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https://api.github.com/repos/huggingface/datasets/issues/6439	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6439/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6439/comments	https://api.github.com/repos/huggingface/datasets/issues/6439/events	https://github.com/huggingface/datasets/issues/6439	2,002,916,514	I_kwDODunzps53YhSi	6,439	Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding	{ "login": "AntreasAntoniou", "id": 10792502, "node_id": "MDQ6VXNlcjEwNzkyNTAy", "avatar_url": "https://avatars.githubusercontent.com/u/10792502?v=4", "gravatar_id": "", "url": "https://api.github.com/users/AntreasAntoniou", "html_url": "https://github.com/AntreasAntoniou", "followers_url": "https://api.github.com/users/AntreasAntoniou/followers", "following_url": "https://api.github.com/users/AntreasAntoniou/following{/other_user}", "gists_url": "https://api.github.com/users/AntreasAntoniou/gists{/gist_id}", "starred_url": "https://api.github.com/users/AntreasAntoniou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/AntreasAntoniou/subscriptions", "organizations_url": "https://api.github.com/users/AntreasAntoniou/orgs", "repos_url": "https://api.github.com/users/AntreasAntoniou/repos", "events_url": "https://api.github.com/users/AntreasAntoniou/events{/privacy}", "received_events_url": "https://api.github.com/users/AntreasAntoniou/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-20T20:07:23	2023-11-20T20:07:37	null	NONE	null	### Describe the bug I am working with a dataset I am trying to publish. The path is Antreas/TALI. It's a fairly large dataset, and contains images, video, audio and text. I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process. With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths. Find the script I am using below: ```python import multiprocessing as mp import pathlib from typing import Optional import datasets from rich import print from tqdm import tqdm def download_dataset_via_hub( dataset_name: str, dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), ): import huggingface_hub as hf_hub download_folder = hf_hub.snapshot_download( repo_id=dataset_name, repo_type="dataset", cache_dir=dataset_download_path, resume_download=True, max_workers=num_download_workers, ignore_patterns=[], ) return pathlib.Path(download_folder) / "data" def load_dataset_via_hub( dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), dataset_name: Optional[str] = None, ): from dataclasses import dataclass, field from datasets import ClassLabel, Features, Image, Sequence, Value dataset_path = download_dataset_via_hub( dataset_download_path=dataset_download_path, num_download_workers=num_download_workers, dataset_name=dataset_name, ) # Building a list of file paths for validation set train_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob(".parquet") if "train" in file.as_posix() ] val_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob(".parquet") if "val" in file.as_posix() ] test_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "test" in file.as_posix() ] print( f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set" ) data_files = { "test": test_files, "val": val_files, "train": train_files, } features = Features( { "image": Image( decode=True ), # Set `decode=True` if you want to decode the images, otherwise `decode=False` "image_url": Value("string"), "item_idx": Value("int64"), "wit_features": Sequence( { "attribution_passes_lang_id": Value("bool"), "caption_alt_text_description": Value("string"), "caption_reference_description": Value("string"), "caption_title_and_reference_description": Value("string"), "context_page_description": Value("string"), "context_section_description": Value("string"), "hierarchical_section_title": Value("string"), "is_main_image": Value("bool"), "language": Value("string"), "page_changed_recently": Value("bool"), "page_title": Value("string"), "page_url": Value("string"), "section_title": Value("string"), } ), "wit_idx": Value("int64"), "youtube_title_text": Value("string"), "youtube_description_text": Value("string"), "youtube_video_content": Value("binary"), "youtube_video_starting_time": Value("string"), "youtube_subtitle_text": Value("string"), "youtube_video_size": Value("int64"), "youtube_video_file_path": Value("string"), } ) dataset = datasets.load_dataset( "parquet" if dataset_name is None else dataset_name, data_files=data_files, features=features, num_proc=1, cache_dir=dataset_download_path / "cache", ) return dataset if __name__ == "__main__": dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/") dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[ "test" ] for sample in tqdm(dataset): print(list(sample.keys())) ``` Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start! ### Steps to reproduce the bug 1. Run the code I provided to get a sense of how fast snapshot + manual is 2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP. 3. You should now have an appreciation of how long these things take. ### Expected behavior The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower. ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6439/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6439/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6438	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6438/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6438/comments	https://api.github.com/repos/huggingface/datasets/issues/6438/events	https://github.com/huggingface/datasets/issues/6438	2,002,032,804	I_kwDODunzps53VJik	6,438	Support GeoParquet	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[ "Thank you, @severo ! I would be more than happy to help in any way I can. I am not familiar with this repo's codebase, but I would be eager to contribute. :)\r\n\r\nFor the preview in Datasets Hub, I think it makes sense to just display the geospatial column as text. If there were a dataset loader, though, I think it should be able to support the geospatial components. Geopandas is probably the most user-friendly interface for that. I'm not sure if it's currently relevant in the context of geoparquet, but I think the pyogrio driver is faster than fiona.\r\n\r\nBut the whole gdal dependency thing can be a real pain. If anything, it would need to be an optional dependency. Maybe it would be best if the loader tries importing relevant geospatial libraries, and in the event of an ImportError, falls back to text for the geometry column.\r\n\r\nPlease let me know if I can be of assistance, and thanks again for creating this Issue. :)" ]	2023-11-20T11:54:58	2023-11-20T14:10:23	null	CONTRIBUTOR	null	### Feature request Support the GeoParquet format ### Motivation GeoParquet (https://geoparquet.org/) is a common format for sharing vectorial geospatial data on the cloud, along with "traditional" data columns. It would be nice to be able to load this format with datasets, and more generally, in the Datasets Hub (see https://huggingface.co/datasets/joshuasundance/govgis_nov2023-slim-spatial/discussions/1). ### Your contribution I would be happy to help work on a PR (but I don't think I can do one on my own). Also, we have to define what we want to support: - load all the columns, but get the "geospatial" column in text-only mode for now - or, fully support the spatial features, maybe taking inspiration from (or depending upon) https://geopandas.org/en/stable/index.html (which itself depends on https://fiona.readthedocs.io/en/stable/, which requires a local install of https://gdal.org/)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6438/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6438/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6437	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6437/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6437/comments	https://api.github.com/repos/huggingface/datasets/issues/6437/events	https://github.com/huggingface/datasets/issues/6437	2,001,272,606	I_kwDODunzps53SP8e	6,437	Problem in training iterable dataset	{ "login": "21Timothy", "id": 38107672, "node_id": "MDQ6VXNlcjM4MTA3Njcy", "avatar_url": "https://avatars.githubusercontent.com/u/38107672?v=4", "gravatar_id": "", "url": "https://api.github.com/users/21Timothy", "html_url": "https://github.com/21Timothy", "followers_url": "https://api.github.com/users/21Timothy/followers", "following_url": "https://api.github.com/users/21Timothy/following{/other_user}", "gists_url": "https://api.github.com/users/21Timothy/gists{/gist_id}", "starred_url": "https://api.github.com/users/21Timothy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/21Timothy/subscriptions", "organizations_url": "https://api.github.com/users/21Timothy/orgs", "repos_url": "https://api.github.com/users/21Timothy/repos", "events_url": "https://api.github.com/users/21Timothy/events{/privacy}", "received_events_url": "https://api.github.com/users/21Timothy/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-20T03:04:02	2023-11-21T02:15:44	null	NONE	null	### Describe the bug I am using PyTorch DDP (Distributed Data Parallel) to train my model. Since the data is too large to load into memory at once, I am using load_dataset to read the data as an iterable dataset. I have used datasets.distributed.split_dataset_by_node to distribute the dataset. However, I have noticed that this distribution results in different processes having different amounts of data to train on. As a result, when the earliest process finishes training and starts predicting on the test set, other processes are still training, causing the overall training speed to be very slow. ### Steps to reproduce the bug ``` def train(args, model, device, train_loader, optimizer, criterion, epoch, length): model.train() idx_length = 0 for batch_idx, data in enumerate(train_loader): s_time = time.time() X = data['X'] target = data['y'].reshape(-1, 28) X, target = X.to(device), target.to(device) optimizer.zero_grad() output = model(X) loss = criterion(output, target) loss.backward() optimizer.step() idx_length += 1 if batch_idx % args.log_interval == 0: # print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( # epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), # 100. * batch_idx * len( # X) * torch.distributed.get_world_size() / length, loss.item())) print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\t'.format( epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), 100. * batch_idx * len( X) * torch.distributed.get_world_size() / length)) if args.dry_run: break print('Process %s length: %s time: %s' % (torch.distributed.get_rank(), idx_length, datetime.datetime.now())) train_iterable_dataset = load_dataset("parquet", data_files=data_files, split="train", streaming=True) test_iterable_dataset = load_dataset("parquet", data_files=data_files, split="test", streaming=True) train_iterable_dataset = train_iterable_dataset.map(process_fn) test_iterable_dataset = test_iterable_dataset.map(process_fn) train_iterable_dataset = train_iterable_dataset.map(scale) test_iterable_dataset = test_iterable_dataset.map(scale) train_iterable_dataset = datasets.distributed.split_dataset_by_node(train_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) test_iterable_dataset = datasets.distributed.split_dataset_by_node(test_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) print(torch.distributed.get_rank(), train_iterable_dataset.n_shards, test_iterable_dataset.n_shards) train_kwargs = {'batch_size': args.batch_size} test_kwargs = {'batch_size': args.test_batch_size} if use_cuda: cuda_kwargs = {'num_workers': 3,#ngpus_per_node, 'pin_memory': True, 'shuffle': False} train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) train_loader = torch.utils.data.DataLoader(train_iterable_dataset, train_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # train_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) test_loader = torch.utils.data.DataLoader(test_iterable_dataset, test_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # test_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) for epoch in range(1, args.epochs + 1): start_time = time.time() train_iterable_dataset.set_epoch(epoch) test_iterable_dataset.set_epoch(epoch) train(args, model, device, train_loader, optimizer, criterion, epoch, train_len) test(args, model, device, criterion2, test_loader) ``` And here’s the part of output: ``` Train Epoch: 1 Batch_idx: 5000 Process: 0 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 1 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 2 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5862 Process: 3 Data_length: 12 coststime: 0.04095172882080078 Train Epoch: 1 Batch_idx: 5862 Process: 0 Data_length: 3 coststime: 0.0751960277557373 Train Epoch: 1 Batch_idx: 5867 Process: 3 Data_length: 49 coststime: 0.0032558441162109375 Train Epoch: 1 Batch_idx: 5872 Process: 1 Data_length: 2 coststime: 0.022842884063720703 Train Epoch: 1 Batch_idx: 5876 Process: 3 Data_length: 63 coststime: 0.002694845199584961 Process 3 length: 5877 time: 2023-11-17 17:03:26.582317 Train epoch 1 costTime: 241.72063446044922s . Process 3 Start to test. 3 0 tensor(45508.8516, device='cuda:3') 3 100 tensor(45309.0469, device='cuda:3') 3 200 tensor(45675.3047, device='cuda:3') 3 300 tensor(45263.0273, device='cuda:3') Process 3 Reduce metrics. Train Epoch: 2 Batch_idx: 0 Process: 3 [0/4710975.0 (0%)] Train Epoch: 1 Batch_idx: 5882 Process: 1 Data_length: 63 coststime: 0.05185818672180176 Train Epoch: 1 Batch_idx: 5887 Process: 1 Data_length: 12 coststime: 0.006895303726196289 Process 1 length: 5888 time: 2023-11-17 17:20:48.578204 Train epoch 1 costTime: 1285.7279663085938s . Process 1 Start to test. 1 0 tensor(45265.9141, device='cuda:1') ``` ### Expected behavior I'd like to know how to fix this problem. ### Environment info ``` torch==2.0 datasets==2.14.0 ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6437/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6437/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6436	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6436/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6436/comments	https://api.github.com/repos/huggingface/datasets/issues/6436/events	https://github.com/huggingface/datasets/issues/6436	2,000,844,474	I_kwDODunzps53Qna6	6,436	TypeError: <lambda>() takes 0 positional arguments but 1 was given	{ "login": "ahmadmustafaanis", "id": 47111429, "node_id": "MDQ6VXNlcjQ3MTExNDI5", "avatar_url": "https://avatars.githubusercontent.com/u/47111429?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ahmadmustafaanis", "html_url": "https://github.com/ahmadmustafaanis", "followers_url": "https://api.github.com/users/ahmadmustafaanis/followers", "following_url": "https://api.github.com/users/ahmadmustafaanis/following{/other_user}", "gists_url": "https://api.github.com/users/ahmadmustafaanis/gists{/gist_id}", "starred_url": "https://api.github.com/users/ahmadmustafaanis/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ahmadmustafaanis/subscriptions", "organizations_url": "https://api.github.com/users/ahmadmustafaanis/orgs", "repos_url": "https://api.github.com/users/ahmadmustafaanis/repos", "events_url": "https://api.github.com/users/ahmadmustafaanis/events{/privacy}", "received_events_url": "https://api.github.com/users/ahmadmustafaanis/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-19T13:10:20	2023-11-19T13:10:20	null	NONE	null	### Describe the bug ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-35-7b6becee3685>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import Dataset 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(args, kwargs) 154 def get_file_type(args, *kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, args, *kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` or ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-36-652e886d387f>](https://localhost:8080/#) in <cell line: 1>() ----> 1 import datasets 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(args, *kwargs) 154 def get_file_type(args, *kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, args, **kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` ### Steps to reproduce the bug `import datasets` on colab ### Expected behavior work fine ### Environment info colab `!pip install datasets`	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6436/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6436/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6435	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6435/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6435/comments	https://api.github.com/repos/huggingface/datasets/issues/6435/events	https://github.com/huggingface/datasets/issues/6435	2,000,690,513	I_kwDODunzps53QB1R	6,435	Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method	{ "login": "kopyl", "id": 17604849, "node_id": "MDQ6VXNlcjE3NjA0ODQ5", "avatar_url": "https://avatars.githubusercontent.com/u/17604849?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kopyl", "html_url": "https://github.com/kopyl", "followers_url": "https://api.github.com/users/kopyl/followers", "following_url": "https://api.github.com/users/kopyl/following{/other_user}", "gists_url": "https://api.github.com/users/kopyl/gists{/gist_id}", "starred_url": "https://api.github.com/users/kopyl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kopyl/subscriptions", "organizations_url": "https://api.github.com/users/kopyl/orgs", "repos_url": "https://api.github.com/users/kopyl/repos", "events_url": "https://api.github.com/users/kopyl/events{/privacy}", "received_events_url": "https://api.github.com/users/kopyl/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-19T04:21:16	2023-11-19T04:21:16	null	NONE	null	### Describe the bug 1. I ran dataset mapping with `num_proc=6` in it and got this error: `RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method` I can't actually find a way to run multi-GPU dataset mapping. Can you help? ### Steps to reproduce the bug 1. Rund SDXL training with `num_proc=6`: https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py ### Expected behavior Should work well ### Environment info 6x A100 SXM, Linux	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6435/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6435/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6434	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6434/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6434/comments	https://api.github.com/repos/huggingface/datasets/issues/6434/events	https://github.com/huggingface/datasets/pull/6434	1,999,554,915	PR_kwDODunzps5fxgUO	6,434	Use `ruff` for formatting	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004293 / 0.011353 (-0.007060) \| 0.002953 / 0.011008 (-0.008055) \| 0.063712 / 0.038508 (0.025204) \| 0.029963 / 0.023109 (0.006854) \| 0.248574 / 0.275898 (-0.027324) \| 0.272757 / 0.323480 (-0.050723) \| 0.003878 / 0.007986 (-0.004108) \| 0.002456 / 0.004328 (-0.001872) \| 0.047959 / 0.004250 (0.043709) \| 0.043277 / 0.037052 (0.006224) \| 0.255071 / 0.258489 (-0.003418) \| 0.283934 / 0.293841 (-0.009907) \| 0.022870 / 0.128546 (-0.105676) \| 0.007224 / 0.075646 (-0.068422) \| 0.221595 / 0.419271 (-0.197677) \| 0.053468 / 0.043533 (0.009935) \| 0.249906 / 0.255139 (-0.005233) \| 0.274894 / 0.283200 (-0.008305) \| 0.017246 / 0.141683 (-0.124437) \| 1.112440 / 1.452155 (-0.339714) \| 1.167293 / 1.492716 (-0.325424) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092684 / 0.018006 (0.074677) \| 0.301721 / 0.000490 (0.301231) \| 0.000220 / 0.000200 (0.000020) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018289 / 0.037411 (-0.019122) \| 0.061898 / 0.014526 (0.047372) \| 0.072904 / 0.176557 (-0.103653) \| 0.118515 / 0.737135 (-0.618621) \| 0.074000 / 0.296338 (-0.222338) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287044 / 0.215209 (0.071835) \| 2.818091 / 2.077655 (0.740436) \| 1.502401 / 1.504120 (-0.001719) \| 1.374688 / 1.541195 (-0.166506) \| 1.410254 / 1.468490 (-0.058236) \| 0.407519 / 4.584777 (-4.177258) \| 2.379199 / 3.745712 (-1.366513) \| 2.585745 / 5.269862 (-2.684117) \| 1.562336 / 4.565676 (-3.003341) \| 0.045977 / 0.424275 (-0.378299) \| 0.004809 / 0.007607 (-0.002798) \| 0.347942 / 0.226044 (0.121897) \| 3.383318 / 2.268929 (1.114390) \| 1.844784 / 55.444624 (-53.599841) \| 1.561949 / 6.876477 (-5.314528) \| 1.571082 / 2.142072 (-0.570990) \| 0.482469 / 4.805227 (-4.322758) \| 0.099357 / 6.500664 (-6.401307) \| 0.041039 / 0.075469 (-0.034430) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944236 / 1.841788 (-0.897551) \| 11.519623 / 8.074308 (3.445315) \| 10.353829 / 10.191392 (0.162437) \| 0.137530 / 0.680424 (-0.542894) \| 0.014454 / 0.534201 (-0.519747) \| 0.268657 / 0.579283 (-0.310626) \| 0.265165 / 0.434364 (-0.169199) \| 0.302626 / 0.540337 (-0.237712) \| 0.426923 / 1.386936 (-0.960013) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004711 / 0.011353 (-0.006641) \| 0.002504 / 0.011008 (-0.008504) \| 0.047671 / 0.038508 (0.009163) \| 0.051147 / 0.023109 (0.028037) \| 0.272848 / 0.275898 (-0.003050) \| 0.291705 / 0.323480 (-0.031775) \| 0.004002 / 0.007986 (-0.003984) \| 0.002382 / 0.004328 (-0.001947) \| 0.047583 / 0.004250 (0.043332) \| 0.038203 / 0.037052 (0.001150) \| 0.278536 / 0.258489 (0.020047) \| 0.305872 / 0.293841 (0.012031) \| 0.023890 / 0.128546 (-0.104657) \| 0.006954 / 0.075646 (-0.068693) \| 0.053716 / 0.419271 (-0.365556) \| 0.032158 / 0.043533 (-0.011375) \| 0.273939 / 0.255139 (0.018800) \| 0.290722 / 0.283200 (0.007522) \| 0.016946 / 0.141683 (-0.124737) \| 1.102726 / 1.452155 (-0.349429) \| 1.169356 / 1.492716 (-0.323360) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092520 / 0.018006 (0.074514) \| 0.301949 / 0.000490 (0.301459) \| 0.000248 / 0.000200 (0.000048) \| 0.000061 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021013 / 0.037411 (-0.016399) \| 0.069965 / 0.014526 (0.055439) \| 0.080105 / 0.176557 (-0.096451) \| 0.119802 / 0.737135 (-0.617334) \| 0.081615 / 0.296338 (-0.214724) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301170 / 0.215209 (0.085960) \| 2.884817 / 2.077655 (0.807162) \| 1.596376 / 1.504120 (0.092256) \| 1.471205 / 1.541195 (-0.069990) \| 1.499061 / 1.468490 (0.030571) \| 0.407729 / 4.584777 (-4.177048) \| 2.432824 / 3.745712 (-1.312888) \| 2.561905 / 5.269862 (-2.707957) \| 1.535364 / 4.565676 (-3.030313) \| 0.046592 / 0.424275 (-0.377683) \| 0.004773 / 0.007607 (-0.002834) \| 0.350872 / 0.226044 (0.124828) \| 3.474874 / 2.268929 (1.205945) \| 1.963114 / 55.444624 (-53.481510) \| 1.688213 / 6.876477 (-5.188263) \| 1.686325 / 2.142072 (-0.455748) \| 0.487151 / 4.805227 (-4.318076) \| 0.104253 / 6.500664 (-6.396411) \| 0.043499 / 0.075469 (-0.031970) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980395 / 1.841788 (-0.861393) \| 11.907393 / 8.074308 (3.833085) \| 10.983688 / 10.191392 (0.792296) \| 0.142875 / 0.680424 (-0.537549) \| 0.015375 / 0.534201 (-0.518826) \| 0.270043 / 0.579283 (-0.309240) \| 0.295092 / 0.434364 (-0.139272) \| 0.309466 / 0.540337 (-0.230871) \| 0.409812 / 1.386936 (-0.977124) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#17f97ca8ec66f6664d3e9b7ceb84fe3ca49a9c18 \"CML watermark\")\n", "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6434). All of your documentation changes will be reflected on that endpoint." ]	2023-11-17T16:53:22	2023-11-20T08:41:15	null	CONTRIBUTOR	null	Use `ruff` instead of `black` for formatting to be consistent with `transformers` ([PR](https://github.com/huggingface/transformers/pull/27144)) and `huggingface_hub` ([PR 1](https://github.com/huggingface/huggingface_hub/pull/1783) and [PR 2](https://github.com/huggingface/huggingface_hub/pull/1789)).	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6434/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6434/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6434", "html_url": "https://github.com/huggingface/datasets/pull/6434", "diff_url": "https://github.com/huggingface/datasets/pull/6434.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6434.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6433	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6433/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6433/comments	https://api.github.com/repos/huggingface/datasets/issues/6433/events	https://github.com/huggingface/datasets/pull/6433	1,999,419,105	PR_kwDODunzps5fxDoG	6,433	Better `tqdm` wrapper	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6433). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005070 / 0.011353 (-0.006283) \| 0.003251 / 0.011008 (-0.007757) \| 0.061528 / 0.038508 (0.023020) \| 0.055386 / 0.023109 (0.032276) \| 0.248536 / 0.275898 (-0.027362) \| 0.272346 / 0.323480 (-0.051134) \| 0.003875 / 0.007986 (-0.004111) \| 0.002396 / 0.004328 (-0.001933) \| 0.047659 / 0.004250 (0.043409) \| 0.037448 / 0.037052 (0.000396) \| 0.251101 / 0.258489 (-0.007388) \| 0.282353 / 0.293841 (-0.011488) \| 0.027784 / 0.128546 (-0.100762) \| 0.010534 / 0.075646 (-0.065113) \| 0.206025 / 0.419271 (-0.213246) \| 0.035410 / 0.043533 (-0.008123) \| 0.250626 / 0.255139 (-0.004513) \| 0.266801 / 0.283200 (-0.016399) \| 0.017704 / 0.141683 (-0.123979) \| 1.089970 / 1.452155 (-0.362185) \| 1.171683 / 1.492716 (-0.321033) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092700 / 0.018006 (0.074694) \| 0.301314 / 0.000490 (0.300824) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018385 / 0.037411 (-0.019026) \| 0.062364 / 0.014526 (0.047838) \| 0.075887 / 0.176557 (-0.100670) \| 0.119484 / 0.737135 (-0.617651) \| 0.074490 / 0.296338 (-0.221849) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283893 / 0.215209 (0.068684) \| 2.746772 / 2.077655 (0.669118) \| 1.486568 / 1.504120 (-0.017552) \| 1.376451 / 1.541195 (-0.164744) \| 1.377928 / 1.468490 (-0.090562) \| 0.572393 / 4.584777 (-4.012384) \| 2.383282 / 3.745712 (-1.362430) \| 2.791614 / 5.269862 (-2.478248) \| 1.753373 / 4.565676 (-2.812303) \| 0.063539 / 0.424275 (-0.360736) \| 0.005014 / 0.007607 (-0.002593) \| 0.341300 / 0.226044 (0.115256) \| 3.376960 / 2.268929 (1.108032) \| 1.914162 / 55.444624 (-53.530462) \| 1.590188 / 6.876477 (-5.286289) \| 1.618420 / 2.142072 (-0.523652) \| 0.648723 / 4.805227 (-4.156504) \| 0.117745 / 6.500664 (-6.382919) \| 0.048858 / 0.075469 (-0.026611) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944422 / 1.841788 (-0.897366) \| 11.603590 / 8.074308 (3.529282) \| 10.707000 / 10.191392 (0.515608) \| 0.130779 / 0.680424 (-0.549645) \| 0.015126 / 0.534201 (-0.519075) \| 0.284869 / 0.579283 (-0.294414) \| 0.266778 / 0.434364 (-0.167585) \| 0.320646 / 0.540337 (-0.219691) \| 0.417167 / 1.386936 (-0.969769) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005384 / 0.011353 (-0.005969) \| 0.003311 / 0.011008 (-0.007698) \| 0.049933 / 0.038508 (0.011425) \| 0.052791 / 0.023109 (0.029681) \| 0.277061 / 0.275898 (0.001162) \| 0.302149 / 0.323480 (-0.021331) \| 0.004006 / 0.007986 (-0.003979) \| 0.002394 / 0.004328 (-0.001934) \| 0.049020 / 0.004250 (0.044770) \| 0.040168 / 0.037052 (0.003116) \| 0.278625 / 0.258489 (0.020136) \| 0.308641 / 0.293841 (0.014800) \| 0.029808 / 0.128546 (-0.098738) \| 0.010873 / 0.075646 (-0.064774) \| 0.058040 / 0.419271 (-0.361231) \| 0.032706 / 0.043533 (-0.010827) \| 0.277254 / 0.255139 (0.022115) \| 0.295208 / 0.283200 (0.012008) \| 0.017769 / 0.141683 (-0.123914) \| 1.126416 / 1.452155 (-0.325739) \| 1.169046 / 1.492716 (-0.323670) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094776 / 0.018006 (0.076770) \| 0.306262 / 0.000490 (0.305772) \| 0.000223 / 0.000200 (0.000023) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022279 / 0.037411 (-0.015132) \| 0.086784 / 0.014526 (0.072258) \| 0.082268 / 0.176557 (-0.094289) \| 0.120131 / 0.737135 (-0.617004) \| 0.082862 / 0.296338 (-0.213476) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300565 / 0.215209 (0.085356) \| 2.923424 / 2.077655 (0.845769) \| 1.594836 / 1.504120 (0.090716) \| 1.504323 / 1.541195 (-0.036872) \| 1.498495 / 1.468490 (0.030005) \| 0.570969 / 4.584777 (-4.013808) \| 2.476966 / 3.745712 (-1.268746) \| 2.785190 / 5.269862 (-2.484672) \| 1.749839 / 4.565676 (-2.815837) \| 0.062809 / 0.424275 (-0.361466) \| 0.004908 / 0.007607 (-0.002699) \| 0.361513 / 0.226044 (0.135469) \| 3.587135 / 2.268929 (1.318207) \| 1.952030 / 55.444624 (-53.492595) \| 1.661552 / 6.876477 (-5.214925) \| 1.678673 / 2.142072 (-0.463399) \| 0.645083 / 4.805227 (-4.160144) \| 0.117098 / 6.500664 (-6.383566) \| 0.041630 / 0.075469 (-0.033839) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.987883 / 1.841788 (-0.853904) \| 12.300764 / 8.074308 (4.226456) \| 10.962068 / 10.191392 (0.770675) \| 0.143200 / 0.680424 (-0.537224) \| 0.015743 / 0.534201 (-0.518458) \| 0.289733 / 0.579283 (-0.289550) \| 0.276384 / 0.434364 (-0.157979) \| 0.328542 / 0.540337 (-0.211795) \| 0.552175 / 1.386936 (-0.834761) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#81a65a57cf9fd0abdf85b664a144c9a06cb2896d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005110 / 0.011353 (-0.006243) \| 0.003311 / 0.011008 (-0.007697) \| 0.061962 / 0.038508 (0.023454) \| 0.050250 / 0.023109 (0.027140) \| 0.245313 / 0.275898 (-0.030585) \| 0.268748 / 0.323480 (-0.054732) \| 0.004693 / 0.007986 (-0.003293) \| 0.002465 / 0.004328 (-0.001863) \| 0.047698 / 0.004250 (0.043447) \| 0.037314 / 0.037052 (0.000262) \| 0.250370 / 0.258489 (-0.008119) \| 0.286023 / 0.293841 (-0.007818) \| 0.027891 / 0.128546 (-0.100655) \| 0.010574 / 0.075646 (-0.065072) \| 0.204895 / 0.419271 (-0.214376) \| 0.036014 / 0.043533 (-0.007519) \| 0.250959 / 0.255139 (-0.004180) \| 0.266710 / 0.283200 (-0.016489) \| 0.018492 / 0.141683 (-0.123191) \| 1.115340 / 1.452155 (-0.336815) \| 1.176488 / 1.492716 (-0.316229) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099409 / 0.018006 (0.081402) \| 0.310151 / 0.000490 (0.309661) \| 0.000223 / 0.000200 (0.000023) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018132 / 0.037411 (-0.019279) \| 0.061820 / 0.014526 (0.047294) \| 0.074960 / 0.176557 (-0.101596) \| 0.119793 / 0.737135 (-0.617342) \| 0.074132 / 0.296338 (-0.222206) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286388 / 0.215209 (0.071179) \| 2.830791 / 2.077655 (0.753137) \| 1.514588 / 1.504120 (0.010468) \| 1.376514 / 1.541195 (-0.164681) \| 1.405080 / 1.468490 (-0.063410) \| 0.555297 / 4.584777 (-4.029480) \| 2.364838 / 3.745712 (-1.380874) \| 2.806050 / 5.269862 (-2.463812) \| 1.756114 / 4.565676 (-2.809562) \| 0.062254 / 0.424275 (-0.362022) \| 0.005020 / 0.007607 (-0.002588) \| 0.346272 / 0.226044 (0.120227) \| 3.453195 / 2.268929 (1.184266) \| 1.837810 / 55.444624 (-53.606814) \| 1.577984 / 6.876477 (-5.298493) \| 1.560821 / 2.142072 (-0.581251) \| 0.633930 / 4.805227 (-4.171297) \| 0.116414 / 6.500664 (-6.384250) \| 0.042007 / 0.075469 (-0.033462) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941322 / 1.841788 (-0.900466) \| 11.740927 / 8.074308 (3.666618) \| 10.450543 / 10.191392 (0.259151) \| 0.128820 / 0.680424 (-0.551604) \| 0.014856 / 0.534201 (-0.519345) \| 0.285636 / 0.579283 (-0.293647) \| 0.270051 / 0.434364 (-0.164313) \| 0.321244 / 0.540337 (-0.219093) \| 0.415486 / 1.386936 (-0.971450) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005333 / 0.011353 (-0.006020) \| 0.003370 / 0.011008 (-0.007638) \| 0.049046 / 0.038508 (0.010538) \| 0.055767 / 0.023109 (0.032658) \| 0.273463 / 0.275898 (-0.002435) \| 0.292909 / 0.323480 (-0.030571) \| 0.004102 / 0.007986 (-0.003883) \| 0.002460 / 0.004328 (-0.001868) \| 0.048025 / 0.004250 (0.043775) \| 0.040342 / 0.037052 (0.003290) \| 0.275114 / 0.258489 (0.016625) \| 0.295988 / 0.293841 (0.002147) \| 0.029461 / 0.128546 (-0.099085) \| 0.010654 / 0.075646 (-0.064992) \| 0.057196 / 0.419271 (-0.362076) \| 0.033238 / 0.043533 (-0.010295) \| 0.275885 / 0.255139 (0.020746) \| 0.288566 / 0.283200 (0.005366) \| 0.018058 / 0.141683 (-0.123625) \| 1.130513 / 1.452155 (-0.321642) \| 1.173608 / 1.492716 (-0.319108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097751 / 0.018006 (0.079745) \| 0.312106 / 0.000490 (0.311616) \| 0.000232 / 0.000200 (0.000032) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021201 / 0.037411 (-0.016211) \| 0.070150 / 0.014526 (0.055624) \| 0.081073 / 0.176557 (-0.095484) \| 0.119520 / 0.737135 (-0.617615) \| 0.084479 / 0.296338 (-0.211859) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292322 / 0.215209 (0.077113) \| 2.844070 / 2.077655 (0.766415) \| 1.581838 / 1.504120 (0.077718) \| 1.462665 / 1.541195 (-0.078530) \| 1.483013 / 1.468490 (0.014523) \| 0.558705 / 4.584777 (-4.026072) \| 2.422368 / 3.745712 (-1.323344) \| 2.772274 / 5.269862 (-2.497587) \| 1.725901 / 4.565676 (-2.839775) \| 0.062993 / 0.424275 (-0.361282) \| 0.004982 / 0.007607 (-0.002625) \| 0.344336 / 0.226044 (0.118292) \| 3.425230 / 2.268929 (1.156302) \| 1.947199 / 55.444624 (-53.497425) \| 1.670362 / 6.876477 (-5.206115) \| 1.674112 / 2.142072 (-0.467961) \| 0.633857 / 4.805227 (-4.171370) \| 0.114837 / 6.500664 (-6.385827) \| 0.042558 / 0.075469 (-0.032911) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979474 / 1.841788 (-0.862314) \| 12.110856 / 8.074308 (4.036548) \| 10.605998 / 10.191392 (0.414606) \| 0.130769 / 0.680424 (-0.549654) \| 0.016057 / 0.534201 (-0.518144) \| 0.296448 / 0.579283 (-0.282835) \| 0.278078 / 0.434364 (-0.156286) \| 0.320809 / 0.540337 (-0.219528) \| 0.570756 / 1.386936 (-0.816180) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eeb9727cc680a8f8172a012920bf84f285fec5a0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005181 / 0.011353 (-0.006172) \| 0.003434 / 0.011008 (-0.007574) \| 0.062333 / 0.038508 (0.023825) \| 0.058544 / 0.023109 (0.035435) \| 0.233794 / 0.275898 (-0.042104) \| 0.258774 / 0.323480 (-0.064706) \| 0.003869 / 0.007986 (-0.004117) \| 0.002478 / 0.004328 (-0.001850) \| 0.047871 / 0.004250 (0.043620) \| 0.037997 / 0.037052 (0.000945) \| 0.241269 / 0.258489 (-0.017220) \| 0.270103 / 0.293841 (-0.023738) \| 0.027710 / 0.128546 (-0.100836) \| 0.010683 / 0.075646 (-0.064963) \| 0.213204 / 0.419271 (-0.206067) \| 0.036156 / 0.043533 (-0.007377) \| 0.240061 / 0.255139 (-0.015078) \| 0.253627 / 0.283200 (-0.029573) \| 0.017880 / 0.141683 (-0.123803) \| 1.102965 / 1.452155 (-0.349189) \| 1.176919 / 1.492716 (-0.315797) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093206 / 0.018006 (0.075200) \| 0.300960 / 0.000490 (0.300470) \| 0.000214 / 0.000200 (0.000014) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019417 / 0.037411 (-0.017994) \| 0.061948 / 0.014526 (0.047422) \| 0.073560 / 0.176557 (-0.102997) \| 0.120682 / 0.737135 (-0.616453) \| 0.074925 / 0.296338 (-0.221413) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280157 / 0.215209 (0.064948) \| 2.760648 / 2.077655 (0.682994) \| 1.482129 / 1.504120 (-0.021991) \| 1.364091 / 1.541195 (-0.177104) \| 1.415680 / 1.468490 (-0.052810) \| 0.564697 / 4.584777 (-4.020080) \| 2.364080 / 3.745712 (-1.381633) \| 2.794018 / 5.269862 (-2.475844) \| 1.752157 / 4.565676 (-2.813520) \| 0.062234 / 0.424275 (-0.362041) \| 0.004927 / 0.007607 (-0.002680) \| 0.337835 / 0.226044 (0.111790) \| 3.313819 / 2.268929 (1.044891) \| 1.834095 / 55.444624 (-53.610530) \| 1.559964 / 6.876477 (-5.316513) \| 1.598489 / 2.142072 (-0.543584) \| 0.636829 / 4.805227 (-4.168399) \| 0.116436 / 6.500664 (-6.384228) \| 0.042506 / 0.075469 (-0.032963) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951508 / 1.841788 (-0.890280) \| 11.599532 / 8.074308 (3.525224) \| 10.492355 / 10.191392 (0.300963) \| 0.151582 / 0.680424 (-0.528842) \| 0.014356 / 0.534201 (-0.519845) \| 0.288448 / 0.579283 (-0.290835) \| 0.265607 / 0.434364 (-0.168757) \| 0.324455 / 0.540337 (-0.215883) \| 0.416718 / 1.386936 (-0.970218) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005489 / 0.011353 (-0.005864) \| 0.003481 / 0.011008 (-0.007527) \| 0.048952 / 0.038508 (0.010444) \| 0.054650 / 0.023109 (0.031540) \| 0.280853 / 0.275898 (0.004955) \| 0.298089 / 0.323480 (-0.025391) \| 0.004762 / 0.007986 (-0.003224) \| 0.002500 / 0.004328 (-0.001828) \| 0.048503 / 0.004250 (0.044253) \| 0.042048 / 0.037052 (0.004995) \| 0.281729 / 0.258489 (0.023240) \| 0.303625 / 0.293841 (0.009785) \| 0.028887 / 0.128546 (-0.099659) \| 0.010687 / 0.075646 (-0.064960) \| 0.058093 / 0.419271 (-0.361178) \| 0.032366 / 0.043533 (-0.011167) \| 0.281987 / 0.255139 (0.026848) \| 0.295554 / 0.283200 (0.012355) \| 0.019242 / 0.141683 (-0.122441) \| 1.127760 / 1.452155 (-0.324395) \| 1.166868 / 1.492716 (-0.325848) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092367 / 0.018006 (0.074361) \| 0.300195 / 0.000490 (0.299706) \| 0.000222 / 0.000200 (0.000022) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022062 / 0.037411 (-0.015349) \| 0.069955 / 0.014526 (0.055429) \| 0.081224 / 0.176557 (-0.095333) \| 0.120183 / 0.737135 (-0.616953) \| 0.082846 / 0.296338 (-0.213492) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295880 / 0.215209 (0.080671) \| 2.902508 / 2.077655 (0.824853) \| 1.616311 / 1.504120 (0.112191) \| 1.491320 / 1.541195 (-0.049875) \| 1.517333 / 1.468490 (0.048843) \| 0.566824 / 4.584777 (-4.017953) \| 2.428397 / 3.745712 (-1.317315) \| 2.807241 / 5.269862 (-2.462620) \| 1.786364 / 4.565676 (-2.779312) \| 0.065253 / 0.424275 (-0.359022) \| 0.004971 / 0.007607 (-0.002636) \| 0.350095 / 0.226044 (0.124051) \| 3.422226 / 2.268929 (1.153297) \| 1.972955 / 55.444624 (-53.471670) \| 1.686142 / 6.876477 (-5.190335) \| 1.694539 / 2.142072 (-0.447533) \| 0.645709 / 4.805227 (-4.159518) \| 0.117774 / 6.500664 (-6.382890) \| 0.041679 / 0.075469 (-0.033790) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976835 / 1.841788 (-0.864952) \| 12.358039 / 8.074308 (4.283730) \| 10.774304 / 10.191392 (0.582912) \| 0.130442 / 0.680424 (-0.549982) \| 0.016071 / 0.534201 (-0.518130) \| 0.289911 / 0.579283 (-0.289372) \| 0.280693 / 0.434364 (-0.153671) \| 0.325598 / 0.540337 (-0.214739) \| 0.549618 / 1.386936 (-0.837318) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1570235228b67a15dce1ed535e905edd7442117f \"CML watermark\")\n" ]	2023-11-17T15:45:15	2023-11-20T00:24:21	null	CONTRIBUTOR	null	This PR aligns the `tqdm` logic with `huggingface_hub` (without introducing breaking changes), as the current one is error-prone. Additionally, it improves the doc page about the `datasets`' utilities, and the handling of `fsspec` paths in `cached_path`. Fix #6409	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6433/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6433/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6433", "html_url": "https://github.com/huggingface/datasets/pull/6433", "diff_url": "https://github.com/huggingface/datasets/pull/6433.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6433.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6432	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6432/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6432/comments	https://api.github.com/repos/huggingface/datasets/issues/6432/events	https://github.com/huggingface/datasets/issues/6432	1,999,258,140	I_kwDODunzps53KkIc	6,432	load_dataset does not load all of the data in my input file	{ "login": "demongolem-biz2", "id": 121301001, "node_id": "U_kgDOBzroCQ", "avatar_url": "https://avatars.githubusercontent.com/u/121301001?v=4", "gravatar_id": "", "url": "https://api.github.com/users/demongolem-biz2", "html_url": "https://github.com/demongolem-biz2", "followers_url": "https://api.github.com/users/demongolem-biz2/followers", "following_url": "https://api.github.com/users/demongolem-biz2/following{/other_user}", "gists_url": "https://api.github.com/users/demongolem-biz2/gists{/gist_id}", "starred_url": "https://api.github.com/users/demongolem-biz2/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/demongolem-biz2/subscriptions", "organizations_url": "https://api.github.com/users/demongolem-biz2/orgs", "repos_url": "https://api.github.com/users/demongolem-biz2/repos", "events_url": "https://api.github.com/users/demongolem-biz2/events{/privacy}", "received_events_url": "https://api.github.com/users/demongolem-biz2/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-17T14:28:50	2023-11-17T14:28:50	null	NONE	null	### Describe the bug I have 127 elements in my input dataset. When I do a len on the dataset after loaded, it is only 124 elements. ### Steps to reproduce the bug train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN) valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION) logger.info(len(train_dataset)) logger.info(len(valid_dataset)) Both train and valid input are 127 items. However, they both only load 124 items. The input format is in json. At the end of the day, I am trying to create .pt files. ### Expected behavior I see all 127 elements in my dataset when performing len ### Environment info Python 3.10. CentOS operating system. nlp==0.40, datasets==2.14.5, transformers==4.26.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6432/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6432/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6431	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6431/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6431/comments	https://api.github.com/repos/huggingface/datasets/issues/6431/events	https://github.com/huggingface/datasets/pull/6431	1,997,202,770	PR_kwDODunzps5fpfos	6,431	Create DatasetNotFoundError and DataFilesNotFoundError	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004459 / 0.011353 (-0.006894) \| 0.002883 / 0.011008 (-0.008125) \| 0.062434 / 0.038508 (0.023925) \| 0.030353 / 0.023109 (0.007244) \| 0.256696 / 0.275898 (-0.019202) \| 0.280557 / 0.323480 (-0.042923) \| 0.003903 / 0.007986 (-0.004083) \| 0.002424 / 0.004328 (-0.001905) \| 0.048509 / 0.004250 (0.044259) \| 0.043583 / 0.037052 (0.006531) \| 0.253900 / 0.258489 (-0.004590) \| 0.309146 / 0.293841 (0.015305) \| 0.023253 / 0.128546 (-0.105294) \| 0.007073 / 0.075646 (-0.068573) \| 0.204118 / 0.419271 (-0.215154) \| 0.056429 / 0.043533 (0.012897) \| 0.247331 / 0.255139 (-0.007808) \| 0.271581 / 0.283200 (-0.011619) \| 0.017021 / 0.141683 (-0.124662) \| 1.115057 / 1.452155 (-0.337098) \| 1.209947 / 1.492716 (-0.282770) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093141 / 0.018006 (0.075134) \| 0.295987 / 0.000490 (0.295497) \| 0.000221 / 0.000200 (0.000021) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019182 / 0.037411 (-0.018230) \| 0.062049 / 0.014526 (0.047523) \| 0.073824 / 0.176557 (-0.102733) \| 0.120175 / 0.737135 (-0.616960) \| 0.074700 / 0.296338 (-0.221639) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280036 / 0.215209 (0.064827) \| 2.731512 / 2.077655 (0.653857) \| 1.414606 / 1.504120 (-0.089514) \| 1.302433 / 1.541195 (-0.238761) \| 1.313012 / 1.468490 (-0.155478) \| 0.399722 / 4.584777 (-4.185055) \| 2.371249 / 3.745712 (-1.374463) \| 2.582520 / 5.269862 (-2.687342) \| 1.558505 / 4.565676 (-3.007171) \| 0.045765 / 0.424275 (-0.378510) \| 0.004748 / 0.007607 (-0.002859) \| 0.327623 / 0.226044 (0.101578) \| 3.258742 / 2.268929 (0.989814) \| 1.756798 / 55.444624 (-53.687826) \| 1.494551 / 6.876477 (-5.381925) \| 1.518161 / 2.142072 (-0.623911) \| 0.468560 / 4.805227 (-4.336667) \| 0.101034 / 6.500664 (-6.399630) \| 0.048259 / 0.075469 (-0.027210) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938146 / 1.841788 (-0.903642) \| 11.636387 / 8.074308 (3.562078) \| 10.638909 / 10.191392 (0.447517) \| 0.128340 / 0.680424 (-0.552084) \| 0.015194 / 0.534201 (-0.519007) \| 0.275961 / 0.579283 (-0.303322) \| 0.264629 / 0.434364 (-0.169735) \| 0.308580 / 0.540337 (-0.231758) \| 0.433658 / 1.386936 (-0.953278) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004797 / 0.011353 (-0.006556) \| 0.002801 / 0.011008 (-0.008208) \| 0.048101 / 0.038508 (0.009593) \| 0.056406 / 0.023109 (0.033296) \| 0.274966 / 0.275898 (-0.000932) \| 0.298310 / 0.323480 (-0.025170) \| 0.004115 / 0.007986 (-0.003871) \| 0.002437 / 0.004328 (-0.001891) \| 0.047921 / 0.004250 (0.043671) \| 0.038812 / 0.037052 (0.001760) \| 0.279594 / 0.258489 (0.021105) \| 0.313703 / 0.293841 (0.019862) \| 0.024485 / 0.128546 (-0.104061) \| 0.007095 / 0.075646 (-0.068551) \| 0.053398 / 0.419271 (-0.365874) \| 0.032306 / 0.043533 (-0.011227) \| 0.278014 / 0.255139 (0.022875) \| 0.301156 / 0.283200 (0.017956) \| 0.017353 / 0.141683 (-0.124330) \| 1.150168 / 1.452155 (-0.301987) \| 1.190822 / 1.492716 (-0.301894) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092162 / 0.018006 (0.074156) \| 0.301031 / 0.000490 (0.300541) \| 0.000244 / 0.000200 (0.000044) \| 0.000062 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020918 / 0.037411 (-0.016494) \| 0.072030 / 0.014526 (0.057504) \| 0.081813 / 0.176557 (-0.094743) \| 0.120233 / 0.737135 (-0.616903) \| 0.082874 / 0.296338 (-0.213465) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291659 / 0.215209 (0.076450) \| 2.841978 / 2.077655 (0.764323) \| 1.594207 / 1.504120 (0.090087) \| 1.473941 / 1.541195 (-0.067254) \| 1.514393 / 1.468490 (0.045903) \| 0.393393 / 4.584777 (-4.191384) \| 2.443663 / 3.745712 (-1.302050) \| 2.545747 / 5.269862 (-2.724114) \| 1.521130 / 4.565676 (-3.044546) \| 0.046246 / 0.424275 (-0.378030) \| 0.004826 / 0.007607 (-0.002781) \| 0.340909 / 0.226044 (0.114865) \| 3.319474 / 2.268929 (1.050546) \| 1.933110 / 55.444624 (-53.511515) \| 1.662463 / 6.876477 (-5.214014) \| 1.670331 / 2.142072 (-0.471742) \| 0.458062 / 4.805227 (-4.347165) \| 0.098397 / 6.500664 (-6.402267) \| 0.041339 / 0.075469 (-0.034130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973718 / 1.841788 (-0.868070) \| 12.095266 / 8.074308 (4.020957) \| 10.761212 / 10.191392 (0.569820) \| 0.142352 / 0.680424 (-0.538072) \| 0.015423 / 0.534201 (-0.518778) \| 0.270912 / 0.579283 (-0.308371) \| 0.276618 / 0.434364 (-0.157746) \| 0.309120 / 0.540337 (-0.231217) \| 0.415330 / 1.386936 (-0.971606) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf4ba6f0e2641056774c01f62984aef5de5d68f1 \"CML watermark\")\n", "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6431). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004676 / 0.011353 (-0.006677) \| 0.003101 / 0.011008 (-0.007907) \| 0.062260 / 0.038508 (0.023752) \| 0.030012 / 0.023109 (0.006903) \| 0.253704 / 0.275898 (-0.022194) \| 0.276404 / 0.323480 (-0.047075) \| 0.004060 / 0.007986 (-0.003926) \| 0.002467 / 0.004328 (-0.001861) \| 0.047921 / 0.004250 (0.043670) \| 0.045760 / 0.037052 (0.008708) \| 0.254529 / 0.258489 (-0.003960) \| 0.286283 / 0.293841 (-0.007558) \| 0.023301 / 0.128546 (-0.105246) \| 0.007407 / 0.075646 (-0.068239) \| 0.204541 / 0.419271 (-0.214730) \| 0.056387 / 0.043533 (0.012854) \| 0.252120 / 0.255139 (-0.003019) \| 0.275795 / 0.283200 (-0.007404) \| 0.018648 / 0.141683 (-0.123034) \| 1.113484 / 1.452155 (-0.338671) \| 1.168685 / 1.492716 (-0.324031) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098286 / 0.018006 (0.080280) \| 0.304619 / 0.000490 (0.304129) \| 0.000225 / 0.000200 (0.000025) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019183 / 0.037411 (-0.018229) \| 0.062183 / 0.014526 (0.047657) \| 0.074288 / 0.176557 (-0.102269) \| 0.120576 / 0.737135 (-0.616560) \| 0.074833 / 0.296338 (-0.221505) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280512 / 0.215209 (0.065303) \| 2.770052 / 2.077655 (0.692397) \| 1.471234 / 1.504120 (-0.032886) \| 1.352080 / 1.541195 (-0.189114) \| 1.374518 / 1.468490 (-0.093973) \| 0.407108 / 4.584777 (-4.177669) \| 2.400581 / 3.745712 (-1.345131) \| 2.677507 / 5.269862 (-2.592355) \| 1.578042 / 4.565676 (-2.987635) \| 0.048539 / 0.424275 (-0.375736) \| 0.004905 / 0.007607 (-0.002703) \| 0.346676 / 0.226044 (0.120631) \| 3.367732 / 2.268929 (1.098803) \| 1.844405 / 55.444624 (-53.600220) \| 1.576883 / 6.876477 (-5.299594) \| 1.666986 / 2.142072 (-0.475086) \| 0.495872 / 4.805227 (-4.309355) \| 0.103142 / 6.500664 (-6.397522) \| 0.044037 / 0.075469 (-0.031432) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980865 / 1.841788 (-0.860923) \| 12.268525 / 8.074308 (4.194217) \| 10.756554 / 10.191392 (0.565162) \| 0.129954 / 0.680424 (-0.550470) \| 0.013864 / 0.534201 (-0.520337) \| 0.267653 / 0.579283 (-0.311630) \| 0.265120 / 0.434364 (-0.169244) \| 0.309050 / 0.540337 (-0.231288) \| 0.423877 / 1.386936 (-0.963059) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005074 / 0.011353 (-0.006279) \| 0.003001 / 0.011008 (-0.008007) \| 0.048271 / 0.038508 (0.009763) \| 0.061206 / 0.023109 (0.038097) \| 0.279268 / 0.275898 (0.003370) \| 0.302592 / 0.323480 (-0.020888) \| 0.004177 / 0.007986 (-0.003809) \| 0.002452 / 0.004328 (-0.001876) \| 0.048259 / 0.004250 (0.044009) \| 0.040032 / 0.037052 (0.002979) \| 0.281398 / 0.258489 (0.022909) \| 0.314121 / 0.293841 (0.020280) \| 0.025137 / 0.128546 (-0.103409) \| 0.007230 / 0.075646 (-0.068416) \| 0.054537 / 0.419271 (-0.364735) \| 0.033266 / 0.043533 (-0.010267) \| 0.277305 / 0.255139 (0.022166) \| 0.295993 / 0.283200 (0.012794) \| 0.019278 / 0.141683 (-0.122405) \| 1.131700 / 1.452155 (-0.320454) \| 1.183848 / 1.492716 (-0.308868) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092258 / 0.018006 (0.074251) \| 0.310668 / 0.000490 (0.310178) \| 0.000219 / 0.000200 (0.000019) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021838 / 0.037411 (-0.015574) \| 0.071382 / 0.014526 (0.056857) \| 0.081389 / 0.176557 (-0.095168) \| 0.120389 / 0.737135 (-0.616746) \| 0.084135 / 0.296338 (-0.212203) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291676 / 0.215209 (0.076467) \| 2.840623 / 2.077655 (0.762968) \| 1.565748 / 1.504120 (0.061628) \| 1.452529 / 1.541195 (-0.088666) \| 1.490633 / 1.468490 (0.022143) \| 0.402878 / 4.584777 (-4.181899) \| 2.486192 / 3.745712 (-1.259520) \| 2.520563 / 5.269862 (-2.749299) \| 1.518550 / 4.565676 (-3.047127) \| 0.047423 / 0.424275 (-0.376852) \| 0.004823 / 0.007607 (-0.002784) \| 0.353122 / 0.226044 (0.127078) \| 3.452136 / 2.268929 (1.183208) \| 1.973798 / 55.444624 (-53.470827) \| 1.669569 / 6.876477 (-5.206907) \| 1.654910 / 2.142072 (-0.487163) \| 0.486746 / 4.805227 (-4.318481) \| 0.097260 / 6.500664 (-6.403404) \| 0.040608 / 0.075469 (-0.034861) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989705 / 1.841788 (-0.852083) \| 12.114386 / 8.074308 (4.040077) \| 11.284551 / 10.191392 (1.093159) \| 0.141408 / 0.680424 (-0.539016) \| 0.015275 / 0.534201 (-0.518926) \| 0.267407 / 0.579283 (-0.311877) \| 0.281007 / 0.434364 (-0.153357) \| 0.309617 / 0.540337 (-0.230720) \| 0.414033 / 1.386936 (-0.972903) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f3e3feec9d7d4d36111401787eb7b5fd51836 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004888 / 0.011353 (-0.006465) \| 0.002775 / 0.011008 (-0.008233) \| 0.062000 / 0.038508 (0.023492) \| 0.050694 / 0.023109 (0.027584) \| 0.257063 / 0.275898 (-0.018835) \| 0.282743 / 0.323480 (-0.040736) \| 0.002862 / 0.007986 (-0.005124) \| 0.002305 / 0.004328 (-0.002023) \| 0.049549 / 0.004250 (0.045299) \| 0.038754 / 0.037052 (0.001701) \| 0.264047 / 0.258489 (0.005558) \| 0.310162 / 0.293841 (0.016321) \| 0.022901 / 0.128546 (-0.105645) \| 0.006894 / 0.075646 (-0.068752) \| 0.202467 / 0.419271 (-0.216805) \| 0.035901 / 0.043533 (-0.007631) \| 0.262344 / 0.255139 (0.007205) \| 0.285563 / 0.283200 (0.002364) \| 0.017070 / 0.141683 (-0.124613) \| 1.113972 / 1.452155 (-0.338182) \| 1.176261 / 1.492716 (-0.316455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092912 / 0.018006 (0.074906) \| 0.302610 / 0.000490 (0.302120) \| 0.000204 / 0.000200 (0.000005) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018232 / 0.037411 (-0.019179) \| 0.062367 / 0.014526 (0.047841) \| 0.074570 / 0.176557 (-0.101987) \| 0.120468 / 0.737135 (-0.616668) \| 0.075187 / 0.296338 (-0.221151) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279760 / 0.215209 (0.064551) \| 2.715372 / 2.077655 (0.637717) \| 1.461636 / 1.504120 (-0.042484) \| 1.324220 / 1.541195 (-0.216975) \| 1.350724 / 1.468490 (-0.117766) \| 0.395648 / 4.584777 (-4.189129) \| 2.376548 / 3.745712 (-1.369164) \| 2.594662 / 5.269862 (-2.675200) \| 1.553528 / 4.565676 (-3.012148) \| 0.047875 / 0.424275 (-0.376400) \| 0.005287 / 0.007607 (-0.002321) \| 0.334734 / 0.226044 (0.108689) \| 3.294753 / 2.268929 (1.025825) \| 1.797901 / 55.444624 (-53.646724) \| 1.510907 / 6.876477 (-5.365570) \| 1.536070 / 2.142072 (-0.606003) \| 0.474672 / 4.805227 (-4.330555) \| 0.099323 / 6.500664 (-6.401341) \| 0.041703 / 0.075469 (-0.033766) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947441 / 1.841788 (-0.894347) \| 11.451378 / 8.074308 (3.377070) \| 10.283213 / 10.191392 (0.091821) \| 0.131032 / 0.680424 (-0.549392) \| 0.014423 / 0.534201 (-0.519777) \| 0.272568 / 0.579283 (-0.306715) \| 0.267127 / 0.434364 (-0.167237) \| 0.307361 / 0.540337 (-0.232976) \| 0.403858 / 1.386936 (-0.983078) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004836 / 0.011353 (-0.006517) \| 0.002544 / 0.011008 (-0.008464) \| 0.047979 / 0.038508 (0.009471) \| 0.052211 / 0.023109 (0.029102) \| 0.273394 / 0.275898 (-0.002504) \| 0.291202 / 0.323480 (-0.032277) \| 0.004094 / 0.007986 (-0.003891) \| 0.002415 / 0.004328 (-0.001914) \| 0.048057 / 0.004250 (0.043807) \| 0.039756 / 0.037052 (0.002703) \| 0.277301 / 0.258489 (0.018812) \| 0.297626 / 0.293841 (0.003785) \| 0.024641 / 0.128546 (-0.103905) \| 0.006957 / 0.075646 (-0.068690) \| 0.053574 / 0.419271 (-0.365697) \| 0.036532 / 0.043533 (-0.007001) \| 0.273753 / 0.255139 (0.018614) \| 0.294254 / 0.283200 (0.011054) \| 0.022252 / 0.141683 (-0.119431) \| 1.128609 / 1.452155 (-0.323546) \| 1.217322 / 1.492716 (-0.275394) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091050 / 0.018006 (0.073044) \| 0.300089 / 0.000490 (0.299600) \| 0.000215 / 0.000200 (0.000015) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021423 / 0.037411 (-0.015988) \| 0.069892 / 0.014526 (0.055366) \| 0.081125 / 0.176557 (-0.095432) \| 0.118725 / 0.737135 (-0.618411) \| 0.081357 / 0.296338 (-0.214981) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295046 / 0.215209 (0.079837) \| 2.868813 / 2.077655 (0.791159) \| 1.579613 / 1.504120 (0.075493) \| 1.449308 / 1.541195 (-0.091887) \| 1.478804 / 1.468490 (0.010314) \| 0.416916 / 4.584777 (-4.167861) \| 2.461093 / 3.745712 (-1.284619) \| 2.449792 / 5.269862 (-2.820070) \| 1.573930 / 4.565676 (-2.991746) \| 0.046808 / 0.424275 (-0.377467) \| 0.004811 / 0.007607 (-0.002796) \| 0.352805 / 0.226044 (0.126761) \| 3.495034 / 2.268929 (1.226105) \| 1.952019 / 55.444624 (-53.492606) \| 1.642607 / 6.876477 (-5.233869) \| 1.775235 / 2.142072 (-0.366837) \| 0.482196 / 4.805227 (-4.323032) \| 0.099562 / 6.500664 (-6.401102) \| 0.040709 / 0.075469 (-0.034760) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972750 / 1.841788 (-0.869038) \| 11.905172 / 8.074308 (3.830864) \| 10.613847 / 10.191392 (0.422455) \| 0.129892 / 0.680424 (-0.550532) \| 0.015611 / 0.534201 (-0.518590) \| 0.271884 / 0.579283 (-0.307400) \| 0.275270 / 0.434364 (-0.159094) \| 0.303213 / 0.540337 (-0.237125) \| 0.402338 / 1.386936 (-0.984598) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf8fa7ad7609ad34d4cc689f529ea606dd2560e0 \"CML watermark\")\n", "I think this PR can be merged.", "you already have an approval, feel free to merge!\r\n" ]	2023-11-16T16:02:55	2023-11-21T01:43:12	null	MEMBER	null	Create `DatasetNotFoundError` and `DataFilesNotFoundError`. Fix #6397. CC: @severo	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6431/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6431/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6431", "html_url": "https://github.com/huggingface/datasets/pull/6431", "diff_url": "https://github.com/huggingface/datasets/pull/6431.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6431.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6429	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6429/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6429/comments	https://api.github.com/repos/huggingface/datasets/issues/6429/events	https://github.com/huggingface/datasets/pull/6429	1,996,723,698	PR_kwDODunzps5fn1r_	6,429	Add trust_remote_code argument	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004947 / 0.011353 (-0.006405) \| 0.002961 / 0.011008 (-0.008047) \| 0.063474 / 0.038508 (0.024966) \| 0.030162 / 0.023109 (0.007053) \| 0.232388 / 0.275898 (-0.043511) \| 0.257654 / 0.323480 (-0.065826) \| 0.002969 / 0.007986 (-0.005017) \| 0.002336 / 0.004328 (-0.001993) \| 0.049724 / 0.004250 (0.045473) \| 0.045608 / 0.037052 (0.008555) \| 0.236079 / 0.258489 (-0.022410) \| 0.267809 / 0.293841 (-0.026032) \| 0.023805 / 0.128546 (-0.104741) \| 0.007177 / 0.075646 (-0.068470) \| 0.202167 / 0.419271 (-0.217104) \| 0.056181 / 0.043533 (0.012648) \| 0.256464 / 0.255139 (0.001325) \| 0.271908 / 0.283200 (-0.011292) \| 0.020211 / 0.141683 (-0.121472) \| 1.114112 / 1.452155 (-0.338042) \| 1.174879 / 1.492716 (-0.317837) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093457 / 0.018006 (0.075451) \| 0.307643 / 0.000490 (0.307154) \| 0.000212 / 0.000200 (0.000012) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018635 / 0.037411 (-0.018777) \| 0.062099 / 0.014526 (0.047573) \| 0.073619 / 0.176557 (-0.102938) \| 0.119986 / 0.737135 (-0.617149) \| 0.075439 / 0.296338 (-0.220899) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280142 / 0.215209 (0.064933) \| 2.733790 / 2.077655 (0.656136) \| 1.457633 / 1.504120 (-0.046487) \| 1.336288 / 1.541195 (-0.204907) \| 1.363191 / 1.468490 (-0.105299) \| 0.399331 / 4.584777 (-4.185446) \| 2.343099 / 3.745712 (-1.402614) \| 2.617059 / 5.269862 (-2.652802) \| 1.575912 / 4.565676 (-2.989765) \| 0.045621 / 0.424275 (-0.378655) \| 0.004825 / 0.007607 (-0.002782) \| 0.346669 / 0.226044 (0.120625) \| 3.225982 / 2.268929 (0.957054) \| 1.787067 / 55.444624 (-53.657557) \| 1.503883 / 6.876477 (-5.372593) \| 1.527593 / 2.142072 (-0.614479) \| 0.466806 / 4.805227 (-4.338421) \| 0.098537 / 6.500664 (-6.402127) \| 0.042028 / 0.075469 (-0.033441) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945040 / 1.841788 (-0.896748) \| 11.970022 / 8.074308 (3.895714) \| 10.261176 / 10.191392 (0.069784) \| 0.138231 / 0.680424 (-0.542193) \| 0.013933 / 0.534201 (-0.520268) \| 0.270640 / 0.579283 (-0.308643) \| 0.263185 / 0.434364 (-0.171178) \| 0.306686 / 0.540337 (-0.233651) \| 0.423164 / 1.386936 (-0.963772) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004765 / 0.011353 (-0.006588) \| 0.003158 / 0.011008 (-0.007850) \| 0.047813 / 0.038508 (0.009305) \| 0.053363 / 0.023109 (0.030254) \| 0.278570 / 0.275898 (0.002671) \| 0.291500 / 0.323480 (-0.031980) \| 0.003987 / 0.007986 (-0.003998) \| 0.002430 / 0.004328 (-0.001898) \| 0.048059 / 0.004250 (0.043809) \| 0.038595 / 0.037052 (0.001542) \| 0.276383 / 0.258489 (0.017894) \| 0.304234 / 0.293841 (0.010393) \| 0.024402 / 0.128546 (-0.104144) \| 0.007303 / 0.075646 (-0.068343) \| 0.055091 / 0.419271 (-0.364180) \| 0.032735 / 0.043533 (-0.010797) \| 0.270905 / 0.255139 (0.015766) \| 0.287181 / 0.283200 (0.003981) \| 0.018919 / 0.141683 (-0.122764) \| 1.153814 / 1.452155 (-0.298341) \| 1.197009 / 1.492716 (-0.295707) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093743 / 0.018006 (0.075737) \| 0.302877 / 0.000490 (0.302387) \| 0.000223 / 0.000200 (0.000023) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021279 / 0.037411 (-0.016133) \| 0.070886 / 0.014526 (0.056360) \| 0.081628 / 0.176557 (-0.094928) \| 0.119721 / 0.737135 (-0.617414) \| 0.083093 / 0.296338 (-0.213245) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297788 / 0.215209 (0.082579) \| 2.915235 / 2.077655 (0.837580) \| 1.587580 / 1.504120 (0.083460) \| 1.461699 / 1.541195 (-0.079495) \| 1.520609 / 1.468490 (0.052119) \| 0.398363 / 4.584777 (-4.186413) \| 2.408415 / 3.745712 (-1.337297) \| 2.552776 / 5.269862 (-2.717086) \| 1.508219 / 4.565676 (-3.057457) \| 0.045884 / 0.424275 (-0.378391) \| 0.004842 / 0.007607 (-0.002765) \| 0.341376 / 0.226044 (0.115331) \| 3.420192 / 2.268929 (1.151264) \| 1.974938 / 55.444624 (-53.469686) \| 1.678283 / 6.876477 (-5.198194) \| 1.702439 / 2.142072 (-0.439633) \| 0.467056 / 4.805227 (-4.338172) \| 0.098684 / 6.500664 (-6.401980) \| 0.041052 / 0.075469 (-0.034417) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.990145 / 1.841788 (-0.851643) \| 12.143198 / 8.074308 (4.068890) \| 10.911039 / 10.191392 (0.719647) \| 0.130384 / 0.680424 (-0.550040) \| 0.015602 / 0.534201 (-0.518599) \| 0.270799 / 0.579283 (-0.308484) \| 0.279060 / 0.434364 (-0.155304) \| 0.315108 / 0.540337 (-0.225230) \| 0.413576 / 1.386936 (-0.973360) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d99b8225e28cca88ed9c2d9b1d8e0342762c4ece \"CML watermark\")\n", "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6429). All of your documentation changes will be reflected on that endpoint." ]	2023-11-16T12:12:54	2023-11-17T13:45:31	null	MEMBER	null	Draft about adding `trust_remote_code` to `load_dataset`. ```python ds = load_dataset(..., trust_remote_code=True) # run remote code (current default) ``` It would default to `True` (current behavior) and in the next major release it will prompt the user to check the code before running it (we'll communicate on this before doing it of course). ```python # in the future ds = load_dataset(...) # prompt the user to check the code before running it (future default) ds = load_dataset(..., trust_remote_code=True) # run remote code ds = load_dataset(..., trust_remote_code=False) # disallow remote code ``` Related to https://github.com/huggingface/datasets/issues/6400 Will do a separate PR to use the parquet export when possible	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6429/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6429/timeline	null	null	true	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6429", "html_url": "https://github.com/huggingface/datasets/pull/6429", "diff_url": "https://github.com/huggingface/datasets/pull/6429.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6429.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6428	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6428/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6428/comments	https://api.github.com/repos/huggingface/datasets/issues/6428/events	https://github.com/huggingface/datasets/pull/6428	1,996,306,394	PR_kwDODunzps5fmakS	6,428	Set dev version	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6428). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004839 / 0.011353 (-0.006514) \| 0.002928 / 0.011008 (-0.008080) \| 0.061730 / 0.038508 (0.023221) \| 0.030523 / 0.023109 (0.007414) \| 0.252679 / 0.275898 (-0.023219) \| 0.281597 / 0.323480 (-0.041883) \| 0.003025 / 0.007986 (-0.004961) \| 0.002374 / 0.004328 (-0.001955) \| 0.048134 / 0.004250 (0.043884) \| 0.045843 / 0.037052 (0.008791) \| 0.256274 / 0.258489 (-0.002215) \| 0.288704 / 0.293841 (-0.005137) \| 0.023486 / 0.128546 (-0.105060) \| 0.007186 / 0.075646 (-0.068461) \| 0.202519 / 0.419271 (-0.216753) \| 0.058192 / 0.043533 (0.014659) \| 0.256448 / 0.255139 (0.001309) \| 0.279417 / 0.283200 (-0.003783) \| 0.019942 / 0.141683 (-0.121740) \| 1.100954 / 1.452155 (-0.351201) \| 1.168183 / 1.492716 (-0.324533) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091314 / 0.018006 (0.073308) \| 0.298614 / 0.000490 (0.298124) \| 0.000232 / 0.000200 (0.000032) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018071 / 0.037411 (-0.019340) \| 0.062265 / 0.014526 (0.047740) \| 0.073228 / 0.176557 (-0.103328) \| 0.119163 / 0.737135 (-0.617972) \| 0.074717 / 0.296338 (-0.221622) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273906 / 0.215209 (0.058697) \| 2.683995 / 2.077655 (0.606340) \| 1.418773 / 1.504120 (-0.085347) \| 1.310473 / 1.541195 (-0.230722) \| 1.303152 / 1.468490 (-0.165339) \| 0.390846 / 4.584777 (-4.193931) \| 2.346407 / 3.745712 (-1.399305) \| 2.582945 / 5.269862 (-2.686916) \| 1.569549 / 4.565676 (-2.996128) \| 0.044893 / 0.424275 (-0.379383) \| 0.004754 / 0.007607 (-0.002853) \| 0.323491 / 0.226044 (0.097447) \| 3.229736 / 2.268929 (0.960808) \| 1.783551 / 55.444624 (-53.661074) \| 1.499685 / 6.876477 (-5.376792) \| 1.515826 / 2.142072 (-0.626246) \| 0.475768 / 4.805227 (-4.329460) \| 0.099579 / 6.500664 (-6.401085) \| 0.042709 / 0.075469 (-0.032760) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.926120 / 1.841788 (-0.915667) \| 11.597189 / 8.074308 (3.522881) \| 10.327055 / 10.191392 (0.135663) \| 0.127479 / 0.680424 (-0.552945) \| 0.014844 / 0.534201 (-0.519357) \| 0.261181 / 0.579283 (-0.318102) \| 0.258407 / 0.434364 (-0.175957) \| 0.303192 / 0.540337 (-0.237146) \| 0.416665 / 1.386936 (-0.970271) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004759 / 0.011353 (-0.006594) \| 0.002780 / 0.011008 (-0.008228) \| 0.047991 / 0.038508 (0.009483) \| 0.052263 / 0.023109 (0.029153) \| 0.261228 / 0.275898 (-0.014670) \| 0.287779 / 0.323480 (-0.035701) \| 0.003961 / 0.007986 (-0.004024) \| 0.002357 / 0.004328 (-0.001971) \| 0.047755 / 0.004250 (0.043505) \| 0.038066 / 0.037052 (0.001014) \| 0.269502 / 0.258489 (0.011013) \| 0.298348 / 0.293841 (0.004507) \| 0.024398 / 0.128546 (-0.104149) \| 0.007189 / 0.075646 (-0.068457) \| 0.053356 / 0.419271 (-0.365915) \| 0.032459 / 0.043533 (-0.011074) \| 0.266389 / 0.255139 (0.011250) \| 0.305367 / 0.283200 (0.022168) \| 0.017629 / 0.141683 (-0.124054) \| 1.145789 / 1.452155 (-0.306366) \| 1.204778 / 1.492716 (-0.287938) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091347 / 0.018006 (0.073341) \| 0.298671 / 0.000490 (0.298181) \| 0.000229 / 0.000200 (0.000029) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021374 / 0.037411 (-0.016037) \| 0.068869 / 0.014526 (0.054344) \| 0.080443 / 0.176557 (-0.096113) \| 0.118759 / 0.737135 (-0.618376) \| 0.081646 / 0.296338 (-0.214692) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295274 / 0.215209 (0.080065) \| 2.889349 / 2.077655 (0.811695) \| 1.561020 / 1.504120 (0.056900) \| 1.425025 / 1.541195 (-0.116170) \| 1.495446 / 1.468490 (0.026956) \| 0.403825 / 4.584777 (-4.180952) \| 2.404905 / 3.745712 (-1.340807) \| 2.590104 / 5.269862 (-2.679758) \| 1.570559 / 4.565676 (-2.995118) \| 0.046342 / 0.424275 (-0.377933) \| 0.004799 / 0.007607 (-0.002809) \| 0.349981 / 0.226044 (0.123937) \| 3.437341 / 2.268929 (1.168412) \| 1.948155 / 55.444624 (-53.496469) \| 1.637765 / 6.876477 (-5.238711) \| 1.671521 / 2.142072 (-0.470551) \| 0.479500 / 4.805227 (-4.325727) \| 0.098305 / 6.500664 (-6.402359) \| 0.040864 / 0.075469 (-0.034605) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979986 / 1.841788 (-0.861801) \| 12.169722 / 8.074308 (4.095413) \| 11.297345 / 10.191392 (1.105953) \| 0.129123 / 0.680424 (-0.551301) \| 0.015389 / 0.534201 (-0.518812) \| 0.270964 / 0.579283 (-0.308319) \| 0.269590 / 0.434364 (-0.164774) \| 0.310662 / 0.540337 (-0.229675) \| 0.406272 / 1.386936 (-0.980664) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31873f1e9acbe013e6d396d1ed5492db8cd59dd3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004620 / 0.011353 (-0.006733) \| 0.002971 / 0.011008 (-0.008038) \| 0.062864 / 0.038508 (0.024355) \| 0.028743 / 0.023109 (0.005634) \| 0.246729 / 0.275898 (-0.029169) \| 0.271165 / 0.323480 (-0.052315) \| 0.003930 / 0.007986 (-0.004056) \| 0.002422 / 0.004328 (-0.001906) \| 0.047430 / 0.004250 (0.043180) \| 0.044895 / 0.037052 (0.007843) \| 0.249128 / 0.258489 (-0.009361) \| 0.283384 / 0.293841 (-0.010457) \| 0.023288 / 0.128546 (-0.105259) \| 0.007241 / 0.075646 (-0.068405) \| 0.207551 / 0.419271 (-0.211720) \| 0.055008 / 0.043533 (0.011475) \| 0.252781 / 0.255139 (-0.002358) \| 0.296924 / 0.283200 (0.013724) \| 0.017860 / 0.141683 (-0.123822) \| 1.094597 / 1.452155 (-0.357558) \| 1.162314 / 1.492716 (-0.330402) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091423 / 0.018006 (0.073417) \| 0.302833 / 0.000490 (0.302343) \| 0.000242 / 0.000200 (0.000042) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018143 / 0.037411 (-0.019268) \| 0.066371 / 0.014526 (0.051845) \| 0.072774 / 0.176557 (-0.103783) \| 0.119062 / 0.737135 (-0.618073) \| 0.102836 / 0.296338 (-0.193502) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280117 / 0.215209 (0.064908) \| 2.757955 / 2.077655 (0.680301) \| 1.494994 / 1.504120 (-0.009126) \| 1.375325 / 1.541195 (-0.165870) \| 1.384179 / 1.468490 (-0.084311) \| 0.399824 / 4.584777 (-4.184953) \| 2.368575 / 3.745712 (-1.377137) \| 2.574035 / 5.269862 (-2.695827) \| 1.548738 / 4.565676 (-3.016939) \| 0.045841 / 0.424275 (-0.378434) \| 0.004799 / 0.007607 (-0.002808) \| 0.331522 / 0.226044 (0.105478) \| 3.324471 / 2.268929 (1.055543) \| 1.838637 / 55.444624 (-53.605987) \| 1.562854 / 6.876477 (-5.313623) \| 1.581736 / 2.142072 (-0.560336) \| 0.468832 / 4.805227 (-4.336396) \| 0.099309 / 6.500664 (-6.401355) \| 0.042078 / 0.075469 (-0.033391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.928468 / 1.841788 (-0.913320) \| 11.331143 / 8.074308 (3.256835) \| 10.296213 / 10.191392 (0.104821) \| 0.138912 / 0.680424 (-0.541511) \| 0.014044 / 0.534201 (-0.520157) \| 0.267293 / 0.579283 (-0.311991) \| 0.267267 / 0.434364 (-0.167097) \| 0.306560 / 0.540337 (-0.233778) \| 0.423926 / 1.386936 (-0.963010) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004842 / 0.011353 (-0.006511) \| 0.002917 / 0.011008 (-0.008091) \| 0.048263 / 0.038508 (0.009755) \| 0.051453 / 0.023109 (0.028344) \| 0.278330 / 0.275898 (0.002432) \| 0.298569 / 0.323480 (-0.024911) \| 0.003936 / 0.007986 (-0.004049) \| 0.002479 / 0.004328 (-0.001850) \| 0.048281 / 0.004250 (0.044031) \| 0.038925 / 0.037052 (0.001872) \| 0.285258 / 0.258489 (0.026769) \| 0.313701 / 0.293841 (0.019860) \| 0.024916 / 0.128546 (-0.103630) \| 0.007142 / 0.075646 (-0.068504) \| 0.053634 / 0.419271 (-0.365638) \| 0.032842 / 0.043533 (-0.010690) \| 0.279373 / 0.255139 (0.024234) \| 0.295844 / 0.283200 (0.012644) \| 0.018142 / 0.141683 (-0.123541) \| 1.136960 / 1.452155 (-0.315195) \| 1.184438 / 1.492716 (-0.308278) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090271 / 0.018006 (0.072264) \| 0.299940 / 0.000490 (0.299450) \| 0.000234 / 0.000200 (0.000034) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021175 / 0.037411 (-0.016237) \| 0.070924 / 0.014526 (0.056398) \| 0.080584 / 0.176557 (-0.095972) \| 0.119278 / 0.737135 (-0.617857) \| 0.082361 / 0.296338 (-0.213977) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298312 / 0.215209 (0.083103) \| 2.895361 / 2.077655 (0.817706) \| 1.616120 / 1.504120 (0.112001) \| 1.484444 / 1.541195 (-0.056750) \| 1.541893 / 1.468490 (0.073403) \| 0.409968 / 4.584777 (-4.174809) \| 2.423639 / 3.745712 (-1.322073) \| 2.585122 / 5.269862 (-2.684740) \| 1.540343 / 4.565676 (-3.025333) \| 0.046604 / 0.424275 (-0.377671) \| 0.004742 / 0.007607 (-0.002865) \| 0.341659 / 0.226044 (0.115614) \| 3.409259 / 2.268929 (1.140330) \| 2.007068 / 55.444624 (-53.437556) \| 1.681348 / 6.876477 (-5.195129) \| 1.719253 / 2.142072 (-0.422819) \| 0.482301 / 4.805227 (-4.322926) \| 0.099619 / 6.500664 (-6.401045) \| 0.041247 / 0.075469 (-0.034222) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971783 / 1.841788 (-0.870004) \| 12.208000 / 8.074308 (4.133692) \| 10.948230 / 10.191392 (0.756838) \| 0.131824 / 0.680424 (-0.548599) \| 0.015696 / 0.534201 (-0.518505) \| 0.272265 / 0.579283 (-0.307018) \| 0.276093 / 0.434364 (-0.158270) \| 0.305897 / 0.540337 (-0.234441) \| 0.411632 / 1.386936 (-0.975304) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bf75fe522c6fedd16d00b4a928f613dee11f73c \"CML watermark\")\n" ]	2023-11-16T08:12:55	2023-11-16T08:19:39	2023-11-16T08:13:28	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6428/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6428/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6428", "html_url": "https://github.com/huggingface/datasets/pull/6428", "diff_url": "https://github.com/huggingface/datasets/pull/6428.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6428.patch", "merged_at": "2023-11-16T08:13:28" }	true
https://api.github.com/repos/huggingface/datasets/issues/6427	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6427/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6427/comments	https://api.github.com/repos/huggingface/datasets/issues/6427/events	https://github.com/huggingface/datasets/pull/6427	1,996,248,605	PR_kwDODunzps5fmN1_	6,427	Release: 2.15.0	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004331 / 0.011353 (-0.007022) \| 0.002573 / 0.011008 (-0.008435) \| 0.061002 / 0.038508 (0.022494) \| 0.029259 / 0.023109 (0.006149) \| 0.242983 / 0.275898 (-0.032915) \| 0.267629 / 0.323480 (-0.055851) \| 0.003906 / 0.007986 (-0.004080) \| 0.002383 / 0.004328 (-0.001946) \| 0.047574 / 0.004250 (0.043323) \| 0.042153 / 0.037052 (0.005101) \| 0.245821 / 0.258489 (-0.012668) \| 0.276479 / 0.293841 (-0.017362) \| 0.022498 / 0.128546 (-0.106049) \| 0.006775 / 0.075646 (-0.068871) \| 0.201795 / 0.419271 (-0.217477) \| 0.052443 / 0.043533 (0.008910) \| 0.248320 / 0.255139 (-0.006819) \| 0.261964 / 0.283200 (-0.021235) \| 0.016764 / 0.141683 (-0.124919) \| 1.118702 / 1.452155 (-0.333453) \| 1.203079 / 1.492716 (-0.289638) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088808 / 0.018006 (0.070801) \| 0.296526 / 0.000490 (0.296037) \| 0.000203 / 0.000200 (0.000003) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018816 / 0.037411 (-0.018595) \| 0.062295 / 0.014526 (0.047769) \| 0.075228 / 0.176557 (-0.101329) \| 0.119916 / 0.737135 (-0.617219) \| 0.077206 / 0.296338 (-0.219132) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276723 / 0.215209 (0.061514) \| 2.711431 / 2.077655 (0.633776) \| 1.425590 / 1.504120 (-0.078530) \| 1.301383 / 1.541195 (-0.239812) \| 1.316314 / 1.468490 (-0.152176) \| 0.402709 / 4.584777 (-4.182068) \| 2.347229 / 3.745712 (-1.398483) \| 2.596937 / 5.269862 (-2.672925) \| 1.560658 / 4.565676 (-3.005018) \| 0.046162 / 0.424275 (-0.378113) \| 0.004760 / 0.007607 (-0.002848) \| 0.330522 / 0.226044 (0.104478) \| 3.244072 / 2.268929 (0.975143) \| 1.747603 / 55.444624 (-53.697021) \| 1.475534 / 6.876477 (-5.400943) \| 1.485135 / 2.142072 (-0.656938) \| 0.476794 / 4.805227 (-4.328433) \| 0.098496 / 6.500664 (-6.402168) \| 0.040740 / 0.075469 (-0.034729) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939020 / 1.841788 (-0.902768) \| 11.235187 / 8.074308 (3.160878) \| 10.194975 / 10.191392 (0.003583) \| 0.126241 / 0.680424 (-0.554182) \| 0.013990 / 0.534201 (-0.520211) \| 0.269149 / 0.579283 (-0.310134) \| 0.256950 / 0.434364 (-0.177414) \| 0.301282 / 0.540337 (-0.239056) \| 0.421490 / 1.386936 (-0.965446) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004956 / 0.011353 (-0.006397) \| 0.002478 / 0.011008 (-0.008530) \| 0.047773 / 0.038508 (0.009265) \| 0.050076 / 0.023109 (0.026967) \| 0.261915 / 0.275898 (-0.013983) \| 0.282553 / 0.323480 (-0.040927) \| 0.003881 / 0.007986 (-0.004105) \| 0.002329 / 0.004328 (-0.001999) \| 0.048091 / 0.004250 (0.043841) \| 0.038188 / 0.037052 (0.001135) \| 0.265502 / 0.258489 (0.007013) \| 0.292568 / 0.293841 (-0.001273) \| 0.024172 / 0.128546 (-0.104374) \| 0.006865 / 0.075646 (-0.068781) \| 0.053199 / 0.419271 (-0.366072) \| 0.032201 / 0.043533 (-0.011332) \| 0.265774 / 0.255139 (0.010635) \| 0.277954 / 0.283200 (-0.005245) \| 0.017798 / 0.141683 (-0.123885) \| 1.121503 / 1.452155 (-0.330652) \| 1.176319 / 1.492716 (-0.316398) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087027 / 0.018006 (0.069020) \| 0.296182 / 0.000490 (0.295693) \| 0.000216 / 0.000200 (0.000017) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020990 / 0.037411 (-0.016421) \| 0.069693 / 0.014526 (0.055168) \| 0.081098 / 0.176557 (-0.095459) \| 0.117760 / 0.737135 (-0.619375) \| 0.081493 / 0.296338 (-0.214845) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295078 / 0.215209 (0.079869) \| 2.876602 / 2.077655 (0.798947) \| 1.558011 / 1.504120 (0.053891) \| 1.426715 / 1.541195 (-0.114480) \| 1.443785 / 1.468490 (-0.024705) \| 0.400826 / 4.584777 (-4.183951) \| 2.378903 / 3.745712 (-1.366810) \| 2.473128 / 5.269862 (-2.796734) \| 1.500785 / 4.565676 (-3.064891) \| 0.045438 / 0.424275 (-0.378837) \| 0.004953 / 0.007607 (-0.002654) \| 0.348182 / 0.226044 (0.122137) \| 3.427751 / 2.268929 (1.158822) \| 1.925173 / 55.444624 (-53.519451) \| 1.633354 / 6.876477 (-5.243123) \| 1.651573 / 2.142072 (-0.490499) \| 0.473260 / 4.805227 (-4.331968) \| 0.097613 / 6.500664 (-6.403051) \| 0.040196 / 0.075469 (-0.035273) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951780 / 1.841788 (-0.890008) \| 11.709342 / 8.074308 (3.635034) \| 10.571831 / 10.191392 (0.380439) \| 0.134344 / 0.680424 (-0.546079) \| 0.022116 / 0.534201 (-0.512084) \| 0.269651 / 0.579283 (-0.309632) \| 0.272310 / 0.434364 (-0.162054) \| 0.306434 / 0.540337 (-0.233903) \| 0.408320 / 1.386936 (-0.978616) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ea64b77079cf76675421917472c05d06ace63fc \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004402 / 0.011353 (-0.006951) \| 0.002732 / 0.011008 (-0.008277) \| 0.062799 / 0.038508 (0.024291) \| 0.029155 / 0.023109 (0.006046) \| 0.241925 / 0.275898 (-0.033973) \| 0.275694 / 0.323480 (-0.047786) \| 0.003989 / 0.007986 (-0.003997) \| 0.002528 / 0.004328 (-0.001801) \| 0.048410 / 0.004250 (0.044160) \| 0.043729 / 0.037052 (0.006677) \| 0.248843 / 0.258489 (-0.009646) \| 0.282980 / 0.293841 (-0.010860) \| 0.023828 / 0.128546 (-0.104718) \| 0.006972 / 0.075646 (-0.068675) \| 0.213222 / 0.419271 (-0.206049) \| 0.054883 / 0.043533 (0.011350) \| 0.251353 / 0.255139 (-0.003786) \| 0.269818 / 0.283200 (-0.013381) \| 0.016906 / 0.141683 (-0.124777) \| 1.114109 / 1.452155 (-0.338045) \| 1.162942 / 1.492716 (-0.329774) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093724 / 0.018006 (0.075718) \| 0.301989 / 0.000490 (0.301499) \| 0.000213 / 0.000200 (0.000014) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018245 / 0.037411 (-0.019166) \| 0.062237 / 0.014526 (0.047712) \| 0.075644 / 0.176557 (-0.100913) \| 0.119655 / 0.737135 (-0.617480) \| 0.074525 / 0.296338 (-0.221814) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274534 / 0.215209 (0.059324) \| 2.683678 / 2.077655 (0.606024) \| 1.453306 / 1.504120 (-0.050814) \| 1.347630 / 1.541195 (-0.193564) \| 1.352875 / 1.468490 (-0.115615) \| 0.398425 / 4.584777 (-4.186352) \| 2.375738 / 3.745712 (-1.369974) \| 2.591573 / 5.269862 (-2.678289) \| 1.555527 / 4.565676 (-3.010150) \| 0.045656 / 0.424275 (-0.378619) \| 0.004898 / 0.007607 (-0.002709) \| 0.330591 / 0.226044 (0.104547) \| 3.247638 / 2.268929 (0.978710) \| 1.816676 / 55.444624 (-53.627948) \| 1.531754 / 6.876477 (-5.344723) \| 1.543196 / 2.142072 (-0.598877) \| 0.472489 / 4.805227 (-4.332739) \| 0.099311 / 6.500664 (-6.401353) \| 0.042139 / 0.075469 (-0.033330) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945472 / 1.841788 (-0.896316) \| 11.476550 / 8.074308 (3.402242) \| 10.281157 / 10.191392 (0.089765) \| 0.141062 / 0.680424 (-0.539362) \| 0.013634 / 0.534201 (-0.520567) \| 0.268778 / 0.579283 (-0.310505) \| 0.263542 / 0.434364 (-0.170822) \| 0.307918 / 0.540337 (-0.232420) \| 0.421231 / 1.386936 (-0.965705) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005090 / 0.011353 (-0.006263) \| 0.003135 / 0.011008 (-0.007873) \| 0.048058 / 0.038508 (0.009550) \| 0.052898 / 0.023109 (0.029789) \| 0.273233 / 0.275898 (-0.002665) \| 0.299516 / 0.323480 (-0.023964) \| 0.004126 / 0.007986 (-0.003860) \| 0.002331 / 0.004328 (-0.001997) \| 0.047627 / 0.004250 (0.043376) \| 0.039076 / 0.037052 (0.002023) \| 0.276625 / 0.258489 (0.018136) \| 0.308180 / 0.293841 (0.014340) \| 0.024929 / 0.128546 (-0.103618) \| 0.007396 / 0.075646 (-0.068251) \| 0.053408 / 0.419271 (-0.365863) \| 0.032896 / 0.043533 (-0.010637) \| 0.275412 / 0.255139 (0.020273) \| 0.292014 / 0.283200 (0.008814) \| 0.018336 / 0.141683 (-0.123347) \| 1.123565 / 1.452155 (-0.328589) \| 1.175382 / 1.492716 (-0.317334) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093799 / 0.018006 (0.075793) \| 0.304219 / 0.000490 (0.303729) \| 0.000231 / 0.000200 (0.000031) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021034 / 0.037411 (-0.016377) \| 0.069961 / 0.014526 (0.055435) \| 0.080311 / 0.176557 (-0.096246) \| 0.118603 / 0.737135 (-0.618532) \| 0.084003 / 0.296338 (-0.212335) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305610 / 0.215209 (0.090401) \| 2.962027 / 2.077655 (0.884372) \| 1.598604 / 1.504120 (0.094484) \| 1.476227 / 1.541195 (-0.064967) \| 1.528960 / 1.468490 (0.060470) \| 0.404545 / 4.584777 (-4.180232) \| 2.423147 / 3.745712 (-1.322565) \| 2.516632 / 5.269862 (-2.753229) \| 1.529000 / 4.565676 (-3.036677) \| 0.045780 / 0.424275 (-0.378495) \| 0.004784 / 0.007607 (-0.002823) \| 0.358836 / 0.226044 (0.132792) \| 3.508782 / 2.268929 (1.239853) \| 1.954513 / 55.444624 (-53.490111) \| 1.672824 / 6.876477 (-5.203653) \| 1.683482 / 2.142072 (-0.458590) \| 0.479014 / 4.805227 (-4.326213) \| 0.098325 / 6.500664 (-6.402340) \| 0.040934 / 0.075469 (-0.034536) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974770 / 1.841788 (-0.867017) \| 11.956137 / 8.074308 (3.881829) \| 10.956458 / 10.191392 (0.765066) \| 0.141800 / 0.680424 (-0.538624) \| 0.015439 / 0.534201 (-0.518762) \| 0.271783 / 0.579283 (-0.307500) \| 0.278058 / 0.434364 (-0.156306) \| 0.305823 / 0.540337 (-0.234514) \| 0.415677 / 1.386936 (-0.971259) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004483 / 0.011353 (-0.006870) \| 0.002560 / 0.011008 (-0.008448) \| 0.061428 / 0.038508 (0.022920) \| 0.029460 / 0.023109 (0.006351) \| 0.238971 / 0.275898 (-0.036927) \| 0.271768 / 0.323480 (-0.051712) \| 0.003970 / 0.007986 (-0.004016) \| 0.002408 / 0.004328 (-0.001921) \| 0.047755 / 0.004250 (0.043505) \| 0.043358 / 0.037052 (0.006306) \| 0.245543 / 0.258489 (-0.012946) \| 0.278230 / 0.293841 (-0.015611) \| 0.023819 / 0.128546 (-0.104727) \| 0.006856 / 0.075646 (-0.068790) \| 0.204603 / 0.419271 (-0.214668) \| 0.054521 / 0.043533 (0.010989) \| 0.246277 / 0.255139 (-0.008862) \| 0.271230 / 0.283200 (-0.011969) \| 0.017283 / 0.141683 (-0.124400) \| 1.088955 / 1.452155 (-0.363200) \| 1.245141 / 1.492716 (-0.247575) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091534 / 0.018006 (0.073528) \| 0.299517 / 0.000490 (0.299027) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018105 / 0.037411 (-0.019306) \| 0.061860 / 0.014526 (0.047334) \| 0.074494 / 0.176557 (-0.102063) \| 0.120107 / 0.737135 (-0.617029) \| 0.073406 / 0.296338 (-0.222932) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278140 / 0.215209 (0.062931) \| 2.746208 / 2.077655 (0.668553) \| 1.476264 / 1.504120 (-0.027856) \| 1.356498 / 1.541195 (-0.184697) \| 1.362998 / 1.468490 (-0.105492) \| 0.401884 / 4.584777 (-4.182893) \| 2.409836 / 3.745712 (-1.335877) \| 2.579087 / 5.269862 (-2.690775) \| 1.545021 / 4.565676 (-3.020656) \| 0.046001 / 0.424275 (-0.378274) \| 0.004812 / 0.007607 (-0.002795) \| 0.339767 / 0.226044 (0.113722) \| 3.341599 / 2.268929 (1.072670) \| 1.821498 / 55.444624 (-53.623127) \| 1.559311 / 6.876477 (-5.317166) \| 1.570368 / 2.142072 (-0.571704) \| 0.472688 / 4.805227 (-4.332539) \| 0.099549 / 6.500664 (-6.401115) \| 0.041644 / 0.075469 (-0.033825) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951988 / 1.841788 (-0.889799) \| 11.371459 / 8.074308 (3.297150) \| 10.229446 / 10.191392 (0.038054) \| 0.128105 / 0.680424 (-0.552319) \| 0.014418 / 0.534201 (-0.519783) \| 0.268615 / 0.579283 (-0.310668) \| 0.263956 / 0.434364 (-0.170407) \| 0.302213 / 0.540337 (-0.238125) \| 0.427224 / 1.386936 (-0.959712) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005150 / 0.011353 (-0.006203) \| 0.002557 / 0.011008 (-0.008451) \| 0.048092 / 0.038508 (0.009584) \| 0.050522 / 0.023109 (0.027413) \| 0.272195 / 0.275898 (-0.003703) \| 0.294191 / 0.323480 (-0.029289) \| 0.004098 / 0.007986 (-0.003887) \| 0.002350 / 0.004328 (-0.001978) \| 0.048682 / 0.004250 (0.044432) \| 0.038381 / 0.037052 (0.001328) \| 0.275530 / 0.258489 (0.017041) \| 0.303991 / 0.293841 (0.010150) \| 0.024734 / 0.128546 (-0.103812) \| 0.006926 / 0.075646 (-0.068720) \| 0.053683 / 0.419271 (-0.365588) \| 0.032675 / 0.043533 (-0.010858) \| 0.272816 / 0.255139 (0.017677) \| 0.291754 / 0.283200 (0.008554) \| 0.018290 / 0.141683 (-0.123392) \| 1.127696 / 1.452155 (-0.324459) \| 1.187080 / 1.492716 (-0.305636) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091224 / 0.018006 (0.073218) \| 0.288838 / 0.000490 (0.288348) \| 0.000226 / 0.000200 (0.000026) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021409 / 0.037411 (-0.016003) \| 0.069846 / 0.014526 (0.055320) \| 0.079962 / 0.176557 (-0.096594) \| 0.118575 / 0.737135 (-0.618560) \| 0.080223 / 0.296338 (-0.216115) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290835 / 0.215209 (0.075626) \| 2.831787 / 2.077655 (0.754133) \| 1.587728 / 1.504120 (0.083608) \| 1.461939 / 1.541195 (-0.079256) \| 1.495257 / 1.468490 (0.026767) \| 0.397653 / 4.584777 (-4.187124) \| 2.399903 / 3.745712 (-1.345809) \| 2.527615 / 5.269862 (-2.742247) \| 1.501555 / 4.565676 (-3.064121) \| 0.045742 / 0.424275 (-0.378533) \| 0.004797 / 0.007607 (-0.002811) \| 0.339139 / 0.226044 (0.113094) \| 3.358340 / 2.268929 (1.089412) \| 1.968955 / 55.444624 (-53.475670) \| 1.663598 / 6.876477 (-5.212879) \| 1.673995 / 2.142072 (-0.468078) \| 0.463444 / 4.805227 (-4.341783) \| 0.098008 / 6.500664 (-6.402656) \| 0.040836 / 0.075469 (-0.034633) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974033 / 1.841788 (-0.867755) \| 11.863206 / 8.074308 (3.788897) \| 10.892389 / 10.191392 (0.700997) \| 0.128884 / 0.680424 (-0.551540) \| 0.015319 / 0.534201 (-0.518882) \| 0.268931 / 0.579283 (-0.310353) \| 0.274148 / 0.434364 (-0.160216) \| 0.305407 / 0.540337 (-0.234930) \| 0.410574 / 1.386936 (-0.976362) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n" ]	2023-11-16T07:37:20	2023-11-16T08:12:12	2023-11-16T07:43:05	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6427/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6427/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6427", "html_url": "https://github.com/huggingface/datasets/pull/6427", "diff_url": "https://github.com/huggingface/datasets/pull/6427.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6427.patch", "merged_at": "2023-11-16T07:43:05" }	true
https://api.github.com/repos/huggingface/datasets/issues/6426	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6426/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6426/comments	https://api.github.com/repos/huggingface/datasets/issues/6426/events	https://github.com/huggingface/datasets/pull/6426	1,995,363,264	PR_kwDODunzps5fjOEK	6,426	More robust temporary directory deletion	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6426). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004750 / 0.011353 (-0.006603) \| 0.002928 / 0.011008 (-0.008080) \| 0.061962 / 0.038508 (0.023454) \| 0.029878 / 0.023109 (0.006768) \| 0.233380 / 0.275898 (-0.042518) \| 0.262221 / 0.323480 (-0.061259) \| 0.002982 / 0.007986 (-0.005004) \| 0.003698 / 0.004328 (-0.000630) \| 0.048565 / 0.004250 (0.044314) \| 0.046107 / 0.037052 (0.009055) \| 0.240090 / 0.258489 (-0.018399) \| 0.267294 / 0.293841 (-0.026547) \| 0.023335 / 0.128546 (-0.105211) \| 0.007221 / 0.075646 (-0.068425) \| 0.200903 / 0.419271 (-0.218369) \| 0.059237 / 0.043533 (0.015705) \| 0.234929 / 0.255139 (-0.020210) \| 0.256326 / 0.283200 (-0.026874) \| 0.018549 / 0.141683 (-0.123134) \| 1.103519 / 1.452155 (-0.348635) \| 1.156573 / 1.492716 (-0.336143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091205 / 0.018006 (0.073199) \| 0.303533 / 0.000490 (0.303043) \| 0.000204 / 0.000200 (0.000004) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018572 / 0.037411 (-0.018839) \| 0.062323 / 0.014526 (0.047797) \| 0.074528 / 0.176557 (-0.102029) \| 0.120295 / 0.737135 (-0.616841) \| 0.076786 / 0.296338 (-0.219552) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278814 / 0.215209 (0.063605) \| 2.745483 / 2.077655 (0.667829) \| 1.486073 / 1.504120 (-0.018047) \| 1.385334 / 1.541195 (-0.155861) \| 1.386351 / 1.468490 (-0.082139) \| 0.395545 / 4.584777 (-4.189232) \| 2.409468 / 3.745712 (-1.336244) \| 2.670702 / 5.269862 (-2.599159) \| 1.629245 / 4.565676 (-2.936432) \| 0.045990 / 0.424275 (-0.378286) \| 0.004782 / 0.007607 (-0.002825) \| 0.332912 / 0.226044 (0.106867) \| 3.249277 / 2.268929 (0.980349) \| 1.888690 / 55.444624 (-53.555934) \| 1.533462 / 6.876477 (-5.343015) \| 1.576045 / 2.142072 (-0.566027) \| 0.473090 / 4.805227 (-4.332138) \| 0.099448 / 6.500664 (-6.401216) \| 0.042613 / 0.075469 (-0.032857) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944229 / 1.841788 (-0.897559) \| 12.103621 / 8.074308 (4.029313) \| 10.643471 / 10.191392 (0.452079) \| 0.143004 / 0.680424 (-0.537420) \| 0.013872 / 0.534201 (-0.520329) \| 0.272026 / 0.579283 (-0.307257) \| 0.298701 / 0.434364 (-0.135663) \| 0.310299 / 0.540337 (-0.230038) \| 0.420934 / 1.386936 (-0.966002) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004904 / 0.011353 (-0.006449) \| 0.003064 / 0.011008 (-0.007945) \| 0.047982 / 0.038508 (0.009474) \| 0.056354 / 0.023109 (0.033245) \| 0.292893 / 0.275898 (0.016995) \| 0.348744 / 0.323480 (0.025264) \| 0.003988 / 0.007986 (-0.003997) \| 0.002431 / 0.004328 (-0.001898) \| 0.049108 / 0.004250 (0.044857) \| 0.039055 / 0.037052 (0.002002) \| 0.278129 / 0.258489 (0.019640) \| 0.318547 / 0.293841 (0.024706) \| 0.025040 / 0.128546 (-0.103507) \| 0.007166 / 0.075646 (-0.068480) \| 0.053967 / 0.419271 (-0.365305) \| 0.033128 / 0.043533 (-0.010405) \| 0.272849 / 0.255139 (0.017710) \| 0.312143 / 0.283200 (0.028943) \| 0.017942 / 0.141683 (-0.123741) \| 1.192297 / 1.452155 (-0.259857) \| 1.328102 / 1.492716 (-0.164615) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090903 / 0.018006 (0.072896) \| 0.301260 / 0.000490 (0.300770) \| 0.000215 / 0.000200 (0.000015) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021112 / 0.037411 (-0.016300) \| 0.070181 / 0.014526 (0.055656) \| 0.082431 / 0.176557 (-0.094126) \| 0.121973 / 0.737135 (-0.615163) \| 0.083617 / 0.296338 (-0.212721) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289587 / 0.215209 (0.074378) \| 2.877895 / 2.077655 (0.800240) \| 1.721417 / 1.504120 (0.217297) \| 1.536023 / 1.541195 (-0.005171) \| 1.550917 / 1.468490 (0.082427) \| 0.402978 / 4.584777 (-4.181799) \| 2.431767 / 3.745712 (-1.313946) \| 2.544419 / 5.269862 (-2.725442) \| 1.554562 / 4.565676 (-3.011115) \| 0.046260 / 0.424275 (-0.378015) \| 0.004923 / 0.007607 (-0.002684) \| 0.341584 / 0.226044 (0.115540) \| 3.362133 / 2.268929 (1.093205) \| 1.928741 / 55.444624 (-53.515884) \| 1.654798 / 6.876477 (-5.221679) \| 1.715111 / 2.142072 (-0.426962) \| 0.471029 / 4.805227 (-4.334198) \| 0.098912 / 6.500664 (-6.401752) \| 0.041018 / 0.075469 (-0.034451) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.992880 / 1.841788 (-0.848907) \| 12.083890 / 8.074308 (4.009582) \| 11.023833 / 10.191392 (0.832441) \| 0.139217 / 0.680424 (-0.541207) \| 0.015183 / 0.534201 (-0.519018) \| 0.271637 / 0.579283 (-0.307646) \| 0.278910 / 0.434364 (-0.155454) \| 0.306891 / 0.540337 (-0.233447) \| 0.424412 / 1.386936 (-0.962524) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d51f37eb9996d4c52250ee6e987ccce0d74f2f4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004545 / 0.011353 (-0.006808) \| 0.002955 / 0.011008 (-0.008054) \| 0.062119 / 0.038508 (0.023611) \| 0.029357 / 0.023109 (0.006248) \| 0.240068 / 0.275898 (-0.035830) \| 0.273376 / 0.323480 (-0.050104) \| 0.003884 / 0.007986 (-0.004102) \| 0.002390 / 0.004328 (-0.001938) \| 0.048621 / 0.004250 (0.044371) \| 0.043867 / 0.037052 (0.006815) \| 0.247240 / 0.258489 (-0.011249) \| 0.279187 / 0.293841 (-0.014654) \| 0.023377 / 0.128546 (-0.105169) \| 0.007261 / 0.075646 (-0.068385) \| 0.201913 / 0.419271 (-0.217359) \| 0.057063 / 0.043533 (0.013530) \| 0.245698 / 0.255139 (-0.009441) \| 0.265644 / 0.283200 (-0.017556) \| 0.018077 / 0.141683 (-0.123606) \| 1.133225 / 1.452155 (-0.318930) \| 1.186380 / 1.492716 (-0.306336) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089639 / 0.018006 (0.071632) \| 0.298918 / 0.000490 (0.298428) \| 0.000198 / 0.000200 (-0.000002) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019037 / 0.037411 (-0.018374) \| 0.062580 / 0.014526 (0.048055) \| 0.072974 / 0.176557 (-0.103582) \| 0.119909 / 0.737135 (-0.617226) \| 0.075021 / 0.296338 (-0.221317) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276561 / 0.215209 (0.061352) \| 2.697281 / 2.077655 (0.619626) \| 1.419772 / 1.504120 (-0.084348) \| 1.302079 / 1.541195 (-0.239115) \| 1.329143 / 1.468490 (-0.139347) \| 0.395528 / 4.584777 (-4.189249) \| 2.365788 / 3.745712 (-1.379925) \| 2.583802 / 5.269862 (-2.686059) \| 1.561983 / 4.565676 (-3.003694) \| 0.045269 / 0.424275 (-0.379006) \| 0.004826 / 0.007607 (-0.002781) \| 0.331041 / 0.226044 (0.104996) \| 3.292523 / 2.268929 (1.023595) \| 1.797865 / 55.444624 (-53.646759) \| 1.509229 / 6.876477 (-5.367248) \| 1.498884 / 2.142072 (-0.643188) \| 0.458518 / 4.805227 (-4.346709) \| 0.098076 / 6.500664 (-6.402588) \| 0.042290 / 0.075469 (-0.033179) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.922331 / 1.841788 (-0.919457) \| 11.605041 / 8.074308 (3.530732) \| 10.471664 / 10.191392 (0.280272) \| 0.130325 / 0.680424 (-0.550098) \| 0.014084 / 0.534201 (-0.520117) \| 0.278877 / 0.579283 (-0.300406) \| 0.263104 / 0.434364 (-0.171259) \| 0.306723 / 0.540337 (-0.233615) \| 0.416238 / 1.386936 (-0.970698) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005094 / 0.011353 (-0.006259) \| 0.002794 / 0.011008 (-0.008214) \| 0.048189 / 0.038508 (0.009680) \| 0.050409 / 0.023109 (0.027300) \| 0.272618 / 0.275898 (-0.003280) \| 0.293589 / 0.323480 (-0.029891) \| 0.003995 / 0.007986 (-0.003991) \| 0.002373 / 0.004328 (-0.001956) \| 0.048269 / 0.004250 (0.044018) \| 0.038751 / 0.037052 (0.001698) \| 0.273495 / 0.258489 (0.015006) \| 0.309244 / 0.293841 (0.015403) \| 0.024681 / 0.128546 (-0.103866) \| 0.007390 / 0.075646 (-0.068256) \| 0.053844 / 0.419271 (-0.365427) \| 0.032395 / 0.043533 (-0.011137) \| 0.271963 / 0.255139 (0.016824) \| 0.289557 / 0.283200 (0.006357) \| 0.018659 / 0.141683 (-0.123024) \| 1.154478 / 1.452155 (-0.297676) \| 1.199772 / 1.492716 (-0.292944) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089771 / 0.018006 (0.071764) \| 0.299468 / 0.000490 (0.298978) \| 0.000219 / 0.000200 (0.000020) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021854 / 0.037411 (-0.015558) \| 0.070280 / 0.014526 (0.055754) \| 0.080956 / 0.176557 (-0.095600) \| 0.119430 / 0.737135 (-0.617705) \| 0.082778 / 0.296338 (-0.213561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304273 / 0.215209 (0.089064) \| 2.968264 / 2.077655 (0.890609) \| 1.592363 / 1.504120 (0.088243) \| 1.460795 / 1.541195 (-0.080400) \| 1.501545 / 1.468490 (0.033055) \| 0.411001 / 4.584777 (-4.173776) \| 2.464273 / 3.745712 (-1.281439) \| 2.524585 / 5.269862 (-2.745277) \| 1.537443 / 4.565676 (-3.028234) \| 0.046163 / 0.424275 (-0.378112) \| 0.004783 / 0.007607 (-0.002824) \| 0.354251 / 0.226044 (0.128206) \| 3.512087 / 2.268929 (1.243158) \| 1.968156 / 55.444624 (-53.476468) \| 1.664966 / 6.876477 (-5.211510) \| 1.685013 / 2.142072 (-0.457060) \| 0.485793 / 4.805227 (-4.319435) \| 0.099789 / 6.500664 (-6.400875) \| 0.040705 / 0.075469 (-0.034764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966570 / 1.841788 (-0.875218) \| 12.023188 / 8.074308 (3.948880) \| 11.122602 / 10.191392 (0.931210) \| 0.141002 / 0.680424 (-0.539422) \| 0.015955 / 0.534201 (-0.518246) \| 0.270293 / 0.579283 (-0.308990) \| 0.281839 / 0.434364 (-0.152525) \| 0.307279 / 0.540337 (-0.233058) \| 0.434687 / 1.386936 (-0.952249) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7eaad71464e85c7358eaa36494227a43257ffcd8 \"CML watermark\")\n", "What would be the impact for non-windows users ?\r\n\r\nAlso I wonder if a gc.collect() after the `del` could help to remove the PermissionError ? Or register the dataset for deletion on copy/pickle maybe ?" ]	2023-11-15T19:06:42	2023-11-20T08:34:28	null	CONTRIBUTOR	null	While fixing the Windows errors in #6362, I noticed that `PermissionError` can still easily be thrown on the session exit by the temporary cache directory's finalizer (we would also have to keep track of intermediate datasets, copies, etc.). Due to the low usage of `datasets` on Windows, this PR takes a simpler approach to the issue than https://github.com/huggingface/datasets/pull/2403 - it tries to delete the temporary cache directory, and if this fails, logs a warning message about using a `delete-temp-cache` CLI command to delete it manually. The problematic references are freed after the session exits, so the CLI command should then succeed.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6426/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6426/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6426", "html_url": "https://github.com/huggingface/datasets/pull/6426", "diff_url": "https://github.com/huggingface/datasets/pull/6426.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6426.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6425	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6425/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6425/comments	https://api.github.com/repos/huggingface/datasets/issues/6425/events	https://github.com/huggingface/datasets/pull/6425	1,995,269,382	PR_kwDODunzps5fi5ye	6,425	Fix deprecation warning when building conda package	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004811 / 0.011353 (-0.006542) \| 0.002478 / 0.011008 (-0.008530) \| 0.062241 / 0.038508 (0.023733) \| 0.031153 / 0.023109 (0.008044) \| 0.248896 / 0.275898 (-0.027002) \| 0.276860 / 0.323480 (-0.046620) \| 0.002934 / 0.007986 (-0.005052) \| 0.002428 / 0.004328 (-0.001901) \| 0.048507 / 0.004250 (0.044257) \| 0.044567 / 0.037052 (0.007515) \| 0.253570 / 0.258489 (-0.004919) \| 0.280762 / 0.293841 (-0.013079) \| 0.023549 / 0.128546 (-0.104997) \| 0.006985 / 0.075646 (-0.068661) \| 0.206227 / 0.419271 (-0.213044) \| 0.054027 / 0.043533 (0.010494) \| 0.257655 / 0.255139 (0.002516) \| 0.273498 / 0.283200 (-0.009702) \| 0.018997 / 0.141683 (-0.122685) \| 1.111732 / 1.452155 (-0.340422) \| 1.162078 / 1.492716 (-0.330639) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091816 / 0.018006 (0.073810) \| 0.299428 / 0.000490 (0.298938) \| 0.000211 / 0.000200 (0.000012) \| 0.000048 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018503 / 0.037411 (-0.018908) \| 0.062933 / 0.014526 (0.048407) \| 0.076349 / 0.176557 (-0.100208) \| 0.123291 / 0.737135 (-0.613844) \| 0.077491 / 0.296338 (-0.218847) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280770 / 0.215209 (0.065561) \| 2.762185 / 2.077655 (0.684530) \| 1.429124 / 1.504120 (-0.074996) \| 1.303162 / 1.541195 (-0.238033) \| 1.307523 / 1.468490 (-0.160967) \| 0.405593 / 4.584777 (-4.179184) \| 2.396992 / 3.745712 (-1.348721) \| 2.550968 / 5.269862 (-2.718894) \| 1.557358 / 4.565676 (-3.008318) \| 0.046149 / 0.424275 (-0.378126) \| 0.004808 / 0.007607 (-0.002799) \| 0.341870 / 0.226044 (0.115825) \| 3.362478 / 2.268929 (1.093550) \| 1.786360 / 55.444624 (-53.658264) \| 1.483419 / 6.876477 (-5.393058) \| 1.493463 / 2.142072 (-0.648609) \| 0.470605 / 4.805227 (-4.334623) \| 0.098372 / 6.500664 (-6.402292) \| 0.041722 / 0.075469 (-0.033748) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938148 / 1.841788 (-0.903640) \| 11.219184 / 8.074308 (3.144876) \| 10.454439 / 10.191392 (0.263047) \| 0.139645 / 0.680424 (-0.540778) \| 0.014453 / 0.534201 (-0.519748) \| 0.268975 / 0.579283 (-0.310308) \| 0.262060 / 0.434364 (-0.172304) \| 0.313652 / 0.540337 (-0.226686) \| 0.423992 / 1.386936 (-0.962944) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004829 / 0.011353 (-0.006524) \| 0.002426 / 0.011008 (-0.008582) \| 0.049064 / 0.038508 (0.010555) \| 0.049728 / 0.023109 (0.026619) \| 0.273263 / 0.275898 (-0.002635) \| 0.295645 / 0.323480 (-0.027835) \| 0.004156 / 0.007986 (-0.003830) \| 0.002397 / 0.004328 (-0.001932) \| 0.048902 / 0.004250 (0.044652) \| 0.038414 / 0.037052 (0.001362) \| 0.276176 / 0.258489 (0.017687) \| 0.306844 / 0.293841 (0.013003) \| 0.024546 / 0.128546 (-0.104000) \| 0.006946 / 0.075646 (-0.068701) \| 0.054024 / 0.419271 (-0.365247) \| 0.032444 / 0.043533 (-0.011089) \| 0.274125 / 0.255139 (0.018986) \| 0.293226 / 0.283200 (0.010027) \| 0.018003 / 0.141683 (-0.123680) \| 1.130402 / 1.452155 (-0.321752) \| 1.195969 / 1.492716 (-0.296748) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090043 / 0.018006 (0.072037) \| 0.298699 / 0.000490 (0.298209) \| 0.000214 / 0.000200 (0.000014) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021284 / 0.037411 (-0.016127) \| 0.069954 / 0.014526 (0.055428) \| 0.080445 / 0.176557 (-0.096111) \| 0.119461 / 0.737135 (-0.617674) \| 0.080632 / 0.296338 (-0.215706) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302246 / 0.215209 (0.087037) \| 2.991936 / 2.077655 (0.914281) \| 1.662969 / 1.504120 (0.158850) \| 1.533141 / 1.541195 (-0.008054) \| 1.583183 / 1.468490 (0.114693) \| 0.402864 / 4.584777 (-4.181913) \| 2.424119 / 3.745712 (-1.321593) \| 2.489558 / 5.269862 (-2.780303) \| 1.502196 / 4.565676 (-3.063481) \| 0.045980 / 0.424275 (-0.378295) \| 0.004768 / 0.007607 (-0.002839) \| 0.356089 / 0.226044 (0.130044) \| 3.481333 / 2.268929 (1.212404) \| 2.009713 / 55.444624 (-53.434912) \| 1.730021 / 6.876477 (-5.146455) \| 1.704656 / 2.142072 (-0.437416) \| 0.470832 / 4.805227 (-4.334395) \| 0.097473 / 6.500664 (-6.403191) \| 0.040437 / 0.075469 (-0.035032) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981497 / 1.841788 (-0.860291) \| 11.827242 / 8.074308 (3.752933) \| 10.888324 / 10.191392 (0.696932) \| 0.129249 / 0.680424 (-0.551174) \| 0.015812 / 0.534201 (-0.518389) \| 0.269657 / 0.579283 (-0.309626) \| 0.275585 / 0.434364 (-0.158779) \| 0.305698 / 0.540337 (-0.234639) \| 0.411497 / 1.386936 (-0.975439) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bcde318293af04fd5044b42ddfcb650f9b092d45 \"CML watermark\")\n", "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6425). All of your documentation changes will be reflected on that endpoint." ]	2023-11-15T18:00:11	2023-11-15T18:05:11	null	MEMBER	null	When building/releasing conda package, we get this deprecation warning: ``` /usr/share/miniconda/envs/build-datasets/bin/conda-build:11: DeprecationWarning: conda_build.cli.main_build.main is deprecated and will be removed in 4.0.0. Use `conda build` instead. ``` This PR fixes the deprecation warning by using `conda build` instead.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6425/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6425/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6425", "html_url": "https://github.com/huggingface/datasets/pull/6425", "diff_url": "https://github.com/huggingface/datasets/pull/6425.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6425.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6424	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6424/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6424/comments	https://api.github.com/repos/huggingface/datasets/issues/6424/events	https://github.com/huggingface/datasets/pull/6424	1,995,224,516	PR_kwDODunzps5fiwDC	6,424	[docs] troubleshooting guide	{ "login": "MKhalusova", "id": 1065417, "node_id": "MDQ6VXNlcjEwNjU0MTc=", "avatar_url": "https://avatars.githubusercontent.com/u/1065417?v=4", "gravatar_id": "", "url": "https://api.github.com/users/MKhalusova", "html_url": "https://github.com/MKhalusova", "followers_url": "https://api.github.com/users/MKhalusova/followers", "following_url": "https://api.github.com/users/MKhalusova/following{/other_user}", "gists_url": "https://api.github.com/users/MKhalusova/gists{/gist_id}", "starred_url": "https://api.github.com/users/MKhalusova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MKhalusova/subscriptions", "organizations_url": "https://api.github.com/users/MKhalusova/orgs", "repos_url": "https://api.github.com/users/MKhalusova/repos", "events_url": "https://api.github.com/users/MKhalusova/events{/privacy}", "received_events_url": "https://api.github.com/users/MKhalusova/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6424). All of your documentation changes will be reflected on that endpoint." ]	2023-11-15T17:28:14	2023-11-17T18:23:10	null	CONTRIBUTOR	null	Hi all! This is a draft for a PR adding a troubleshooting guide for Datasets docs. I went through the library's GitHub Issues and Forum questions and identified a few issues that are common enough that I think it would be valuable to include them in the troubleshooting guide. These are: - creating a dataset from a folder and not following the required format - authentication issues when using `push_to_hub` - `Too Many Requests` with `push_to_hub` - Pickling issues when using Dataset.from_generator() There's also a section on asking for help. Please let me know if there are other common issues or advice that we can include here.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6424/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 1, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6424/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6424", "html_url": "https://github.com/huggingface/datasets/pull/6424", "diff_url": "https://github.com/huggingface/datasets/pull/6424.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6424.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6423	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6423/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6423/comments	https://api.github.com/repos/huggingface/datasets/issues/6423/events	https://github.com/huggingface/datasets/pull/6423	1,994,946,847	PR_kwDODunzps5fhzD6	6,423	Fix conda release by adding pyarrow-hotfix dependency	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004476 / 0.011353 (-0.006877) \| 0.002691 / 0.011008 (-0.008317) \| 0.061400 / 0.038508 (0.022892) \| 0.030096 / 0.023109 (0.006986) \| 0.279868 / 0.275898 (0.003970) \| 0.310320 / 0.323480 (-0.013159) \| 0.003873 / 0.007986 (-0.004112) \| 0.002394 / 0.004328 (-0.001935) \| 0.048307 / 0.004250 (0.044056) \| 0.043326 / 0.037052 (0.006273) \| 0.288256 / 0.258489 (0.029767) \| 0.311449 / 0.293841 (0.017609) \| 0.022970 / 0.128546 (-0.105576) \| 0.006714 / 0.075646 (-0.068932) \| 0.201656 / 0.419271 (-0.217615) \| 0.052811 / 0.043533 (0.009278) \| 0.285123 / 0.255139 (0.029984) \| 0.301495 / 0.283200 (0.018295) \| 0.017531 / 0.141683 (-0.124152) \| 1.097660 / 1.452155 (-0.354494) \| 1.161986 / 1.492716 (-0.330731) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089223 / 0.018006 (0.071217) \| 0.297815 / 0.000490 (0.297326) \| 0.000205 / 0.000200 (0.000005) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018679 / 0.037411 (-0.018732) \| 0.062742 / 0.014526 (0.048216) \| 0.072869 / 0.176557 (-0.103687) \| 0.120730 / 0.737135 (-0.616406) \| 0.074526 / 0.296338 (-0.221813) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299977 / 0.215209 (0.084768) \| 2.921029 / 2.077655 (0.843375) \| 1.632283 / 1.504120 (0.128163) \| 1.508008 / 1.541195 (-0.033187) \| 1.513967 / 1.468490 (0.045477) \| 0.403056 / 4.584777 (-4.181721) \| 2.340011 / 3.745712 (-1.405701) \| 2.552319 / 5.269862 (-2.717543) \| 1.549741 / 4.565676 (-3.015935) \| 0.046303 / 0.424275 (-0.377972) \| 0.004768 / 0.007607 (-0.002839) \| 0.356921 / 0.226044 (0.130877) \| 3.506410 / 2.268929 (1.237482) \| 1.975394 / 55.444624 (-53.469230) \| 1.688683 / 6.876477 (-5.187794) \| 1.715502 / 2.142072 (-0.426571) \| 0.471016 / 4.805227 (-4.334212) \| 0.099552 / 6.500664 (-6.401112) \| 0.042095 / 0.075469 (-0.033374) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.955784 / 1.841788 (-0.886004) \| 11.191802 / 8.074308 (3.117494) \| 10.127818 / 10.191392 (-0.063574) \| 0.141225 / 0.680424 (-0.539199) \| 0.014486 / 0.534201 (-0.519715) \| 0.267204 / 0.579283 (-0.312079) \| 0.289108 / 0.434364 (-0.145256) \| 0.309458 / 0.540337 (-0.230880) \| 0.422802 / 1.386936 (-0.964134) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004797 / 0.011353 (-0.006556) \| 0.002907 / 0.011008 (-0.008101) \| 0.047666 / 0.038508 (0.009158) \| 0.051183 / 0.023109 (0.028074) \| 0.266315 / 0.275898 (-0.009583) \| 0.286429 / 0.323480 (-0.037051) \| 0.003954 / 0.007986 (-0.004031) \| 0.002041 / 0.004328 (-0.002288) \| 0.047652 / 0.004250 (0.043401) \| 0.038211 / 0.037052 (0.001158) \| 0.272210 / 0.258489 (0.013721) \| 0.299425 / 0.293841 (0.005584) \| 0.024266 / 0.128546 (-0.104280) \| 0.006747 / 0.075646 (-0.068900) \| 0.052959 / 0.419271 (-0.366312) \| 0.032094 / 0.043533 (-0.011439) \| 0.265677 / 0.255139 (0.010538) \| 0.285373 / 0.283200 (0.002174) \| 0.017577 / 0.141683 (-0.124106) \| 1.114514 / 1.452155 (-0.337640) \| 1.212970 / 1.492716 (-0.279746) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088347 / 0.018006 (0.070341) \| 0.296678 / 0.000490 (0.296188) \| 0.000209 / 0.000200 (0.000009) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021159 / 0.037411 (-0.016253) \| 0.069886 / 0.014526 (0.055360) \| 0.079832 / 0.176557 (-0.096725) \| 0.115512 / 0.737135 (-0.621623) \| 0.081600 / 0.296338 (-0.214739) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292659 / 0.215209 (0.077450) \| 2.872556 / 2.077655 (0.794901) \| 1.573017 / 1.504120 (0.068897) \| 1.445122 / 1.541195 (-0.096072) \| 1.485584 / 1.468490 (0.017094) \| 0.388638 / 4.584777 (-4.196139) \| 2.434847 / 3.745712 (-1.310865) \| 2.518167 / 5.269862 (-2.751695) \| 1.503000 / 4.565676 (-3.062676) \| 0.045123 / 0.424275 (-0.379153) \| 0.004778 / 0.007607 (-0.002829) \| 0.347955 / 0.226044 (0.121910) \| 3.384819 / 2.268929 (1.115891) \| 1.920185 / 55.444624 (-53.524439) \| 1.646910 / 6.876477 (-5.229567) \| 1.638092 / 2.142072 (-0.503980) \| 0.450535 / 4.805227 (-4.354692) \| 0.095301 / 6.500664 (-6.405363) \| 0.040275 / 0.075469 (-0.035194) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956088 / 1.841788 (-0.885700) \| 11.776642 / 8.074308 (3.702334) \| 10.651063 / 10.191392 (0.459671) \| 0.127079 / 0.680424 (-0.553345) \| 0.015080 / 0.534201 (-0.519121) \| 0.273737 / 0.579283 (-0.305546) \| 0.271434 / 0.434364 (-0.162929) \| 0.308448 / 0.540337 (-0.231889) \| 0.412467 / 1.386936 (-0.974469) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#af014830363401a0166a2b8435ca2f863cb468d4 \"CML watermark\")\n", "Once this PR is merged, we should upload the missing version to conda.\r\n\r\n@lhoestq you did this in the past. If you tell me your approach (I see a tag called `VERSION`...), I could do it myself.", "Maybe open a PR against the 2.14 branch and update `release-conda.yml` like this ?\r\n\r\n```diff\r\n- on:\r\n- push:\r\n- tags:\r\n- - \"[0-9]+.[0-9]+.[0-9]+*\"\r\n+ on: push\r\n```\r\n\r\nand then set it back to normal after the release is done", "After having cherry-picked the commit in this PR, I have released the conda package. See: \r\n- https://github.com/huggingface/datasets/actions/runs/6880182419/job/18713812449\r\n- https://anaconda.org/HuggingFace/datasets/files?version=2.14.7\r\n\r\nI am merging this PR.\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004993 / 0.011353 (-0.006360) \| 0.002964 / 0.011008 (-0.008044) \| 0.062588 / 0.038508 (0.024080) \| 0.030794 / 0.023109 (0.007685) \| 0.234856 / 0.275898 (-0.041042) \| 0.264807 / 0.323480 (-0.058673) \| 0.003139 / 0.007986 (-0.004847) \| 0.002498 / 0.004328 (-0.001831) \| 0.048058 / 0.004250 (0.043807) \| 0.048349 / 0.037052 (0.011296) \| 0.238210 / 0.258489 (-0.020279) \| 0.278144 / 0.293841 (-0.015697) \| 0.023219 / 0.128546 (-0.105327) \| 0.007296 / 0.075646 (-0.068351) \| 0.203263 / 0.419271 (-0.216008) \| 0.058844 / 0.043533 (0.015311) \| 0.246330 / 0.255139 (-0.008809) \| 0.264550 / 0.283200 (-0.018649) \| 0.018580 / 0.141683 (-0.123103) \| 1.084163 / 1.452155 (-0.367992) \| 1.154891 / 1.492716 (-0.337825) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092393 / 0.018006 (0.074387) \| 0.300545 / 0.000490 (0.300055) \| 0.000203 / 0.000200 (0.000003) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018648 / 0.037411 (-0.018763) \| 0.063151 / 0.014526 (0.048625) \| 0.074206 / 0.176557 (-0.102350) \| 0.120929 / 0.737135 (-0.616207) \| 0.075970 / 0.296338 (-0.220368) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278489 / 0.215209 (0.063279) \| 2.664804 / 2.077655 (0.587150) \| 1.433040 / 1.504120 (-0.071080) \| 1.321416 / 1.541195 (-0.219779) \| 1.320964 / 1.468490 (-0.147526) \| 0.401289 / 4.584777 (-4.183488) \| 2.365310 / 3.745712 (-1.380402) \| 2.635798 / 5.269862 (-2.634063) \| 1.584384 / 4.565676 (-2.981293) \| 0.045675 / 0.424275 (-0.378600) \| 0.004854 / 0.007607 (-0.002753) \| 0.337592 / 0.226044 (0.111548) \| 3.330462 / 2.268929 (1.061534) \| 1.794507 / 55.444624 (-53.650117) \| 1.531284 / 6.876477 (-5.345193) \| 1.507165 / 2.142072 (-0.634908) \| 0.478622 / 4.805227 (-4.326606) \| 0.099105 / 6.500664 (-6.401560) \| 0.041575 / 0.075469 (-0.033894) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941790 / 1.841788 (-0.899997) \| 11.609871 / 8.074308 (3.535563) \| 10.770869 / 10.191392 (0.579477) \| 0.138931 / 0.680424 (-0.541493) \| 0.014406 / 0.534201 (-0.519795) \| 0.269681 / 0.579283 (-0.309602) \| 0.260556 / 0.434364 (-0.173808) \| 0.308244 / 0.540337 (-0.232093) \| 0.428867 / 1.386936 (-0.958069) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004803 / 0.011353 (-0.006550) \| 0.003263 / 0.011008 (-0.007745) \| 0.049143 / 0.038508 (0.010635) \| 0.052033 / 0.023109 (0.028924) \| 0.267815 / 0.275898 (-0.008083) \| 0.288733 / 0.323480 (-0.034747) \| 0.004159 / 0.007986 (-0.003826) \| 0.002407 / 0.004328 (-0.001921) \| 0.048978 / 0.004250 (0.044728) \| 0.038994 / 0.037052 (0.001942) \| 0.264028 / 0.258489 (0.005539) \| 0.303930 / 0.293841 (0.010090) \| 0.024283 / 0.128546 (-0.104263) \| 0.007201 / 0.075646 (-0.068446) \| 0.053810 / 0.419271 (-0.365461) \| 0.032611 / 0.043533 (-0.010922) \| 0.266730 / 0.255139 (0.011591) \| 0.281564 / 0.283200 (-0.001635) \| 0.018720 / 0.141683 (-0.122963) \| 1.140676 / 1.452155 (-0.311479) \| 1.206604 / 1.492716 (-0.286113) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.109390 / 0.018006 (0.091384) \| 0.313783 / 0.000490 (0.313294) \| 0.000228 / 0.000200 (0.000028) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021228 / 0.037411 (-0.016183) \| 0.070505 / 0.014526 (0.055979) \| 0.081961 / 0.176557 (-0.094595) \| 0.119943 / 0.737135 (-0.617193) \| 0.083582 / 0.296338 (-0.212757) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295702 / 0.215209 (0.080493) \| 2.886865 / 2.077655 (0.809210) \| 1.583206 / 1.504120 (0.079086) \| 1.451129 / 1.541195 (-0.090065) \| 1.486253 / 1.468490 (0.017763) \| 0.403207 / 4.584777 (-4.181570) \| 2.408889 / 3.745712 (-1.336824) \| 2.578480 / 5.269862 (-2.691381) \| 1.533066 / 4.565676 (-3.032610) \| 0.046075 / 0.424275 (-0.378200) \| 0.004877 / 0.007607 (-0.002730) \| 0.345995 / 0.226044 (0.119950) \| 3.377039 / 2.268929 (1.108110) \| 1.944614 / 55.444624 (-53.500010) \| 1.677691 / 6.876477 (-5.198786) \| 1.672828 / 2.142072 (-0.469244) \| 0.468426 / 4.805227 (-4.336802) \| 0.097290 / 6.500664 (-6.403374) \| 0.040695 / 0.075469 (-0.034774) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965778 / 1.841788 (-0.876010) \| 12.092639 / 8.074308 (4.018331) \| 11.210968 / 10.191392 (1.019576) \| 0.131212 / 0.680424 (-0.549212) \| 0.015865 / 0.534201 (-0.518336) \| 0.285702 / 0.579283 (-0.293581) \| 0.278319 / 0.434364 (-0.156045) \| 0.336063 / 0.540337 (-0.204275) \| 0.426265 / 1.386936 (-0.960671) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d122b3ddc67705cc2b622bcbd79de9ff943a5742 \"CML watermark\")\n" ]	2023-11-15T14:57:12	2023-11-15T17:15:33	2023-11-15T17:09:24	MEMBER	null	Fix conda release by adding pyarrow-hotfix dependency. Note that conda release failed in latest 2.14.7 release: https://github.com/huggingface/datasets/actions/runs/6874667214/job/18696761723 ``` Traceback (most recent call last): File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/test_tmp/run_test.py", line 2, in <module> import datasets File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/__init__.py", line 22, in <module> from .arrow_dataset import Dataset File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 67, in <module> from .arrow_writer import ArrowWriter, OptimizedTypedSequence File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_writer.py", line 27, in <module> from .features import Features, Image, Value File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/__init__.py", line 18, in <module> from .features import Array2D, Array3D, Array4D, Array5D, ClassLabel, Features, Sequence, Value File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/features.py", line 34, in <module> import pyarrow_hotfix # noqa: F401 # to fix vulnerability on pyarrow<14.0.1 ^^^^^^^^^^^^^^^^^^^^^ ModuleNotFoundError: No module named 'pyarrow_hotfix' ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6423/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6423/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6423", "html_url": "https://github.com/huggingface/datasets/pull/6423", "diff_url": "https://github.com/huggingface/datasets/pull/6423.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6423.patch", "merged_at": "2023-11-15T17:09:24" }	true
https://api.github.com/repos/huggingface/datasets/issues/6422	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6422/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6422/comments	https://api.github.com/repos/huggingface/datasets/issues/6422/events	https://github.com/huggingface/datasets/issues/6422	1,994,579,267	I_kwDODunzps524t1D	6,422	Allow to choose the `writer_batch_size` when using `save_to_disk`	{ "login": "NathanGodey", "id": 38216711, "node_id": "MDQ6VXNlcjM4MjE2NzEx", "avatar_url": "https://avatars.githubusercontent.com/u/38216711?v=4", "gravatar_id": "", "url": "https://api.github.com/users/NathanGodey", "html_url": "https://github.com/NathanGodey", "followers_url": "https://api.github.com/users/NathanGodey/followers", "following_url": "https://api.github.com/users/NathanGodey/following{/other_user}", "gists_url": "https://api.github.com/users/NathanGodey/gists{/gist_id}", "starred_url": "https://api.github.com/users/NathanGodey/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NathanGodey/subscriptions", "organizations_url": "https://api.github.com/users/NathanGodey/orgs", "repos_url": "https://api.github.com/users/NathanGodey/repos", "events_url": "https://api.github.com/users/NathanGodey/events{/privacy}", "received_events_url": "https://api.github.com/users/NathanGodey/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[ "We have a config variable that controls the batch size in `save_to_disk`:\r\n```python\r\nimport datasets\r\ndatasets.config.DEFAULT_MAX_BATCH_SIZE = <smaller_batch_size>\r\n...\r\nds.save_to_disk(...)\r\n```", "Thank you for your answer!\r\n\r\nFrom what I am reading in `https://github.com/huggingface/datasets/blob/2.14.5/src/datasets/arrow_dataset.py`, every function involved (`select`, `shard`, ...) has a default hardcoded batch size of 1000, as such:\r\n```python\r\ndef select(\r\n self,\r\n indices: Iterable,\r\n keep_in_memory: bool = False,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n ) -> \"Dataset\":\r\n...\r\n```\r\nThen, `ArrowWriter` is instantiated with the specified `writer_batch_size`. In `ArrowWriter`, `writer_batch_size` is set to `datasets.config.DEFAULT_MAX_BATCH_SIZE` if it is `None`(https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L345C14-L345C31). However, in our case, it is already set to 1000 by \"parent\" methods, so it won't happen.\r\n\r\nNevertheless, due to this: \r\n```python\r\ndef _save_to_disk_single(job_id: int, shard: \"Dataset\", fpath: str, storage_options: Optional[dict]):\r\n batch_size = config.DEFAULT_MAX_BATCH_SIZE\r\n...\r\n```\r\nit seems to work. I will use it as such, but it should maybe be added to documentation? And maybe improved in next versions?" ]	2023-11-15T11:18:34	2023-11-16T10:00:21	null	NONE	null	### Feature request Add an argument in `save_to_disk` regarding batch size, which would be passed to `shard` and other methods. ### Motivation The `Dataset.save_to_disk` method currently calls `shard` without passing a `writer_batch_size` argument, thus implicitly using the default value (1000). This can result in RAM saturation when using a lot of processes on long text sequences or other modalities, or for specific IO configs. ### Your contribution I would be glad to submit a PR, as long as it does not imply extensive tests refactoring.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6422/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6422/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6421	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6421/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6421/comments	https://api.github.com/repos/huggingface/datasets/issues/6421/events	https://github.com/huggingface/datasets/pull/6421	1,994,451,553	PR_kwDODunzps5fgG1h	6,421	Add pyarrow-hotfix to release docs	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "id": 4296013012, "node_id": "LA_kwDODunzps8AAAABAA_01A", "url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance", "name": "maintenance", "color": "d4c5f9", "default": false, "description": "Maintenance tasks" } ]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004755 / 0.011353 (-0.006598) \| 0.002683 / 0.011008 (-0.008325) \| 0.061701 / 0.038508 (0.023193) \| 0.030123 / 0.023109 (0.007013) \| 0.238186 / 0.275898 (-0.037712) \| 0.266570 / 0.323480 (-0.056910) \| 0.002898 / 0.007986 (-0.005088) \| 0.002381 / 0.004328 (-0.001948) \| 0.048033 / 0.004250 (0.043782) \| 0.044529 / 0.037052 (0.007477) \| 0.246728 / 0.258489 (-0.011761) \| 0.302066 / 0.293841 (0.008225) \| 0.024008 / 0.128546 (-0.104539) \| 0.006626 / 0.075646 (-0.069020) \| 0.202000 / 0.419271 (-0.217272) \| 0.056492 / 0.043533 (0.012959) \| 0.243417 / 0.255139 (-0.011722) \| 0.263947 / 0.283200 (-0.019253) \| 0.020481 / 0.141683 (-0.121202) \| 1.130635 / 1.452155 (-0.321520) \| 1.180570 / 1.492716 (-0.312146) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095541 / 0.018006 (0.077535) \| 0.306152 / 0.000490 (0.305662) \| 0.000217 / 0.000200 (0.000017) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018593 / 0.037411 (-0.018818) \| 0.063029 / 0.014526 (0.048503) \| 0.074312 / 0.176557 (-0.102245) \| 0.119882 / 0.737135 (-0.617254) \| 0.074066 / 0.296338 (-0.222273) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275409 / 0.215209 (0.060200) \| 2.727061 / 2.077655 (0.649407) \| 1.415632 / 1.504120 (-0.088488) \| 1.294922 / 1.541195 (-0.246273) \| 1.341636 / 1.468490 (-0.126854) \| 0.403250 / 4.584777 (-4.181527) \| 2.384657 / 3.745712 (-1.361055) \| 2.604131 / 5.269862 (-2.665731) \| 1.558888 / 4.565676 (-3.006789) \| 0.046008 / 0.424275 (-0.378267) \| 0.004819 / 0.007607 (-0.002789) \| 0.331046 / 0.226044 (0.105002) \| 3.340950 / 2.268929 (1.072021) \| 1.801077 / 55.444624 (-53.643548) \| 1.479162 / 6.876477 (-5.397315) \| 1.503713 / 2.142072 (-0.638359) \| 0.474931 / 4.805227 (-4.330296) \| 0.101869 / 6.500664 (-6.398795) \| 0.041946 / 0.075469 (-0.033523) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.955641 / 1.841788 (-0.886147) \| 11.441032 / 8.074308 (3.366724) \| 10.267731 / 10.191392 (0.076339) \| 0.128735 / 0.680424 (-0.551689) \| 0.013942 / 0.534201 (-0.520259) \| 0.266620 / 0.579283 (-0.312663) \| 0.262334 / 0.434364 (-0.172029) \| 0.302713 / 0.540337 (-0.237624) \| 0.430323 / 1.386936 (-0.956613) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004670 / 0.011353 (-0.006683) \| 0.002671 / 0.011008 (-0.008338) \| 0.048949 / 0.038508 (0.010441) \| 0.052520 / 0.023109 (0.029411) \| 0.272614 / 0.275898 (-0.003284) \| 0.292618 / 0.323480 (-0.030862) \| 0.004016 / 0.007986 (-0.003969) \| 0.002430 / 0.004328 (-0.001899) \| 0.048313 / 0.004250 (0.044063) \| 0.038647 / 0.037052 (0.001595) \| 0.279893 / 0.258489 (0.021404) \| 0.305371 / 0.293841 (0.011530) \| 0.023710 / 0.128546 (-0.104836) \| 0.006999 / 0.075646 (-0.068648) \| 0.053315 / 0.419271 (-0.365956) \| 0.032417 / 0.043533 (-0.011115) \| 0.272066 / 0.255139 (0.016927) \| 0.291717 / 0.283200 (0.008518) \| 0.018127 / 0.141683 (-0.123556) \| 1.173611 / 1.452155 (-0.278544) \| 1.183659 / 1.492716 (-0.309057) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094831 / 0.018006 (0.076824) \| 0.304911 / 0.000490 (0.304421) \| 0.000225 / 0.000200 (0.000025) \| 0.000049 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020948 / 0.037411 (-0.016463) \| 0.070255 / 0.014526 (0.055729) \| 0.081371 / 0.176557 (-0.095186) \| 0.118932 / 0.737135 (-0.618203) \| 0.082207 / 0.296338 (-0.214132) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294067 / 0.215209 (0.078858) \| 2.856981 / 2.077655 (0.779326) \| 1.598392 / 1.504120 (0.094273) \| 1.479093 / 1.541195 (-0.062102) \| 1.509495 / 1.468490 (0.041005) \| 0.396303 / 4.584777 (-4.188473) \| 2.429077 / 3.745712 (-1.316635) \| 2.525037 / 5.269862 (-2.744824) \| 1.503332 / 4.565676 (-3.062345) \| 0.046191 / 0.424275 (-0.378084) \| 0.004858 / 0.007607 (-0.002750) \| 0.349528 / 0.226044 (0.123484) \| 3.401451 / 2.268929 (1.132522) \| 1.989613 / 55.444624 (-53.455012) \| 1.664528 / 6.876477 (-5.211949) \| 1.669076 / 2.142072 (-0.472997) \| 0.467090 / 4.805227 (-4.338137) \| 0.098137 / 6.500664 (-6.402527) \| 0.040448 / 0.075469 (-0.035021) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969578 / 1.841788 (-0.872210) \| 12.064705 / 8.074308 (3.990396) \| 10.991438 / 10.191392 (0.800046) \| 0.130149 / 0.680424 (-0.550275) \| 0.015357 / 0.534201 (-0.518844) \| 0.266567 / 0.579283 (-0.312717) \| 0.270619 / 0.434364 (-0.163744) \| 0.305978 / 0.540337 (-0.234359) \| 0.411164 / 1.386936 (-0.975772) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#86a2cf3174c55899535ee5f1707892a430ee53bc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009810 / 0.011353 (-0.001543) \| 0.005411 / 0.011008 (-0.005598) \| 0.111670 / 0.038508 (0.073162) \| 0.050288 / 0.023109 (0.027179) \| 0.415625 / 0.275898 (0.139727) \| 0.479382 / 0.323480 (0.155902) \| 0.005104 / 0.007986 (-0.002882) \| 0.007122 / 0.004328 (0.002793) \| 0.079626 / 0.004250 (0.075375) \| 0.079421 / 0.037052 (0.042369) \| 0.406722 / 0.258489 (0.148233) \| 0.461511 / 0.293841 (0.167670) \| 0.053812 / 0.128546 (-0.074734) \| 0.014315 / 0.075646 (-0.061331) \| 0.389636 / 0.419271 (-0.029636) \| 0.111859 / 0.043533 (0.068326) \| 0.411703 / 0.255139 (0.156564) \| 0.457072 / 0.283200 (0.173872) \| 0.039807 / 0.141683 (-0.101876) \| 1.744064 / 1.452155 (0.291909) \| 1.968321 / 1.492716 (0.475604) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.341839 / 0.018006 (0.323833) \| 0.628083 / 0.000490 (0.627593) \| 0.023787 / 0.000200 (0.023587) \| 0.000601 / 0.000054 (0.000547) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034170 / 0.037411 (-0.003241) \| 0.091159 / 0.014526 (0.076633) \| 0.108993 / 0.176557 (-0.067563) \| 0.186906 / 0.737135 (-0.550229) \| 0.109753 / 0.296338 (-0.186586) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.684138 / 0.215209 (0.468929) \| 6.634852 / 2.077655 (4.557198) \| 3.102870 / 1.504120 (1.598750) \| 2.831023 / 1.541195 (1.289828) \| 2.831597 / 1.468490 (1.363107) \| 0.903584 / 4.584777 (-3.681193) \| 5.503341 / 3.745712 (1.757629) \| 4.970283 / 5.269862 (-0.299579) \| 3.139413 / 4.565676 (-1.426264) \| 0.109848 / 0.424275 (-0.314427) \| 0.008501 / 0.007607 (0.000894) \| 0.823815 / 0.226044 (0.597770) \| 7.963355 / 2.268929 (5.694426) \| 4.002010 / 55.444624 (-51.442614) \| 3.229390 / 6.876477 (-3.647087) \| 3.166413 / 2.142072 (1.024341) \| 1.030313 / 4.805227 (-3.774914) \| 0.219394 / 6.500664 (-6.281270) \| 0.077760 / 0.075469 (0.002291) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.580309 / 1.841788 (-0.261479) \| 24.279185 / 8.074308 (16.204877) \| 22.305293 / 10.191392 (12.113901) \| 0.235711 / 0.680424 (-0.444713) \| 0.030342 / 0.534201 (-0.503859) \| 0.498137 / 0.579283 (-0.081146) \| 0.619173 / 0.434364 (0.184809) \| 0.529904 / 0.540337 (-0.010434) \| 0.822547 / 1.386936 (-0.564389) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009375 / 0.011353 (-0.001978) \| 0.006009 / 0.011008 (-0.004999) \| 0.074080 / 0.038508 (0.035572) \| 0.089454 / 0.023109 (0.066345) \| 0.473458 / 0.275898 (0.197560) \| 0.462558 / 0.323480 (0.139078) \| 0.006415 / 0.007986 (-0.001571) \| 0.004777 / 0.004328 (0.000448) \| 0.076563 / 0.004250 (0.072313) \| 0.062793 / 0.037052 (0.025741) \| 0.455860 / 0.258489 (0.197371) \| 0.485281 / 0.293841 (0.191440) \| 0.052966 / 0.128546 (-0.075580) \| 0.021600 / 0.075646 (-0.054046) \| 0.090407 / 0.419271 (-0.328864) \| 0.063951 / 0.043533 (0.020418) \| 0.487561 / 0.255139 (0.232422) \| 0.479958 / 0.283200 (0.196758) \| 0.039263 / 0.141683 (-0.102420) \| 1.727215 / 1.452155 (0.275061) \| 1.962039 / 1.492716 (0.469323) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296267 / 0.018006 (0.278261) \| 0.604982 / 0.000490 (0.604493) \| 0.007842 / 0.000200 (0.007642) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034317 / 0.037411 (-0.003094) \| 0.097796 / 0.014526 (0.083270) \| 0.126034 / 0.176557 (-0.050522) \| 0.180873 / 0.737135 (-0.556262) \| 0.125410 / 0.296338 (-0.170928) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.608278 / 0.215209 (0.393069) \| 6.154006 / 2.077655 (4.076351) \| 2.822342 / 1.504120 (1.318222) \| 2.568263 / 1.541195 (1.027068) \| 2.518545 / 1.468490 (1.050055) \| 0.863186 / 4.584777 (-3.721591) \| 5.367969 / 3.745712 (1.622257) \| 4.737691 / 5.269862 (-0.532170) \| 2.917620 / 4.565676 (-1.648056) \| 0.100731 / 0.424275 (-0.323544) \| 0.008611 / 0.007607 (0.001004) \| 0.735523 / 0.226044 (0.509479) \| 7.552790 / 2.268929 (5.283862) \| 3.821835 / 55.444624 (-51.622789) \| 2.878259 / 6.876477 (-3.998217) \| 2.957686 / 2.142072 (0.815613) \| 0.964630 / 4.805227 (-3.840598) \| 0.207098 / 6.500664 (-6.293566) \| 0.084215 / 0.075469 (0.008746) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.711020 / 1.841788 (-0.130768) \| 24.034122 / 8.074308 (15.959814) \| 21.378504 / 10.191392 (11.187112) \| 0.233433 / 0.680424 (-0.446990) \| 0.037214 / 0.534201 (-0.496987) \| 0.511952 / 0.579283 (-0.067332) \| 0.591486 / 0.434364 (0.157123) \| 0.606549 / 0.540337 (0.066211) \| 0.833773 / 1.386936 (-0.553163) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#671f9b32fc559a35996c1b9070fad1a2647a7fef \"CML watermark\")\n" ]	2023-11-15T10:06:44	2023-11-15T13:49:55	2023-11-15T13:38:22	MEMBER	null	Add `pyarrow-hotfix` to release docs.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6421/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6421/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6421", "html_url": "https://github.com/huggingface/datasets/pull/6421", "diff_url": "https://github.com/huggingface/datasets/pull/6421.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6421.patch", "merged_at": "2023-11-15T13:38:22" }	true
https://api.github.com/repos/huggingface/datasets/issues/6420	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6420/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6420/comments	https://api.github.com/repos/huggingface/datasets/issues/6420/events	https://github.com/huggingface/datasets/pull/6420	1,994,278,903	PR_kwDODunzps5ffhdi	6,420	Set dev version	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6420). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004536 / 0.011353 (-0.006816) \| 0.002979 / 0.011008 (-0.008030) \| 0.061984 / 0.038508 (0.023476) \| 0.029382 / 0.023109 (0.006273) \| 0.245237 / 0.275898 (-0.030661) \| 0.270571 / 0.323480 (-0.052909) \| 0.003956 / 0.007986 (-0.004029) \| 0.002453 / 0.004328 (-0.001876) \| 0.047967 / 0.004250 (0.043717) \| 0.043695 / 0.037052 (0.006643) \| 0.248457 / 0.258489 (-0.010032) \| 0.283293 / 0.293841 (-0.010548) \| 0.023603 / 0.128546 (-0.104943) \| 0.007225 / 0.075646 (-0.068422) \| 0.200533 / 0.419271 (-0.218739) \| 0.055310 / 0.043533 (0.011777) \| 0.245152 / 0.255139 (-0.009987) \| 0.267187 / 0.283200 (-0.016012) \| 0.018158 / 0.141683 (-0.123525) \| 1.126079 / 1.452155 (-0.326075) \| 1.185137 / 1.492716 (-0.307580) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092436 / 0.018006 (0.074430) \| 0.300132 / 0.000490 (0.299642) \| 0.000206 / 0.000200 (0.000006) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018476 / 0.037411 (-0.018935) \| 0.062827 / 0.014526 (0.048301) \| 0.074605 / 0.176557 (-0.101952) \| 0.119768 / 0.737135 (-0.617368) \| 0.076044 / 0.296338 (-0.220294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279717 / 0.215209 (0.064508) \| 2.752308 / 2.077655 (0.674654) \| 1.434954 / 1.504120 (-0.069166) \| 1.314700 / 1.541195 (-0.226495) \| 1.347689 / 1.468490 (-0.120802) \| 0.400332 / 4.584777 (-4.184445) \| 2.383024 / 3.745712 (-1.362689) \| 2.583130 / 5.269862 (-2.686732) \| 1.567670 / 4.565676 (-2.998007) \| 0.045446 / 0.424275 (-0.378829) \| 0.004813 / 0.007607 (-0.002794) \| 0.336191 / 0.226044 (0.110147) \| 3.319837 / 2.268929 (1.050909) \| 1.816808 / 55.444624 (-53.627817) \| 1.539052 / 6.876477 (-5.337424) \| 1.550765 / 2.142072 (-0.591307) \| 0.484253 / 4.805227 (-4.320974) \| 0.100494 / 6.500664 (-6.400170) \| 0.041614 / 0.075469 (-0.033855) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.940857 / 1.841788 (-0.900931) \| 11.784946 / 8.074308 (3.710638) \| 10.397038 / 10.191392 (0.205646) \| 0.141458 / 0.680424 (-0.538965) \| 0.014193 / 0.534201 (-0.520008) \| 0.268304 / 0.579283 (-0.310979) \| 0.267059 / 0.434364 (-0.167305) \| 0.309389 / 0.540337 (-0.230949) \| 0.420628 / 1.386936 (-0.966308) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004776 / 0.011353 (-0.006577) \| 0.002941 / 0.011008 (-0.008067) \| 0.048659 / 0.038508 (0.010151) \| 0.053334 / 0.023109 (0.030225) \| 0.273342 / 0.275898 (-0.002556) \| 0.302278 / 0.323480 (-0.021202) \| 0.004001 / 0.007986 (-0.003984) \| 0.002414 / 0.004328 (-0.001914) \| 0.047504 / 0.004250 (0.043254) \| 0.038581 / 0.037052 (0.001529) \| 0.277768 / 0.258489 (0.019279) \| 0.306772 / 0.293841 (0.012931) \| 0.024146 / 0.128546 (-0.104400) \| 0.007233 / 0.075646 (-0.068413) \| 0.053308 / 0.419271 (-0.365964) \| 0.032617 / 0.043533 (-0.010916) \| 0.277390 / 0.255139 (0.022251) \| 0.296015 / 0.283200 (0.012816) \| 0.018733 / 0.141683 (-0.122950) \| 1.124895 / 1.452155 (-0.327260) \| 1.182579 / 1.492716 (-0.310137) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093375 / 0.018006 (0.075369) \| 0.301555 / 0.000490 (0.301066) \| 0.000217 / 0.000200 (0.000017) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021284 / 0.037411 (-0.016127) \| 0.070158 / 0.014526 (0.055632) \| 0.080187 / 0.176557 (-0.096370) \| 0.119282 / 0.737135 (-0.617854) \| 0.081672 / 0.296338 (-0.214666) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314396 / 0.215209 (0.099187) \| 2.975114 / 2.077655 (0.897459) \| 1.724658 / 1.504120 (0.220539) \| 1.604464 / 1.541195 (0.063269) \| 1.652736 / 1.468490 (0.184246) \| 0.395064 / 4.584777 (-4.189713) \| 2.412768 / 3.745712 (-1.332944) \| 2.564427 / 5.269862 (-2.705435) \| 1.507627 / 4.565676 (-3.058050) \| 0.045463 / 0.424275 (-0.378812) \| 0.004797 / 0.007607 (-0.002810) \| 0.383115 / 0.226044 (0.157071) \| 3.501976 / 2.268929 (1.233048) \| 2.087512 / 55.444624 (-53.357113) \| 1.793132 / 6.876477 (-5.083345) \| 1.804178 / 2.142072 (-0.337895) \| 0.468287 / 4.805227 (-4.336940) \| 0.097247 / 6.500664 (-6.403417) \| 0.041139 / 0.075469 (-0.034330) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976034 / 1.841788 (-0.865754) \| 12.431248 / 8.074308 (4.356940) \| 10.896064 / 10.191392 (0.704672) \| 0.129137 / 0.680424 (-0.551287) \| 0.015636 / 0.534201 (-0.518565) \| 0.268219 / 0.579283 (-0.311064) \| 0.278345 / 0.434364 (-0.156019) \| 0.302696 / 0.540337 (-0.237642) \| 0.408465 / 1.386936 (-0.978471) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51c53e94acd7a273c24899c045446df021314cd2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007703 / 0.011353 (-0.003650) \| 0.004614 / 0.011008 (-0.006394) \| 0.101425 / 0.038508 (0.062917) \| 0.040122 / 0.023109 (0.017013) \| 0.398890 / 0.275898 (0.122992) \| 0.424392 / 0.323480 (0.100912) \| 0.005411 / 0.007986 (-0.002575) \| 0.003747 / 0.004328 (-0.000582) \| 0.080494 / 0.004250 (0.076243) \| 0.059392 / 0.037052 (0.022340) \| 0.398025 / 0.258489 (0.139536) \| 0.454293 / 0.293841 (0.160452) \| 0.043662 / 0.128546 (-0.084884) \| 0.013726 / 0.075646 (-0.061920) \| 0.352910 / 0.419271 (-0.066362) \| 0.088572 / 0.043533 (0.045039) \| 0.401677 / 0.255139 (0.146538) \| 0.421774 / 0.283200 (0.138575) \| 0.033377 / 0.141683 (-0.108305) \| 1.728499 / 1.452155 (0.276344) \| 1.821557 / 1.492716 (0.328841) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230744 / 0.018006 (0.212738) \| 0.496188 / 0.000490 (0.495698) \| 0.010315 / 0.000200 (0.010115) \| 0.000402 / 0.000054 (0.000348) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028859 / 0.037411 (-0.008552) \| 0.089688 / 0.014526 (0.075163) \| 0.111697 / 0.176557 (-0.064860) \| 0.183238 / 0.737135 (-0.553898) \| 0.112407 / 0.296338 (-0.183931) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.558394 / 0.215209 (0.343185) \| 5.643048 / 2.077655 (3.565393) \| 2.454622 / 1.504120 (0.950502) \| 2.183338 / 1.541195 (0.642143) \| 2.324793 / 1.468490 (0.856303) \| 0.859482 / 4.584777 (-3.725295) \| 4.959346 / 3.745712 (1.213634) \| 4.599224 / 5.269862 (-0.670638) \| 2.764382 / 4.565676 (-1.801295) \| 0.089976 / 0.424275 (-0.334299) \| 0.008144 / 0.007607 (0.000537) \| 0.634675 / 0.226044 (0.408631) \| 6.555693 / 2.268929 (4.286765) \| 3.080252 / 55.444624 (-52.364373) \| 2.442715 / 6.876477 (-4.433762) \| 2.475126 / 2.142072 (0.333053) \| 0.986459 / 4.805227 (-3.818768) \| 0.193859 / 6.500664 (-6.306805) \| 0.063652 / 0.075469 (-0.011817) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.545318 / 1.841788 (-0.296469) \| 21.928751 / 8.074308 (13.854442) \| 20.598229 / 10.191392 (10.406837) \| 0.234046 / 0.680424 (-0.446377) \| 0.025947 / 0.534201 (-0.508254) \| 0.459773 / 0.579283 (-0.119510) \| 0.598026 / 0.434364 (0.163662) \| 0.555260 / 0.540337 (0.014922) \| 0.782767 / 1.386936 (-0.604169) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009322 / 0.011353 (-0.002030) \| 0.004650 / 0.011008 (-0.006358) \| 0.079326 / 0.038508 (0.040818) \| 0.079112 / 0.023109 (0.056003) \| 0.428708 / 0.275898 (0.152810) \| 0.481647 / 0.323480 (0.158168) \| 0.006419 / 0.007986 (-0.001566) \| 0.003878 / 0.004328 (-0.000450) \| 0.079013 / 0.004250 (0.074762) \| 0.058107 / 0.037052 (0.021055) \| 0.436967 / 0.258489 (0.178478) \| 0.501120 / 0.293841 (0.207279) \| 0.052972 / 0.128546 (-0.075574) \| 0.014414 / 0.075646 (-0.061232) \| 0.098587 / 0.419271 (-0.320685) \| 0.061626 / 0.043533 (0.018093) \| 0.451623 / 0.255139 (0.196484) \| 0.468893 / 0.283200 (0.185693) \| 0.032479 / 0.141683 (-0.109203) \| 1.911743 / 1.452155 (0.459588) \| 1.969024 / 1.492716 (0.476308) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232015 / 0.018006 (0.214009) \| 0.508637 / 0.000490 (0.508147) \| 0.005470 / 0.000200 (0.005270) \| 0.000131 / 0.000054 (0.000076) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035345 / 0.037411 (-0.002066) \| 0.106319 / 0.014526 (0.091794) \| 0.117205 / 0.176557 (-0.059352) \| 0.176527 / 0.737135 (-0.560608) \| 0.121566 / 0.296338 (-0.174773) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.584920 / 0.215209 (0.369711) \| 5.745688 / 2.077655 (3.668034) \| 2.519875 / 1.504120 (1.015755) \| 2.197593 / 1.541195 (0.656398) \| 2.296670 / 1.468490 (0.828180) \| 0.831938 / 4.584777 (-3.752839) \| 5.130594 / 3.745712 (1.384882) \| 4.581385 / 5.269862 (-0.688476) \| 2.829516 / 4.565676 (-1.736161) \| 0.099015 / 0.424275 (-0.325260) \| 0.011468 / 0.007607 (0.003861) \| 0.702717 / 0.226044 (0.476672) \| 6.856099 / 2.268929 (4.587170) \| 3.372966 / 55.444624 (-52.071658) \| 2.567664 / 6.876477 (-4.308812) \| 2.699200 / 2.142072 (0.557127) \| 0.992316 / 4.805227 (-3.812911) \| 0.190463 / 6.500664 (-6.310201) \| 0.063305 / 0.075469 (-0.012165) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.591491 / 1.841788 (-0.250296) \| 21.696492 / 8.074308 (13.622184) \| 19.695404 / 10.191392 (9.504012) \| 0.222853 / 0.680424 (-0.457571) \| 0.032936 / 0.534201 (-0.501265) \| 0.431209 / 0.579283 (-0.148074) \| 0.543101 / 0.434364 (0.108737) \| 0.543427 / 0.540337 (0.003089) \| 0.742102 / 1.386936 (-0.644834) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#534a227179265df9093230885613c95390325705 \"CML watermark\")\n" ]	2023-11-15T08:22:19	2023-11-15T08:33:36	2023-11-15T08:22:33	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6420/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6420/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6420", "html_url": "https://github.com/huggingface/datasets/pull/6420", "diff_url": "https://github.com/huggingface/datasets/pull/6420.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6420.patch", "merged_at": "2023-11-15T08:22:33" }	true
https://api.github.com/repos/huggingface/datasets/issues/6419	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6419/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6419/comments	https://api.github.com/repos/huggingface/datasets/issues/6419/events	https://github.com/huggingface/datasets/pull/6419	1,994,257,873	PR_kwDODunzps5ffc7d	6,419	Release: 2.14.7	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004943 / 0.011353 (-0.006410) \| 0.002900 / 0.011008 (-0.008109) \| 0.061495 / 0.038508 (0.022987) \| 0.053575 / 0.023109 (0.030466) \| 0.249318 / 0.275898 (-0.026580) \| 0.271773 / 0.323480 (-0.051706) \| 0.003074 / 0.007986 (-0.004911) \| 0.003738 / 0.004328 (-0.000590) \| 0.047624 / 0.004250 (0.043373) \| 0.045141 / 0.037052 (0.008089) \| 0.255467 / 0.258489 (-0.003022) \| 0.286577 / 0.293841 (-0.007264) \| 0.023113 / 0.128546 (-0.105433) \| 0.007189 / 0.075646 (-0.068458) \| 0.204441 / 0.419271 (-0.214830) \| 0.036829 / 0.043533 (-0.006704) \| 0.252474 / 0.255139 (-0.002665) \| 0.270960 / 0.283200 (-0.012239) \| 0.019666 / 0.141683 (-0.122017) \| 1.095139 / 1.452155 (-0.357015) \| 1.158659 / 1.492716 (-0.334057) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091046 / 0.018006 (0.073040) \| 0.298346 / 0.000490 (0.297856) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018702 / 0.037411 (-0.018709) \| 0.062213 / 0.014526 (0.047687) \| 0.073364 / 0.176557 (-0.103193) \| 0.119841 / 0.737135 (-0.617294) \| 0.074070 / 0.296338 (-0.222268) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282388 / 0.215209 (0.067179) \| 2.792029 / 2.077655 (0.714375) \| 1.471483 / 1.504120 (-0.032637) \| 1.386236 / 1.541195 (-0.154959) \| 1.377489 / 1.468490 (-0.091001) \| 0.410335 / 4.584777 (-4.174442) \| 2.424866 / 3.745712 (-1.320846) \| 2.610609 / 5.269862 (-2.659253) \| 1.574636 / 4.565676 (-2.991041) \| 0.046716 / 0.424275 (-0.377559) \| 0.004768 / 0.007607 (-0.002839) \| 0.339831 / 0.226044 (0.113787) \| 3.297579 / 2.268929 (1.028651) \| 1.851410 / 55.444624 (-53.593214) \| 1.550048 / 6.876477 (-5.326428) \| 1.576647 / 2.142072 (-0.565425) \| 0.482538 / 4.805227 (-4.322689) \| 0.101381 / 6.500664 (-6.399283) \| 0.042066 / 0.075469 (-0.033403) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972664 / 1.841788 (-0.869123) \| 11.580700 / 8.074308 (3.506392) \| 10.586747 / 10.191392 (0.395355) \| 0.127844 / 0.680424 (-0.552580) \| 0.014270 / 0.534201 (-0.519931) \| 0.269678 / 0.579283 (-0.309605) \| 0.264022 / 0.434364 (-0.170342) \| 0.309395 / 0.540337 (-0.230942) \| 0.429228 / 1.386936 (-0.957708) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004815 / 0.011353 (-0.006538) \| 0.002890 / 0.011008 (-0.008119) \| 0.048039 / 0.038508 (0.009531) \| 0.053029 / 0.023109 (0.029920) \| 0.271346 / 0.275898 (-0.004552) \| 0.294488 / 0.323480 (-0.028992) \| 0.003983 / 0.007986 (-0.004003) \| 0.002439 / 0.004328 (-0.001889) \| 0.048250 / 0.004250 (0.044000) \| 0.038855 / 0.037052 (0.001803) \| 0.284723 / 0.258489 (0.026234) \| 0.303604 / 0.293841 (0.009763) \| 0.024384 / 0.128546 (-0.104163) \| 0.007021 / 0.075646 (-0.068625) \| 0.053850 / 0.419271 (-0.365422) \| 0.032177 / 0.043533 (-0.011356) \| 0.270039 / 0.255139 (0.014900) \| 0.289669 / 0.283200 (0.006469) \| 0.018840 / 0.141683 (-0.122842) \| 1.122191 / 1.452155 (-0.329963) \| 1.187083 / 1.492716 (-0.305634) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090609 / 0.018006 (0.072603) \| 0.298915 / 0.000490 (0.298425) \| 0.000216 / 0.000200 (0.000016) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020919 / 0.037411 (-0.016492) \| 0.070474 / 0.014526 (0.055948) \| 0.082421 / 0.176557 (-0.094135) \| 0.126967 / 0.737135 (-0.610168) \| 0.083447 / 0.296338 (-0.212892) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300153 / 0.215209 (0.084944) \| 2.958992 / 2.077655 (0.881337) \| 1.631228 / 1.504120 (0.127108) \| 1.497991 / 1.541195 (-0.043204) \| 1.536963 / 1.468490 (0.068473) \| 0.403047 / 4.584777 (-4.181730) \| 2.448782 / 3.745712 (-1.296930) \| 2.571954 / 5.269862 (-2.697908) \| 1.556346 / 4.565676 (-3.009331) \| 0.045992 / 0.424275 (-0.378283) \| 0.004785 / 0.007607 (-0.002822) \| 0.357448 / 0.226044 (0.131404) \| 3.558808 / 2.268929 (1.289880) \| 1.992624 / 55.444624 (-53.452001) \| 1.695027 / 6.876477 (-5.181450) \| 1.695183 / 2.142072 (-0.446889) \| 0.477001 / 4.805227 (-4.328226) \| 0.097485 / 6.500664 (-6.403179) \| 0.040530 / 0.075469 (-0.034939) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976342 / 1.841788 (-0.865445) \| 12.141698 / 8.074308 (4.067390) \| 10.881101 / 10.191392 (0.689709) \| 0.142443 / 0.680424 (-0.537981) \| 0.015583 / 0.534201 (-0.518618) \| 0.269727 / 0.579283 (-0.309556) \| 0.275890 / 0.434364 (-0.158474) \| 0.306351 / 0.540337 (-0.233987) \| 0.412003 / 1.386936 (-0.974933) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7c744261000fd684f54c54de8ac4f15a726092d7 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004946 / 0.011353 (-0.006407) \| 0.002863 / 0.011008 (-0.008146) \| 0.061888 / 0.038508 (0.023380) \| 0.050664 / 0.023109 (0.027554) \| 0.242635 / 0.275898 (-0.033263) \| 0.271741 / 0.323480 (-0.051739) \| 0.003023 / 0.007986 (-0.004963) \| 0.003088 / 0.004328 (-0.001241) \| 0.049286 / 0.004250 (0.045036) \| 0.044699 / 0.037052 (0.007647) \| 0.249581 / 0.258489 (-0.008908) \| 0.285633 / 0.293841 (-0.008208) \| 0.023048 / 0.128546 (-0.105499) \| 0.007235 / 0.075646 (-0.068412) \| 0.202989 / 0.419271 (-0.216282) \| 0.036357 / 0.043533 (-0.007175) \| 0.245980 / 0.255139 (-0.009159) \| 0.277486 / 0.283200 (-0.005713) \| 0.019215 / 0.141683 (-0.122468) \| 1.096456 / 1.452155 (-0.355699) \| 1.152196 / 1.492716 (-0.340520) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092026 / 0.018006 (0.074020) \| 0.303038 / 0.000490 (0.302549) \| 0.000209 / 0.000200 (0.000009) \| 0.000048 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018670 / 0.037411 (-0.018741) \| 0.061972 / 0.014526 (0.047446) \| 0.072963 / 0.176557 (-0.103594) \| 0.119984 / 0.737135 (-0.617151) \| 0.074074 / 0.296338 (-0.222265) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282444 / 0.215209 (0.067235) \| 2.754571 / 2.077655 (0.676916) \| 1.482635 / 1.504120 (-0.021485) \| 1.352039 / 1.541195 (-0.189155) \| 1.359333 / 1.468490 (-0.109157) \| 0.399690 / 4.584777 (-4.185087) \| 2.364844 / 3.745712 (-1.380868) \| 2.603942 / 5.269862 (-2.665919) \| 1.569512 / 4.565676 (-2.996164) \| 0.046074 / 0.424275 (-0.378201) \| 0.004745 / 0.007607 (-0.002862) \| 0.339066 / 0.226044 (0.113022) \| 3.363456 / 2.268929 (1.094527) \| 1.822213 / 55.444624 (-53.622411) \| 1.536622 / 6.876477 (-5.339854) \| 1.574772 / 2.142072 (-0.567300) \| 0.474418 / 4.805227 (-4.330809) \| 0.099572 / 6.500664 (-6.401092) \| 0.041824 / 0.075469 (-0.033645) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956300 / 1.841788 (-0.885487) \| 11.648886 / 8.074308 (3.574578) \| 10.645700 / 10.191392 (0.454308) \| 0.138924 / 0.680424 (-0.541499) \| 0.013936 / 0.534201 (-0.520265) \| 0.270319 / 0.579283 (-0.308964) \| 0.269735 / 0.434364 (-0.164629) \| 0.309699 / 0.540337 (-0.230639) \| 0.429139 / 1.386936 (-0.957797) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004838 / 0.011353 (-0.006515) \| 0.002937 / 0.011008 (-0.008072) \| 0.048094 / 0.038508 (0.009586) \| 0.053131 / 0.023109 (0.030022) \| 0.271893 / 0.275898 (-0.004005) \| 0.291025 / 0.323480 (-0.032454) \| 0.004058 / 0.007986 (-0.003928) \| 0.002410 / 0.004328 (-0.001919) \| 0.047939 / 0.004250 (0.043689) \| 0.038996 / 0.037052 (0.001944) \| 0.274983 / 0.258489 (0.016494) \| 0.306175 / 0.293841 (0.012334) \| 0.024388 / 0.128546 (-0.104159) \| 0.007242 / 0.075646 (-0.068404) \| 0.054011 / 0.419271 (-0.365261) \| 0.032750 / 0.043533 (-0.010783) \| 0.271147 / 0.255139 (0.016008) \| 0.288163 / 0.283200 (0.004963) \| 0.018383 / 0.141683 (-0.123299) \| 1.116134 / 1.452155 (-0.336021) \| 1.185964 / 1.492716 (-0.306752) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093289 / 0.018006 (0.075283) \| 0.303058 / 0.000490 (0.302568) \| 0.000241 / 0.000200 (0.000041) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021422 / 0.037411 (-0.015990) \| 0.069974 / 0.014526 (0.055449) \| 0.081164 / 0.176557 (-0.095392) \| 0.119991 / 0.737135 (-0.617144) \| 0.082154 / 0.296338 (-0.214184) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292298 / 0.215209 (0.077089) \| 2.851475 / 2.077655 (0.773821) \| 1.558283 / 1.504120 (0.054163) \| 1.432431 / 1.541195 (-0.108764) \| 1.479282 / 1.468490 (0.010792) \| 0.413124 / 4.584777 (-4.171653) \| 2.473005 / 3.745712 (-1.272707) \| 2.548779 / 5.269862 (-2.721082) \| 1.520776 / 4.565676 (-3.044900) \| 0.046476 / 0.424275 (-0.377799) \| 0.004814 / 0.007607 (-0.002794) \| 0.347036 / 0.226044 (0.120992) \| 3.424928 / 2.268929 (1.155999) \| 1.963274 / 55.444624 (-53.481351) \| 1.653794 / 6.876477 (-5.222683) \| 1.643874 / 2.142072 (-0.498198) \| 0.469086 / 4.805227 (-4.336141) \| 0.097417 / 6.500664 (-6.403247) \| 0.040468 / 0.075469 (-0.035002) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972783 / 1.841788 (-0.869005) \| 12.122994 / 8.074308 (4.048686) \| 10.876396 / 10.191392 (0.685004) \| 0.130573 / 0.680424 (-0.549850) \| 0.016693 / 0.534201 (-0.517508) \| 0.270952 / 0.579283 (-0.308331) \| 0.273834 / 0.434364 (-0.160530) \| 0.305049 / 0.540337 (-0.235289) \| 0.408776 / 1.386936 (-0.978160) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4216e5d57ea07e6b1ed73a3ec2cf845c6e59f70 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004606 / 0.011353 (-0.006747) \| 0.002433 / 0.011008 (-0.008576) \| 0.061985 / 0.038508 (0.023477) \| 0.048853 / 0.023109 (0.025744) \| 0.244506 / 0.275898 (-0.031392) \| 0.270159 / 0.323480 (-0.053321) \| 0.003962 / 0.007986 (-0.004024) \| 0.002376 / 0.004328 (-0.001952) \| 0.048067 / 0.004250 (0.043817) \| 0.041864 / 0.037052 (0.004812) \| 0.249743 / 0.258489 (-0.008746) \| 0.287723 / 0.293841 (-0.006117) \| 0.022954 / 0.128546 (-0.105593) \| 0.006845 / 0.075646 (-0.068801) \| 0.206313 / 0.419271 (-0.212959) \| 0.035780 / 0.043533 (-0.007753) \| 0.244286 / 0.255139 (-0.010853) \| 0.270026 / 0.283200 (-0.013173) \| 0.018177 / 0.141683 (-0.123506) \| 1.083998 / 1.452155 (-0.368157) \| 1.156086 / 1.492716 (-0.336630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093754 / 0.018006 (0.075748) \| 0.302157 / 0.000490 (0.301667) \| 0.000215 / 0.000200 (0.000015) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018745 / 0.037411 (-0.018666) \| 0.061707 / 0.014526 (0.047181) \| 0.074356 / 0.176557 (-0.102200) \| 0.121643 / 0.737135 (-0.615492) \| 0.075885 / 0.296338 (-0.220454) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289156 / 0.215209 (0.073947) \| 2.881327 / 2.077655 (0.803672) \| 1.483568 / 1.504120 (-0.020552) \| 1.355933 / 1.541195 (-0.185262) \| 1.389693 / 1.468490 (-0.078797) \| 0.402834 / 4.584777 (-4.181943) \| 2.390634 / 3.745712 (-1.355078) \| 2.596761 / 5.269862 (-2.673101) \| 1.527602 / 4.565676 (-3.038074) \| 0.046434 / 0.424275 (-0.377841) \| 0.004783 / 0.007607 (-0.002824) \| 0.341017 / 0.226044 (0.114972) \| 3.429023 / 2.268929 (1.160095) \| 1.832988 / 55.444624 (-53.611637) \| 1.526510 / 6.876477 (-5.349967) \| 1.539382 / 2.142072 (-0.602690) \| 0.475734 / 4.805227 (-4.329493) \| 0.098710 / 6.500664 (-6.401954) \| 0.041136 / 0.075469 (-0.034333) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.922023 / 1.841788 (-0.919765) \| 11.428215 / 8.074308 (3.353907) \| 10.356668 / 10.191392 (0.165276) \| 0.139575 / 0.680424 (-0.540848) \| 0.014541 / 0.534201 (-0.519660) \| 0.271359 / 0.579283 (-0.307924) \| 0.266701 / 0.434364 (-0.167663) \| 0.309449 / 0.540337 (-0.230888) \| 0.422047 / 1.386936 (-0.964889) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004892 / 0.011353 (-0.006461) \| 0.002792 / 0.011008 (-0.008216) \| 0.048027 / 0.038508 (0.009519) \| 0.059256 / 0.023109 (0.036147) \| 0.270150 / 0.275898 (-0.005748) \| 0.294530 / 0.323480 (-0.028950) \| 0.004162 / 0.007986 (-0.003823) \| 0.002470 / 0.004328 (-0.001858) \| 0.047993 / 0.004250 (0.043743) \| 0.040380 / 0.037052 (0.003328) \| 0.275247 / 0.258489 (0.016758) \| 0.305684 / 0.293841 (0.011843) \| 0.025072 / 0.128546 (-0.103474) \| 0.007183 / 0.075646 (-0.068463) \| 0.054875 / 0.419271 (-0.364397) \| 0.033053 / 0.043533 (-0.010480) \| 0.271281 / 0.255139 (0.016142) \| 0.288057 / 0.283200 (0.004858) \| 0.018692 / 0.141683 (-0.122991) \| 1.125224 / 1.452155 (-0.326930) \| 1.171083 / 1.492716 (-0.321633) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103102 / 0.018006 (0.085096) \| 0.309099 / 0.000490 (0.308609) \| 0.000232 / 0.000200 (0.000032) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021532 / 0.037411 (-0.015879) \| 0.069927 / 0.014526 (0.055401) \| 0.080920 / 0.176557 (-0.095637) \| 0.122214 / 0.737135 (-0.614921) \| 0.082268 / 0.296338 (-0.214071) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298121 / 0.215209 (0.082912) \| 2.933000 / 2.077655 (0.855345) \| 1.608782 / 1.504120 (0.104662) \| 1.554083 / 1.541195 (0.012889) \| 1.552700 / 1.468490 (0.084209) \| 0.400576 / 4.584777 (-4.184201) \| 2.412914 / 3.745712 (-1.332798) \| 2.545706 / 5.269862 (-2.724155) \| 1.548797 / 4.565676 (-3.016879) \| 0.045553 / 0.424275 (-0.378722) \| 0.004751 / 0.007607 (-0.002857) \| 0.343002 / 0.226044 (0.116958) \| 3.402866 / 2.268929 (1.133937) \| 1.969910 / 55.444624 (-53.474715) \| 1.686639 / 6.876477 (-5.189838) \| 1.768474 / 2.142072 (-0.373599) \| 0.471299 / 4.805227 (-4.333928) \| 0.097696 / 6.500664 (-6.402968) \| 0.041693 / 0.075469 (-0.033776) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971380 / 1.841788 (-0.870408) \| 12.686033 / 8.074308 (4.611725) \| 11.370946 / 10.191392 (1.179554) \| 0.138377 / 0.680424 (-0.542047) \| 0.015623 / 0.534201 (-0.518578) \| 0.270935 / 0.579283 (-0.308348) \| 0.276235 / 0.434364 (-0.158129) \| 0.310196 / 0.540337 (-0.230141) \| 0.416908 / 1.386936 (-0.970028) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004581 / 0.011353 (-0.006772) \| 0.002468 / 0.011008 (-0.008541) \| 0.061420 / 0.038508 (0.022912) \| 0.047685 / 0.023109 (0.024575) \| 0.237756 / 0.275898 (-0.038142) \| 0.267548 / 0.323480 (-0.055932) \| 0.003899 / 0.007986 (-0.004086) \| 0.002338 / 0.004328 (-0.001990) \| 0.048794 / 0.004250 (0.044543) \| 0.042485 / 0.037052 (0.005433) \| 0.250165 / 0.258489 (-0.008324) \| 0.278791 / 0.293841 (-0.015050) \| 0.022371 / 0.128546 (-0.106175) \| 0.006923 / 0.075646 (-0.068723) \| 0.201401 / 0.419271 (-0.217870) \| 0.035867 / 0.043533 (-0.007665) \| 0.244628 / 0.255139 (-0.010511) \| 0.271137 / 0.283200 (-0.012063) \| 0.017257 / 0.141683 (-0.124426) \| 1.097261 / 1.452155 (-0.354894) \| 1.163314 / 1.492716 (-0.329402) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089060 / 0.018006 (0.071054) \| 0.297489 / 0.000490 (0.296999) \| 0.000207 / 0.000200 (0.000007) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018583 / 0.037411 (-0.018828) \| 0.061974 / 0.014526 (0.047449) \| 0.073300 / 0.176557 (-0.103256) \| 0.118871 / 0.737135 (-0.618264) \| 0.075513 / 0.296338 (-0.220826) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285544 / 0.215209 (0.070335) \| 2.799871 / 2.077655 (0.722216) \| 1.479871 / 1.504120 (-0.024249) \| 1.351128 / 1.541195 (-0.190067) \| 1.377540 / 1.468490 (-0.090950) \| 0.393056 / 4.584777 (-4.191721) \| 2.341791 / 3.745712 (-1.403921) \| 2.546854 / 5.269862 (-2.723007) \| 1.547368 / 4.565676 (-3.018309) \| 0.046056 / 0.424275 (-0.378219) \| 0.004765 / 0.007607 (-0.002842) \| 0.336384 / 0.226044 (0.110339) \| 3.283277 / 2.268929 (1.014348) \| 1.784535 / 55.444624 (-53.660089) \| 1.557809 / 6.876477 (-5.318667) \| 1.581728 / 2.142072 (-0.560344) \| 0.470527 / 4.805227 (-4.334700) \| 0.098383 / 6.500664 (-6.402281) \| 0.041563 / 0.075469 (-0.033906) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946924 / 1.841788 (-0.894863) \| 11.202775 / 8.074308 (3.128467) \| 10.249760 / 10.191392 (0.058368) \| 0.142337 / 0.680424 (-0.538087) \| 0.013784 / 0.534201 (-0.520417) \| 0.267237 / 0.579283 (-0.312046) \| 0.264142 / 0.434364 (-0.170222) \| 0.306343 / 0.540337 (-0.233994) \| 0.423681 / 1.386936 (-0.963255) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004786 / 0.011353 (-0.006567) \| 0.002398 / 0.011008 (-0.008610) \| 0.047325 / 0.038508 (0.008817) \| 0.050753 / 0.023109 (0.027644) \| 0.271132 / 0.275898 (-0.004766) \| 0.290854 / 0.323480 (-0.032626) \| 0.003953 / 0.007986 (-0.004033) \| 0.002238 / 0.004328 (-0.002090) \| 0.047463 / 0.004250 (0.043213) \| 0.038504 / 0.037052 (0.001451) \| 0.273182 / 0.258489 (0.014693) \| 0.303449 / 0.293841 (0.009608) \| 0.024069 / 0.128546 (-0.104477) \| 0.006712 / 0.075646 (-0.068934) \| 0.053032 / 0.419271 (-0.366239) \| 0.032221 / 0.043533 (-0.011312) \| 0.271770 / 0.255139 (0.016631) \| 0.287876 / 0.283200 (0.004677) \| 0.018040 / 0.141683 (-0.123643) \| 1.138749 / 1.452155 (-0.313405) \| 1.192048 / 1.492716 (-0.300668) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089132 / 0.018006 (0.071126) \| 0.298636 / 0.000490 (0.298146) \| 0.000220 / 0.000200 (0.000020) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020808 / 0.037411 (-0.016603) \| 0.069506 / 0.014526 (0.054980) \| 0.079412 / 0.176557 (-0.097145) \| 0.118188 / 0.737135 (-0.618947) \| 0.083044 / 0.296338 (-0.213294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293502 / 0.215209 (0.078293) \| 2.863692 / 2.077655 (0.786037) \| 1.590877 / 1.504120 (0.086757) \| 1.483634 / 1.541195 (-0.057561) \| 1.502113 / 1.468490 (0.033623) \| 0.402170 / 4.584777 (-4.182607) \| 2.414188 / 3.745712 (-1.331524) \| 2.500146 / 5.269862 (-2.769716) \| 1.506977 / 4.565676 (-3.058699) \| 0.045849 / 0.424275 (-0.378426) \| 0.004755 / 0.007607 (-0.002852) \| 0.343073 / 0.226044 (0.117029) \| 3.354985 / 2.268929 (1.086056) \| 1.952594 / 55.444624 (-53.492030) \| 1.664084 / 6.876477 (-5.212392) \| 1.664203 / 2.142072 (-0.477869) \| 0.475858 / 4.805227 (-4.329370) \| 0.097539 / 6.500664 (-6.403125) \| 0.040201 / 0.075469 (-0.035268) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980051 / 1.841788 (-0.861736) \| 11.615291 / 8.074308 (3.540983) \| 10.492092 / 10.191392 (0.300700) \| 0.130450 / 0.680424 (-0.549974) \| 0.015883 / 0.534201 (-0.518318) \| 0.267575 / 0.579283 (-0.311708) \| 0.276981 / 0.434364 (-0.157383) \| 0.310221 / 0.540337 (-0.230116) \| 0.417143 / 1.386936 (-0.969793) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004721 / 0.011353 (-0.006632) \| 0.002931 / 0.011008 (-0.008077) \| 0.061948 / 0.038508 (0.023440) \| 0.051066 / 0.023109 (0.027957) \| 0.245431 / 0.275898 (-0.030467) \| 0.295627 / 0.323480 (-0.027852) \| 0.003997 / 0.007986 (-0.003988) \| 0.002408 / 0.004328 (-0.001920) \| 0.048292 / 0.004250 (0.044041) \| 0.044716 / 0.037052 (0.007664) \| 0.255119 / 0.258489 (-0.003371) \| 0.287384 / 0.293841 (-0.006457) \| 0.022835 / 0.128546 (-0.105711) \| 0.007162 / 0.075646 (-0.068484) \| 0.201352 / 0.419271 (-0.217920) \| 0.036626 / 0.043533 (-0.006906) \| 0.249590 / 0.255139 (-0.005549) \| 0.270822 / 0.283200 (-0.012378) \| 0.018152 / 0.141683 (-0.123531) \| 1.097046 / 1.452155 (-0.355109) \| 1.160461 / 1.492716 (-0.332255) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091712 / 0.018006 (0.073705) \| 0.299121 / 0.000490 (0.298631) \| 0.000244 / 0.000200 (0.000044) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018998 / 0.037411 (-0.018413) \| 0.062811 / 0.014526 (0.048285) \| 0.076348 / 0.176557 (-0.100209) \| 0.123898 / 0.737135 (-0.613238) \| 0.076249 / 0.296338 (-0.220090) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282780 / 0.215209 (0.067571) \| 2.739028 / 2.077655 (0.661373) \| 1.472564 / 1.504120 (-0.031556) \| 1.347343 / 1.541195 (-0.193852) \| 1.387130 / 1.468490 (-0.081360) \| 0.403348 / 4.584777 (-4.181429) \| 2.369924 / 3.745712 (-1.375788) \| 2.612875 / 5.269862 (-2.656987) \| 1.588079 / 4.565676 (-2.977598) \| 0.045233 / 0.424275 (-0.379042) \| 0.004767 / 0.007607 (-0.002840) \| 0.336614 / 0.226044 (0.110570) \| 3.300485 / 2.268929 (1.031556) \| 1.834365 / 55.444624 (-53.610259) \| 1.559799 / 6.876477 (-5.316677) \| 1.601265 / 2.142072 (-0.540808) \| 0.468158 / 4.805227 (-4.337069) \| 0.099811 / 6.500664 (-6.400853) \| 0.042688 / 0.075469 (-0.032782) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.934097 / 1.841788 (-0.907691) \| 11.687713 / 8.074308 (3.613405) \| 10.412723 / 10.191392 (0.221331) \| 0.139276 / 0.680424 (-0.541148) \| 0.014042 / 0.534201 (-0.520159) \| 0.270306 / 0.579283 (-0.308978) \| 0.266609 / 0.434364 (-0.167755) \| 0.314179 / 0.540337 (-0.226158) \| 0.437744 / 1.386936 (-0.949192) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004893 / 0.011353 (-0.006460) \| 0.002952 / 0.011008 (-0.008056) \| 0.050441 / 0.038508 (0.011933) \| 0.051838 / 0.023109 (0.028729) \| 0.271163 / 0.275898 (-0.004735) \| 0.293031 / 0.323480 (-0.030449) \| 0.003976 / 0.007986 (-0.004010) \| 0.002396 / 0.004328 (-0.001933) \| 0.048103 / 0.004250 (0.043852) \| 0.038732 / 0.037052 (0.001680) \| 0.274276 / 0.258489 (0.015787) \| 0.305112 / 0.293841 (0.011271) \| 0.024112 / 0.128546 (-0.104434) \| 0.007203 / 0.075646 (-0.068443) \| 0.053502 / 0.419271 (-0.365770) \| 0.032360 / 0.043533 (-0.011173) \| 0.270154 / 0.255139 (0.015015) \| 0.286689 / 0.283200 (0.003489) \| 0.018285 / 0.141683 (-0.123397) \| 1.141421 / 1.452155 (-0.310734) \| 1.244062 / 1.492716 (-0.248654) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090960 / 0.018006 (0.072954) \| 0.286134 / 0.000490 (0.285644) \| 0.000207 / 0.000200 (0.000007) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020789 / 0.037411 (-0.016622) \| 0.070850 / 0.014526 (0.056324) \| 0.080750 / 0.176557 (-0.095807) \| 0.120046 / 0.737135 (-0.617089) \| 0.083630 / 0.296338 (-0.212708) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290654 / 0.215209 (0.075445) \| 2.846669 / 2.077655 (0.769014) \| 1.561752 / 1.504120 (0.057632) \| 1.442968 / 1.541195 (-0.098227) \| 1.503551 / 1.468490 (0.035061) \| 0.399731 / 4.584777 (-4.185046) \| 2.430099 / 3.745712 (-1.315613) \| 2.556169 / 5.269862 (-2.713692) \| 1.545591 / 4.565676 (-3.020085) \| 0.045967 / 0.424275 (-0.378309) \| 0.004851 / 0.007607 (-0.002756) \| 0.340167 / 0.226044 (0.114122) \| 3.392738 / 2.268929 (1.123809) \| 1.943577 / 55.444624 (-53.501047) \| 1.650057 / 6.876477 (-5.226420) \| 1.686872 / 2.142072 (-0.455201) \| 0.470305 / 4.805227 (-4.334923) \| 0.097296 / 6.500664 (-6.403368) \| 0.041399 / 0.075469 (-0.034070) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985660 / 1.841788 (-0.856128) \| 12.300826 / 8.074308 (4.226518) \| 10.972591 / 10.191392 (0.781199) \| 0.131512 / 0.680424 (-0.548912) \| 0.015742 / 0.534201 (-0.518459) \| 0.270630 / 0.579283 (-0.308653) \| 0.276039 / 0.434364 (-0.158325) \| 0.302288 / 0.540337 (-0.238050) \| 0.409415 / 1.386936 (-0.977521) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n" ]	2023-11-15T08:07:37	2023-11-15T17:35:30	2023-11-15T08:12:59	MEMBER	null	Release 2.14.7.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6419/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6419/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6419", "html_url": "https://github.com/huggingface/datasets/pull/6419", "diff_url": "https://github.com/huggingface/datasets/pull/6419.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6419.patch", "merged_at": "2023-11-15T08:12:59" }	true
https://api.github.com/repos/huggingface/datasets/issues/6418	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6418/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6418/comments	https://api.github.com/repos/huggingface/datasets/issues/6418/events	https://github.com/huggingface/datasets/pull/6418	1,993,224,629	PR_kwDODunzps5fb7lu	6,418	Remove token value from warnings	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005135 / 0.011353 (-0.006218) \| 0.002950 / 0.011008 (-0.008058) \| 0.062316 / 0.038508 (0.023808) \| 0.030068 / 0.023109 (0.006959) \| 0.251998 / 0.275898 (-0.023900) \| 0.274806 / 0.323480 (-0.048674) \| 0.003067 / 0.007986 (-0.004919) \| 0.003082 / 0.004328 (-0.001247) \| 0.048503 / 0.004250 (0.044253) \| 0.045167 / 0.037052 (0.008114) \| 0.254277 / 0.258489 (-0.004212) \| 0.290528 / 0.293841 (-0.003313) \| 0.023666 / 0.128546 (-0.104880) \| 0.007049 / 0.075646 (-0.068597) \| 0.202367 / 0.419271 (-0.216905) \| 0.056291 / 0.043533 (0.012758) \| 0.251923 / 0.255139 (-0.003216) \| 0.273595 / 0.283200 (-0.009605) \| 0.019065 / 0.141683 (-0.122618) \| 1.100832 / 1.452155 (-0.351322) \| 1.266758 / 1.492716 (-0.225959) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094311 / 0.018006 (0.076305) \| 0.303199 / 0.000490 (0.302709) \| 0.000238 / 0.000200 (0.000039) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019413 / 0.037411 (-0.017999) \| 0.062618 / 0.014526 (0.048092) \| 0.072850 / 0.176557 (-0.103707) \| 0.119124 / 0.737135 (-0.618012) \| 0.074044 / 0.296338 (-0.222294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273660 / 0.215209 (0.058451) \| 2.682371 / 2.077655 (0.604716) \| 1.426041 / 1.504120 (-0.078079) \| 1.317186 / 1.541195 (-0.224009) \| 1.332385 / 1.468490 (-0.136106) \| 0.394599 / 4.584777 (-4.190178) \| 2.368167 / 3.745712 (-1.377545) \| 2.683728 / 5.269862 (-2.586134) \| 1.668348 / 4.565676 (-2.897329) \| 0.046177 / 0.424275 (-0.378098) \| 0.004833 / 0.007607 (-0.002774) \| 0.331413 / 0.226044 (0.105369) \| 3.278984 / 2.268929 (1.010055) \| 1.797600 / 55.444624 (-53.647024) \| 1.492202 / 6.876477 (-5.384274) \| 1.536039 / 2.142072 (-0.606034) \| 0.470601 / 4.805227 (-4.334626) \| 0.100833 / 6.500664 (-6.399831) \| 0.042787 / 0.075469 (-0.032682) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959036 / 1.841788 (-0.882752) \| 11.632956 / 8.074308 (3.558648) \| 10.384574 / 10.191392 (0.193182) \| 0.127477 / 0.680424 (-0.552946) \| 0.014072 / 0.534201 (-0.520129) \| 0.269534 / 0.579283 (-0.309749) \| 0.259753 / 0.434364 (-0.174611) \| 0.313450 / 0.540337 (-0.226888) \| 0.431799 / 1.386936 (-0.955137) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004964 / 0.011353 (-0.006389) \| 0.002906 / 0.011008 (-0.008102) \| 0.048145 / 0.038508 (0.009637) \| 0.056457 / 0.023109 (0.033348) \| 0.274131 / 0.275898 (-0.001767) \| 0.298534 / 0.323480 (-0.024946) \| 0.004145 / 0.007986 (-0.003841) \| 0.002415 / 0.004328 (-0.001913) \| 0.048558 / 0.004250 (0.044308) \| 0.039031 / 0.037052 (0.001978) \| 0.278948 / 0.258489 (0.020459) \| 0.312358 / 0.293841 (0.018517) \| 0.024902 / 0.128546 (-0.103645) \| 0.007286 / 0.075646 (-0.068360) \| 0.053839 / 0.419271 (-0.365433) \| 0.032510 / 0.043533 (-0.011023) \| 0.272023 / 0.255139 (0.016884) \| 0.293420 / 0.283200 (0.010221) \| 0.018932 / 0.141683 (-0.122750) \| 1.122792 / 1.452155 (-0.329362) \| 1.167385 / 1.492716 (-0.325331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094574 / 0.018006 (0.076567) \| 0.303810 / 0.000490 (0.303321) \| 0.000227 / 0.000200 (0.000027) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021675 / 0.037411 (-0.015737) \| 0.070289 / 0.014526 (0.055763) \| 0.080345 / 0.176557 (-0.096211) \| 0.120220 / 0.737135 (-0.616915) \| 0.084080 / 0.296338 (-0.212259) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300134 / 0.215209 (0.084925) \| 2.945831 / 2.077655 (0.868176) \| 1.605303 / 1.504120 (0.101183) \| 1.480135 / 1.541195 (-0.061059) \| 1.526039 / 1.468490 (0.057549) \| 0.398264 / 4.584777 (-4.186512) \| 2.461391 / 3.745712 (-1.284321) \| 2.559929 / 5.269862 (-2.709933) \| 1.541391 / 4.565676 (-3.024286) \| 0.045319 / 0.424275 (-0.378957) \| 0.004834 / 0.007607 (-0.002773) \| 0.352186 / 0.226044 (0.126141) \| 3.500108 / 2.268929 (1.231180) \| 1.966394 / 55.444624 (-53.478230) \| 1.675500 / 6.876477 (-5.200977) \| 1.683134 / 2.142072 (-0.458938) \| 0.465085 / 4.805227 (-4.340142) \| 0.097235 / 6.500664 (-6.403429) \| 0.040764 / 0.075469 (-0.034705) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982813 / 1.841788 (-0.858975) \| 12.382529 / 8.074308 (4.308221) \| 11.082660 / 10.191392 (0.891268) \| 0.129113 / 0.680424 (-0.551310) \| 0.015718 / 0.534201 (-0.518483) \| 0.272776 / 0.579283 (-0.306507) \| 0.275513 / 0.434364 (-0.158850) \| 0.304933 / 0.540337 (-0.235404) \| 0.414591 / 1.386936 (-0.972345) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8723b129a64928eba40baf70ffd462060ade9f97 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004400 / 0.011353 (-0.006953) \| 0.002580 / 0.011008 (-0.008428) \| 0.060975 / 0.038508 (0.022467) \| 0.029337 / 0.023109 (0.006228) \| 0.248643 / 0.275898 (-0.027255) \| 0.274476 / 0.323480 (-0.049004) \| 0.003925 / 0.007986 (-0.004061) \| 0.002332 / 0.004328 (-0.001997) \| 0.049501 / 0.004250 (0.045251) \| 0.042730 / 0.037052 (0.005678) \| 0.255823 / 0.258489 (-0.002666) \| 0.281748 / 0.293841 (-0.012093) \| 0.023118 / 0.128546 (-0.105428) \| 0.006957 / 0.075646 (-0.068690) \| 0.201630 / 0.419271 (-0.217641) \| 0.054258 / 0.043533 (0.010725) \| 0.252289 / 0.255139 (-0.002850) \| 0.267561 / 0.283200 (-0.015639) \| 0.016903 / 0.141683 (-0.124780) \| 1.104322 / 1.452155 (-0.347833) \| 1.160027 / 1.492716 (-0.332689) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096340 / 0.018006 (0.078333) \| 0.305187 / 0.000490 (0.304697) \| 0.000222 / 0.000200 (0.000022) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018733 / 0.037411 (-0.018678) \| 0.062382 / 0.014526 (0.047856) \| 0.072309 / 0.176557 (-0.104248) \| 0.119772 / 0.737135 (-0.617364) \| 0.074655 / 0.296338 (-0.221683) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286150 / 0.215209 (0.070941) \| 2.770328 / 2.077655 (0.692673) \| 1.494593 / 1.504120 (-0.009527) \| 1.358611 / 1.541195 (-0.182583) \| 1.396308 / 1.468490 (-0.072182) \| 0.394806 / 4.584777 (-4.189971) \| 2.349100 / 3.745712 (-1.396613) \| 2.600541 / 5.269862 (-2.669321) \| 1.568975 / 4.565676 (-2.996701) \| 0.046212 / 0.424275 (-0.378063) \| 0.004821 / 0.007607 (-0.002786) \| 0.332286 / 0.226044 (0.106242) \| 3.302643 / 2.268929 (1.033714) \| 1.838992 / 55.444624 (-53.605633) \| 1.571919 / 6.876477 (-5.304557) \| 1.574956 / 2.142072 (-0.567117) \| 0.464156 / 4.805227 (-4.341071) \| 0.097983 / 6.500664 (-6.402681) \| 0.042243 / 0.075469 (-0.033226) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941675 / 1.841788 (-0.900113) \| 11.450326 / 8.074308 (3.376017) \| 10.169943 / 10.191392 (-0.021449) \| 0.137879 / 0.680424 (-0.542545) \| 0.013765 / 0.534201 (-0.520436) \| 0.268633 / 0.579283 (-0.310650) \| 0.265083 / 0.434364 (-0.169281) \| 0.302099 / 0.540337 (-0.238238) \| 0.423033 / 1.386936 (-0.963903) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004998 / 0.011353 (-0.006355) \| 0.003174 / 0.011008 (-0.007834) \| 0.047924 / 0.038508 (0.009416) \| 0.057598 / 0.023109 (0.034489) \| 0.278823 / 0.275898 (0.002925) \| 0.334349 / 0.323480 (0.010869) \| 0.004053 / 0.007986 (-0.003932) \| 0.002554 / 0.004328 (-0.001774) \| 0.047797 / 0.004250 (0.043547) \| 0.039802 / 0.037052 (0.002749) \| 0.278295 / 0.258489 (0.019806) \| 0.319597 / 0.293841 (0.025757) \| 0.024802 / 0.128546 (-0.103744) \| 0.007362 / 0.075646 (-0.068284) \| 0.066983 / 0.419271 (-0.352288) \| 0.032707 / 0.043533 (-0.010826) \| 0.277350 / 0.255139 (0.022211) \| 0.296829 / 0.283200 (0.013629) \| 0.017902 / 0.141683 (-0.123781) \| 1.129765 / 1.452155 (-0.322390) \| 1.201940 / 1.492716 (-0.290777) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095631 / 0.018006 (0.077625) \| 0.296999 / 0.000490 (0.296510) \| 0.000234 / 0.000200 (0.000034) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021547 / 0.037411 (-0.015865) \| 0.070003 / 0.014526 (0.055477) \| 0.083173 / 0.176557 (-0.093384) \| 0.121676 / 0.737135 (-0.615459) \| 0.082974 / 0.296338 (-0.213364) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298982 / 0.215209 (0.083773) \| 2.918666 / 2.077655 (0.841011) \| 1.582054 / 1.504120 (0.077934) \| 1.463804 / 1.541195 (-0.077391) \| 1.484384 / 1.468490 (0.015893) \| 0.399443 / 4.584777 (-4.185334) \| 2.393515 / 3.745712 (-1.352197) \| 2.533004 / 5.269862 (-2.736858) \| 1.490411 / 4.565676 (-3.075266) \| 0.045274 / 0.424275 (-0.379002) \| 0.004783 / 0.007607 (-0.002824) \| 0.350510 / 0.226044 (0.124465) \| 3.437927 / 2.268929 (1.168998) \| 1.940115 / 55.444624 (-53.504509) \| 1.662025 / 6.876477 (-5.214452) \| 1.640621 / 2.142072 (-0.501452) \| 0.464014 / 4.805227 (-4.341214) \| 0.095506 / 6.500664 (-6.405158) \| 0.040172 / 0.075469 (-0.035297) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975618 / 1.841788 (-0.866169) \| 12.561067 / 8.074308 (4.486759) \| 11.408037 / 10.191392 (1.216645) \| 0.130699 / 0.680424 (-0.549725) \| 0.016796 / 0.534201 (-0.517405) \| 0.271130 / 0.579283 (-0.308153) \| 0.283506 / 0.434364 (-0.150857) \| 0.304482 / 0.540337 (-0.235856) \| 0.413673 / 1.386936 (-0.973263) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#723038a73248dd12dc0673d2b341e9295c441ea3 \"CML watermark\")\n" ]	2023-11-14T17:34:06	2023-11-14T22:26:04	2023-11-14T22:19:45	CONTRIBUTOR	null	Fix #6412	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6418/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6418/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6418", "html_url": "https://github.com/huggingface/datasets/pull/6418", "diff_url": "https://github.com/huggingface/datasets/pull/6418.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6418.patch", "merged_at": "2023-11-14T22:19:45" }	true
https://api.github.com/repos/huggingface/datasets/issues/6417	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6417/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6417/comments	https://api.github.com/repos/huggingface/datasets/issues/6417/events	https://github.com/huggingface/datasets/issues/6417	1,993,149,416	I_kwDODunzps52zQvo	6,417	Bug: LayoutLMv3 finetuning on FUNSD Notebook; Arrow Error	{ "login": "Davo00", "id": 57496007, "node_id": "MDQ6VXNlcjU3NDk2MDA3", "avatar_url": "https://avatars.githubusercontent.com/u/57496007?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Davo00", "html_url": "https://github.com/Davo00", "followers_url": "https://api.github.com/users/Davo00/followers", "following_url": "https://api.github.com/users/Davo00/following{/other_user}", "gists_url": "https://api.github.com/users/Davo00/gists{/gist_id}", "starred_url": "https://api.github.com/users/Davo00/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Davo00/subscriptions", "organizations_url": "https://api.github.com/users/Davo00/orgs", "repos_url": "https://api.github.com/users/Davo00/repos", "events_url": "https://api.github.com/users/Davo00/events{/privacy}", "received_events_url": "https://api.github.com/users/Davo00/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Very strange: `datasets-cli env`\r\n> \r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.9.0\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.13\r\n> - PyArrow version: 8.0.0\r\n> - Pandas version: 1.3.5\r\n\r\nAfter updating datasets and pyarrow on base environment, although I am using a different one called layoutLM\r\n\r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.14.6\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.18\r\n> - Huggingface_hub version: 0.17.3\r\n> - PyArrow version: 14.0.1\r\n> - Pandas version: 2.1.3", "Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.", "> Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.\r\n\r\nThanks for the info and the latest release, it seems this has also solved my issue. First run after the update worked and I am training right now :D\r\nWill close the Issu" ]	2023-11-14T16:53:20	2023-11-16T20:23:41	2023-11-16T20:23:41	NONE	null	### Describe the bug Arrow issues when running the example Notebook laptop locally on Mac with M1. Works on Google Collab. Notebook: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv3/Fine_tune_LayoutLMv3_on_FUNSD_(HuggingFace_Trainer).ipynb Error: `ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.` Caused by: ``` # we need to define custom features for `set_format` (used later on) to work properly features = Features({ 'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'labels': Sequence(feature=Value(dtype='int64')), }) ``` ### Steps to reproduce the bug Run the notebook provided, locally. If possible also on M1. ### Expected behavior The cell where features are mapped to Array2D and Array3D should work without any issues. ### Environment info Tried with Python 3.9 and 3.10 conda envs. Running Mac M1. `pip show datasets` > Name: datasets Version: 2.14.6 Summary: HuggingFace community-driven open-source library of datasets `pip list` > Package Version > ------------------------- ------------ > accelerate 0.24.1 > aiohttp 3.8.6 > aiosignal 1.3.1 > anyio 3.5.0 > appnope 0.1.2 > argon2-cffi 21.3.0 > argon2-cffi-bindings 21.2.0 > asttokens 2.0.5 > async-timeout 4.0.3 > attrs 23.1.0 > backcall 0.2.0 > beautifulsoup4 4.12.2 > bleach 4.1.0 > certifi 2023.7.22 > cffi 1.15.1 > charset-normalizer 3.3.2 > comm 0.1.2 > datasets 2.14.6 > debugpy 1.6.7 > decorator 5.1.1 > defusedxml 0.7.1 > dill 0.3.7 > entrypoints 0.4 > exceptiongroup 1.0.4 > executing 0.8.3 > fastjsonschema 2.16.2 > filelock 3.13.1 > frozenlist 1.4.0 > fsspec 2023.10.0 > huggingface-hub 0.17.3 > idna 3.4 > importlib-metadata 6.0.0 > IProgress 0.4 > ipykernel 6.25.0 > ipython 8.15.0 > ipython-genutils 0.2.0 > jedi 0.18.1 > Jinja2 3.1.2 > joblib 1.3.2 > jsonschema 4.19.2 > jsonschema-specifications 2023.7.1 > jupyter_client 7.4.9 > jupyter_core 5.5.0 > jupyter-server 1.23.4 > jupyterlab-pygments 0.1.2 > MarkupSafe 2.1.1 > matplotlib-inline 0.1.6 > mistune 2.0.4 > mpmath 1.3.0 > multidict 6.0.4 > multiprocess 0.70.15 > nbclassic 1.0.0 > nbclient 0.8.0 > nbconvert 7.10.0 > nbformat 5.9.2 > nest-asyncio 1.5.6 > networkx 3.2.1 > notebook 6.5.4 > notebook_shim 0.2.3 > numpy 1.26.1 > packaging 23.1 > pandas 2.1.3 > pandocfilters 1.5.0 > parso 0.8.3 > pexpect 4.8.0 > pickleshare 0.7.5 > Pillow 10.1.0 > pip 23.3 > platformdirs 3.10.0 > prometheus-client 0.14.1 > prompt-toolkit 3.0.36 > psutil 5.9.0 > ptyprocess 0.7.0 > pure-eval 0.2.2 > pyarrow 14.0.1 > pycparser 2.21 > Pygments 2.15.1 > python-dateutil 2.8.2 > pytz 2023.3.post1 > PyYAML 6.0.1 > pyzmq 23.2.0 > referencing 0.30.2 > regex 2023.10.3 > requests 2.31.0 > rpds-py 0.10.6 > safetensors 0.4.0 > scikit-learn 1.3.2 > scipy 1.11.3 > Send2Trash 1.8.2 > seqeval 1.2.2 > setuptools 68.0.0 > six 1.16.0 > sniffio 1.2.0 > soupsieve 2.5 > stack-data 0.2.0 > sympy 1.12 > terminado 0.17.1 > threadpoolctl 3.2.0 > tinycss2 1.2.1 > tokenizers 0.14.1 > torch 2.1.0 > tornado 6.3.3 > tqdm 4.66.1 > traitlets 5.7.1 > transformers 4.36.0.dev0 > typing_extensions 4.7.1 > tzdata 2023.3 > urllib3 2.0.7 > wcwidth 0.2.5 > webencodings 0.5.1 > websocket-client 0.58.0 > wheel 0.41.2 > xxhash 3.4.1 > yarl 1.9.2 > zipp 3.11.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6417/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6417/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6416	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6416/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6416/comments	https://api.github.com/repos/huggingface/datasets/issues/6416/events	https://github.com/huggingface/datasets/pull/6416	1,992,954,723	PR_kwDODunzps5fbA4H	6,416	Rename audio_classificiation.py to audio_classification.py	{ "login": "carlthome", "id": 1595907, "node_id": "MDQ6VXNlcjE1OTU5MDc=", "avatar_url": "https://avatars.githubusercontent.com/u/1595907?v=4", "gravatar_id": "", "url": "https://api.github.com/users/carlthome", "html_url": "https://github.com/carlthome", "followers_url": "https://api.github.com/users/carlthome/followers", "following_url": "https://api.github.com/users/carlthome/following{/other_user}", "gists_url": "https://api.github.com/users/carlthome/gists{/gist_id}", "starred_url": "https://api.github.com/users/carlthome/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/carlthome/subscriptions", "organizations_url": "https://api.github.com/users/carlthome/orgs", "repos_url": "https://api.github.com/users/carlthome/repos", "events_url": "https://api.github.com/users/carlthome/events{/privacy}", "received_events_url": "https://api.github.com/users/carlthome/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Oh good catch. Can you also rename it in `src/datasets/tasks/__init__.py` ?", "Fixed! \r\n\r\n(I think, tough word to spell right TBH)", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004737 / 0.011353 (-0.006616) \| 0.002446 / 0.011008 (-0.008563) \| 0.060928 / 0.038508 (0.022420) \| 0.030479 / 0.023109 (0.007370) \| 0.238385 / 0.275898 (-0.037513) \| 0.265563 / 0.323480 (-0.057917) \| 0.002910 / 0.007986 (-0.005076) \| 0.002325 / 0.004328 (-0.002004) \| 0.047817 / 0.004250 (0.043566) \| 0.044243 / 0.037052 (0.007191) \| 0.245190 / 0.258489 (-0.013299) \| 0.275449 / 0.293841 (-0.018392) \| 0.023384 / 0.128546 (-0.105162) \| 0.006820 / 0.075646 (-0.068826) \| 0.201488 / 0.419271 (-0.217783) \| 0.057758 / 0.043533 (0.014225) \| 0.245279 / 0.255139 (-0.009860) \| 0.266094 / 0.283200 (-0.017106) \| 0.019254 / 0.141683 (-0.122429) \| 1.107497 / 1.452155 (-0.344658) \| 1.161412 / 1.492716 (-0.331304) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094909 / 0.018006 (0.076903) \| 0.305185 / 0.000490 (0.304695) \| 0.000221 / 0.000200 (0.000021) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018352 / 0.037411 (-0.019059) \| 0.062441 / 0.014526 (0.047915) \| 0.072386 / 0.176557 (-0.104171) \| 0.118836 / 0.737135 (-0.618299) \| 0.074514 / 0.296338 (-0.221824) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283632 / 0.215209 (0.068423) \| 2.751845 / 2.077655 (0.674190) \| 1.478620 / 1.504120 (-0.025499) \| 1.357221 / 1.541195 (-0.183974) \| 1.415297 / 1.468490 (-0.053194) \| 0.400093 / 4.584777 (-4.184684) \| 2.404607 / 3.745712 (-1.341105) \| 2.617572 / 5.269862 (-2.652289) \| 1.587622 / 4.565676 (-2.978055) \| 0.045997 / 0.424275 (-0.378278) \| 0.004872 / 0.007607 (-0.002735) \| 0.338901 / 0.226044 (0.112856) \| 3.371362 / 2.268929 (1.102434) \| 1.870469 / 55.444624 (-53.574155) \| 1.561670 / 6.876477 (-5.314807) \| 1.573186 / 2.142072 (-0.568886) \| 0.478735 / 4.805227 (-4.326492) \| 0.098743 / 6.500664 (-6.401921) \| 0.041780 / 0.075469 (-0.033689) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945422 / 1.841788 (-0.896366) \| 11.563464 / 8.074308 (3.489156) \| 10.368731 / 10.191392 (0.177339) \| 0.129910 / 0.680424 (-0.550513) \| 0.014014 / 0.534201 (-0.520187) \| 0.269036 / 0.579283 (-0.310247) \| 0.265516 / 0.434364 (-0.168848) \| 0.311082 / 0.540337 (-0.229255) \| 0.431510 / 1.386936 (-0.955426) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005068 / 0.011353 (-0.006284) \| 0.002989 / 0.011008 (-0.008019) \| 0.048213 / 0.038508 (0.009705) \| 0.056133 / 0.023109 (0.033024) \| 0.283347 / 0.275898 (0.007449) \| 0.307505 / 0.323480 (-0.015975) \| 0.004041 / 0.007986 (-0.003944) \| 0.002477 / 0.004328 (-0.001852) \| 0.047771 / 0.004250 (0.043521) \| 0.039361 / 0.037052 (0.002309) \| 0.283764 / 0.258489 (0.025275) \| 0.320644 / 0.293841 (0.026803) \| 0.024972 / 0.128546 (-0.103575) \| 0.007599 / 0.075646 (-0.068048) \| 0.054732 / 0.419271 (-0.364539) \| 0.032774 / 0.043533 (-0.010759) \| 0.285594 / 0.255139 (0.030455) \| 0.301500 / 0.283200 (0.018300) \| 0.018181 / 0.141683 (-0.123501) \| 1.126311 / 1.452155 (-0.325843) \| 1.187147 / 1.492716 (-0.305569) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097397 / 0.018006 (0.079391) \| 0.315112 / 0.000490 (0.314622) \| 0.000224 / 0.000200 (0.000024) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021529 / 0.037411 (-0.015882) \| 0.073208 / 0.014526 (0.058682) \| 0.081683 / 0.176557 (-0.094874) \| 0.120475 / 0.737135 (-0.616660) \| 0.083265 / 0.296338 (-0.213073) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289976 / 0.215209 (0.074767) \| 2.839860 / 2.077655 (0.762205) \| 1.592635 / 1.504120 (0.088515) \| 1.466722 / 1.541195 (-0.074472) \| 1.552850 / 1.468490 (0.084360) \| 0.418693 / 4.584777 (-4.166084) \| 2.526620 / 3.745712 (-1.219093) \| 2.706182 / 5.269862 (-2.563680) \| 1.618514 / 4.565676 (-2.947162) \| 0.046303 / 0.424275 (-0.377972) \| 0.004873 / 0.007607 (-0.002734) \| 0.345146 / 0.226044 (0.119102) \| 3.378448 / 2.268929 (1.109520) \| 1.986393 / 55.444624 (-53.458231) \| 1.681838 / 6.876477 (-5.194639) \| 1.738093 / 2.142072 (-0.403980) \| 0.484386 / 4.805227 (-4.320842) \| 0.100693 / 6.500664 (-6.399971) \| 0.043084 / 0.075469 (-0.032385) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976399 / 1.841788 (-0.865389) \| 13.122968 / 8.074308 (5.048660) \| 11.245031 / 10.191392 (1.053639) \| 0.134433 / 0.680424 (-0.545991) \| 0.017439 / 0.534201 (-0.516762) \| 0.274083 / 0.579283 (-0.305200) \| 0.287353 / 0.434364 (-0.147011) \| 0.309231 / 0.540337 (-0.231106) \| 0.418003 / 1.386936 (-0.968933) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#939f136f255eab68a5bf6441db2a395f8af78511 \"CML watermark\")\n" ]	2023-11-14T15:15:29	2023-11-15T11:59:32	2023-11-15T11:53:20	CONTRIBUTOR	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6416/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6416/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6416", "html_url": "https://github.com/huggingface/datasets/pull/6416", "diff_url": "https://github.com/huggingface/datasets/pull/6416.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6416.patch", "merged_at": "2023-11-15T11:53:20" }	true
https://api.github.com/repos/huggingface/datasets/issues/6415	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6415/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6415/comments	https://api.github.com/repos/huggingface/datasets/issues/6415/events	https://github.com/huggingface/datasets/pull/6415	1,992,917,248	PR_kwDODunzps5fa4n7	6,415	Fix multi gpu map example	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004537 / 0.011353 (-0.006816) \| 0.002844 / 0.011008 (-0.008164) \| 0.062506 / 0.038508 (0.023998) \| 0.029675 / 0.023109 (0.006566) \| 0.238080 / 0.275898 (-0.037818) \| 0.259858 / 0.323480 (-0.063622) \| 0.004015 / 0.007986 (-0.003970) \| 0.002432 / 0.004328 (-0.001897) \| 0.049477 / 0.004250 (0.045227) \| 0.045383 / 0.037052 (0.008331) \| 0.241934 / 0.258489 (-0.016555) \| 0.270759 / 0.293841 (-0.023082) \| 0.023207 / 0.128546 (-0.105339) \| 0.007107 / 0.075646 (-0.068539) \| 0.207626 / 0.419271 (-0.211645) \| 0.056706 / 0.043533 (0.013173) \| 0.239713 / 0.255139 (-0.015426) \| 0.256639 / 0.283200 (-0.026560) \| 0.017514 / 0.141683 (-0.124169) \| 1.105201 / 1.452155 (-0.346953) \| 1.173087 / 1.492716 (-0.319629) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093391 / 0.018006 (0.075384) \| 0.302673 / 0.000490 (0.302184) \| 0.000218 / 0.000200 (0.000018) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019447 / 0.037411 (-0.017965) \| 0.063349 / 0.014526 (0.048823) \| 0.075600 / 0.176557 (-0.100957) \| 0.121098 / 0.737135 (-0.616037) \| 0.075028 / 0.296338 (-0.221311) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291479 / 0.215209 (0.076270) \| 2.787231 / 2.077655 (0.709576) \| 1.480205 / 1.504120 (-0.023915) \| 1.417656 / 1.541195 (-0.123538) \| 1.394529 / 1.468490 (-0.073962) \| 0.408843 / 4.584777 (-4.175934) \| 2.398691 / 3.745712 (-1.347021) \| 2.635457 / 5.269862 (-2.634404) \| 1.591722 / 4.565676 (-2.973955) \| 0.048445 / 0.424275 (-0.375830) \| 0.004864 / 0.007607 (-0.002743) \| 0.349014 / 0.226044 (0.122969) \| 3.436962 / 2.268929 (1.168033) \| 1.839266 / 55.444624 (-53.605359) \| 1.535252 / 6.876477 (-5.341225) \| 1.581048 / 2.142072 (-0.561025) \| 0.491150 / 4.805227 (-4.314078) \| 0.101279 / 6.500664 (-6.399385) \| 0.041938 / 0.075469 (-0.033532) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946986 / 1.841788 (-0.894801) \| 11.766196 / 8.074308 (3.691888) \| 10.425615 / 10.191392 (0.234223) \| 0.129957 / 0.680424 (-0.550467) \| 0.014859 / 0.534201 (-0.519342) \| 0.268046 / 0.579283 (-0.311237) \| 0.263724 / 0.434364 (-0.170640) \| 0.311028 / 0.540337 (-0.229309) \| 0.434715 / 1.386936 (-0.952221) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004874 / 0.011353 (-0.006479) \| 0.002942 / 0.011008 (-0.008067) \| 0.048250 / 0.038508 (0.009742) \| 0.053726 / 0.023109 (0.030617) \| 0.268870 / 0.275898 (-0.007028) \| 0.289152 / 0.323480 (-0.034328) \| 0.003982 / 0.007986 (-0.004004) \| 0.002488 / 0.004328 (-0.001840) \| 0.047902 / 0.004250 (0.043652) \| 0.038732 / 0.037052 (0.001680) \| 0.271021 / 0.258489 (0.012532) \| 0.299967 / 0.293841 (0.006126) \| 0.024672 / 0.128546 (-0.103874) \| 0.007311 / 0.075646 (-0.068336) \| 0.053721 / 0.419271 (-0.365550) \| 0.032407 / 0.043533 (-0.011126) \| 0.266604 / 0.255139 (0.011465) \| 0.286816 / 0.283200 (0.003617) \| 0.018973 / 0.141683 (-0.122710) \| 1.122460 / 1.452155 (-0.329695) \| 1.177720 / 1.492716 (-0.314997) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093968 / 0.018006 (0.075962) \| 0.304010 / 0.000490 (0.303521) \| 0.000228 / 0.000200 (0.000028) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021203 / 0.037411 (-0.016208) \| 0.070318 / 0.014526 (0.055793) \| 0.081688 / 0.176557 (-0.094869) \| 0.120916 / 0.737135 (-0.616219) \| 0.083452 / 0.296338 (-0.212886) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293961 / 0.215209 (0.078752) \| 2.858514 / 2.077655 (0.780860) \| 1.556169 / 1.504120 (0.052049) \| 1.431523 / 1.541195 (-0.109671) \| 1.478145 / 1.468490 (0.009654) \| 0.408927 / 4.584777 (-4.175850) \| 2.440630 / 3.745712 (-1.305082) \| 2.586327 / 5.269862 (-2.683534) \| 1.529495 / 4.565676 (-3.036182) \| 0.047387 / 0.424275 (-0.376888) \| 0.004817 / 0.007607 (-0.002790) \| 0.345009 / 0.226044 (0.118965) \| 3.386313 / 2.268929 (1.117384) \| 1.922361 / 55.444624 (-53.522264) \| 1.640814 / 6.876477 (-5.235663) \| 1.657005 / 2.142072 (-0.485068) \| 0.483844 / 4.805227 (-4.321383) \| 0.099470 / 6.500664 (-6.401194) \| 0.040735 / 0.075469 (-0.034734) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986311 / 1.841788 (-0.855476) \| 12.327425 / 8.074308 (4.253117) \| 10.995135 / 10.191392 (0.803743) \| 0.146814 / 0.680424 (-0.533610) \| 0.015820 / 0.534201 (-0.518381) \| 0.272319 / 0.579283 (-0.306964) \| 0.274858 / 0.434364 (-0.159506) \| 0.305728 / 0.540337 (-0.234609) \| 0.421400 / 1.386936 (-0.965536) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#611a03d70378d6e48a19fac89e7616cf556b920a \"CML watermark\")\n", "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6415). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007995 / 0.011353 (-0.003358) \| 0.004596 / 0.011008 (-0.006412) \| 0.099818 / 0.038508 (0.061310) \| 0.053539 / 0.023109 (0.030429) \| 0.367757 / 0.275898 (0.091859) \| 0.409351 / 0.323480 (0.085871) \| 0.007423 / 0.007986 (-0.000563) \| 0.003770 / 0.004328 (-0.000558) \| 0.075635 / 0.004250 (0.071385) \| 0.078844 / 0.037052 (0.041791) \| 0.374523 / 0.258489 (0.116034) \| 0.423378 / 0.293841 (0.129537) \| 0.038901 / 0.128546 (-0.089645) \| 0.009985 / 0.075646 (-0.065661) \| 0.342793 / 0.419271 (-0.076479) \| 0.098045 / 0.043533 (0.054512) \| 0.368077 / 0.255139 (0.112938) \| 0.394251 / 0.283200 (0.111051) \| 0.030624 / 0.141683 (-0.111059) \| 1.782728 / 1.452155 (0.330574) \| 1.867571 / 1.492716 (0.374855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265550 / 0.018006 (0.247544) \| 0.504045 / 0.000490 (0.503555) \| 0.016523 / 0.000200 (0.016323) \| 0.000757 / 0.000054 (0.000702) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034239 / 0.037411 (-0.003172) \| 0.099953 / 0.014526 (0.085427) \| 0.113728 / 0.176557 (-0.062829) \| 0.180113 / 0.737135 (-0.557023) \| 0.114506 / 0.296338 (-0.181833) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.507186 / 0.215209 (0.291977) \| 5.033590 / 2.077655 (2.955935) \| 2.480111 / 1.504120 (0.975991) \| 2.258966 / 1.541195 (0.717771) \| 2.316045 / 1.468490 (0.847555) \| 0.622482 / 4.584777 (-3.962295) \| 4.400909 / 3.745712 (0.655197) \| 4.012443 / 5.269862 (-1.257419) \| 2.408294 / 4.565676 (-2.157383) \| 0.067608 / 0.424275 (-0.356668) \| 0.008638 / 0.007607 (0.001031) \| 0.546558 / 0.226044 (0.320513) \| 5.472973 / 2.268929 (3.204044) \| 2.795147 / 55.444624 (-52.649477) \| 2.371153 / 6.876477 (-4.505324) \| 2.440883 / 2.142072 (0.298811) \| 0.682380 / 4.805227 (-4.122847) \| 0.156819 / 6.500664 (-6.343845) \| 0.071969 / 0.075469 (-0.003500) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.500200 / 1.841788 (-0.341588) \| 22.854103 / 8.074308 (14.779795) \| 16.691945 / 10.191392 (6.500553) \| 0.210945 / 0.680424 (-0.469479) \| 0.023234 / 0.534201 (-0.510967) \| 0.475641 / 0.579283 (-0.103642) \| 0.491553 / 0.434364 (0.057189) \| 0.549311 / 0.540337 (0.008974) \| 0.858498 / 1.386936 (-0.528439) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009020 / 0.011353 (-0.002333) \| 0.004768 / 0.011008 (-0.006240) \| 0.082841 / 0.038508 (0.044333) \| 0.095111 / 0.023109 (0.072002) \| 0.486050 / 0.275898 (0.210151) \| 0.527074 / 0.323480 (0.203594) \| 0.006622 / 0.007986 (-0.001364) \| 0.003961 / 0.004328 (-0.000367) \| 0.083361 / 0.004250 (0.079111) \| 0.068571 / 0.037052 (0.031518) \| 0.494575 / 0.258489 (0.236086) \| 0.545593 / 0.293841 (0.251752) \| 0.047671 / 0.128546 (-0.080875) \| 0.010715 / 0.075646 (-0.064932) \| 0.096239 / 0.419271 (-0.323033) \| 0.061556 / 0.043533 (0.018023) \| 0.484301 / 0.255139 (0.229162) \| 0.492189 / 0.283200 (0.208989) \| 0.029374 / 0.141683 (-0.112309) \| 1.911833 / 1.452155 (0.459678) \| 2.005744 / 1.492716 (0.513028) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265402 / 0.018006 (0.247396) \| 0.501034 / 0.000490 (0.500545) \| 0.004039 / 0.000200 (0.003839) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.041005 / 0.037411 (0.003594) \| 0.119204 / 0.014526 (0.104678) \| 0.134583 / 0.176557 (-0.041973) \| 0.195995 / 0.737135 (-0.541140) \| 0.133125 / 0.296338 (-0.163214) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.503012 / 0.215209 (0.287803) \| 5.021972 / 2.077655 (2.944318) \| 2.912987 / 1.504120 (1.408867) \| 2.707637 / 1.541195 (1.166442) \| 2.824065 / 1.468490 (1.355575) \| 0.664285 / 4.584777 (-3.920492) \| 4.341905 / 3.745712 (0.596193) \| 4.152839 / 5.269862 (-1.117022) \| 2.438138 / 4.565676 (-2.127539) \| 0.076169 / 0.424275 (-0.348106) \| 0.010471 / 0.007607 (0.002864) \| 0.680918 / 0.226044 (0.454874) \| 6.424209 / 2.268929 (4.155281) \| 3.285353 / 55.444624 (-52.159271) \| 2.865458 / 6.876477 (-4.011019) \| 2.946246 / 2.142072 (0.804173) \| 0.700051 / 4.805227 (-4.105176) \| 0.155299 / 6.500664 (-6.345365) \| 0.069372 / 0.075469 (-0.006097) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.749517 / 1.841788 (-0.092271) \| 23.382582 / 8.074308 (15.308274) \| 17.708718 / 10.191392 (7.517326) \| 0.197042 / 0.680424 (-0.483382) \| 0.023874 / 0.534201 (-0.510327) \| 0.471631 / 0.579283 (-0.107652) \| 0.512649 / 0.434364 (0.078285) \| 0.614479 / 0.540337 (0.074142) \| 0.771859 / 1.386936 (-0.615077) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f084b2d85f5004ed969d2387027093b2d765a4f \"CML watermark\")\n" ]	2023-11-14T14:57:18	2023-11-15T11:18:05	null	MEMBER	null	- use `orch.cuda.set_device` instead of `CUDA_VISIBLE_DEVICES ` - add `if __name__ == "__main__"` fix https://github.com/huggingface/datasets/issues/6186	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6415/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6415/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6415", "html_url": "https://github.com/huggingface/datasets/pull/6415", "diff_url": "https://github.com/huggingface/datasets/pull/6415.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6415.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6414	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6414/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6414/comments	https://api.github.com/repos/huggingface/datasets/issues/6414/events	https://github.com/huggingface/datasets/pull/6414	1,992,482,491	PR_kwDODunzps5fZZ2l	6,414	Set `usedforsecurity=False` in hashlib methods (FIPS compliance)	{ "login": "Wauplin", "id": 11801849, "node_id": "MDQ6VXNlcjExODAxODQ5", "avatar_url": "https://avatars.githubusercontent.com/u/11801849?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Wauplin", "html_url": "https://github.com/Wauplin", "followers_url": "https://api.github.com/users/Wauplin/followers", "following_url": "https://api.github.com/users/Wauplin/following{/other_user}", "gists_url": "https://api.github.com/users/Wauplin/gists{/gist_id}", "starred_url": "https://api.github.com/users/Wauplin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Wauplin/subscriptions", "organizations_url": "https://api.github.com/users/Wauplin/orgs", "repos_url": "https://api.github.com/users/Wauplin/repos", "events_url": "https://api.github.com/users/Wauplin/events{/privacy}", "received_events_url": "https://api.github.com/users/Wauplin/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008434 / 0.011353 (-0.002919) \| 0.006755 / 0.011008 (-0.004253) \| 0.106169 / 0.038508 (0.067661) \| 0.049329 / 0.023109 (0.026220) \| 0.433610 / 0.275898 (0.157712) \| 0.441993 / 0.323480 (0.118513) \| 0.004703 / 0.007986 (-0.003282) \| 0.006996 / 0.004328 (0.002667) \| 0.080330 / 0.004250 (0.076080) \| 0.066098 / 0.037052 (0.029045) \| 0.435444 / 0.258489 (0.176955) \| 0.490442 / 0.293841 (0.196601) \| 0.047050 / 0.128546 (-0.081496) \| 0.014520 / 0.075646 (-0.061127) \| 0.339805 / 0.419271 (-0.079467) \| 0.101161 / 0.043533 (0.057629) \| 0.423236 / 0.255139 (0.168097) \| 0.455627 / 0.283200 (0.172427) \| 0.036218 / 0.141683 (-0.105465) \| 1.766128 / 1.452155 (0.313973) \| 1.923919 / 1.492716 (0.431203) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242939 / 0.018006 (0.224933) \| 0.515582 / 0.000490 (0.515093) \| 0.020271 / 0.000200 (0.020071) \| 0.000383 / 0.000054 (0.000328) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030927 / 0.037411 (-0.006484) \| 0.093951 / 0.014526 (0.079425) \| 0.109028 / 0.176557 (-0.067529) \| 0.174947 / 0.737135 (-0.562188) \| 0.120538 / 0.296338 (-0.175800) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.553884 / 0.215209 (0.338675) \| 5.424566 / 2.077655 (3.346911) \| 2.439420 / 1.504120 (0.935301) \| 2.019324 / 1.541195 (0.478129) \| 2.170781 / 1.468490 (0.702290) \| 0.924424 / 4.584777 (-3.660353) \| 5.706029 / 3.745712 (1.960317) \| 5.096911 / 5.269862 (-0.172951) \| 3.168261 / 4.565676 (-1.397416) \| 0.094336 / 0.424275 (-0.329940) \| 0.015899 / 0.007607 (0.008292) \| 0.709684 / 0.226044 (0.483639) \| 7.476865 / 2.268929 (5.207936) \| 3.350983 / 55.444624 (-52.093641) \| 2.653419 / 6.876477 (-4.223058) \| 2.802201 / 2.142072 (0.660129) \| 1.081442 / 4.805227 (-3.723785) \| 0.217025 / 6.500664 (-6.283639) \| 0.077248 / 0.075469 (0.001779) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.598621 / 1.841788 (-0.243167) \| 23.490338 / 8.074308 (15.416030) \| 21.853488 / 10.191392 (11.662096) \| 0.209625 / 0.680424 (-0.470799) \| 0.028166 / 0.534201 (-0.506035) \| 0.473883 / 0.579283 (-0.105400) \| 0.584226 / 0.434364 (0.149862) \| 0.538605 / 0.540337 (-0.001732) \| 0.837060 / 1.386936 (-0.549876) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009029 / 0.011353 (-0.002324) \| 0.004945 / 0.011008 (-0.006063) \| 0.084539 / 0.038508 (0.046031) \| 0.081014 / 0.023109 (0.057905) \| 0.431291 / 0.275898 (0.155393) \| 0.478913 / 0.323480 (0.155433) \| 0.006107 / 0.007986 (-0.001879) \| 0.003939 / 0.004328 (-0.000390) \| 0.079932 / 0.004250 (0.075682) \| 0.057936 / 0.037052 (0.020884) \| 0.437295 / 0.258489 (0.178806) \| 0.489790 / 0.293841 (0.195949) \| 0.049544 / 0.128546 (-0.079003) \| 0.013675 / 0.075646 (-0.061972) \| 0.093143 / 0.419271 (-0.326128) \| 0.064104 / 0.043533 (0.020571) \| 0.444699 / 0.255139 (0.189560) \| 0.443688 / 0.283200 (0.160489) \| 0.034331 / 0.141683 (-0.107352) \| 1.753014 / 1.452155 (0.300859) \| 1.877274 / 1.492716 (0.384558) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250460 / 0.018006 (0.232454) \| 0.527241 / 0.000490 (0.526752) \| 0.007679 / 0.000200 (0.007479) \| 0.000115 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033269 / 0.037411 (-0.004142) \| 0.111262 / 0.014526 (0.096736) \| 0.133503 / 0.176557 (-0.043053) \| 0.177998 / 0.737135 (-0.559137) \| 0.117899 / 0.296338 (-0.178440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.633588 / 0.215209 (0.418379) \| 6.105283 / 2.077655 (4.027628) \| 2.779309 / 1.504120 (1.275189) \| 2.445788 / 1.541195 (0.904594) \| 2.396443 / 1.468490 (0.927953) \| 0.925928 / 4.584777 (-3.658849) \| 5.266142 / 3.745712 (1.520430) \| 4.868830 / 5.269862 (-0.401031) \| 2.998768 / 4.565676 (-1.566909) \| 0.103135 / 0.424275 (-0.321140) \| 0.008059 / 0.007607 (0.000452) \| 0.753159 / 0.226044 (0.527115) \| 7.532170 / 2.268929 (5.263242) \| 3.563941 / 55.444624 (-51.880683) \| 2.829208 / 6.876477 (-4.047269) \| 2.913954 / 2.142072 (0.771881) \| 1.085843 / 4.805227 (-3.719384) \| 0.214195 / 6.500664 (-6.286469) \| 0.071509 / 0.075469 (-0.003960) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.544819 / 1.841788 (-0.296968) \| 23.790149 / 8.074308 (15.715841) \| 23.086019 / 10.191392 (12.894627) \| 0.242695 / 0.680424 (-0.437729) \| 0.041706 / 0.534201 (-0.492495) \| 0.552402 / 0.579283 (-0.026881) \| 0.652518 / 0.434364 (0.218154) \| 0.581876 / 0.540337 (0.041539) \| 0.795425 / 1.386936 (-0.591511) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#117fdfccc8523fe150521ad74e478459fe2f297c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004573 / 0.011353 (-0.006780) \| 0.002965 / 0.011008 (-0.008043) \| 0.061913 / 0.038508 (0.023405) \| 0.029474 / 0.023109 (0.006365) \| 0.258117 / 0.275898 (-0.017781) \| 0.279854 / 0.323480 (-0.043626) \| 0.003954 / 0.007986 (-0.004031) \| 0.002479 / 0.004328 (-0.001850) \| 0.048685 / 0.004250 (0.044434) \| 0.044733 / 0.037052 (0.007681) \| 0.256659 / 0.258489 (-0.001830) \| 0.285235 / 0.293841 (-0.008606) \| 0.023566 / 0.128546 (-0.104981) \| 0.007291 / 0.075646 (-0.068355) \| 0.202701 / 0.419271 (-0.216570) \| 0.055706 / 0.043533 (0.012173) \| 0.258790 / 0.255139 (0.003651) \| 0.278675 / 0.283200 (-0.004525) \| 0.018574 / 0.141683 (-0.123109) \| 1.109359 / 1.452155 (-0.342796) \| 1.184434 / 1.492716 (-0.308282) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095048 / 0.018006 (0.077042) \| 0.305027 / 0.000490 (0.304537) \| 0.000310 / 0.000200 (0.000110) \| 0.000066 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018183 / 0.037411 (-0.019228) \| 0.066130 / 0.014526 (0.051604) \| 0.073948 / 0.176557 (-0.102608) \| 0.120458 / 0.737135 (-0.616678) \| 0.075995 / 0.296338 (-0.220343) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279419 / 0.215209 (0.064210) \| 2.728591 / 2.077655 (0.650936) \| 1.439016 / 1.504120 (-0.065104) \| 1.325798 / 1.541195 (-0.215397) \| 1.352050 / 1.468490 (-0.116440) \| 0.395041 / 4.584777 (-4.189736) \| 2.377651 / 3.745712 (-1.368061) \| 2.618473 / 5.269862 (-2.651389) \| 1.587580 / 4.565676 (-2.978096) \| 0.045910 / 0.424275 (-0.378365) \| 0.004843 / 0.007607 (-0.002764) \| 0.335491 / 0.226044 (0.109447) \| 3.378441 / 2.268929 (1.109512) \| 1.827757 / 55.444624 (-53.616868) \| 1.502360 / 6.876477 (-5.374117) \| 1.508460 / 2.142072 (-0.633612) \| 0.471309 / 4.805227 (-4.333918) \| 0.098934 / 6.500664 (-6.401730) \| 0.041705 / 0.075469 (-0.033764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945067 / 1.841788 (-0.896720) \| 11.548209 / 8.074308 (3.473900) \| 10.422628 / 10.191392 (0.231236) \| 0.141494 / 0.680424 (-0.538929) \| 0.014345 / 0.534201 (-0.519856) \| 0.267750 / 0.579283 (-0.311533) \| 0.261488 / 0.434364 (-0.172876) \| 0.307192 / 0.540337 (-0.233145) \| 0.427926 / 1.386936 (-0.959010) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004831 / 0.011353 (-0.006522) \| 0.002876 / 0.011008 (-0.008132) \| 0.048629 / 0.038508 (0.010121) \| 0.055090 / 0.023109 (0.031981) \| 0.271381 / 0.275898 (-0.004517) \| 0.292350 / 0.323480 (-0.031130) \| 0.004001 / 0.007986 (-0.003985) \| 0.002389 / 0.004328 (-0.001939) \| 0.047527 / 0.004250 (0.043277) \| 0.038065 / 0.037052 (0.001012) \| 0.277387 / 0.258489 (0.018898) \| 0.307209 / 0.293841 (0.013368) \| 0.025136 / 0.128546 (-0.103411) \| 0.007309 / 0.075646 (-0.068338) \| 0.054483 / 0.419271 (-0.364789) \| 0.032807 / 0.043533 (-0.010726) \| 0.274364 / 0.255139 (0.019225) \| 0.290280 / 0.283200 (0.007080) \| 0.017855 / 0.141683 (-0.123828) \| 1.185912 / 1.452155 (-0.266243) \| 1.228141 / 1.492716 (-0.264576) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094787 / 0.018006 (0.076781) \| 0.314191 / 0.000490 (0.313701) \| 0.000217 / 0.000200 (0.000017) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020920 / 0.037411 (-0.016491) \| 0.070446 / 0.014526 (0.055920) \| 0.081371 / 0.176557 (-0.095186) \| 0.119127 / 0.737135 (-0.618009) \| 0.085658 / 0.296338 (-0.210680) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290601 / 0.215209 (0.075392) \| 2.874091 / 2.077655 (0.796436) \| 1.598934 / 1.504120 (0.094814) \| 1.464329 / 1.541195 (-0.076866) \| 1.504943 / 1.468490 (0.036453) \| 0.410457 / 4.584777 (-4.174320) \| 2.428706 / 3.745712 (-1.317006) \| 2.596510 / 5.269862 (-2.673352) \| 1.547084 / 4.565676 (-3.018592) \| 0.047546 / 0.424275 (-0.376729) \| 0.004740 / 0.007607 (-0.002867) \| 0.351168 / 0.226044 (0.125123) \| 3.424554 / 2.268929 (1.155626) \| 1.969792 / 55.444624 (-53.474832) \| 1.676731 / 6.876477 (-5.199745) \| 1.668769 / 2.142072 (-0.473304) \| 0.482486 / 4.805227 (-4.322741) \| 0.100018 / 6.500664 (-6.400646) \| 0.040956 / 0.075469 (-0.034513) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966306 / 1.841788 (-0.875482) \| 12.158909 / 8.074308 (4.084601) \| 10.926447 / 10.191392 (0.735055) \| 0.130359 / 0.680424 (-0.550065) \| 0.016162 / 0.534201 (-0.518039) \| 0.269977 / 0.579283 (-0.309306) \| 0.283366 / 0.434364 (-0.150997) \| 0.304517 / 0.540337 (-0.235821) \| 0.410398 / 1.386936 (-0.976539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d5d6e57913465c22bb8074b0c0f968252cb12b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004686 / 0.011353 (-0.006667) \| 0.002764 / 0.011008 (-0.008244) \| 0.061411 / 0.038508 (0.022902) \| 0.030450 / 0.023109 (0.007341) \| 0.247648 / 0.275898 (-0.028250) \| 0.278033 / 0.323480 (-0.045447) \| 0.002903 / 0.007986 (-0.005082) \| 0.002350 / 0.004328 (-0.001979) \| 0.047514 / 0.004250 (0.043264) \| 0.044446 / 0.037052 (0.007393) \| 0.256170 / 0.258489 (-0.002319) \| 0.285977 / 0.293841 (-0.007864) \| 0.023407 / 0.128546 (-0.105139) \| 0.007223 / 0.075646 (-0.068423) \| 0.201274 / 0.419271 (-0.217997) \| 0.054022 / 0.043533 (0.010489) \| 0.253841 / 0.255139 (-0.001298) \| 0.278219 / 0.283200 (-0.004980) \| 0.017796 / 0.141683 (-0.123886) \| 1.105950 / 1.452155 (-0.346205) \| 1.182021 / 1.492716 (-0.310695) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089584 / 0.018006 (0.071578) \| 0.299338 / 0.000490 (0.298849) \| 0.000202 / 0.000200 (0.000003) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018974 / 0.037411 (-0.018437) \| 0.062352 / 0.014526 (0.047826) \| 0.073667 / 0.176557 (-0.102889) \| 0.119225 / 0.737135 (-0.617911) \| 0.075393 / 0.296338 (-0.220945) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282749 / 0.215209 (0.067540) \| 2.795822 / 2.077655 (0.718167) \| 1.492946 / 1.504120 (-0.011174) \| 1.382340 / 1.541195 (-0.158855) \| 1.377281 / 1.468490 (-0.091209) \| 0.397361 / 4.584777 (-4.187415) \| 2.379416 / 3.745712 (-1.366296) \| 2.552967 / 5.269862 (-2.716895) \| 1.546347 / 4.565676 (-3.019330) \| 0.045851 / 0.424275 (-0.378424) \| 0.004830 / 0.007607 (-0.002777) \| 0.351194 / 0.226044 (0.125150) \| 3.407406 / 2.268929 (1.138478) \| 1.852983 / 55.444624 (-53.591641) \| 1.536381 / 6.876477 (-5.340095) \| 1.542786 / 2.142072 (-0.599287) \| 0.471960 / 4.805227 (-4.333267) \| 0.098336 / 6.500664 (-6.402328) \| 0.041569 / 0.075469 (-0.033900) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.912718 / 1.841788 (-0.929070) \| 11.339404 / 8.074308 (3.265095) \| 10.480593 / 10.191392 (0.289201) \| 0.139508 / 0.680424 (-0.540916) \| 0.014210 / 0.534201 (-0.519991) \| 0.268152 / 0.579283 (-0.311131) \| 0.260503 / 0.434364 (-0.173860) \| 0.304735 / 0.540337 (-0.235602) \| 0.422155 / 1.386936 (-0.964781) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004714 / 0.011353 (-0.006638) \| 0.002638 / 0.011008 (-0.008370) \| 0.047967 / 0.038508 (0.009459) \| 0.050758 / 0.023109 (0.027649) \| 0.265619 / 0.275898 (-0.010279) \| 0.286920 / 0.323480 (-0.036560) \| 0.003936 / 0.007986 (-0.004050) \| 0.002351 / 0.004328 (-0.001977) \| 0.047642 / 0.004250 (0.043392) \| 0.038412 / 0.037052 (0.001360) \| 0.269561 / 0.258489 (0.011072) \| 0.302057 / 0.293841 (0.008216) \| 0.023893 / 0.128546 (-0.104653) \| 0.006793 / 0.075646 (-0.068854) \| 0.053091 / 0.419271 (-0.366180) \| 0.032228 / 0.043533 (-0.011305) \| 0.267110 / 0.255139 (0.011971) \| 0.287211 / 0.283200 (0.004011) \| 0.017945 / 0.141683 (-0.123738) \| 1.191770 / 1.452155 (-0.260384) \| 1.269644 / 1.492716 (-0.223072) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088067 / 0.018006 (0.070061) \| 0.298383 / 0.000490 (0.297893) \| 0.000202 / 0.000200 (0.000002) \| 0.000048 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020685 / 0.037411 (-0.016726) \| 0.069883 / 0.014526 (0.055357) \| 0.080107 / 0.176557 (-0.096450) \| 0.119311 / 0.737135 (-0.617825) \| 0.080791 / 0.296338 (-0.215548) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295781 / 0.215209 (0.080572) \| 2.905536 / 2.077655 (0.827881) \| 1.579184 / 1.504120 (0.075064) \| 1.475937 / 1.541195 (-0.065258) \| 1.533708 / 1.468490 (0.065218) \| 0.409851 / 4.584777 (-4.174926) \| 2.443217 / 3.745712 (-1.302496) \| 2.543980 / 5.269862 (-2.725882) \| 1.512187 / 4.565676 (-3.053489) \| 0.046390 / 0.424275 (-0.377885) \| 0.004762 / 0.007607 (-0.002845) \| 0.345066 / 0.226044 (0.119021) \| 3.485133 / 2.268929 (1.216204) \| 1.954690 / 55.444624 (-53.489934) \| 1.671104 / 6.876477 (-5.205372) \| 1.655330 / 2.142072 (-0.486743) \| 0.487910 / 4.805227 (-4.317317) \| 0.097707 / 6.500664 (-6.402957) \| 0.040379 / 0.075469 (-0.035090) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981620 / 1.841788 (-0.860168) \| 11.806530 / 8.074308 (3.732222) \| 10.868275 / 10.191392 (0.676883) \| 0.141230 / 0.680424 (-0.539194) \| 0.015785 / 0.534201 (-0.518416) \| 0.271416 / 0.579283 (-0.307867) \| 0.276048 / 0.434364 (-0.158316) \| 0.310988 / 0.540337 (-0.229349) \| 0.410078 / 1.386936 (-0.976858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ec565740dee10c466ade16f81dee2783e442ba55 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004803 / 0.011353 (-0.006550) \| 0.002961 / 0.011008 (-0.008047) \| 0.061431 / 0.038508 (0.022923) \| 0.030189 / 0.023109 (0.007080) \| 0.255755 / 0.275898 (-0.020143) \| 0.277841 / 0.323480 (-0.045639) \| 0.003083 / 0.007986 (-0.004902) \| 0.002432 / 0.004328 (-0.001896) \| 0.047674 / 0.004250 (0.043424) \| 0.045066 / 0.037052 (0.008014) \| 0.268701 / 0.258489 (0.010211) \| 0.286673 / 0.293841 (-0.007168) \| 0.023663 / 0.128546 (-0.104883) \| 0.007148 / 0.075646 (-0.068499) \| 0.201962 / 0.419271 (-0.217310) \| 0.054953 / 0.043533 (0.011420) \| 0.257155 / 0.255139 (0.002016) \| 0.277769 / 0.283200 (-0.005431) \| 0.017803 / 0.141683 (-0.123880) \| 1.100270 / 1.452155 (-0.351884) \| 1.146975 / 1.492716 (-0.345741) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092776 / 0.018006 (0.074770) \| 0.303786 / 0.000490 (0.303296) \| 0.000237 / 0.000200 (0.000037) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019647 / 0.037411 (-0.017765) \| 0.063211 / 0.014526 (0.048686) \| 0.076684 / 0.176557 (-0.099873) \| 0.121952 / 0.737135 (-0.615184) \| 0.077202 / 0.296338 (-0.219137) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282087 / 0.215209 (0.066878) \| 2.789204 / 2.077655 (0.711550) \| 1.510376 / 1.504120 (0.006256) \| 1.384241 / 1.541195 (-0.156954) \| 1.414949 / 1.468490 (-0.053541) \| 0.402206 / 4.584777 (-4.182570) \| 2.377601 / 3.745712 (-1.368111) \| 2.585354 / 5.269862 (-2.684508) \| 1.592937 / 4.565676 (-2.972740) \| 0.045217 / 0.424275 (-0.379058) \| 0.004772 / 0.007607 (-0.002835) \| 0.339584 / 0.226044 (0.113539) \| 3.373184 / 2.268929 (1.104256) \| 1.855196 / 55.444624 (-53.589428) \| 1.599559 / 6.876477 (-5.276918) \| 1.604421 / 2.142072 (-0.537651) \| 0.467754 / 4.805227 (-4.337474) \| 0.098244 / 6.500664 (-6.402420) \| 0.042631 / 0.075469 (-0.032838) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947680 / 1.841788 (-0.894108) \| 11.539875 / 8.074308 (3.465567) \| 10.340830 / 10.191392 (0.149438) \| 0.145591 / 0.680424 (-0.534833) \| 0.014367 / 0.534201 (-0.519834) \| 0.270506 / 0.579283 (-0.308777) \| 0.268825 / 0.434364 (-0.165539) \| 0.308372 / 0.540337 (-0.231966) \| 0.425039 / 1.386936 (-0.961897) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004813 / 0.011353 (-0.006540) \| 0.002931 / 0.011008 (-0.008078) \| 0.047997 / 0.038508 (0.009489) \| 0.050753 / 0.023109 (0.027644) \| 0.272704 / 0.275898 (-0.003194) \| 0.294045 / 0.323480 (-0.029435) \| 0.004059 / 0.007986 (-0.003927) \| 0.002491 / 0.004328 (-0.001838) \| 0.047621 / 0.004250 (0.043371) \| 0.038824 / 0.037052 (0.001772) \| 0.275322 / 0.258489 (0.016833) \| 0.306447 / 0.293841 (0.012606) \| 0.024402 / 0.128546 (-0.104145) \| 0.007252 / 0.075646 (-0.068394) \| 0.053346 / 0.419271 (-0.365925) \| 0.032224 / 0.043533 (-0.011309) \| 0.271468 / 0.255139 (0.016329) \| 0.289429 / 0.283200 (0.006229) \| 0.018285 / 0.141683 (-0.123398) \| 1.116743 / 1.452155 (-0.335412) \| 1.182724 / 1.492716 (-0.309993) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091899 / 0.018006 (0.073893) \| 0.299161 / 0.000490 (0.298671) \| 0.000224 / 0.000200 (0.000024) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021823 / 0.037411 (-0.015588) \| 0.071227 / 0.014526 (0.056701) \| 0.080503 / 0.176557 (-0.096053) \| 0.120243 / 0.737135 (-0.616892) \| 0.082328 / 0.296338 (-0.214010) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.324951 / 0.215209 (0.109742) \| 2.842358 / 2.077655 (0.764703) \| 1.602317 / 1.504120 (0.098197) \| 1.481103 / 1.541195 (-0.060091) \| 1.497557 / 1.468490 (0.029067) \| 0.406523 / 4.584777 (-4.178254) \| 2.402743 / 3.745712 (-1.342970) \| 2.545435 / 5.269862 (-2.724427) \| 1.534071 / 4.565676 (-3.031605) \| 0.046914 / 0.424275 (-0.377361) \| 0.004728 / 0.007607 (-0.002879) \| 0.341544 / 0.226044 (0.115499) \| 3.412017 / 2.268929 (1.143089) \| 1.937442 / 55.444624 (-53.507182) \| 1.668774 / 6.876477 (-5.207703) \| 1.668908 / 2.142072 (-0.473165) \| 0.477398 / 4.805227 (-4.327829) \| 0.098531 / 6.500664 (-6.402133) \| 0.041077 / 0.075469 (-0.034392) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983888 / 1.841788 (-0.857900) \| 12.072703 / 8.074308 (3.998395) \| 11.028622 / 10.191392 (0.837230) \| 0.148097 / 0.680424 (-0.532327) \| 0.015869 / 0.534201 (-0.518332) \| 0.267609 / 0.579283 (-0.311674) \| 0.272345 / 0.434364 (-0.162019) \| 0.303840 / 0.540337 (-0.236497) \| 0.409199 / 1.386936 (-0.977737) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1487df064580bd23458234fab2e85876d9364e03 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005016 / 0.011353 (-0.006337) \| 0.002931 / 0.011008 (-0.008077) \| 0.062142 / 0.038508 (0.023634) \| 0.030758 / 0.023109 (0.007648) \| 0.251689 / 0.275898 (-0.024209) \| 0.272114 / 0.323480 (-0.051366) \| 0.004102 / 0.007986 (-0.003884) \| 0.002500 / 0.004328 (-0.001828) \| 0.049187 / 0.004250 (0.044937) \| 0.047150 / 0.037052 (0.010098) \| 0.256497 / 0.258489 (-0.001992) \| 0.288069 / 0.293841 (-0.005772) \| 0.023915 / 0.128546 (-0.104632) \| 0.007204 / 0.075646 (-0.068442) \| 0.204257 / 0.419271 (-0.215015) \| 0.063879 / 0.043533 (0.020346) \| 0.253008 / 0.255139 (-0.002131) \| 0.266554 / 0.283200 (-0.016645) \| 0.018929 / 0.141683 (-0.122754) \| 1.140547 / 1.452155 (-0.311608) \| 1.197049 / 1.492716 (-0.295668) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094111 / 0.018006 (0.076105) \| 0.301618 / 0.000490 (0.301128) \| 0.000219 / 0.000200 (0.000019) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018614 / 0.037411 (-0.018797) \| 0.062426 / 0.014526 (0.047900) \| 0.073079 / 0.176557 (-0.103477) \| 0.120313 / 0.737135 (-0.616823) \| 0.076445 / 0.296338 (-0.219894) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285151 / 0.215209 (0.069942) \| 2.754272 / 2.077655 (0.676617) \| 1.485254 / 1.504120 (-0.018866) \| 1.368412 / 1.541195 (-0.172783) \| 1.402819 / 1.468490 (-0.065671) \| 0.396561 / 4.584777 (-4.188216) \| 2.375708 / 3.745712 (-1.370004) \| 2.656088 / 5.269862 (-2.613773) \| 1.588676 / 4.565676 (-2.977001) \| 0.048662 / 0.424275 (-0.375613) \| 0.004963 / 0.007607 (-0.002644) \| 0.339747 / 0.226044 (0.113702) \| 3.315841 / 2.268929 (1.046912) \| 1.841439 / 55.444624 (-53.603186) \| 1.547803 / 6.876477 (-5.328674) \| 1.601872 / 2.142072 (-0.540200) \| 0.468637 / 4.805227 (-4.336591) \| 0.099423 / 6.500664 (-6.401241) \| 0.041926 / 0.075469 (-0.033543) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933058 / 1.841788 (-0.908730) \| 11.680870 / 8.074308 (3.606561) \| 10.239009 / 10.191392 (0.047617) \| 0.129974 / 0.680424 (-0.550450) \| 0.014081 / 0.534201 (-0.520120) \| 0.273076 / 0.579283 (-0.306207) \| 0.261914 / 0.434364 (-0.172450) \| 0.305982 / 0.540337 (-0.234356) \| 0.430623 / 1.386936 (-0.956313) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004969 / 0.011353 (-0.006384) \| 0.003084 / 0.011008 (-0.007924) \| 0.048686 / 0.038508 (0.010178) \| 0.057234 / 0.023109 (0.034125) \| 0.295408 / 0.275898 (0.019510) \| 0.323774 / 0.323480 (0.000294) \| 0.004014 / 0.007986 (-0.003972) \| 0.002423 / 0.004328 (-0.001905) \| 0.048000 / 0.004250 (0.043749) \| 0.039872 / 0.037052 (0.002820) \| 0.294717 / 0.258489 (0.036228) \| 0.331149 / 0.293841 (0.037309) \| 0.027884 / 0.128546 (-0.100662) \| 0.007155 / 0.075646 (-0.068491) \| 0.053812 / 0.419271 (-0.365460) \| 0.032483 / 0.043533 (-0.011050) \| 0.293402 / 0.255139 (0.038263) \| 0.312553 / 0.283200 (0.029354) \| 0.017848 / 0.141683 (-0.123835) \| 1.125600 / 1.452155 (-0.326554) \| 1.189469 / 1.492716 (-0.303248) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096198 / 0.018006 (0.078191) \| 0.305096 / 0.000490 (0.304607) \| 0.000229 / 0.000200 (0.000029) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021992 / 0.037411 (-0.015419) \| 0.072082 / 0.014526 (0.057556) \| 0.082704 / 0.176557 (-0.093853) \| 0.124512 / 0.737135 (-0.612624) \| 0.084541 / 0.296338 (-0.211797) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296440 / 0.215209 (0.081231) \| 2.923392 / 2.077655 (0.845738) \| 1.599057 / 1.504120 (0.094937) \| 1.480473 / 1.541195 (-0.060722) \| 1.551837 / 1.468490 (0.083347) \| 0.418618 / 4.584777 (-4.166159) \| 2.472727 / 3.745712 (-1.272985) \| 2.796141 / 5.269862 (-2.473721) \| 1.629139 / 4.565676 (-2.936538) \| 0.047703 / 0.424275 (-0.376572) \| 0.004971 / 0.007607 (-0.002636) \| 0.354453 / 0.226044 (0.128408) \| 3.514861 / 2.268929 (1.245932) \| 1.993597 / 55.444624 (-53.451028) \| 1.694386 / 6.876477 (-5.182090) \| 1.748562 / 2.142072 (-0.393510) \| 0.487158 / 4.805227 (-4.318070) \| 0.102021 / 6.500664 (-6.398643) \| 0.042648 / 0.075469 (-0.032821) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974950 / 1.841788 (-0.866837) \| 13.391204 / 8.074308 (5.316896) \| 11.474696 / 10.191392 (1.283304) \| 0.142618 / 0.680424 (-0.537806) \| 0.016163 / 0.534201 (-0.518038) \| 0.271453 / 0.579283 (-0.307830) \| 0.287049 / 0.434364 (-0.147315) \| 0.309069 / 0.540337 (-0.231268) \| 0.417117 / 1.386936 (-0.969819) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35a3422cfcebfef5b09ae70c22843ffadaf44c46 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004974 / 0.011353 (-0.006379) \| 0.002950 / 0.011008 (-0.008058) \| 0.061856 / 0.038508 (0.023348) \| 0.030539 / 0.023109 (0.007429) \| 0.250105 / 0.275898 (-0.025793) \| 0.276687 / 0.323480 (-0.046793) \| 0.003077 / 0.007986 (-0.004908) \| 0.002412 / 0.004328 (-0.001916) \| 0.048336 / 0.004250 (0.044086) \| 0.045849 / 0.037052 (0.008797) \| 0.251757 / 0.258489 (-0.006732) \| 0.284914 / 0.293841 (-0.008927) \| 0.024033 / 0.128546 (-0.104513) \| 0.007343 / 0.075646 (-0.068303) \| 0.202867 / 0.419271 (-0.216405) \| 0.061294 / 0.043533 (0.017762) \| 0.263590 / 0.255139 (0.008451) \| 0.272744 / 0.283200 (-0.010455) \| 0.019613 / 0.141683 (-0.122070) \| 1.104263 / 1.452155 (-0.347892) \| 1.164128 / 1.492716 (-0.328588) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094261 / 0.018006 (0.076255) \| 0.303340 / 0.000490 (0.302850) \| 0.000215 / 0.000200 (0.000015) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018381 / 0.037411 (-0.019030) \| 0.062727 / 0.014526 (0.048201) \| 0.074955 / 0.176557 (-0.101602) \| 0.124810 / 0.737135 (-0.612326) \| 0.074335 / 0.296338 (-0.222004) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279368 / 0.215209 (0.064159) \| 2.721641 / 2.077655 (0.643986) \| 1.510773 / 1.504120 (0.006653) \| 1.364349 / 1.541195 (-0.176845) \| 1.386044 / 1.468490 (-0.082446) \| 0.403051 / 4.584777 (-4.181726) \| 2.416525 / 3.745712 (-1.329187) \| 2.623198 / 5.269862 (-2.646663) \| 1.560869 / 4.565676 (-3.004808) \| 0.046613 / 0.424275 (-0.377662) \| 0.004861 / 0.007607 (-0.002746) \| 0.337875 / 0.226044 (0.111830) \| 3.289956 / 2.268929 (1.021028) \| 1.851707 / 55.444624 (-53.592917) \| 1.571092 / 6.876477 (-5.305385) \| 1.600328 / 2.142072 (-0.541745) \| 0.480766 / 4.805227 (-4.324461) \| 0.099138 / 6.500664 (-6.401526) \| 0.041691 / 0.075469 (-0.033779) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941162 / 1.841788 (-0.900626) \| 11.745335 / 8.074308 (3.671027) \| 10.645509 / 10.191392 (0.454117) \| 0.132506 / 0.680424 (-0.547918) \| 0.015192 / 0.534201 (-0.519009) \| 0.272483 / 0.579283 (-0.306800) \| 0.270269 / 0.434364 (-0.164094) \| 0.309580 / 0.540337 (-0.230758) \| 0.431513 / 1.386936 (-0.955423) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005068 / 0.011353 (-0.006285) \| 0.003069 / 0.011008 (-0.007939) \| 0.048605 / 0.038508 (0.010097) \| 0.059557 / 0.023109 (0.036448) \| 0.275092 / 0.275898 (-0.000806) \| 0.298910 / 0.323480 (-0.024570) \| 0.004198 / 0.007986 (-0.003788) \| 0.002499 / 0.004328 (-0.001830) \| 0.048248 / 0.004250 (0.043997) \| 0.040302 / 0.037052 (0.003249) \| 0.279539 / 0.258489 (0.021050) \| 0.312500 / 0.293841 (0.018659) \| 0.025407 / 0.128546 (-0.103140) \| 0.007364 / 0.075646 (-0.068282) \| 0.053086 / 0.419271 (-0.366186) \| 0.033291 / 0.043533 (-0.010242) \| 0.276521 / 0.255139 (0.021382) \| 0.292943 / 0.283200 (0.009743) \| 0.019416 / 0.141683 (-0.122267) \| 1.151734 / 1.452155 (-0.300421) \| 1.205021 / 1.492716 (-0.287695) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094112 / 0.018006 (0.076106) \| 0.309534 / 0.000490 (0.309044) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021539 / 0.037411 (-0.015872) \| 0.070325 / 0.014526 (0.055799) \| 0.080468 / 0.176557 (-0.096089) \| 0.121095 / 0.737135 (-0.616040) \| 0.082008 / 0.296338 (-0.214331) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302591 / 0.215209 (0.087382) \| 2.943475 / 2.077655 (0.865820) \| 1.597970 / 1.504120 (0.093850) \| 1.468774 / 1.541195 (-0.072421) \| 1.504812 / 1.468490 (0.036322) \| 0.413715 / 4.584777 (-4.171062) \| 2.418319 / 3.745712 (-1.327393) \| 2.616656 / 5.269862 (-2.653206) \| 1.558165 / 4.565676 (-3.007512) \| 0.047169 / 0.424275 (-0.377106) \| 0.004761 / 0.007607 (-0.002846) \| 0.347225 / 0.226044 (0.121180) \| 3.479624 / 2.268929 (1.210696) \| 1.961253 / 55.444624 (-53.483371) \| 1.673532 / 6.876477 (-5.202944) \| 1.698900 / 2.142072 (-0.443172) \| 0.488373 / 4.805227 (-4.316855) \| 0.098322 / 6.500664 (-6.402342) \| 0.040832 / 0.075469 (-0.034637) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.009133 / 1.841788 (-0.832655) \| 13.373258 / 8.074308 (5.298949) \| 11.327360 / 10.191392 (1.135968) \| 0.135778 / 0.680424 (-0.544646) \| 0.015813 / 0.534201 (-0.518388) \| 0.275404 / 0.579283 (-0.303879) \| 0.282564 / 0.434364 (-0.151799) \| 0.311830 / 0.540337 (-0.228507) \| 0.419008 / 1.386936 (-0.967928) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4592709e5399f91b5b392f4fd73687985365c909 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004899 / 0.011353 (-0.006454) \| 0.002780 / 0.011008 (-0.008229) \| 0.061997 / 0.038508 (0.023489) \| 0.029909 / 0.023109 (0.006800) \| 0.233445 / 0.275898 (-0.042453) \| 0.254128 / 0.323480 (-0.069351) \| 0.002927 / 0.007986 (-0.005058) \| 0.002396 / 0.004328 (-0.001932) \| 0.048118 / 0.004250 (0.043868) \| 0.044520 / 0.037052 (0.007468) \| 0.237594 / 0.258489 (-0.020895) \| 0.268407 / 0.293841 (-0.025434) \| 0.023517 / 0.128546 (-0.105029) \| 0.007035 / 0.075646 (-0.068612) \| 0.202803 / 0.419271 (-0.216469) \| 0.057692 / 0.043533 (0.014159) \| 0.237058 / 0.255139 (-0.018081) \| 0.252966 / 0.283200 (-0.030233) \| 0.017934 / 0.141683 (-0.123748) \| 1.096406 / 1.452155 (-0.355749) \| 1.153509 / 1.492716 (-0.339207) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091812 / 0.018006 (0.073806) \| 0.298410 / 0.000490 (0.297920) \| 0.000228 / 0.000200 (0.000028) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018333 / 0.037411 (-0.019078) \| 0.062685 / 0.014526 (0.048159) \| 0.073295 / 0.176557 (-0.103261) \| 0.119234 / 0.737135 (-0.617901) \| 0.074603 / 0.296338 (-0.221736) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279078 / 0.215209 (0.063869) \| 2.768535 / 2.077655 (0.690880) \| 1.457049 / 1.504120 (-0.047071) \| 1.326870 / 1.541195 (-0.214325) \| 1.349657 / 1.468490 (-0.118833) \| 0.405003 / 4.584777 (-4.179774) \| 2.428726 / 3.745712 (-1.316986) \| 2.595776 / 5.269862 (-2.674086) \| 1.557879 / 4.565676 (-3.007797) \| 0.045985 / 0.424275 (-0.378291) \| 0.004854 / 0.007607 (-0.002753) \| 0.336437 / 0.226044 (0.110392) \| 3.317330 / 2.268929 (1.048401) \| 1.784525 / 55.444624 (-53.660100) \| 1.500295 / 6.876477 (-5.376182) \| 1.529869 / 2.142072 (-0.612203) \| 0.473426 / 4.805227 (-4.331801) \| 0.099609 / 6.500664 (-6.401055) \| 0.042054 / 0.075469 (-0.033415) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937154 / 1.841788 (-0.904633) \| 11.482383 / 8.074308 (3.408075) \| 10.468769 / 10.191392 (0.277377) \| 0.132724 / 0.680424 (-0.547700) \| 0.015242 / 0.534201 (-0.518959) \| 0.281124 / 0.579283 (-0.298159) \| 0.268603 / 0.434364 (-0.165761) \| 0.311410 / 0.540337 (-0.228928) \| 0.431817 / 1.386936 (-0.955119) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004695 / 0.011353 (-0.006658) \| 0.002873 / 0.011008 (-0.008135) \| 0.048133 / 0.038508 (0.009625) \| 0.052505 / 0.023109 (0.029396) \| 0.271679 / 0.275898 (-0.004219) \| 0.292530 / 0.323480 (-0.030950) \| 0.003844 / 0.007986 (-0.004142) \| 0.002417 / 0.004328 (-0.001912) \| 0.048619 / 0.004250 (0.044369) \| 0.039152 / 0.037052 (0.002100) \| 0.276575 / 0.258489 (0.018086) \| 0.307836 / 0.293841 (0.013995) \| 0.023877 / 0.128546 (-0.104669) \| 0.006897 / 0.075646 (-0.068749) \| 0.053241 / 0.419271 (-0.366031) \| 0.032487 / 0.043533 (-0.011046) \| 0.274205 / 0.255139 (0.019066) \| 0.289701 / 0.283200 (0.006502) \| 0.018250 / 0.141683 (-0.123432) \| 1.137902 / 1.452155 (-0.314253) \| 1.202043 / 1.492716 (-0.290673) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091453 / 0.018006 (0.073446) \| 0.297032 / 0.000490 (0.296543) \| 0.000224 / 0.000200 (0.000024) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021062 / 0.037411 (-0.016349) \| 0.069848 / 0.014526 (0.055322) \| 0.084337 / 0.176557 (-0.092219) \| 0.119951 / 0.737135 (-0.617184) \| 0.082805 / 0.296338 (-0.213533) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297056 / 0.215209 (0.081846) \| 2.890110 / 2.077655 (0.812456) \| 1.609918 / 1.504120 (0.105798) \| 1.491184 / 1.541195 (-0.050011) \| 1.529433 / 1.468490 (0.060943) \| 0.396081 / 4.584777 (-4.188696) \| 2.408310 / 3.745712 (-1.337402) \| 2.567905 / 5.269862 (-2.701957) \| 1.514465 / 4.565676 (-3.051212) \| 0.045329 / 0.424275 (-0.378946) \| 0.004738 / 0.007607 (-0.002869) \| 0.344373 / 0.226044 (0.118328) \| 3.428333 / 2.268929 (1.159404) \| 1.981401 / 55.444624 (-53.463223) \| 1.688007 / 6.876477 (-5.188470) \| 1.685542 / 2.142072 (-0.456531) \| 0.478045 / 4.805227 (-4.327182) \| 0.096664 / 6.500664 (-6.404001) \| 0.040335 / 0.075469 (-0.035135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972912 / 1.841788 (-0.868876) \| 12.055045 / 8.074308 (3.980737) \| 10.821073 / 10.191392 (0.629681) \| 0.139177 / 0.680424 (-0.541247) \| 0.015046 / 0.534201 (-0.519155) \| 0.275670 / 0.579283 (-0.303613) \| 0.280366 / 0.434364 (-0.153998) \| 0.315781 / 0.540337 (-0.224556) \| 0.424536 / 1.386936 (-0.962400) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0684b471d6ca8a235162f5575f624b6eda7956c5 \"CML watermark\")\n", "I'm finally merging as `transformers`/`tokenizers` dependency pins have been removed + `huggingface_hub 0.19.4` has fixed the deps incompatibility issue. All good now :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004435 / 0.011353 (-0.006918) \| 0.002924 / 0.011008 (-0.008084) \| 0.062159 / 0.038508 (0.023651) \| 0.029639 / 0.023109 (0.006529) \| 0.237470 / 0.275898 (-0.038428) \| 0.269641 / 0.323480 (-0.053839) \| 0.004124 / 0.007986 (-0.003862) \| 0.002528 / 0.004328 (-0.001800) \| 0.048114 / 0.004250 (0.043864) \| 0.046055 / 0.037052 (0.009002) \| 0.245844 / 0.258489 (-0.012645) \| 0.278085 / 0.293841 (-0.015756) \| 0.023152 / 0.128546 (-0.105394) \| 0.007194 / 0.075646 (-0.068452) \| 0.206493 / 0.419271 (-0.212778) \| 0.055687 / 0.043533 (0.012155) \| 0.243301 / 0.255139 (-0.011838) \| 0.267645 / 0.283200 (-0.015555) \| 0.017413 / 0.141683 (-0.124270) \| 1.113071 / 1.452155 (-0.339083) \| 1.201436 / 1.492716 (-0.291280) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092576 / 0.018006 (0.074570) \| 0.303516 / 0.000490 (0.303027) \| 0.000213 / 0.000200 (0.000013) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019108 / 0.037411 (-0.018303) \| 0.062326 / 0.014526 (0.047800) \| 0.073711 / 0.176557 (-0.102846) \| 0.120414 / 0.737135 (-0.616721) \| 0.075837 / 0.296338 (-0.220501) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278267 / 0.215209 (0.063058) \| 2.766231 / 2.077655 (0.688576) \| 1.455613 / 1.504120 (-0.048507) \| 1.337128 / 1.541195 (-0.204066) \| 1.357659 / 1.468490 (-0.110831) \| 0.404549 / 4.584777 (-4.180228) \| 2.409084 / 3.745712 (-1.336628) \| 2.645000 / 5.269862 (-2.624861) \| 1.600475 / 4.565676 (-2.965201) \| 0.046680 / 0.424275 (-0.377595) \| 0.004887 / 0.007607 (-0.002720) \| 0.340338 / 0.226044 (0.114294) \| 3.332647 / 2.268929 (1.063719) \| 1.852529 / 55.444624 (-53.592096) \| 1.532442 / 6.876477 (-5.344035) \| 1.550383 / 2.142072 (-0.591689) \| 0.482702 / 4.805227 (-4.322525) \| 0.101067 / 6.500664 (-6.399597) \| 0.042132 / 0.075469 (-0.033337) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945481 / 1.841788 (-0.896307) \| 11.886240 / 8.074308 (3.811932) \| 10.484620 / 10.191392 (0.293228) \| 0.130906 / 0.680424 (-0.549518) \| 0.014880 / 0.534201 (-0.519321) \| 0.268836 / 0.579283 (-0.310447) \| 0.268112 / 0.434364 (-0.166251) \| 0.304300 / 0.540337 (-0.236038) \| 0.440262 / 1.386936 (-0.946674) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005028 / 0.011353 (-0.006325) \| 0.002937 / 0.011008 (-0.008071) \| 0.049038 / 0.038508 (0.010530) \| 0.057763 / 0.023109 (0.034653) \| 0.273196 / 0.275898 (-0.002702) \| 0.295519 / 0.323480 (-0.027961) \| 0.004102 / 0.007986 (-0.003883) \| 0.002487 / 0.004328 (-0.001841) \| 0.049148 / 0.004250 (0.044898) \| 0.040303 / 0.037052 (0.003251) \| 0.279187 / 0.258489 (0.020698) \| 0.311086 / 0.293841 (0.017245) \| 0.024961 / 0.128546 (-0.103585) \| 0.007264 / 0.075646 (-0.068382) \| 0.055711 / 0.419271 (-0.363561) \| 0.032355 / 0.043533 (-0.011178) \| 0.274304 / 0.255139 (0.019165) \| 0.290953 / 0.283200 (0.007753) \| 0.018358 / 0.141683 (-0.123325) \| 1.115984 / 1.452155 (-0.336170) \| 1.190409 / 1.492716 (-0.302308) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095765 / 0.018006 (0.077759) \| 0.287947 / 0.000490 (0.287457) \| 0.000242 / 0.000200 (0.000042) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022165 / 0.037411 (-0.015246) \| 0.070465 / 0.014526 (0.055940) \| 0.082078 / 0.176557 (-0.094479) \| 0.120209 / 0.737135 (-0.616926) \| 0.084573 / 0.296338 (-0.211765) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298492 / 0.215209 (0.083283) \| 2.924981 / 2.077655 (0.847327) \| 1.597326 / 1.504120 (0.093206) \| 1.459132 / 1.541195 (-0.082062) \| 1.511471 / 1.468490 (0.042981) \| 0.406671 / 4.584777 (-4.178106) \| 2.443154 / 3.745712 (-1.302558) \| 2.591131 / 5.269862 (-2.678731) \| 1.549931 / 4.565676 (-3.015745) \| 0.047042 / 0.424275 (-0.377234) \| 0.004891 / 0.007607 (-0.002716) \| 0.346274 / 0.226044 (0.120230) \| 3.456050 / 2.268929 (1.187121) \| 1.959328 / 55.444624 (-53.485296) \| 1.647631 / 6.876477 (-5.228845) \| 1.692024 / 2.142072 (-0.450049) \| 0.478307 / 4.805227 (-4.326920) \| 0.098738 / 6.500664 (-6.401926) \| 0.041743 / 0.075469 (-0.033726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984619 / 1.841788 (-0.857168) \| 12.403984 / 8.074308 (4.329676) \| 10.974347 / 10.191392 (0.782955) \| 0.132893 / 0.680424 (-0.547530) \| 0.015504 / 0.534201 (-0.518697) \| 0.275354 / 0.579283 (-0.303929) \| 0.283312 / 0.434364 (-0.151052) \| 0.313661 / 0.540337 (-0.226677) \| 0.419065 / 1.386936 (-0.967871) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c65315e4a8308f04fcb025039afe2a2e43b5684e \"CML watermark\")\n" ]	2023-11-14T10:47:09	2023-11-17T14:23:20	2023-11-17T14:17:00	CONTRIBUTOR	null	Related to https://github.com/huggingface/transformers/issues/27034 and https://github.com/huggingface/huggingface_hub/pull/1782. TL;DR: `hashlib` is not a secure library for cryptography-related stuff. We are only using `hashlib` for non-security-related purposes in `datasets` so it's fine. From Python 3.9 we set can `usedforsecurity=False` in any `hashlib` method which is mandatory for companies that forbid the use of `hashlib` for security purposes. This PR fixes that. Note: before merging this we need to release a new tokenizers version that would allow the newest `huggingface_hub` version (see https://github.com/huggingface/tokenizers/pull/1385). Otherwise it might create friction to users that want to install `datasets` + `tokenizers` at the same time.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6414/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6414/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6414", "html_url": "https://github.com/huggingface/datasets/pull/6414", "diff_url": "https://github.com/huggingface/datasets/pull/6414.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6414.patch", "merged_at": "2023-11-17T14:17:00" }	true
https://api.github.com/repos/huggingface/datasets/issues/6412	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6412/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6412/comments	https://api.github.com/repos/huggingface/datasets/issues/6412/events	https://github.com/huggingface/datasets/issues/6412	1,992,401,594	I_kwDODunzps52waK6	6,412	User token is printed out!	{ "login": "mohsen-goodarzi", "id": 25702692, "node_id": "MDQ6VXNlcjI1NzAyNjky", "avatar_url": "https://avatars.githubusercontent.com/u/25702692?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mohsen-goodarzi", "html_url": "https://github.com/mohsen-goodarzi", "followers_url": "https://api.github.com/users/mohsen-goodarzi/followers", "following_url": "https://api.github.com/users/mohsen-goodarzi/following{/other_user}", "gists_url": "https://api.github.com/users/mohsen-goodarzi/gists{/gist_id}", "starred_url": "https://api.github.com/users/mohsen-goodarzi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mohsen-goodarzi/subscriptions", "organizations_url": "https://api.github.com/users/mohsen-goodarzi/orgs", "repos_url": "https://api.github.com/users/mohsen-goodarzi/repos", "events_url": "https://api.github.com/users/mohsen-goodarzi/events{/privacy}", "received_events_url": "https://api.github.com/users/mohsen-goodarzi/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Indeed, this is not a good practice. I've opened a PR that removes the token value from the (deprecation) warning." ]	2023-11-14T10:01:34	2023-11-14T22:19:46	2023-11-14T22:19:46	NONE	null	This line prints user token on command line! Is it safe? https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/load.py#L2091	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6412/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6412/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6411	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6411/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6411/comments	https://api.github.com/repos/huggingface/datasets/issues/6411/events	https://github.com/huggingface/datasets/pull/6411	1,992,386,630	PR_kwDODunzps5fZE9F	6,411	Fix dependency conflict within CI build documentation	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2023-11-14T09:52:51	2023-11-14T10:05:59	2023-11-14T10:05:35	MEMBER	null	Manually fix dependency conflict on `typing-extensions` version originated by `apache-beam` + `pydantic` (now a dependency of `huggingface-hub`). This is a temporary hot fix of our CI build documentation until we stop using `apache-beam`. Fix #6406.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6411/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6411/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6411", "html_url": "https://github.com/huggingface/datasets/pull/6411", "diff_url": "https://github.com/huggingface/datasets/pull/6411.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6411.patch", "merged_at": "2023-11-14T10:05:34" }	true
https://api.github.com/repos/huggingface/datasets/issues/6410	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6410/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6410/comments	https://api.github.com/repos/huggingface/datasets/issues/6410/events	https://github.com/huggingface/datasets/issues/6410	1,992,100,209	I_kwDODunzps52vQlx	6,410	Datasets does not load HuggingFace Repository properly	{ "login": "MikeDoes", "id": 40600201, "node_id": "MDQ6VXNlcjQwNjAwMjAx", "avatar_url": "https://avatars.githubusercontent.com/u/40600201?v=4", "gravatar_id": "", "url": "https://api.github.com/users/MikeDoes", "html_url": "https://github.com/MikeDoes", "followers_url": "https://api.github.com/users/MikeDoes/followers", "following_url": "https://api.github.com/users/MikeDoes/following{/other_user}", "gists_url": "https://api.github.com/users/MikeDoes/gists{/gist_id}", "starred_url": "https://api.github.com/users/MikeDoes/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MikeDoes/subscriptions", "organizations_url": "https://api.github.com/users/MikeDoes/orgs", "repos_url": "https://api.github.com/users/MikeDoes/repos", "events_url": "https://api.github.com/users/MikeDoes/events{/privacy}", "received_events_url": "https://api.github.com/users/MikeDoes/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hi! You can avoid the error by requesting only the `jsonl` files. `dataset = load_dataset(\"ai4privacy/pii-masking-200k\", data_files=[\"*.jsonl\"])`.\r\n\r\nOur data file inference does not filter out (incompatible) `json` files because `json` and `jsonl` use the same builder. Still, I think the inference should differentiate these extensions because it's safe to assume that loading them together will lead to an error. WDYT @lhoestq? ", "Raising an error if there is a mix of json and jsonl in the builder makes sense yea" ]	2023-11-14T06:50:49	2023-11-16T06:54:36	null	NONE	null	### Describe the bug Dear Datasets team, We just have published a dataset on Huggingface: https://huggingface.co/ai4privacy However, when trying to read it using the Dataset library we get an error. As I understand jsonl files are compatible, could you please clarify how we can solve the issue? Please let me know and we would be more than happy to adapt the structure of the repository or meta data so it works easier: ```python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ``` ``` Downloading readme: 100% 11.8k/11.8k [00:00<00:00, 512kB/s] Downloading data files: 100% 1/1 [00:11<00:00, 11.16s/it] Downloading data: 100% 64.3M/64.3M [00:02<00:00, 32.9MB/s] Downloading data: 100% 113M/113M [00:03<00:00, 35.0MB/s] Downloading data: 100% 97.7M/97.7M [00:02<00:00, 46.1MB/s] Downloading data: 100% 90.8M/90.8M [00:02<00:00, 44.9MB/s] Downloading data: 100% 7.63k/7.63k [00:00<00:00, 41.0kB/s] Downloading data: 100% 1.03k/1.03k [00:00<00:00, 9.44kB/s] Extracting data files: 100% 1/1 [00:00<00:00, 29.26it/s] Generating train split: 209261/0 [00:05<00:00, 41201.25 examples/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1939 ) -> 1940 writer.write_table(table) 1941 num_examples_progress_update += len(table) 8 frames [/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size) 571 pa_table = pa_table.combine_chunks() --> 572 pa_table = table_cast(pa_table, self._schema) 573 if self.embed_local_files: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema) 2327 if table.schema != schema: -> 2328 return cast_table_to_schema(table, schema) 2329 elif table.schema.metadata != schema.metadata: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema) 2285 if sorted(table.column_names) != sorted(features): -> 2286 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2287 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast JOBTYPE: int64 PHONEIMEI: int64 ACCOUNTNAME: int64 VEHICLEVIN: int64 GENDER: int64 CURRENCYCODE: int64 CREDITCARDISSUER: int64 JOBTITLE: int64 SEX: int64 CURRENCYSYMBOL: int64 IP: int64 EYECOLOR: int64 MASKEDNUMBER: int64 SECONDARYADDRESS: int64 JOBAREA: int64 ACCOUNTNUMBER: int64 language: string BITCOINADDRESS: int64 MAC: int64 SSN: int64 EMAIL: int64 ETHEREUMADDRESS: int64 DOB: int64 VEHICLEVRM: int64 IPV6: int64 AMOUNT: int64 URL: int64 PHONENUMBER: int64 PIN: int64 TIME: int64 CREDITCARDNUMBER: int64 FIRSTNAME: int64 IBAN: int64 BIC: int64 COUNTY: int64 STATE: int64 LASTNAME: int64 ZIPCODE: int64 HEIGHT: int64 ORDINALDIRECTION: int64 MIDDLENAME: int64 STREET: int64 USERNAME: int64 CURRENCY: int64 PREFIX: int64 USERAGENT: int64 CURRENCYNAME: int64 LITECOINADDRESS: int64 CREDITCARDCVV: int64 AGE: int64 CITY: int64 PASSWORD: int64 BUILDINGNUMBER: int64 IPV4: int64 NEARBYGPSCOORDINATE: int64 DATE: int64 COMPANYNAME: int64 to {'masked_text': Value(dtype='string', id=None), 'unmasked_text': Value(dtype='string', id=None), 'privacy_mask': Value(dtype='string', id=None), 'span_labels': Value(dtype='string', id=None), 'bio_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'tokenised_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [<ipython-input-2-f1c6811e9c83>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("ai4privacy/pii-masking-200k") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2151 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1959 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Thank you and have a great day ahead ### Steps to reproduce the bug Open Google Colab Notebook: Run command: !pip3 install datasets Run code: from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ### Expected behavior Download the dataset successfully from HuggingFace to the notebook so that we can start working with it ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6410/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6410/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6409	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6409/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6409/comments	https://api.github.com/repos/huggingface/datasets/issues/6409/events	https://github.com/huggingface/datasets/issues/6409	1,991,960,865	I_kwDODunzps52uukh	6,409	using DownloadManager to download from local filesystem and disable_progress_bar, there will be an exception	{ "login": "neiblegy", "id": 16574677, "node_id": "MDQ6VXNlcjE2NTc0Njc3", "avatar_url": "https://avatars.githubusercontent.com/u/16574677?v=4", "gravatar_id": "", "url": "https://api.github.com/users/neiblegy", "html_url": "https://github.com/neiblegy", "followers_url": "https://api.github.com/users/neiblegy/followers", "following_url": "https://api.github.com/users/neiblegy/following{/other_user}", "gists_url": "https://api.github.com/users/neiblegy/gists{/gist_id}", "starred_url": "https://api.github.com/users/neiblegy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/neiblegy/subscriptions", "organizations_url": "https://api.github.com/users/neiblegy/orgs", "repos_url": "https://api.github.com/users/neiblegy/repos", "events_url": "https://api.github.com/users/neiblegy/events{/privacy}", "received_events_url": "https://api.github.com/users/neiblegy/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-14T04:21:01	2023-11-14T04:21:01	null	NONE	null	### Describe the bug i'm using datasets.download.download_manager.DownloadManager to download files like "file:///a/b/c.txt", and i disable_progress_bar() to disable bar. there will be an exception as follows: `AttributeError: 'function' object has no attribute 'close' Exception ignored in: <function TqdmCallback.__del__ at 0x7fa8683d84c0> Traceback (most recent call last): File "/home/protoss.gao/.local/lib/python3.9/site-packages/fsspec/callbacks.py", line 233, in __del__ self.tqdm.close()` i check your source code in datasets/utils/file_utils.py:348 you define TqdmCallback derive from fsspec.callbacks.TqdmCallback but in the newest fsspec code [https://github.com/fsspec/filesystem_spec/blob/master/fsspec/callbacks.py](url) , line 146, in this case, _DEFAULT_CALLBACK will take effect, but in line 234, it calls "close()" function which _DEFAULT_CALLBACK don't have such thing. so i think the class "TqdmCallback" in datasets/utils/file_utils.py may override "__del__" function or report this bug to fsspec. ### Steps to reproduce the bug as i said ### Expected behavior no exception ### Environment info datasets: 2.14.4 python: 3.9 platform: x86_64	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6409/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6409/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6408	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6408/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6408/comments	https://api.github.com/repos/huggingface/datasets/issues/6408/events	https://github.com/huggingface/datasets/issues/6408	1,991,902,972	I_kwDODunzps52ugb8	6,408	`IterableDataset` lost but not keep columns when map function adding columns with names in `remove_columns`	{ "login": "shmily326", "id": 24571857, "node_id": "MDQ6VXNlcjI0NTcxODU3", "avatar_url": "https://avatars.githubusercontent.com/u/24571857?v=4", "gravatar_id": "", "url": "https://api.github.com/users/shmily326", "html_url": "https://github.com/shmily326", "followers_url": "https://api.github.com/users/shmily326/followers", "following_url": "https://api.github.com/users/shmily326/following{/other_user}", "gists_url": "https://api.github.com/users/shmily326/gists{/gist_id}", "starred_url": "https://api.github.com/users/shmily326/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/shmily326/subscriptions", "organizations_url": "https://api.github.com/users/shmily326/orgs", "repos_url": "https://api.github.com/users/shmily326/repos", "events_url": "https://api.github.com/users/shmily326/events{/privacy}", "received_events_url": "https://api.github.com/users/shmily326/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-14T03:12:08	2023-11-16T06:24:10	null	NONE	null	### Describe the bug IterableDataset lost but not keep columns when map function adding columns with names in remove_columns, Dataset not. May be related to the code below: https://github.com/huggingface/datasets/blob/06c3ffb8d068b6307b247164b10f7c7311cefed4/src/datasets/iterable_dataset.py#L750-L756 ### Steps to reproduce the bug ```python dataset: IterableDataset = load_dataset("Anthropic/hh-rlhf", streaming=True, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx} ``` ```python # when load_dataset with streaming=False, the column_names are kept: dataset: Dataset = load_dataset("Anthropic/hh-rlhf", streaming=False, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx, "chosen": xxx, "rejected": xxx} ``` ### Expected behavior IterableDataset keep columns when map function adding columns with names in remove_columns ### Environment info datasets==2.14.6	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6408/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6408/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6407	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6407/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6407/comments	https://api.github.com/repos/huggingface/datasets/issues/6407/events	https://github.com/huggingface/datasets/issues/6407	1,991,514,079	I_kwDODunzps52tBff	6,407	Loading the dataset from private S3 bucket gives "TypeError: cannot pickle '_contextvars.Context' object"	{ "login": "eawer", "id": 1741779, "node_id": "MDQ6VXNlcjE3NDE3Nzk=", "avatar_url": "https://avatars.githubusercontent.com/u/1741779?v=4", "gravatar_id": "", "url": "https://api.github.com/users/eawer", "html_url": "https://github.com/eawer", "followers_url": "https://api.github.com/users/eawer/followers", "following_url": "https://api.github.com/users/eawer/following{/other_user}", "gists_url": "https://api.github.com/users/eawer/gists{/gist_id}", "starred_url": "https://api.github.com/users/eawer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/eawer/subscriptions", "organizations_url": "https://api.github.com/users/eawer/orgs", "repos_url": "https://api.github.com/users/eawer/repos", "events_url": "https://api.github.com/users/eawer/events{/privacy}", "received_events_url": "https://api.github.com/users/eawer/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-13T21:27:43	2023-11-13T21:27:43	null	NONE	null	### Describe the bug I'm trying to read the parquet file from the private s3 bucket using the `load_dataset` function, but I receive `TypeError: cannot pickle '_contextvars.Context' object` error I'm working on a machine with `~/.aws/credentials` file. I can't give credentials and the path to a file in a private bucket for obvious reasons, but I'll try to give all possible outputs. ### Steps to reproduce the bug ```python import s3fs from datasets import load_dataset from aiobotocore.session import get_session DATA_PATH = "s3://bucket_name/path/validation.parquet" fs = s3fs.S3FileSystem(session=get_session()) ``` `fs.stat` returns the data, so we can say that fs is working and we have all permissions ```python fs.stat(DATA_PATH) # Returns: # {'ETag': '"123123a-19"', # 'LastModified': datetime.datetime(2023, 11, 1, 10, 16, 57, tzinfo=tzutc()), # 'size': 312237170, # 'name': 'bucket_name/path/validation.parquet', # 'type': 'file', # 'StorageClass': 'STANDARD', # 'VersionId': 'Abc.HtmsC9h.as', # 'ContentType': 'binary/octet-stream'} ``` ```python fs.storage_options # Returns: # {'session': <aiobotocore.session.AioSession at 0x7f9193fa53c0>} ``` ```python ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) ``` <details> <summary>Returns such error (expandable)</summary> ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[88], line 1 ----> 1 ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 prepare_split_kwargs, 958 download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1025 split_dict = SplitDict(dataset_name=self.dataset_name) 1026 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) -> 1027 split_generators = self._split_generators(dl_manager, split_generators_kwargs) 1029 # Checksums verification 1030 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py:34, in Parquet._split_generators(self, dl_manager) 32 if not self.config.data_files: 33 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}") ---> 34 data_files = dl_manager.download_and_extract(self.config.data_files) 35 if isinstance(data_files, (str, list, tuple)): 36 files = data_files File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:565, in DownloadManager.download_and_extract(self, url_or_urls) 549 def download_and_extract(self, url_or_urls): 550 """Download and extract given `url_or_urls`. 551 552 Is roughly equivalent to: (...) 563 extracted_path(s): `str`, extracted paths of given URL(s). 564 """ --> 565 return self.extract(self.download(url_or_urls)) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:420, in DownloadManager.download(self, url_or_urls) 401 def download(self, url_or_urls): 402 """Download given URL(s). 403 404 By default, only one process is used for download. Pass customized `download_config.num_proc` to change this behavior. (...) 418 ``` 419 """ --> 420 download_config = self.download_config.copy() 421 download_config.extract_compressed_file = False 422 if download_config.download_desc is None: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in DownloadConfig.copy(self) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__({k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in <dictcomp>(.0) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__({k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (2 times), deepcopy at line 146 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:206, in _deepcopy_list(x, memo, deepcopy) 204 append = y.append 205 for a in x: --> 206 append(deepcopy(a, memo)) 207 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:238, in _deepcopy_method(x, memo) 237 def _deepcopy_method(x, memo): # Copy instance methods --> 238 return type(x)(x.__func__, deepcopy(x.__self__, memo)) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (3 times), deepcopy at line 146 (3 times), deepcopy at line 172 (3 times), _reconstruct at line 271 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (1 times), deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:265, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 263 if deep and args: 264 args = (deepcopy(arg, memo) for arg in args) --> 265 y = func(args) 266 if deep: 267 memo[id(x)] = y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:264, in <genexpr>(.0) 262 deep = memo is not None 263 if deep and args: --> 264 args = (deepcopy(arg, memo) for arg in args) 265 y = func(args) 266 if deep: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:161, in deepcopy(x, memo, _nil) 159 reductor = getattr(x, "__reduce_ex__", None) 160 if reductor is not None: --> 161 rv = reductor(4) 162 else: 163 reductor = getattr(x, "__reduce__", None) TypeError: cannot pickle '_contextvars.Context' object ``` </details> ### Expected behavior If I choose to load the file from the public bucket with `anon=True` passed - everything works, so I expected loading from the private bucket to work as well ### Environment info - `datasets` version: 2.14.6 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.10.13 - Huggingface_hub version: 0.19.1 - PyArrow version: 14.0.1 - Pandas version: 1.5.3 - s3fs version: 2023.10.0 - fsspec version: 2023.10.0 - aiobotocore version: 2.7.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6407/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6407/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6406	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6406/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6406/comments	https://api.github.com/repos/huggingface/datasets/issues/6406/events	https://github.com/huggingface/datasets/issues/6406	1,990,469,045	I_kwDODunzps52pCW1	6,406	CI Build PR Documentation is broken: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	2023-11-13T11:36:10	2023-11-14T10:05:36	2023-11-14T10:05:36	MEMBER	null	Our CI Build PR Documentation is broken. See: https://github.com/huggingface/datasets/actions/runs/6799554060/job/18486828777?pr=6390 ``` ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6406/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6406/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6405	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6405/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6405/comments	https://api.github.com/repos/huggingface/datasets/issues/6405/events	https://github.com/huggingface/datasets/issues/6405	1,990,358,743	I_kwDODunzps52onbX	6,405	ConfigNamesError on a simple CSV file	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" } ]	closed	false	null	[]	null	[ "The viewer is working now. \r\n\r\nBased on the repo commit history, the bug was due to the incorrect format of the `features` field in the README YAML (`Value` requires `dtype`, e.g., `Value(\"string\")`, but it was not specified)", "Feel free to close the issue", "Oh, OK! Thanks. So, there was no reason to open an issue" ]	2023-11-13T10:28:29	2023-11-13T20:01:24	2023-11-13T20:01:24	CONTRIBUTOR	null	See https://huggingface.co/datasets/Nguyendo1999/mmath/discussions/1 ``` Error code: ConfigNamesError Exception: TypeError Message: __init__() missing 1 required positional argument: 'dtype' Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1512, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1489, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1039, in get_module dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 468, in from_dataset_card_data dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 399, in _from_yaml_dict yaml_data["features"] = Features._from_yaml_list(yaml_data["features"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1838, in _from_yaml_list return cls.from_dict(from_yaml_inner(yaml_data)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1690, in from_dict obj = generate_from_dict(dic) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1353, in generate_from_dict return class_type(**{k: v for k, v in obj.items() if k in field_names}) TypeError: __init__() missing 1 required positional argument: 'dtype' ``` This is the CSV file: https://huggingface.co/datasets/Nguyendo1999/mmath/blob/dbcdd7c2c6fc447f852ec136a7532292802bb46f/math_train.csv	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6405/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6405/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6404	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6404/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6404/comments	https://api.github.com/repos/huggingface/datasets/issues/6404/events	https://github.com/huggingface/datasets/pull/6404	1,990,211,901	PR_kwDODunzps5fRrJ-	6,404	Support pyarrow 14.0.1 and fix vulnerability CVE-2023-47248	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005974 / 0.011353 (-0.005378) \| 0.003707 / 0.011008 (-0.007301) \| 0.079908 / 0.038508 (0.041399) \| 0.036891 / 0.023109 (0.013781) \| 0.390355 / 0.275898 (0.114457) \| 0.424439 / 0.323480 (0.100960) \| 0.004936 / 0.007986 (-0.003050) \| 0.002886 / 0.004328 (-0.001442) \| 0.062793 / 0.004250 (0.058542) \| 0.054192 / 0.037052 (0.017139) \| 0.394697 / 0.258489 (0.136208) \| 0.437775 / 0.293841 (0.143934) \| 0.027596 / 0.128546 (-0.100950) \| 0.008006 / 0.075646 (-0.067640) \| 0.262515 / 0.419271 (-0.156757) \| 0.071014 / 0.043533 (0.027481) \| 0.392964 / 0.255139 (0.137825) \| 0.417449 / 0.283200 (0.134249) \| 0.021819 / 0.141683 (-0.119864) \| 1.458083 / 1.452155 (0.005929) \| 1.489042 / 1.492716 (-0.003674) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230303 / 0.018006 (0.212297) \| 0.439361 / 0.000490 (0.438871) \| 0.010615 / 0.000200 (0.010415) \| 0.000303 / 0.000054 (0.000249) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026600 / 0.037411 (-0.010811) \| 0.078605 / 0.014526 (0.064079) \| 0.088552 / 0.176557 (-0.088005) \| 0.149429 / 0.737135 (-0.587706) \| 0.087921 / 0.296338 (-0.208417) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422063 / 0.215209 (0.206854) \| 4.201333 / 2.077655 (2.123678) \| 1.982284 / 1.504120 (0.478164) \| 1.779625 / 1.541195 (0.238431) \| 1.872454 / 1.468490 (0.403964) \| 0.502713 / 4.584777 (-4.082063) \| 3.103372 / 3.745712 (-0.642340) \| 3.030516 / 5.269862 (-2.239346) \| 1.909123 / 4.565676 (-2.656554) \| 0.057134 / 0.424275 (-0.367141) \| 0.006405 / 0.007607 (-0.001202) \| 0.494452 / 0.226044 (0.268408) \| 4.839345 / 2.268929 (2.570417) \| 2.424721 / 55.444624 (-53.019904) \| 2.028618 / 6.876477 (-4.847859) \| 2.082528 / 2.142072 (-0.059545) \| 0.587396 / 4.805227 (-4.217831) \| 0.125013 / 6.500664 (-6.375651) \| 0.061369 / 0.075469 (-0.014100) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235799 / 1.841788 (-0.605989) \| 17.919977 / 8.074308 (9.845669) \| 13.868524 / 10.191392 (3.677132) \| 0.146058 / 0.680424 (-0.534366) \| 0.016826 / 0.534201 (-0.517375) \| 0.337512 / 0.579283 (-0.241771) \| 0.390263 / 0.434364 (-0.044101) \| 0.385336 / 0.540337 (-0.155001) \| 0.566004 / 1.386936 (-0.820932) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006537 / 0.011353 (-0.004816) \| 0.003787 / 0.011008 (-0.007221) \| 0.062568 / 0.038508 (0.024060) \| 0.066672 / 0.023109 (0.043563) \| 0.420447 / 0.275898 (0.144549) \| 0.457260 / 0.323480 (0.133780) \| 0.005005 / 0.007986 (-0.002981) \| 0.003037 / 0.004328 (-0.001291) \| 0.062095 / 0.004250 (0.057844) \| 0.049619 / 0.037052 (0.012567) \| 0.429935 / 0.258489 (0.171446) \| 0.471566 / 0.293841 (0.177725) \| 0.029688 / 0.128546 (-0.098859) \| 0.008028 / 0.075646 (-0.067619) \| 0.067915 / 0.419271 (-0.351356) \| 0.042066 / 0.043533 (-0.001467) \| 0.419275 / 0.255139 (0.164136) \| 0.444819 / 0.283200 (0.161619) \| 0.020100 / 0.141683 (-0.121583) \| 1.439057 / 1.452155 (-0.013098) \| 1.495657 / 1.492716 (0.002940) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211148 / 0.018006 (0.193142) \| 0.423777 / 0.000490 (0.423288) \| 0.005892 / 0.000200 (0.005693) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026469 / 0.037411 (-0.010942) \| 0.081438 / 0.014526 (0.066912) \| 0.092007 / 0.176557 (-0.084550) \| 0.143433 / 0.737135 (-0.593703) \| 0.093039 / 0.296338 (-0.203300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.410468 / 0.215209 (0.195259) \| 4.083783 / 2.077655 (2.006128) \| 2.234501 / 1.504120 (0.730381) \| 2.122323 / 1.541195 (0.581128) \| 2.255036 / 1.468490 (0.786546) \| 0.497712 / 4.584777 (-4.087065) \| 3.231187 / 3.745712 (-0.514525) \| 3.005399 / 5.269862 (-2.264463) \| 1.909516 / 4.565676 (-2.656161) \| 0.057529 / 0.424275 (-0.366746) \| 0.006475 / 0.007607 (-0.001132) \| 0.477282 / 0.226044 (0.251238) \| 4.799566 / 2.268929 (2.530637) \| 2.497070 / 55.444624 (-52.947554) \| 2.206359 / 6.876477 (-4.670118) \| 2.281614 / 2.142072 (0.139541) \| 0.581710 / 4.805227 (-4.223518) \| 0.121572 / 6.500664 (-6.379092) \| 0.058774 / 0.075469 (-0.016695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.301880 / 1.841788 (-0.539908) \| 18.287330 / 8.074308 (10.213021) \| 14.939642 / 10.191392 (4.748250) \| 0.153941 / 0.680424 (-0.526483) \| 0.018345 / 0.534201 (-0.515856) \| 0.335986 / 0.579283 (-0.243297) \| 0.384264 / 0.434364 (-0.050099) \| 0.393115 / 0.540337 (-0.147223) \| 0.573343 / 1.386936 (-0.813594) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d54b6459f4ed0b2519ddec605dd71956d2d1d3e4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004805 / 0.011353 (-0.006548) \| 0.003261 / 0.011008 (-0.007747) \| 0.061585 / 0.038508 (0.023077) \| 0.030236 / 0.023109 (0.007127) \| 0.234767 / 0.275898 (-0.041131) \| 0.260478 / 0.323480 (-0.063002) \| 0.004121 / 0.007986 (-0.003865) \| 0.002525 / 0.004328 (-0.001803) \| 0.048213 / 0.004250 (0.043962) \| 0.045229 / 0.037052 (0.008176) \| 0.245143 / 0.258489 (-0.013346) \| 0.271818 / 0.293841 (-0.022023) \| 0.023594 / 0.128546 (-0.104952) \| 0.007335 / 0.075646 (-0.068311) \| 0.206246 / 0.419271 (-0.213026) \| 0.060783 / 0.043533 (0.017250) \| 0.238588 / 0.255139 (-0.016551) \| 0.274985 / 0.283200 (-0.008214) \| 0.018342 / 0.141683 (-0.123341) \| 1.135445 / 1.452155 (-0.316710) \| 1.184836 / 1.492716 (-0.307881) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095603 / 0.018006 (0.077597) \| 0.290340 / 0.000490 (0.289850) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018804 / 0.037411 (-0.018607) \| 0.062525 / 0.014526 (0.047999) \| 0.074797 / 0.176557 (-0.101760) \| 0.120360 / 0.737135 (-0.616775) \| 0.076182 / 0.296338 (-0.220156) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274981 / 0.215209 (0.059772) \| 2.684931 / 2.077655 (0.607276) \| 1.453845 / 1.504120 (-0.050275) \| 1.348361 / 1.541195 (-0.192834) \| 1.402820 / 1.468490 (-0.065670) \| 0.396311 / 4.584777 (-4.188466) \| 2.396314 / 3.745712 (-1.349398) \| 2.744379 / 5.269862 (-2.525482) \| 1.615268 / 4.565676 (-2.950409) \| 0.045920 / 0.424275 (-0.378355) \| 0.004844 / 0.007607 (-0.002763) \| 0.331132 / 0.226044 (0.105087) \| 3.325484 / 2.268929 (1.056556) \| 1.845734 / 55.444624 (-53.598890) \| 1.537268 / 6.876477 (-5.339209) \| 1.565155 / 2.142072 (-0.576918) \| 0.480032 / 4.805227 (-4.325195) \| 0.099917 / 6.500664 (-6.400747) \| 0.042276 / 0.075469 (-0.033193) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973128 / 1.841788 (-0.868660) \| 12.643790 / 8.074308 (4.569482) \| 10.319586 / 10.191392 (0.128194) \| 0.131733 / 0.680424 (-0.548691) \| 0.014849 / 0.534201 (-0.519352) \| 0.270960 / 0.579283 (-0.308323) \| 0.265409 / 0.434364 (-0.168955) \| 0.309073 / 0.540337 (-0.231264) \| 0.466204 / 1.386936 (-0.920732) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005067 / 0.011353 (-0.006286) \| 0.003344 / 0.011008 (-0.007665) \| 0.047917 / 0.038508 (0.009409) \| 0.059556 / 0.023109 (0.036447) \| 0.275777 / 0.275898 (-0.000121) \| 0.299703 / 0.323480 (-0.023777) \| 0.004185 / 0.007986 (-0.003801) \| 0.002602 / 0.004328 (-0.001726) \| 0.048723 / 0.004250 (0.044472) \| 0.040686 / 0.037052 (0.003634) \| 0.281078 / 0.258489 (0.022589) \| 0.314725 / 0.293841 (0.020885) \| 0.024645 / 0.128546 (-0.103901) \| 0.007465 / 0.075646 (-0.068182) \| 0.053827 / 0.419271 (-0.365445) \| 0.033395 / 0.043533 (-0.010138) \| 0.273675 / 0.255139 (0.018536) \| 0.291261 / 0.283200 (0.008062) \| 0.019733 / 0.141683 (-0.121950) \| 1.134084 / 1.452155 (-0.318071) \| 1.189186 / 1.492716 (-0.303531) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.114960 / 0.018006 (0.096954) \| 0.308800 / 0.000490 (0.308311) \| 0.000237 / 0.000200 (0.000037) \| 0.000061 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021633 / 0.037411 (-0.015778) \| 0.073192 / 0.014526 (0.058666) \| 0.081598 / 0.176557 (-0.094959) \| 0.123085 / 0.737135 (-0.614050) \| 0.088677 / 0.296338 (-0.207661) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300865 / 0.215209 (0.085656) \| 2.956847 / 2.077655 (0.879192) \| 1.613890 / 1.504120 (0.109770) \| 1.494074 / 1.541195 (-0.047121) \| 1.550345 / 1.468490 (0.081855) \| 0.408880 / 4.584777 (-4.175897) \| 2.422848 / 3.745712 (-1.322865) \| 2.690623 / 5.269862 (-2.579239) \| 1.546922 / 4.565676 (-3.018755) \| 0.047192 / 0.424275 (-0.377083) \| 0.004882 / 0.007607 (-0.002725) \| 0.360625 / 0.226044 (0.134580) \| 3.512678 / 2.268929 (1.243749) \| 1.978633 / 55.444624 (-53.465992) \| 1.686927 / 6.876477 (-5.189549) \| 1.748387 / 2.142072 (-0.393685) \| 0.480780 / 4.805227 (-4.324447) \| 0.099163 / 6.500664 (-6.401501) \| 0.041194 / 0.075469 (-0.034275) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989087 / 1.841788 (-0.852700) \| 12.341951 / 8.074308 (4.267643) \| 11.109329 / 10.191392 (0.917936) \| 0.143329 / 0.680424 (-0.537095) \| 0.015565 / 0.534201 (-0.518636) \| 0.269532 / 0.579283 (-0.309751) \| 0.274899 / 0.434364 (-0.159465) \| 0.309308 / 0.540337 (-0.231030) \| 0.439651 / 1.386936 (-0.947285) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a3f006a1a88c894ea10610d66dfddd73ad1490 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007880 / 0.011353 (-0.003473) \| 0.004386 / 0.011008 (-0.006622) \| 0.099067 / 0.038508 (0.060559) \| 0.048036 / 0.023109 (0.024927) \| 0.368349 / 0.275898 (0.092451) \| 0.400052 / 0.323480 (0.076572) \| 0.004493 / 0.007986 (-0.003493) \| 0.003732 / 0.004328 (-0.000597) \| 0.076153 / 0.004250 (0.071902) \| 0.071024 / 0.037052 (0.033972) \| 0.379771 / 0.258489 (0.121282) \| 0.425005 / 0.293841 (0.131164) \| 0.036092 / 0.128546 (-0.092454) \| 0.009825 / 0.075646 (-0.065822) \| 0.340217 / 0.419271 (-0.079055) \| 0.089571 / 0.043533 (0.046038) \| 0.371426 / 0.255139 (0.116287) \| 0.397864 / 0.283200 (0.114664) \| 0.029440 / 0.141683 (-0.112243) \| 1.778100 / 1.452155 (0.325945) \| 1.857202 / 1.492716 (0.364486) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254022 / 0.018006 (0.236015) \| 0.549844 / 0.000490 (0.549354) \| 0.012824 / 0.000200 (0.012624) \| 0.000378 / 0.000054 (0.000324) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032334 / 0.037411 (-0.005077) \| 0.096101 / 0.014526 (0.081576) \| 0.117825 / 0.176557 (-0.058731) \| 0.179277 / 0.737135 (-0.557858) \| 0.112614 / 0.296338 (-0.183724) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.455051 / 0.215209 (0.239842) \| 4.537086 / 2.077655 (2.459431) \| 2.198662 / 1.504120 (0.694542) \| 1.982772 / 1.541195 (0.441578) \| 2.058673 / 1.468490 (0.590182) \| 0.569268 / 4.584777 (-4.015509) \| 4.095000 / 3.745712 (0.349288) \| 3.891680 / 5.269862 (-1.378182) \| 2.345129 / 4.565676 (-2.220548) \| 0.066974 / 0.424275 (-0.357301) \| 0.008557 / 0.007607 (0.000950) \| 0.545290 / 0.226044 (0.319245) \| 5.453377 / 2.268929 (3.184448) \| 2.858688 / 55.444624 (-52.585936) \| 2.502367 / 6.876477 (-4.374109) \| 2.515658 / 2.142072 (0.373586) \| 0.681423 / 4.805227 (-4.123804) \| 0.155975 / 6.500664 (-6.344689) \| 0.070872 / 0.075469 (-0.004597) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.474674 / 1.841788 (-0.367114) \| 21.653619 / 8.074308 (13.579311) \| 16.277111 / 10.191392 (6.085719) \| 0.166445 / 0.680424 (-0.513979) \| 0.021676 / 0.534201 (-0.512525) \| 0.466949 / 0.579283 (-0.112334) \| 0.500953 / 0.434364 (0.066589) \| 0.540413 / 0.540337 (0.000076) \| 0.792989 / 1.386936 (-0.593947) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007633 / 0.011353 (-0.003720) \| 0.004468 / 0.011008 (-0.006540) \| 0.075573 / 0.038508 (0.037065) \| 0.081174 / 0.023109 (0.058064) \| 0.440741 / 0.275898 (0.164843) \| 0.489493 / 0.323480 (0.166013) \| 0.006180 / 0.007986 (-0.001805) \| 0.003693 / 0.004328 (-0.000636) \| 0.074692 / 0.004250 (0.070441) \| 0.061732 / 0.037052 (0.024680) \| 0.460391 / 0.258489 (0.201902) \| 0.505575 / 0.293841 (0.211734) \| 0.037692 / 0.128546 (-0.090854) \| 0.009870 / 0.075646 (-0.065776) \| 0.083830 / 0.419271 (-0.335442) \| 0.056255 / 0.043533 (0.012723) \| 0.439330 / 0.255139 (0.184191) \| 0.475598 / 0.283200 (0.192399) \| 0.026626 / 0.141683 (-0.115056) \| 1.794410 / 1.452155 (0.342255) \| 1.882510 / 1.492716 (0.389794) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236194 / 0.018006 (0.218187) \| 0.486109 / 0.000490 (0.485619) \| 0.006652 / 0.000200 (0.006453) \| 0.000108 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037277 / 0.037411 (-0.000134) \| 0.108904 / 0.014526 (0.094378) \| 0.122699 / 0.176557 (-0.053857) \| 0.182388 / 0.737135 (-0.554747) \| 0.122826 / 0.296338 (-0.173512) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485989 / 0.215209 (0.270780) \| 4.913263 / 2.077655 (2.835609) \| 2.571618 / 1.504120 (1.067498) \| 2.401248 / 1.541195 (0.860054) \| 2.501117 / 1.468490 (1.032627) \| 0.570989 / 4.584777 (-4.013788) \| 4.107420 / 3.745712 (0.361708) \| 3.814977 / 5.269862 (-1.454885) \| 2.282539 / 4.565676 (-2.283138) \| 0.067765 / 0.424275 (-0.356511) \| 0.008561 / 0.007607 (0.000954) \| 0.584515 / 0.226044 (0.358471) \| 5.817821 / 2.268929 (3.548893) \| 3.211202 / 55.444624 (-52.233422) \| 2.764480 / 6.876477 (-4.111996) \| 2.807301 / 2.142072 (0.665229) \| 0.676882 / 4.805227 (-4.128346) \| 0.150124 / 6.500664 (-6.350540) \| 0.067205 / 0.075469 (-0.008265) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.594945 / 1.841788 (-0.246843) \| 22.533511 / 8.074308 (14.459203) \| 17.099693 / 10.191392 (6.908301) \| 0.195954 / 0.680424 (-0.484470) \| 0.023968 / 0.534201 (-0.510233) \| 0.471337 / 0.579283 (-0.107946) \| 0.491017 / 0.434364 (0.056653) \| 0.561342 / 0.540337 (0.021004) \| 0.797116 / 1.386936 (-0.589820) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98871b9ba46e89e75e9d0dddc49f4241373c575d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006235 / 0.011353 (-0.005118) \| 0.003688 / 0.011008 (-0.007321) \| 0.080801 / 0.038508 (0.042293) \| 0.036243 / 0.023109 (0.013134) \| 0.312173 / 0.275898 (0.036275) \| 0.346239 / 0.323480 (0.022759) \| 0.003429 / 0.007986 (-0.004556) \| 0.003806 / 0.004328 (-0.000523) \| 0.063236 / 0.004250 (0.058986) \| 0.053229 / 0.037052 (0.016177) \| 0.315184 / 0.258489 (0.056695) \| 0.360124 / 0.293841 (0.066283) \| 0.027447 / 0.128546 (-0.101099) \| 0.008029 / 0.075646 (-0.067618) \| 0.262766 / 0.419271 (-0.156505) \| 0.068421 / 0.043533 (0.024888) \| 0.309028 / 0.255139 (0.053889) \| 0.345859 / 0.283200 (0.062659) \| 0.021388 / 0.141683 (-0.120295) \| 1.452807 / 1.452155 (0.000652) \| 1.502803 / 1.492716 (0.010087) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211297 / 0.018006 (0.193291) \| 0.423364 / 0.000490 (0.422874) \| 0.004574 / 0.000200 (0.004374) \| 0.000272 / 0.000054 (0.000218) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023805 / 0.037411 (-0.013606) \| 0.072309 / 0.014526 (0.057783) \| 0.083274 / 0.176557 (-0.093283) \| 0.143594 / 0.737135 (-0.593541) \| 0.083777 / 0.296338 (-0.212561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415691 / 0.215209 (0.200482) \| 4.128621 / 2.077655 (2.050967) \| 1.931128 / 1.504120 (0.427008) \| 1.737486 / 1.541195 (0.196292) \| 1.806314 / 1.468490 (0.337823) \| 0.501405 / 4.584777 (-4.083372) \| 3.082042 / 3.745712 (-0.663670) \| 2.980224 / 5.269862 (-2.289637) \| 1.879780 / 4.565676 (-2.685897) \| 0.057546 / 0.424275 (-0.366729) \| 0.006422 / 0.007607 (-0.001186) \| 0.479813 / 0.226044 (0.253768) \| 4.854497 / 2.268929 (2.585568) \| 2.529674 / 55.444624 (-52.914950) \| 2.283041 / 6.876477 (-4.593436) \| 2.377173 / 2.142072 (0.235101) \| 0.589654 / 4.805227 (-4.215573) \| 0.126190 / 6.500664 (-6.374474) \| 0.062391 / 0.075469 (-0.013079) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.232023 / 1.841788 (-0.609764) \| 17.576621 / 8.074308 (9.502313) \| 13.437075 / 10.191392 (3.245683) \| 0.143367 / 0.680424 (-0.537057) \| 0.016638 / 0.534201 (-0.517563) \| 0.332806 / 0.579283 (-0.246477) \| 0.356029 / 0.434364 (-0.078335) \| 0.385610 / 0.540337 (-0.154727) \| 0.563268 / 1.386936 (-0.823668) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006293 / 0.011353 (-0.005060) \| 0.003692 / 0.011008 (-0.007317) \| 0.062075 / 0.038508 (0.023567) \| 0.062104 / 0.023109 (0.038995) \| 0.407478 / 0.275898 (0.131580) \| 0.434982 / 0.323480 (0.111502) \| 0.004889 / 0.007986 (-0.003097) \| 0.002915 / 0.004328 (-0.001413) \| 0.061426 / 0.004250 (0.057176) \| 0.048027 / 0.037052 (0.010974) \| 0.410504 / 0.258489 (0.152015) \| 0.435383 / 0.293841 (0.141542) \| 0.029419 / 0.128546 (-0.099127) \| 0.008275 / 0.075646 (-0.067371) \| 0.067796 / 0.419271 (-0.351476) \| 0.041696 / 0.043533 (-0.001837) \| 0.398882 / 0.255139 (0.143743) \| 0.419480 / 0.283200 (0.136281) \| 0.021519 / 0.141683 (-0.120164) \| 1.436961 / 1.452155 (-0.015194) \| 1.507961 / 1.492716 (0.015245) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223190 / 0.018006 (0.205184) \| 0.416281 / 0.000490 (0.415791) \| 0.003370 / 0.000200 (0.003170) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025923 / 0.037411 (-0.011488) \| 0.079989 / 0.014526 (0.065463) \| 0.091289 / 0.176557 (-0.085268) \| 0.141212 / 0.737135 (-0.595923) \| 0.091717 / 0.296338 (-0.204622) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434640 / 0.215209 (0.219431) \| 4.326154 / 2.077655 (2.248500) \| 2.364845 / 1.504120 (0.860725) \| 2.194040 / 1.541195 (0.652846) \| 2.276665 / 1.468490 (0.808175) \| 0.501879 / 4.584777 (-4.082898) \| 3.073307 / 3.745712 (-0.672405) \| 2.893823 / 5.269862 (-2.376039) \| 1.820594 / 4.565676 (-2.745083) \| 0.057595 / 0.424275 (-0.366680) \| 0.006516 / 0.007607 (-0.001091) \| 0.513633 / 0.226044 (0.287589) \| 5.104799 / 2.268929 (2.835870) \| 2.845025 / 55.444624 (-52.599599) \| 2.513852 / 6.876477 (-4.362624) \| 2.561044 / 2.142072 (0.418972) \| 0.582711 / 4.805227 (-4.222516) \| 0.120631 / 6.500664 (-6.380034) \| 0.056738 / 0.075469 (-0.018731) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303370 / 1.841788 (-0.538418) \| 18.023568 / 8.074308 (9.949259) \| 14.637973 / 10.191392 (4.446581) \| 0.145182 / 0.680424 (-0.535241) \| 0.018061 / 0.534201 (-0.516140) \| 0.333219 / 0.579283 (-0.246065) \| 0.373184 / 0.434364 (-0.061180) \| 0.388176 / 0.540337 (-0.152161) \| 0.564752 / 1.386936 (-0.822184) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aecdc94580d105d4b70c94e8e238ce097f97af90 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007230 / 0.011353 (-0.004122) \| 0.003727 / 0.011008 (-0.007281) \| 0.078893 / 0.038508 (0.040385) \| 0.042600 / 0.023109 (0.019491) \| 0.301905 / 0.275898 (0.026007) \| 0.328478 / 0.323480 (0.004998) \| 0.003960 / 0.007986 (-0.004026) \| 0.004530 / 0.004328 (0.000201) \| 0.059446 / 0.004250 (0.055196) \| 0.061241 / 0.037052 (0.024189) \| 0.301878 / 0.258489 (0.043389) \| 0.340935 / 0.293841 (0.047095) \| 0.030559 / 0.128546 (-0.097988) \| 0.008016 / 0.075646 (-0.067630) \| 0.305174 / 0.419271 (-0.114097) \| 0.080374 / 0.043533 (0.036842) \| 0.307162 / 0.255139 (0.052023) \| 0.342459 / 0.283200 (0.059259) \| 0.025881 / 0.141683 (-0.115801) \| 1.443311 / 1.452155 (-0.008844) \| 1.631060 / 1.492716 (0.138344) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242676 / 0.018006 (0.224670) \| 0.463941 / 0.000490 (0.463451) \| 0.007762 / 0.000200 (0.007562) \| 0.000582 / 0.000054 (0.000527) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027334 / 0.037411 (-0.010077) \| 0.078910 / 0.014526 (0.064384) \| 0.091399 / 0.176557 (-0.085157) \| 0.143318 / 0.737135 (-0.593818) \| 0.089761 / 0.296338 (-0.206577) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463002 / 0.215209 (0.247793) \| 4.627235 / 2.077655 (2.549580) \| 2.256699 / 1.504120 (0.752579) \| 2.057615 / 1.541195 (0.516421) \| 2.126424 / 1.468490 (0.657934) \| 0.571969 / 4.584777 (-4.012808) \| 4.130260 / 3.745712 (0.384548) \| 3.833521 / 5.269862 (-1.436341) \| 2.320141 / 4.565676 (-2.245535) \| 0.067587 / 0.424275 (-0.356688) \| 0.008452 / 0.007607 (0.000845) \| 0.546478 / 0.226044 (0.320433) \| 5.070678 / 2.268929 (2.801750) \| 2.325387 / 55.444624 (-53.119237) \| 2.044041 / 6.876477 (-4.832435) \| 2.019714 / 2.142072 (-0.122358) \| 0.563589 / 4.805227 (-4.241639) \| 0.135269 / 6.500664 (-6.365395) \| 0.058208 / 0.075469 (-0.017261) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283156 / 1.841788 (-0.558631) \| 18.617776 / 8.074308 (10.543468) \| 13.360700 / 10.191392 (3.169308) \| 0.160001 / 0.680424 (-0.520423) \| 0.021538 / 0.534201 (-0.512663) \| 0.384169 / 0.579283 (-0.195114) \| 0.407517 / 0.434364 (-0.026847) \| 0.427295 / 0.540337 (-0.113042) \| 0.655288 / 1.386936 (-0.731648) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006854 / 0.011353 (-0.004499) \| 0.003442 / 0.011008 (-0.007566) \| 0.060622 / 0.038508 (0.022114) \| 0.074649 / 0.023109 (0.051540) \| 0.341733 / 0.275898 (0.065835) \| 0.360096 / 0.323480 (0.036616) \| 0.006235 / 0.007986 (-0.001751) \| 0.003447 / 0.004328 (-0.000882) \| 0.057301 / 0.004250 (0.053051) \| 0.059022 / 0.037052 (0.021970) \| 0.369523 / 0.258489 (0.111034) \| 0.386280 / 0.293841 (0.092439) \| 0.034319 / 0.128546 (-0.094228) \| 0.008291 / 0.075646 (-0.067355) \| 0.070403 / 0.419271 (-0.348868) \| 0.050433 / 0.043533 (0.006901) \| 0.347262 / 0.255139 (0.092123) \| 0.380543 / 0.283200 (0.097343) \| 0.024492 / 0.141683 (-0.117191) \| 1.446721 / 1.452155 (-0.005433) \| 1.541614 / 1.492716 (0.048898) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226148 / 0.018006 (0.208142) \| 0.442150 / 0.000490 (0.441660) \| 0.004997 / 0.000200 (0.004797) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032866 / 0.037411 (-0.004546) \| 0.088097 / 0.014526 (0.073571) \| 0.102178 / 0.176557 (-0.074379) \| 0.151129 / 0.737135 (-0.586006) \| 0.103953 / 0.296338 (-0.192386) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.376701 / 0.215209 (0.161492) \| 3.886997 / 2.077655 (1.809342) \| 2.027143 / 1.504120 (0.523023) \| 1.808647 / 1.541195 (0.267453) \| 1.867664 / 1.468490 (0.399173) \| 0.459487 / 4.584777 (-4.125290) \| 3.640801 / 3.745712 (-0.104911) \| 3.242512 / 5.269862 (-2.027350) \| 1.889174 / 4.565676 (-2.676503) \| 0.052415 / 0.424275 (-0.371860) \| 0.007479 / 0.007607 (-0.000128) \| 0.457706 / 0.226044 (0.231662) \| 4.815041 / 2.268929 (2.546112) \| 2.542470 / 55.444624 (-52.902154) \| 2.137084 / 6.876477 (-4.739392) \| 2.122867 / 2.142072 (-0.019205) \| 0.553756 / 4.805227 (-4.251471) \| 0.118902 / 6.500664 (-6.381763) \| 0.058149 / 0.075469 (-0.017320) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272615 / 1.841788 (-0.569173) \| 19.455709 / 8.074308 (11.381401) \| 14.111693 / 10.191392 (3.920301) \| 0.165741 / 0.680424 (-0.514683) \| 0.023680 / 0.534201 (-0.510521) \| 0.431458 / 0.579283 (-0.147825) \| 0.433612 / 0.434364 (-0.000752) \| 0.465615 / 0.540337 (-0.074722) \| 0.678177 / 1.386936 (-0.708759) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#998623fa51991320740b945d0853ee20807304d7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004870 / 0.011353 (-0.006483) \| 0.002834 / 0.011008 (-0.008175) \| 0.061359 / 0.038508 (0.022851) \| 0.031286 / 0.023109 (0.008177) \| 0.236701 / 0.275898 (-0.039197) \| 0.258139 / 0.323480 (-0.065341) \| 0.002943 / 0.007986 (-0.005043) \| 0.002989 / 0.004328 (-0.001339) \| 0.048046 / 0.004250 (0.043796) \| 0.044927 / 0.037052 (0.007874) \| 0.241339 / 0.258489 (-0.017151) \| 0.273912 / 0.293841 (-0.019929) \| 0.023427 / 0.128546 (-0.105119) \| 0.007251 / 0.075646 (-0.068395) \| 0.202730 / 0.419271 (-0.216542) \| 0.056223 / 0.043533 (0.012691) \| 0.239908 / 0.255139 (-0.015231) \| 0.254723 / 0.283200 (-0.028476) \| 0.018223 / 0.141683 (-0.123460) \| 1.119691 / 1.452155 (-0.332464) \| 1.163802 / 1.492716 (-0.328915) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091303 / 0.018006 (0.073297) \| 0.302097 / 0.000490 (0.301607) \| 0.000214 / 0.000200 (0.000014) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018201 / 0.037411 (-0.019210) \| 0.062092 / 0.014526 (0.047566) \| 0.074806 / 0.176557 (-0.101751) \| 0.119625 / 0.737135 (-0.617510) \| 0.074680 / 0.296338 (-0.221659) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281140 / 0.215209 (0.065931) \| 2.752094 / 2.077655 (0.674439) \| 1.436813 / 1.504120 (-0.067307) \| 1.312947 / 1.541195 (-0.228247) \| 1.331022 / 1.468490 (-0.137468) \| 0.396579 / 4.584777 (-4.188198) \| 2.406181 / 3.745712 (-1.339531) \| 2.597180 / 5.269862 (-2.672682) \| 1.565879 / 4.565676 (-2.999798) \| 0.046330 / 0.424275 (-0.377945) \| 0.004776 / 0.007607 (-0.002831) \| 0.339681 / 0.226044 (0.113637) \| 3.279533 / 2.268929 (1.010605) \| 1.793352 / 55.444624 (-53.651272) \| 1.493910 / 6.876477 (-5.382567) \| 1.514494 / 2.142072 (-0.627579) \| 0.467955 / 4.805227 (-4.337272) \| 0.097764 / 6.500664 (-6.402900) \| 0.041659 / 0.075469 (-0.033810) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943204 / 1.841788 (-0.898583) \| 11.350848 / 8.074308 (3.276540) \| 10.169944 / 10.191392 (-0.021448) \| 0.130882 / 0.680424 (-0.549542) \| 0.013804 / 0.534201 (-0.520397) \| 0.269107 / 0.579283 (-0.310177) \| 0.261685 / 0.434364 (-0.172679) \| 0.305610 / 0.540337 (-0.234727) \| 0.430586 / 1.386936 (-0.956350) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004835 / 0.011353 (-0.006518) \| 0.002530 / 0.011008 (-0.008479) \| 0.047383 / 0.038508 (0.008875) \| 0.052559 / 0.023109 (0.029450) \| 0.265015 / 0.275898 (-0.010883) \| 0.286955 / 0.323480 (-0.036525) \| 0.003931 / 0.007986 (-0.004054) \| 0.002038 / 0.004328 (-0.002290) \| 0.047458 / 0.004250 (0.043207) \| 0.038257 / 0.037052 (0.001205) \| 0.270569 / 0.258489 (0.012080) \| 0.298968 / 0.293841 (0.005127) \| 0.024615 / 0.128546 (-0.103932) \| 0.006969 / 0.075646 (-0.068677) \| 0.052361 / 0.419271 (-0.366911) \| 0.032701 / 0.043533 (-0.010832) \| 0.269126 / 0.255139 (0.013987) \| 0.285934 / 0.283200 (0.002735) \| 0.018121 / 0.141683 (-0.123562) \| 1.129796 / 1.452155 (-0.322359) \| 1.272831 / 1.492716 (-0.219885) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092058 / 0.018006 (0.074051) \| 0.303544 / 0.000490 (0.303054) \| 0.000232 / 0.000200 (0.000032) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020983 / 0.037411 (-0.016428) \| 0.069798 / 0.014526 (0.055272) \| 0.081410 / 0.176557 (-0.095146) \| 0.120403 / 0.737135 (-0.616732) \| 0.082813 / 0.296338 (-0.213525) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295943 / 0.215209 (0.080734) \| 2.895761 / 2.077655 (0.818106) \| 1.583534 / 1.504120 (0.079414) \| 1.458397 / 1.541195 (-0.082798) \| 1.492113 / 1.468490 (0.023623) \| 0.402364 / 4.584777 (-4.182413) \| 2.469777 / 3.745712 (-1.275935) \| 2.565262 / 5.269862 (-2.704599) \| 1.525914 / 4.565676 (-3.039763) \| 0.047168 / 0.424275 (-0.377107) \| 0.004800 / 0.007607 (-0.002808) \| 0.348356 / 0.226044 (0.122311) \| 3.463184 / 2.268929 (1.194255) \| 1.930240 / 55.444624 (-53.514385) \| 1.644312 / 6.876477 (-5.232165) \| 1.625477 / 2.142072 (-0.516596) \| 0.480781 / 4.805227 (-4.324446) \| 0.098431 / 6.500664 (-6.402233) \| 0.041071 / 0.075469 (-0.034398) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973633 / 1.841788 (-0.868154) \| 11.952261 / 8.074308 (3.877953) \| 11.038222 / 10.191392 (0.846830) \| 0.142755 / 0.680424 (-0.537669) \| 0.015389 / 0.534201 (-0.518812) \| 0.274144 / 0.579283 (-0.305139) \| 0.282319 / 0.434364 (-0.152045) \| 0.314330 / 0.540337 (-0.226007) \| 0.435315 / 1.386936 (-0.951621) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05200c0a4f8f02c3890ab79a10b44ab0bcf11629 \"CML watermark\")\n", "The red CI job is unrelated to this PR. It appeared 5 days ago. See:\r\n- https://github.com/huggingface/datasets/pull/6390#pullrequestreview-1721070927\r\n- https://github.com/huggingface/datasets/issues/6406", "Let's do a new release once this is merged ? cc @mariosasko as well let us know if the fix sounds good to you", "@lhoestq Yes, this sounds good to me!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004932 / 0.011353 (-0.006421) \| 0.002956 / 0.011008 (-0.008052) \| 0.061999 / 0.038508 (0.023491) \| 0.030174 / 0.023109 (0.007065) \| 0.241483 / 0.275898 (-0.034415) \| 0.261578 / 0.323480 (-0.061902) \| 0.002881 / 0.007986 (-0.005105) \| 0.002451 / 0.004328 (-0.001878) \| 0.048176 / 0.004250 (0.043925) \| 0.045028 / 0.037052 (0.007976) \| 0.244304 / 0.258489 (-0.014185) \| 0.275834 / 0.293841 (-0.018007) \| 0.023312 / 0.128546 (-0.105234) \| 0.007361 / 0.075646 (-0.068286) \| 0.204433 / 0.419271 (-0.214838) \| 0.054561 / 0.043533 (0.011028) \| 0.236902 / 0.255139 (-0.018237) \| 0.269358 / 0.283200 (-0.013842) \| 0.017736 / 0.141683 (-0.123947) \| 1.112444 / 1.452155 (-0.339711) \| 1.170260 / 1.492716 (-0.322456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093081 / 0.018006 (0.075074) \| 0.311470 / 0.000490 (0.310981) \| 0.000212 / 0.000200 (0.000013) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018654 / 0.037411 (-0.018757) \| 0.063239 / 0.014526 (0.048714) \| 0.073759 / 0.176557 (-0.102798) \| 0.120279 / 0.737135 (-0.616857) \| 0.076214 / 0.296338 (-0.220124) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287219 / 0.215209 (0.072010) \| 2.765378 / 2.077655 (0.687723) \| 1.459733 / 1.504120 (-0.044387) \| 1.325999 / 1.541195 (-0.215196) \| 1.349957 / 1.468490 (-0.118533) \| 0.413093 / 4.584777 (-4.171684) \| 2.394758 / 3.745712 (-1.350954) \| 2.633916 / 5.269862 (-2.635945) \| 1.621629 / 4.565676 (-2.944047) \| 0.046839 / 0.424275 (-0.377436) \| 0.004786 / 0.007607 (-0.002822) \| 0.336261 / 0.226044 (0.110217) \| 3.348196 / 2.268929 (1.079267) \| 1.853050 / 55.444624 (-53.591574) \| 1.543926 / 6.876477 (-5.332551) \| 1.573675 / 2.142072 (-0.568398) \| 0.484088 / 4.805227 (-4.321139) \| 0.100820 / 6.500664 (-6.399845) \| 0.042194 / 0.075469 (-0.033275) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945186 / 1.841788 (-0.896601) \| 11.859855 / 8.074308 (3.785547) \| 10.459883 / 10.191392 (0.268491) \| 0.142024 / 0.680424 (-0.538400) \| 0.013882 / 0.534201 (-0.520319) \| 0.269584 / 0.579283 (-0.309699) \| 0.264353 / 0.434364 (-0.170011) \| 0.307988 / 0.540337 (-0.232349) \| 0.423655 / 1.386936 (-0.963281) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004891 / 0.011353 (-0.006461) \| 0.003087 / 0.011008 (-0.007921) \| 0.048206 / 0.038508 (0.009697) \| 0.058570 / 0.023109 (0.035461) \| 0.268552 / 0.275898 (-0.007346) \| 0.287839 / 0.323480 (-0.035641) \| 0.004044 / 0.007986 (-0.003942) \| 0.002388 / 0.004328 (-0.001940) \| 0.048186 / 0.004250 (0.043935) \| 0.038719 / 0.037052 (0.001667) \| 0.271940 / 0.258489 (0.013451) \| 0.299716 / 0.293841 (0.005875) \| 0.027166 / 0.128546 (-0.101380) \| 0.007388 / 0.075646 (-0.068258) \| 0.053885 / 0.419271 (-0.365387) \| 0.032804 / 0.043533 (-0.010729) \| 0.271664 / 0.255139 (0.016525) \| 0.284613 / 0.283200 (0.001414) \| 0.018488 / 0.141683 (-0.123195) \| 1.125854 / 1.452155 (-0.326301) \| 1.195896 / 1.492716 (-0.296820) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092438 / 0.018006 (0.074431) \| 0.315265 / 0.000490 (0.314775) \| 0.000228 / 0.000200 (0.000028) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021373 / 0.037411 (-0.016038) \| 0.070611 / 0.014526 (0.056085) \| 0.080391 / 0.176557 (-0.096165) \| 0.118749 / 0.737135 (-0.618386) \| 0.082340 / 0.296338 (-0.213999) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295583 / 0.215209 (0.080374) \| 2.882152 / 2.077655 (0.804497) \| 1.565088 / 1.504120 (0.060968) \| 1.451954 / 1.541195 (-0.089241) \| 1.505783 / 1.468490 (0.037293) \| 0.404699 / 4.584777 (-4.180078) \| 2.451703 / 3.745712 (-1.294009) \| 2.596301 / 5.269862 (-2.673560) \| 1.547014 / 4.565676 (-3.018662) \| 0.047750 / 0.424275 (-0.376525) \| 0.004850 / 0.007607 (-0.002757) \| 0.346893 / 0.226044 (0.120849) \| 3.383355 / 2.268929 (1.114426) \| 1.943933 / 55.444624 (-53.500692) \| 1.657513 / 6.876477 (-5.218964) \| 1.687166 / 2.142072 (-0.454906) \| 0.478543 / 4.805227 (-4.326685) \| 0.097804 / 6.500664 (-6.402860) \| 0.041392 / 0.075469 (-0.034078) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983894 / 1.841788 (-0.857893) \| 12.446443 / 8.074308 (4.372135) \| 10.973461 / 10.191392 (0.782069) \| 0.131630 / 0.680424 (-0.548794) \| 0.017196 / 0.534201 (-0.517005) \| 0.270873 / 0.579283 (-0.308411) \| 0.284379 / 0.434364 (-0.149985) \| 0.306103 / 0.540337 (-0.234234) \| 0.413762 / 1.386936 (-0.973174) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#980ad4c6e6e33f0129db8745e84de8c298741aa2 \"CML watermark\")\n", "Note I had to add `pa.ExtensionType.__reduce__` because this is used by `copy.deepcopy` when using `.with_format`. See error below.\r\n\r\nThis method was added in pyarrow-13.0.0: https://github.com/apache/arrow/pull/36170\r\n- We need to re-implement it as long we support lower pyarrow versions\r\n\r\nErrors: https://github.com/huggingface/datasets/actions/runs/6861278161/job/18656665772\r\n```\r\n ____________________________ test_dataset_map[True] ____________________________\r\n[gw1] linux -- Python 3.8.18 /opt/hostedtoolcache/Python/3.8.18/x64/bin/python\r\n\r\n> ???\r\nE KeyError: 'extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>'\r\n\r\npyarrow/types.pxi:3155: KeyError\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nwith_none = True\r\n\r\n @pytest.mark.parametrize(\"with_none\", [False, True])\r\n def test_dataset_map(with_none):\r\n ds = datasets.Dataset.from_dict({\"path\": [\"path1\", \"path2\"]})\r\n \r\n def process_data(batch):\r\n batch = {\r\n \"image\": [\r\n np.array(\r\n [\r\n [[1, 2, 3], [4, 5, 6], [7, 8, 9]],\r\n [[10, 20, 30], [40, 50, 60], [70, 80, 90]],\r\n [[100, 200, 300], [400, 500, 600], [700, 800, 900]],\r\n ]\r\n )\r\n for _ in batch[\"path\"]\r\n ]\r\n }\r\n if with_none:\r\n batch[\"image\"][0] = None\r\n return batch\r\n \r\n features = datasets.Features({\"image\": Array3D(dtype=\"int32\", shape=(3, 3, 3))})\r\n processed_ds = ds.map(process_data, batched=True, remove_columns=ds.column_names, features=features)\r\n assert processed_ds.shape == (2, 1)\r\n> with processed_ds.with_format(\"numpy\") as pds:\r\n\r\ntests/features/test_array_xd.py:459: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:2669: in with_format\r\n dataset = copy.deepcopy(self)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:270: in _reconstruct\r\n state = deepcopy(state, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:230: in _deepcopy_dict\r\n y[deepcopy(key, memo)] = deepcopy(value, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:153: in deepcopy\r\n y = copier(memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:188: in __deepcopy__\r\n return _deepcopy(self, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:86: in _deepcopy\r\n setattr(result, k, copy.deepcopy(v, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:205: in _deepcopy_list\r\n append(deepcopy(a, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(*args)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\n> ???\r\nE ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n\r\npyarrow/types.pxi:3157: ValueError\r\n```\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_class_encode_column_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_dummy_dataset_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_csv_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_sql_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[True] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[False] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[mix] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::ArrayXDDynamicTest::test_map_dataset - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[False] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[True] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n===== 15 failed,\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007338 / 0.011353 (-0.004015) \| 0.004308 / 0.011008 (-0.006700) \| 0.088788 / 0.038508 (0.050280) \| 0.039369 / 0.023109 (0.016260) \| 0.334527 / 0.275898 (0.058629) \| 0.373748 / 0.323480 (0.050268) \| 0.005550 / 0.007986 (-0.002435) \| 0.003606 / 0.004328 (-0.000723) \| 0.072238 / 0.004250 (0.067988) \| 0.061271 / 0.037052 (0.024218) \| 0.336333 / 0.258489 (0.077844) \| 0.398256 / 0.293841 (0.104415) \| 0.041941 / 0.128546 (-0.086605) \| 0.013372 / 0.075646 (-0.062274) \| 0.336221 / 0.419271 (-0.083050) \| 0.083013 / 0.043533 (0.039480) \| 0.334743 / 0.255139 (0.079604) \| 0.362572 / 0.283200 (0.079373) \| 0.031161 / 0.141683 (-0.110521) \| 1.563441 / 1.452155 (0.111287) \| 1.704059 / 1.492716 (0.211343) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252978 / 0.018006 (0.234972) \| 0.506348 / 0.000490 (0.505859) \| 0.011679 / 0.000200 (0.011479) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026257 / 0.037411 (-0.011154) \| 0.085936 / 0.014526 (0.071410) \| 0.098542 / 0.176557 (-0.078015) \| 0.154507 / 0.737135 (-0.582628) \| 0.111493 / 0.296338 (-0.184845) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.575941 / 0.215209 (0.360732) \| 5.590230 / 2.077655 (3.512576) \| 2.463330 / 1.504120 (0.959211) \| 2.125760 / 1.541195 (0.584565) \| 2.095933 / 1.468490 (0.627443) \| 0.844768 / 4.584777 (-3.740009) \| 4.768995 / 3.745712 (1.023282) \| 4.670484 / 5.269862 (-0.599377) \| 2.630386 / 4.565676 (-1.935290) \| 0.085996 / 0.424275 (-0.338279) \| 0.007900 / 0.007607 (0.000293) \| 0.685463 / 0.226044 (0.459419) \| 6.699310 / 2.268929 (4.430381) \| 3.132542 / 55.444624 (-52.312083) \| 2.527963 / 6.876477 (-4.348513) \| 2.381835 / 2.142072 (0.239763) \| 0.909668 / 4.805227 (-3.895559) \| 0.209979 / 6.500664 (-6.290685) \| 0.079222 / 0.075469 (0.003753) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.444895 / 1.841788 (-0.396892) \| 20.388140 / 8.074308 (12.313832) \| 19.354148 / 10.191392 (9.162756) \| 0.222433 / 0.680424 (-0.457991) \| 0.029710 / 0.534201 (-0.504491) \| 0.427153 / 0.579283 (-0.152130) \| 0.537500 / 0.434364 (0.103136) \| 0.506917 / 0.540337 (-0.033421) \| 0.726088 / 1.386936 (-0.660848) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007652 / 0.011353 (-0.003701) \| 0.004320 / 0.011008 (-0.006688) \| 0.072721 / 0.038508 (0.034212) \| 0.068204 / 0.023109 (0.045095) \| 0.392087 / 0.275898 (0.116189) \| 0.431638 / 0.323480 (0.108158) \| 0.005419 / 0.007986 (-0.002566) \| 0.004305 / 0.004328 (-0.000023) \| 0.069042 / 0.004250 (0.064791) \| 0.051555 / 0.037052 (0.014503) \| 0.412141 / 0.258489 (0.153651) \| 0.438802 / 0.293841 (0.144961) \| 0.043631 / 0.128546 (-0.084915) \| 0.014169 / 0.075646 (-0.061478) \| 0.079571 / 0.419271 (-0.339701) \| 0.056707 / 0.043533 (0.013174) \| 0.413698 / 0.255139 (0.158559) \| 0.414127 / 0.283200 (0.130928) \| 0.031380 / 0.141683 (-0.110303) \| 1.677157 / 1.452155 (0.225003) \| 1.755155 / 1.492716 (0.262439) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257236 / 0.018006 (0.239230) \| 0.521347 / 0.000490 (0.520858) \| 0.006282 / 0.000200 (0.006082) \| 0.000139 / 0.000054 (0.000085) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028433 / 0.037411 (-0.008978) \| 0.087698 / 0.014526 (0.073172) \| 0.108840 / 0.176557 (-0.067716) \| 0.157432 / 0.737135 (-0.579704) \| 0.103144 / 0.296338 (-0.193195) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.598745 / 0.215209 (0.383536) \| 5.981460 / 2.077655 (3.903805) \| 2.556931 / 1.504120 (1.052811) \| 2.179915 / 1.541195 (0.638720) \| 2.240841 / 1.468490 (0.772351) \| 0.811501 / 4.584777 (-3.773276) \| 4.718282 / 3.745712 (0.972570) \| 4.365738 / 5.269862 (-0.904124) \| 2.669798 / 4.565676 (-1.895878) \| 0.099135 / 0.424275 (-0.325140) \| 0.007369 / 0.007607 (-0.000238) \| 0.669491 / 0.226044 (0.443447) \| 6.700389 / 2.268929 (4.431461) \| 3.155328 / 55.444624 (-52.289296) \| 2.563375 / 6.876477 (-4.313102) \| 2.545191 / 2.142072 (0.403119) \| 0.961359 / 4.805227 (-3.843868) \| 0.189391 / 6.500664 (-6.311273) \| 0.061597 / 0.075469 (-0.013873) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.564008 / 1.841788 (-0.277780) \| 21.401307 / 8.074308 (13.326999) \| 20.693441 / 10.191392 (10.502049) \| 0.229340 / 0.680424 (-0.451084) \| 0.033637 / 0.534201 (-0.500564) \| 0.429394 / 0.579283 (-0.149889) \| 0.557202 / 0.434364 (0.122838) \| 0.510284 / 0.540337 (-0.030054) \| 0.725661 / 1.386936 (-0.661276) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45abe297c178b829afcee853f9958b0c5a6469aa \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004820 / 0.011353 (-0.006533) \| 0.003152 / 0.011008 (-0.007856) \| 0.061842 / 0.038508 (0.023334) \| 0.030127 / 0.023109 (0.007018) \| 0.257409 / 0.275898 (-0.018489) \| 0.269382 / 0.323480 (-0.054097) \| 0.004288 / 0.007986 (-0.003698) \| 0.002500 / 0.004328 (-0.001829) \| 0.048520 / 0.004250 (0.044270) \| 0.046815 / 0.037052 (0.009763) \| 0.245858 / 0.258489 (-0.012631) \| 0.289636 / 0.293841 (-0.004205) \| 0.023983 / 0.128546 (-0.104563) \| 0.007336 / 0.075646 (-0.068310) \| 0.202347 / 0.419271 (-0.216924) \| 0.057737 / 0.043533 (0.014204) \| 0.245922 / 0.255139 (-0.009217) \| 0.268788 / 0.283200 (-0.014412) \| 0.017819 / 0.141683 (-0.123864) \| 1.149889 / 1.452155 (-0.302265) \| 1.227192 / 1.492716 (-0.265524) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092234 / 0.018006 (0.074228) \| 0.310259 / 0.000490 (0.309769) \| 0.000223 / 0.000200 (0.000023) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019059 / 0.037411 (-0.018352) \| 0.064904 / 0.014526 (0.050378) \| 0.073531 / 0.176557 (-0.103026) \| 0.120879 / 0.737135 (-0.616257) \| 0.075410 / 0.296338 (-0.220929) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275364 / 0.215209 (0.060155) \| 2.724379 / 2.077655 (0.646725) \| 1.447617 / 1.504120 (-0.056503) \| 1.366794 / 1.541195 (-0.174401) \| 1.345849 / 1.468490 (-0.122641) \| 0.411205 / 4.584777 (-4.173572) \| 2.412712 / 3.745712 (-1.333000) \| 2.612469 / 5.269862 (-2.657393) \| 1.552113 / 4.565676 (-3.013564) \| 0.045783 / 0.424275 (-0.378492) \| 0.004782 / 0.007607 (-0.002825) \| 0.339218 / 0.226044 (0.113174) \| 3.359540 / 2.268929 (1.090612) \| 1.821369 / 55.444624 (-53.623256) \| 1.540742 / 6.876477 (-5.335734) \| 1.531845 / 2.142072 (-0.610227) \| 0.462009 / 4.805227 (-4.343218) \| 0.097794 / 6.500664 (-6.402870) \| 0.041222 / 0.075469 (-0.034247) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938319 / 1.841788 (-0.903469) \| 11.712003 / 8.074308 (3.637695) \| 10.325317 / 10.191392 (0.133925) \| 0.126812 / 0.680424 (-0.553612) \| 0.013734 / 0.534201 (-0.520467) \| 0.279509 / 0.579283 (-0.299774) \| 0.269265 / 0.434364 (-0.165099) \| 0.322033 / 0.540337 (-0.218304) \| 0.441610 / 1.386936 (-0.945326) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004882 / 0.011353 (-0.006471) \| 0.002984 / 0.011008 (-0.008024) \| 0.048318 / 0.038508 (0.009810) \| 0.054642 / 0.023109 (0.031533) \| 0.268599 / 0.275898 (-0.007299) \| 0.292916 / 0.323480 (-0.030564) \| 0.004108 / 0.007986 (-0.003878) \| 0.002500 / 0.004328 (-0.001829) \| 0.048452 / 0.004250 (0.044202) \| 0.038835 / 0.037052 (0.001782) \| 0.275410 / 0.258489 (0.016921) \| 0.307284 / 0.293841 (0.013443) \| 0.024720 / 0.128546 (-0.103826) \| 0.007274 / 0.075646 (-0.068372) \| 0.054419 / 0.419271 (-0.364853) \| 0.032815 / 0.043533 (-0.010718) \| 0.273660 / 0.255139 (0.018521) \| 0.289183 / 0.283200 (0.005984) \| 0.017746 / 0.141683 (-0.123937) \| 1.153876 / 1.452155 (-0.298278) \| 1.212778 / 1.492716 (-0.279938) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095286 / 0.018006 (0.077280) \| 0.305185 / 0.000490 (0.304696) \| 0.000230 / 0.000200 (0.000030) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021556 / 0.037411 (-0.015855) \| 0.071029 / 0.014526 (0.056503) \| 0.081914 / 0.176557 (-0.094643) \| 0.120553 / 0.737135 (-0.616582) \| 0.086696 / 0.296338 (-0.209642) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289750 / 0.215209 (0.074541) \| 2.794247 / 2.077655 (0.716592) \| 1.577105 / 1.504120 (0.072985) \| 1.457706 / 1.541195 (-0.083489) \| 1.500481 / 1.468490 (0.031991) \| 0.403834 / 4.584777 (-4.180943) \| 2.466810 / 3.745712 (-1.278902) \| 2.701008 / 5.269862 (-2.568854) \| 1.634821 / 4.565676 (-2.930856) \| 0.046954 / 0.424275 (-0.377322) \| 0.004811 / 0.007607 (-0.002796) \| 0.347622 / 0.226044 (0.121578) \| 3.407125 / 2.268929 (1.138197) \| 1.987121 / 55.444624 (-53.457504) \| 1.689978 / 6.876477 (-5.186499) \| 1.731801 / 2.142072 (-0.410271) \| 0.478926 / 4.805227 (-4.326301) \| 0.100730 / 6.500664 (-6.399934) \| 0.043078 / 0.075469 (-0.032391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963575 / 1.841788 (-0.878212) \| 12.675331 / 8.074308 (4.601023) \| 11.167584 / 10.191392 (0.976192) \| 0.131199 / 0.680424 (-0.549225) \| 0.016030 / 0.534201 (-0.518171) \| 0.277783 / 0.579283 (-0.301500) \| 0.278693 / 0.434364 (-0.155671) \| 0.315141 / 0.540337 (-0.225196) \| 0.429104 / 1.386936 (-0.957832) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#825c1d25835b64fc3533a63d60bd237f4465f15e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004807 / 0.011353 (-0.006546) \| 0.002925 / 0.011008 (-0.008083) \| 0.062560 / 0.038508 (0.024052) \| 0.029926 / 0.023109 (0.006817) \| 0.264708 / 0.275898 (-0.011190) \| 0.273464 / 0.323480 (-0.050016) \| 0.003197 / 0.007986 (-0.004788) \| 0.002544 / 0.004328 (-0.001784) \| 0.048230 / 0.004250 (0.043980) \| 0.046552 / 0.037052 (0.009500) \| 0.249553 / 0.258489 (-0.008936) \| 0.282078 / 0.293841 (-0.011762) \| 0.023201 / 0.128546 (-0.105346) \| 0.007306 / 0.075646 (-0.068340) \| 0.241361 / 0.419271 (-0.177910) \| 0.058286 / 0.043533 (0.014753) \| 0.245854 / 0.255139 (-0.009285) \| 0.266053 / 0.283200 (-0.017146) \| 0.020294 / 0.141683 (-0.121388) \| 1.102215 / 1.452155 (-0.349939) \| 1.170733 / 1.492716 (-0.321984) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094647 / 0.018006 (0.076641) \| 0.303819 / 0.000490 (0.303329) \| 0.000250 / 0.000200 (0.000050) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019036 / 0.037411 (-0.018375) \| 0.064729 / 0.014526 (0.050203) \| 0.074143 / 0.176557 (-0.102414) \| 0.120082 / 0.737135 (-0.617054) \| 0.076835 / 0.296338 (-0.219503) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283786 / 0.215209 (0.068577) \| 2.751446 / 2.077655 (0.673791) \| 1.473789 / 1.504120 (-0.030331) \| 1.336968 / 1.541195 (-0.204226) \| 1.384148 / 1.468490 (-0.084342) \| 0.397452 / 4.584777 (-4.187325) \| 2.388042 / 3.745712 (-1.357670) \| 2.661291 / 5.269862 (-2.608571) \| 1.595454 / 4.565676 (-2.970223) \| 0.045919 / 0.424275 (-0.378356) \| 0.004879 / 0.007607 (-0.002728) \| 0.337862 / 0.226044 (0.111818) \| 3.355665 / 2.268929 (1.086737) \| 1.875261 / 55.444624 (-53.569363) \| 1.540874 / 6.876477 (-5.335603) \| 1.653632 / 2.142072 (-0.488440) \| 0.473090 / 4.805227 (-4.332138) \| 0.100151 / 6.500664 (-6.400513) \| 0.042357 / 0.075469 (-0.033112) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959550 / 1.841788 (-0.882238) \| 12.307145 / 8.074308 (4.232837) \| 10.719321 / 10.191392 (0.527929) \| 0.128376 / 0.680424 (-0.552048) \| 0.014406 / 0.534201 (-0.519795) \| 0.295208 / 0.579283 (-0.284075) \| 0.268891 / 0.434364 (-0.165473) \| 0.305446 / 0.540337 (-0.234892) \| 0.429591 / 1.386936 (-0.957345) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005189 / 0.011353 (-0.006164) \| 0.003082 / 0.011008 (-0.007926) \| 0.048956 / 0.038508 (0.010448) \| 0.063403 / 0.023109 (0.040294) \| 0.272858 / 0.275898 (-0.003040) \| 0.295207 / 0.323480 (-0.028273) \| 0.004253 / 0.007986 (-0.003733) \| 0.002552 / 0.004328 (-0.001776) \| 0.048042 / 0.004250 (0.043792) \| 0.040429 / 0.037052 (0.003377) \| 0.269614 / 0.258489 (0.011125) \| 0.307205 / 0.293841 (0.013364) \| 0.027912 / 0.128546 (-0.100634) \| 0.007621 / 0.075646 (-0.068026) \| 0.054020 / 0.419271 (-0.365251) \| 0.036958 / 0.043533 (-0.006574) \| 0.272457 / 0.255139 (0.017318) \| 0.287966 / 0.283200 (0.004766) \| 0.019542 / 0.141683 (-0.122141) \| 1.116742 / 1.452155 (-0.335413) \| 1.194739 / 1.492716 (-0.297977) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093532 / 0.018006 (0.075526) \| 0.303262 / 0.000490 (0.302773) \| 0.000217 / 0.000200 (0.000017) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021984 / 0.037411 (-0.015428) \| 0.075024 / 0.014526 (0.060498) \| 0.080959 / 0.176557 (-0.095598) \| 0.121780 / 0.737135 (-0.615356) \| 0.082817 / 0.296338 (-0.213522) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292766 / 0.215209 (0.077557) \| 2.857457 / 2.077655 (0.779802) \| 1.621860 / 1.504120 (0.117740) \| 1.473783 / 1.541195 (-0.067412) \| 1.535211 / 1.468490 (0.066721) \| 0.402212 / 4.584777 (-4.182565) \| 2.467143 / 3.745712 (-1.278569) \| 2.618162 / 5.269862 (-2.651700) \| 1.568682 / 4.565676 (-2.996994) \| 0.047123 / 0.424275 (-0.377152) \| 0.004780 / 0.007607 (-0.002827) \| 0.346959 / 0.226044 (0.120914) \| 3.395196 / 2.268929 (1.126268) \| 1.957835 / 55.444624 (-53.486789) \| 1.674287 / 6.876477 (-5.202190) \| 1.715879 / 2.142072 (-0.426193) \| 0.479481 / 4.805227 (-4.325746) \| 0.100043 / 6.500664 (-6.400621) \| 0.041289 / 0.075469 (-0.034180) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965418 / 1.841788 (-0.876370) \| 12.703830 / 8.074308 (4.629522) \| 11.301401 / 10.191392 (1.110009) \| 0.131429 / 0.680424 (-0.548995) \| 0.016597 / 0.534201 (-0.517604) \| 0.273290 / 0.579283 (-0.305993) \| 0.285400 / 0.434364 (-0.148964) \| 0.307327 / 0.540337 (-0.233011) \| 0.434186 / 1.386936 (-0.952750) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c096bd288d07ed86f340ae090e5d4d9c5351f76f \"CML watermark\")\n" ]	2023-11-13T09:15:39	2023-11-14T10:29:48	2023-11-14T10:23:29	MEMBER	null	Support `pyarrow` 14.0.1 and fix vulnerability [CVE-2023-47248](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). Fix #6396.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6404/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6404/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6404", "html_url": "https://github.com/huggingface/datasets/pull/6404", "diff_url": "https://github.com/huggingface/datasets/pull/6404.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6404.patch", "merged_at": "2023-11-14T10:23:29" }	true
https://api.github.com/repos/huggingface/datasets/issues/6403	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6403/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6403/comments	https://api.github.com/repos/huggingface/datasets/issues/6403/events	https://github.com/huggingface/datasets/issues/6403	1,990,098,817	I_kwDODunzps52nn-B	6,403	Cannot import datasets on google colab (python 3.10.12)	{ "login": "nabilaannisa", "id": 15389235, "node_id": "MDQ6VXNlcjE1Mzg5MjM1", "avatar_url": "https://avatars.githubusercontent.com/u/15389235?v=4", "gravatar_id": "", "url": "https://api.github.com/users/nabilaannisa", "html_url": "https://github.com/nabilaannisa", "followers_url": "https://api.github.com/users/nabilaannisa/followers", "following_url": "https://api.github.com/users/nabilaannisa/following{/other_user}", "gists_url": "https://api.github.com/users/nabilaannisa/gists{/gist_id}", "starred_url": "https://api.github.com/users/nabilaannisa/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/nabilaannisa/subscriptions", "organizations_url": "https://api.github.com/users/nabilaannisa/orgs", "repos_url": "https://api.github.com/users/nabilaannisa/repos", "events_url": "https://api.github.com/users/nabilaannisa/events{/privacy}", "received_events_url": "https://api.github.com/users/nabilaannisa/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "You are most likely using an outdated version of `datasets` in the notebook, which can be verified with the `!datasets-cli env` command. You can run `!pip install -U datasets` to update the installation.", "okay, it works! thank you so much! 😄 " ]	2023-11-13T08:14:43	2023-11-16T05:04:22	2023-11-16T05:04:21	NONE	null	### Describe the bug I'm trying A full colab demo notebook of zero-shot-distillation from https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation but i got this type of error when importing datasets on my google colab (python version is 3.10.12) ![image](https://github.com/huggingface/datasets/assets/15389235/6f7758a2-681d-4436-87d0-5e557838e368) I found the same problem that have been solved in [#3326 ] but it seem still error on the google colab. I can't try on my local using jupyter notebook because of my laptop resource doesn't fulfill the requirements. Please can anyone help me solve this problem. Thank you 😅 ### Steps to reproduce the bug Error: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-8-b6e092f83978>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import load_dataset 2 3 # Print all the available datasets 4 from huggingface_hub import list_datasets 5 print([dataset.id for dataset in list_datasets()]) 6 frames [/usr/lib/python3.10/functools.py](https://localhost:8080/#) in update_wrapper(wrapper, wrapped, assigned, updated) 59 # Issue #17482: set __wrapped__ last so we don't inadvertently copy it 60 # from the wrapped function when updating __dict__ ---> 61 wrapper.__wrapped__ = wrapped 62 # Return the wrapper so this can be used as a decorator via partial() 63 return wrapper AttributeError: readonly attribute ``` ### Expected behavior Run success on Google Colab (free) ### Environment info Windows 11 x64, Google Colab free	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6403/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6403/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6402	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6402/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6402/comments	https://api.github.com/repos/huggingface/datasets/issues/6402/events	https://github.com/huggingface/datasets/pull/6402	1,989,094,542	PR_kwDODunzps5fOBdK	6,402	Update torch_formatter.py	{ "login": "VarunNSrivastava", "id": 32204417, "node_id": "MDQ6VXNlcjMyMjA0NDE3", "avatar_url": "https://avatars.githubusercontent.com/u/32204417?v=4", "gravatar_id": "", "url": "https://api.github.com/users/VarunNSrivastava", "html_url": "https://github.com/VarunNSrivastava", "followers_url": "https://api.github.com/users/VarunNSrivastava/followers", "following_url": "https://api.github.com/users/VarunNSrivastava/following{/other_user}", "gists_url": "https://api.github.com/users/VarunNSrivastava/gists{/gist_id}", "starred_url": "https://api.github.com/users/VarunNSrivastava/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/VarunNSrivastava/subscriptions", "organizations_url": "https://api.github.com/users/VarunNSrivastava/orgs", "repos_url": "https://api.github.com/users/VarunNSrivastava/repos", "events_url": "https://api.github.com/users/VarunNSrivastava/events{/privacy}", "received_events_url": "https://api.github.com/users/VarunNSrivastava/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-11T19:40:41	2023-11-11T19:41:53	null	NONE	null	Ensure PyTorch images are converted to (C, H, W) instead of (H, W, C). See #6394 for motivation.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6402/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6402/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6402", "html_url": "https://github.com/huggingface/datasets/pull/6402", "diff_url": "https://github.com/huggingface/datasets/pull/6402.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6402.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6401	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6401/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6401/comments	https://api.github.com/repos/huggingface/datasets/issues/6401/events	https://github.com/huggingface/datasets/issues/6401	1,988,710,061	I_kwDODunzps52iU6t	6,401	dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") not working	{ "login": "userbox020", "id": 47074021, "node_id": "MDQ6VXNlcjQ3MDc0MDIx", "avatar_url": "https://avatars.githubusercontent.com/u/47074021?v=4", "gravatar_id": "", "url": "https://api.github.com/users/userbox020", "html_url": "https://github.com/userbox020", "followers_url": "https://api.github.com/users/userbox020/followers", "following_url": "https://api.github.com/users/userbox020/following{/other_user}", "gists_url": "https://api.github.com/users/userbox020/gists{/gist_id}", "starred_url": "https://api.github.com/users/userbox020/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/userbox020/subscriptions", "organizations_url": "https://api.github.com/users/userbox020/orgs", "repos_url": "https://api.github.com/users/userbox020/repos", "events_url": "https://api.github.com/users/userbox020/events{/privacy}", "received_events_url": "https://api.github.com/users/userbox020/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Seems like it's a problem with the dataset, since in the [README](https://huggingface.co/datasets/Hyperspace-Technologies/scp-wiki-text/blob/main/README.md) the validation is not specified. Try cloning the dataset, removing the README (or validation split), and loading it locally/ ", "@VarunNSrivastava thanks brother, working beautiful now\r\n\r\n```\r\nC:\\_Work\\_datasets>py dataset.py\r\nDownloading data files: 100%\|████████████████████████████████████████████████████████████████████\| 3/3 [00:00<?, ?it/s]\r\nExtracting data files: 100%\|████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 599.90it/s]\r\nGenerating train split: 314294 examples [00:00, 1293222.03 examples/s]\r\nGenerating validation split: 120 examples [00:00, 59053.91 examples/s]\r\nGenerating test split: 34922 examples [00:00, 1343275.84 examples/s]\r\n```" ]	2023-11-11T04:09:07	2023-11-20T17:45:20	2023-11-20T17:45:20	NONE	null	### Describe the bug ``` (datasets) mruserbox@guru-X99:/media/10TB_HHD/_LLM_DATASETS$ python dataset.py Downloading readme: 100%\|███████████████████████████████████\| 360/360 [00:00<00:00, 2.16MB/s] Downloading data: 100%\|█████████████████████████████████\| 65.1M/65.1M [00:19<00:00, 3.38MB/s] Downloading data: 100%\|█████████████████████████████████\| 6.35k/6.35k [00:00<00:00, 20.7kB/s] Downloading data: 100%\|█████████████████████████████████\| 7.29M/7.29M [00:01<00:00, 3.99MB/s] Downloading data files: 100%\|██████████████████████████████████\| 3/3 [00:21<00:00, 7.14s/it] Extracting data files: 100%\|█████████████████████████████████\| 3/3 [00:00<00:00, 1624.23it/s] Generating train split: 100%\|█████████████\| 314294/314294 [00:00<00:00, 668186.58 examples/s] Generating validation split: 120 examples [00:00, 100422.28 examples/s] Generating test split: 100%\|████████████████\| 34922/34922 [00:00<00:00, 754683.41 examples/s] Traceback (most recent call last): File "/media/10TB_HHD/_LLM_DATASETS/dataset.py", line 3, in <module> dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 1067, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 93, in verify_splits raise UnexpectedSplits(str(set(recorded_splits) - set(expected_splits))) datasets.utils.info_utils.UnexpectedSplits: {'validation'} ``` ### Steps to reproduce the bug Name: `dataset.py` Code: ``` from datasets import load_dataset dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") ``` ### Expected behavior Run without errors ### Environment info ``` name: datasets channels: - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h7b6447c_0 - ca-certificates=2023.08.22=h06a4308_0 - ld_impl_linux-64=2.38=h1181459_1 - libffi=3.4.4=h6a678d5_0 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.12=h7f8727e_0 - python=3.10.13=h955ad1f_0 - readline=8.2=h5eee18b_0 - setuptools=68.0.0=py310h06a4308_0 - sqlite=3.41.2=h5eee18b_0 - tk=8.6.12=h1ccaba5_0 - wheel=0.41.2=py310h06a4308_0 - xz=5.4.2=h5eee18b_0 - zlib=1.2.13=h5eee18b_0 - pip: - aiohttp==3.8.6 - aiosignal==1.3.1 - async-timeout==4.0.3 - attrs==23.1.0 - certifi==2023.7.22 - charset-normalizer==3.3.2 - click==8.1.7 - datasets==2.14.6 - dill==0.3.7 - filelock==3.13.1 - frozenlist==1.4.0 - fsspec==2023.10.0 - huggingface-hub==0.19.0 - idna==3.4 - multidict==6.0.4 - multiprocess==0.70.15 - numpy==1.26.1 - openai==0.27.8 - packaging==23.2 - pandas==2.1.3 - pip==23.3.1 - platformdirs==4.0.0 - pyarrow==14.0.1 - python-dateutil==2.8.2 - pytz==2023.3.post1 - pyyaml==6.0.1 - requests==2.31.0 - six==1.16.0 - tomli==2.0.1 - tqdm==4.66.1 - typer==0.9.0 - typing-extensions==4.8.0 - tzdata==2023.3 - urllib3==2.0.7 - xxhash==3.4.1 - yarl==1.9.2 prefix: /home/mruserbox/miniconda3/envs/datasets ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6401/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6401/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6400	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6400/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6400/comments	https://api.github.com/repos/huggingface/datasets/issues/6400/events	https://github.com/huggingface/datasets/issues/6400	1,988,571,317	I_kwDODunzps52hzC1	6,400	Safely load datasets by disabling execution of dataset loading script	{ "login": "irenedea", "id": 14367635, "node_id": "MDQ6VXNlcjE0MzY3NjM1", "avatar_url": "https://avatars.githubusercontent.com/u/14367635?v=4", "gravatar_id": "", "url": "https://api.github.com/users/irenedea", "html_url": "https://github.com/irenedea", "followers_url": "https://api.github.com/users/irenedea/followers", "following_url": "https://api.github.com/users/irenedea/following{/other_user}", "gists_url": "https://api.github.com/users/irenedea/gists{/gist_id}", "starred_url": "https://api.github.com/users/irenedea/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/irenedea/subscriptions", "organizations_url": "https://api.github.com/users/irenedea/orgs", "repos_url": "https://api.github.com/users/irenedea/repos", "events_url": "https://api.github.com/users/irenedea/events{/privacy}", "received_events_url": "https://api.github.com/users/irenedea/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]	null	[ "great idea IMO\r\n\r\nthis could be a `trust_remote_code=True` flag like in transformers. We could also default to loading the Parquet conversion rather than executing code (for dataset repos that have both)", "@julien-c that would be great!" ]	2023-11-10T23:48:29	2023-11-15T14:46:43	null	NONE	null	### Feature request Is there a way to disable execution of dataset loading script using `load_dataset`? This is a security vulnerability that could lead to arbitrary code execution. Any suggested workarounds are welcome as well. ### Motivation This is a security vulnerability that could lead to arbitrary code execution. ### Your contribution n/a	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6400/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6400/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6399	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6399/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6399/comments	https://api.github.com/repos/huggingface/datasets/issues/6399/events	https://github.com/huggingface/datasets/issues/6399	1,988,368,503	I_kwDODunzps52hBh3	6,399	TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array	{ "login": "y-hwang", "id": 76236359, "node_id": "MDQ6VXNlcjc2MjM2MzU5", "avatar_url": "https://avatars.githubusercontent.com/u/76236359?v=4", "gravatar_id": "", "url": "https://api.github.com/users/y-hwang", "html_url": "https://github.com/y-hwang", "followers_url": "https://api.github.com/users/y-hwang/followers", "following_url": "https://api.github.com/users/y-hwang/following{/other_user}", "gists_url": "https://api.github.com/users/y-hwang/gists{/gist_id}", "starred_url": "https://api.github.com/users/y-hwang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/y-hwang/subscriptions", "organizations_url": "https://api.github.com/users/y-hwang/orgs", "repos_url": "https://api.github.com/users/y-hwang/repos", "events_url": "https://api.github.com/users/y-hwang/events{/privacy}", "received_events_url": "https://api.github.com/users/y-hwang/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-10T20:48:46	2023-11-10T20:48:46	null	NONE	null	### Describe the bug Hi, I am preprocessing a large custom dataset with numpy arrays. I am running into this TypeError during writing in a dataset.map() function. I've tried decreasing writer batch size, but this error persists. This error does not occur for smaller datasets. Thank you! ### Steps to reproduce the bug Traceback (most recent call last): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(args, kwds)) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1354, in _write_generator_to_queue for i, result in enumerate(func(*kwargs)): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3493, in _map_single writer.write_batch(batch) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 555, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 243, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 184, in __arrow_array__ out = numpy_to_pyarrow_listarray(data) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/features/features.py", line 1394, in numpy_to_pyarrow_listarray values = pa.ListArray.from_arrays(offsets, values) File "pyarrow/array.pxi", line 2004, in pyarrow.lib.ListArray.from_arrays TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array ### Expected behavior Type should not be a ChunkedArray ### Environment info datasets v2.14.5 arrow v1.2.3 pyarrow v12.0.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6399/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6399/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6398	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6398/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6398/comments	https://api.github.com/repos/huggingface/datasets/issues/6398/events	https://github.com/huggingface/datasets/pull/6398	1,987,786,446	PR_kwDODunzps5fJlP7	6,398	Remove redundant condition in builders	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004475 / 0.011353 (-0.006878) \| 0.002840 / 0.011008 (-0.008168) \| 0.061544 / 0.038508 (0.023036) \| 0.031237 / 0.023109 (0.008128) \| 0.243270 / 0.275898 (-0.032628) \| 0.271903 / 0.323480 (-0.051577) \| 0.002906 / 0.007986 (-0.005080) \| 0.003118 / 0.004328 (-0.001210) \| 0.047362 / 0.004250 (0.043112) \| 0.047840 / 0.037052 (0.010788) \| 0.244044 / 0.258489 (-0.014445) \| 0.279310 / 0.293841 (-0.014531) \| 0.023408 / 0.128546 (-0.105138) \| 0.007110 / 0.075646 (-0.068536) \| 0.207328 / 0.419271 (-0.211943) \| 0.058463 / 0.043533 (0.014930) \| 0.245631 / 0.255139 (-0.009508) \| 0.267755 / 0.283200 (-0.015445) \| 0.018147 / 0.141683 (-0.123536) \| 1.086877 / 1.452155 (-0.365278) \| 1.155380 / 1.492716 (-0.337337) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091925 / 0.018006 (0.073919) \| 0.299858 / 0.000490 (0.299368) \| 0.000232 / 0.000200 (0.000032) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018416 / 0.037411 (-0.018995) \| 0.062608 / 0.014526 (0.048082) \| 0.073897 / 0.176557 (-0.102660) \| 0.120216 / 0.737135 (-0.616919) \| 0.075788 / 0.296338 (-0.220550) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287823 / 0.215209 (0.072614) \| 2.797546 / 2.077655 (0.719891) \| 1.470878 / 1.504120 (-0.033242) \| 1.347497 / 1.541195 (-0.193698) \| 1.363837 / 1.468490 (-0.104653) \| 0.400069 / 4.584777 (-4.184708) \| 2.338870 / 3.745712 (-1.406842) \| 2.564075 / 5.269862 (-2.705787) \| 1.568454 / 4.565676 (-2.997222) \| 0.047103 / 0.424275 (-0.377172) \| 0.004783 / 0.007607 (-0.002824) \| 0.345244 / 0.226044 (0.119200) \| 3.407752 / 2.268929 (1.138823) \| 1.826552 / 55.444624 (-53.618073) \| 1.536714 / 6.876477 (-5.339763) \| 1.543138 / 2.142072 (-0.598934) \| 0.478996 / 4.805227 (-4.326232) \| 0.099580 / 6.500664 (-6.401085) \| 0.041994 / 0.075469 (-0.033475) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947106 / 1.841788 (-0.894682) \| 11.391262 / 8.074308 (3.316954) \| 10.531141 / 10.191392 (0.339749) \| 0.141497 / 0.680424 (-0.538927) \| 0.014214 / 0.534201 (-0.519987) \| 0.269346 / 0.579283 (-0.309937) \| 0.268129 / 0.434364 (-0.166235) \| 0.309496 / 0.540337 (-0.230841) \| 0.429207 / 1.386936 (-0.957729) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004770 / 0.011353 (-0.006583) \| 0.002878 / 0.011008 (-0.008130) \| 0.048248 / 0.038508 (0.009740) \| 0.051068 / 0.023109 (0.027959) \| 0.272076 / 0.275898 (-0.003822) \| 0.292423 / 0.323480 (-0.031057) \| 0.004016 / 0.007986 (-0.003970) \| 0.002522 / 0.004328 (-0.001807) \| 0.047617 / 0.004250 (0.043367) \| 0.038168 / 0.037052 (0.001115) \| 0.275236 / 0.258489 (0.016746) \| 0.303811 / 0.293841 (0.009970) \| 0.023816 / 0.128546 (-0.104730) \| 0.007177 / 0.075646 (-0.068469) \| 0.053453 / 0.419271 (-0.365818) \| 0.032425 / 0.043533 (-0.011108) \| 0.271620 / 0.255139 (0.016481) \| 0.289618 / 0.283200 (0.006418) \| 0.017986 / 0.141683 (-0.123697) \| 1.154225 / 1.452155 (-0.297930) \| 1.224244 / 1.492716 (-0.268472) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090477 / 0.018006 (0.072471) \| 0.299461 / 0.000490 (0.298971) \| 0.000224 / 0.000200 (0.000024) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022043 / 0.037411 (-0.015369) \| 0.070327 / 0.014526 (0.055801) \| 0.080132 / 0.176557 (-0.096425) \| 0.120007 / 0.737135 (-0.617128) \| 0.083037 / 0.296338 (-0.213301) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294538 / 0.215209 (0.079329) \| 2.882791 / 2.077655 (0.805136) \| 1.582923 / 1.504120 (0.078803) \| 1.457091 / 1.541195 (-0.084104) \| 1.536149 / 1.468490 (0.067659) \| 0.401539 / 4.584777 (-4.183238) \| 2.440919 / 3.745712 (-1.304793) \| 2.503108 / 5.269862 (-2.766753) \| 1.509216 / 4.565676 (-3.056460) \| 0.046267 / 0.424275 (-0.378008) \| 0.004790 / 0.007607 (-0.002817) \| 0.336137 / 0.226044 (0.110093) \| 3.331655 / 2.268929 (1.062726) \| 1.954228 / 55.444624 (-53.490396) \| 1.686637 / 6.876477 (-5.189840) \| 1.650278 / 2.142072 (-0.491794) \| 0.473895 / 4.805227 (-4.331333) \| 0.096908 / 6.500664 (-6.403756) \| 0.040387 / 0.075469 (-0.035082) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972999 / 1.841788 (-0.868789) \| 11.978367 / 8.074308 (3.904059) \| 10.861092 / 10.191392 (0.669699) \| 0.129054 / 0.680424 (-0.551369) \| 0.015988 / 0.534201 (-0.518213) \| 0.268827 / 0.579283 (-0.310456) \| 0.271714 / 0.434364 (-0.162649) \| 0.304045 / 0.540337 (-0.236293) \| 0.413158 / 1.386936 (-0.973778) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e4348a233a75907c305b3159ac9cb183cf30ea5 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005286 / 0.011353 (-0.006067) \| 0.002860 / 0.011008 (-0.008149) \| 0.062449 / 0.038508 (0.023941) \| 0.035346 / 0.023109 (0.012237) \| 0.241685 / 0.275898 (-0.034213) \| 0.268116 / 0.323480 (-0.055364) \| 0.003050 / 0.007986 (-0.004935) \| 0.003134 / 0.004328 (-0.001194) \| 0.048818 / 0.004250 (0.044567) \| 0.049187 / 0.037052 (0.012135) \| 0.247395 / 0.258489 (-0.011094) \| 0.280301 / 0.293841 (-0.013540) \| 0.023801 / 0.128546 (-0.104745) \| 0.007653 / 0.075646 (-0.067994) \| 0.204185 / 0.419271 (-0.215087) \| 0.071251 / 0.043533 (0.027718) \| 0.244409 / 0.255139 (-0.010730) \| 0.262363 / 0.283200 (-0.020836) \| 0.018631 / 0.141683 (-0.123052) \| 1.110152 / 1.452155 (-0.342003) \| 1.165093 / 1.492716 (-0.327624) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099536 / 0.018006 (0.081530) \| 0.309598 / 0.000490 (0.309109) \| 0.000207 / 0.000200 (0.000007) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019213 / 0.037411 (-0.018198) \| 0.069296 / 0.014526 (0.054770) \| 0.074752 / 0.176557 (-0.101804) \| 0.121314 / 0.737135 (-0.615822) \| 0.081274 / 0.296338 (-0.215065) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281345 / 0.215209 (0.066136) \| 2.755435 / 2.077655 (0.677780) \| 1.453358 / 1.504120 (-0.050762) \| 1.328222 / 1.541195 (-0.212973) \| 1.392281 / 1.468490 (-0.076209) \| 0.410539 / 4.584777 (-4.174238) \| 2.452072 / 3.745712 (-1.293640) \| 2.777757 / 5.269862 (-2.492105) \| 1.656719 / 4.565676 (-2.908958) \| 0.046844 / 0.424275 (-0.377431) \| 0.004785 / 0.007607 (-0.002822) \| 0.336567 / 0.226044 (0.110522) \| 3.317564 / 2.268929 (1.048635) \| 1.830737 / 55.444624 (-53.613888) \| 1.528464 / 6.876477 (-5.348013) \| 1.620527 / 2.142072 (-0.521545) \| 0.480662 / 4.805227 (-4.324565) \| 0.100819 / 6.500664 (-6.399845) \| 0.042501 / 0.075469 (-0.032968) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962593 / 1.841788 (-0.879195) \| 12.508048 / 8.074308 (4.433740) \| 11.117398 / 10.191392 (0.926006) \| 0.131265 / 0.680424 (-0.549159) \| 0.014469 / 0.534201 (-0.519732) \| 0.271627 / 0.579283 (-0.307656) \| 0.274966 / 0.434364 (-0.159398) \| 0.313260 / 0.540337 (-0.227077) \| 0.444741 / 1.386936 (-0.942195) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004974 / 0.011353 (-0.006379) \| 0.003383 / 0.011008 (-0.007626) \| 0.048792 / 0.038508 (0.010284) \| 0.052821 / 0.023109 (0.029712) \| 0.267123 / 0.275898 (-0.008775) \| 0.293604 / 0.323480 (-0.029876) \| 0.003968 / 0.007986 (-0.004018) \| 0.002594 / 0.004328 (-0.001735) \| 0.047690 / 0.004250 (0.043439) \| 0.040236 / 0.037052 (0.003183) \| 0.267805 / 0.258489 (0.009315) \| 0.310543 / 0.293841 (0.016702) \| 0.025707 / 0.128546 (-0.102839) \| 0.008012 / 0.075646 (-0.067634) \| 0.054460 / 0.419271 (-0.364812) \| 0.033545 / 0.043533 (-0.009988) \| 0.270166 / 0.255139 (0.015027) \| 0.285965 / 0.283200 (0.002765) \| 0.019391 / 0.141683 (-0.122292) \| 1.144991 / 1.452155 (-0.307164) \| 1.198491 / 1.492716 (-0.294225) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094757 / 0.018006 (0.076751) \| 0.306712 / 0.000490 (0.306222) \| 0.000218 / 0.000200 (0.000018) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020995 / 0.037411 (-0.016417) \| 0.070293 / 0.014526 (0.055767) \| 0.081441 / 0.176557 (-0.095116) \| 0.119538 / 0.737135 (-0.617597) \| 0.081454 / 0.296338 (-0.214885) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293451 / 0.215209 (0.078242) \| 2.880378 / 2.077655 (0.802723) \| 1.572547 / 1.504120 (0.068427) \| 1.439172 / 1.541195 (-0.102023) \| 1.506343 / 1.468490 (0.037853) \| 0.402764 / 4.584777 (-4.182013) \| 2.501341 / 3.745712 (-1.244371) \| 2.538494 / 5.269862 (-2.731367) \| 1.524306 / 4.565676 (-3.041371) \| 0.046401 / 0.424275 (-0.377874) \| 0.004781 / 0.007607 (-0.002826) \| 0.349448 / 0.226044 (0.123404) \| 3.416181 / 2.268929 (1.147252) \| 1.964204 / 55.444624 (-53.480420) \| 1.648564 / 6.876477 (-5.227912) \| 1.675977 / 2.142072 (-0.466095) \| 0.475717 / 4.805227 (-4.329511) \| 0.098416 / 6.500664 (-6.402248) \| 0.041212 / 0.075469 (-0.034257) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975928 / 1.841788 (-0.865860) \| 12.066648 / 8.074308 (3.992340) \| 10.943181 / 10.191392 (0.751789) \| 0.149687 / 0.680424 (-0.530736) \| 0.015107 / 0.534201 (-0.519094) \| 0.268950 / 0.579283 (-0.310333) \| 0.280419 / 0.434364 (-0.153945) \| 0.305263 / 0.540337 (-0.235074) \| 0.408486 / 1.386936 (-0.978450) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#344086a7a1707ef20b57399f813ef64ce679e956 \"CML watermark\")\n" ]	2023-11-10T14:56:43	2023-11-14T10:49:15	2023-11-14T10:43:00	MEMBER	null	Minor refactoring to remove redundant condition.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6398/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6398/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6398", "html_url": "https://github.com/huggingface/datasets/pull/6398", "diff_url": "https://github.com/huggingface/datasets/pull/6398.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6398.patch", "merged_at": "2023-11-14T10:43:00" }	true
https://api.github.com/repos/huggingface/datasets/issues/6397	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6397/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6397/comments	https://api.github.com/repos/huggingface/datasets/issues/6397/events	https://github.com/huggingface/datasets/issues/6397	1,987,622,152	I_kwDODunzps52eLUI	6,397	Raise a different exception for inexisting dataset vs files without known extension	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-10T13:22:14	2023-11-10T13:22:14	null	CONTRIBUTOR	null	See https://github.com/huggingface/datasets-server/issues/2082#issuecomment-1805716557 We have the same error for: - https://huggingface.co/datasets/severo/a_dataset_that_does_not_exist: a dataset that does not exist - https://huggingface.co/datasets/severo/test_files_without_extension: a dataset with files without a known extension ``` >>> import datasets >>> datasets.get_dataset_config_names('severo/a_dataset_that_does_not_exist') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/a_dataset_that_does_not_exist/a_dataset_that_does_not_exist.py or any data file in the same directory. Couldn't find 'severo/a_dataset_that_does_not_exist' on the Hugging Face Hub either: FileNotFoundError: Dataset 'severo/a_dataset_that_does_not_exist' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`. >>> datasets.get_dataset_config_names('severo/test_files_without_extension') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/test_files_without_extension/test_files_without_extension.py or any data file in the same directory. Couldn't find 'severo/test_files_without_extension' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in severo/test_files_without_extension. ``` To differentiate, we must parse the error message (only the end is different). We should have a different exception for these two errors.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6397/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6397/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6396	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6396/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6396/comments	https://api.github.com/repos/huggingface/datasets/issues/6396/events	https://github.com/huggingface/datasets/issues/6396	1,987,308,077	I_kwDODunzps52c-ot	6,396	Issue with pyarrow 14.0.1	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Looks like we should stop using `PyExtensionType` and use `ExtensionType` instead\r\n\r\nsee https://github.com/apache/arrow/commit/f14170976372436ec1d03a724d8d3f3925484ecf", "https://github.com/huggingface/datasets-server/pull/2089#pullrequestreview-1724449532\r\n\r\n> Yes, I understand now: they have disabled their `PyExtensionType` and we use it in `datasets` for arrays... ", "related?\r\n\r\nhttps://huggingface.co/datasets/ssbuild/tools_data/discussions/1#654e663b77c8ec680d10479c", "> related?\r\n>\r\n> https://huggingface.co/datasets/ssbuild/tools_data/discussions/1#654e663b77c8ec680d10479c\r\n\r\nNo, related to https://github.com/huggingface/datasets/issues/5706", "Running the following is a workaround:\r\n\r\n```\r\nimport pyarrow\r\npyarrow.PyExtensionType.set_auto_load(True)\r\n```" ]	2023-11-10T10:02:12	2023-11-14T10:23:30	2023-11-14T10:23:30	CONTRIBUTOR	null	See https://github.com/huggingface/datasets-server/pull/2089 for reference ``` from datasets import (Array2D, Dataset, Features) feature_type = Array2D(shape=(2, 2), dtype="float32") content = [[0.0, 0.0], [0.0, 0.0]] features = Features({"col": feature_type}) dataset = Dataset.from_dict({"col": [content]}, features=features) ``` generates ``` /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:648: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. pa.PyExtensionType.__init__(self, self.storage_dtype) /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: RuntimeWarning: pickle-based deserialization of pyarrow.PyExtensionType subclasses is disabled by default; if you only ingest trusted data files, you may re-enable this using `pyarrow.PyExtensionType.set_auto_load(True)`. In the future, Python-defined extension subclasses should derive from pyarrow.ExtensionType (not pyarrow.PyExtensionType) and implement their own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 924, in from_dict return cls(pa_table, info=info, split=split) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 693, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in from_arrow_schema obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in <dictcomp> obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1381, in generate_from_arrow_type return Value(dtype=_arrow_to_datasets_dtype(pa_type)) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 111, in _arrow_to_datasets_dtype raise ValueError(f"Arrow type {arrow_type} does not have a datasets dtype equivalent.") ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent. ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6396/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6396/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6395	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6395/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6395/comments	https://api.github.com/repos/huggingface/datasets/issues/6395/events	https://github.com/huggingface/datasets/issues/6395	1,986,484,124	I_kwDODunzps52Z1ec	6,395	Add ability to set lock type	{ "login": "leoleoasd", "id": 37735580, "node_id": "MDQ6VXNlcjM3NzM1NTgw", "avatar_url": "https://avatars.githubusercontent.com/u/37735580?v=4", "gravatar_id": "", "url": "https://api.github.com/users/leoleoasd", "html_url": "https://github.com/leoleoasd", "followers_url": "https://api.github.com/users/leoleoasd/followers", "following_url": "https://api.github.com/users/leoleoasd/following{/other_user}", "gists_url": "https://api.github.com/users/leoleoasd/gists{/gist_id}", "starred_url": "https://api.github.com/users/leoleoasd/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/leoleoasd/subscriptions", "organizations_url": "https://api.github.com/users/leoleoasd/orgs", "repos_url": "https://api.github.com/users/leoleoasd/repos", "events_url": "https://api.github.com/users/leoleoasd/events{/privacy}", "received_events_url": "https://api.github.com/users/leoleoasd/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	2023-11-09T22:12:30	2023-11-09T22:13:13	null	NONE	null	### Feature request Allow setting file lock type, maybe from an environment variable Currently, it only depends on whether fnctl is available: https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/utils/filelock.py#L463-L470C16 ### Motivation In my environment, flock isn't supported on a network attached drive ### Your contribution I'll be happy to submit a pr.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6395/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6395/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6394	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6394/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6394/comments	https://api.github.com/repos/huggingface/datasets/issues/6394/events	https://github.com/huggingface/datasets/issues/6394	1,985,947,116	I_kwDODunzps52XyXs	6,394	TorchFormatter images (H, W, C) instead of (C, H, W) format	{ "login": "Modexus", "id": 37351874, "node_id": "MDQ6VXNlcjM3MzUxODc0", "avatar_url": "https://avatars.githubusercontent.com/u/37351874?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Modexus", "html_url": "https://github.com/Modexus", "followers_url": "https://api.github.com/users/Modexus/followers", "following_url": "https://api.github.com/users/Modexus/following{/other_user}", "gists_url": "https://api.github.com/users/Modexus/gists{/gist_id}", "starred_url": "https://api.github.com/users/Modexus/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Modexus/subscriptions", "organizations_url": "https://api.github.com/users/Modexus/orgs", "repos_url": "https://api.github.com/users/Modexus/repos", "events_url": "https://api.github.com/users/Modexus/events{/privacy}", "received_events_url": "https://api.github.com/users/Modexus/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Here's a PR for that. https://github.com/huggingface/datasets/pull/6402\r\n\r\nIt's not backward compatible, unfortunately. " ]	2023-11-09T16:02:15	2023-11-11T19:41:03	null	NONE	null	### Describe the bug Using .set_format("torch") leads to images having shape (H, W, C), the same as in numpy. However, pytorch normally uses (C, H, W) format. Maybe I'm missing something but this makes the format a lot less useful as I then have to permute it anyways. If not using the format it is possible to directly use torchvision transforms but any non-transformed value will not be a tensor. Is there a reason for this choice? ### Steps to reproduce the bug ```python from datasets import Dataset, Features, Audio, Image images = ["path/to/image.png"] * 10 features = Features({"image": Image()}) ds = Dataset.from_dict({"image": images}, features=features) ds = ds.with_format("torch") ds[0]["image"].shape ``` ```python torch.Size([512, 512, 4]) ``` ### Expected behavior ```python from datasets import Dataset, Features, Audio, Image images = ["path/to/image.png"] * 10 features = Features({"image": Image()}) ds = Dataset.from_dict({"image": images}, features=features) ds = ds.with_format("torch") ds[0]["image"].shape ``` ```python torch.Size([4, 512, 512]) ``` ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-6.5.9-100.fc37.x86_64-x86_64-with-glibc2.31 - Python version: 3.11.6 - Huggingface_hub version: 0.18.0 - PyArrow version: 14.0.1 - Pandas version: 2.1.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6394/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6394/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6393	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6393/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6393/comments	https://api.github.com/repos/huggingface/datasets/issues/6393/events	https://github.com/huggingface/datasets/issues/6393	1,984,913,259	I_kwDODunzps52T19r	6,393	Filter occasionally hangs	{ "login": "dakinggg", "id": 43149077, "node_id": "MDQ6VXNlcjQzMTQ5MDc3", "avatar_url": "https://avatars.githubusercontent.com/u/43149077?v=4", "gravatar_id": "", "url": "https://api.github.com/users/dakinggg", "html_url": "https://github.com/dakinggg", "followers_url": "https://api.github.com/users/dakinggg/followers", "following_url": "https://api.github.com/users/dakinggg/following{/other_user}", "gists_url": "https://api.github.com/users/dakinggg/gists{/gist_id}", "starred_url": "https://api.github.com/users/dakinggg/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dakinggg/subscriptions", "organizations_url": "https://api.github.com/users/dakinggg/orgs", "repos_url": "https://api.github.com/users/dakinggg/repos", "events_url": "https://api.github.com/users/dakinggg/events{/privacy}", "received_events_url": "https://api.github.com/users/dakinggg/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "It looks like I may not be the first to encounter this: https://github.com/huggingface/datasets/issues/3172", "Adding some more information, it seems to occur more frequently with large (millions of samples) datasets.", "More information. My code is structured as (1) load (2) map (3) filter (4) filter. It was always the second filter that failed. Combining the two filters into one seems to reliably work.", "@lhoestq it'd be great if someone had a chance to look at this. I suspect it is impacting many users given the other issue that I linked.", "Hi ! Sorry for the late response. Was it happening after the first or the second filter ?\r\n\r\nIt looks like an issue with the garbage collector (which makes it random). Maybe datasets created with `filter` are not always handled properly ? cc @mariosasko", "It was after the second filter (and combining the two filters into one seemingly resolved it). I obviously haven't tried all settings to know that these details are causal, but it did work for me." ]	2023-11-09T06:18:30	2023-11-20T18:43:47	null	NONE	null	### Describe the bug A call to `.filter` occasionally hangs (after the filter is complete, according to tqdm) There is a trace produced ``` Exception ignored in: <function Dataset.__del__ at 0x7efb48130c10> Traceback (most recent call last): File "/usr/lib/python3/dist-packages/datasets/arrow_dataset.py", line 1366, in __del__ if hasattr(self, "_indices"): File "/usr/lib/python3/dist-packages/composer/core/engine.py", line 123, in sigterm_handler sys.exit(128 + signal) SystemExit: 143 ``` but I'm not sure if the trace is actually from `datasets`, or from surrounding code that is trying to clean up after datasets gets stuck. Unfortunately I can't reproduce this issue anywhere close to reliably. It happens infrequently when using `num_procs > 1`. Anecdotally I started seeing it when using larger datasets (~10M samples). ### Steps to reproduce the bug N/A see description ### Expected behavior map/filter calls always complete sucessfully ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.4.0-137-generic-x86_64-with-glibc2.31 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6393/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6393/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6392	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6392/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6392/comments	https://api.github.com/repos/huggingface/datasets/issues/6392/events	https://github.com/huggingface/datasets/issues/6392	1,984,369,545	I_kwDODunzps52RxOJ	6,392	`push_to_hub` is not robust to hub closing connection	{ "login": "msis", "id": 577139, "node_id": "MDQ6VXNlcjU3NzEzOQ==", "avatar_url": "https://avatars.githubusercontent.com/u/577139?v=4", "gravatar_id": "", "url": "https://api.github.com/users/msis", "html_url": "https://github.com/msis", "followers_url": "https://api.github.com/users/msis/followers", "following_url": "https://api.github.com/users/msis/following{/other_user}", "gists_url": "https://api.github.com/users/msis/gists{/gist_id}", "starred_url": "https://api.github.com/users/msis/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/msis/subscriptions", "organizations_url": "https://api.github.com/users/msis/orgs", "repos_url": "https://api.github.com/users/msis/repos", "events_url": "https://api.github.com/users/msis/events{/privacy}", "received_events_url": "https://api.github.com/users/msis/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hi! We made some improvements to `push_to_hub` to make it more robust a couple of weeks ago but haven't published a release in the meantime, so it would help if you could install `datasets` from `main` (`pip install https://github.com/huggingface/datasets`) and let us know if this improved version of `push_to_hub` resolves the issue (in case the `ConnectionError` happens, re-running `push_to_hub` should be faster now).\r\n\r\nAlso, note that the previous implementation retries the upload, but sometimes this is not enough, so re-running the op is the only option.", "The update helped push more data.\r\nHowever it still crashed a little later:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(func_args, func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00206-of-00261.parquet' to the Hub.\r\n```", "I think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n\r\nThe implementation in `main` pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n\r\nBelow is the another error log from another run with `main`. I've reverting back to the current release as it does the job for me.\r\n\r\n```\r\nUploading the dataset shards: 86%\|████████▌ \| 224/261 [21:46<03:35, 5.83s/it]s]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n p, glob_pattern_to_regex(PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n token = token if token is not None else HfFolder.get_token()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(func_args, *func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00224-of-00261.parquet' to the Hub.\r\n```", "There's a new error from the hub now:\r\n```\r\nPushing dataset shards to the dataset hub: 49%\|████▉ \| 128/261 [11:38<12:05, 5.45s/it]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1641, in push_to_hub\r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5308, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 293, in _retry\r\n raise err\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(func_args, *func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3850, in upload_file\r\n commit_info = self.create_commit(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3237, in create_commit\r\n hf_raise_for_status(commit_resp, endpoint_name=\"commit\")\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main (Request ID: Root=1-654e48e6-598511b14413bb293fa67084;783522b4-66f9-4f8a-8a74-2accf7cabd17)\r\n\r\nYou have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n```\r\n\r\nAt least this is more explicit from the server side.", "> think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n>\r\n>The implementation in main pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n>\r\n>Below is the another error log from another run with main. I've reverting back to the current release as it does the job for me.\r\n\r\nThe `preupload` step is instant for the already uploaded shards, so only the Parquet conversion is repeated without uploading the actual Parquet data (only to check the SHAs). The previous implementation manually checks the Parquet shard's fingerprint to resume uploading, so the current implementation is cleaner.\r\n\r\n> You have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n\r\nThis is the problem with the previous implementation. If the number of shards is large, it creates too many commits for the Hub in a short period.", "But I agree that the `500 Server Error` returned by the Hub is annoying. Earlier today, I also got it on a small 5GB dataset (with 500 MB shards).\r\n\r\n@Wauplin @julien-c Is there something we can do about this?", "@mariosasko can't do much if AWS raises a HTTP 500 unfortunately (we are simply pushing data to a S3 bucket).\r\nWhat we can do is to add a retry mechanism in the multi-part upload logic here: https://github.com/huggingface/huggingface_hub/blob/c972cba1fecb456a7b3325cdd1fdbcc425f21f94/src/huggingface_hub/lfs.py#L370 :confused: ", "@Wauplin That code already retries the request using `http_backoff`, no?", "> That code already retries the request using http_backoff, no?\r\n\r\nCurrently only on HTTP 503 by default. We should add 500 as well (and hope it is a transient error from AWS)", "Opened a PR to retry in case S3 raises HTTP 500. Will also retry on any `ConnectionError` (connection reset by peer, connection lost,...). Hopefully this should make the upload process more robust to transient errors." ]	2023-11-08T20:44:53	2023-11-15T09:46:04	null	NONE	null	### Describe the bug Like to #6172, `push_to_hub` will crash if Hub resets the connection and raise the following error: ``` Pushing dataset shards to the dataset hub: 32%\|███▏ \| 54/171 [06:38<14:23, 7.38s/it] Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen httplib_response = self._make_request( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request six.raise_from(e, None) File "<string>", line 3, in raise_from File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request httplib_response = conn.getresponse() File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse response.begin() File "/usr/lib/python3.8/http/client.py", line 316, in begin version, status, reason = self._read_status() File "/usr/lib/python3.8/http/client.py", line 285, in _read_status raise RemoteDisconnected("Remote end closed connection without" http.client.RemoteDisconnected: Remote end closed connection without response During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 486, in send resp = conn.urlopen( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 799, in urlopen retries = retries.increment( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/util/retry.py", line 550, in increment raise six.reraise(type(error), error, _stacktrace) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/packages/six.py", line 769, in reraise raise value.with_traceback(tb) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen httplib_response = self._make_request( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request six.raise_from(e, None) File "<string>", line 3, in raise_from File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request httplib_response = conn.getresponse() File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse response.begin() File "/usr/lib/python3.8/http/client.py", line 316, in begin version, status, reason = self._read_status() File "/usr/lib/python3.8/http/client.py", line 285, in _read_status raise RemoteDisconnected("Remote end closed connection without" urllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 383, in _wrapped_lfs_upload lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 223, in lfs_upload _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action["href"]) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 319, in _upload_multi_part else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 375, in _upload_parts_iteratively part_upload_res = http_backoff("PUT", part_upload_url, data=fileobj_slice) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 258, in http_backoff response = session.request(method=method, url=url, kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 589, in request resp = self.send(prep, send_kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 703, in send r = adapter.send(request, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 63, in send return super().send(request, args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 501, in send raise ConnectionError(err, request=request) requests.exceptions.ConnectionError: (ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 2bab8c06-b701-4266-aead-fe2e0dc0e3ed)') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "convert_to_hf.py", line 116, in <module> main() File "convert_to_hf.py", line 108, in main audio_dataset.push_to_hub( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1641, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 5308, in _push_parquet_shards_to_hub _retry( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 290, in _retry return func(func_args, *func_kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file commit_info = self.create_commit( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 2695, in create_commit upload_lfs_files( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 393, in upload_lfs_files _wrapped_lfs_upload(filtered_actions[0]) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 385, in _wrapped_lfs_upload raise RuntimeError(f"Error while uploading '{operation.path_in_repo}' to the Hub.") from exc RuntimeError: Error while uploading 'batch_19/train-00054-of-00171-932beb4082c034bf.parquet' to the Hub. ``` The function should retry if the operations fails, or at least offer a way to recover after such a failure. Right now, calling the function again will start sending all the parquets files leading to duplicates in the repository, with no guarantee that it will actually be pushed. Previously, it would crash with an error 400 #4677 . ### Steps to reproduce the bug Any large dataset pushed the hub: ```py audio_dataset.push_to_hub( repo_id="org/dataset", ) ``` ### Expected behavior `push_to_hub` should have an option for max retries or resume. ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.0-1044-aws-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6392/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6392/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6391	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6391/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6391/comments	https://api.github.com/repos/huggingface/datasets/issues/6391/events	https://github.com/huggingface/datasets/pull/6391	1,984,091,776	PR_kwDODunzps5e9BDO	6,391	Webdataset dataset builder	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6391). All of your documentation changes will be reflected on that endpoint.", "I added an error message if the first examples don't appear to be in webdataset format\r\n```\r\n\"The TAR archives of the dataset should be in Webdataset format, \"\r\n\"but the files in the archive don't share the same prefix or the same types.\"\r\n```" ]	2023-11-08T17:31:59	2023-11-15T11:57:33	null	MEMBER	null	Allow `load_dataset` to support the Webdataset format. It allows users to download/stream data from local files or from the Hugging Face Hub. Moreover it will enable the Dataset Viewer for Webdataset datasets on HF. ## Implementation details - I added a new Webdataset builder - dataset with TAR files are now read using the Webdataset builder - Basic decoding from `webdataset` is used by default, except unsafe ones like pickle - HF authentication support is done by registering a `webdataset.gopen` reader - `webdataset` uses buffering when reading files, so I had to add buffering support in `xopen` ## TODOS - [x] tests - [x] docs	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6391/reactions", "total_count": 2, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 2, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6391/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6391", "html_url": "https://github.com/huggingface/datasets/pull/6391", "diff_url": "https://github.com/huggingface/datasets/pull/6391.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6391.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6390	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6390/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6390/comments	https://api.github.com/repos/huggingface/datasets/issues/6390/events	https://github.com/huggingface/datasets/pull/6390	1,983,725,707	PR_kwDODunzps5e7xQ3	6,390	handle future deprecation argument	{ "login": "winglian", "id": 381258, "node_id": "MDQ6VXNlcjM4MTI1OA==", "avatar_url": "https://avatars.githubusercontent.com/u/381258?v=4", "gravatar_id": "", "url": "https://api.github.com/users/winglian", "html_url": "https://github.com/winglian", "followers_url": "https://api.github.com/users/winglian/followers", "following_url": "https://api.github.com/users/winglian/following{/other_user}", "gists_url": "https://api.github.com/users/winglian/gists{/gist_id}", "starred_url": "https://api.github.com/users/winglian/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/winglian/subscriptions", "organizations_url": "https://api.github.com/users/winglian/orgs", "repos_url": "https://api.github.com/users/winglian/repos", "events_url": "https://api.github.com/users/winglian/events{/privacy}", "received_events_url": "https://api.github.com/users/winglian/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004368 / 0.011353 (-0.006985) \| 0.002613 / 0.011008 (-0.008396) \| 0.061365 / 0.038508 (0.022856) \| 0.029553 / 0.023109 (0.006444) \| 0.240535 / 0.275898 (-0.035363) \| 0.280634 / 0.323480 (-0.042845) \| 0.002923 / 0.007986 (-0.005063) \| 0.003696 / 0.004328 (-0.000632) \| 0.049824 / 0.004250 (0.045573) \| 0.044935 / 0.037052 (0.007882) \| 0.246870 / 0.258489 (-0.011619) \| 0.317248 / 0.293841 (0.023407) \| 0.022717 / 0.128546 (-0.105829) \| 0.006933 / 0.075646 (-0.068713) \| 0.201118 / 0.419271 (-0.218154) \| 0.053422 / 0.043533 (0.009890) \| 0.266262 / 0.255139 (0.011123) \| 0.269114 / 0.283200 (-0.014086) \| 0.016908 / 0.141683 (-0.124775) \| 1.154296 / 1.452155 (-0.297859) \| 1.218825 / 1.492716 (-0.273892) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089908 / 0.018006 (0.071902) \| 0.300029 / 0.000490 (0.299539) \| 0.000209 / 0.000200 (0.000009) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018518 / 0.037411 (-0.018894) \| 0.062246 / 0.014526 (0.047720) \| 0.073542 / 0.176557 (-0.103014) \| 0.119386 / 0.737135 (-0.617749) \| 0.075256 / 0.296338 (-0.221082) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280812 / 0.215209 (0.065603) \| 2.701282 / 2.077655 (0.623628) \| 1.455146 / 1.504120 (-0.048974) \| 1.310198 / 1.541195 (-0.230996) \| 1.335287 / 1.468490 (-0.133203) \| 0.388245 / 4.584777 (-4.196532) \| 2.357770 / 3.745712 (-1.387942) \| 2.534640 / 5.269862 (-2.735222) \| 1.541382 / 4.565676 (-3.024295) \| 0.045597 / 0.424275 (-0.378678) \| 0.004842 / 0.007607 (-0.002765) \| 0.325416 / 0.226044 (0.099371) \| 3.221873 / 2.268929 (0.952944) \| 1.791061 / 55.444624 (-53.653563) \| 1.485094 / 6.876477 (-5.391382) \| 1.512354 / 2.142072 (-0.629718) \| 0.471241 / 4.805227 (-4.333986) \| 0.098672 / 6.500664 (-6.401992) \| 0.041668 / 0.075469 (-0.033801) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953553 / 1.841788 (-0.888234) \| 11.378394 / 8.074308 (3.304086) \| 10.355970 / 10.191392 (0.164578) \| 0.126891 / 0.680424 (-0.553533) \| 0.013808 / 0.534201 (-0.520393) \| 0.267800 / 0.579283 (-0.311484) \| 0.266436 / 0.434364 (-0.167928) \| 0.306668 / 0.540337 (-0.233670) \| 0.427666 / 1.386936 (-0.959270) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004908 / 0.011353 (-0.006445) \| 0.002698 / 0.011008 (-0.008310) \| 0.047492 / 0.038508 (0.008984) \| 0.049906 / 0.023109 (0.026797) \| 0.271466 / 0.275898 (-0.004432) \| 0.291030 / 0.323480 (-0.032449) \| 0.003938 / 0.007986 (-0.004047) \| 0.002457 / 0.004328 (-0.001871) \| 0.047347 / 0.004250 (0.043096) \| 0.038599 / 0.037052 (0.001547) \| 0.269950 / 0.258489 (0.011461) \| 0.303026 / 0.293841 (0.009185) \| 0.024196 / 0.128546 (-0.104351) \| 0.006889 / 0.075646 (-0.068757) \| 0.053357 / 0.419271 (-0.365914) \| 0.032249 / 0.043533 (-0.011284) \| 0.271660 / 0.255139 (0.016521) \| 0.286395 / 0.283200 (0.003196) \| 0.017914 / 0.141683 (-0.123769) \| 1.128762 / 1.452155 (-0.323393) \| 1.206495 / 1.492716 (-0.286221) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093384 / 0.018006 (0.075378) \| 0.305504 / 0.000490 (0.305014) \| 0.000227 / 0.000200 (0.000027) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021183 / 0.037411 (-0.016229) \| 0.070113 / 0.014526 (0.055587) \| 0.080288 / 0.176557 (-0.096269) \| 0.120798 / 0.737135 (-0.616337) \| 0.082896 / 0.296338 (-0.213442) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292758 / 0.215209 (0.077549) \| 2.893975 / 2.077655 (0.816320) \| 1.584909 / 1.504120 (0.080789) \| 1.455509 / 1.541195 (-0.085686) \| 1.501625 / 1.468490 (0.033135) \| 0.400772 / 4.584777 (-4.184005) \| 2.446319 / 3.745712 (-1.299393) \| 2.530690 / 5.269862 (-2.739172) \| 1.525957 / 4.565676 (-3.039719) \| 0.046070 / 0.424275 (-0.378205) \| 0.004756 / 0.007607 (-0.002851) \| 0.343039 / 0.226044 (0.116995) \| 3.366772 / 2.268929 (1.097844) \| 1.948895 / 55.444624 (-53.495729) \| 1.666419 / 6.876477 (-5.210058) \| 1.658258 / 2.142072 (-0.483814) \| 0.470835 / 4.805227 (-4.334392) \| 0.098008 / 6.500664 (-6.402656) \| 0.040743 / 0.075469 (-0.034726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978025 / 1.841788 (-0.863763) \| 11.945229 / 8.074308 (3.870920) \| 11.025810 / 10.191392 (0.834418) \| 0.129706 / 0.680424 (-0.550717) \| 0.015148 / 0.534201 (-0.519053) \| 0.269160 / 0.579283 (-0.310123) \| 0.284306 / 0.434364 (-0.150058) \| 0.307154 / 0.540337 (-0.233183) \| 0.409153 / 1.386936 (-0.977783) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c75c104fd79cbf53be25f0fbbeb001e535f7e9b \"CML watermark\")\n" ]	2023-11-08T14:21:25	2023-11-21T02:10:24	2023-11-14T15:15:59	CONTRIBUTOR	null	getting this error: ``` /root/miniconda3/envs/py3.10/lib/python3.10/site-packages/datasets/table.py:1387: FutureWarning: promote has been superseded by mode='default'. return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0) ``` Since datasets supports arrow greater than 8.0.0, we need to handle both cases. [Arrow v14 docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html) [Arrow v13 docs](https://arrow.apache.org/docs/13.0/python/generated/pyarrow.concat_tables.html)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6390/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6390/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6390", "html_url": "https://github.com/huggingface/datasets/pull/6390", "diff_url": "https://github.com/huggingface/datasets/pull/6390.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6390.patch", "merged_at": "2023-11-14T15:15:59" }	true
https://api.github.com/repos/huggingface/datasets/issues/6389	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6389/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6389/comments	https://api.github.com/repos/huggingface/datasets/issues/6389/events	https://github.com/huggingface/datasets/issues/6389	1,983,545,744	I_kwDODunzps52OoGQ	6,389	Index 339 out of range for dataset of size 339 <-- save_to_file()	{ "login": "jaggzh", "id": 20318973, "node_id": "MDQ6VXNlcjIwMzE4OTcz", "avatar_url": "https://avatars.githubusercontent.com/u/20318973?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jaggzh", "html_url": "https://github.com/jaggzh", "followers_url": "https://api.github.com/users/jaggzh/followers", "following_url": "https://api.github.com/users/jaggzh/following{/other_user}", "gists_url": "https://api.github.com/users/jaggzh/gists{/gist_id}", "starred_url": "https://api.github.com/users/jaggzh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jaggzh/subscriptions", "organizations_url": "https://api.github.com/users/jaggzh/orgs", "repos_url": "https://api.github.com/users/jaggzh/repos", "events_url": "https://api.github.com/users/jaggzh/events{/privacy}", "received_events_url": "https://api.github.com/users/jaggzh/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hi! Can you make the above reproducer self-contained by adding code that generates the data?" ]	2023-11-08T12:52:09	2023-11-15T16:03:09	null	NONE	null	### Describe the bug When saving out some Audio() data. The data is audio recordings with associated 'sentences'. (They use the audio 'bytes' approach because they're clips within audio files). Code is below the traceback (I can't upload the voice audio/text (it's not even me)). ``` Traceback (most recent call last): File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 156, in <module> create_dataset(args) File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 138, in create_dataset hf_dataset.save_to_disk(args.outds, max_shard_size='50MB') File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1531, in save_to_disk for kwargs in kwargs_per_job: File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1508, in <genexpr> "shard": self.shard(num_shards=num_shards, index=shard_idx, contiguous=True), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 4609, in shard return self.select( ^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3797, in select return self._select_contiguous(start, length, new_fingerprint=new_fingerprint) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3857, in _select_contiguous _check_valid_indices_value(start, len(self)) File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 648, in _check_valid_indices_value raise IndexError(f"Index {index} out of range for dataset of size {size}.") IndexError: Index 339 out of range for dataset of size 339. ``` ### Steps to reproduce the bug (I had to set the default max batch size down due to a different bug... or maybe it's related: https://github.com/huggingface/datasets/issues/5717) ```python3 #!/usr/bin/env python3 import argparse import os from pathlib import Path import soundfile as sf import datasets datasets.config.DEFAULT_MAX_BATCH_SIZE=35 from datasets import Features, Array2D, Value, Dataset, Sequence, Audio import numpy as np import librosa import sys import soundfile as sf import io import logging logging.basicConfig(level=logging.DEBUG, filename='debug.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s') # Define the arguments for the command-line interface def parse_args(): parser = argparse.ArgumentParser(description="Create a Huggingface dataset from labeled audio files.") parser.add_argument("--indir_labeled", action="append", help="Directory containing labeled audio files.", required=True) parser.add_argument("--outds", help="Path to save the dataset file.", required=True) parser.add_argument("--max_clips", type=int, help="Max count of audio samples to add to the dataset.", default=None) parser.add_argument("-r", "--sr", type=int, help="Sample rate for the audio files.", default=16000) parser.add_argument("--no-resample", action="store_true", help="Disable resampling of the audio files.") parser.add_argument("--max_clip_secs", type=float, help="Max length of audio clips in seconds.", default=3.0) parser.add_argument("-v", "--verbose", action='count', default=1, help="Increase verbosity") return parser.parse_args() # Convert the NumPy arrays to audio bytes in WAV format def numpy_to_bytes(audio_array, sampling_rate=16000): with io.BytesIO() as bytes_io: sf.write(bytes_io, audio_array, samplerate=sampling_rate, format='wav', subtype='FLOAT') # float32 return bytes_io.getvalue() # Function to find audio and label files in a directory def find_audio_label_pairs(indir_labeled): audio_label_pairs = [] for root, _, files in os.walk(indir_labeled): for file in files: if file.endswith(('.mp3', '.wav', '.aac', '.flac')): audio_path = Path(root) / file if args.verbose>1: print(f'File: {audio_path}') label_path = audio_path.with_suffix('.labels.txt') if label_path.exists(): if args.verbose>0: print(f' Pair: {audio_path}') audio_label_pairs.append((audio_path, label_path)) return audio_label_pairs def process_audio_label_pair(audio_path, label_path, sampling_rate, no_resample, max_clip_secs): # Read the label file with open(label_path, 'r') as label_file: labels = label_file.readlines() # Load the full audio file full_audio, current_sr = sf.read(audio_path) if not no_resample and current_sr != sampling_rate: # You can use librosa.resample here if librosa is available full_audio = librosa.resample(full_audio, orig_sr=current_sr, target_sr=sampling_rate) audio_segments = [] sentences = [] # Process each label for label in labels: start_secs, end_secs, label_text = label.strip().split('\t') start_sample = int(float(start_secs) sampling_rate) end_sample = int(float(end_secs) * sampling_rate) # Extract segment and truncate or pad to max_clip_secs audio_segment = full_audio[start_sample:end_sample] max_samples = int(max_clip_secs * sampling_rate) if len(audio_segment) > max_samples: # Truncate audio_segment = audio_segment[:max_samples] elif len(audio_segment) < max_samples: # Pad padding = np.zeros(max_samples - len(audio_segment), dtype=audio_segment.dtype) audio_segment = np.concatenate((audio_segment, padding)) audio_segment = numpy_to_bytes(audio_segment) audio_data = { 'path': str(audio_path), 'bytes': audio_segment, } audio_segments.append(audio_data) sentences.append(label_text) return audio_segments, sentences # Main function to create the dataset def create_dataset(args): audio_label_pairs = [] for indir in args.indir_labeled: audio_label_pairs.extend(find_audio_label_pairs(indir)) # Initialize our dataset data dataset_data = { 'path': [], # This will be a list of strings 'audio': [], # This will be a list of dictionaries 'sentence': [], # This will be a list of strings } # Process each audio-label pair and add the data to the dataset for audio_path, label_path in audio_label_pairs[:args.max_clips]: audio_segments, sentences = process_audio_label_pair(audio_path, label_path, args.sr, args.no_resample, args.max_clip_secs) if audio_segments and sentences: for audio_data, sentence in zip(audio_segments, sentences): if args.verbose>1: print(f'Appending {audio_data["path"]}') dataset_data['path'].append(audio_data['path']) dataset_data['audio'].append({ 'path': audio_data['path'], 'bytes': audio_data['bytes'], }) dataset_data['sentence'].append(sentence) features = Features({ 'path': Value('string'), # Path is redundant in common voice set also 'audio': Audio(sampling_rate=16000), 'sentence': Value('string'), }) hf_dataset = Dataset.from_dict(dataset_data, features=features) for key in dataset_data: for i, item in enumerate(dataset_data[key]): if item is None or (isinstance(item, bytes) and len(item) == 0): logging.error(f"Invalid {key} at index {i}: {item}") import ipdb; ipdb.set_trace(context=16); pass hf_dataset.save_to_disk(args.outds, max_shard_size='50MB') # try: # hf_dataset.save_to_disk(args.outds) # except TypeError as e: # # If there's a TypeError, log the exception and the dataset data that might have caused it # logging.exception("An error occurred while saving the dataset.") # import ipdb; ipdb.set_trace(context=16); pass # for key in dataset_data: # logging.debug(f"{key} length: {len(dataset_data[key])}") # if key == 'audio': # # Log the first 100 bytes of the audio data to avoid huge log files # for i, audio in enumerate(dataset_data[key]): # logging.debug(f"Audio {i}: {audio['bytes'][:100]}") # raise # Run the script if __name__ == "__main__": args = parse_args() create_dataset(args) ``` ### Expected behavior It shouldn't fail. ### Environment info - `datasets` version: 2.14.7.dev0 - Platform: Linux-6.1.0-13-amd64-x86_64-with-glibc2.36 - Python version: 3.11.2 - `huggingface_hub` version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6389/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6389/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6388	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6388/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6388/comments	https://api.github.com/repos/huggingface/datasets/issues/6388/events	https://github.com/huggingface/datasets/issues/6388	1,981,136,093	I_kwDODunzps52Fbzd	6,388	How to create 3d medical imgae dataset?	{ "login": "QingYunA", "id": 41177312, "node_id": "MDQ6VXNlcjQxMTc3MzEy", "avatar_url": "https://avatars.githubusercontent.com/u/41177312?v=4", "gravatar_id": "", "url": "https://api.github.com/users/QingYunA", "html_url": "https://github.com/QingYunA", "followers_url": "https://api.github.com/users/QingYunA/followers", "following_url": "https://api.github.com/users/QingYunA/following{/other_user}", "gists_url": "https://api.github.com/users/QingYunA/gists{/gist_id}", "starred_url": "https://api.github.com/users/QingYunA/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/QingYunA/subscriptions", "organizations_url": "https://api.github.com/users/QingYunA/orgs", "repos_url": "https://api.github.com/users/QingYunA/repos", "events_url": "https://api.github.com/users/QingYunA/events{/privacy}", "received_events_url": "https://api.github.com/users/QingYunA/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	2023-11-07T11:27:36	2023-11-07T11:28:53	null	NONE	null	### Feature request I am newer to huggingface, after i look up `datasets` docs, I can't find how to create the dataset contains 3d medical image (ends with '.mhd', '.dcm', '.nii') ### Motivation help us to upload 3d medical dataset to huggingface! ### Your contribution I'll submit a PR if I find a way to add this feature	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6388/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6388/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6387	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6387/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6387/comments	https://api.github.com/repos/huggingface/datasets/issues/6387/events	https://github.com/huggingface/datasets/issues/6387	1,980,224,020	I_kwDODunzps52B9IU	6,387	How to load existing downloaded dataset ?	{ "login": "liming-ai", "id": 73068772, "node_id": "MDQ6VXNlcjczMDY4Nzcy", "avatar_url": "https://avatars.githubusercontent.com/u/73068772?v=4", "gravatar_id": "", "url": "https://api.github.com/users/liming-ai", "html_url": "https://github.com/liming-ai", "followers_url": "https://api.github.com/users/liming-ai/followers", "following_url": "https://api.github.com/users/liming-ai/following{/other_user}", "gists_url": "https://api.github.com/users/liming-ai/gists{/gist_id}", "starred_url": "https://api.github.com/users/liming-ai/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/liming-ai/subscriptions", "organizations_url": "https://api.github.com/users/liming-ai/orgs", "repos_url": "https://api.github.com/users/liming-ai/repos", "events_url": "https://api.github.com/users/liming-ai/events{/privacy}", "received_events_url": "https://api.github.com/users/liming-ai/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	closed	false	null	[]	null	[ "Feel free to use `dataset.save_to_disk(...)`, then scp the directory containing the saved dataset and reload it on your other machine using `dataset = load_from_disk(...)`" ]	2023-11-06T22:51:44	2023-11-16T18:07:01	2023-11-16T18:07:01	NONE	null	Hi @mariosasko @lhoestq @katielink Thanks for your contribution and hard work. ### Feature request First, I download a dataset as normal by: ``` from datasets import load_dataset dataset = load_dataset('username/data_name', cache_dir='data') ``` The dataset format in `data` directory will be: ``` -data \|-data_name \|-test-00000-of-00001-bf4c733542e35fcb.parquet \|-train-00000-of-00001-2a1df75c6bce91ab.parquet ``` Then I use SCP to clone this dataset into another machine, and then try: ``` from datasets import load_dataset dataset = load_dataset('data/data_name') # load from local path ``` This leads to re-generating training and validation split for each time, and the disk quota will be duplicated occupation. How can I just load the dataset without generating and saving these splits again? ### Motivation I do not want to download the same dataset in two machines, scp is much faster and better than HuggingFace API. I hope we can directly load the downloaded datasets (.parquest) ### Your contribution Please refer to the feature	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6387/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6387/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6386	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6386/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6386/comments	https://api.github.com/repos/huggingface/datasets/issues/6386/events	https://github.com/huggingface/datasets/issues/6386	1,979,878,014	I_kwDODunzps52Aop-	6,386	Formatting overhead	{ "login": "d-miketa", "id": 320321, "node_id": "MDQ6VXNlcjMyMDMyMQ==", "avatar_url": "https://avatars.githubusercontent.com/u/320321?v=4", "gravatar_id": "", "url": "https://api.github.com/users/d-miketa", "html_url": "https://github.com/d-miketa", "followers_url": "https://api.github.com/users/d-miketa/followers", "following_url": "https://api.github.com/users/d-miketa/following{/other_user}", "gists_url": "https://api.github.com/users/d-miketa/gists{/gist_id}", "starred_url": "https://api.github.com/users/d-miketa/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/d-miketa/subscriptions", "organizations_url": "https://api.github.com/users/d-miketa/orgs", "repos_url": "https://api.github.com/users/d-miketa/repos", "events_url": "https://api.github.com/users/d-miketa/events{/privacy}", "received_events_url": "https://api.github.com/users/d-miketa/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Ah I think the `line-profiler` log is off-by-one and it is in fact the `extract_batch` method that's taking forever. Will investigate further.", "I tracked it down to a quirk of my setup. Apologies." ]	2023-11-06T19:06:38	2023-11-06T23:56:12	2023-11-06T23:56:12	NONE	null	### Describe the bug Hi! I very recently noticed that my training time is dominated by batch formatting. Using Lightning's profilers, I located the bottleneck within `datasets.formatting.formatting` and then narrowed it down with `line-profiler`. It turns out that almost all of the overhead is due to creating new instances of `self.python_arrow_extractor`. I admit I'm confused why that could be the case - as far as I can tell there's no complex `__init__` logic to execute. ![image](https://github.com/huggingface/datasets/assets/320321/5e022e0b-0d21-43d0-8e6f-9e641142e96b) ### Steps to reproduce the bug 1. Set up a dataset `ds` with potentially several (4+) columns (not sure if this is necessary, but it did at one point of the investigation make overhead worse) 2. Process it using a custom transform, `ds = ds.with_transform(transform_func)` 3. Decorate this function https://github.com/huggingface/datasets/blob/main/src/datasets/formatting/formatting.py#L512 with `@profile` from https://pypi.org/project/line-profiler/ 4. Profile with `$ kernprof -l script_to_profile.py` ### Expected behavior Batch formatting should have acceptable overhead. ### Environment info ``` datasets=2.14.6 pyarrow=14.0.0 ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6386/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6386/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6385	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6385/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6385/comments	https://api.github.com/repos/huggingface/datasets/issues/6385/events	https://github.com/huggingface/datasets/issues/6385	1,979,308,338	I_kwDODunzps51-dky	6,385	Get an error when i try to concatenate the squad dataset with my own dataset	{ "login": "CCDXDX", "id": 149378500, "node_id": "U_kgDOCOdVxA", "avatar_url": "https://avatars.githubusercontent.com/u/149378500?v=4", "gravatar_id": "", "url": "https://api.github.com/users/CCDXDX", "html_url": "https://github.com/CCDXDX", "followers_url": "https://api.github.com/users/CCDXDX/followers", "following_url": "https://api.github.com/users/CCDXDX/following{/other_user}", "gists_url": "https://api.github.com/users/CCDXDX/gists{/gist_id}", "starred_url": "https://api.github.com/users/CCDXDX/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/CCDXDX/subscriptions", "organizations_url": "https://api.github.com/users/CCDXDX/orgs", "repos_url": "https://api.github.com/users/CCDXDX/repos", "events_url": "https://api.github.com/users/CCDXDX/events{/privacy}", "received_events_url": "https://api.github.com/users/CCDXDX/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The `answers.text` field in the JSON dataset needs to be a list of strings, not a string.\r\n\r\nSo, here is the fixed code:\r\n```python\r\nfrom huggingface_hub import notebook_login\r\nfrom datasets import load_dataset\r\n\r\n\r\n\r\nnotebook_login(\"mymailadresse\", \"mypassword\")\r\nsquad = load_dataset(\"squad\", split=\"train[:5000]\")\r\nsquad = squad.train_test_split(test_size=0.2)\r\ndataset1 = squad[\"train\"]\r\n\r\n\r\n\r\n\r\nimport json\r\n\r\nmybase = [\r\n {\r\n \"id\": \"1\",\r\n \"context\": \"She lives in Nantes\",\r\n \"question\": \"Where does she live?\",\r\n \"answers\": {\r\n \"text\": [\"Nantes\"],\r\n \"answer_start\": [13],\r\n }\r\n }\r\n]\r\n\r\n\r\n\r\n\r\n# Save the data to a JSON file\r\njson_file_path = r\"data\"\r\nwith open(json_file_path, \"w\", encoding= \"utf-8\") as json_file:\r\n json.dump(mybase, json_file, indent=4)\r\n\r\n\r\n\r\n\r\n# Load the JSON file as a dataset\r\ncustom_dataset = load_dataset(\"json\", data_files=json_file_path, features=dataset1.features)\r\n\r\n\r\n# Access the train split\r\ntrain_dataset = custom_dataset[\"train\"]\r\n\r\n\r\nfrom datasets import concatenate_datasets\r\n\r\n\r\n# Concatenate the datasets\r\nconcatenated_dataset = concatenate_datasets([train_dataset, dataset1])\r\n```", "Thank you @mariosasko for your help ! It works !" ]	2023-11-06T14:29:22	2023-11-06T16:50:45	2023-11-06T16:50:45	NONE	null	### Describe the bug Hello, I'm new here and I need to concatenate the squad dataset with my own dataset i created. I find the following error when i try to do it: Traceback (most recent call last): Cell In[9], line 1 concatenated_dataset = concatenate_datasets([train_dataset, dataset1]) File ~\anaconda3\Lib\site-packages\datasets\combine.py:213 in concatenate_datasets return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis) File ~\anaconda3\Lib\site-packages\datasets\arrow_dataset.py:6002 in _concatenate_map_style_datasets _check_if_features_can_be_aligned([dset.features for dset in dsets]) File ~\anaconda3\Lib\site-packages\datasets\features\features.py:2122 in _check_if_features_can_be_aligned raise ValueError( ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Value(dtype='string', id=None)} or Value("null"). ### Steps to reproduce the bug ```python from huggingface_hub import notebook_login from datasets import load_dataset notebook_login("mymailadresse", "mypassword") squad = load_dataset("squad", split="train[:5000]") squad = squad.train_test_split(test_size=0.2) dataset1 = squad["train"] import json mybase = [ { "id": "1", "context": "She lives in Nantes", "question": "Where does she live?", "answers": { "text": "Nantes", "answer_start": [13], } } ] # Save the data to a JSON file json_file_path = r"C:\Users\mypath\thefile.json" with open(json_file_path, "w", encoding= "utf-8") as json_file: json.dump(mybase, json_file, indent=4) # Load the JSON file as a dataset custom_dataset = load_dataset("json", data_files=json_file_path) # Access the train split train_dataset = custom_dataset["train"] from datasets import concatenate_datasets # Concatenate the datasets concatenated_dataset = concatenate_datasets([train_dataset, dataset1]) ``` ### Expected behavior I would expect the two datasets to be concatenated without error. The len(dataset1) is equal to 4000 and the len(train_dataset) is equal to 1 so I would exepect concatenated_dataset to be created and having lenght 4001. ### Environment info Python 3.11.4 and using windows Thank you for your help	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6385/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6385/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6384	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6384/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6384/comments	https://api.github.com/repos/huggingface/datasets/issues/6384/events	https://github.com/huggingface/datasets/issues/6384	1,979,117,069	I_kwDODunzps519u4N	6,384	Load the local dataset folder from other place	{ "login": "OrangeSodahub", "id": 54439582, "node_id": "MDQ6VXNlcjU0NDM5NTgy", "avatar_url": "https://avatars.githubusercontent.com/u/54439582?v=4", "gravatar_id": "", "url": "https://api.github.com/users/OrangeSodahub", "html_url": "https://github.com/OrangeSodahub", "followers_url": "https://api.github.com/users/OrangeSodahub/followers", "following_url": "https://api.github.com/users/OrangeSodahub/following{/other_user}", "gists_url": "https://api.github.com/users/OrangeSodahub/gists{/gist_id}", "starred_url": "https://api.github.com/users/OrangeSodahub/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/OrangeSodahub/subscriptions", "organizations_url": "https://api.github.com/users/OrangeSodahub/orgs", "repos_url": "https://api.github.com/users/OrangeSodahub/repos", "events_url": "https://api.github.com/users/OrangeSodahub/events{/privacy}", "received_events_url": "https://api.github.com/users/OrangeSodahub/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Solved" ]	2023-11-06T13:07:04	2023-11-19T05:42:06	2023-11-19T05:42:05	NONE	null	This is from https://github.com/huggingface/diffusers/issues/5573	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6384/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6384/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6383	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6383/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6383/comments	https://api.github.com/repos/huggingface/datasets/issues/6383/events	https://github.com/huggingface/datasets/issues/6383	1,978,189,389	I_kwDODunzps516MZN	6,383	imagenet-1k downloads over and over	{ "login": "seann999", "id": 6847529, "node_id": "MDQ6VXNlcjY4NDc1Mjk=", "avatar_url": "https://avatars.githubusercontent.com/u/6847529?v=4", "gravatar_id": "", "url": "https://api.github.com/users/seann999", "html_url": "https://github.com/seann999", "followers_url": "https://api.github.com/users/seann999/followers", "following_url": "https://api.github.com/users/seann999/following{/other_user}", "gists_url": "https://api.github.com/users/seann999/gists{/gist_id}", "starred_url": "https://api.github.com/users/seann999/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/seann999/subscriptions", "organizations_url": "https://api.github.com/users/seann999/orgs", "repos_url": "https://api.github.com/users/seann999/repos", "events_url": "https://api.github.com/users/seann999/events{/privacy}", "received_events_url": "https://api.github.com/users/seann999/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	2023-11-06T02:58:58	2023-11-06T06:02:39	2023-11-06T06:02:39	NONE	null	### Describe the bug What could be causing this? ``` $ python3 Python 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> load_dataset("imagenet-1k") Downloading builder script: 100%\|██████████\| 4.72k/4.72k [00:00<00:00, 7.51MB/s] Downloading readme: 100%\|███████████████████\| 85.4k/85.4k [00:00<00:00, 510kB/s] Downloading extra modules: 100%\|████████████\| 46.4k/46.4k [00:00<00:00, 300kB/s] Downloading data: 100%\|████████████████████\| 29.1G/29.1G [19:36<00:00, 24.8MB/s] Downloading data: 100%\|████████████████████\| 29.3G/29.3G [08:38<00:00, 56.5MB/s] Downloading data: 100%\|████████████████████\| 29.0G/29.0G [09:26<00:00, 51.2MB/s] Downloading data: 100%\|████████████████████\| 29.2G/29.2G [09:38<00:00, 50.6MB/s] Downloading data: 100%\|███████████████████▉\| 29.2G/29.2G [09:37<00:00, 44.1MB/s^Downloading data: 0%\| \| 106M/29.1G [00:05<23:49, 20.3MB/s] ``` ### Steps to reproduce the bug See above commands/code ### Expected behavior imagenet-1k is downloaded ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-6.2.0-34-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - Huggingface_hub version: 0.15.1 - PyArrow version: 14.0.0 - Pandas version: 1.5.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6383/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6383/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6382	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6382/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6382/comments	https://api.github.com/repos/huggingface/datasets/issues/6382/events	https://github.com/huggingface/datasets/issues/6382	1,977,400,799	I_kwDODunzps513L3f	6,382	Add CheXpert dataset for vision	{ "login": "SauravMaheshkar", "id": 61241031, "node_id": "MDQ6VXNlcjYxMjQxMDMx", "avatar_url": "https://avatars.githubusercontent.com/u/61241031?v=4", "gravatar_id": "", "url": "https://api.github.com/users/SauravMaheshkar", "html_url": "https://github.com/SauravMaheshkar", "followers_url": "https://api.github.com/users/SauravMaheshkar/followers", "following_url": "https://api.github.com/users/SauravMaheshkar/following{/other_user}", "gists_url": "https://api.github.com/users/SauravMaheshkar/gists{/gist_id}", "starred_url": "https://api.github.com/users/SauravMaheshkar/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/SauravMaheshkar/subscriptions", "organizations_url": "https://api.github.com/users/SauravMaheshkar/orgs", "repos_url": "https://api.github.com/users/SauravMaheshkar/repos", "events_url": "https://api.github.com/users/SauravMaheshkar/events{/privacy}", "received_events_url": "https://api.github.com/users/SauravMaheshkar/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" }, { "id": 2067376369, "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request", "name": "dataset request", "color": "e99695", "default": false, "description": "Requesting to add a new dataset" } ]	open	false	null	[]	null	[ "Hey @SauravMaheshkar ! Just responded to your email.\r\n\r\n_For transparency, copying part of my response here:_\r\nI agree, it would be really great to have this and other BenchMD datasets easily accessible on the hub.\r\n\r\nI think the main limiting factor is that the ChexPert dataset is currently hosted on the Stanford AIMI Shared Datasets website, with a license that does not permit redistribution IIRC. Thus, I believe we would need to create a [dataset loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script) that would check authentication with the Stanford AIMI site before downloading and extracting the data. \r\n\r\nI've started a HF dataset repo [here](https://huggingface.co/datasets/katielink/CheXpert), in case you want to collaborate on writing up this loading script! I'm also happy to take a stab when I have some more time next week." ]	2023-11-04T15:36:11	2023-11-05T02:00:49	null	NONE	null	### Feature request ### Name CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison ### Paper https://arxiv.org/abs/1901.07031 ### Data https://stanfordaimi.azurewebsites.net/datasets/8cbd9ed4-2eb9-4565-affc-111cf4f7ebe2 ### Motivation CheXpert is one of the fundamental models in medical image classification and can serve as a viable pre-training dataset for radiology classification or low-scale ablation / exploratory studies. This could also serve as a good pre-training dataset for Kaggle competitions. ### Your contribution Would love to make a PR and pre-process / get this into 🤗	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6382/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6382/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6381	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6381/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6381/comments	https://api.github.com/repos/huggingface/datasets/issues/6381/events	https://github.com/huggingface/datasets/pull/6381	1,975,028,470	PR_kwDODunzps5eeYty	6,381	Add my dataset	{ "login": "keyur536", "id": 103646675, "node_id": "U_kgDOBi2F0w", "avatar_url": "https://avatars.githubusercontent.com/u/103646675?v=4", "gravatar_id": "", "url": "https://api.github.com/users/keyur536", "html_url": "https://github.com/keyur536", "followers_url": "https://api.github.com/users/keyur536/followers", "following_url": "https://api.github.com/users/keyur536/following{/other_user}", "gists_url": "https://api.github.com/users/keyur536/gists{/gist_id}", "starred_url": "https://api.github.com/users/keyur536/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/keyur536/subscriptions", "organizations_url": "https://api.github.com/users/keyur536/orgs", "repos_url": "https://api.github.com/users/keyur536/repos", "events_url": "https://api.github.com/users/keyur536/events{/privacy}", "received_events_url": "https://api.github.com/users/keyur536/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! We do not host datasets in this repo. Instead, you should use `dataset.push_to_hub` to upload the dataset to the HF Hub.", "@mariosasko could you provide me proper guide to push data on HF hub ", "You can find this info here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingface.co/docs/datasets/loading for how to load a local dataset (before pushing it to the Hub)." ]	2023-11-02T20:59:52	2023-11-08T14:37:46	2023-11-06T15:50:14	NONE	null	## medical data Description: This dataset, named "medical data," is a collection of text data from various sources, carefully curated and cleaned for use in natural language processing (NLP) tasks. It consists of a diverse range of text, including articles, books, and online content, covering topics from science to literature. Citation: If applicable, please include a citation for this dataset to give credit to the original sources or contributors. Key Features: - Language: The text is primarily in English, but it may include content in other languages as well. - Use Cases: This dataset is suitable for text classification, language modeling, sentiment analysis, and other NLP tasks. Usage: To access this dataset, use the `load_your_dataset` function provided in the `your_dataset.py` script within this repository. You can specify the dataset split you need, such as "train," "test," or "validation," to get the data for your specific task. Contributors: - [Keyur Chaudhari] Contact: If you have any questions or need assistance regarding this dataset, please feel free to contact [[email protected]]. Please note that this dataset is shared under a specific license, which can be found in the [LICENSE](link to your dataset's license) file. Make sure to review and adhere to the terms of the license when using this dataset for your projects.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6381/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6381/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6381", "html_url": "https://github.com/huggingface/datasets/pull/6381", "diff_url": "https://github.com/huggingface/datasets/pull/6381.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6381.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6380	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6380/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6380/comments	https://api.github.com/repos/huggingface/datasets/issues/6380/events	https://github.com/huggingface/datasets/pull/6380	1,974,741,221	PR_kwDODunzps5edaO6	6,380	Fix for continuation behaviour on broken dataset archives due to starving download connections via HTTP-GET	{ "login": "RuntimeRacer", "id": 49956579, "node_id": "MDQ6VXNlcjQ5OTU2NTc5", "avatar_url": "https://avatars.githubusercontent.com/u/49956579?v=4", "gravatar_id": "", "url": "https://api.github.com/users/RuntimeRacer", "html_url": "https://github.com/RuntimeRacer", "followers_url": "https://api.github.com/users/RuntimeRacer/followers", "following_url": "https://api.github.com/users/RuntimeRacer/following{/other_user}", "gists_url": "https://api.github.com/users/RuntimeRacer/gists{/gist_id}", "starred_url": "https://api.github.com/users/RuntimeRacer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/RuntimeRacer/subscriptions", "organizations_url": "https://api.github.com/users/RuntimeRacer/orgs", "repos_url": "https://api.github.com/users/RuntimeRacer/repos", "events_url": "https://api.github.com/users/RuntimeRacer/events{/privacy}", "received_events_url": "https://api.github.com/users/RuntimeRacer/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-11-02T17:28:23	2023-11-02T17:31:19	null	NONE	null	This PR proposes a (slightly hacky) fix for an Issue that can occur when downloading large dataset parts over unstable connections. The underlying issue is also being discussed in https://github.com/huggingface/datasets/issues/5594. Issue Symptoms & Behaviour: - Download of a large archive file during dataset download via HTTP-GET fails. - An silent net exception (which I was unable to identify) is thrown within the `tqdm` download progress. - Due to missing exception catch code, the above process just continues processing, assuming `http_get` completed successfully. - Pending Archive file gets renamed to remove the `.incomplete` extension, despite not all data has been downloaded. - Also, for reasons I did not investigate, there seems to be no real integrity check for the downloaded files; or it does not detect this problem. This is especially problematic, since the downloader script won't retry downloading this archive after CRC-Checking, even if it is being manually restarted / executed again after running into errors on extraction. Fix proposal: Adding a retry mechanic for HTTP-GET downloads, which adds the following behaviour: - Download Progress Thread checks for download size validity in case the HTTP connection starves mid download. If the check fails, a RuntimeError is thrown - Cache Downloader code with retry mechanic monitors for an exception thrown by the download progress thread, and retries download with updated `resume_size`. - Cache Downloader will not mark incomplete files which have thrown an exception during download, and exceeded retries, as complete.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6380/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6380/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6380", "html_url": "https://github.com/huggingface/datasets/pull/6380", "diff_url": "https://github.com/huggingface/datasets/pull/6380.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6380.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6379	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6379/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6379/comments	https://api.github.com/repos/huggingface/datasets/issues/6379/events	https://github.com/huggingface/datasets/pull/6379	1,974,638,850	PR_kwDODunzps5edDZL	6,379	Avoid redundant warning when encoding NumPy array as `Image`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008649 / 0.011353 (-0.002704) \| 0.005754 / 0.011008 (-0.005254) \| 0.101992 / 0.038508 (0.063484) \| 0.084932 / 0.023109 (0.061823) \| 0.393928 / 0.275898 (0.118030) \| 0.414059 / 0.323480 (0.090579) \| 0.006564 / 0.007986 (-0.001422) \| 0.004746 / 0.004328 (0.000418) \| 0.078624 / 0.004250 (0.074373) \| 0.060465 / 0.037052 (0.023412) \| 0.420767 / 0.258489 (0.162278) \| 0.497797 / 0.293841 (0.203956) \| 0.047031 / 0.128546 (-0.081516) \| 0.014316 / 0.075646 (-0.061330) \| 0.340347 / 0.419271 (-0.078925) \| 0.067126 / 0.043533 (0.023593) \| 0.390806 / 0.255139 (0.135667) \| 0.413711 / 0.283200 (0.130512) \| 0.037838 / 0.141683 (-0.103845) \| 1.713547 / 1.452155 (0.261393) \| 1.825591 / 1.492716 (0.332874) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.316357 / 0.018006 (0.298350) \| 0.594279 / 0.000490 (0.593789) \| 0.013659 / 0.000200 (0.013459) \| 0.000547 / 0.000054 (0.000492) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031310 / 0.037411 (-0.006101) \| 0.090410 / 0.014526 (0.075884) \| 0.114620 / 0.176557 (-0.061936) \| 0.183036 / 0.737135 (-0.554099) \| 0.112700 / 0.296338 (-0.183638) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.582424 / 0.215209 (0.367215) \| 5.670424 / 2.077655 (3.592769) \| 2.444326 / 1.504120 (0.940206) \| 2.108555 / 1.541195 (0.567360) \| 2.091594 / 1.468490 (0.623104) \| 0.839067 / 4.584777 (-3.745710) \| 5.280942 / 3.745712 (1.535230) \| 4.611059 / 5.269862 (-0.658803) \| 2.911145 / 4.565676 (-1.654531) \| 0.091929 / 0.424275 (-0.332346) \| 0.008774 / 0.007607 (0.001167) \| 0.657948 / 0.226044 (0.431904) \| 6.816300 / 2.268929 (4.547371) \| 3.232260 / 55.444624 (-52.212364) \| 2.479626 / 6.876477 (-4.396851) \| 2.497886 / 2.142072 (0.355813) \| 0.959160 / 4.805227 (-3.846068) \| 0.222306 / 6.500664 (-6.278358) \| 0.072962 / 0.075469 (-0.002507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.580415 / 1.841788 (-0.261372) \| 23.689597 / 8.074308 (15.615289) \| 20.430709 / 10.191392 (10.239317) \| 0.237891 / 0.680424 (-0.442533) \| 0.028194 / 0.534201 (-0.506007) \| 0.464915 / 0.579283 (-0.114368) \| 0.611512 / 0.434364 (0.177148) \| 0.556564 / 0.540337 (0.016227) \| 0.811075 / 1.386936 (-0.575861) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008703 / 0.011353 (-0.002649) \| 0.005030 / 0.011008 (-0.005978) \| 0.079251 / 0.038508 (0.040743) \| 0.079054 / 0.023109 (0.055945) \| 0.440220 / 0.275898 (0.164322) \| 0.479824 / 0.323480 (0.156344) \| 0.006312 / 0.007986 (-0.001673) \| 0.004506 / 0.004328 (0.000177) \| 0.078454 / 0.004250 (0.074203) \| 0.061041 / 0.037052 (0.023989) \| 0.490104 / 0.258489 (0.231615) \| 0.480925 / 0.293841 (0.187084) \| 0.049601 / 0.128546 (-0.078945) \| 0.013114 / 0.075646 (-0.062532) \| 0.092576 / 0.419271 (-0.326696) \| 0.059516 / 0.043533 (0.015983) \| 0.433728 / 0.255139 (0.178589) \| 0.490039 / 0.283200 (0.206839) \| 0.035359 / 0.141683 (-0.106324) \| 1.823618 / 1.452155 (0.371463) \| 1.980894 / 1.492716 (0.488178) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284679 / 0.018006 (0.266673) \| 0.606623 / 0.000490 (0.606133) \| 0.007531 / 0.000200 (0.007331) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033261 / 0.037411 (-0.004150) \| 0.102908 / 0.014526 (0.088382) \| 0.123912 / 0.176557 (-0.052644) \| 0.169893 / 0.737135 (-0.567242) \| 0.115366 / 0.296338 (-0.180973) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.598239 / 0.215209 (0.383030) \| 6.003464 / 2.077655 (3.925809) \| 2.828483 / 1.504120 (1.324363) \| 2.485996 / 1.541195 (0.944802) \| 2.434986 / 1.468490 (0.966496) \| 0.832718 / 4.584777 (-3.752058) \| 5.327407 / 3.745712 (1.581694) \| 4.732271 / 5.269862 (-0.537590) \| 3.047555 / 4.565676 (-1.518121) \| 0.103576 / 0.424275 (-0.320699) \| 0.009795 / 0.007607 (0.002188) \| 0.755443 / 0.226044 (0.529399) \| 7.465857 / 2.268929 (5.196928) \| 3.564923 / 55.444624 (-51.879701) \| 2.740483 / 6.876477 (-4.135994) \| 3.044993 / 2.142072 (0.902920) \| 1.012925 / 4.805227 (-3.792302) \| 0.207498 / 6.500664 (-6.293167) \| 0.073361 / 0.075469 (-0.002108) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.704988 / 1.841788 (-0.136800) \| 24.669992 / 8.074308 (16.595684) \| 21.103096 / 10.191392 (10.911704) \| 0.253759 / 0.680424 (-0.426665) \| 0.040109 / 0.534201 (-0.494092) \| 0.465646 / 0.579283 (-0.113637) \| 0.619696 / 0.434364 (0.185332) \| 0.552228 / 0.540337 (0.011890) \| 0.794907 / 1.386936 (-0.592029) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#85bba8991f6a2d9ed9fd4769d945eeaf318d3aa6 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006347 / 0.011353 (-0.005006) \| 0.003725 / 0.011008 (-0.007283) \| 0.080233 / 0.038508 (0.041725) \| 0.061013 / 0.023109 (0.037904) \| 0.390046 / 0.275898 (0.114148) \| 0.420526 / 0.323480 (0.097046) \| 0.003579 / 0.007986 (-0.004407) \| 0.002837 / 0.004328 (-0.001491) \| 0.062929 / 0.004250 (0.058678) \| 0.048781 / 0.037052 (0.011729) \| 0.400722 / 0.258489 (0.142233) \| 0.435022 / 0.293841 (0.141182) \| 0.027560 / 0.128546 (-0.100986) \| 0.007981 / 0.075646 (-0.067666) \| 0.262838 / 0.419271 (-0.156433) \| 0.045480 / 0.043533 (0.001947) \| 0.394443 / 0.255139 (0.139304) \| 0.413828 / 0.283200 (0.130628) \| 0.023375 / 0.141683 (-0.118307) \| 1.412865 / 1.452155 (-0.039290) \| 1.495761 / 1.492716 (0.003044) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224876 / 0.018006 (0.206870) \| 0.424234 / 0.000490 (0.423745) \| 0.007502 / 0.000200 (0.007302) \| 0.000220 / 0.000054 (0.000166) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024246 / 0.037411 (-0.013165) \| 0.073982 / 0.014526 (0.059456) \| 0.082704 / 0.176557 (-0.093852) \| 0.143137 / 0.737135 (-0.593998) \| 0.083398 / 0.296338 (-0.212941) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400220 / 0.215209 (0.185010) \| 3.973037 / 2.077655 (1.895382) \| 2.025903 / 1.504120 (0.521783) \| 1.912888 / 1.541195 (0.371693) \| 1.999578 / 1.468490 (0.531088) \| 0.499378 / 4.584777 (-4.085399) \| 3.025715 / 3.745712 (-0.719997) \| 2.992338 / 5.269862 (-2.277524) \| 1.851155 / 4.565676 (-2.714522) \| 0.057528 / 0.424275 (-0.366747) \| 0.006802 / 0.007607 (-0.000805) \| 0.469516 / 0.226044 (0.243471) \| 4.675630 / 2.268929 (2.406702) \| 2.472166 / 55.444624 (-52.972458) \| 2.238052 / 6.876477 (-4.638424) \| 2.288255 / 2.142072 (0.146183) \| 0.584906 / 4.805227 (-4.220321) \| 0.125902 / 6.500664 (-6.374762) \| 0.060681 / 0.075469 (-0.014788) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.236383 / 1.841788 (-0.605404) \| 17.554238 / 8.074308 (9.479930) \| 13.749298 / 10.191392 (3.557906) \| 0.144715 / 0.680424 (-0.535708) \| 0.017449 / 0.534201 (-0.516752) \| 0.334831 / 0.579283 (-0.244452) \| 0.362660 / 0.434364 (-0.071704) \| 0.385295 / 0.540337 (-0.155043) \| 0.541173 / 1.386936 (-0.845763) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006118 / 0.011353 (-0.005235) \| 0.003660 / 0.011008 (-0.007348) \| 0.062373 / 0.038508 (0.023865) \| 0.063404 / 0.023109 (0.040295) \| 0.354149 / 0.275898 (0.078251) \| 0.410324 / 0.323480 (0.086844) \| 0.004826 / 0.007986 (-0.003160) \| 0.002881 / 0.004328 (-0.001448) \| 0.061631 / 0.004250 (0.057381) \| 0.048052 / 0.037052 (0.010999) \| 0.352905 / 0.258489 (0.094416) \| 0.400096 / 0.293841 (0.106255) \| 0.028472 / 0.128546 (-0.100075) \| 0.008076 / 0.075646 (-0.067571) \| 0.067910 / 0.419271 (-0.351362) \| 0.040671 / 0.043533 (-0.002862) \| 0.352131 / 0.255139 (0.096992) \| 0.402140 / 0.283200 (0.118940) \| 0.020065 / 0.141683 (-0.121618) \| 1.456938 / 1.452155 (0.004783) \| 1.506484 / 1.492716 (0.013767) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222295 / 0.018006 (0.204288) \| 0.416672 / 0.000490 (0.416183) \| 0.003015 / 0.000200 (0.002815) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026428 / 0.037411 (-0.010983) \| 0.080072 / 0.014526 (0.065547) \| 0.089992 / 0.176557 (-0.086564) \| 0.141739 / 0.737135 (-0.595397) \| 0.092281 / 0.296338 (-0.204058) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417758 / 0.215209 (0.202549) \| 4.175673 / 2.077655 (2.098018) \| 2.262369 / 1.504120 (0.758249) \| 2.100440 / 1.541195 (0.559246) \| 2.075827 / 1.468490 (0.607337) \| 0.505673 / 4.584777 (-4.079104) \| 3.129020 / 3.745712 (-0.616692) \| 2.843255 / 5.269862 (-2.426607) \| 1.853288 / 4.565676 (-2.712389) \| 0.058337 / 0.424275 (-0.365938) \| 0.006461 / 0.007607 (-0.001147) \| 0.491797 / 0.226044 (0.265753) \| 4.933327 / 2.268929 (2.664399) \| 2.675374 / 55.444624 (-52.769250) \| 2.358103 / 6.876477 (-4.518374) \| 2.540436 / 2.142072 (0.398363) \| 0.591550 / 4.805227 (-4.213677) \| 0.121572 / 6.500664 (-6.379092) \| 0.057311 / 0.075469 (-0.018158) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.365368 / 1.841788 (-0.476419) \| 17.763413 / 8.074308 (9.689105) \| 14.368754 / 10.191392 (4.177362) \| 0.132979 / 0.680424 (-0.547445) \| 0.017957 / 0.534201 (-0.516244) \| 0.334035 / 0.579283 (-0.245248) \| 0.385349 / 0.434364 (-0.049015) \| 0.392636 / 0.540337 (-0.147702) \| 0.537957 / 1.386936 (-0.848979) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#92503c94839b31125b4d5288d0a49d81b9b9b3cc \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008053 / 0.011353 (-0.003300) \| 0.004966 / 0.011008 (-0.006043) \| 0.102219 / 0.038508 (0.063711) \| 0.099319 / 0.023109 (0.076210) \| 0.418458 / 0.275898 (0.142559) \| 0.459344 / 0.323480 (0.135864) \| 0.004756 / 0.007986 (-0.003229) \| 0.003940 / 0.004328 (-0.000388) \| 0.076824 / 0.004250 (0.072573) \| 0.068090 / 0.037052 (0.031038) \| 0.428689 / 0.258489 (0.170200) \| 0.476153 / 0.293841 (0.182312) \| 0.036927 / 0.128546 (-0.091619) \| 0.010232 / 0.075646 (-0.065414) \| 0.345126 / 0.419271 (-0.074145) \| 0.063182 / 0.043533 (0.019649) \| 0.416633 / 0.255139 (0.161494) \| 0.437418 / 0.283200 (0.154218) \| 0.028192 / 0.141683 (-0.113491) \| 1.768869 / 1.452155 (0.316715) \| 1.847022 / 1.492716 (0.354306) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269997 / 0.018006 (0.251991) \| 0.544246 / 0.000490 (0.543756) \| 0.012940 / 0.000200 (0.012740) \| 0.000754 / 0.000054 (0.000699) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035570 / 0.037411 (-0.001842) \| 0.104318 / 0.014526 (0.089792) \| 0.115263 / 0.176557 (-0.061294) \| 0.184693 / 0.737135 (-0.552442) \| 0.116023 / 0.296338 (-0.180315) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472361 / 0.215209 (0.257152) \| 4.714327 / 2.077655 (2.636673) \| 2.405434 / 1.504120 (0.901314) \| 2.197871 / 1.541195 (0.656677) \| 2.312901 / 1.468490 (0.844411) \| 0.569736 / 4.584777 (-4.015041) \| 4.600008 / 3.745712 (0.854296) \| 4.127967 / 5.269862 (-1.141895) \| 2.462232 / 4.565676 (-2.103445) \| 0.067759 / 0.424275 (-0.356516) \| 0.009277 / 0.007607 (0.001670) \| 0.569658 / 0.226044 (0.343614) \| 5.694050 / 2.268929 (3.425121) \| 3.041495 / 55.444624 (-52.403129) \| 2.688418 / 6.876477 (-4.188059) \| 2.762175 / 2.142072 (0.620102) \| 0.683250 / 4.805227 (-4.121977) \| 0.158772 / 6.500664 (-6.341892) \| 0.073364 / 0.075469 (-0.002105) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.627241 / 1.841788 (-0.214547) \| 23.054465 / 8.074308 (14.980157) \| 17.122451 / 10.191392 (6.931059) \| 0.170272 / 0.680424 (-0.510152) \| 0.021678 / 0.534201 (-0.512523) \| 0.467301 / 0.579283 (-0.111982) \| 0.509480 / 0.434364 (0.075116) \| 0.555077 / 0.540337 (0.014740) \| 0.816199 / 1.386936 (-0.570737) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008499 / 0.011353 (-0.002854) \| 0.004724 / 0.011008 (-0.006284) \| 0.077519 / 0.038508 (0.039011) \| 0.103237 / 0.023109 (0.080127) \| 0.447470 / 0.275898 (0.171572) \| 0.484778 / 0.323480 (0.161298) \| 0.006475 / 0.007986 (-0.001511) \| 0.003946 / 0.004328 (-0.000383) \| 0.075596 / 0.004250 (0.071346) \| 0.069265 / 0.037052 (0.032213) \| 0.454185 / 0.258489 (0.195696) \| 0.491039 / 0.293841 (0.197198) \| 0.038611 / 0.128546 (-0.089935) \| 0.009889 / 0.075646 (-0.065758) \| 0.084012 / 0.419271 (-0.335260) \| 0.057265 / 0.043533 (0.013732) \| 0.448622 / 0.255139 (0.193483) \| 0.470961 / 0.283200 (0.187762) \| 0.029220 / 0.141683 (-0.112463) \| 1.773347 / 1.452155 (0.321192) \| 1.872669 / 1.492716 (0.379953) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272429 / 0.018006 (0.254423) \| 0.569907 / 0.000490 (0.569418) \| 0.013359 / 0.000200 (0.013159) \| 0.000187 / 0.000054 (0.000133) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038784 / 0.037411 (0.001373) \| 0.114958 / 0.014526 (0.100432) \| 0.132745 / 0.176557 (-0.043811) \| 0.186283 / 0.737135 (-0.550852) \| 0.126652 / 0.296338 (-0.169686) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.482753 / 0.215209 (0.267544) \| 4.827287 / 2.077655 (2.749633) \| 2.539959 / 1.504120 (1.035839) \| 2.348483 / 1.541195 (0.807288) \| 2.421739 / 1.468490 (0.953249) \| 0.586064 / 4.584777 (-3.998713) \| 4.579865 / 3.745712 (0.834152) \| 3.950617 / 5.269862 (-1.319244) \| 2.528447 / 4.565676 (-2.037229) \| 0.070280 / 0.424275 (-0.353995) \| 0.008801 / 0.007607 (0.001194) \| 0.568857 / 0.226044 (0.342812) \| 5.692739 / 2.268929 (3.423810) \| 3.192045 / 55.444624 (-52.252579) \| 2.768092 / 6.876477 (-4.108384) \| 3.002934 / 2.142072 (0.860862) \| 0.701887 / 4.805227 (-4.103340) \| 0.155563 / 6.500664 (-6.345102) \| 0.069397 / 0.075469 (-0.006072) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.607991 / 1.841788 (-0.233796) \| 24.658060 / 8.074308 (16.583752) \| 17.616229 / 10.191392 (7.424837) \| 0.209730 / 0.680424 (-0.470693) \| 0.024052 / 0.534201 (-0.510149) \| 0.476648 / 0.579283 (-0.102635) \| 0.534452 / 0.434364 (0.100089) \| 0.567702 / 0.540337 (0.027365) \| 0.772933 / 1.386936 (-0.614003) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49e78ede85c2a680adddacbb6b9638cba4062f3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004684 / 0.011353 (-0.006669) \| 0.002944 / 0.011008 (-0.008064) \| 0.063065 / 0.038508 (0.024557) \| 0.051627 / 0.023109 (0.028518) \| 0.243485 / 0.275898 (-0.032413) \| 0.275144 / 0.323480 (-0.048336) \| 0.002934 / 0.007986 (-0.005052) \| 0.002395 / 0.004328 (-0.001934) \| 0.048579 / 0.004250 (0.044328) \| 0.038940 / 0.037052 (0.001887) \| 0.250244 / 0.258489 (-0.008245) \| 0.287404 / 0.293841 (-0.006437) \| 0.022958 / 0.128546 (-0.105588) \| 0.007189 / 0.075646 (-0.068458) \| 0.202483 / 0.419271 (-0.216788) \| 0.035477 / 0.043533 (-0.008056) \| 0.243793 / 0.255139 (-0.011346) \| 0.265990 / 0.283200 (-0.017209) \| 0.019675 / 0.141683 (-0.122008) \| 1.119127 / 1.452155 (-0.333028) \| 1.183230 / 1.492716 (-0.309486) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097090 / 0.018006 (0.079084) \| 0.305815 / 0.000490 (0.305325) \| 0.000228 / 0.000200 (0.000028) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019233 / 0.037411 (-0.018178) \| 0.061743 / 0.014526 (0.047217) \| 0.077033 / 0.176557 (-0.099524) \| 0.119786 / 0.737135 (-0.617349) \| 0.074740 / 0.296338 (-0.221598) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284361 / 0.215209 (0.069152) \| 2.761501 / 2.077655 (0.683846) \| 1.464980 / 1.504120 (-0.039140) \| 1.348026 / 1.541195 (-0.193169) \| 1.362690 / 1.468490 (-0.105800) \| 0.392022 / 4.584777 (-4.192755) \| 2.401330 / 3.745712 (-1.344382) \| 2.618999 / 5.269862 (-2.650863) \| 1.599526 / 4.565676 (-2.966150) \| 0.045621 / 0.424275 (-0.378654) \| 0.005153 / 0.007607 (-0.002454) \| 0.337279 / 0.226044 (0.111234) \| 3.330135 / 2.268929 (1.061206) \| 1.803544 / 55.444624 (-53.641081) \| 1.515545 / 6.876477 (-5.360932) \| 1.561745 / 2.142072 (-0.580327) \| 0.468735 / 4.805227 (-4.336492) \| 0.098882 / 6.500664 (-6.401782) \| 0.042923 / 0.075469 (-0.032546) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.961106 / 1.841788 (-0.880682) \| 12.030489 / 8.074308 (3.956181) \| 10.824166 / 10.191392 (0.632774) \| 0.132135 / 0.680424 (-0.548289) \| 0.015320 / 0.534201 (-0.518881) \| 0.269691 / 0.579283 (-0.309592) \| 0.270700 / 0.434364 (-0.163664) \| 0.308317 / 0.540337 (-0.232020) \| 0.397871 / 1.386936 (-0.989065) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004859 / 0.011353 (-0.006494) \| 0.003400 / 0.011008 (-0.007609) \| 0.048095 / 0.038508 (0.009587) \| 0.054885 / 0.023109 (0.031776) \| 0.276976 / 0.275898 (0.001078) \| 0.302298 / 0.323480 (-0.021182) \| 0.004084 / 0.007986 (-0.003902) \| 0.002647 / 0.004328 (-0.001681) \| 0.048570 / 0.004250 (0.044319) \| 0.040683 / 0.037052 (0.003631) \| 0.279828 / 0.258489 (0.021339) \| 0.306037 / 0.293841 (0.012196) \| 0.024263 / 0.128546 (-0.104283) \| 0.007336 / 0.075646 (-0.068310) \| 0.053768 / 0.419271 (-0.365503) \| 0.032284 / 0.043533 (-0.011248) \| 0.276706 / 0.255139 (0.021567) \| 0.294706 / 0.283200 (0.011506) \| 0.018092 / 0.141683 (-0.123591) \| 1.153430 / 1.452155 (-0.298725) \| 1.208783 / 1.492716 (-0.283933) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096946 / 0.018006 (0.078939) \| 0.308118 / 0.000490 (0.307628) \| 0.000234 / 0.000200 (0.000034) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021834 / 0.037411 (-0.015577) \| 0.070934 / 0.014526 (0.056408) \| 0.080310 / 0.176557 (-0.096247) \| 0.123299 / 0.737135 (-0.613836) \| 0.081591 / 0.296338 (-0.214748) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302242 / 0.215209 (0.087033) \| 2.934477 / 2.077655 (0.856822) \| 1.623768 / 1.504120 (0.119648) \| 1.493868 / 1.541195 (-0.047326) \| 1.516553 / 1.468490 (0.048063) \| 0.410319 / 4.584777 (-4.174458) \| 2.471346 / 3.745712 (-1.274366) \| 2.667371 / 5.269862 (-2.602491) \| 1.625390 / 4.565676 (-2.940286) \| 0.046465 / 0.424275 (-0.377810) \| 0.004867 / 0.007607 (-0.002740) \| 0.355516 / 0.226044 (0.129471) \| 3.442294 / 2.268929 (1.173365) \| 1.973859 / 55.444624 (-53.470765) \| 1.682089 / 6.876477 (-5.194388) \| 1.865253 / 2.142072 (-0.276819) \| 0.475750 / 4.805227 (-4.329477) \| 0.098298 / 6.500664 (-6.402366) \| 0.041025 / 0.075469 (-0.034445) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969864 / 1.841788 (-0.871924) \| 12.437806 / 8.074308 (4.363498) \| 10.461262 / 10.191392 (0.269870) \| 0.131051 / 0.680424 (-0.549373) \| 0.016232 / 0.534201 (-0.517969) \| 0.273968 / 0.579283 (-0.305315) \| 0.285369 / 0.434364 (-0.148995) \| 0.309046 / 0.540337 (-0.231291) \| 0.398776 / 1.386936 (-0.988160) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49e78ede85c2a680adddacbb6b9638cba4062f3 \"CML watermark\")\n" ]	2023-11-02T16:37:58	2023-11-06T17:53:27	2023-11-02T17:08:07	CONTRIBUTOR	null	Avoid a redundant warning in `encode_np_array` by removing the identity check as NumPy `dtype`s can be equal without having identical `id`s. Additionally, fix "unreachable" checks in `encode_np_array`.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6379/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6379/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6379", "html_url": "https://github.com/huggingface/datasets/pull/6379", "diff_url": "https://github.com/huggingface/datasets/pull/6379.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6379.patch", "merged_at": "2023-11-02T17:08:07" }	true
https://api.github.com/repos/huggingface/datasets/issues/6378	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6378/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6378/comments	https://api.github.com/repos/huggingface/datasets/issues/6378/events	https://github.com/huggingface/datasets/pull/6378	1,973,942,770	PR_kwDODunzps5eaqhv	6,378	Support pyarrow 14.0.0	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007561 / 0.011353 (-0.003792) \| 0.004824 / 0.011008 (-0.006184) \| 0.110372 / 0.038508 (0.071864) \| 0.076767 / 0.023109 (0.053657) \| 0.357094 / 0.275898 (0.081196) \| 0.420566 / 0.323480 (0.097086) \| 0.004753 / 0.007986 (-0.003232) \| 0.004734 / 0.004328 (0.000405) \| 0.072926 / 0.004250 (0.068675) \| 0.058045 / 0.037052 (0.020992) \| 0.401109 / 0.258489 (0.142620) \| 0.444585 / 0.293841 (0.150744) \| 0.046492 / 0.128546 (-0.082055) \| 0.013948 / 0.075646 (-0.061698) \| 0.305188 / 0.419271 (-0.114083) \| 0.063112 / 0.043533 (0.019579) \| 0.384711 / 0.255139 (0.129572) \| 0.411375 / 0.283200 (0.128175) \| 0.048147 / 0.141683 (-0.093536) \| 1.632357 / 1.452155 (0.180202) \| 1.661021 / 1.492716 (0.168304) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281104 / 0.018006 (0.263098) \| 0.567152 / 0.000490 (0.566662) \| 0.007178 / 0.000200 (0.006978) \| 0.000121 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029337 / 0.037411 (-0.008075) \| 0.081644 / 0.014526 (0.067118) \| 0.103326 / 0.176557 (-0.073230) \| 0.155299 / 0.737135 (-0.581836) \| 0.093518 / 0.296338 (-0.202821) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.517979 / 0.215209 (0.302769) \| 5.250052 / 2.077655 (3.172397) \| 2.220543 / 1.504120 (0.716424) \| 1.901087 / 1.541195 (0.359892) \| 1.920564 / 1.468490 (0.452073) \| 0.766289 / 4.584777 (-3.818488) \| 5.130968 / 3.745712 (1.385256) \| 4.561874 / 5.269862 (-0.707988) \| 2.702808 / 4.565676 (-1.862868) \| 0.078929 / 0.424275 (-0.345346) \| 0.007834 / 0.007607 (0.000226) \| 0.636628 / 0.226044 (0.410583) \| 6.309391 / 2.268929 (4.040463) \| 2.942180 / 55.444624 (-52.502445) \| 2.369557 / 6.876477 (-4.506920) \| 2.347528 / 2.142072 (0.205456) \| 0.911110 / 4.805227 (-3.894117) \| 0.189102 / 6.500664 (-6.311562) \| 0.068012 / 0.075469 (-0.007457) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.494431 / 1.841788 (-0.347356) \| 22.161476 / 8.074308 (14.087168) \| 19.426403 / 10.191392 (9.235011) \| 0.211154 / 0.680424 (-0.469270) \| 0.030655 / 0.534201 (-0.503546) \| 0.440449 / 0.579283 (-0.138834) \| 0.526522 / 0.434364 (0.092158) \| 0.517494 / 0.540337 (-0.022844) \| 0.727387 / 1.386936 (-0.659549) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008354 / 0.011353 (-0.002999) \| 0.006108 / 0.011008 (-0.004900) \| 0.069079 / 0.038508 (0.030571) \| 0.080402 / 0.023109 (0.057292) \| 0.452166 / 0.275898 (0.176268) \| 0.440264 / 0.323480 (0.116784) \| 0.005942 / 0.007986 (-0.002043) \| 0.003397 / 0.004328 (-0.000932) \| 0.079856 / 0.004250 (0.075606) \| 0.056329 / 0.037052 (0.019276) \| 0.424261 / 0.258489 (0.165772) \| 0.464362 / 0.293841 (0.170521) \| 0.051968 / 0.128546 (-0.076578) \| 0.015204 / 0.075646 (-0.060442) \| 0.085940 / 0.419271 (-0.333332) \| 0.066673 / 0.043533 (0.023140) \| 0.436481 / 0.255139 (0.181342) \| 0.445285 / 0.283200 (0.162085) \| 0.035188 / 0.141683 (-0.106495) \| 1.579442 / 1.452155 (0.127288) \| 1.686120 / 1.492716 (0.193404) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319039 / 0.018006 (0.301032) \| 0.655080 / 0.000490 (0.654591) \| 0.005445 / 0.000200 (0.005245) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028566 / 0.037411 (-0.008845) \| 0.092131 / 0.014526 (0.077605) \| 0.103654 / 0.176557 (-0.072902) \| 0.158082 / 0.737135 (-0.579054) \| 0.107520 / 0.296338 (-0.188819) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.573479 / 0.215209 (0.358270) \| 5.629751 / 2.077655 (3.552096) \| 2.501722 / 1.504120 (0.997602) \| 2.156255 / 1.541195 (0.615061) \| 2.251296 / 1.468490 (0.782805) \| 0.767686 / 4.584777 (-3.817091) \| 5.080866 / 3.745712 (1.335154) \| 4.353351 / 5.269862 (-0.916510) \| 2.818707 / 4.565676 (-1.746970) \| 0.082617 / 0.424275 (-0.341658) \| 0.008045 / 0.007607 (0.000438) \| 0.665462 / 0.226044 (0.439417) \| 6.961380 / 2.268929 (4.692452) \| 3.308717 / 55.444624 (-52.135907) \| 2.664239 / 6.876477 (-4.212238) \| 2.782790 / 2.142072 (0.640718) \| 0.919567 / 4.805227 (-3.885660) \| 0.186731 / 6.500664 (-6.313933) \| 0.063437 / 0.075469 (-0.012032) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.668076 / 1.841788 (-0.173712) \| 22.720187 / 8.074308 (14.645879) \| 19.803359 / 10.191392 (9.611967) \| 0.237201 / 0.680424 (-0.443223) \| 0.041156 / 0.534201 (-0.493045) \| 0.458974 / 0.579283 (-0.120309) \| 0.620276 / 0.434364 (0.185912) \| 0.544079 / 0.540337 (0.003741) \| 0.722715 / 1.386936 (-0.664221) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ed9306b6c512befb721b681fba3222221c8468e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006882 / 0.011353 (-0.004471) \| 0.004238 / 0.011008 (-0.006770) \| 0.084042 / 0.038508 (0.045534) \| 0.074175 / 0.023109 (0.051065) \| 0.308771 / 0.275898 (0.032873) \| 0.346300 / 0.323480 (0.022820) \| 0.005455 / 0.007986 (-0.002530) \| 0.003638 / 0.004328 (-0.000690) \| 0.065326 / 0.004250 (0.061076) \| 0.056080 / 0.037052 (0.019028) \| 0.326324 / 0.258489 (0.067834) \| 0.360133 / 0.293841 (0.066292) \| 0.031577 / 0.128546 (-0.096969) \| 0.008675 / 0.075646 (-0.066971) \| 0.288051 / 0.419271 (-0.131221) \| 0.052769 / 0.043533 (0.009236) \| 0.308689 / 0.255139 (0.053550) \| 0.328270 / 0.283200 (0.045070) \| 0.025028 / 0.141683 (-0.116655) \| 1.520670 / 1.452155 (0.068515) \| 1.585229 / 1.492716 (0.092513) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284078 / 0.018006 (0.266072) \| 0.558134 / 0.000490 (0.557644) \| 0.015042 / 0.000200 (0.014842) \| 0.000429 / 0.000054 (0.000375) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028747 / 0.037411 (-0.008664) \| 0.083816 / 0.014526 (0.069290) \| 0.207467 / 0.176557 (0.030911) \| 0.163527 / 0.737135 (-0.573608) \| 0.100148 / 0.296338 (-0.196190) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.376109 / 0.215209 (0.160900) \| 3.749639 / 2.077655 (1.671984) \| 1.827081 / 1.504120 (0.322961) \| 1.662021 / 1.541195 (0.120827) \| 1.734655 / 1.468490 (0.266165) \| 0.483701 / 4.584777 (-4.101075) \| 3.454772 / 3.745712 (-0.290941) \| 3.465079 / 5.269862 (-1.804783) \| 2.070874 / 4.565676 (-2.494802) \| 0.056714 / 0.424275 (-0.367561) \| 0.007786 / 0.007607 (0.000179) \| 0.455980 / 0.226044 (0.229936) \| 4.530612 / 2.268929 (2.261683) \| 2.345757 / 55.444624 (-53.098867) \| 2.030289 / 6.876477 (-4.846188) \| 2.068440 / 2.142072 (-0.073632) \| 0.576502 / 4.805227 (-4.228725) \| 0.131787 / 6.500664 (-6.368878) \| 0.060038 / 0.075469 (-0.015431) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272225 / 1.841788 (-0.569563) \| 19.373635 / 8.074308 (11.299327) \| 14.167831 / 10.191392 (3.976439) \| 0.166336 / 0.680424 (-0.514088) \| 0.018420 / 0.534201 (-0.515781) \| 0.387878 / 0.579283 (-0.191405) \| 0.413105 / 0.434364 (-0.021259) \| 0.458618 / 0.540337 (-0.081720) \| 0.639031 / 1.386936 (-0.747905) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007122 / 0.011353 (-0.004230) \| 0.004193 / 0.011008 (-0.006815) \| 0.066194 / 0.038508 (0.027686) \| 0.077775 / 0.023109 (0.054666) \| 0.349780 / 0.275898 (0.073882) \| 0.383417 / 0.323480 (0.059937) \| 0.006416 / 0.007986 (-0.001570) \| 0.003651 / 0.004328 (-0.000677) \| 0.064837 / 0.004250 (0.060587) \| 0.058012 / 0.037052 (0.020959) \| 0.351085 / 0.258489 (0.092596) \| 0.387302 / 0.293841 (0.093462) \| 0.032447 / 0.128546 (-0.096099) \| 0.008636 / 0.075646 (-0.067011) \| 0.071962 / 0.419271 (-0.347309) \| 0.047839 / 0.043533 (0.004306) \| 0.349508 / 0.255139 (0.094369) \| 0.361892 / 0.283200 (0.078693) \| 0.024129 / 0.141683 (-0.117554) \| 1.523828 / 1.452155 (0.071673) \| 1.607371 / 1.492716 (0.114655) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245928 / 0.018006 (0.227922) \| 0.567708 / 0.000490 (0.567218) \| 0.003789 / 0.000200 (0.003589) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034107 / 0.037411 (-0.003304) \| 0.092539 / 0.014526 (0.078014) \| 0.110735 / 0.176557 (-0.065821) \| 0.163251 / 0.737135 (-0.573884) \| 0.110353 / 0.296338 (-0.185985) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399992 / 0.215209 (0.184783) \| 3.976526 / 2.077655 (1.898872) \| 2.056182 / 1.504120 (0.552062) \| 1.856624 / 1.541195 (0.315429) \| 1.941540 / 1.468490 (0.473050) \| 0.484662 / 4.584777 (-4.100115) \| 3.548228 / 3.745712 (-0.197484) \| 3.352900 / 5.269862 (-1.916962) \| 2.056310 / 4.565676 (-2.509366) \| 0.056952 / 0.424275 (-0.367323) \| 0.007284 / 0.007607 (-0.000323) \| 0.473749 / 0.226044 (0.247704) \| 4.736510 / 2.268929 (2.467581) \| 2.570711 / 55.444624 (-52.873913) \| 2.204237 / 6.876477 (-4.672239) \| 2.438512 / 2.142072 (0.296439) \| 0.575542 / 4.805227 (-4.229685) \| 0.129260 / 6.500664 (-6.371404) \| 0.057704 / 0.075469 (-0.017765) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.316659 / 1.841788 (-0.525128) \| 20.103340 / 8.074308 (12.029032) \| 14.488385 / 10.191392 (4.296993) \| 0.171841 / 0.680424 (-0.508583) \| 0.020148 / 0.534201 (-0.514053) \| 0.398456 / 0.579283 (-0.180828) \| 0.443516 / 0.434364 (0.009152) \| 0.479597 / 0.540337 (-0.060741) \| 0.643665 / 1.386936 (-0.743271) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#370be814b0c18769ea8e699e3647fadcf431e6df \"CML watermark\")\n" ]	2023-11-02T10:25:10	2023-11-02T15:24:28	2023-11-02T15:15:44	MEMBER	null	Support `pyarrow` 14.0.0. Fix #6377 and fix #6374 (root cause). This fix is analog to a previous one: - #6175	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6378/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6378/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6378", "html_url": "https://github.com/huggingface/datasets/pull/6378", "diff_url": "https://github.com/huggingface/datasets/pull/6378.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6378.patch", "merged_at": "2023-11-02T15:15:44" }	true
https://api.github.com/repos/huggingface/datasets/issues/6377	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6377/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6377/comments	https://api.github.com/repos/huggingface/datasets/issues/6377/events	https://github.com/huggingface/datasets/issues/6377	1,973,937,612	I_kwDODunzps51p-XM	6,377	Support pyarrow 14.0.0	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[]	2023-11-02T10:22:08	2023-11-02T15:15:45	2023-11-02T15:15:45	MEMBER	null	Support pyarrow 14.0.0 by fixing the root cause of: - #6374 and revert: - #6375	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6377/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6377/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6376	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6376/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6376/comments	https://api.github.com/repos/huggingface/datasets/issues/6376/events	https://github.com/huggingface/datasets/issues/6376	1,973,927,468	I_kwDODunzps51p74s	6,376	Caching problem when deleting a dataset	{ "login": "clefourrier", "id": 22726840, "node_id": "MDQ6VXNlcjIyNzI2ODQw", "avatar_url": "https://avatars.githubusercontent.com/u/22726840?v=4", "gravatar_id": "", "url": "https://api.github.com/users/clefourrier", "html_url": "https://github.com/clefourrier", "followers_url": "https://api.github.com/users/clefourrier/followers", "following_url": "https://api.github.com/users/clefourrier/following{/other_user}", "gists_url": "https://api.github.com/users/clefourrier/gists{/gist_id}", "starred_url": "https://api.github.com/users/clefourrier/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/clefourrier/subscriptions", "organizations_url": "https://api.github.com/users/clefourrier/orgs", "repos_url": "https://api.github.com/users/clefourrier/repos", "events_url": "https://api.github.com/users/clefourrier/events{/privacy}", "received_events_url": "https://api.github.com/users/clefourrier/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Thanks for reporting! Can you also share the error message printed in step 5?", "I did not store it at the time but I'll try to re-do a mwe next week to get it again" ]	2023-11-02T10:15:58	2023-11-03T07:38:14	null	MEMBER	null	### Describe the bug Pushing a dataset with n + m features to a repo which was deleted, but contained n features, will fail. ### Steps to reproduce the bug 1. Create a dataset with n features per row 2. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` 3. Go on the hub, delete the repo at `YOUR_PATH` 4. Update your local dataset to have n + m features per row 5. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` will fail because of a mismatch in features number ### Expected behavior Step 5 should work or display a message to indicate the cache has not been cleared ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-88-generic-x86_64-with-glibc2.31 - Python version: 3.10.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6376/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6376/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6375	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6375/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6375/comments	https://api.github.com/repos/huggingface/datasets/issues/6375/events	https://github.com/huggingface/datasets/pull/6375	1,973,877,879	PR_kwDODunzps5eacao	6,375	Temporarily pin pyarrow < 14.0.0	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008947 / 0.011353 (-0.002406) \| 0.005602 / 0.011008 (-0.005406) \| 0.111208 / 0.038508 (0.072700) \| 0.082750 / 0.023109 (0.059641) \| 0.453277 / 0.275898 (0.177379) \| 0.480072 / 0.323480 (0.156592) \| 0.005254 / 0.007986 (-0.002731) \| 0.005421 / 0.004328 (0.001092) \| 0.082899 / 0.004250 (0.078648) \| 0.062859 / 0.037052 (0.025807) \| 0.466703 / 0.258489 (0.208214) \| 0.478241 / 0.293841 (0.184400) \| 0.050754 / 0.128546 (-0.077792) \| 0.017726 / 0.075646 (-0.057920) \| 0.374830 / 0.419271 (-0.044442) \| 0.068577 / 0.043533 (0.025044) \| 0.453643 / 0.255139 (0.198504) \| 0.453736 / 0.283200 (0.170537) \| 0.037313 / 0.141683 (-0.104369) \| 1.741215 / 1.452155 (0.289060) \| 1.862247 / 1.492716 (0.369531) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314174 / 0.018006 (0.296168) \| 0.644439 / 0.000490 (0.643949) \| 0.013914 / 0.000200 (0.013715) \| 0.000478 / 0.000054 (0.000424) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030462 / 0.037411 (-0.006949) \| 0.096789 / 0.014526 (0.082263) \| 0.109999 / 0.176557 (-0.066557) \| 0.184610 / 0.737135 (-0.552525) \| 0.113846 / 0.296338 (-0.182493) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.586508 / 0.215209 (0.371299) \| 5.785138 / 2.077655 (3.707484) \| 2.578512 / 1.504120 (1.074392) \| 2.266981 / 1.541195 (0.725786) \| 2.442463 / 1.468490 (0.973973) \| 0.880973 / 4.584777 (-3.703804) \| 5.410327 / 3.745712 (1.664615) \| 4.976842 / 5.269862 (-0.293020) \| 3.020535 / 4.565676 (-1.545142) \| 0.089640 / 0.424275 (-0.334635) \| 0.009126 / 0.007607 (0.001519) \| 0.682364 / 0.226044 (0.456319) \| 6.840507 / 2.268929 (4.571579) \| 3.313314 / 55.444624 (-52.131310) \| 2.815313 / 6.876477 (-4.061164) \| 2.851787 / 2.142072 (0.709715) \| 1.044916 / 4.805227 (-3.760312) \| 0.218346 / 6.500664 (-6.282318) \| 0.075655 / 0.075469 (0.000186) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.641767 / 1.841788 (-0.200020) \| 24.618096 / 8.074308 (16.543788) \| 21.557652 / 10.191392 (11.366260) \| 0.211622 / 0.680424 (-0.468801) \| 0.028775 / 0.534201 (-0.505426) \| 0.480469 / 0.579283 (-0.098814) \| 0.593311 / 0.434364 (0.158948) \| 0.560620 / 0.540337 (0.020283) \| 0.827026 / 1.386936 (-0.559910) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009347 / 0.011353 (-0.002006) \| 0.005184 / 0.011008 (-0.005824) \| 0.078878 / 0.038508 (0.040370) \| 0.083067 / 0.023109 (0.059957) \| 0.446591 / 0.275898 (0.170693) \| 0.512934 / 0.323480 (0.189454) \| 0.006614 / 0.007986 (-0.001372) \| 0.004477 / 0.004328 (0.000148) \| 0.087403 / 0.004250 (0.083153) \| 0.060710 / 0.037052 (0.023658) \| 0.451811 / 0.258489 (0.193322) \| 0.482031 / 0.293841 (0.188190) \| 0.051685 / 0.128546 (-0.076862) \| 0.013436 / 0.075646 (-0.062210) \| 0.109012 / 0.419271 (-0.310259) \| 0.059654 / 0.043533 (0.016121) \| 0.439041 / 0.255139 (0.183902) \| 0.481708 / 0.283200 (0.198508) \| 0.037393 / 0.141683 (-0.104290) \| 1.761704 / 1.452155 (0.309549) \| 1.946711 / 1.492716 (0.453995) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287981 / 0.018006 (0.269975) \| 0.610219 / 0.000490 (0.609729) \| 0.006733 / 0.000200 (0.006533) \| 0.000128 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038999 / 0.037411 (0.001588) \| 0.100613 / 0.014526 (0.086087) \| 0.126445 / 0.176557 (-0.050111) \| 0.187596 / 0.737135 (-0.549540) \| 0.122130 / 0.296338 (-0.174208) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.647686 / 0.215209 (0.432477) \| 6.176079 / 2.077655 (4.098424) \| 2.800232 / 1.504120 (1.296112) \| 2.434625 / 1.541195 (0.893430) \| 2.460646 / 1.468490 (0.992155) \| 0.923736 / 4.584777 (-3.661041) \| 5.480197 / 3.745712 (1.734485) \| 4.849250 / 5.269862 (-0.420612) \| 3.031576 / 4.565676 (-1.534101) \| 0.102525 / 0.424275 (-0.321750) \| 0.008688 / 0.007607 (0.001081) \| 0.766097 / 0.226044 (0.540052) \| 7.626822 / 2.268929 (5.357893) \| 3.719155 / 55.444624 (-51.725469) \| 2.967121 / 6.876477 (-3.909356) \| 3.182464 / 2.142072 (1.040392) \| 1.018315 / 4.805227 (-3.786912) \| 0.211300 / 6.500664 (-6.289364) \| 0.083055 / 0.075469 (0.007586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.731619 / 1.841788 (-0.110168) \| 25.315978 / 8.074308 (17.241669) \| 22.736306 / 10.191392 (12.544914) \| 0.270330 / 0.680424 (-0.410094) \| 0.034790 / 0.534201 (-0.499411) \| 0.488675 / 0.579283 (-0.090608) \| 0.603426 / 0.434364 (0.169062) \| 0.572547 / 0.540337 (0.032210) \| 0.825719 / 1.386936 (-0.561217) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1eaa85a4ad79aa0e411218d61a8894cc14a75fa0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008992 / 0.011353 (-0.002360) \| 0.005086 / 0.011008 (-0.005923) \| 0.107400 / 0.038508 (0.068892) \| 0.091894 / 0.023109 (0.068785) \| 0.382347 / 0.275898 (0.106449) \| 0.446581 / 0.323480 (0.123101) \| 0.005179 / 0.007986 (-0.002807) \| 0.006356 / 0.004328 (0.002028) \| 0.084979 / 0.004250 (0.080729) \| 0.060647 / 0.037052 (0.023594) \| 0.385940 / 0.258489 (0.127451) \| 0.444817 / 0.293841 (0.150976) \| 0.049484 / 0.128546 (-0.079062) \| 0.014173 / 0.075646 (-0.061473) \| 0.345704 / 0.419271 (-0.073567) \| 0.068082 / 0.043533 (0.024550) \| 0.377170 / 0.255139 (0.122031) \| 0.411816 / 0.283200 (0.128616) \| 0.043049 / 0.141683 (-0.098633) \| 1.681499 / 1.452155 (0.229344) \| 1.805428 / 1.492716 (0.312712) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.323170 / 0.018006 (0.305164) \| 0.693845 / 0.000490 (0.693355) \| 0.015499 / 0.000200 (0.015299) \| 0.000603 / 0.000054 (0.000548) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031629 / 0.037411 (-0.005783) \| 0.093511 / 0.014526 (0.078985) \| 0.112400 / 0.176557 (-0.064157) \| 0.173731 / 0.737135 (-0.563405) \| 0.116013 / 0.296338 (-0.180325) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.576724 / 0.215209 (0.361515) \| 5.775055 / 2.077655 (3.697400) \| 2.755869 / 1.504120 (1.251749) \| 2.430253 / 1.541195 (0.889058) \| 2.479629 / 1.468490 (1.011139) \| 0.841472 / 4.584777 (-3.743305) \| 5.120536 / 3.745712 (1.374824) \| 4.813281 / 5.269862 (-0.456581) \| 3.054617 / 4.565676 (-1.511059) \| 0.091459 / 0.424275 (-0.332816) \| 0.009072 / 0.007607 (0.001465) \| 0.742674 / 0.226044 (0.516629) \| 7.137861 / 2.268929 (4.868933) \| 3.497568 / 55.444624 (-51.947056) \| 2.814658 / 6.876477 (-4.061819) \| 2.934415 / 2.142072 (0.792343) \| 0.970855 / 4.805227 (-3.834372) \| 0.213366 / 6.500664 (-6.287299) \| 0.078763 / 0.075469 (0.003293) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.584716 / 1.841788 (-0.257072) \| 24.098173 / 8.074308 (16.023865) \| 20.746352 / 10.191392 (10.554960) \| 0.215313 / 0.680424 (-0.465111) \| 0.029538 / 0.534201 (-0.504663) \| 0.448672 / 0.579283 (-0.130611) \| 0.580023 / 0.434364 (0.145659) \| 0.537867 / 0.540337 (-0.002471) \| 0.804622 / 1.386936 (-0.582314) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008965 / 0.011353 (-0.002388) \| 0.005544 / 0.011008 (-0.005464) \| 0.076806 / 0.038508 (0.038298) \| 0.085333 / 0.023109 (0.062224) \| 0.509974 / 0.275898 (0.234076) \| 0.511548 / 0.323480 (0.188068) \| 0.007136 / 0.007986 (-0.000849) \| 0.004491 / 0.004328 (0.000163) \| 0.086687 / 0.004250 (0.082437) \| 0.066539 / 0.037052 (0.029486) \| 0.483663 / 0.258489 (0.225174) \| 0.529480 / 0.293841 (0.235639) \| 0.046296 / 0.128546 (-0.082250) \| 0.014736 / 0.075646 (-0.060910) \| 0.088261 / 0.419271 (-0.331010) \| 0.056753 / 0.043533 (0.013220) \| 0.511698 / 0.255139 (0.256559) \| 0.497956 / 0.283200 (0.214756) \| 0.034753 / 0.141683 (-0.106930) \| 1.828354 / 1.452155 (0.376199) \| 1.799211 / 1.492716 (0.306494) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.389652 / 0.018006 (0.371645) \| 0.602522 / 0.000490 (0.602033) \| 0.068363 / 0.000200 (0.068163) \| 0.000493 / 0.000054 (0.000439) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036431 / 0.037411 (-0.000980) \| 0.102162 / 0.014526 (0.087636) \| 0.122466 / 0.176557 (-0.054091) \| 0.181001 / 0.737135 (-0.556134) \| 0.125743 / 0.296338 (-0.170596) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.583847 / 0.215209 (0.368638) \| 5.913008 / 2.077655 (3.835354) \| 2.716088 / 1.504120 (1.211968) \| 2.328631 / 1.541195 (0.787437) \| 2.459953 / 1.468490 (0.991463) \| 0.792829 / 4.584777 (-3.791948) \| 5.183965 / 3.745712 (1.438253) \| 4.508264 / 5.269862 (-0.761598) \| 2.855444 / 4.565676 (-1.710232) \| 0.090704 / 0.424275 (-0.333571) \| 0.009303 / 0.007607 (0.001696) \| 0.694303 / 0.226044 (0.468258) \| 6.951876 / 2.268929 (4.682947) \| 3.418244 / 55.444624 (-52.026381) \| 2.799743 / 6.876477 (-4.076734) \| 3.043657 / 2.142072 (0.901584) \| 0.921537 / 4.805227 (-3.883691) \| 0.191774 / 6.500664 (-6.308890) \| 0.068602 / 0.075469 (-0.006867) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.624842 / 1.841788 (-0.216946) \| 24.570622 / 8.074308 (16.496314) \| 21.207566 / 10.191392 (11.016174) \| 0.217734 / 0.680424 (-0.462689) \| 0.033109 / 0.534201 (-0.501091) \| 0.451651 / 0.579283 (-0.127632) \| 0.590890 / 0.434364 (0.156526) \| 0.546195 / 0.540337 (0.005858) \| 0.730298 / 1.386936 (-0.656638) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f6bdecff73303cf97f279a4e36622faf53133f9c \"CML watermark\")\n" ]	2023-11-02T09:48:58	2023-11-02T10:22:33	2023-11-02T10:11:19	MEMBER	null	Temporarily pin `pyarrow` < 14.0.0 until permanent solution is found. Hot fix #6374.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6375/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6375/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6375", "html_url": "https://github.com/huggingface/datasets/pull/6375", "diff_url": "https://github.com/huggingface/datasets/pull/6375.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6375.patch", "merged_at": "2023-11-02T10:11:19" }	true
https://api.github.com/repos/huggingface/datasets/issues/6374	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6374/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6374/comments	https://api.github.com/repos/huggingface/datasets/issues/6374/events	https://github.com/huggingface/datasets/issues/6374	1,973,857,428	I_kwDODunzps51pqyU	6,374	CI is broken: TypeError: Couldn't cast array	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[]	2023-11-02T09:37:06	2023-11-02T10:11:20	2023-11-02T10:11:20	MEMBER	null	See: https://github.com/huggingface/datasets/actions/runs/6730567226/job/18293518039 ``` FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type fixed_size_list<item: int32>[3] to Sequence(feature=Value(dtype='int64', id=None), length=3, id=None) ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6374/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6374/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6373	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6373/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6373/comments	https://api.github.com/repos/huggingface/datasets/issues/6373/events	https://github.com/huggingface/datasets/pull/6373	1,973,349,695	PR_kwDODunzps5eYsZc	6,373	Fix typo in `Dataset.map` docstring	{ "login": "bryant1410", "id": 3905501, "node_id": "MDQ6VXNlcjM5MDU1MDE=", "avatar_url": "https://avatars.githubusercontent.com/u/3905501?v=4", "gravatar_id": "", "url": "https://api.github.com/users/bryant1410", "html_url": "https://github.com/bryant1410", "followers_url": "https://api.github.com/users/bryant1410/followers", "following_url": "https://api.github.com/users/bryant1410/following{/other_user}", "gists_url": "https://api.github.com/users/bryant1410/gists{/gist_id}", "starred_url": "https://api.github.com/users/bryant1410/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bryant1410/subscriptions", "organizations_url": "https://api.github.com/users/bryant1410/orgs", "repos_url": "https://api.github.com/users/bryant1410/repos", "events_url": "https://api.github.com/users/bryant1410/events{/privacy}", "received_events_url": "https://api.github.com/users/bryant1410/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006709 / 0.011353 (-0.004643) \| 0.004102 / 0.011008 (-0.006906) \| 0.084449 / 0.038508 (0.045941) \| 0.076078 / 0.023109 (0.052969) \| 0.319831 / 0.275898 (0.043933) \| 0.359918 / 0.323480 (0.036438) \| 0.006092 / 0.007986 (-0.001894) \| 0.003402 / 0.004328 (-0.000926) \| 0.064715 / 0.004250 (0.060465) \| 0.054541 / 0.037052 (0.017488) \| 0.330394 / 0.258489 (0.071905) \| 0.366048 / 0.293841 (0.072207) \| 0.031594 / 0.128546 (-0.096952) \| 0.008591 / 0.075646 (-0.067056) \| 0.292983 / 0.419271 (-0.126288) \| 0.052986 / 0.043533 (0.009453) \| 0.322253 / 0.255139 (0.067114) \| 0.340082 / 0.283200 (0.056882) \| 0.023390 / 0.141683 (-0.118293) \| 1.459038 / 1.452155 (0.006883) \| 1.536256 / 1.492716 (0.043540) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233527 / 0.018006 (0.215521) \| 0.459145 / 0.000490 (0.458655) \| 0.007471 / 0.000200 (0.007271) \| 0.000281 / 0.000054 (0.000227) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028158 / 0.037411 (-0.009253) \| 0.083079 / 0.014526 (0.068553) \| 0.097159 / 0.176557 (-0.079397) \| 0.151927 / 0.737135 (-0.585208) \| 0.098024 / 0.296338 (-0.198314) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386882 / 0.215209 (0.171673) \| 3.849635 / 2.077655 (1.771981) \| 1.832885 / 1.504120 (0.328765) \| 1.668356 / 1.541195 (0.127162) \| 1.745066 / 1.468490 (0.276576) \| 0.484476 / 4.584777 (-4.100301) \| 3.547604 / 3.745712 (-0.198108) \| 3.480338 / 5.269862 (-1.789523) \| 2.066837 / 4.565676 (-2.498840) \| 0.056755 / 0.424275 (-0.367520) \| 0.007747 / 0.007607 (0.000140) \| 0.467999 / 0.226044 (0.241955) \| 4.678875 / 2.268929 (2.409946) \| 2.341930 / 55.444624 (-53.102695) \| 1.985632 / 6.876477 (-4.890844) \| 2.046998 / 2.142072 (-0.095074) \| 0.579860 / 4.805227 (-4.225367) \| 0.131488 / 6.500664 (-6.369176) \| 0.060193 / 0.075469 (-0.015276) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249656 / 1.841788 (-0.592132) \| 19.079517 / 8.074308 (11.005209) \| 14.328827 / 10.191392 (4.137435) \| 0.173707 / 0.680424 (-0.506717) \| 0.018250 / 0.534201 (-0.515951) \| 0.392225 / 0.579283 (-0.187058) \| 0.413920 / 0.434364 (-0.020444) \| 0.464124 / 0.540337 (-0.076214) \| 0.640283 / 1.386936 (-0.746653) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006859 / 0.011353 (-0.004494) \| 0.004068 / 0.011008 (-0.006940) \| 0.063936 / 0.038508 (0.025428) \| 0.077187 / 0.023109 (0.054078) \| 0.365098 / 0.275898 (0.089200) \| 0.391003 / 0.323480 (0.067523) \| 0.005571 / 0.007986 (-0.002415) \| 0.003425 / 0.004328 (-0.000904) \| 0.063220 / 0.004250 (0.058970) \| 0.056964 / 0.037052 (0.019912) \| 0.367793 / 0.258489 (0.109304) \| 0.398776 / 0.293841 (0.104935) \| 0.033182 / 0.128546 (-0.095364) \| 0.008601 / 0.075646 (-0.067045) \| 0.070276 / 0.419271 (-0.348996) \| 0.048383 / 0.043533 (0.004850) \| 0.360414 / 0.255139 (0.105275) \| 0.368171 / 0.283200 (0.084971) \| 0.023114 / 0.141683 (-0.118569) \| 1.503503 / 1.452155 (0.051349) \| 1.567279 / 1.492716 (0.074562) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224296 / 0.018006 (0.206290) \| 0.455138 / 0.000490 (0.454648) \| 0.004014 / 0.000200 (0.003814) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032337 / 0.037411 (-0.005074) \| 0.094385 / 0.014526 (0.079859) \| 0.109870 / 0.176557 (-0.066687) \| 0.156978 / 0.737135 (-0.580157) \| 0.107559 / 0.296338 (-0.188780) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.427409 / 0.215209 (0.212200) \| 4.261772 / 2.077655 (2.184117) \| 2.276106 / 1.504120 (0.771986) \| 2.115232 / 1.541195 (0.574038) \| 2.192048 / 1.468490 (0.723558) \| 0.488459 / 4.584777 (-4.096318) \| 3.675463 / 3.745712 (-0.070249) \| 3.322475 / 5.269862 (-1.947387) \| 2.072253 / 4.565676 (-2.493424) \| 0.058259 / 0.424275 (-0.366017) \| 0.007319 / 0.007607 (-0.000288) \| 0.499513 / 0.226044 (0.273469) \| 4.994774 / 2.268929 (2.725845) \| 2.760927 / 55.444624 (-52.683697) \| 2.391947 / 6.876477 (-4.484530) \| 2.600557 / 2.142072 (0.458484) \| 0.587597 / 4.805227 (-4.217630) \| 0.131444 / 6.500664 (-6.369220) \| 0.057334 / 0.075469 (-0.018135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354636 / 1.841788 (-0.487152) \| 19.685735 / 8.074308 (11.611427) \| 14.295920 / 10.191392 (4.104528) \| 0.171921 / 0.680424 (-0.508503) \| 0.019926 / 0.534201 (-0.514274) \| 0.395216 / 0.579283 (-0.184068) \| 0.432791 / 0.434364 (-0.001573) \| 0.473055 / 0.540337 (-0.067282) \| 0.638633 / 1.386936 (-0.748303) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fad7c899ec9218a717311223aa6ef5c09a6c7885 \"CML watermark\")\n" ]	2023-11-02T01:36:49	2023-11-02T15:18:22	2023-11-02T10:11:38	CONTRIBUTOR	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6373/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6373/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6373", "html_url": "https://github.com/huggingface/datasets/pull/6373", "diff_url": "https://github.com/huggingface/datasets/pull/6373.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6373.patch", "merged_at": "2023-11-02T10:11:38" }	true
https://api.github.com/repos/huggingface/datasets/issues/6372	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6372/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6372/comments	https://api.github.com/repos/huggingface/datasets/issues/6372/events	https://github.com/huggingface/datasets/pull/6372	1,972,837,794	PR_kwDODunzps5eW9kO	6,372	do not try to download from HF GCS for generator	{ "login": "yundai424", "id": 43726198, "node_id": "MDQ6VXNlcjQzNzI2MTk4", "avatar_url": "https://avatars.githubusercontent.com/u/43726198?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yundai424", "html_url": "https://github.com/yundai424", "followers_url": "https://api.github.com/users/yundai424/followers", "following_url": "https://api.github.com/users/yundai424/following{/other_user}", "gists_url": "https://api.github.com/users/yundai424/gists{/gist_id}", "starred_url": "https://api.github.com/users/yundai424/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yundai424/subscriptions", "organizations_url": "https://api.github.com/users/yundai424/orgs", "repos_url": "https://api.github.com/users/yundai424/repos", "events_url": "https://api.github.com/users/yundai424/events{/privacy}", "received_events_url": "https://api.github.com/users/yundai424/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007617 / 0.011353 (-0.003735) \| 0.005371 / 0.011008 (-0.005638) \| 0.092110 / 0.038508 (0.053602) \| 0.070654 / 0.023109 (0.047544) \| 0.362501 / 0.275898 (0.086603) \| 0.412835 / 0.323480 (0.089355) \| 0.006752 / 0.007986 (-0.001234) \| 0.003752 / 0.004328 (-0.000576) \| 0.075644 / 0.004250 (0.071394) \| 0.055666 / 0.037052 (0.018614) \| 0.355906 / 0.258489 (0.097417) \| 0.405078 / 0.293841 (0.111237) \| 0.045767 / 0.128546 (-0.082779) \| 0.013778 / 0.075646 (-0.061868) \| 0.324696 / 0.419271 (-0.094575) \| 0.062200 / 0.043533 (0.018667) \| 0.359571 / 0.255139 (0.104432) \| 0.387274 / 0.283200 (0.104075) \| 0.035323 / 0.141683 (-0.106360) \| 1.586294 / 1.452155 (0.134139) \| 1.707564 / 1.492716 (0.214847) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303940 / 0.018006 (0.285934) \| 0.583349 / 0.000490 (0.582859) \| 0.014845 / 0.000200 (0.014645) \| 0.000698 / 0.000054 (0.000643) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028994 / 0.037411 (-0.008417) \| 0.085555 / 0.014526 (0.071029) \| 0.097856 / 0.176557 (-0.078701) \| 0.161480 / 0.737135 (-0.575655) \| 0.098573 / 0.296338 (-0.197766) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.591294 / 0.215209 (0.376085) \| 5.751350 / 2.077655 (3.673695) \| 2.241620 / 1.504120 (0.737500) \| 1.991083 / 1.541195 (0.449888) \| 2.006711 / 1.468490 (0.538221) \| 0.832339 / 4.584777 (-3.752438) \| 5.213808 / 3.745712 (1.468095) \| 4.650355 / 5.269862 (-0.619506) \| 2.860494 / 4.565676 (-1.705182) \| 0.093090 / 0.424275 (-0.331185) \| 0.009740 / 0.007607 (0.002133) \| 0.693509 / 0.226044 (0.467464) \| 6.828735 / 2.268929 (4.559807) \| 2.967763 / 55.444624 (-52.476862) \| 2.311461 / 6.876477 (-4.565016) \| 2.400051 / 2.142072 (0.257979) \| 0.914753 / 4.805227 (-3.890474) \| 0.202804 / 6.500664 (-6.297860) \| 0.076905 / 0.075469 (0.001436) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.576424 / 1.841788 (-0.265363) \| 22.963472 / 8.074308 (14.889164) \| 19.948105 / 10.191392 (9.756713) \| 0.228982 / 0.680424 (-0.451442) \| 0.029038 / 0.534201 (-0.505163) \| 0.477715 / 0.579283 (-0.101568) \| 0.554924 / 0.434364 (0.120560) \| 0.532118 / 0.540337 (-0.008219) \| 0.775096 / 1.386936 (-0.611840) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009127 / 0.011353 (-0.002226) \| 0.004978 / 0.011008 (-0.006030) \| 0.084166 / 0.038508 (0.045658) \| 0.083391 / 0.023109 (0.060282) \| 0.420760 / 0.275898 (0.144862) \| 0.459072 / 0.323480 (0.135592) \| 0.007102 / 0.007986 (-0.000883) \| 0.004175 / 0.004328 (-0.000154) \| 0.082922 / 0.004250 (0.078672) \| 0.059010 / 0.037052 (0.021957) \| 0.416959 / 0.258489 (0.158470) \| 0.472220 / 0.293841 (0.178379) \| 0.049999 / 0.128546 (-0.078547) \| 0.014126 / 0.075646 (-0.061520) \| 0.096894 / 0.419271 (-0.322378) \| 0.057920 / 0.043533 (0.014387) \| 0.405779 / 0.255139 (0.150640) \| 0.464286 / 0.283200 (0.181087) \| 0.034957 / 0.141683 (-0.106726) \| 1.637921 / 1.452155 (0.185767) \| 1.768231 / 1.492716 (0.275515) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.354875 / 0.018006 (0.336868) \| 0.554667 / 0.000490 (0.554177) \| 0.074127 / 0.000200 (0.073927) \| 0.000411 / 0.000054 (0.000357) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027681 / 0.037411 (-0.009730) \| 0.087746 / 0.014526 (0.073220) \| 0.093714 / 0.176557 (-0.082843) \| 0.145380 / 0.737135 (-0.591755) \| 0.095686 / 0.296338 (-0.200652) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.522079 / 0.215209 (0.306870) \| 5.197366 / 2.077655 (3.119711) \| 2.300744 / 1.504120 (0.796624) \| 2.056846 / 1.541195 (0.515652) \| 2.009897 / 1.468490 (0.541407) \| 0.813025 / 4.584777 (-3.771751) \| 5.177732 / 3.745712 (1.432020) \| 4.076749 / 5.269862 (-1.193112) \| 2.545588 / 4.565676 (-2.020088) \| 0.083507 / 0.424275 (-0.340769) \| 0.007011 / 0.007607 (-0.000596) \| 0.598820 / 0.226044 (0.372776) \| 6.203730 / 2.268929 (3.934801) \| 2.945385 / 55.444624 (-52.499239) \| 2.304849 / 6.876477 (-4.571628) \| 2.599035 / 2.142072 (0.456962) \| 1.002721 / 4.805227 (-3.802506) \| 0.191781 / 6.500664 (-6.308883) \| 0.064178 / 0.075469 (-0.011292) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.549560 / 1.841788 (-0.292228) \| 22.395727 / 8.074308 (14.321418) \| 20.537895 / 10.191392 (10.346503) \| 0.246542 / 0.680424 (-0.433882) \| 0.031673 / 0.534201 (-0.502528) \| 0.442490 / 0.579283 (-0.136793) \| 0.589838 / 0.434364 (0.155474) \| 0.535201 / 0.540337 (-0.005136) \| 0.733660 / 1.386936 (-0.653276) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e742718e504a372cce0b1f87c2cac65eb8c35792 \"CML watermark\")\n" ]	2023-11-01T17:57:11	2023-11-02T16:02:52	2023-11-02T15:52:09	CONTRIBUTOR	null	attempt to fix https://github.com/huggingface/datasets/issues/6371	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6372/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6372/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6372", "html_url": "https://github.com/huggingface/datasets/pull/6372", "diff_url": "https://github.com/huggingface/datasets/pull/6372.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6372.patch", "merged_at": "2023-11-02T15:52:09" }	true
https://api.github.com/repos/huggingface/datasets/issues/6371	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6371/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6371/comments	https://api.github.com/repos/huggingface/datasets/issues/6371/events	https://github.com/huggingface/datasets/issues/6371	1,972,807,579	I_kwDODunzps51lqeb	6,371	`Dataset.from_generator` should not try to download from HF GCS	{ "login": "yundai424", "id": 43726198, "node_id": "MDQ6VXNlcjQzNzI2MTk4", "avatar_url": "https://avatars.githubusercontent.com/u/43726198?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yundai424", "html_url": "https://github.com/yundai424", "followers_url": "https://api.github.com/users/yundai424/followers", "following_url": "https://api.github.com/users/yundai424/following{/other_user}", "gists_url": "https://api.github.com/users/yundai424/gists{/gist_id}", "starred_url": "https://api.github.com/users/yundai424/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yundai424/subscriptions", "organizations_url": "https://api.github.com/users/yundai424/orgs", "repos_url": "https://api.github.com/users/yundai424/repos", "events_url": "https://api.github.com/users/yundai424/events{/privacy}", "received_events_url": "https://api.github.com/users/yundai424/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Indeed, setting `try_from_gcs` to `False` makes sense for `from_generator`.\r\n\r\nWe plan to deprecate and remove `try_from_hf_gcs` soon, as we can use Hub for file hosting now, but this is a good temporary fix.\r\n" ]	2023-11-01T17:36:17	2023-11-02T15:52:10	2023-11-02T15:52:10	CONTRIBUTOR	null	### Describe the bug When using [`Dataset.from_generator`](https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/arrow_dataset.py#L1072) with `streaming=False`, the internal logic will call [`download_and_prepare`](https://github.com/huggingface/datasets/blob/main/src/datasets/io/generator.py#L47) which will attempt to download from HF GCS which is redundant, because user has already provided the generator from which the data should be drawn. If someone attempts to call `Dataset.from_generator` from an environment that doesn't have external internet access (for example internal production machine) and doesn't set `HF_DATASETS_OFFLINE=1`, this will result in process being stuck at building connection. ### Steps to reproduce the bug ```python import datasets def gen(): for _ in range(100): yield {"text": "dummy text"} dataset = datasets.Dataset.from_generator(gen) ``` A minimum example executed on any environment that doesn't have access to HF GCS can result in the error ### Expected behavior `try_from_hf_gcs` should be set to False here https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/io/generator.py#L51 ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-3.10.0-1160.90.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.17.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6371/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6371/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6370	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6370/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6370/comments	https://api.github.com/repos/huggingface/datasets/issues/6370/events	https://github.com/huggingface/datasets/issues/6370	1,972,073,909	I_kwDODunzps51i3W1	6,370	TensorDataset format does not work with Trainer from transformers	{ "login": "jinzzasol", "id": 49014051, "node_id": "MDQ6VXNlcjQ5MDE0MDUx", "avatar_url": "https://avatars.githubusercontent.com/u/49014051?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jinzzasol", "html_url": "https://github.com/jinzzasol", "followers_url": "https://api.github.com/users/jinzzasol/followers", "following_url": "https://api.github.com/users/jinzzasol/following{/other_user}", "gists_url": "https://api.github.com/users/jinzzasol/gists{/gist_id}", "starred_url": "https://api.github.com/users/jinzzasol/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jinzzasol/subscriptions", "organizations_url": "https://api.github.com/users/jinzzasol/orgs", "repos_url": "https://api.github.com/users/jinzzasol/repos", "events_url": "https://api.github.com/users/jinzzasol/events{/privacy}", "received_events_url": "https://api.github.com/users/jinzzasol/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "I figured it out. I found that `Trainer` does not work with TensorDataset even though the document says it uses it. Instead, I ended up creating a dictionary and converting it to a dataset using `dataset.Dataset.from_dict()`.\r\n\r\nI will leave this post open for a while. If someone knows a better approach, please leave a comment.", "Only issues directly related to the HF datasets library should be reported here. ~So, I'm transferring this issue to the `transformers` repo.~ I'm not a `transformers` maintainer, so GitHub doesn't let me transfer it there :(. This means you need to do it manually." ]	2023-11-01T10:09:54	2023-11-02T15:05:26	null	NONE	null	### Describe the bug The model was built to do fine tunning on BERT model for relation extraction. trainer.train() returns an error message ```TypeError: vars() argument must have __dict__ attribute``` when it has `train_dataset` generated from `torch.utils.data.TensorDataset` However, in the document, the required data format is `torch.utils.data.TensorDataset`. ![image](https://github.com/huggingface/datasets/assets/49014051/36fa34ac-3127-4c64-9580-9ab736136d83) Transformers trainer is supposed to accept the train_dataset in the format of torch.utils.data.TensorDataset, but it returns error message "TypeError: vars() argument must have __dict__ attribute" ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-30-5df728c929a2> in <cell line: 1>() ----> 1 trainer.train() 2 trainer.evaluate(test_dataset) 9 frames /usr/local/lib/python3.10/dist-packages/transformers/data/data_collator.py in <listcomp>(.0) 107 108 if not isinstance(features[0], Mapping): --> 109 features = [vars(f) for f in features] 110 first = features[0] 111 batch = {} TypeError: vars() argument must have __dict__ attribute ``` ### Steps to reproduce the bug Create train_dataset using `torch.utils.data.TensorDataset`, for instance, ```train_dataset = torch.utils.data.TensorDataset(train_input_ids, train_attention_masks, train_labels)``` Feed this `train_dataset` to your trainer and run trainer.train ``` trainer = Trainer(model, training_args, train_dataset=train_dataset, eval_dataset=dev_dataset, compute_metrics=compute_metrics, ) ``` ### Expected behavior Trainer should start training ### Environment info It is running on Google Colab - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.17.3 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6370/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6370/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6369	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6369/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6369/comments	https://api.github.com/repos/huggingface/datasets/issues/6369/events	https://github.com/huggingface/datasets/issues/6369	1,971,794,108	I_kwDODunzps51hzC8	6,369	Multi process map did not load cache file correctly	{ "login": "enze5088", "id": 14285786, "node_id": "MDQ6VXNlcjE0Mjg1Nzg2", "avatar_url": "https://avatars.githubusercontent.com/u/14285786?v=4", "gravatar_id": "", "url": "https://api.github.com/users/enze5088", "html_url": "https://github.com/enze5088", "followers_url": "https://api.github.com/users/enze5088/followers", "following_url": "https://api.github.com/users/enze5088/following{/other_user}", "gists_url": "https://api.github.com/users/enze5088/gists{/gist_id}", "starred_url": "https://api.github.com/users/enze5088/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/enze5088/subscriptions", "organizations_url": "https://api.github.com/users/enze5088/orgs", "repos_url": "https://api.github.com/users/enze5088/repos", "events_url": "https://api.github.com/users/enze5088/events{/privacy}", "received_events_url": "https://api.github.com/users/enze5088/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The inconsistency may be caused by the usage of \"update_fingerprint\" and setting \"trust_remote_code\" to \"True.\"\r\nWhen the tokenizer employs \"trust_remote_code,\" the behavior of the map function varies with each code execution. Even if the remote code of the tokenizer remains the same, the result of \"asher.hexdigest()\" is found to be inconsistent each time.\r\nThis may result in different processes executing multiple maps\r\n![1698841094290](https://github.com/huggingface/datasets/assets/14285786/21fc3c65-e9fd-4a79-b12e-a1d4b9c6cf32)\r\n![1698841117416](https://github.com/huggingface/datasets/assets/14285786/c3e5a530-54d2-4ae6-b902-ce9f85de373b)\r\n\r\n", "The issue may be related to problems previously discussed in GitHub issues [#3847](https://github.com/huggingface/datasets/issues/3847) and [#6318](https://github.com/huggingface/datasets/pull/6318). \r\nThis arises from the fact that tokenizer.tokens_trie._tokens is an unordered set, leading to varying hash results:\r\n`value = hash_bytes(dumps(tokenizer.tokens_trie._tokens))`\r\nConsequently, this results in different outcomes each time for:\r\n`new_fingerprint = update_fingerprint(datasets._fingerprint, transform, kwargs_for_fingerprint)`\r\n\r\nTo address this issue, it's essential to make `Trie._tokens` a deterministic set while ensuring a consistent order after the final update of `_tokens`.\r\n" ]	2023-11-01T06:36:54	2023-11-01T18:33:55	null	NONE	null	### Describe the bug When I was training model on Multiple GPUs by DDP, the dataset is tokenized multiple times after main process. ![1698820541284](https://github.com/huggingface/datasets/assets/14285786/0b2fe054-54d8-4e00-96e6-6ca5b69e662b) ![1698820501568](https://github.com/huggingface/datasets/assets/14285786/dd62bf6f-a58f-41bf-9848-ea4fb3b62b9b) Code is modified from [run_clm.py](https://github.com/huggingface/transformers/blob/7d8ff3629b2725ec43ace99c1a6e87ac1978d433/examples/pytorch/language-modeling/run_clm.py#L484) ### Steps to reproduce the bug ``` block_size = data_args.block_size IGNORE_INDEX = -100 Ignore_Input = False def tokenize_function(examples): sources = [] targets = [] for instruction, inputs, output in zip(examples['instruction'], examples['input'], examples['output']): source = instruction + inputs target = f"{output}{tokenizer.eos_token}" sources.append(source) targets.append(target) tokenized_sources = tokenizer(sources, return_attention_mask=False) tokenized_targets = tokenizer(targets, return_attention_mask=False, add_special_tokens=False ) all_input_ids = [] all_labels = [] for s, t in zip(tokenized_sources['input_ids'], tokenized_targets['input_ids']): if len(s) > block_size and Ignore_Input == False: # print(s) continue input_ids = torch.LongTensor(s + t)[:block_size] if Ignore_Input: labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:block_size] else: labels = input_ids assert len(input_ids) == len(labels) all_input_ids.append(input_ids) all_labels.append(labels) results = { 'input_ids': all_input_ids, 'labels': all_labels, } return results with training_args.main_process_first(desc="dataset map tokenization ", local=False): # print('local_rank',training_args.local_rank) if not data_args.streaming: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on dataset ", ) else: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, desc="Running tokenizer on dataset " ) ``` ### Expected behavior This code should only tokenize the dataset in the main process, and the other processes load the dataset after waiting ### Environment info transformers == 4.34.1 datasets == 2.14.5	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6369/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6369/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6368	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6368/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6368/comments	https://api.github.com/repos/huggingface/datasets/issues/6368/events	https://github.com/huggingface/datasets/pull/6368	1,971,193,692	PR_kwDODunzps5eRZwQ	6,368	Fix python formatting for complex types in `format_table`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008047 / 0.011353 (-0.003305) \| 0.004649 / 0.011008 (-0.006359) \| 0.100275 / 0.038508 (0.061767) \| 0.089551 / 0.023109 (0.066442) \| 0.369831 / 0.275898 (0.093933) \| 0.431023 / 0.323480 (0.107544) \| 0.004721 / 0.007986 (-0.003265) \| 0.004904 / 0.004328 (0.000575) \| 0.076345 / 0.004250 (0.072095) \| 0.066902 / 0.037052 (0.029849) \| 0.377208 / 0.258489 (0.118718) \| 0.430989 / 0.293841 (0.137148) \| 0.036260 / 0.128546 (-0.092287) \| 0.010158 / 0.075646 (-0.065488) \| 0.344923 / 0.419271 (-0.074349) \| 0.062504 / 0.043533 (0.018971) \| 0.373038 / 0.255139 (0.117899) \| 0.399918 / 0.283200 (0.116718) \| 0.028257 / 0.141683 (-0.113425) \| 1.782546 / 1.452155 (0.330391) \| 1.920010 / 1.492716 (0.427293) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277670 / 0.018006 (0.259664) \| 0.500543 / 0.000490 (0.500053) \| 0.018256 / 0.000200 (0.018056) \| 0.000343 / 0.000054 (0.000289) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033337 / 0.037411 (-0.004074) \| 0.100542 / 0.014526 (0.086017) \| 0.114903 / 0.176557 (-0.061654) \| 0.181267 / 0.737135 (-0.555868) \| 0.115019 / 0.296338 (-0.181320) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457333 / 0.215209 (0.242124) \| 4.542082 / 2.077655 (2.464427) \| 2.231817 / 1.504120 (0.727697) \| 2.028523 / 1.541195 (0.487328) \| 2.110715 / 1.468490 (0.642225) \| 0.583162 / 4.584777 (-4.001615) \| 4.179413 / 3.745712 (0.433701) \| 4.145620 / 5.269862 (-1.124241) \| 2.452458 / 4.565676 (-2.113218) \| 0.068229 / 0.424275 (-0.356046) \| 0.009027 / 0.007607 (0.001420) \| 0.549002 / 0.226044 (0.322957) \| 5.485707 / 2.268929 (3.216779) \| 2.789467 / 55.444624 (-52.655157) \| 2.397499 / 6.876477 (-4.478977) \| 2.492083 / 2.142072 (0.350010) \| 0.692445 / 4.805227 (-4.112782) \| 0.160527 / 6.500664 (-6.340137) \| 0.071597 / 0.075469 (-0.003872) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.486043 / 1.841788 (-0.355744) \| 22.377207 / 8.074308 (14.302899) \| 16.443719 / 10.191392 (6.252327) \| 0.170740 / 0.680424 (-0.509684) \| 0.021511 / 0.534201 (-0.512690) \| 0.470798 / 0.579283 (-0.108485) \| 0.511851 / 0.434364 (0.077487) \| 0.551154 / 0.540337 (0.010817) \| 0.768420 / 1.386936 (-0.618516) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008049 / 0.011353 (-0.003303) \| 0.004676 / 0.011008 (-0.006332) \| 0.076360 / 0.038508 (0.037852) \| 0.093648 / 0.023109 (0.070539) \| 0.480597 / 0.275898 (0.204699) \| 0.524674 / 0.323480 (0.201194) \| 0.006242 / 0.007986 (-0.001744) \| 0.003827 / 0.004328 (-0.000501) \| 0.077039 / 0.004250 (0.072788) \| 0.067992 / 0.037052 (0.030940) \| 0.480287 / 0.258489 (0.221798) \| 0.528546 / 0.293841 (0.234706) \| 0.038347 / 0.128546 (-0.090199) \| 0.010036 / 0.075646 (-0.065611) \| 0.084386 / 0.419271 (-0.334885) \| 0.057211 / 0.043533 (0.013678) \| 0.475993 / 0.255139 (0.220854) \| 0.504881 / 0.283200 (0.221682) \| 0.026658 / 0.141683 (-0.115025) \| 1.777095 / 1.452155 (0.324940) \| 1.896446 / 1.492716 (0.403730) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242450 / 0.018006 (0.224443) \| 0.488864 / 0.000490 (0.488374) \| 0.007329 / 0.000200 (0.007129) \| 0.000108 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039093 / 0.037411 (0.001682) \| 0.114724 / 0.014526 (0.100198) \| 0.124965 / 0.176557 (-0.051591) \| 0.188165 / 0.737135 (-0.548971) \| 0.125336 / 0.296338 (-0.171002) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.515718 / 0.215209 (0.300509) \| 5.150865 / 2.077655 (3.073210) \| 2.767866 / 1.504120 (1.263746) \| 2.571003 / 1.541195 (1.029808) \| 2.656224 / 1.468490 (1.187734) \| 0.583771 / 4.584777 (-4.001006) \| 4.268713 / 3.745712 (0.523001) \| 3.938699 / 5.269862 (-1.331163) \| 2.413569 / 4.565676 (-2.152108) \| 0.068848 / 0.424275 (-0.355427) \| 0.008758 / 0.007607 (0.001151) \| 0.610831 / 0.226044 (0.384786) \| 6.099965 / 2.268929 (3.831037) \| 3.337530 / 55.444624 (-52.107095) \| 2.910962 / 6.876477 (-3.965514) \| 3.149813 / 2.142072 (1.007740) \| 0.700576 / 4.805227 (-4.104651) \| 0.157569 / 6.500664 (-6.343095) \| 0.072237 / 0.075469 (-0.003232) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.655840 / 1.841788 (-0.185947) \| 23.639061 / 8.074308 (15.564753) \| 17.301593 / 10.191392 (7.110201) \| 0.201717 / 0.680424 (-0.478707) \| 0.023836 / 0.534201 (-0.510365) \| 0.470941 / 0.579283 (-0.108342) \| 0.498157 / 0.434364 (0.063794) \| 0.581195 / 0.540337 (0.040857) \| 0.788304 / 1.386936 (-0.598632) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f657900acfd8ea1afaf47267e552a7ad2c6ef28b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004823 / 0.011353 (-0.006530) \| 0.002976 / 0.011008 (-0.008032) \| 0.062070 / 0.038508 (0.023562) \| 0.051623 / 0.023109 (0.028513) \| 0.242249 / 0.275898 (-0.033649) \| 0.271223 / 0.323480 (-0.052257) \| 0.003906 / 0.007986 (-0.004079) \| 0.002709 / 0.004328 (-0.001620) \| 0.047874 / 0.004250 (0.043624) \| 0.038123 / 0.037052 (0.001071) \| 0.253737 / 0.258489 (-0.004752) \| 0.281942 / 0.293841 (-0.011899) \| 0.023750 / 0.128546 (-0.104797) \| 0.007227 / 0.075646 (-0.068420) \| 0.203137 / 0.419271 (-0.216134) \| 0.036254 / 0.043533 (-0.007278) \| 0.243923 / 0.255139 (-0.011216) \| 0.263908 / 0.283200 (-0.019291) \| 0.017795 / 0.141683 (-0.123888) \| 1.105680 / 1.452155 (-0.346475) \| 1.166804 / 1.492716 (-0.325912) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097388 / 0.018006 (0.079381) \| 0.305481 / 0.000490 (0.304991) \| 0.000210 / 0.000200 (0.000010) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020096 / 0.037411 (-0.017315) \| 0.063990 / 0.014526 (0.049464) \| 0.073694 / 0.176557 (-0.102863) \| 0.122909 / 0.737135 (-0.614227) \| 0.076199 / 0.296338 (-0.220140) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285612 / 0.215209 (0.070403) \| 2.770524 / 2.077655 (0.692869) \| 1.451624 / 1.504120 (-0.052496) \| 1.329223 / 1.541195 (-0.211972) \| 1.369980 / 1.468490 (-0.098510) \| 0.398269 / 4.584777 (-4.186507) \| 2.418740 / 3.745712 (-1.326972) \| 2.796384 / 5.269862 (-2.473478) \| 1.686490 / 4.565676 (-2.879186) \| 0.046417 / 0.424275 (-0.377858) \| 0.005414 / 0.007607 (-0.002193) \| 0.345505 / 0.226044 (0.119460) \| 3.391857 / 2.268929 (1.122929) \| 1.856696 / 55.444624 (-53.587929) \| 1.538061 / 6.876477 (-5.338416) \| 1.631489 / 2.142072 (-0.510584) \| 0.479188 / 4.805227 (-4.326039) \| 0.101549 / 6.500664 (-6.399116) \| 0.042150 / 0.075469 (-0.033319) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.957961 / 1.841788 (-0.883827) \| 12.349371 / 8.074308 (4.275063) \| 10.778214 / 10.191392 (0.586822) \| 0.141265 / 0.680424 (-0.539158) \| 0.014559 / 0.534201 (-0.519642) \| 0.272071 / 0.579283 (-0.307212) \| 0.262493 / 0.434364 (-0.171871) \| 0.310351 / 0.540337 (-0.229986) \| 0.399220 / 1.386936 (-0.987716) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005127 / 0.011353 (-0.006226) \| 0.002926 / 0.011008 (-0.008082) \| 0.048320 / 0.038508 (0.009812) \| 0.063082 / 0.023109 (0.039973) \| 0.269846 / 0.275898 (-0.006052) \| 0.294470 / 0.323480 (-0.029010) \| 0.004201 / 0.007986 (-0.003784) \| 0.002434 / 0.004328 (-0.001894) \| 0.048020 / 0.004250 (0.043770) \| 0.043909 / 0.037052 (0.006856) \| 0.271328 / 0.258489 (0.012839) \| 0.298820 / 0.293841 (0.004979) \| 0.024565 / 0.128546 (-0.103981) \| 0.007752 / 0.075646 (-0.067894) \| 0.054171 / 0.419271 (-0.365101) \| 0.033147 / 0.043533 (-0.010386) \| 0.266628 / 0.255139 (0.011489) \| 0.288651 / 0.283200 (0.005452) \| 0.018910 / 0.141683 (-0.122773) \| 1.153679 / 1.452155 (-0.298476) \| 1.214979 / 1.492716 (-0.277737) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097064 / 0.018006 (0.079057) \| 0.307504 / 0.000490 (0.307014) \| 0.000230 / 0.000200 (0.000030) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021848 / 0.037411 (-0.015563) \| 0.071159 / 0.014526 (0.056633) \| 0.081310 / 0.176557 (-0.095247) \| 0.120175 / 0.737135 (-0.616961) \| 0.082619 / 0.296338 (-0.213720) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296606 / 0.215209 (0.081397) \| 2.908495 / 2.077655 (0.830840) \| 1.606522 / 1.504120 (0.102402) \| 1.528599 / 1.541195 (-0.012596) \| 1.508332 / 1.468490 (0.039842) \| 0.396336 / 4.584777 (-4.188441) \| 2.449163 / 3.745712 (-1.296549) \| 2.533372 / 5.269862 (-2.736490) \| 1.623061 / 4.565676 (-2.942615) \| 0.046723 / 0.424275 (-0.377552) \| 0.005120 / 0.007607 (-0.002487) \| 0.345763 / 0.226044 (0.119718) \| 3.427382 / 2.268929 (1.158454) \| 1.962806 / 55.444624 (-53.481819) \| 1.678548 / 6.876477 (-5.197929) \| 1.865773 / 2.142072 (-0.276300) \| 0.477932 / 4.805227 (-4.327295) \| 0.100994 / 6.500664 (-6.399670) \| 0.042212 / 0.075469 (-0.033258) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.992766 / 1.841788 (-0.849022) \| 12.764885 / 8.074308 (4.690577) \| 10.892094 / 10.191392 (0.700702) \| 0.143211 / 0.680424 (-0.537213) \| 0.016347 / 0.534201 (-0.517853) \| 0.270181 / 0.579283 (-0.309102) \| 0.278658 / 0.434364 (-0.155706) \| 0.307134 / 0.540337 (-0.233203) \| 0.396792 / 1.386936 (-0.990144) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6d2f2a5e0fea3827eccfd1717d8021c15fc4292a \"CML watermark\")\n", "Thanks for the fix ! It was probably my mistake (forgot to re-apply the features)" ]	2023-10-31T19:48:08	2023-11-02T14:42:28	2023-11-02T14:21:16	CONTRIBUTOR	null	Fix #6366	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6368/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6368/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6368", "html_url": "https://github.com/huggingface/datasets/pull/6368", "diff_url": "https://github.com/huggingface/datasets/pull/6368.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6368.patch", "merged_at": "2023-11-02T14:21:16" }	true
https://api.github.com/repos/huggingface/datasets/issues/6367	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6367/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6367/comments	https://api.github.com/repos/huggingface/datasets/issues/6367/events	https://github.com/huggingface/datasets/pull/6367	1,971,015,861	PR_kwDODunzps5eQy1D	6,367	Fix time measuring snippet in docs	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007683 / 0.011353 (-0.003670) \| 0.004159 / 0.011008 (-0.006849) \| 0.097017 / 0.038508 (0.058509) \| 0.074216 / 0.023109 (0.051107) \| 0.323115 / 0.275898 (0.047217) \| 0.412836 / 0.323480 (0.089356) \| 0.005151 / 0.007986 (-0.002834) \| 0.004037 / 0.004328 (-0.000292) \| 0.067881 / 0.004250 (0.063631) \| 0.051395 / 0.037052 (0.014342) \| 0.356391 / 0.258489 (0.097901) \| 0.386744 / 0.293841 (0.092903) \| 0.043571 / 0.128546 (-0.084975) \| 0.012844 / 0.075646 (-0.062803) \| 0.369440 / 0.419271 (-0.049832) \| 0.056944 / 0.043533 (0.013411) \| 0.316159 / 0.255139 (0.061020) \| 0.435530 / 0.283200 (0.152330) \| 0.033622 / 0.141683 (-0.108061) \| 1.379602 / 1.452155 (-0.072553) \| 1.766400 / 1.492716 (0.273683) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304151 / 0.018006 (0.286145) \| 0.616365 / 0.000490 (0.615875) \| 0.013588 / 0.000200 (0.013389) \| 0.000441 / 0.000054 (0.000387) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032812 / 0.037411 (-0.004600) \| 0.100914 / 0.014526 (0.086388) \| 0.124004 / 0.176557 (-0.052552) \| 0.195087 / 0.737135 (-0.542048) \| 0.124388 / 0.296338 (-0.171951) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.575649 / 0.215209 (0.360440) \| 5.665461 / 2.077655 (3.587806) \| 2.474892 / 1.504120 (0.970773) \| 2.142687 / 1.541195 (0.601492) \| 2.254962 / 1.468490 (0.786472) \| 0.816635 / 4.584777 (-3.768141) \| 5.044279 / 3.745712 (1.298567) \| 4.566728 / 5.269862 (-0.703134) \| 2.867146 / 4.565676 (-1.698531) \| 0.092994 / 0.424275 (-0.331281) \| 0.008395 / 0.007607 (0.000788) \| 0.680346 / 0.226044 (0.454302) \| 6.909875 / 2.268929 (4.640946) \| 3.275602 / 55.444624 (-52.169022) \| 2.556000 / 6.876477 (-4.320477) \| 2.581337 / 2.142072 (0.439264) \| 0.997883 / 4.805227 (-3.807344) \| 0.204109 / 6.500664 (-6.296555) \| 0.069705 / 0.075469 (-0.005764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.504573 / 1.841788 (-0.337215) \| 22.219363 / 8.074308 (14.145055) \| 19.078040 / 10.191392 (8.886648) \| 0.234970 / 0.680424 (-0.445454) \| 0.027324 / 0.534201 (-0.506877) \| 0.427960 / 0.579283 (-0.151323) \| 0.570258 / 0.434364 (0.135894) \| 0.502335 / 0.540337 (-0.038003) \| 0.788078 / 1.386936 (-0.598858) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008370 / 0.011353 (-0.002982) \| 0.004573 / 0.011008 (-0.006435) \| 0.073080 / 0.038508 (0.034572) \| 0.068752 / 0.023109 (0.045643) \| 0.439648 / 0.275898 (0.163750) \| 0.499700 / 0.323480 (0.176220) \| 0.006119 / 0.007986 (-0.001866) \| 0.004300 / 0.004328 (-0.000028) \| 0.073173 / 0.004250 (0.068923) \| 0.055676 / 0.037052 (0.018624) \| 0.464152 / 0.258489 (0.205663) \| 0.476954 / 0.293841 (0.183113) \| 0.046335 / 0.128546 (-0.082211) \| 0.013373 / 0.075646 (-0.062274) \| 0.092006 / 0.419271 (-0.327265) \| 0.054802 / 0.043533 (0.011269) \| 0.456594 / 0.255139 (0.201455) \| 0.491931 / 0.283200 (0.208732) \| 0.034021 / 0.141683 (-0.107662) \| 1.575200 / 1.452155 (0.123045) \| 1.689742 / 1.492716 (0.197026) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299432 / 0.018006 (0.281426) \| 0.605643 / 0.000490 (0.605153) \| 0.006280 / 0.000200 (0.006080) \| 0.000120 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028414 / 0.037411 (-0.008997) \| 0.085812 / 0.014526 (0.071286) \| 0.109142 / 0.176557 (-0.067414) \| 0.163458 / 0.737135 (-0.573677) \| 0.100837 / 0.296338 (-0.195501) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.615557 / 0.215209 (0.400348) \| 6.051599 / 2.077655 (3.973944) \| 2.872353 / 1.504120 (1.368234) \| 2.508322 / 1.541195 (0.967128) \| 2.550073 / 1.468490 (1.081583) \| 0.835793 / 4.584777 (-3.748983) \| 5.208484 / 3.745712 (1.462772) \| 4.361846 / 5.269862 (-0.908016) \| 2.776164 / 4.565676 (-1.789513) \| 0.090831 / 0.424275 (-0.333444) \| 0.007320 / 0.007607 (-0.000287) \| 0.725533 / 0.226044 (0.499488) \| 7.051321 / 2.268929 (4.782393) \| 3.515464 / 55.444624 (-51.929160) \| 2.798193 / 6.876477 (-4.078284) \| 3.022512 / 2.142072 (0.880440) \| 0.986744 / 4.805227 (-3.818484) \| 0.198050 / 6.500664 (-6.302615) \| 0.069200 / 0.075469 (-0.006269) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.623759 / 1.841788 (-0.218029) \| 22.269700 / 8.074308 (14.195392) \| 19.577429 / 10.191392 (9.386037) \| 0.215990 / 0.680424 (-0.464434) \| 0.033005 / 0.534201 (-0.501196) \| 0.436848 / 0.579283 (-0.142435) \| 0.591442 / 0.434364 (0.157078) \| 0.547701 / 0.540337 (0.007364) \| 0.741695 / 1.386936 (-0.645241) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e17e139b1323aca3321a5d2c2da40d82c458bae \"CML watermark\")\n", "CI failures are unrelated", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009027 / 0.011353 (-0.002326) \| 0.006118 / 0.011008 (-0.004890) \| 0.118939 / 0.038508 (0.080431) \| 0.089979 / 0.023109 (0.066869) \| 0.412425 / 0.275898 (0.136527) \| 0.455706 / 0.323480 (0.132227) \| 0.006762 / 0.007986 (-0.001224) \| 0.004409 / 0.004328 (0.000080) \| 0.088002 / 0.004250 (0.083751) \| 0.063708 / 0.037052 (0.026656) \| 0.417373 / 0.258489 (0.158884) \| 0.489582 / 0.293841 (0.195741) \| 0.050222 / 0.128546 (-0.078324) \| 0.014386 / 0.075646 (-0.061260) \| 0.435363 / 0.419271 (0.016092) \| 0.069375 / 0.043533 (0.025842) \| 0.410242 / 0.255139 (0.155103) \| 0.436439 / 0.283200 (0.153239) \| 0.039318 / 0.141683 (-0.102365) \| 1.857574 / 1.452155 (0.405419) \| 1.919402 / 1.492716 (0.426686) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.343916 / 0.018006 (0.325910) \| 0.633639 / 0.000490 (0.633150) \| 0.014756 / 0.000200 (0.014557) \| 0.000707 / 0.000054 (0.000652) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031983 / 0.037411 (-0.005429) \| 0.097222 / 0.014526 (0.082697) \| 0.114644 / 0.176557 (-0.061912) \| 0.187787 / 0.737135 (-0.549348) \| 0.120595 / 0.296338 (-0.175743) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.605861 / 0.215209 (0.390652) \| 6.039318 / 2.077655 (3.961664) \| 2.699251 / 1.504120 (1.195132) \| 2.436398 / 1.541195 (0.895203) \| 2.493653 / 1.468490 (1.025163) \| 0.889423 / 4.584777 (-3.695354) \| 5.384769 / 3.745712 (1.639056) \| 5.033033 / 5.269862 (-0.236829) \| 3.056894 / 4.565676 (-1.508783) \| 0.100683 / 0.424275 (-0.323592) \| 0.009103 / 0.007607 (0.001495) \| 0.737066 / 0.226044 (0.511021) \| 7.370485 / 2.268929 (5.101556) \| 3.422670 / 55.444624 (-52.021954) \| 2.830392 / 6.876477 (-4.046084) \| 2.985789 / 2.142072 (0.843717) \| 0.999239 / 4.805227 (-3.805989) \| 0.203506 / 6.500664 (-6.297158) \| 0.076135 / 0.075469 (0.000666) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.697001 / 1.841788 (-0.144787) \| 24.653975 / 8.074308 (16.579667) \| 22.241622 / 10.191392 (12.050230) \| 0.257075 / 0.680424 (-0.423349) \| 0.029159 / 0.534201 (-0.505041) \| 0.493329 / 0.579283 (-0.085954) \| 0.596661 / 0.434364 (0.162297) \| 0.569431 / 0.540337 (0.029094) \| 0.812231 / 1.386936 (-0.574705) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009815 / 0.011353 (-0.001538) \| 0.005136 / 0.011008 (-0.005872) \| 0.078224 / 0.038508 (0.039716) \| 0.103276 / 0.023109 (0.080166) \| 0.512742 / 0.275898 (0.236844) \| 0.544010 / 0.323480 (0.220530) \| 0.007957 / 0.007986 (-0.000029) \| 0.004629 / 0.004328 (0.000300) \| 0.074983 / 0.004250 (0.070733) \| 0.071831 / 0.037052 (0.034778) \| 0.542752 / 0.258489 (0.284262) \| 0.573176 / 0.293841 (0.279335) \| 0.053939 / 0.128546 (-0.074607) \| 0.015007 / 0.075646 (-0.060640) \| 0.085389 / 0.419271 (-0.333882) \| 0.063587 / 0.043533 (0.020055) \| 0.509580 / 0.255139 (0.254441) \| 0.563374 / 0.283200 (0.280174) \| 0.037575 / 0.141683 (-0.104108) \| 1.840740 / 1.452155 (0.388585) \| 1.836414 / 1.492716 (0.343698) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.310188 / 0.018006 (0.292182) \| 0.641478 / 0.000490 (0.640988) \| 0.011057 / 0.000200 (0.010857) \| 0.000173 / 0.000054 (0.000119) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.043280 / 0.037411 (0.005869) \| 0.109256 / 0.014526 (0.094730) \| 0.126701 / 0.176557 (-0.049856) \| 0.199172 / 0.737135 (-0.537963) \| 0.123584 / 0.296338 (-0.172755) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.649272 / 0.215209 (0.434063) \| 6.487501 / 2.077655 (4.409846) \| 3.170330 / 1.504120 (1.666210) \| 2.960912 / 1.541195 (1.419718) \| 3.024531 / 1.468490 (1.556041) \| 0.905112 / 4.584777 (-3.679665) \| 5.560961 / 3.745712 (1.815249) \| 4.920463 / 5.269862 (-0.349399) \| 3.158989 / 4.565676 (-1.406687) \| 0.095444 / 0.424275 (-0.328831) \| 0.008264 / 0.007607 (0.000657) \| 0.819292 / 0.226044 (0.593247) \| 7.982695 / 2.268929 (5.713767) \| 4.098704 / 55.444624 (-51.345921) \| 3.442330 / 6.876477 (-3.434147) \| 3.763426 / 2.142072 (1.621354) \| 1.065464 / 4.805227 (-3.739763) \| 0.215089 / 6.500664 (-6.285575) \| 0.085280 / 0.075469 (0.009811) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.881770 / 1.841788 (0.039983) \| 25.671479 / 8.074308 (17.597171) \| 22.367019 / 10.191392 (12.175627) \| 0.241377 / 0.680424 (-0.439047) \| 0.033555 / 0.534201 (-0.500646) \| 0.501786 / 0.579283 (-0.077497) \| 0.596376 / 0.434364 (0.162012) \| 0.579674 / 0.540337 (0.039337) \| 0.855534 / 1.386936 (-0.531402) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c9c1166e1cf81d38534020f9c167b326585339e5 \"CML watermark\")\n" ]	2023-10-31T17:57:17	2023-10-31T18:35:53	2023-10-31T18:24:02	CONTRIBUTOR	null	Fix https://discuss.huggingface.co/t/attributeerror-enter/60509	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6367/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6367/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6367", "html_url": "https://github.com/huggingface/datasets/pull/6367", "diff_url": "https://github.com/huggingface/datasets/pull/6367.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6367.patch", "merged_at": "2023-10-31T18:24:02" }	true
https://api.github.com/repos/huggingface/datasets/issues/6366	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6366/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6366/comments	https://api.github.com/repos/huggingface/datasets/issues/6366/events	https://github.com/huggingface/datasets/issues/6366	1,970,213,490	I_kwDODunzps51bxJy	6,366	with_format() function returns bytes instead of PIL images even when image column is not part of "columns"	{ "login": "leot13", "id": 17809020, "node_id": "MDQ6VXNlcjE3ODA5MDIw", "avatar_url": "https://avatars.githubusercontent.com/u/17809020?v=4", "gravatar_id": "", "url": "https://api.github.com/users/leot13", "html_url": "https://github.com/leot13", "followers_url": "https://api.github.com/users/leot13/followers", "following_url": "https://api.github.com/users/leot13/following{/other_user}", "gists_url": "https://api.github.com/users/leot13/gists{/gist_id}", "starred_url": "https://api.github.com/users/leot13/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/leot13/subscriptions", "organizations_url": "https://api.github.com/users/leot13/orgs", "repos_url": "https://api.github.com/users/leot13/repos", "events_url": "https://api.github.com/users/leot13/events{/privacy}", "received_events_url": "https://api.github.com/users/leot13/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Thanks for reporting! I've opened a PR with a fix." ]	2023-10-31T11:10:48	2023-11-02T14:21:17	2023-11-02T14:21:17	NONE	null	### Describe the bug When using the with_format() function on a dataset containing images, even if the image column is not part of the columns provided in the function, its type will be changed to bytes. Here is a minimal reproduction of the bug: https://colab.research.google.com/drive/1hyaOspgyhB41oiR1-tXE3k_gJCdJUQCf?usp=sharing ### Steps to reproduce the bug 1. Load the image dataset 2. apply with_format(columns=["text"]) 3. Check the type of images in the "image" column before and after applying with_format ### Expected behavior The type should stay the same, but it does not ### Environment info datasets==2.14.6	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6366/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6366/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6365	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6365/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6365/comments	https://api.github.com/repos/huggingface/datasets/issues/6365/events	https://github.com/huggingface/datasets/issues/6365	1,970,140,392	I_kwDODunzps51bfTo	6,365	Parquet size grows exponential for categorical data	{ "login": "aseganti", "id": 82567957, "node_id": "MDQ6VXNlcjgyNTY3OTU3", "avatar_url": "https://avatars.githubusercontent.com/u/82567957?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aseganti", "html_url": "https://github.com/aseganti", "followers_url": "https://api.github.com/users/aseganti/followers", "following_url": "https://api.github.com/users/aseganti/following{/other_user}", "gists_url": "https://api.github.com/users/aseganti/gists{/gist_id}", "starred_url": "https://api.github.com/users/aseganti/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/aseganti/subscriptions", "organizations_url": "https://api.github.com/users/aseganti/orgs", "repos_url": "https://api.github.com/users/aseganti/repos", "events_url": "https://api.github.com/users/aseganti/events{/privacy}", "received_events_url": "https://api.github.com/users/aseganti/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Wrong repo." ]	2023-10-31T10:29:02	2023-10-31T10:49:17	2023-10-31T10:49:17	NONE	null	### Describe the bug It seems that when saving a data frame with a categorical column inside the size can grow exponentially. This seems to happen because when we save the categorical data to parquet, we are saving the data + all the categories existing in the original data. This happens even when the categories are not present in the original data. ### Steps to reproduce the bug To reproduce the bug, it is enough to run this script: ``` import pandas as pd import os if __name__ == "__main__": for n in [10, 1e2, 1e3, 1e4, 1e5]: for n_col in [1, 10, 100, 1000, 10000]: input = pd.DataFrame([{"{i}": f"{i}_cat" for col in range(n_col)} for i in range(int(n))]) input.iloc[0:100].to_parquet("a.parquet") for col in input.columns: input[col] = input[col].astype("category") input.iloc[0:100].to_parquet("b.parquet") a_size_mb = os.stat("a.parquet").st_size / (1024 * 1024) b_size_mb = os.stat("b.parquet").st_size / (1024 * 1024) print(f"{n} {n_col} {a_size_mb} {b_size_mb} {100*b_size_mb/a_size_mb:.2f}") ``` That produces this output: <img width="464" alt="Screenshot 2023-10-31 at 11 25 25" src="https://github.com/huggingface/datasets/assets/82567957/2b8a9284-7f9e-4c10-a006-0a27236ebd15"> ### Expected behavior In my opinion either: 1. The two file should have (almost) the same size 2. There should be warning telling the user that such difference in size is possible ### Environment info Python 3.8.18 pandas==2.0.3 numpy==1.24.4	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6365/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6365/timeline	null	not_planned	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6364	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6364/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6364/comments	https://api.github.com/repos/huggingface/datasets/issues/6364/events	https://github.com/huggingface/datasets/issues/6364	1,969,136,106	I_kwDODunzps51XqHq	6,364	ArrowNotImplementedError: Unsupported cast from string to list using function cast_list	{ "login": "divyakrishna-devisetty", "id": 32887094, "node_id": "MDQ6VXNlcjMyODg3MDk0", "avatar_url": "https://avatars.githubusercontent.com/u/32887094?v=4", "gravatar_id": "", "url": "https://api.github.com/users/divyakrishna-devisetty", "html_url": "https://github.com/divyakrishna-devisetty", "followers_url": "https://api.github.com/users/divyakrishna-devisetty/followers", "following_url": "https://api.github.com/users/divyakrishna-devisetty/following{/other_user}", "gists_url": "https://api.github.com/users/divyakrishna-devisetty/gists{/gist_id}", "starred_url": "https://api.github.com/users/divyakrishna-devisetty/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/divyakrishna-devisetty/subscriptions", "organizations_url": "https://api.github.com/users/divyakrishna-devisetty/orgs", "repos_url": "https://api.github.com/users/divyakrishna-devisetty/repos", "events_url": "https://api.github.com/users/divyakrishna-devisetty/events{/privacy}", "received_events_url": "https://api.github.com/users/divyakrishna-devisetty/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "You can use the following code to load this CSV with the list values preserved:\r\n```python\r\nfrom datasets import load_dataset\r\nimport ast\r\n\r\nconverters = {\r\n \"contexts\" : ast.literal_eval,\r\n \"ground_truths\" : ast.literal_eval,\r\n}\r\n\r\nds = load_dataset(\"csv\", data_files=\"golden_dataset.csv\", converters=converters)\r\n```", "Thank you! it worked :)" ]	2023-10-30T20:14:01	2023-10-31T19:21:23	2023-10-31T19:21:23	NONE	null	Hi, I am trying to load a local csv dataset(similar to explodinggradients_fiqa) using load_dataset. When I try to pass features, I am facing the mentioned issue. CSV Data sample(golden_dataset.csv): Question \| Context \| answer \| groundtruth "what is abc?" \| "abc is this and that" \| "abc is this " \| "abc is this and that" ``` import csv # built it based on https://huggingface.co/datasets/explodinggradients/fiqa/viewer/ragas_eval?row=0 mydict = [ {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]}, {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]}, {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]} ] fields = ['question', 'contexts', 'answer', 'ground_truths'] with open('golden_dataset.csv', 'w', newline='\n') as file: writer = csv.DictWriter(file, fieldnames = fields) writer.writeheader() for row in mydict: writer.writerow(row) ``` Retrieved dataset: DatasetDict({ train: Dataset({ features: ['question', 'contexts', 'answer', 'ground_truths'], num_rows: 1 }) }) Code to reproduce issue: ``` from datasets import load_dataset, Features, Sequence, Value encode_features = Features( { "question": Value(dtype='string', id=0), "contexts": Sequence(feature=Value(dtype='string', id=1)), "answer": Value(dtype='string', id=2), "ground_truths": Sequence(feature=Value(dtype='string',id=3)), } ) eval_dataset = load_dataset('csv', data_files='/golden_dataset.csv', features = encode_features ) ``` Error trace: ``` --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1925, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1924 _time = time.time() -> 1925 for _, table in generator: 1926 if max_shard_size is not None and writer._num_bytes > max_shard_size: File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/packaged_modules/csv/csv.py:192, in Csv._generate_tables(self, files) 189 # Uncomment for debugging (will print the Arrow table size and elements) 190 # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") 191 # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) --> 192 yield (file_idx, batch_idx), self._cast_table(pa_table) 193 except ValueError as e: File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/packaged_modules/csv/csv.py:167, in Csv._cast_table(self, pa_table) 165 if all(not require_storage_cast(feature) for feature in self.config.features.values()): 166 # cheaper cast --> 167 pa_table = pa.Table.from_arrays([pa_table[field.name] for field in schema], schema=schema) 168 else: 169 # more expensive cast; allows str <-> int/float or str to Audio for example File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:3781, in pyarrow.lib.Table.from_arrays() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:1449, in pyarrow.lib._sanitize_arrays() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/array.pxi:354, in pyarrow.lib.asarray() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:551, in pyarrow.lib.ChunkedArray.cast() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/compute.py:400, in cast(arr, target_type, safe, options, memory_pool) 399 options = CastOptions.safe(target_type) --> 400 return call_function("cast", [arr], options, memory_pool) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/_compute.pyx:572, in pyarrow._compute.call_function() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/_compute.pyx:367, in pyarrow._compute.Function.call() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from string to list using function cast_list The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[57], line 1 ----> 1 eval_dataset = load_dataset('csv', data_files='/golden_dataset.csv', features = encode_features ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 prepare_split_kwargs, 958 download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Environment Info: datasets version: 2.14.5 Python version: 3.10.8 PyArrow version: 12.0.1 Pandas version: 2.0.3 I have also tried to load dataset first and then use cast_column, or save_to_disk and load_from_disk.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6364/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6364/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6363	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6363/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6363/comments	https://api.github.com/repos/huggingface/datasets/issues/6363/events	https://github.com/huggingface/datasets/issues/6363	1,968,891,277	I_kwDODunzps51WuWN	6,363	dataset.transform() hangs indefinitely while finetuning the stable diffusion XL	{ "login": "bhosalems", "id": 10846405, "node_id": "MDQ6VXNlcjEwODQ2NDA1", "avatar_url": "https://avatars.githubusercontent.com/u/10846405?v=4", "gravatar_id": "", "url": "https://api.github.com/users/bhosalems", "html_url": "https://github.com/bhosalems", "followers_url": "https://api.github.com/users/bhosalems/followers", "following_url": "https://api.github.com/users/bhosalems/following{/other_user}", "gists_url": "https://api.github.com/users/bhosalems/gists{/gist_id}", "starred_url": "https://api.github.com/users/bhosalems/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bhosalems/subscriptions", "organizations_url": "https://api.github.com/users/bhosalems/orgs", "repos_url": "https://api.github.com/users/bhosalems/repos", "events_url": "https://api.github.com/users/bhosalems/events{/privacy}", "received_events_url": "https://api.github.com/users/bhosalems/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "I think the code hangs on the `accelerator.main_process_first()` context manager exit. To verify this, you can append a print statement to the end of the `accelerator.main_process_first()` block. \r\n\r\n\r\nIf the problem is in `with_transform`, it would help if you could share the error stack trace printed when you interrupt the process (while it hangs)", "@bhosalems Were you able to fix that ? I face this issue as well", "@matankley No I am not able to resolve this issue yet.", "@mariosasko yes the problem seems to be to exit from accelerator.main_process_first(). Is there any known problem?", "NCCL debug info I get below output, if it helps.\r\n```\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 1\r\nLocal process index: 1\r\nDevice: cuda:1\r\n\r\nMixed precision type: fp16\r\n\r\nDetected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 0\r\nLocal process index: 0\r\nDevice: cuda:0\r\n\r\nMixed precision type: fp16\r\n\r\n{'timestep_spacing', 'thresholding', 'variance_type', 'clip_sample_range', 'prediction_type', 'dynamic_thresholding_ratio', 'sample_max_value'} was not found in config. Values will be initialized to default values.\r\n{'norm_num_groups', 'force_upcast'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\ndeepbull5:1311249:1311249 [0] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311249 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311249:1311249 [0] NCCL INFO cudaDriverVersion 11070\r\nNCCL version 2.14.3+cuda11.7\r\ndeepbull5:1311250:1311250 [1] NCCL INFO cudaDriverVersion 11070\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311250 [1] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311250 [1] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Setting affinity for GPU 1 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/04 : 0 1\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Trees [0] -1/-1/-1->1->0 [1] 0/-1/-1->1->-1 [2] -1/-1/-1->1->0 [3] 0/-1/-1->1->-1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] -1/-1/-1->0->1 [2] 1/-1/-1->0->-1 [3] -1/-1/-1->0->1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 00/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 01/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 02/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 03/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all rings\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all trees\r\ndeepbull5:1311249:1311365 [0] NCCL INFO threadThresholds 8/8/64 \| 16/8/64 \| 512 \| 512\r\ndeepbull5:1311249:1311365 [0] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all rings\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all trees\r\ndeepbull5:1311250:1311366 [1] NCCL INFO threadThresholds 8/8/64 \| 16/8/64 \| 512 \| 512\r\ndeepbull5:1311250:1311366 [1] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311249:1311365 [0] NCCL INFO comm 0x88a84ee0 rank 0 nranks 2 cudaDev 0 busId 1000 - Init COMPLETE\r\ndeepbull5:1311250:1311366 [1] NCCL INFO comm 0x89a42f60 rank 1 nranks 2 cudaDev 1 busId 24000 - Init COMPLETE\r\n\r\n```", "Maybe @muellerzr can help as an `accelerate` maintainer.", "I don't know what the issue was, but after going through the thread here I loved the issue with https://github.com/huggingface/accelerate/issues/314#issuecomment-1565259831" ]	2023-10-30T17:34:05	2023-11-09T19:16:07	null	NONE	null	### Describe the bug Multi-GPU fine-tuning the stable diffusion X by following https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/README_sdxl.md hangs indefinitely. ### Steps to reproduce the bug accelerate launch train_text_to_image_sdxl.py --pretrained_model_name_or_path=$MODEL_NAME --pretrained_vae_model_name_or_path=$VAE_NAME --dataset_name=$DATASET_NAME --enable_xformers_memory_efficient_attention --resolution=512 --center_crop --random_flip --proportion_empty_prompts=0.2 --train_batch_size=1 --gradient_accumulation_steps=4 --gradient_checkpointing --max_train_steps=10000 --use_8bit_adam --learning_rate=1e-06 --lr_scheduler="constant" --lr_warmup_steps=0 --mixed_precision="fp16" --report_to="wandb" --validation_prompt="a cute Sundar Pichai creature" --validation_epochs 5 --checkpointing_steps=5000 --output_dir="sdxl-pokemon-model" ### Expected behavior It should start the training as it does for the single GPU training. I opened the issue in diffusers **https://github.com/huggingface/diffusers/issues/5534 but it does seem to be an issue with the Pokemon dataset. I added some debug prints ``` print("==========HERE3=============") with accelerator.main_process_first(): print(accelerator.is_main_process) print("===========Here3.1===========") if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) print("===========Here3.2===========") # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) print("==========HERE4=============") Corresponding Output Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 1 Local process index: 1 Device: cuda:1 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 2 Local process index: 2 Device: cuda:2 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 0 Local process index: 0 Device: cuda:0 Mixed precision type: fp16 You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. {‘variance_type’, ‘clip_sample_range’, ‘thresholding’, ‘dynamic_thresholding_ratio’} was not found in config. Values will be initialized to default values. {‘attention_type’, ‘reverse_transformer_layers_per_block’, ‘dropout’} was not found in config. Values will be initialized to default values. ==========HERE1============= ==========HERE1============= ==========HERE1============= ==========HERE2============= ==========HERE2============= ==========HERE2============= ==========HERE3============= True ===========Here3.1=========== ===========Here3.2=========== ==========HERE3============= ==========HERE3========= ``` ### Environment info _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_kmp_llvm conda-forge absl-py 2.0.0 pypi_0 pypi accelerate 0.24.0 pypi_0 pypi aiohttp 3.8.6 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi appdirs 1.4.4 pyh9f0ad1d_0 conda-forge async-timeout 4.0.3 pypi_0 pypi attrs 23.1.0 pypi_0 pypi bitsandbytes 0.41.1 pypi_0 pypi blas 1.0 mkl blessings 1.7 py39h06a4308_1002 brotli-python 1.0.9 py39h6a678d5_7 bzip2 1.0.8 h7b6447c_0 ca-certificates 2023.08.22 h06a4308_0 cachetools 5.3.2 pypi_0 pypi certifi 2023.7.22 py39h06a4308_0 cffi 1.15.1 py39h5eee18b_3 charset-normalizer 2.0.4 pyhd3eb1b0_0 click 8.1.7 unix_pyh707e725_0 conda-forge cryptography 41.0.3 py39hdda0065_0 cuda-cudart 11.7.99 0 nvidia cuda-cupti 11.7.101 0 nvidia cuda-libraries 11.7.1 0 nvidia cuda-nvrtc 11.7.99 0 nvidia cuda-nvtx 11.7.91 0 nvidia cuda-runtime 11.7.1 0 nvidia datasets 2.14.6 pypi_0 pypi diffusers 0.22.0.dev0 pypi_0 pypi dill 0.3.7 pypi_0 pypi docker-pycreds 0.4.0 py_0 conda-forge ffmpeg 4.3 hf484d3e_0 pytorch filelock 3.12.4 pypi_0 pypi freetype 2.12.1 h4a9f257_0 frozenlist 1.4.0 pypi_0 pypi fsspec 2023.10.0 pypi_0 pypi ftfy 6.1.1 pypi_0 pypi giflib 5.2.1 h5eee18b_3 gitdb 4.0.11 pyhd8ed1ab_0 conda-forge gitpython 3.1.40 pyhd8ed1ab_0 conda-forge gmp 6.2.1 h295c915_3 gnutls 3.6.15 he1e5248_0 google-auth 2.23.3 pypi_0 pypi google-auth-oauthlib 1.1.0 pypi_0 pypi gpustat 0.6.0 pyhd3eb1b0_1 grpcio 1.59.0 pypi_0 pypi huggingface-hub 0.17.3 pypi_0 pypi idna 3.4 py39h06a4308_0 importlib-metadata 6.8.0 pypi_0 pypi intel-openmp 2023.1.0 hdb19cb5_46305 jinja2 3.1.2 pypi_0 pypi jpeg 9e h5eee18b_1 lame 3.100 h7b6447c_0 lcms2 2.12 h3be6417_0 ld_impl_linux-64 2.38 h1181459_1 lerc 3.0 h295c915_0 libcublas 11.10.3.66 0 nvidia libcufft 10.7.2.124 h4fbf590_0 nvidia libcufile 1.8.0.34 0 nvidia libcurand 10.3.4.52 0 nvidia libcusolver 11.4.0.1 0 nvidia libcusparse 11.7.4.91 0 nvidia libdeflate 1.17 h5eee18b_1 libffi 3.4.4 h6a678d5_0 libgcc-ng 13.2.0 h807b86a_2 conda-forge libgfortran-ng 13.2.0 h69a702a_2 conda-forge libgfortran5 13.2.0 ha4646dd_2 conda-forge libiconv 1.16 h7f8727e_2 libidn2 2.3.4 h5eee18b_0 libnpp 11.7.4.75 0 nvidia libnvjpeg 11.8.0.2 0 nvidia libpng 1.6.39 h5eee18b_0 libprotobuf 3.20.3 he621ea3_0 libstdcxx-ng 13.2.0 h7e041cc_2 conda-forge libtasn1 4.19.0 h5eee18b_0 libtiff 4.5.1 h6a678d5_0 libunistring 0.9.10 h27cfd23_0 libwebp 1.3.2 h11a3e52_0 libwebp-base 1.3.2 h5eee18b_0 llvm-openmp 14.0.6 h9e868ea_0 lz4-c 1.9.4 h6a678d5_0 markdown 3.5 pypi_0 pypi markupsafe 2.1.3 pypi_0 pypi mkl 2023.1.0 h213fc3f_46343 mkl-service 2.4.0 py39h5eee18b_1 mkl_fft 1.3.8 py39h5eee18b_0 mkl_random 1.2.4 py39hdb19cb5_0 multidict 6.0.4 pypi_0 pypi multiprocess 0.70.15 pypi_0 pypi ncurses 6.4 h6a678d5_0 nettle 3.7.3 hbbd107a_1 numpy 1.26.0 py39h5f9d8c6_0 numpy-base 1.26.0 py39hb5e798b_0 nvidia-ml 7.352.0 pyhd3eb1b0_0 oauthlib 3.2.2 pypi_0 pypi openh264 2.1.1 h4ff587b_0 openjpeg 2.4.0 h3ad879b_0 openssl 3.0.11 h7f8727e_2 packaging 23.2 pypi_0 pypi pandas 2.1.1 pypi_0 pypi pathtools 0.1.2 py_1 conda-forge pillow 10.0.1 py39ha6cbd5a_0 pip 23.3 py39h06a4308_0 protobuf 4.23.4 pypi_0 pypi psutil 5.9.6 pypi_0 pypi pyarrow 13.0.0 pypi_0 pypi pyasn1 0.5.0 pypi_0 pypi pyasn1-modules 0.3.0 pypi_0 pypi pycparser 2.21 pyhd3eb1b0_0 pyopenssl 23.2.0 py39h06a4308_0 pysocks 1.7.1 py39h06a4308_0 python 3.9.18 h955ad1f_0 python-dateutil 2.8.2 pypi_0 pypi python_abi 3.9 2_cp39 conda-forge pytorch 1.13.1 py3.9_cuda11.7_cudnn8.5.0_0 pytorch pytorch-cuda 11.7 h778d358_5 pytorch pytorch-mutex 1.0 cuda pytorch pytz 2023.3.post1 pypi_0 pypi pyyaml 6.0.1 pypi_0 pypi readline 8.2 h5eee18b_0 regex 2023.10.3 pypi_0 pypi requests 2.31.0 py39h06a4308_0 requests-oauthlib 1.3.1 pypi_0 pypi rsa 4.9 pypi_0 pypi safetensors 0.4.0 pypi_0 pypi scipy 1.11.3 py39h5f9d8c6_0 sentry-sdk 1.32.0 pyhd8ed1ab_0 conda-forge setproctitle 1.1.10 py39h3811e60_1004 conda-forge setuptools 68.0.0 py39h06a4308_0 six 1.16.0 pyh6c4a22f_0 conda-forge smmap 5.0.0 pyhd8ed1ab_0 conda-forge sqlite 3.41.2 h5eee18b_0 tbb 2021.8.0 hdb19cb5_0 tensorboard 2.15.0 pypi_0 pypi tensorboard-data-server 0.7.2 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tokenizers 0.14.1 pypi_0 pypi torchaudio 0.13.1 py39_cu117 pytorch torchtriton 2.1.0 py39 pytorch torchvision 0.14.1 py39_cu117 pytorch tqdm 4.66.1 pypi_0 pypi transformers 4.34.1 pypi_0 pypi typing_extensions 4.7.1 py39h06a4308_0 tzdata 2023.3 pypi_0 pypi urllib3 1.26.18 py39h06a4308_0 wandb 0.15.12 pyhd8ed1ab_0 conda-forge wcwidth 0.2.8 pypi_0 pypi werkzeug 3.0.1 pypi_0 pypi wheel 0.41.2 py39h06a4308_0 xformers 0.0.22.post7 py39_cu11.7.1_pyt1.13.1 xformers xxhash 3.4.1 pypi_0 pypi xz 5.4.2 h5eee18b_0 yaml 0.2.5 h7f98852_2 conda-forge yarl 1.9.2 pypi_0 pypi zipp 3.17.0 pypi_0 pypi zlib 1.2.13 h5eee18b_0 zstd 1.5.5 hc292b87_0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6363/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6363/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6362	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6362/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6362/comments	https://api.github.com/repos/huggingface/datasets/issues/6362/events	https://github.com/huggingface/datasets/pull/6362	1,965,794,569	PR_kwDODunzps5d_MxD	6,362	Simplify filesystem logic	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008852 / 0.011353 (-0.002501) \| 0.004613 / 0.011008 (-0.006396) \| 0.096153 / 0.038508 (0.057645) \| 0.074945 / 0.023109 (0.051836) \| 0.365960 / 0.275898 (0.090062) \| 0.385450 / 0.323480 (0.061970) \| 0.004757 / 0.007986 (-0.003229) \| 0.003453 / 0.004328 (-0.000876) \| 0.069944 / 0.004250 (0.065693) \| 0.057781 / 0.037052 (0.020729) \| 0.361056 / 0.258489 (0.102567) \| 0.409218 / 0.293841 (0.115377) \| 0.045714 / 0.128546 (-0.082833) \| 0.013776 / 0.075646 (-0.061871) \| 0.328797 / 0.419271 (-0.090474) \| 0.063431 / 0.043533 (0.019899) \| 0.370799 / 0.255139 (0.115660) \| 0.370701 / 0.283200 (0.087502) \| 0.034894 / 0.141683 (-0.106789) \| 1.730290 / 1.452155 (0.278136) \| 1.863600 / 1.492716 (0.370883) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245571 / 0.018006 (0.227565) \| 0.509666 / 0.000490 (0.509176) \| 0.008051 / 0.000200 (0.007851) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027854 / 0.037411 (-0.009557) \| 0.090735 / 0.014526 (0.076209) \| 0.100100 / 0.176557 (-0.076457) \| 0.158267 / 0.737135 (-0.578868) \| 0.107537 / 0.296338 (-0.188801) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.565455 / 0.215209 (0.350246) \| 5.671436 / 2.077655 (3.593781) \| 2.438078 / 1.504120 (0.933958) \| 2.072403 / 1.541195 (0.531208) \| 2.127830 / 1.468490 (0.659340) \| 0.840101 / 4.584777 (-3.744675) \| 4.945952 / 3.745712 (1.200240) \| 4.840904 / 5.269862 (-0.428957) \| 3.037936 / 4.565676 (-1.527740) \| 0.099027 / 0.424275 (-0.325248) \| 0.008448 / 0.007607 (0.000841) \| 0.703315 / 0.226044 (0.477271) \| 6.837550 / 2.268929 (4.568621) \| 3.204232 / 55.444624 (-52.240393) \| 2.492985 / 6.876477 (-4.383492) \| 2.426792 / 2.142072 (0.284720) \| 0.998430 / 4.805227 (-3.806797) \| 0.203854 / 6.500664 (-6.296811) \| 0.072386 / 0.075469 (-0.003083) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.606627 / 1.841788 (-0.235161) \| 22.287391 / 8.074308 (14.213082) \| 20.245654 / 10.191392 (10.054262) \| 0.229377 / 0.680424 (-0.451046) \| 0.028399 / 0.534201 (-0.505802) \| 0.446567 / 0.579283 (-0.132716) \| 0.565277 / 0.434364 (0.130913) \| 0.502957 / 0.540337 (-0.037381) \| 0.749268 / 1.386936 (-0.637668) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008253 / 0.011353 (-0.003100) \| 0.004432 / 0.011008 (-0.006576) \| 0.081995 / 0.038508 (0.043487) \| 0.075443 / 0.023109 (0.052334) \| 0.442139 / 0.275898 (0.166241) \| 0.507308 / 0.323480 (0.183829) \| 0.007343 / 0.007986 (-0.000643) \| 0.003850 / 0.004328 (-0.000478) \| 0.072656 / 0.004250 (0.068406) \| 0.054585 / 0.037052 (0.017533) \| 0.430057 / 0.258489 (0.171568) \| 0.466953 / 0.293841 (0.173112) \| 0.050350 / 0.128546 (-0.078196) \| 0.013682 / 0.075646 (-0.061965) \| 0.088164 / 0.419271 (-0.331107) \| 0.061726 / 0.043533 (0.018193) \| 0.444420 / 0.255139 (0.189281) \| 0.470406 / 0.283200 (0.187206) \| 0.033258 / 0.141683 (-0.108425) \| 1.635977 / 1.452155 (0.183823) \| 1.732767 / 1.492716 (0.240051) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227350 / 0.018006 (0.209344) \| 0.500805 / 0.000490 (0.500316) \| 0.006473 / 0.000200 (0.006273) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034456 / 0.037411 (-0.002955) \| 0.094832 / 0.014526 (0.080306) \| 0.118549 / 0.176557 (-0.058008) \| 0.177971 / 0.737135 (-0.559164) \| 0.114165 / 0.296338 (-0.182174) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.664805 / 0.215209 (0.449596) \| 6.509756 / 2.077655 (4.432101) \| 2.936840 / 1.504120 (1.432720) \| 2.662645 / 1.541195 (1.121450) \| 2.659957 / 1.468490 (1.191467) \| 0.903019 / 4.584777 (-3.681758) \| 5.237191 / 3.745712 (1.491479) \| 4.791917 / 5.269862 (-0.477945) \| 3.130905 / 4.565676 (-1.434772) \| 0.100953 / 0.424275 (-0.323322) \| 0.008388 / 0.007607 (0.000781) \| 0.776393 / 0.226044 (0.550348) \| 7.726230 / 2.268929 (5.457301) \| 3.669223 / 55.444624 (-51.775401) \| 2.904556 / 6.876477 (-3.971921) \| 3.205546 / 2.142072 (1.063473) \| 1.058899 / 4.805227 (-3.746329) \| 0.213733 / 6.500664 (-6.286931) \| 0.071374 / 0.075469 (-0.004096) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.713384 / 1.841788 (-0.128403) \| 23.325498 / 8.074308 (15.251190) \| 20.140510 / 10.191392 (9.949118) \| 0.211565 / 0.680424 (-0.468859) \| 0.032916 / 0.534201 (-0.501285) \| 0.460504 / 0.579283 (-0.118779) \| 0.594352 / 0.434364 (0.159988) \| 0.556384 / 0.540337 (0.016047) \| 0.788586 / 1.386936 (-0.598350) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3c7249c11d8330ce49b1fe119c34fc6100f10774 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008840 / 0.011353 (-0.002513) \| 0.005045 / 0.011008 (-0.005963) \| 0.110777 / 0.038508 (0.072269) \| 0.100495 / 0.023109 (0.077386) \| 0.420302 / 0.275898 (0.144404) \| 0.456423 / 0.323480 (0.132943) \| 0.006873 / 0.007986 (-0.001113) \| 0.005230 / 0.004328 (0.000902) \| 0.081316 / 0.004250 (0.077066) \| 0.063047 / 0.037052 (0.025995) \| 0.439469 / 0.258489 (0.180979) \| 0.488477 / 0.293841 (0.194636) \| 0.048553 / 0.128546 (-0.079994) \| 0.014984 / 0.075646 (-0.060662) \| 0.401317 / 0.419271 (-0.017955) \| 0.074578 / 0.043533 (0.031045) \| 0.435298 / 0.255139 (0.180159) \| 0.464406 / 0.283200 (0.181206) \| 0.048788 / 0.141683 (-0.092895) \| 1.836166 / 1.452155 (0.384011) \| 1.959808 / 1.492716 (0.467091) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.321419 / 0.018006 (0.303412) \| 0.595736 / 0.000490 (0.595246) \| 0.021144 / 0.000200 (0.020944) \| 0.000626 / 0.000054 (0.000571) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033033 / 0.037411 (-0.004379) \| 0.112621 / 0.014526 (0.098095) \| 0.118736 / 0.176557 (-0.057821) \| 0.195533 / 0.737135 (-0.541602) \| 0.120807 / 0.296338 (-0.175531) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616692 / 0.215209 (0.401483) \| 6.033674 / 2.077655 (3.956019) \| 2.630106 / 1.504120 (1.125986) \| 2.316739 / 1.541195 (0.775544) \| 2.387525 / 1.468490 (0.919035) \| 0.863385 / 4.584777 (-3.721392) \| 5.288193 / 3.745712 (1.542481) \| 5.115766 / 5.269862 (-0.154096) \| 3.083055 / 4.565676 (-1.482621) \| 0.104885 / 0.424275 (-0.319391) \| 0.012233 / 0.007607 (0.004626) \| 0.739924 / 0.226044 (0.513880) \| 7.422996 / 2.268929 (5.154067) \| 3.403316 / 55.444624 (-52.041309) \| 2.778740 / 6.876477 (-4.097736) \| 2.836937 / 2.142072 (0.694864) \| 1.059683 / 4.805227 (-3.745544) \| 0.235838 / 6.500664 (-6.264826) \| 0.083725 / 0.075469 (0.008256) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.755843 / 1.841788 (-0.085944) \| 25.186642 / 8.074308 (17.112334) \| 24.133582 / 10.191392 (13.942190) \| 0.240511 / 0.680424 (-0.439913) \| 0.029563 / 0.534201 (-0.504638) \| 0.486049 / 0.579283 (-0.093234) \| 0.610064 / 0.434364 (0.175700) \| 0.559521 / 0.540337 (0.019184) \| 0.828289 / 1.386936 (-0.558647) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012134 / 0.011353 (0.000781) \| 0.005133 / 0.011008 (-0.005875) \| 0.084521 / 0.038508 (0.046013) \| 0.095172 / 0.023109 (0.072063) \| 0.527298 / 0.275898 (0.251400) \| 0.558915 / 0.323480 (0.235435) \| 0.006996 / 0.007986 (-0.000989) \| 0.004283 / 0.004328 (-0.000045) \| 0.082975 / 0.004250 (0.078725) \| 0.067976 / 0.037052 (0.030924) \| 0.534020 / 0.258489 (0.275531) \| 0.560810 / 0.293841 (0.266969) \| 0.051603 / 0.128546 (-0.076943) \| 0.013330 / 0.075646 (-0.062316) \| 0.094093 / 0.419271 (-0.325178) \| 0.068967 / 0.043533 (0.025434) \| 0.512527 / 0.255139 (0.257388) \| 0.542182 / 0.283200 (0.258982) \| 0.039159 / 0.141683 (-0.102524) \| 1.858841 / 1.452155 (0.406686) \| 1.915450 / 1.492716 (0.422734) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269013 / 0.018006 (0.251007) \| 0.601711 / 0.000490 (0.601222) \| 0.013950 / 0.000200 (0.013750) \| 0.000166 / 0.000054 (0.000112) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038817 / 0.037411 (0.001405) \| 0.138528 / 0.014526 (0.124002) \| 0.130691 / 0.176557 (-0.045865) \| 0.192825 / 0.737135 (-0.544310) \| 0.128337 / 0.296338 (-0.168002) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.678725 / 0.215209 (0.463516) \| 6.869763 / 2.077655 (4.792108) \| 3.416224 / 1.504120 (1.912104) \| 3.106971 / 1.541195 (1.565776) \| 3.117248 / 1.468490 (1.648757) \| 0.895004 / 4.584777 (-3.689773) \| 5.551618 / 3.745712 (1.805906) \| 4.964811 / 5.269862 (-0.305051) \| 3.239555 / 4.565676 (-1.326121) \| 0.099776 / 0.424275 (-0.324500) \| 0.008723 / 0.007607 (0.001116) \| 0.818554 / 0.226044 (0.592510) \| 8.015976 / 2.268929 (5.747047) \| 4.200392 / 55.444624 (-51.244232) \| 3.566942 / 6.876477 (-3.309535) \| 3.766249 / 2.142072 (1.624177) \| 1.083428 / 4.805227 (-3.721799) \| 0.214614 / 6.500664 (-6.286050) \| 0.081951 / 0.075469 (0.006482) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.854400 / 1.841788 (0.012612) \| 26.002556 / 8.074308 (17.928248) \| 24.315194 / 10.191392 (14.123802) \| 0.249012 / 0.680424 (-0.431412) \| 0.032681 / 0.534201 (-0.501520) \| 0.502360 / 0.579283 (-0.076923) \| 0.606014 / 0.434364 (0.171650) \| 0.616852 / 0.540337 (0.076514) \| 0.861785 / 1.386936 (-0.525151) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53c5c01583153cc112a507082aff4679433a1cce \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006723 / 0.011353 (-0.004630) \| 0.004135 / 0.011008 (-0.006873) \| 0.079241 / 0.038508 (0.040733) \| 0.065484 / 0.023109 (0.042374) \| 0.302831 / 0.275898 (0.026933) \| 0.343747 / 0.323480 (0.020268) \| 0.005910 / 0.007986 (-0.002076) \| 0.006028 / 0.004328 (0.001699) \| 0.064000 / 0.004250 (0.059750) \| 0.047872 / 0.037052 (0.010820) \| 0.336928 / 0.258489 (0.078439) \| 0.357726 / 0.293841 (0.063885) \| 0.039375 / 0.128546 (-0.089171) \| 0.010439 / 0.075646 (-0.065207) \| 0.310453 / 0.419271 (-0.108819) \| 0.055320 / 0.043533 (0.011787) \| 0.294722 / 0.255139 (0.039583) \| 0.314649 / 0.283200 (0.031450) \| 0.033223 / 0.141683 (-0.108460) \| 1.386705 / 1.452155 (-0.065450) \| 1.420546 / 1.492716 (-0.072170) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.262649 / 0.018006 (0.244643) \| 0.536764 / 0.000490 (0.536274) \| 0.011090 / 0.000200 (0.010891) \| 0.000118 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023822 / 0.037411 (-0.013590) \| 0.074279 / 0.014526 (0.059753) \| 0.081295 / 0.176557 (-0.095262) \| 0.135853 / 0.737135 (-0.601282) \| 0.080193 / 0.296338 (-0.216146) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468577 / 0.215209 (0.253368) \| 4.615975 / 2.077655 (2.538321) \| 2.059232 / 1.504120 (0.555112) \| 1.798578 / 1.541195 (0.257383) \| 1.801436 / 1.468490 (0.332946) \| 0.660489 / 4.584777 (-3.924288) \| 4.394652 / 3.745712 (0.648940) \| 3.956277 / 5.269862 (-1.313585) \| 2.406700 / 4.565676 (-2.158976) \| 0.077174 / 0.424275 (-0.347101) \| 0.007121 / 0.007607 (-0.000486) \| 0.568213 / 0.226044 (0.342168) \| 5.721217 / 2.268929 (3.452289) \| 2.662741 / 55.444624 (-52.781883) \| 2.207333 / 6.876477 (-4.669144) \| 2.165279 / 2.142072 (0.023206) \| 0.772566 / 4.805227 (-4.032661) \| 0.162845 / 6.500664 (-6.337819) \| 0.057515 / 0.075469 (-0.017954) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.313565 / 1.841788 (-0.528223) \| 19.298926 / 8.074308 (11.224618) \| 17.194320 / 10.191392 (7.002928) \| 0.223404 / 0.680424 (-0.457020) \| 0.024735 / 0.534201 (-0.509466) \| 0.388452 / 0.579283 (-0.190831) \| 0.489354 / 0.434364 (0.054990) \| 0.427962 / 0.540337 (-0.112375) \| 0.629483 / 1.386936 (-0.757453) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007404 / 0.011353 (-0.003949) \| 0.004434 / 0.011008 (-0.006574) \| 0.061633 / 0.038508 (0.023125) \| 0.058446 / 0.023109 (0.035336) \| 0.386107 / 0.275898 (0.110209) \| 0.397676 / 0.323480 (0.074197) \| 0.005463 / 0.007986 (-0.002523) \| 0.003797 / 0.004328 (-0.000531) \| 0.067323 / 0.004250 (0.063072) \| 0.053826 / 0.037052 (0.016774) \| 0.387910 / 0.258489 (0.129421) \| 0.409364 / 0.293841 (0.115523) \| 0.039836 / 0.128546 (-0.088710) \| 0.011940 / 0.075646 (-0.063706) \| 0.071812 / 0.419271 (-0.347459) \| 0.047952 / 0.043533 (0.004419) \| 0.386826 / 0.255139 (0.131687) \| 0.392845 / 0.283200 (0.109645) \| 0.029430 / 0.141683 (-0.112253) \| 1.390961 / 1.452155 (-0.061194) \| 1.482744 / 1.492716 (-0.009972) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.258814 / 0.018006 (0.240807) \| 0.535505 / 0.000490 (0.535015) \| 0.006097 / 0.000200 (0.005897) \| 0.000130 / 0.000054 (0.000075) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028046 / 0.037411 (-0.009365) \| 0.078077 / 0.014526 (0.063552) \| 0.087713 / 0.176557 (-0.088843) \| 0.140856 / 0.737135 (-0.596279) \| 0.090565 / 0.296338 (-0.205773) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.504375 / 0.215209 (0.289165) \| 5.133472 / 2.077655 (3.055817) \| 2.368968 / 1.504120 (0.864848) \| 2.176939 / 1.541195 (0.635744) \| 2.151976 / 1.468490 (0.683486) \| 0.720566 / 4.584777 (-3.864211) \| 5.050505 / 3.745712 (1.304793) \| 3.993614 / 5.269862 (-1.276248) \| 2.492234 / 4.565676 (-2.073443) \| 0.089629 / 0.424275 (-0.334646) \| 0.008074 / 0.007607 (0.000467) \| 0.677706 / 0.226044 (0.451661) \| 6.208332 / 2.268929 (3.939403) \| 3.058299 / 55.444624 (-52.386325) \| 2.461078 / 6.876477 (-4.415399) \| 2.622681 / 2.142072 (0.480609) \| 0.873573 / 4.805227 (-3.931654) \| 0.176321 / 6.500664 (-6.324343) \| 0.062410 / 0.075469 (-0.013059) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.454767 / 1.841788 (-0.387021) \| 19.544225 / 8.074308 (11.469917) \| 17.365997 / 10.191392 (7.174605) \| 0.225461 / 0.680424 (-0.454963) \| 0.027679 / 0.534201 (-0.506522) \| 0.396419 / 0.579283 (-0.182864) \| 0.513244 / 0.434364 (0.078880) \| 0.469054 / 0.540337 (-0.071283) \| 0.676458 / 1.386936 (-0.710478) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d44d8a649b541cd0b10ea99fbfe7a02c3ba50a63 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007606 / 0.011353 (-0.003747) \| 0.004692 / 0.011008 (-0.006317) \| 0.100525 / 0.038508 (0.062017) \| 0.085426 / 0.023109 (0.062317) \| 0.378568 / 0.275898 (0.102670) \| 0.412268 / 0.323480 (0.088788) \| 0.004756 / 0.007986 (-0.003230) \| 0.003871 / 0.004328 (-0.000457) \| 0.075244 / 0.004250 (0.070994) \| 0.064969 / 0.037052 (0.027916) \| 0.385569 / 0.258489 (0.127079) \| 0.429117 / 0.293841 (0.135276) \| 0.035798 / 0.128546 (-0.092749) \| 0.009999 / 0.075646 (-0.065647) \| 0.351380 / 0.419271 (-0.067891) \| 0.060850 / 0.043533 (0.017317) \| 0.381327 / 0.255139 (0.126188) \| 0.403663 / 0.283200 (0.120464) \| 0.028103 / 0.141683 (-0.113580) \| 1.814143 / 1.452155 (0.361988) \| 1.895062 / 1.492716 (0.402346) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.263581 / 0.018006 (0.245575) \| 0.506988 / 0.000490 (0.506499) \| 0.012775 / 0.000200 (0.012575) \| 0.000456 / 0.000054 (0.000402) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033452 / 0.037411 (-0.003959) \| 0.104950 / 0.014526 (0.090425) \| 0.114803 / 0.176557 (-0.061754) \| 0.182465 / 0.737135 (-0.554671) \| 0.116156 / 0.296338 (-0.180183) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441574 / 0.215209 (0.226365) \| 4.394601 / 2.077655 (2.316946) \| 2.170797 / 1.504120 (0.666677) \| 1.926675 / 1.541195 (0.385480) \| 1.974867 / 1.468490 (0.506377) \| 0.546777 / 4.584777 (-4.038000) \| 4.053612 / 3.745712 (0.307900) \| 3.934278 / 5.269862 (-1.335583) \| 2.354660 / 4.565676 (-2.211017) \| 0.067706 / 0.424275 (-0.356569) \| 0.009217 / 0.007607 (0.001610) \| 0.539261 / 0.226044 (0.313217) \| 5.409552 / 2.268929 (3.140623) \| 2.835739 / 55.444624 (-52.608886) \| 2.282246 / 6.876477 (-4.594230) \| 2.359930 / 2.142072 (0.217858) \| 0.696363 / 4.805227 (-4.108864) \| 0.155947 / 6.500664 (-6.344717) \| 0.071293 / 0.075469 (-0.004176) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.495512 / 1.841788 (-0.346275) \| 22.027128 / 8.074308 (13.952820) \| 16.226068 / 10.191392 (6.034676) \| 0.180281 / 0.680424 (-0.500142) \| 0.021839 / 0.534201 (-0.512362) \| 0.446151 / 0.579283 (-0.133132) \| 0.476872 / 0.434364 (0.042508) \| 0.515171 / 0.540337 (-0.025166) \| 0.731372 / 1.386936 (-0.655564) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006843 / 0.011353 (-0.004510) \| 0.004286 / 0.011008 (-0.006722) \| 0.074104 / 0.038508 (0.035596) \| 0.076789 / 0.023109 (0.053680) \| 0.441506 / 0.275898 (0.165608) \| 0.500999 / 0.323480 (0.177519) \| 0.006041 / 0.007986 (-0.001945) \| 0.003718 / 0.004328 (-0.000610) \| 0.074189 / 0.004250 (0.069938) \| 0.060513 / 0.037052 (0.023461) \| 0.460812 / 0.258489 (0.202323) \| 0.503631 / 0.293841 (0.209790) \| 0.037026 / 0.128546 (-0.091520) \| 0.009611 / 0.075646 (-0.066035) \| 0.077037 / 0.419271 (-0.342234) \| 0.052191 / 0.043533 (0.008658) \| 0.444567 / 0.255139 (0.189428) \| 0.486730 / 0.283200 (0.203530) \| 0.023846 / 0.141683 (-0.117837) \| 1.692422 / 1.452155 (0.240267) \| 1.809648 / 1.492716 (0.316932) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240007 / 0.018006 (0.222001) \| 0.481980 / 0.000490 (0.481490) \| 0.006945 / 0.000200 (0.006746) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037198 / 0.037411 (-0.000213) \| 0.119413 / 0.014526 (0.104887) \| 0.137409 / 0.176557 (-0.039148) \| 0.199130 / 0.737135 (-0.538005) \| 0.133137 / 0.296338 (-0.163202) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.521747 / 0.215209 (0.306538) \| 4.955653 / 2.077655 (2.877999) \| 2.694323 / 1.504120 (1.190203) \| 2.496629 / 1.541195 (0.955434) \| 2.661151 / 1.468490 (1.192660) \| 0.576687 / 4.584777 (-4.008089) \| 4.251437 / 3.745712 (0.505725) \| 3.683020 / 5.269862 (-1.586842) \| 2.363951 / 4.565676 (-2.201726) \| 0.064631 / 0.424275 (-0.359644) \| 0.007958 / 0.007607 (0.000351) \| 0.616498 / 0.226044 (0.390454) \| 5.919424 / 2.268929 (3.650496) \| 3.255936 / 55.444624 (-52.188689) \| 2.866167 / 6.876477 (-4.010309) \| 3.007272 / 2.142072 (0.865199) \| 0.660259 / 4.805227 (-4.144968) \| 0.152469 / 6.500664 (-6.348195) \| 0.065254 / 0.075469 (-0.010215) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.547912 / 1.841788 (-0.293876) \| 22.494611 / 8.074308 (14.420303) \| 16.400746 / 10.191392 (6.209354) \| 0.184137 / 0.680424 (-0.496287) \| 0.023615 / 0.534201 (-0.510586) \| 0.473923 / 0.579283 (-0.105360) \| 0.473030 / 0.434364 (0.038666) \| 0.534264 / 0.540337 (-0.006073) \| 0.770178 / 1.386936 (-0.616758) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#565efb7f43839072ef01247681645ca404ba0b94 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006812 / 0.011353 (-0.004541) \| 0.004254 / 0.011008 (-0.006754) \| 0.084271 / 0.038508 (0.045763) \| 0.084299 / 0.023109 (0.061189) \| 0.317437 / 0.275898 (0.041539) \| 0.350855 / 0.323480 (0.027375) \| 0.004296 / 0.007986 (-0.003690) \| 0.003610 / 0.004328 (-0.000718) \| 0.065205 / 0.004250 (0.060955) \| 0.057734 / 0.037052 (0.020682) \| 0.324049 / 0.258489 (0.065560) \| 0.365042 / 0.293841 (0.071201) \| 0.031454 / 0.128546 (-0.097092) \| 0.008703 / 0.075646 (-0.066943) \| 0.286603 / 0.419271 (-0.132668) \| 0.052251 / 0.043533 (0.008719) \| 0.312404 / 0.255139 (0.057265) \| 0.335902 / 0.283200 (0.052703) \| 0.025087 / 0.141683 (-0.116595) \| 1.478573 / 1.452155 (0.026418) \| 1.559548 / 1.492716 (0.066831) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307637 / 0.018006 (0.289631) \| 0.567169 / 0.000490 (0.566679) \| 0.006782 / 0.000200 (0.006582) \| 0.000235 / 0.000054 (0.000180) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030979 / 0.037411 (-0.006433) \| 0.089972 / 0.014526 (0.075446) \| 0.101689 / 0.176557 (-0.074868) \| 0.162038 / 0.737135 (-0.575097) \| 0.103107 / 0.296338 (-0.193232) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382458 / 0.215209 (0.167248) \| 3.813105 / 2.077655 (1.735450) \| 1.855198 / 1.504120 (0.351078) \| 1.699850 / 1.541195 (0.158656) \| 1.902818 / 1.468490 (0.434328) \| 0.478654 / 4.584777 (-4.106123) \| 3.536926 / 3.745712 (-0.208786) \| 3.558557 / 5.269862 (-1.711304) \| 2.121098 / 4.565676 (-2.444579) \| 0.056584 / 0.424275 (-0.367691) \| 0.007693 / 0.007607 (0.000086) \| 0.471157 / 0.226044 (0.245112) \| 4.717742 / 2.268929 (2.448813) \| 2.389033 / 55.444624 (-53.055591) \| 2.102898 / 6.876477 (-4.773579) \| 2.233404 / 2.142072 (0.091332) \| 0.585829 / 4.805227 (-4.219398) \| 0.133784 / 6.500664 (-6.366880) \| 0.063963 / 0.075469 (-0.011506) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272234 / 1.841788 (-0.569554) \| 19.897647 / 8.074308 (11.823339) \| 14.808090 / 10.191392 (4.616698) \| 0.167199 / 0.680424 (-0.513224) \| 0.018357 / 0.534201 (-0.515844) \| 0.391635 / 0.579283 (-0.187648) \| 0.409603 / 0.434364 (-0.024761) \| 0.467670 / 0.540337 (-0.072668) \| 0.639763 / 1.386936 (-0.747173) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006794 / 0.011353 (-0.004559) \| 0.004317 / 0.011008 (-0.006692) \| 0.065434 / 0.038508 (0.026926) \| 0.079066 / 0.023109 (0.055957) \| 0.415486 / 0.275898 (0.139588) \| 0.448072 / 0.323480 (0.124593) \| 0.005705 / 0.007986 (-0.002281) \| 0.003589 / 0.004328 (-0.000739) \| 0.065195 / 0.004250 (0.060945) \| 0.058951 / 0.037052 (0.021899) \| 0.414466 / 0.258489 (0.155977) \| 0.453844 / 0.293841 (0.160003) \| 0.032437 / 0.128546 (-0.096110) \| 0.008805 / 0.075646 (-0.066841) \| 0.071741 / 0.419271 (-0.347530) \| 0.048051 / 0.043533 (0.004518) \| 0.413197 / 0.255139 (0.158058) \| 0.430071 / 0.283200 (0.146872) \| 0.023144 / 0.141683 (-0.118539) \| 1.507756 / 1.452155 (0.055601) \| 1.572180 / 1.492716 (0.079464) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.326556 / 0.018006 (0.308550) \| 0.533664 / 0.000490 (0.533174) \| 0.007400 / 0.000200 (0.007200) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033397 / 0.037411 (-0.004014) \| 0.092486 / 0.014526 (0.077960) \| 0.108454 / 0.176557 (-0.068103) \| 0.163885 / 0.737135 (-0.573250) \| 0.109682 / 0.296338 (-0.186657) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429283 / 0.215209 (0.214074) \| 4.285774 / 2.077655 (2.208119) \| 2.245646 / 1.504120 (0.741526) \| 2.088460 / 1.541195 (0.547265) \| 2.217908 / 1.468490 (0.749418) \| 0.500126 / 4.584777 (-4.084651) \| 3.640253 / 3.745712 (-0.105459) \| 3.435069 / 5.269862 (-1.834793) \| 2.158015 / 4.565676 (-2.407662) \| 0.059087 / 0.424275 (-0.365188) \| 0.007479 / 0.007607 (-0.000128) \| 0.518067 / 0.226044 (0.292023) \| 5.181891 / 2.268929 (2.912963) \| 2.759156 / 55.444624 (-52.685468) \| 2.452164 / 6.876477 (-4.424313) \| 2.712764 / 2.142072 (0.570692) \| 0.604871 / 4.805227 (-4.200356) \| 0.137810 / 6.500664 (-6.362854) \| 0.061999 / 0.075469 (-0.013470) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.338081 / 1.841788 (-0.503706) \| 19.934668 / 8.074308 (11.860360) \| 14.482526 / 10.191392 (4.291134) \| 0.167615 / 0.680424 (-0.512809) \| 0.020257 / 0.534201 (-0.513944) \| 0.399103 / 0.579283 (-0.180180) \| 0.431785 / 0.434364 (-0.002579) \| 0.475470 / 0.540337 (-0.064868) \| 0.648003 / 1.386936 (-0.738933) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#153a6c0b0897fc30203ded5a6d6c358c53aa3a0e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011916 / 0.011353 (0.000563) \| 0.004696 / 0.011008 (-0.006313) \| 0.101061 / 0.038508 (0.062553) \| 0.093383 / 0.023109 (0.070274) \| 0.391517 / 0.275898 (0.115619) \| 0.434374 / 0.323480 (0.110894) \| 0.006193 / 0.007986 (-0.001792) \| 0.003840 / 0.004328 (-0.000489) \| 0.077946 / 0.004250 (0.073696) \| 0.066332 / 0.037052 (0.029280) \| 0.413103 / 0.258489 (0.154614) \| 0.452988 / 0.293841 (0.159148) \| 0.044899 / 0.128546 (-0.083647) \| 0.009969 / 0.075646 (-0.065677) \| 0.344569 / 0.419271 (-0.074703) \| 0.064688 / 0.043533 (0.021155) \| 0.388042 / 0.255139 (0.132903) \| 0.417615 / 0.283200 (0.134416) \| 0.032899 / 0.141683 (-0.108784) \| 1.738834 / 1.452155 (0.286679) \| 1.837562 / 1.492716 (0.344845) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.255265 / 0.018006 (0.237259) \| 0.547550 / 0.000490 (0.547061) \| 0.009018 / 0.000200 (0.008818) \| 0.001232 / 0.000054 (0.001178) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033171 / 0.037411 (-0.004241) \| 0.102569 / 0.014526 (0.088043) \| 0.113611 / 0.176557 (-0.062946) \| 0.181805 / 0.737135 (-0.555330) \| 0.115015 / 0.296338 (-0.181323) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456430 / 0.215209 (0.241221) \| 4.536000 / 2.077655 (2.458346) \| 2.220554 / 1.504120 (0.716434) \| 2.037965 / 1.541195 (0.496770) \| 2.223780 / 1.468490 (0.755290) \| 0.565732 / 4.584777 (-4.019045) \| 4.574917 / 3.745712 (0.829205) \| 4.085683 / 5.269862 (-1.184178) \| 2.529052 / 4.565676 (-2.036624) \| 0.067061 / 0.424275 (-0.357214) \| 0.009161 / 0.007607 (0.001554) \| 0.551377 / 0.226044 (0.325332) \| 5.510422 / 2.268929 (3.241493) \| 2.788264 / 55.444624 (-52.656360) \| 2.432821 / 6.876477 (-4.443656) \| 2.500835 / 2.142072 (0.358762) \| 0.683645 / 4.805227 (-4.121582) \| 0.155595 / 6.500664 (-6.345069) \| 0.072265 / 0.075469 (-0.003204) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.512571 / 1.841788 (-0.329217) \| 23.752582 / 8.074308 (15.678273) \| 16.798834 / 10.191392 (6.607442) \| 0.210325 / 0.680424 (-0.470099) \| 0.023446 / 0.534201 (-0.510755) \| 0.472964 / 0.579283 (-0.106319) \| 0.518003 / 0.434364 (0.083639) \| 0.588422 / 0.540337 (0.048085) \| 0.830762 / 1.386936 (-0.556174) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008075 / 0.011353 (-0.003278) \| 0.004569 / 0.011008 (-0.006439) \| 0.079786 / 0.038508 (0.041278) \| 0.092741 / 0.023109 (0.069632) \| 0.500732 / 0.275898 (0.224834) \| 0.544108 / 0.323480 (0.220628) \| 0.006305 / 0.007986 (-0.001680) \| 0.003843 / 0.004328 (-0.000486) \| 0.078347 / 0.004250 (0.074096) \| 0.066969 / 0.037052 (0.029916) \| 0.504116 / 0.258489 (0.245627) \| 0.548109 / 0.293841 (0.254268) \| 0.038263 / 0.128546 (-0.090283) \| 0.010006 / 0.075646 (-0.065640) \| 0.085582 / 0.419271 (-0.333690) \| 0.056937 / 0.043533 (0.013404) \| 0.502861 / 0.255139 (0.247722) \| 0.532002 / 0.283200 (0.248802) \| 0.027003 / 0.141683 (-0.114679) \| 1.811658 / 1.452155 (0.359503) \| 1.878863 / 1.492716 (0.386147) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242297 / 0.018006 (0.224291) \| 0.489060 / 0.000490 (0.488570) \| 0.005770 / 0.000200 (0.005570) \| 0.000129 / 0.000054 (0.000075) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.040368 / 0.037411 (0.002956) \| 0.116221 / 0.014526 (0.101695) \| 0.125195 / 0.176557 (-0.051361) \| 0.188616 / 0.737135 (-0.548519) \| 0.126473 / 0.296338 (-0.169866) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.513975 / 0.215209 (0.298766) \| 5.122407 / 2.077655 (3.044752) \| 2.854024 / 1.504120 (1.349904) \| 2.611101 / 1.541195 (1.069906) \| 2.704880 / 1.468490 (1.236390) \| 0.581568 / 4.584777 (-4.003209) \| 4.628965 / 3.745712 (0.883253) \| 4.069359 / 5.269862 (-1.200503) \| 2.433793 / 4.565676 (-2.131883) \| 0.068624 / 0.424275 (-0.355651) \| 0.008843 / 0.007607 (0.001235) \| 0.609147 / 0.226044 (0.383102) \| 6.096923 / 2.268929 (3.827995) \| 3.411687 / 55.444624 (-52.032937) \| 2.972037 / 6.876477 (-3.904440) \| 3.210266 / 2.142072 (1.068194) \| 0.697935 / 4.805227 (-4.107292) \| 0.156855 / 6.500664 (-6.343809) \| 0.072600 / 0.075469 (-0.002869) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.673126 / 1.841788 (-0.168661) \| 24.782231 / 8.074308 (16.707923) \| 17.945937 / 10.191392 (7.754545) \| 0.229063 / 0.680424 (-0.451361) \| 0.024264 / 0.534201 (-0.509937) \| 0.474904 / 0.579283 (-0.104379) \| 0.616602 / 0.434364 (0.182238) \| 0.587687 / 0.540337 (0.047350) \| 0.875600 / 1.386936 (-0.511336) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ae63ad72c0733c947fa0f2996fa739d80e1ef \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004866 / 0.011353 (-0.006487) \| 0.002877 / 0.011008 (-0.008132) \| 0.061786 / 0.038508 (0.023277) \| 0.051555 / 0.023109 (0.028446) \| 0.262182 / 0.275898 (-0.013716) \| 0.288908 / 0.323480 (-0.034572) \| 0.002929 / 0.007986 (-0.005057) \| 0.002358 / 0.004328 (-0.001971) \| 0.048246 / 0.004250 (0.043995) \| 0.040391 / 0.037052 (0.003339) \| 0.268165 / 0.258489 (0.009675) \| 0.304844 / 0.293841 (0.011003) \| 0.023280 / 0.128546 (-0.105266) \| 0.007274 / 0.075646 (-0.068372) \| 0.200698 / 0.419271 (-0.218574) \| 0.036181 / 0.043533 (-0.007352) \| 0.267292 / 0.255139 (0.012153) \| 0.286981 / 0.283200 (0.003781) \| 0.018686 / 0.141683 (-0.122996) \| 1.131903 / 1.452155 (-0.320251) \| 1.196631 / 1.492716 (-0.296086) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092158 / 0.018006 (0.074152) \| 0.300621 / 0.000490 (0.300132) \| 0.000205 / 0.000200 (0.000006) \| 0.000041 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018101 / 0.037411 (-0.019310) \| 0.062478 / 0.014526 (0.047952) \| 0.073092 / 0.176557 (-0.103464) \| 0.119397 / 0.737135 (-0.617738) \| 0.073768 / 0.296338 (-0.222570) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286711 / 0.215209 (0.071502) \| 2.766663 / 2.077655 (0.689008) \| 1.431238 / 1.504120 (-0.072882) \| 1.308312 / 1.541195 (-0.232883) \| 1.344886 / 1.468490 (-0.123605) \| 0.396719 / 4.584777 (-4.188058) \| 2.371154 / 3.745712 (-1.374558) \| 2.626471 / 5.269862 (-2.643391) \| 1.574837 / 4.565676 (-2.990840) \| 0.046344 / 0.424275 (-0.377931) \| 0.005108 / 0.007607 (-0.002499) \| 0.334200 / 0.226044 (0.108156) \| 3.277034 / 2.268929 (1.008106) \| 1.789338 / 55.444624 (-53.655286) \| 1.527584 / 6.876477 (-5.348892) \| 1.570417 / 2.142072 (-0.571656) \| 0.472663 / 4.805227 (-4.332564) \| 0.100825 / 6.500664 (-6.399839) \| 0.042270 / 0.075469 (-0.033199) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965416 / 1.841788 (-0.876372) \| 11.827406 / 8.074308 (3.753098) \| 10.820703 / 10.191392 (0.629311) \| 0.128636 / 0.680424 (-0.551788) \| 0.014696 / 0.534201 (-0.519505) \| 0.271019 / 0.579283 (-0.308264) \| 0.270077 / 0.434364 (-0.164287) \| 0.313054 / 0.540337 (-0.227284) \| 0.402941 / 1.386936 (-0.983995) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005204 / 0.011353 (-0.006149) \| 0.002976 / 0.011008 (-0.008032) \| 0.047723 / 0.038508 (0.009215) \| 0.056180 / 0.023109 (0.033071) \| 0.277751 / 0.275898 (0.001853) \| 0.304109 / 0.323480 (-0.019371) \| 0.004254 / 0.007986 (-0.003732) \| 0.002386 / 0.004328 (-0.001943) \| 0.047815 / 0.004250 (0.043564) \| 0.041553 / 0.037052 (0.004501) \| 0.280958 / 0.258489 (0.022469) \| 0.308639 / 0.293841 (0.014799) \| 0.023549 / 0.128546 (-0.104997) \| 0.007846 / 0.075646 (-0.067800) \| 0.053762 / 0.419271 (-0.365509) \| 0.031763 / 0.043533 (-0.011770) \| 0.278208 / 0.255139 (0.023069) \| 0.294024 / 0.283200 (0.010825) \| 0.018648 / 0.141683 (-0.123035) \| 1.140664 / 1.452155 (-0.311490) \| 1.206706 / 1.492716 (-0.286010) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093211 / 0.018006 (0.075205) \| 0.303067 / 0.000490 (0.302577) \| 0.000222 / 0.000200 (0.000022) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021745 / 0.037411 (-0.015666) \| 0.070400 / 0.014526 (0.055874) \| 0.083250 / 0.176557 (-0.093307) \| 0.119745 / 0.737135 (-0.617391) \| 0.083004 / 0.296338 (-0.213335) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305841 / 0.215209 (0.090632) \| 2.958171 / 2.077655 (0.880516) \| 1.596990 / 1.504120 (0.092870) \| 1.466522 / 1.541195 (-0.074673) \| 1.487050 / 1.468490 (0.018560) \| 0.402866 / 4.584777 (-4.181911) \| 2.425415 / 3.745712 (-1.320297) \| 2.545245 / 5.269862 (-2.724617) \| 1.569719 / 4.565676 (-2.995958) \| 0.046344 / 0.424275 (-0.377931) \| 0.005275 / 0.007607 (-0.002332) \| 0.362024 / 0.226044 (0.135980) \| 3.556721 / 2.268929 (1.287792) \| 1.961359 / 55.444624 (-53.483266) \| 1.672835 / 6.876477 (-5.203641) \| 1.814036 / 2.142072 (-0.328036) \| 0.482012 / 4.805227 (-4.323215) \| 0.099275 / 6.500664 (-6.401389) \| 0.040988 / 0.075469 (-0.034481) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984368 / 1.841788 (-0.857420) \| 12.251555 / 8.074308 (4.177247) \| 10.645975 / 10.191392 (0.454583) \| 0.128955 / 0.680424 (-0.551468) \| 0.015355 / 0.534201 (-0.518846) \| 0.272498 / 0.579283 (-0.306785) \| 0.279342 / 0.434364 (-0.155022) \| 0.303055 / 0.540337 (-0.237282) \| 0.392437 / 1.386936 (-0.994499) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56458fa98d2670ff6bf47a782b6f418785c017fd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009502 / 0.011353 (-0.001851) \| 0.004957 / 0.011008 (-0.006052) \| 0.111062 / 0.038508 (0.072553) \| 0.100012 / 0.023109 (0.076903) \| 0.415747 / 0.275898 (0.139849) \| 0.453910 / 0.323480 (0.130430) \| 0.006030 / 0.007986 (-0.001956) \| 0.004271 / 0.004328 (-0.000057) \| 0.088694 / 0.004250 (0.084444) \| 0.064529 / 0.037052 (0.027477) \| 0.414999 / 0.258489 (0.156510) \| 0.477115 / 0.293841 (0.183274) \| 0.047565 / 0.128546 (-0.080982) \| 0.013352 / 0.075646 (-0.062294) \| 0.367948 / 0.419271 (-0.051324) \| 0.067577 / 0.043533 (0.024044) \| 0.405107 / 0.255139 (0.149968) \| 0.430281 / 0.283200 (0.147081) \| 0.041629 / 0.141683 (-0.100054) \| 1.784746 / 1.452155 (0.332591) \| 1.901539 / 1.492716 (0.408822) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.308456 / 0.018006 (0.290450) \| 0.623253 / 0.000490 (0.622763) \| 0.014966 / 0.000200 (0.014766) \| 0.000393 / 0.000054 (0.000338) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031538 / 0.037411 (-0.005873) \| 0.100321 / 0.014526 (0.085796) \| 0.112788 / 0.176557 (-0.063769) \| 0.180998 / 0.737135 (-0.556138) \| 0.111589 / 0.296338 (-0.184750) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.603121 / 0.215209 (0.387912) \| 5.769795 / 2.077655 (3.692140) \| 2.501168 / 1.504120 (0.997048) \| 2.240982 / 1.541195 (0.699787) \| 2.333123 / 1.468490 (0.864633) \| 0.799246 / 4.584777 (-3.785531) \| 5.148529 / 3.745712 (1.402817) \| 4.737782 / 5.269862 (-0.532080) \| 3.003032 / 4.565676 (-1.562644) \| 0.087457 / 0.424275 (-0.336818) \| 0.008777 / 0.007607 (0.001170) \| 0.692961 / 0.226044 (0.466916) \| 7.235537 / 2.268929 (4.966608) \| 3.464074 / 55.444624 (-51.980551) \| 2.817360 / 6.876477 (-4.059116) \| 2.903121 / 2.142072 (0.761049) \| 1.026150 / 4.805227 (-3.779077) \| 0.231814 / 6.500664 (-6.268850) \| 0.088358 / 0.075469 (0.012888) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.527889 / 1.841788 (-0.313898) \| 24.374770 / 8.074308 (16.300462) \| 21.720415 / 10.191392 (11.529023) \| 0.209357 / 0.680424 (-0.471067) \| 0.027587 / 0.534201 (-0.506614) \| 0.479136 / 0.579283 (-0.100147) \| 0.573005 / 0.434364 (0.138641) \| 0.537713 / 0.540337 (-0.002625) \| 0.753628 / 1.386936 (-0.633308) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009724 / 0.011353 (-0.001629) \| 0.004798 / 0.011008 (-0.006210) \| 0.076423 / 0.038508 (0.037915) \| 0.085693 / 0.023109 (0.062584) \| 0.446864 / 0.275898 (0.170966) \| 0.482700 / 0.323480 (0.159220) \| 0.006448 / 0.007986 (-0.001537) \| 0.004451 / 0.004328 (0.000122) \| 0.078295 / 0.004250 (0.074045) \| 0.061940 / 0.037052 (0.024888) \| 0.446091 / 0.258489 (0.187601) \| 0.478567 / 0.293841 (0.184726) \| 0.047206 / 0.128546 (-0.081340) \| 0.012608 / 0.075646 (-0.063038) \| 0.089719 / 0.419271 (-0.329552) \| 0.057791 / 0.043533 (0.014258) \| 0.438357 / 0.255139 (0.183218) \| 0.475060 / 0.283200 (0.191860) \| 0.035466 / 0.141683 (-0.106216) \| 1.691982 / 1.452155 (0.239827) \| 1.773834 / 1.492716 (0.281118) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290053 / 0.018006 (0.272047) \| 0.595465 / 0.000490 (0.594976) \| 0.007531 / 0.000200 (0.007331) \| 0.000179 / 0.000054 (0.000124) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034625 / 0.037411 (-0.002786) \| 0.098725 / 0.014526 (0.084200) \| 0.111248 / 0.176557 (-0.065308) \| 0.172113 / 0.737135 (-0.565022) \| 0.111299 / 0.296338 (-0.185040) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.581773 / 0.215209 (0.366564) \| 6.150993 / 2.077655 (4.073338) \| 2.761099 / 1.504120 (1.256980) \| 2.431459 / 1.541195 (0.890264) \| 2.501471 / 1.468490 (1.032981) \| 0.805751 / 4.584777 (-3.779026) \| 5.375406 / 3.745712 (1.629693) \| 4.829323 / 5.269862 (-0.440538) \| 3.095235 / 4.565676 (-1.470442) \| 0.103336 / 0.424275 (-0.320939) \| 0.012678 / 0.007607 (0.005071) \| 0.730121 / 0.226044 (0.504077) \| 7.272025 / 2.268929 (5.003097) \| 3.607889 / 55.444624 (-51.836735) \| 2.904797 / 6.876477 (-3.971680) \| 3.179139 / 2.142072 (1.037067) \| 0.997510 / 4.805227 (-3.807717) \| 0.219023 / 6.500664 (-6.281641) \| 0.076680 / 0.075469 (0.001211) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.712838 / 1.841788 (-0.128950) \| 24.242240 / 8.074308 (16.167932) \| 19.746825 / 10.191392 (9.555433) \| 0.234590 / 0.680424 (-0.445833) \| 0.032015 / 0.534201 (-0.502186) \| 0.462554 / 0.579283 (-0.116729) \| 0.604529 / 0.434364 (0.170165) \| 0.537779 / 0.540337 (-0.002558) \| 0.777386 / 1.386936 (-0.609550) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#89c1b13d85b8971925440deb84f558e23c224a47 \"CML watermark\")\n", "Cool ! Nice to simplify this", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004659 / 0.011353 (-0.006693) \| 0.002672 / 0.011008 (-0.008337) \| 0.062385 / 0.038508 (0.023877) \| 0.030581 / 0.023109 (0.007471) \| 0.243210 / 0.275898 (-0.032688) \| 0.271441 / 0.323480 (-0.052039) \| 0.002909 / 0.007986 (-0.005076) \| 0.002371 / 0.004328 (-0.001957) \| 0.049213 / 0.004250 (0.044962) \| 0.043952 / 0.037052 (0.006900) \| 0.250257 / 0.258489 (-0.008232) \| 0.280470 / 0.293841 (-0.013371) \| 0.023048 / 0.128546 (-0.105499) \| 0.006893 / 0.075646 (-0.068754) \| 0.204026 / 0.419271 (-0.215245) \| 0.054067 / 0.043533 (0.010534) \| 0.248730 / 0.255139 (-0.006409) \| 0.272325 / 0.283200 (-0.010874) \| 0.019028 / 0.141683 (-0.122655) \| 1.103477 / 1.452155 (-0.348678) \| 1.185775 / 1.492716 (-0.306942) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097295 / 0.018006 (0.079289) \| 0.302997 / 0.000490 (0.302507) \| 0.000216 / 0.000200 (0.000016) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018653 / 0.037411 (-0.018759) \| 0.062604 / 0.014526 (0.048079) \| 0.075652 / 0.176557 (-0.100904) \| 0.121298 / 0.737135 (-0.615838) \| 0.074129 / 0.296338 (-0.222209) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283315 / 0.215209 (0.068106) \| 2.833975 / 2.077655 (0.756320) \| 1.463877 / 1.504120 (-0.040243) \| 1.352197 / 1.541195 (-0.188998) \| 1.337623 / 1.468490 (-0.130867) \| 0.405282 / 4.584777 (-4.179495) \| 2.371381 / 3.745712 (-1.374331) \| 2.584853 / 5.269862 (-2.685009) \| 1.565902 / 4.565676 (-2.999775) \| 0.046398 / 0.424275 (-0.377877) \| 0.004795 / 0.007607 (-0.002812) \| 0.345949 / 0.226044 (0.119905) \| 3.326662 / 2.268929 (1.057733) \| 1.778394 / 55.444624 (-53.666230) \| 1.520788 / 6.876477 (-5.355688) \| 1.526517 / 2.142072 (-0.615556) \| 0.471788 / 4.805227 (-4.333439) \| 0.099236 / 6.500664 (-6.401428) \| 0.041886 / 0.075469 (-0.033583) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.958183 / 1.841788 (-0.883605) \| 11.474476 / 8.074308 (3.400168) \| 10.547550 / 10.191392 (0.356158) \| 0.129316 / 0.680424 (-0.551108) \| 0.013969 / 0.534201 (-0.520232) \| 0.272028 / 0.579283 (-0.307255) \| 0.271027 / 0.434364 (-0.163337) \| 0.312124 / 0.540337 (-0.228214) \| 0.423879 / 1.386936 (-0.963057) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004743 / 0.011353 (-0.006610) \| 0.002724 / 0.011008 (-0.008284) \| 0.049526 / 0.038508 (0.011018) \| 0.051429 / 0.023109 (0.028319) \| 0.265202 / 0.275898 (-0.010696) \| 0.287498 / 0.323480 (-0.035981) \| 0.004034 / 0.007986 (-0.003951) \| 0.002460 / 0.004328 (-0.001868) \| 0.049367 / 0.004250 (0.045116) \| 0.038526 / 0.037052 (0.001474) \| 0.271496 / 0.258489 (0.013007) \| 0.300969 / 0.293841 (0.007128) \| 0.024159 / 0.128546 (-0.104387) \| 0.006959 / 0.075646 (-0.068687) \| 0.055316 / 0.419271 (-0.363955) \| 0.032409 / 0.043533 (-0.011124) \| 0.267524 / 0.255139 (0.012385) \| 0.284667 / 0.283200 (0.001467) \| 0.017305 / 0.141683 (-0.124378) \| 1.127560 / 1.452155 (-0.324595) \| 1.188271 / 1.492716 (-0.304445) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093587 / 0.018006 (0.075581) \| 0.301834 / 0.000490 (0.301344) \| 0.000211 / 0.000200 (0.000011) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020899 / 0.037411 (-0.016512) \| 0.069999 / 0.014526 (0.055473) \| 0.081434 / 0.176557 (-0.095123) \| 0.120538 / 0.737135 (-0.616598) \| 0.082708 / 0.296338 (-0.213630) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291845 / 0.215209 (0.076636) \| 2.872476 / 2.077655 (0.794822) \| 1.579330 / 1.504120 (0.075210) \| 1.453083 / 1.541195 (-0.088112) \| 1.496675 / 1.468490 (0.028185) \| 0.406178 / 4.584777 (-4.178599) \| 2.434121 / 3.745712 (-1.311592) \| 2.519760 / 5.269862 (-2.750101) \| 1.535781 / 4.565676 (-3.029895) \| 0.046331 / 0.424275 (-0.377944) \| 0.004749 / 0.007607 (-0.002858) \| 0.340862 / 0.226044 (0.114817) \| 3.362750 / 2.268929 (1.093822) \| 1.924707 / 55.444624 (-53.519917) \| 1.646820 / 6.876477 (-5.229657) \| 1.630885 / 2.142072 (-0.511188) \| 0.478623 / 4.805227 (-4.326605) \| 0.098235 / 6.500664 (-6.402429) \| 0.040741 / 0.075469 (-0.034728) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989858 / 1.841788 (-0.851929) \| 12.111035 / 8.074308 (4.036727) \| 11.065284 / 10.191392 (0.873892) \| 0.143443 / 0.680424 (-0.536981) \| 0.015873 / 0.534201 (-0.518328) \| 0.271932 / 0.579283 (-0.307351) \| 0.281440 / 0.434364 (-0.152924) \| 0.309518 / 0.540337 (-0.230819) \| 0.414701 / 1.386936 (-0.972235) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#322ee4bd7d460a5789f9991a45453b9fb5f5aed1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005840 / 0.011353 (-0.005513) \| 0.003580 / 0.011008 (-0.007428) \| 0.079921 / 0.038508 (0.041413) \| 0.036316 / 0.023109 (0.013206) \| 0.321065 / 0.275898 (0.045167) \| 0.348594 / 0.323480 (0.025115) \| 0.004662 / 0.007986 (-0.003324) \| 0.002884 / 0.004328 (-0.001444) \| 0.062964 / 0.004250 (0.058714) \| 0.052856 / 0.037052 (0.015804) \| 0.322087 / 0.258489 (0.063598) \| 0.355546 / 0.293841 (0.061705) \| 0.027025 / 0.128546 (-0.101521) \| 0.007969 / 0.075646 (-0.067678) \| 0.261416 / 0.419271 (-0.157855) \| 0.066612 / 0.043533 (0.023079) \| 0.314631 / 0.255139 (0.059492) \| 0.340939 / 0.283200 (0.057739) \| 0.019710 / 0.141683 (-0.121972) \| 1.446068 / 1.452155 (-0.006086) \| 1.510342 / 1.492716 (0.017625) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219742 / 0.018006 (0.201736) \| 0.431794 / 0.000490 (0.431304) \| 0.005717 / 0.000200 (0.005517) \| 0.000195 / 0.000054 (0.000141) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024486 / 0.037411 (-0.012926) \| 0.073231 / 0.014526 (0.058706) \| 0.084053 / 0.176557 (-0.092503) \| 0.145857 / 0.737135 (-0.591279) \| 0.083050 / 0.296338 (-0.213289) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400532 / 0.215209 (0.185323) \| 3.989293 / 2.077655 (1.911638) \| 1.935520 / 1.504120 (0.431400) \| 1.754146 / 1.541195 (0.212951) \| 1.821060 / 1.468490 (0.352570) \| 0.512603 / 4.584777 (-4.072173) \| 3.070974 / 3.745712 (-0.674738) \| 2.984617 / 5.269862 (-2.285245) \| 1.875790 / 4.565676 (-2.689886) \| 0.057881 / 0.424275 (-0.366394) \| 0.006403 / 0.007607 (-0.001204) \| 0.465542 / 0.226044 (0.239498) \| 4.659589 / 2.268929 (2.390661) \| 2.349637 / 55.444624 (-53.094987) \| 2.011511 / 6.876477 (-4.864965) \| 2.071893 / 2.142072 (-0.070179) \| 0.591113 / 4.805227 (-4.214114) \| 0.125000 / 6.500664 (-6.375664) \| 0.061372 / 0.075469 (-0.014097) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237068 / 1.841788 (-0.604720) \| 17.493192 / 8.074308 (9.418884) \| 13.600688 / 10.191392 (3.409296) \| 0.142508 / 0.680424 (-0.537916) \| 0.017305 / 0.534201 (-0.516896) \| 0.333352 / 0.579283 (-0.245931) \| 0.366699 / 0.434364 (-0.067665) \| 0.381104 / 0.540337 (-0.159233) \| 0.562645 / 1.386936 (-0.824291) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006337 / 0.011353 (-0.005016) \| 0.003584 / 0.011008 (-0.007424) \| 0.063351 / 0.038508 (0.024843) \| 0.061351 / 0.023109 (0.038242) \| 0.430690 / 0.275898 (0.154792) \| 0.462158 / 0.323480 (0.138678) \| 0.004922 / 0.007986 (-0.003064) \| 0.002898 / 0.004328 (-0.001430) \| 0.063722 / 0.004250 (0.059472) \| 0.046970 / 0.037052 (0.009918) \| 0.436340 / 0.258489 (0.177851) \| 0.472842 / 0.293841 (0.179001) \| 0.029238 / 0.128546 (-0.099309) \| 0.008079 / 0.075646 (-0.067568) \| 0.068425 / 0.419271 (-0.350846) \| 0.041272 / 0.043533 (-0.002261) \| 0.429150 / 0.255139 (0.174011) \| 0.451859 / 0.283200 (0.168659) \| 0.020135 / 0.141683 (-0.121547) \| 1.440388 / 1.452155 (-0.011767) \| 1.506784 / 1.492716 (0.014068) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225810 / 0.018006 (0.207804) \| 0.408447 / 0.000490 (0.407957) \| 0.002484 / 0.000200 (0.002284) \| 0.000079 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026162 / 0.037411 (-0.011250) \| 0.079292 / 0.014526 (0.064766) \| 0.091126 / 0.176557 (-0.085431) \| 0.141607 / 0.737135 (-0.595528) \| 0.090073 / 0.296338 (-0.206266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420689 / 0.215209 (0.205479) \| 4.207631 / 2.077655 (2.129976) \| 2.163469 / 1.504120 (0.659350) \| 2.098208 / 1.541195 (0.557013) \| 2.217340 / 1.468490 (0.748850) \| 0.502599 / 4.584777 (-4.082178) \| 3.128151 / 3.745712 (-0.617561) \| 2.921041 / 5.269862 (-2.348820) \| 1.808352 / 4.565676 (-2.757325) \| 0.057724 / 0.424275 (-0.366551) \| 0.006423 / 0.007607 (-0.001184) \| 0.490631 / 0.226044 (0.264587) \| 4.878761 / 2.268929 (2.609833) \| 2.614831 / 55.444624 (-52.829793) \| 2.214611 / 6.876477 (-4.661866) \| 2.253313 / 2.142072 (0.111241) \| 0.585643 / 4.805227 (-4.219584) \| 0.122436 / 6.500664 (-6.378228) \| 0.057974 / 0.075469 (-0.017495) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.334290 / 1.841788 (-0.507498) \| 17.778981 / 8.074308 (9.704672) \| 14.982837 / 10.191392 (4.791445) \| 0.135731 / 0.680424 (-0.544693) \| 0.018314 / 0.534201 (-0.515887) \| 0.332318 / 0.579283 (-0.246966) \| 0.380185 / 0.434364 (-0.054179) \| 0.391430 / 0.540337 (-0.148907) \| 0.554577 / 1.386936 (-0.832359) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1715b61a096cafb20caeb136111522432aba04f5 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005248 / 0.011353 (-0.006105) \| 0.003188 / 0.011008 (-0.007820) \| 0.063045 / 0.038508 (0.024537) \| 0.033620 / 0.023109 (0.010511) \| 0.244725 / 0.275898 (-0.031173) \| 0.283259 / 0.323480 (-0.040220) \| 0.003013 / 0.007986 (-0.004973) \| 0.002486 / 0.004328 (-0.001842) \| 0.048873 / 0.004250 (0.044623) \| 0.049431 / 0.037052 (0.012379) \| 0.245297 / 0.258489 (-0.013192) \| 0.283127 / 0.293841 (-0.010714) \| 0.024204 / 0.128546 (-0.104342) \| 0.007542 / 0.075646 (-0.068104) \| 0.204831 / 0.419271 (-0.214440) \| 0.067487 / 0.043533 (0.023954) \| 0.251477 / 0.255139 (-0.003662) \| 0.273108 / 0.283200 (-0.010091) \| 0.021035 / 0.141683 (-0.120648) \| 1.108361 / 1.452155 (-0.343793) \| 1.172923 / 1.492716 (-0.319793) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094729 / 0.018006 (0.076722) \| 0.301877 / 0.000490 (0.301388) \| 0.000223 / 0.000200 (0.000023) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019901 / 0.037411 (-0.017511) \| 0.068059 / 0.014526 (0.053534) \| 0.075333 / 0.176557 (-0.101224) \| 0.123276 / 0.737135 (-0.613859) \| 0.076810 / 0.296338 (-0.219528) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283421 / 0.215209 (0.068211) \| 2.775511 / 2.077655 (0.697857) \| 1.430927 / 1.504120 (-0.073193) \| 1.317334 / 1.541195 (-0.223860) \| 1.359483 / 1.468490 (-0.109007) \| 0.403186 / 4.584777 (-4.181591) \| 2.405789 / 3.745712 (-1.339923) \| 2.773039 / 5.269862 (-2.496823) \| 1.666722 / 4.565676 (-2.898954) \| 0.047937 / 0.424275 (-0.376338) \| 0.004879 / 0.007607 (-0.002728) \| 0.347225 / 0.226044 (0.121180) \| 3.380860 / 2.268929 (1.111931) \| 1.838532 / 55.444624 (-53.606092) \| 1.597681 / 6.876477 (-5.278796) \| 1.600123 / 2.142072 (-0.541949) \| 0.478836 / 4.805227 (-4.326391) \| 0.100332 / 6.500664 (-6.400332) \| 0.043334 / 0.075469 (-0.032135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.942591 / 1.841788 (-0.899196) \| 12.588886 / 8.074308 (4.514578) \| 11.375666 / 10.191392 (1.184274) \| 0.143460 / 0.680424 (-0.536964) \| 0.014990 / 0.534201 (-0.519211) \| 0.271068 / 0.579283 (-0.308216) \| 0.265478 / 0.434364 (-0.168885) \| 0.310914 / 0.540337 (-0.229423) \| 0.428310 / 1.386936 (-0.958626) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004986 / 0.011353 (-0.006367) \| 0.003263 / 0.011008 (-0.007745) \| 0.049076 / 0.038508 (0.010567) \| 0.063665 / 0.023109 (0.040556) \| 0.270352 / 0.275898 (-0.005546) \| 0.298849 / 0.323480 (-0.024631) \| 0.004083 / 0.007986 (-0.003903) \| 0.002503 / 0.004328 (-0.001826) \| 0.048586 / 0.004250 (0.044335) \| 0.040701 / 0.037052 (0.003648) \| 0.274082 / 0.258489 (0.015593) \| 0.308279 / 0.293841 (0.014438) \| 0.024734 / 0.128546 (-0.103812) \| 0.007535 / 0.075646 (-0.068111) \| 0.054670 / 0.419271 (-0.364602) \| 0.032828 / 0.043533 (-0.010705) \| 0.276226 / 0.255139 (0.021087) \| 0.289322 / 0.283200 (0.006122) \| 0.018789 / 0.141683 (-0.122893) \| 1.279837 / 1.452155 (-0.172318) \| 1.203010 / 1.492716 (-0.289706) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095674 / 0.018006 (0.077667) \| 0.309754 / 0.000490 (0.309265) \| 0.000229 / 0.000200 (0.000029) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021733 / 0.037411 (-0.015678) \| 0.074858 / 0.014526 (0.060332) \| 0.081845 / 0.176557 (-0.094711) \| 0.121991 / 0.737135 (-0.615145) \| 0.084057 / 0.296338 (-0.212281) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298456 / 0.215209 (0.083246) \| 2.884930 / 2.077655 (0.807276) \| 1.574875 / 1.504120 (0.070755) \| 1.451598 / 1.541195 (-0.089597) \| 1.548106 / 1.468490 (0.079616) \| 0.408662 / 4.584777 (-4.176115) \| 2.444306 / 3.745712 (-1.301406) \| 2.737027 / 5.269862 (-2.532835) \| 1.633085 / 4.565676 (-2.932592) \| 0.047349 / 0.424275 (-0.376926) \| 0.004864 / 0.007607 (-0.002744) \| 0.355434 / 0.226044 (0.129389) \| 3.495531 / 2.268929 (1.226603) \| 1.972737 / 55.444624 (-53.471888) \| 1.706973 / 6.876477 (-5.169504) \| 1.798985 / 2.142072 (-0.343087) \| 0.490353 / 4.805227 (-4.314874) \| 0.099533 / 6.500664 (-6.401131) \| 0.042397 / 0.075469 (-0.033073) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978092 / 1.841788 (-0.863696) \| 13.166220 / 8.074308 (5.091912) \| 11.673518 / 10.191392 (1.482126) \| 0.134253 / 0.680424 (-0.546171) \| 0.016478 / 0.534201 (-0.517723) \| 0.271629 / 0.579283 (-0.307654) \| 0.284082 / 0.434364 (-0.150282) \| 0.313352 / 0.540337 (-0.226986) \| 0.416913 / 1.386936 (-0.970023) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01e7144a02825fea3418872c51a8ca93950f3080 \"CML watermark\")\n" ]	2023-10-27T15:54:18	2023-11-15T14:08:29	2023-11-15T14:02:02	CONTRIBUTOR	null	Simplifies the existing filesystem logic (e.g., to avoid unnecessary if-else as mentioned in https://github.com/huggingface/datasets/pull/6098#issue-1827655071)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6362/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6362/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6362", "html_url": "https://github.com/huggingface/datasets/pull/6362", "diff_url": "https://github.com/huggingface/datasets/pull/6362.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6362.patch", "merged_at": "2023-11-15T14:02:02" }	true
https://api.github.com/repos/huggingface/datasets/issues/6360	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6360/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6360/comments	https://api.github.com/repos/huggingface/datasets/issues/6360/events	https://github.com/huggingface/datasets/issues/6360	1,965,672,950	I_kwDODunzps51Kcn2	6,360	Add support for `Sequence(Audio/Image)` feature in `push_to_hub`	{ "login": "Laurent2916", "id": 21087104, "node_id": "MDQ6VXNlcjIxMDg3MTA0", "avatar_url": "https://avatars.githubusercontent.com/u/21087104?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Laurent2916", "html_url": "https://github.com/Laurent2916", "followers_url": "https://api.github.com/users/Laurent2916/followers", "following_url": "https://api.github.com/users/Laurent2916/following{/other_user}", "gists_url": "https://api.github.com/users/Laurent2916/gists{/gist_id}", "starred_url": "https://api.github.com/users/Laurent2916/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Laurent2916/subscriptions", "organizations_url": "https://api.github.com/users/Laurent2916/orgs", "repos_url": "https://api.github.com/users/Laurent2916/repos", "events_url": "https://api.github.com/users/Laurent2916/events{/privacy}", "received_events_url": "https://api.github.com/users/Laurent2916/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[ { "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false } ]	null	[ "This issue stems from https://github.com/huggingface/datasets/blob/6d2f2a5e0fea3827eccfd1717d8021c15fc4292a/src/datasets/table.py#L2203-L2205\r\n\r\nI'll address it as part of https://github.com/huggingface/datasets/pull/6283.\r\n\r\nIn the meantime, this should work\r\n\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets import Image\r\n\r\ndataset = dataset.with_format(\"arrow\")\r\n\r\ndef embed_images(pa_table):\r\n images_arr = pa.chunked_array(\r\n [\r\n pa.ListArray.from_arrays(chunk.offsets, Image().embed_storage(chunk.values), mask=chunk.is_null())\r\n for chunk in pa_table[\"images\"].chunks\r\n ]\r\n )\r\n return pa_table.set_column(pa_table.schema.get_field_index(\"images\"), \"images\", images_arr)\r\n\r\ndataset = dataset.map(embed_images, batched=True)\r\n\r\ndataset = dataset.with_format(\"python\")\r\n\r\ndataset.push_to_hub(...)\r\n```" ]	2023-10-27T14:39:57	2023-11-02T17:49:28	null	CONTRIBUTOR	null	### Feature request Allow for `Sequence` of `Image` (or `Audio`) to be embedded inside the shards. ### Motivation Currently, thanks to #3685, when `embed_external_files` is set to True (which is the default) in `push_to_hub`, features of type `Image` and `Audio` are embedded inside the arrow/parquet shards, instead of only storing paths to the files. I've noticed that this behavior does not extend to `Sequence` of `Image`, when working with a [dataset of timelapse images](https://huggingface.co/datasets/1aurent/Human-Embryo-Timelapse). ### Your contribution I'll submit a PR if I find a way to add this feature	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6360/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6360/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6359	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6359/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6359/comments	https://api.github.com/repos/huggingface/datasets/issues/6359/events	https://github.com/huggingface/datasets/issues/6359	1,965,378,583	I_kwDODunzps51JUwX	6,359	Stuck in "Resolving data files..."	{ "login": "Luciennnnnnn", "id": 20135317, "node_id": "MDQ6VXNlcjIwMTM1MzE3", "avatar_url": "https://avatars.githubusercontent.com/u/20135317?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Luciennnnnnn", "html_url": "https://github.com/Luciennnnnnn", "followers_url": "https://api.github.com/users/Luciennnnnnn/followers", "following_url": "https://api.github.com/users/Luciennnnnnn/following{/other_user}", "gists_url": "https://api.github.com/users/Luciennnnnnn/gists{/gist_id}", "starred_url": "https://api.github.com/users/Luciennnnnnn/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Luciennnnnnn/subscriptions", "organizations_url": "https://api.github.com/users/Luciennnnnnn/orgs", "repos_url": "https://api.github.com/users/Luciennnnnnn/repos", "events_url": "https://api.github.com/users/Luciennnnnnn/events{/privacy}", "received_events_url": "https://api.github.com/users/Luciennnnnnn/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Most likely, the data file inference logic is the problem here.\r\n\r\nYou can run the following code to verify this:\r\n```python\r\nimport time\r\nfrom datasets.data_files import get_data_patterns\r\nstart_time = time.time()\r\nget_data_patterns(\"/path/to/img_dir\")\r\nend_time = time.time()\r\nprint(f\"Elapsed time: {end_time - start_time:.2f}s\")\r\n```\r\n \r\nWe plan to optimize this for the next version (or version after that). In the meantime, specifying the split patterns manually should give better performance:\r\n```python\r\nds = load_dataset(\"imagefolder\", data_files={\"train\": \"path/to/img_dir/train/**\", ...}, split=\"train\")\r\n```", "Hi, @mariosasko, you are right; data file inference logic is extremely slow.\r\n\r\nI have done a similar test, that is I modify the source code of datasets/load.py to measure the cost of two suspicious operations:\r\n```python\r\ndef get_module(self) -> DatasetModule:\r\n base_path = Path(self.data_dir or \"\").expanduser().resolve().as_posix()\r\n start = time.time()\r\n patterns = sanitize_patterns(self.data_files) if self.data_files is not None else get_data_patterns(base_path)\r\n print(f\"patterns: {time.time() - start}\")\r\n start = time.time()\r\n data_files = DataFilesDict.from_patterns(\r\n patterns,\r\n download_config=self.download_config,\r\n base_path=base_path,\r\n )\r\n print(f\"data_files: {time.time() - start}\")\r\n```\r\nIt gaves:\r\npatterns: 3062.2050700187683\r\ndata_files: 413.9576675891876\r\n\r\nThus, these two operations contribute to almost all of load time. What's going on in them?", "Furthermore, what's my current workaround about this problem? Should I save it by `save_to_disk()` and load dataset through `load_from_disk`?" ]	2023-10-27T12:01:51	2023-10-28T01:38:21	null	NONE	null	### Describe the bug I have an image dataset with 300k images, the size of image is 768 * 768. When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going on in this part? From my understand, after Arrow files been created in the first run, the second run should not take time longer than one or two minutes. ### Steps to reproduce the bug # Run following code two times dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train') ### Expected behavior Fast dataset building ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Huggingface_hub version: 0.17.3 - PyArrow version: 10.0.1 - Pandas version: 1.5.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6359/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6359/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6358	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6358/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6358/comments	https://api.github.com/repos/huggingface/datasets/issues/6358/events	https://github.com/huggingface/datasets/issues/6358	1,965,014,595	I_kwDODunzps51H75D	6,358	Mounting datasets cache fails due to absolute paths.	{ "login": "charliebudd", "id": 72921588, "node_id": "MDQ6VXNlcjcyOTIxNTg4", "avatar_url": "https://avatars.githubusercontent.com/u/72921588?v=4", "gravatar_id": "", "url": "https://api.github.com/users/charliebudd", "html_url": "https://github.com/charliebudd", "followers_url": "https://api.github.com/users/charliebudd/followers", "following_url": "https://api.github.com/users/charliebudd/following{/other_user}", "gists_url": "https://api.github.com/users/charliebudd/gists{/gist_id}", "starred_url": "https://api.github.com/users/charliebudd/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/charliebudd/subscriptions", "organizations_url": "https://api.github.com/users/charliebudd/orgs", "repos_url": "https://api.github.com/users/charliebudd/repos", "events_url": "https://api.github.com/users/charliebudd/events{/privacy}", "received_events_url": "https://api.github.com/users/charliebudd/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "You may be able to make it work by tweaking some environment variables, such as [`HF_HOME`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#hfhome) or [`HF_DATASETS_CACHE`](https://huggingface.co/docs/datasets/cache#cache-directory).", "> You may be able to make it work by tweaking some environment variables, such as [`HF_HOME`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#hfhome) or [`HF_DATASETS_CACHE`](https://huggingface.co/docs/datasets/cache#cache-directory).\r\n\r\nI am already doing this. The problem is that, while this seemingly allows flexibility, the absolute paths written into the cache still have the old cache directory. The paths written into the cache should be relative to the cache location to allow this sort of flexibility. Sorry, I omitted this in the reproduction steps, I have now added it.", "I'm unable to reproduce this with the cache\r\n```bash\r\nexport HF_CACHE=$PWD/hf_cache\r\npython -c \"import datasets; datasets.load_dataset('imdb')\"\r\n```\r\nimported inside a dummy container that is built from\r\n```bash\r\nFROM python:3.9\r\n\r\nWORKDIR /usr/src/app\r\n\r\nRUN pip install datasets\r\n\r\nCOPY ./hf_cache ./hf_cache\r\n\r\nENV HF_HOME=./hf_cache\r\nENV HF_DATASETS_OFFLINE=1\r\n\r\nCMD [\"python\"]\r\n```\r\nWhat do you mean by \"absolute paths written into the cache\"? Paths inside the HF cache paths are based on hash (hashed URL of the downloaded files, etc.)" ]	2023-10-27T08:20:27	2023-11-03T14:20:03	null	NONE	null	### Describe the bug Creating a datasets cache and mounting this into, for example, a docker container, renders the data unreadable due to absolute paths written into the cache. ### Steps to reproduce the bug 1. Create a datasets cache by downloading some data 2. Mount the dataset folder into a docker container or remote system. 3. (Edit) Set `HF_HOME` or `HF_DATASET_CACHE` to point to the mounted cache. 4. Attempt to access the data from within the docker container. 5. An error is thrown saying no file exists at \<absolute path to original cache location\> ### Expected behavior The data is loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-162-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6358/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6358/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6357	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6357/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6357/comments	https://api.github.com/repos/huggingface/datasets/issues/6357/events	https://github.com/huggingface/datasets/issues/6357	1,964,653,995	I_kwDODunzps51Gj2r	6,357	Allow passing a multiprocessing context to functions that support `num_proc`	{ "login": "bryant1410", "id": 3905501, "node_id": "MDQ6VXNlcjM5MDU1MDE=", "avatar_url": "https://avatars.githubusercontent.com/u/3905501?v=4", "gravatar_id": "", "url": "https://api.github.com/users/bryant1410", "html_url": "https://github.com/bryant1410", "followers_url": "https://api.github.com/users/bryant1410/followers", "following_url": "https://api.github.com/users/bryant1410/following{/other_user}", "gists_url": "https://api.github.com/users/bryant1410/gists{/gist_id}", "starred_url": "https://api.github.com/users/bryant1410/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bryant1410/subscriptions", "organizations_url": "https://api.github.com/users/bryant1410/orgs", "repos_url": "https://api.github.com/users/bryant1410/repos", "events_url": "https://api.github.com/users/bryant1410/events{/privacy}", "received_events_url": "https://api.github.com/users/bryant1410/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	2023-10-27T02:31:16	2023-10-27T02:31:16	null	CONTRIBUTOR	null	### Feature request Allow specifying [a multiprocessing context](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods) to functions that support `num_proc` or use multiprocessing pools. For example, the following could be done: ```python dataset = dataset.map(_func, num_proc=2, mp_context=multiprocess.get_context("spawn")) ``` Or at least the multiprocessing start method ("fork", "spawn", "fork_server" or `None`): ```python dataset = dataset.map(_func, num_proc=2, mp_start_method="spawn") ``` Another option could be passing the `pool` as an argument. ### Motivation By default, `multiprocess` (the `multiprocessing`-fork library that this repo uses) uses the "fork" start method for multiprocessing pools (for the default context). It could be changed by using `set_start_method`. However, this conditions the multiprocessing start method from all processing in a Python program that uses the default context, because [you can't call that function more than once](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods:~:text=set_start_method()%20should%20not%20be%20used%20more%20than%20once%20in%20the%20program.). My proposal is to allow using a different multiprocessing context, not to condition the whole Python program. One reason to change the start method is that "fork" (the default) makes child processes likely deadlock if thread pools were created before (and also this is not supported by POSIX). For example, this happens when using PyTorch because OpenMP threads are used for CPU intra-op parallelism, which is enabled by default (e.g., for context see [`torch.set_num_threads`](https://pytorch.org/docs/stable/generated/torch.set_num_threads.html)). This can also be fixed by setting `torch.set_num_threads(1)` (or similarly by other methods) but this conditions the whole Python program as it can only be set once to guarantee its behavior (similarly to). There are noticeable performance differences when setting this number to 1 even when using GPU(s). Using, e.g., a "spawn" start method would solve this issue. For more context, see: * https://discuss.huggingface.co/t/dataset-map-stuck-with-torch-set-num-threads-set-to-2-or-larger/37984 * https://discuss.huggingface.co/t/using-num-proc-1-in-dataset-map-hangs/44310 ### Your contribution I'd be happy to review a PR that makes such a change. And if you really don't have the bandwidth for it, I'd consider creating one.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6357/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6357/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6356	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6356/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6356/comments	https://api.github.com/repos/huggingface/datasets/issues/6356/events	https://github.com/huggingface/datasets/pull/6356	1,964,015,802	PR_kwDODunzps5d5Jri	6,356	Add `fsspec` version to the `datasets-cli env` command output	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008775 / 0.011353 (-0.002578) \| 0.005304 / 0.011008 (-0.005704) \| 0.108912 / 0.038508 (0.070404) \| 0.075589 / 0.023109 (0.052479) \| 0.456612 / 0.275898 (0.180713) \| 0.502303 / 0.323480 (0.178823) \| 0.006695 / 0.007986 (-0.001291) \| 0.004404 / 0.004328 (0.000076) \| 0.084802 / 0.004250 (0.080552) \| 0.062711 / 0.037052 (0.025659) \| 0.465062 / 0.258489 (0.206573) \| 0.505321 / 0.293841 (0.211480) \| 0.049401 / 0.128546 (-0.079146) \| 0.014784 / 0.075646 (-0.060862) \| 0.378202 / 0.419271 (-0.041069) \| 0.069826 / 0.043533 (0.026293) \| 0.461161 / 0.255139 (0.206022) \| 0.484616 / 0.283200 (0.201416) \| 0.035998 / 0.141683 (-0.105685) \| 1.846343 / 1.452155 (0.394189) \| 1.999439 / 1.492716 (0.506723) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.317779 / 0.018006 (0.299773) \| 0.605967 / 0.000490 (0.605477) \| 0.011412 / 0.000200 (0.011212) \| 0.000410 / 0.000054 (0.000356) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031118 / 0.037411 (-0.006293) \| 0.095425 / 0.014526 (0.080900) \| 0.108002 / 0.176557 (-0.068554) \| 0.184625 / 0.737135 (-0.552511) \| 0.108180 / 0.296338 (-0.188159) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.587497 / 0.215209 (0.372288) \| 5.818632 / 2.077655 (3.740977) \| 2.629776 / 1.504120 (1.125656) \| 2.266129 / 1.541195 (0.724934) \| 2.324618 / 1.468490 (0.856128) \| 0.830049 / 4.584777 (-3.754728) \| 5.380062 / 3.745712 (1.634350) \| 4.808525 / 5.269862 (-0.461336) \| 2.960368 / 4.565676 (-1.605309) \| 0.093637 / 0.424275 (-0.330638) \| 0.009187 / 0.007607 (0.001580) \| 0.703468 / 0.226044 (0.477424) \| 6.924509 / 2.268929 (4.655580) \| 3.380582 / 55.444624 (-52.064043) \| 2.689118 / 6.876477 (-4.187358) \| 2.712418 / 2.142072 (0.570345) \| 1.017144 / 4.805227 (-3.788084) \| 0.212874 / 6.500664 (-6.287791) \| 0.080053 / 0.075469 (0.004584) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.623663 / 1.841788 (-0.218125) \| 23.668872 / 8.074308 (15.594564) \| 20.245972 / 10.191392 (10.054580) \| 0.236448 / 0.680424 (-0.443976) \| 0.029730 / 0.534201 (-0.504470) \| 0.491525 / 0.579283 (-0.087758) \| 0.593780 / 0.434364 (0.159416) \| 0.548776 / 0.540337 (0.008438) \| 0.799370 / 1.386936 (-0.587566) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009714 / 0.011353 (-0.001639) \| 0.005328 / 0.011008 (-0.005681) \| 0.078460 / 0.038508 (0.039952) \| 0.077791 / 0.023109 (0.054682) \| 0.510124 / 0.275898 (0.234226) \| 0.547769 / 0.323480 (0.224289) \| 0.006868 / 0.007986 (-0.001118) \| 0.004145 / 0.004328 (-0.000183) \| 0.088696 / 0.004250 (0.084445) \| 0.072387 / 0.037052 (0.035334) \| 0.527373 / 0.258489 (0.268884) \| 0.561948 / 0.293841 (0.268107) \| 0.049769 / 0.128546 (-0.078777) \| 0.014401 / 0.075646 (-0.061246) \| 0.097541 / 0.419271 (-0.321731) \| 0.062237 / 0.043533 (0.018705) \| 0.531001 / 0.255139 (0.275862) \| 0.561797 / 0.283200 (0.278597) \| 0.038482 / 0.141683 (-0.103201) \| 1.783558 / 1.452155 (0.331404) \| 1.864339 / 1.492716 (0.371622) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289389 / 0.018006 (0.271383) \| 0.595326 / 0.000490 (0.594836) \| 0.004583 / 0.000200 (0.004383) \| 0.000114 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034492 / 0.037411 (-0.002919) \| 0.102934 / 0.014526 (0.088409) \| 0.121689 / 0.176557 (-0.054868) \| 0.182121 / 0.737135 (-0.555015) \| 0.127087 / 0.296338 (-0.169252) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.645726 / 0.215209 (0.430517) \| 6.462235 / 2.077655 (4.384580) \| 3.044176 / 1.504120 (1.540056) \| 2.731181 / 1.541195 (1.189986) \| 2.805508 / 1.468490 (1.337018) \| 0.846324 / 4.584777 (-3.738453) \| 5.341074 / 3.745712 (1.595362) \| 4.687111 / 5.269862 (-0.582751) \| 3.035472 / 4.565676 (-1.530205) \| 0.099193 / 0.424275 (-0.325082) \| 0.008825 / 0.007607 (0.001218) \| 0.795102 / 0.226044 (0.569058) \| 7.895770 / 2.268929 (5.626842) \| 3.826752 / 55.444624 (-51.617873) \| 3.112217 / 6.876477 (-3.764259) \| 3.526878 / 2.142072 (1.384806) \| 1.011352 / 4.805227 (-3.793875) \| 0.213424 / 6.500664 (-6.287240) \| 0.076228 / 0.075469 (0.000759) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.805232 / 1.841788 (-0.036556) \| 24.049100 / 8.074308 (15.974792) \| 23.056011 / 10.191392 (12.864619) \| 0.261656 / 0.680424 (-0.418767) \| 0.032021 / 0.534201 (-0.502179) \| 0.483829 / 0.579283 (-0.095454) \| 0.602208 / 0.434364 (0.167844) \| 0.565848 / 0.540337 (0.025511) \| 0.818678 / 1.386936 (-0.568258) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#71fc5e2ca41f5f725b9117f4cf99f348534902f3 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008043 / 0.011353 (-0.003310) \| 0.004642 / 0.011008 (-0.006366) \| 0.102592 / 0.038508 (0.064084) \| 0.099508 / 0.023109 (0.076399) \| 0.377692 / 0.275898 (0.101794) \| 0.409929 / 0.323480 (0.086450) \| 0.006363 / 0.007986 (-0.001622) \| 0.003881 / 0.004328 (-0.000447) \| 0.076636 / 0.004250 (0.072386) \| 0.067021 / 0.037052 (0.029969) \| 0.371454 / 0.258489 (0.112964) \| 0.423637 / 0.293841 (0.129796) \| 0.038632 / 0.128546 (-0.089914) \| 0.010055 / 0.075646 (-0.065591) \| 0.352021 / 0.419271 (-0.067251) \| 0.064988 / 0.043533 (0.021456) \| 0.369614 / 0.255139 (0.114475) \| 0.396972 / 0.283200 (0.113773) \| 0.028866 / 0.141683 (-0.112817) \| 1.757620 / 1.452155 (0.305465) \| 1.886283 / 1.492716 (0.393567) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257579 / 0.018006 (0.239572) \| 0.529859 / 0.000490 (0.529369) \| 0.011720 / 0.000200 (0.011520) \| 0.000455 / 0.000054 (0.000401) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034163 / 0.037411 (-0.003248) \| 0.101422 / 0.014526 (0.086896) \| 0.114858 / 0.176557 (-0.061698) \| 0.180265 / 0.737135 (-0.556870) \| 0.116034 / 0.296338 (-0.180305) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477609 / 0.215209 (0.262400) \| 4.830116 / 2.077655 (2.752461) \| 2.323844 / 1.504120 (0.819724) \| 2.174496 / 1.541195 (0.633301) \| 2.268594 / 1.468490 (0.800104) \| 0.612429 / 4.584777 (-3.972348) \| 4.265277 / 3.745712 (0.519565) \| 4.095741 / 5.269862 (-1.174121) \| 2.561532 / 4.565676 (-2.004144) \| 0.068043 / 0.424275 (-0.356233) \| 0.009139 / 0.007607 (0.001532) \| 0.545512 / 0.226044 (0.319467) \| 5.456403 / 2.268929 (3.187475) \| 2.778937 / 55.444624 (-52.665688) \| 2.428560 / 6.876477 (-4.447917) \| 2.557483 / 2.142072 (0.415411) \| 0.696721 / 4.805227 (-4.108506) \| 0.157217 / 6.500664 (-6.343447) \| 0.071334 / 0.075469 (-0.004135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.617755 / 1.841788 (-0.224032) \| 23.368508 / 8.074308 (15.294200) \| 17.028591 / 10.191392 (6.837199) \| 0.195881 / 0.680424 (-0.484542) \| 0.021788 / 0.534201 (-0.512413) \| 0.468484 / 0.579283 (-0.110799) \| 0.474604 / 0.434364 (0.040240) \| 0.544738 / 0.540337 (0.004400) \| 0.771722 / 1.386936 (-0.615214) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007939 / 0.011353 (-0.003414) \| 0.004684 / 0.011008 (-0.006324) \| 0.077273 / 0.038508 (0.038765) \| 0.088763 / 0.023109 (0.065654) \| 0.489178 / 0.275898 (0.213280) \| 0.531547 / 0.323480 (0.208067) \| 0.006214 / 0.007986 (-0.001772) \| 0.003988 / 0.004328 (-0.000340) \| 0.076685 / 0.004250 (0.072434) \| 0.066628 / 0.037052 (0.029576) \| 0.497153 / 0.258489 (0.238664) \| 0.538301 / 0.293841 (0.244460) \| 0.037939 / 0.128546 (-0.090607) \| 0.010054 / 0.075646 (-0.065592) \| 0.084642 / 0.419271 (-0.334629) \| 0.057140 / 0.043533 (0.013608) \| 0.487701 / 0.255139 (0.232562) \| 0.519676 / 0.283200 (0.236477) \| 0.026560 / 0.141683 (-0.115123) \| 1.809676 / 1.452155 (0.357521) \| 1.864884 / 1.492716 (0.372168) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.259005 / 0.018006 (0.240998) \| 0.522900 / 0.000490 (0.522410) \| 0.006885 / 0.000200 (0.006685) \| 0.000156 / 0.000054 (0.000102) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039838 / 0.037411 (0.002426) \| 0.117777 / 0.014526 (0.103251) \| 0.129189 / 0.176557 (-0.047368) \| 0.198584 / 0.737135 (-0.538552) \| 0.129753 / 0.296338 (-0.166586) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.543366 / 0.215209 (0.328157) \| 5.241502 / 2.077655 (3.163847) \| 2.719079 / 1.504120 (1.214959) \| 2.525337 / 1.541195 (0.984142) \| 2.648908 / 1.468490 (1.180418) \| 0.589239 / 4.584777 (-3.995538) \| 4.379856 / 3.745712 (0.634144) \| 4.139919 / 5.269862 (-1.129943) \| 2.633412 / 4.565676 (-1.932264) \| 0.074582 / 0.424275 (-0.349693) \| 0.009106 / 0.007607 (0.001499) \| 0.635540 / 0.226044 (0.409495) \| 6.072965 / 2.268929 (3.804037) \| 3.327233 / 55.444624 (-52.117391) \| 3.012637 / 6.876477 (-3.863840) \| 3.113226 / 2.142072 (0.971154) \| 0.712705 / 4.805227 (-4.092523) \| 0.159550 / 6.500664 (-6.341114) \| 0.073446 / 0.075469 (-0.002023) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.718732 / 1.841788 (-0.123055) \| 23.249445 / 8.074308 (15.175137) \| 17.630643 / 10.191392 (7.439251) \| 0.201017 / 0.680424 (-0.479407) \| 0.024162 / 0.534201 (-0.510039) \| 0.475054 / 0.579283 (-0.104229) \| 0.492348 / 0.434364 (0.057985) \| 0.587118 / 0.540337 (0.046781) \| 0.777462 / 1.386936 (-0.609474) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#feb036956a592b9a9ecdf048cc801549f233dbef \"CML watermark\")\n" ]	2023-10-26T17:19:25	2023-10-26T18:42:56	2023-10-26T18:32:21	CONTRIBUTOR	null	... to make debugging issues easier, as `fsspec`'s releases often introduce breaking changes.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6356/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6356/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6356", "html_url": "https://github.com/huggingface/datasets/pull/6356", "diff_url": "https://github.com/huggingface/datasets/pull/6356.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6356.patch", "merged_at": "2023-10-26T18:32:21" }	true
https://api.github.com/repos/huggingface/datasets/issues/6355	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6355/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6355/comments	https://api.github.com/repos/huggingface/datasets/issues/6355/events	https://github.com/huggingface/datasets/pull/6355	1,963,979,896	PR_kwDODunzps5d5B2B	6,355	More hub centric docs	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006941 / 0.011353 (-0.004412) \| 0.004255 / 0.011008 (-0.006753) \| 0.085237 / 0.038508 (0.046729) \| 0.080962 / 0.023109 (0.057853) \| 0.312016 / 0.275898 (0.036118) \| 0.353161 / 0.323480 (0.029681) \| 0.005756 / 0.007986 (-0.002230) \| 0.003591 / 0.004328 (-0.000738) \| 0.065416 / 0.004250 (0.061166) \| 0.057837 / 0.037052 (0.020785) \| 0.316169 / 0.258489 (0.057680) \| 0.372345 / 0.293841 (0.078504) \| 0.031958 / 0.128546 (-0.096588) \| 0.008798 / 0.075646 (-0.066848) \| 0.294764 / 0.419271 (-0.124507) \| 0.053954 / 0.043533 (0.010421) \| 0.310961 / 0.255139 (0.055822) \| 0.330063 / 0.283200 (0.046864) \| 0.025298 / 0.141683 (-0.116385) \| 1.454715 / 1.452155 (0.002560) \| 1.557915 / 1.492716 (0.065198) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274830 / 0.018006 (0.256824) \| 0.565890 / 0.000490 (0.565400) \| 0.009242 / 0.000200 (0.009042) \| 0.000321 / 0.000054 (0.000266) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031092 / 0.037411 (-0.006320) \| 0.087558 / 0.014526 (0.073033) \| 0.103395 / 0.176557 (-0.073162) \| 0.160078 / 0.737135 (-0.577057) \| 0.102356 / 0.296338 (-0.193983) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402912 / 0.215209 (0.187703) \| 4.029374 / 2.077655 (1.951719) \| 2.048237 / 1.504120 (0.544117) \| 1.887470 / 1.541195 (0.346276) \| 1.994807 / 1.468490 (0.526316) \| 0.491109 / 4.584777 (-4.093668) \| 3.645059 / 3.745712 (-0.100653) \| 3.516376 / 5.269862 (-1.753486) \| 2.103267 / 4.565676 (-2.462409) \| 0.058072 / 0.424275 (-0.366203) \| 0.007796 / 0.007607 (0.000189) \| 0.480544 / 0.226044 (0.254499) \| 4.795422 / 2.268929 (2.526494) \| 2.507770 / 55.444624 (-52.936854) \| 2.187106 / 6.876477 (-4.689371) \| 2.271005 / 2.142072 (0.128933) \| 0.585376 / 4.805227 (-4.219851) \| 0.134741 / 6.500664 (-6.365923) \| 0.060684 / 0.075469 (-0.014785) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.264349 / 1.841788 (-0.577439) \| 19.448735 / 8.074308 (11.374427) \| 14.521197 / 10.191392 (4.329805) \| 0.167295 / 0.680424 (-0.513129) \| 0.018352 / 0.534201 (-0.515849) \| 0.396345 / 0.579283 (-0.182938) \| 0.418690 / 0.434364 (-0.015674) \| 0.469703 / 0.540337 (-0.070635) \| 0.637852 / 1.386936 (-0.749084) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006939 / 0.011353 (-0.004414) \| 0.004196 / 0.011008 (-0.006812) \| 0.064719 / 0.038508 (0.026211) \| 0.077517 / 0.023109 (0.054407) \| 0.401977 / 0.275898 (0.126079) \| 0.431089 / 0.323480 (0.107609) \| 0.005624 / 0.007986 (-0.002362) \| 0.003680 / 0.004328 (-0.000649) \| 0.065817 / 0.004250 (0.061567) \| 0.058297 / 0.037052 (0.021245) \| 0.399614 / 0.258489 (0.141125) \| 0.440089 / 0.293841 (0.146248) \| 0.032492 / 0.128546 (-0.096054) \| 0.008974 / 0.075646 (-0.066672) \| 0.071311 / 0.419271 (-0.347961) \| 0.048001 / 0.043533 (0.004468) \| 0.394763 / 0.255139 (0.139624) \| 0.416754 / 0.283200 (0.133554) \| 0.023730 / 0.141683 (-0.117953) \| 1.509677 / 1.452155 (0.057522) \| 1.605711 / 1.492716 (0.112994) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265490 / 0.018006 (0.247483) \| 0.561745 / 0.000490 (0.561255) \| 0.004616 / 0.000200 (0.004417) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033371 / 0.037411 (-0.004040) \| 0.092763 / 0.014526 (0.078238) \| 0.108905 / 0.176557 (-0.067652) \| 0.160380 / 0.737135 (-0.576756) \| 0.106968 / 0.296338 (-0.189370) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430268 / 0.215209 (0.215059) \| 4.299313 / 2.077655 (2.221658) \| 2.308971 / 1.504120 (0.804851) \| 2.155855 / 1.541195 (0.614661) \| 2.392698 / 1.468490 (0.924208) \| 0.498464 / 4.584777 (-4.086313) \| 3.694473 / 3.745712 (-0.051239) \| 3.409625 / 5.269862 (-1.860236) \| 2.106144 / 4.565676 (-2.459532) \| 0.058992 / 0.424275 (-0.365283) \| 0.007395 / 0.007607 (-0.000212) \| 0.511291 / 0.226044 (0.285247) \| 5.101806 / 2.268929 (2.832877) \| 2.853100 / 55.444624 (-52.591524) \| 2.527216 / 6.876477 (-4.349260) \| 2.819380 / 2.142072 (0.677308) \| 0.635155 / 4.805227 (-4.170072) \| 0.135816 / 6.500664 (-6.364848) \| 0.062056 / 0.075469 (-0.013413) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353479 / 1.841788 (-0.488308) \| 20.318513 / 8.074308 (12.244205) \| 15.105336 / 10.191392 (4.913944) \| 0.166186 / 0.680424 (-0.514238) \| 0.020742 / 0.534201 (-0.513459) \| 0.399286 / 0.579283 (-0.179997) \| 0.431785 / 0.434364 (-0.002579) \| 0.478667 / 0.540337 (-0.061671) \| 0.654683 / 1.386936 (-0.732253) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b39d1ce0b8f231649752f28cb724971f4df1c7ae \"CML watermark\")\n", "Yea I think some of it should be in the Hub docs indeed, let me open a new PR there.\r\n\r\nThen I'll update the `datasets` docs anyway to avoid redundant stuff and add redirects instead" ]	2023-10-26T16:54:46	2023-10-30T17:33:32	2023-10-30T17:32:57	MEMBER	null	Let's have more hub-centric documentation in the datasets docs Tutorials - Add “Configure the dataset viewer” page - Change order: - Overview - and more focused on the Hub rather than the library - Then all the hub related things - and mention how to read/write with other tools like pandas - Then all the datasets lib related things in a subsection Also: - Rename “know your dataset” page to “Explore your dataset” - Remove “Evaluate Predictions” page since it's 'evaluate' stuff (or move to legacy section ?) TODO: - [ ] write the “Configure the dataset viewer” page	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6355/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 1, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6355/timeline	null	null	true	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6355", "html_url": "https://github.com/huggingface/datasets/pull/6355", "diff_url": "https://github.com/huggingface/datasets/pull/6355.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6355.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6354	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6354/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6354/comments	https://api.github.com/repos/huggingface/datasets/issues/6354/events	https://github.com/huggingface/datasets/issues/6354	1,963,483,324	I_kwDODunzps51CGC8	6,354	`IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader`	{ "login": "NazyS", "id": 50199774, "node_id": "MDQ6VXNlcjUwMTk5Nzc0", "avatar_url": "https://avatars.githubusercontent.com/u/50199774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/NazyS", "html_url": "https://github.com/NazyS", "followers_url": "https://api.github.com/users/NazyS/followers", "following_url": "https://api.github.com/users/NazyS/following{/other_user}", "gists_url": "https://api.github.com/users/NazyS/gists{/gist_id}", "starred_url": "https://api.github.com/users/NazyS/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NazyS/subscriptions", "organizations_url": "https://api.github.com/users/NazyS/orgs", "repos_url": "https://api.github.com/users/NazyS/repos", "events_url": "https://api.github.com/users/NazyS/events{/privacy}", "received_events_url": "https://api.github.com/users/NazyS/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "I am having issues as well with this. \r\n\r\nHowever, the error I am getting is :\r\n`RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.`\r\n\r\nAlso did not work with pyspark==3.3.0 and py4j==0.10.9.5" ]	2023-10-26T12:43:36	2023-11-14T18:46:03	null	NONE	null	### Describe the bug Looks like `IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader` if I'm not missing anything. Also, returns not consistent error messages, which probably depend on the nondeterministic order of worker executions Some exampes I've encountered: ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/instrumentation_utils.py", line 54, in wrapper logger.log_failure( File "/databricks/spark/python/pyspark/databricks/usage_logger.py", line 70, in log_failure self.logger.recordFunctionCallFailureEvent( File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__ return_value = get_return_value( File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco return f(a, *kw) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value return OUTPUT_CONVERTER[type](answer[2:], gateway_client) KeyError: 'c' ``` ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped return f(args, kwargs) File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id return _invoke_function("spark_partition_id") File "/databricks/spark/python/pyspark/sql/functions.py", line 98, in _invoke_function return Column(jf(args)) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__ return_value = get_return_value( File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco return f(a, kw) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value return OUTPUT_CONVERTER[type](answer[2:], gateway_client) KeyError: 'm' ``` ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped return f(args, kwargs) File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id return _invoke_function("spark_partition_id") File "/databricks/spark/python/pyspark/sql/functions.py", line 97, in _invoke_function jf = _get_jvm_function(name, SparkContext._active_spark_context) File "/databricks/spark/python/pyspark/sql/functions.py", line 88, in _get_jvm_function return getattr(sc._jvm.functions, name) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1725, in __getattr__ raise Py4JError(message) py4j.protocol.Py4JError: functions does not exist in the JVM ``` ### Steps to reproduce the bug ```python import pandas as pd import numpy as np batch_size = 16 pdf = pd.DataFrame({ key: np.random.rand(16100) for key in ['feature', 'target'] }) test_df = spark.createDataFrame(pdf) from datasets import IterableDataset from torch.utils.data import DataLoader ids = IterableDataset.from_spark(test_df) for batch in DataLoader(ids, batch_size=16, num_workers=4): for k, b in batch.items(): print(k, b.shape, sep='\t') print('\n') ``` ### Expected behavior For `num_workers` equal to 0 or 1 works fine as expected: ``` feature torch.Size([16]) target torch.Size([16]) feature torch.Size([16]) target torch.Size([16]) .... ``` Expected to support workers >1. ### Environment info Databricks 13.3 LTS ML runtime - Spark 3.4.1 pyspark==3.4.1 py4j==0.10.9.7 datasets==2.13.1 and also tested with datasets==2.14.6	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6354/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6354/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6353	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6353/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6353/comments	https://api.github.com/repos/huggingface/datasets/issues/6353/events	https://github.com/huggingface/datasets/issues/6353	1,962,646,450	I_kwDODunzps50-5uy	6,353	load_dataset save_to_disk load_from_disk error	{ "login": "brisker", "id": 13804492, "node_id": "MDQ6VXNlcjEzODA0NDky", "avatar_url": "https://avatars.githubusercontent.com/u/13804492?v=4", "gravatar_id": "", "url": "https://api.github.com/users/brisker", "html_url": "https://github.com/brisker", "followers_url": "https://api.github.com/users/brisker/followers", "following_url": "https://api.github.com/users/brisker/following{/other_user}", "gists_url": "https://api.github.com/users/brisker/gists{/gist_id}", "starred_url": "https://api.github.com/users/brisker/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/brisker/subscriptions", "organizations_url": "https://api.github.com/users/brisker/orgs", "repos_url": "https://api.github.com/users/brisker/repos", "events_url": "https://api.github.com/users/brisker/events{/privacy}", "received_events_url": "https://api.github.com/users/brisker/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "solved.\r\nfsspec version problem" ]	2023-10-26T03:47:06	2023-10-26T10:18:05	2023-10-26T10:18:04	NONE	null	### Describe the bug datasets version： 2.10.1 I `load_dataset `and `save_to_disk` sucessfully on windows10( and I `load_from_disk(/LLM/data/wiki)` succcesfully on windows10), and I copy the dataset `/LLM/data/wiki` into a ubuntu system, but when I `load_from_disk(/LLM/data/wiki)` on ubuntu, something weird happens: ``` load_from_disk('/LLM/data/wiki') File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1874, in load_from_disk return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1309, in load_from_disk dataset_dict[k] = Dataset.load_from_disk( File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1543, in load_from_disk fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 610, in get_fs_token_paths chain = _un_chain(urlpath0, storage_options or {}) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 325, in _un_chain cls = get_filesystem_class(protocol) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/registry.py", line 232, in get_filesystem_class raise ValueError(f"Protocol not known: {protocol}") ValueError: Protocol not known: /LLM/data/wiki ``` It seems that something went wrong on the arrow file? How can I solve this , since currently I can not save_to_disk on ubuntu system ### Steps to reproduce the bug datasets version： 2.10.1 ### Expected behavior datasets version： 2.10.1 ### Environment info datasets version： 2.10.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6353/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6353/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6352	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6352/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6352/comments	https://api.github.com/repos/huggingface/datasets/issues/6352/events	https://github.com/huggingface/datasets/issues/6352	1,962,296,057	I_kwDODunzps509kL5	6,352	Error loading wikitext data raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")	{ "login": "Ahmed-Roushdy", "id": 68569076, "node_id": "MDQ6VXNlcjY4NTY5MDc2", "avatar_url": "https://avatars.githubusercontent.com/u/68569076?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Ahmed-Roushdy", "html_url": "https://github.com/Ahmed-Roushdy", "followers_url": "https://api.github.com/users/Ahmed-Roushdy/followers", "following_url": "https://api.github.com/users/Ahmed-Roushdy/following{/other_user}", "gists_url": "https://api.github.com/users/Ahmed-Roushdy/gists{/gist_id}", "starred_url": "https://api.github.com/users/Ahmed-Roushdy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Ahmed-Roushdy/subscriptions", "organizations_url": "https://api.github.com/users/Ahmed-Roushdy/orgs", "repos_url": "https://api.github.com/users/Ahmed-Roushdy/repos", "events_url": "https://api.github.com/users/Ahmed-Roushdy/events{/privacy}", "received_events_url": "https://api.github.com/users/Ahmed-Roushdy/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "+1 \r\n```\r\nFound cached dataset csv (file:///home/ubuntu/.cache/huggingface/datasets/theSquarePond___csv/theSquarePond--XXXXX-bbf0a8365d693d2c/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d)\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[14], line 4\r\n 1 get_ipython().system('pip install -U datasets')\r\n 3 # Load dataset from the hub\r\n----> 4 dataset = load_dataset(dataset_name)\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/load.py:1810, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs)\r\n 1806 # Build dataset for splits\r\n 1807 keep_in_memory = (\r\n 1808 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1809 )\r\n-> 1810 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1811 # Rename and cast features to match task schema\r\n 1812 if task is not None:\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/builder.py:1128, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1126 is_local = not is_remote_filesystem(self._fs)\r\n 1127 if not is_local:\r\n-> 1128 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1129 if not os.path.exists(self._output_dir):\r\n 1130 raise FileNotFoundError(\r\n 1131 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1132 \"builder.download_and_prepare(), or use \"\r\n 1133 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1134 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```", "+1\r\n\r\n```\r\nFound cached dataset csv ([file://C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1](file:///C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1))\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[38], line 3\r\n 1 huggingface_dataset_name = \"knkarthick/dialogsum\"\r\n----> 3 dataset = load_dataset(huggingface_dataset_name)\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\load.py:1804, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs)\r\n 1800 # Build dataset for splits\r\n 1801 keep_in_memory = (\r\n 1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1803 )\r\n-> 1804 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1805 # Rename and cast features to match task schema\r\n 1806 if task is not None:\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\builder.py:1108, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1106 is_local = not is_remote_filesystem(self._fs)\r\n 1107 if not is_local:\r\n-> 1108 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1109 if not os.path.exists(self._output_dir):\r\n 1110 raise FileNotFoundError(\r\n 1111 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1112 \"builder.download_and_prepare(), or use \"\r\n 1113 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1114 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```", "This error stems from a breaking change in `fsspec`. It has been fixed in the latest `datasets` release (`2.14.6`). Updating the installation with `pip install -U datasets` should fix the issue.\r\n", "> 此错误源于中的重大更改。此问题已在最新版本（）中修复。更新安装应该可以解决此问题。`fsspec``datasets``2.14.6``pip install -U datasets`\r\n\r\nthanks , 太好啦，刚好解决了我的问题，GPT都没解决了，终于被你搞定了", "https://stackoverflow.com/questions/77433096/notimplementederror-loading-a-dataset-cached-in-a-localfilesystem-is-not-suppor/77433141#77433141", "Fixed by:\r\n- https://github.com/huggingface/datasets/pull/6334\r\n\r\nThe fix was released in `datasets-2.14.6`." ]	2023-10-25T21:55:31	2023-11-07T07:26:54	2023-11-07T07:26:54	NONE	null	I was trying to load the wiki dataset, but i got this error traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train') File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/load.py", line 1804, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/builder.py", line 1108, in as_dataset raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.") NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6352/reactions", "total_count": 4, "+1": 4, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6352/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6351	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6351/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6351/comments	https://api.github.com/repos/huggingface/datasets/issues/6351/events	https://github.com/huggingface/datasets/pull/6351	1,961,982,988	PR_kwDODunzps5dyMvh	6,351	Fix use_dataset.mdx	{ "login": "angel-luis", "id": 17672548, "node_id": "MDQ6VXNlcjE3NjcyNTQ4", "avatar_url": "https://avatars.githubusercontent.com/u/17672548?v=4", "gravatar_id": "", "url": "https://api.github.com/users/angel-luis", "html_url": "https://github.com/angel-luis", "followers_url": "https://api.github.com/users/angel-luis/followers", "following_url": "https://api.github.com/users/angel-luis/following{/other_user}", "gists_url": "https://api.github.com/users/angel-luis/gists{/gist_id}", "starred_url": "https://api.github.com/users/angel-luis/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/angel-luis/subscriptions", "organizations_url": "https://api.github.com/users/angel-luis/orgs", "repos_url": "https://api.github.com/users/angel-luis/repos", "events_url": "https://api.github.com/users/angel-luis/events{/privacy}", "received_events_url": "https://api.github.com/users/angel-luis/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007718 / 0.011353 (-0.003635) \| 0.004730 / 0.011008 (-0.006278) \| 0.097262 / 0.038508 (0.058754) \| 0.077880 / 0.023109 (0.054771) \| 0.363855 / 0.275898 (0.087957) \| 0.394470 / 0.323480 (0.070990) \| 0.006416 / 0.007986 (-0.001570) \| 0.003596 / 0.004328 (-0.000732) \| 0.076494 / 0.004250 (0.072243) \| 0.062656 / 0.037052 (0.025603) \| 0.366160 / 0.258489 (0.107671) \| 0.421383 / 0.293841 (0.127542) \| 0.035756 / 0.128546 (-0.092791) \| 0.009430 / 0.075646 (-0.066217) \| 0.327722 / 0.419271 (-0.091550) \| 0.061252 / 0.043533 (0.017719) \| 0.352167 / 0.255139 (0.097028) \| 0.385166 / 0.283200 (0.101966) \| 0.026656 / 0.141683 (-0.115027) \| 1.718533 / 1.452155 (0.266378) \| 1.886646 / 1.492716 (0.393930) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254564 / 0.018006 (0.236558) \| 0.490942 / 0.000490 (0.490452) \| 0.011656 / 0.000200 (0.011456) \| 0.000313 / 0.000054 (0.000259) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028753 / 0.037411 (-0.008659) \| 0.093076 / 0.014526 (0.078550) \| 0.096441 / 0.176557 (-0.080116) \| 0.154848 / 0.737135 (-0.582287) \| 0.092903 / 0.296338 (-0.203435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395611 / 0.215209 (0.180402) \| 3.860736 / 2.077655 (1.783082) \| 1.908808 / 1.504120 (0.404688) \| 1.708975 / 1.541195 (0.167781) \| 1.848173 / 1.468490 (0.379683) \| 0.527022 / 4.584777 (-4.057755) \| 3.815171 / 3.745712 (0.069459) \| 3.621132 / 5.269862 (-1.648730) \| 2.220238 / 4.565676 (-2.345439) \| 0.063169 / 0.424275 (-0.361106) \| 0.008906 / 0.007607 (0.001299) \| 0.510478 / 0.226044 (0.284433) \| 4.828116 / 2.268929 (2.559187) \| 2.340801 / 55.444624 (-53.103824) \| 2.040834 / 6.876477 (-4.835642) \| 2.092316 / 2.142072 (-0.049757) \| 0.579194 / 4.805227 (-4.226033) \| 0.135525 / 6.500664 (-6.365139) \| 0.062720 / 0.075469 (-0.012749) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.393091 / 1.841788 (-0.448697) \| 19.751526 / 8.074308 (11.677218) \| 14.161795 / 10.191392 (3.970403) \| 0.163340 / 0.680424 (-0.517084) \| 0.021504 / 0.534201 (-0.512697) \| 0.393183 / 0.579283 (-0.186100) \| 0.448407 / 0.434364 (0.014043) \| 0.504169 / 0.540337 (-0.036169) \| 0.663698 / 1.386936 (-0.723238) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007390 / 0.011353 (-0.003962) \| 0.004381 / 0.011008 (-0.006628) \| 0.074501 / 0.038508 (0.035993) \| 0.078242 / 0.023109 (0.055133) \| 0.481108 / 0.275898 (0.205210) \| 0.512111 / 0.323480 (0.188631) \| 0.006280 / 0.007986 (-0.001705) \| 0.003820 / 0.004328 (-0.000509) \| 0.071602 / 0.004250 (0.067351) \| 0.068359 / 0.037052 (0.031307) \| 0.478484 / 0.258489 (0.219995) \| 0.519543 / 0.293841 (0.225702) \| 0.036211 / 0.128546 (-0.092335) \| 0.009433 / 0.075646 (-0.066213) \| 0.086140 / 0.419271 (-0.333132) \| 0.054177 / 0.043533 (0.010644) \| 0.466726 / 0.255139 (0.211587) \| 0.514085 / 0.283200 (0.230885) \| 0.026729 / 0.141683 (-0.114954) \| 1.743770 / 1.452155 (0.291615) \| 1.833469 / 1.492716 (0.340753) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.251339 / 0.018006 (0.233333) \| 0.472294 / 0.000490 (0.471804) \| 0.013381 / 0.000200 (0.013181) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037845 / 0.037411 (0.000433) \| 0.105977 / 0.014526 (0.091451) \| 0.124446 / 0.176557 (-0.052111) \| 0.180432 / 0.737135 (-0.556703) \| 0.120844 / 0.296338 (-0.175495) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.470928 / 0.215209 (0.255719) \| 4.738154 / 2.077655 (2.660499) \| 2.558618 / 1.504120 (1.054498) \| 2.359745 / 1.541195 (0.818550) \| 2.458438 / 1.468490 (0.989948) \| 0.548580 / 4.584777 (-4.036197) \| 3.912145 / 3.745712 (0.166433) \| 3.764174 / 5.269862 (-1.505687) \| 2.325265 / 4.565676 (-2.240411) \| 0.078022 / 0.424275 (-0.346254) \| 0.008279 / 0.007607 (0.000672) \| 0.571635 / 0.226044 (0.345590) \| 5.672445 / 2.268929 (3.403517) \| 2.760577 / 55.444624 (-52.684047) \| 2.544229 / 6.876477 (-4.332248) \| 2.537509 / 2.142072 (0.395436) \| 0.609858 / 4.805227 (-4.195369) \| 0.131053 / 6.500664 (-6.369611) \| 0.056433 / 0.075469 (-0.019036) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.567231 / 1.841788 (-0.274556) \| 21.415586 / 8.074308 (13.341278) \| 15.982328 / 10.191392 (5.790936) \| 0.167648 / 0.680424 (-0.512776) \| 0.023562 / 0.534201 (-0.510639) \| 0.477307 / 0.579283 (-0.101976) \| 0.471929 / 0.434364 (0.037566) \| 0.549996 / 0.540337 (0.009659) \| 0.753927 / 1.386936 (-0.633009) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fb2785be9198997e8b9006225b0e231f4d8ed31 \"CML watermark\")\n" ]	2023-10-25T18:21:08	2023-10-26T17:19:49	2023-10-26T17:10:27	CONTRIBUTOR	null	The current example isn't working because it can't find `labels` inside the Dataset object. So I've added an extra step to the process. Tested and working in Colab.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6351/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6351/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6351", "html_url": "https://github.com/huggingface/datasets/pull/6351", "diff_url": "https://github.com/huggingface/datasets/pull/6351.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6351.patch", "merged_at": "2023-10-26T17:10:27" }	true
https://api.github.com/repos/huggingface/datasets/issues/6350	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6350/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6350/comments	https://api.github.com/repos/huggingface/datasets/issues/6350/events	https://github.com/huggingface/datasets/issues/6350	1,961,869,203	I_kwDODunzps5077-T	6,350	Different objects are returned from calls that should be returning the same kind of object.	{ "login": "phalexo", "id": 4603365, "node_id": "MDQ6VXNlcjQ2MDMzNjU=", "avatar_url": "https://avatars.githubusercontent.com/u/4603365?v=4", "gravatar_id": "", "url": "https://api.github.com/users/phalexo", "html_url": "https://github.com/phalexo", "followers_url": "https://api.github.com/users/phalexo/followers", "following_url": "https://api.github.com/users/phalexo/following{/other_user}", "gists_url": "https://api.github.com/users/phalexo/gists{/gist_id}", "starred_url": "https://api.github.com/users/phalexo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/phalexo/subscriptions", "organizations_url": "https://api.github.com/users/phalexo/orgs", "repos_url": "https://api.github.com/users/phalexo/repos", "events_url": "https://api.github.com/users/phalexo/events{/privacy}", "received_events_url": "https://api.github.com/users/phalexo/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "`load_dataset` returns a `DatasetDict` object unless `split` is defined, in which case it returns a `Dataset` (or a list of datasets if `split` is a list). We've discussed dropping `DatasetDict` from the API in https://github.com/huggingface/datasets/issues/5189 to always return the same type in `load_dataset` and support datasets without (explicit) splits. IIRC the main discussion point is deciding what to return when loading a dataset with multiple splits, but `split` is not specified. What would you expect as a return value in that scenario?", "> `load_dataset` returns a `DatasetDict` object unless `split` is defined, in which case it returns a `Dataset` (or a list of datasets if `split` is a list). We've discussed dropping `DatasetDict` from the API in #5189 to always return the same type in `load_dataset` and support datasets without (explicit) splits. IIRC the main discussion point is deciding what to return when loading a dataset with multiple splits, but `split` is not specified. What would you expect as a return value in that scenario?\r\n\r\nWouldn't a dataset with multiple splits already have keys and their related data arrays?\r\n\r\nLets say the dataset has \"train\" : trainset, \"valid\": validset and \"test\": testset\r\n\r\nSo a dictionary can be returned,, i.e.\r\n\r\n{ \r\n\"train\": trainset,\r\n\"valid\": validset,\r\n\"test\": testset\r\n}\r\n\r\nif a split is provided split=['train[:80%]', 'valid[80%:90%]', 'test[90%:100%]']\r\n\r\nwould also return the same dictionary as above.\r\n\r\nsplit='train[:10%]' should return the same value as split=['train[:10%]']\r\n\r\n{\r\n\"train\": trainset\r\n}\r\n " ]	2023-10-25T17:08:39	2023-10-26T21:03:06	null	NONE	null	### Describe the bug 1. dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir, split='train[:1%]') 2. dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir) The only difference I would expect these calls to have is the size of the dataset. But, while 2. returns a dictionary with "train" key in it, 1. returns a dataset WITHOUT any initial "train" keyword. Both calls are to be used within exactly the same context. They should return identically structured datasets of different size. ### Steps to reproduce the bug See above. ### Expected behavior Expect both calls to return the same structured Dataset structure but with different number of elements, i.e. call 1. should have 1% of the data of the call 2.0 ### Environment info Ubuntu 20.04 gcc 9.x.x. It is really irrelevant.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6350/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6350/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6349	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6349/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6349/comments	https://api.github.com/repos/huggingface/datasets/issues/6349/events	https://github.com/huggingface/datasets/issues/6349	1,961,435,673	I_kwDODunzps506SIZ	6,349	Can't load ds = load_dataset("imdb")	{ "login": "vivianc2", "id": 86415736, "node_id": "MDQ6VXNlcjg2NDE1NzM2", "avatar_url": "https://avatars.githubusercontent.com/u/86415736?v=4", "gravatar_id": "", "url": "https://api.github.com/users/vivianc2", "html_url": "https://github.com/vivianc2", "followers_url": "https://api.github.com/users/vivianc2/followers", "following_url": "https://api.github.com/users/vivianc2/following{/other_user}", "gists_url": "https://api.github.com/users/vivianc2/gists{/gist_id}", "starred_url": "https://api.github.com/users/vivianc2/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vivianc2/subscriptions", "organizations_url": "https://api.github.com/users/vivianc2/orgs", "repos_url": "https://api.github.com/users/vivianc2/repos", "events_url": "https://api.github.com/users/vivianc2/events{/privacy}", "received_events_url": "https://api.github.com/users/vivianc2/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "I'm unable to reproduce this error. The server hosting the files may have been down temporarily, so try again." ]	2023-10-25T13:29:51	2023-10-31T19:59:35	2023-10-31T19:59:35	NONE	null	### Describe the bug I did `from datasets import load_dataset, load_metric` and then `ds = load_dataset("imdb")` and it gave me the error: ExpectedMoreDownloadedFiles: {'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'} I tried doing `ds = load_dataset("imdb",download_mode="force_redownload")` as well as reinstalling dataset. I still face this problem. ### Steps to reproduce the bug 1. from datasets import load_dataset, load_metric 2. ds = load_dataset("imdb") ### Expected behavior It should load and give me this when I run `ds` DatasetDict({ train: Dataset({ features: ['text', 'label'], num_rows: 25000 }) test: Dataset({ features: ['text', 'label'], num_rows: 25000 }) unsupervised: Dataset({ features: ['text', 'label'], num_rows: 50000 }) }) ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.4.0-164-generic-x86_64-with-glibc2.17 - Python version: 3.8.18 - Huggingface_hub version: 0.16.2 - PyArrow version: 13.0.0 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6349/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6349/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6348	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6348/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6348/comments	https://api.github.com/repos/huggingface/datasets/issues/6348/events	https://github.com/huggingface/datasets/issues/6348	1,961,268,504	I_kwDODunzps505pUY	6,348	Parquet stream-conversion fails to embed images/audio files from gated repos	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" } ]	open	false	null	[]	null	[]	2023-10-25T12:12:44	2023-10-25T12:13:07	null	CONTRIBUTOR	null	it seems to be an issue with datasets not passing the token to embed_table_storage when generating a dataset See https://github.com/huggingface/datasets-server/issues/2010	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6348/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6348/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6347	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6347/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6347/comments	https://api.github.com/repos/huggingface/datasets/issues/6347/events	https://github.com/huggingface/datasets/issues/6347	1,959,004,835	I_kwDODunzps50xAqj	6,347	Incorrect example code in 'Create a dataset' docs	{ "login": "rwood-97", "id": 72076688, "node_id": "MDQ6VXNlcjcyMDc2Njg4", "avatar_url": "https://avatars.githubusercontent.com/u/72076688?v=4", "gravatar_id": "", "url": "https://api.github.com/users/rwood-97", "html_url": "https://github.com/rwood-97", "followers_url": "https://api.github.com/users/rwood-97/followers", "following_url": "https://api.github.com/users/rwood-97/following{/other_user}", "gists_url": "https://api.github.com/users/rwood-97/gists{/gist_id}", "starred_url": "https://api.github.com/users/rwood-97/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rwood-97/subscriptions", "organizations_url": "https://api.github.com/users/rwood-97/orgs", "repos_url": "https://api.github.com/users/rwood-97/repos", "events_url": "https://api.github.com/users/rwood-97/events{/privacy}", "received_events_url": "https://api.github.com/users/rwood-97/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "This was fixed in https://github.com/huggingface/datasets/pull/6247. You can find the fix in the `main` version of the docs", "Ah great, thanks :)" ]	2023-10-24T11:01:21	2023-10-25T13:05:21	2023-10-25T13:05:21	NONE	null	### Describe the bug On [this](https://huggingface.co/docs/datasets/create_dataset) page, the example code for loading in images and audio is incorrect. Currently, examples are: ``` python from datasets import ImageFolder dataset = load_dataset("imagefolder", data_dir="/path/to/pokemon") ``` and ``` python from datasets import AudioFolder dataset = load_dataset("audiofolder", data_dir="/path/to/folder") ``` I'm pretty sure the imports are wrong and should be: ``` python from datasets import load_dataset dataset = load_dataset("audiofolder", data_dir="/path/to/folder") ``` I am happy to update this if this is right but just wanted to check before making any changes. ### Steps to reproduce the bug Go to https://huggingface.co/docs/datasets/create_dataset ### Expected behavior N/A ### Environment info N/A	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6347/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6347/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6346	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6346/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6346/comments	https://api.github.com/repos/huggingface/datasets/issues/6346/events	https://github.com/huggingface/datasets/pull/6346	1,958,777,076	PR_kwDODunzps5dnZM_	6,346	Fix UnboundLocalError if preprocessing returns an empty list	{ "login": "cwallenwein", "id": 40916592, "node_id": "MDQ6VXNlcjQwOTE2NTky", "avatar_url": "https://avatars.githubusercontent.com/u/40916592?v=4", "gravatar_id": "", "url": "https://api.github.com/users/cwallenwein", "html_url": "https://github.com/cwallenwein", "followers_url": "https://api.github.com/users/cwallenwein/followers", "following_url": "https://api.github.com/users/cwallenwein/following{/other_user}", "gists_url": "https://api.github.com/users/cwallenwein/gists{/gist_id}", "starred_url": "https://api.github.com/users/cwallenwein/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/cwallenwein/subscriptions", "organizations_url": "https://api.github.com/users/cwallenwein/orgs", "repos_url": "https://api.github.com/users/cwallenwein/repos", "events_url": "https://api.github.com/users/cwallenwein/events{/privacy}", "received_events_url": "https://api.github.com/users/cwallenwein/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009286 / 0.011353 (-0.002067) \| 0.005478 / 0.011008 (-0.005530) \| 0.109768 / 0.038508 (0.071260) \| 0.088460 / 0.023109 (0.065351) \| 0.387664 / 0.275898 (0.111766) \| 0.457379 / 0.323480 (0.133899) \| 0.006517 / 0.007986 (-0.001469) \| 0.004037 / 0.004328 (-0.000292) \| 0.083911 / 0.004250 (0.079661) \| 0.071658 / 0.037052 (0.034605) \| 0.385065 / 0.258489 (0.126576) \| 0.460928 / 0.293841 (0.167087) \| 0.048062 / 0.128546 (-0.080484) \| 0.016343 / 0.075646 (-0.059303) \| 0.373675 / 0.419271 (-0.045597) \| 0.067640 / 0.043533 (0.024108) \| 0.391730 / 0.255139 (0.136591) \| 0.432908 / 0.283200 (0.149708) \| 0.035748 / 0.141683 (-0.105935) \| 1.767625 / 1.452155 (0.315471) \| 1.965606 / 1.492716 (0.472889) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277405 / 0.018006 (0.259399) \| 0.538448 / 0.000490 (0.537958) \| 0.013795 / 0.000200 (0.013595) \| 0.000518 / 0.000054 (0.000464) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.043962 / 0.037411 (0.006550) \| 0.115305 / 0.014526 (0.100780) \| 0.117572 / 0.176557 (-0.058985) \| 0.182168 / 0.737135 (-0.554968) \| 0.114833 / 0.296338 (-0.181505) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.604209 / 0.215209 (0.389000) \| 6.186113 / 2.077655 (4.108458) \| 2.771067 / 1.504120 (1.266947) \| 2.425420 / 1.541195 (0.884226) \| 2.475200 / 1.468490 (1.006710) \| 0.887096 / 4.584777 (-3.697681) \| 5.214349 / 3.745712 (1.468637) \| 4.989606 / 5.269862 (-0.280256) \| 3.092135 / 4.565676 (-1.473541) \| 0.104464 / 0.424275 (-0.319811) \| 0.008994 / 0.007607 (0.001387) \| 0.732819 / 0.226044 (0.506775) \| 7.396007 / 2.268929 (5.127078) \| 3.371167 / 55.444624 (-52.073457) \| 2.645475 / 6.876477 (-4.231001) \| 2.704215 / 2.142072 (0.562143) \| 1.034724 / 4.805227 (-3.770504) \| 0.219063 / 6.500664 (-6.281601) \| 0.073863 / 0.075469 (-0.001606) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.625020 / 1.841788 (-0.216768) \| 23.369980 / 8.074308 (15.295671) \| 22.480951 / 10.191392 (12.289559) \| 0.228219 / 0.680424 (-0.452204) \| 0.026981 / 0.534201 (-0.507220) \| 0.487670 / 0.579283 (-0.091613) \| 0.582310 / 0.434364 (0.147946) \| 0.539182 / 0.540337 (-0.001156) \| 0.791962 / 1.386936 (-0.594974) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008657 / 0.011353 (-0.002696) \| 0.004971 / 0.011008 (-0.006037) \| 0.089499 / 0.038508 (0.050991) \| 0.075963 / 0.023109 (0.052854) \| 0.497719 / 0.275898 (0.221821) \| 0.507912 / 0.323480 (0.184432) \| 0.006067 / 0.007986 (-0.001919) \| 0.004118 / 0.004328 (-0.000210) \| 0.079397 / 0.004250 (0.075146) \| 0.059181 / 0.037052 (0.022129) \| 0.501108 / 0.258489 (0.242619) \| 0.565792 / 0.293841 (0.271951) \| 0.048818 / 0.128546 (-0.079729) \| 0.014813 / 0.075646 (-0.060833) \| 0.093863 / 0.419271 (-0.325409) \| 0.060824 / 0.043533 (0.017292) \| 0.489289 / 0.255139 (0.234150) \| 0.533624 / 0.283200 (0.250425) \| 0.034997 / 0.141683 (-0.106685) \| 1.770574 / 1.452155 (0.318419) \| 1.837213 / 1.492716 (0.344496) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237319 / 0.018006 (0.219313) \| 0.594976 / 0.000490 (0.594486) \| 0.008888 / 0.000200 (0.008688) \| 0.000124 / 0.000054 (0.000070) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036955 / 0.037411 (-0.000456) \| 0.097825 / 0.014526 (0.083299) \| 0.111139 / 0.176557 (-0.065418) \| 0.174776 / 0.737135 (-0.562359) \| 0.117755 / 0.296338 (-0.178584) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.606498 / 0.215209 (0.391289) \| 6.089874 / 2.077655 (4.012219) \| 2.811135 / 1.504120 (1.307015) \| 2.428486 / 1.541195 (0.887292) \| 2.399512 / 1.468490 (0.931022) \| 0.823492 / 4.584777 (-3.761285) \| 4.897107 / 3.745712 (1.151395) \| 4.407589 / 5.269862 (-0.862272) \| 2.868442 / 4.565676 (-1.697235) \| 0.098774 / 0.424275 (-0.325502) \| 0.007998 / 0.007607 (0.000391) \| 0.699489 / 0.226044 (0.473445) \| 7.139214 / 2.268929 (4.870285) \| 3.511158 / 55.444624 (-51.933466) \| 2.775459 / 6.876477 (-4.101018) \| 2.951549 / 2.142072 (0.809477) \| 1.006921 / 4.805227 (-3.798306) \| 0.200105 / 6.500664 (-6.300559) \| 0.071064 / 0.075469 (-0.004405) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.680599 / 1.841788 (-0.161189) \| 23.399777 / 8.074308 (15.325469) \| 21.776357 / 10.191392 (11.584965) \| 0.264697 / 0.680424 (-0.415726) \| 0.034272 / 0.534201 (-0.499929) \| 0.506984 / 0.579283 (-0.072299) \| 0.609556 / 0.434364 (0.175192) \| 0.599014 / 0.540337 (0.058677) \| 0.824068 / 1.386936 (-0.562868) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ab9de69420de8bd5d057579d71d07187b3a2c60 \"CML watermark\")\n" ]	2023-10-24T08:38:43	2023-10-25T17:39:17	2023-10-25T16:36:38	CONTRIBUTOR	null	If this tokenization function is used with IterableDatasets and no sample is as big as the context length, `input_batch` will be an empty list. ``` def tokenize(batch, tokenizer, context_length): outputs = tokenizer( batch["text"], truncation=True, max_length=context_length, return_overflowing_tokens=True, return_length=True ) input_batch = [] for length, input_ids in zip(outputs["length"], outputs["input_ids"]): if length == context_length: input_batch.append(input_ids) return {"input_ids": input_batch} dataset.map(tokenize, batched=True, batch_size=batch_size, fn_kwargs={"context_length": context_length, "tokenizer": tokenizer}, remove_columns=dataset.column_names) ``` This will throw the following error: UnboundLocalError: local variable 'batch_idx' referenced before assignment, because the for loop was not executed a single time ``` for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)): yield new_key, example current_idx += batch_idx + 1 ``` Some of the possible solutions ``` for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)): yield new_key, example try: current_idx += batch_idx + 1 except: current_idx += 1 ``` or ``` batch_idx = 0 for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)): yield new_key, example current_idx += batch_idx + 1 ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6346/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6346/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6346", "html_url": "https://github.com/huggingface/datasets/pull/6346", "diff_url": "https://github.com/huggingface/datasets/pull/6346.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6346.patch", "merged_at": "2023-10-25T16:36:38" }	true
https://api.github.com/repos/huggingface/datasets/issues/6345	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6345/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6345/comments	https://api.github.com/repos/huggingface/datasets/issues/6345/events	https://github.com/huggingface/datasets/issues/6345	1,957,707,870	I_kwDODunzps50sEBe	6,345	support squad structure datasets using a YAML parameter	{ "login": "MajdTannous1", "id": 138524319, "node_id": "U_kgDOCEG2nw", "avatar_url": "https://avatars.githubusercontent.com/u/138524319?v=4", "gravatar_id": "", "url": "https://api.github.com/users/MajdTannous1", "html_url": "https://github.com/MajdTannous1", "followers_url": "https://api.github.com/users/MajdTannous1/followers", "following_url": "https://api.github.com/users/MajdTannous1/following{/other_user}", "gists_url": "https://api.github.com/users/MajdTannous1/gists{/gist_id}", "starred_url": "https://api.github.com/users/MajdTannous1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MajdTannous1/subscriptions", "organizations_url": "https://api.github.com/users/MajdTannous1/orgs", "repos_url": "https://api.github.com/users/MajdTannous1/repos", "events_url": "https://api.github.com/users/MajdTannous1/events{/privacy}", "received_events_url": "https://api.github.com/users/MajdTannous1/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	2023-10-23T17:55:37	2023-10-23T17:55:37	null	NONE	null	### Feature request Since the squad structure is widely used, I think it could be beneficial to support it using a YAML parameter. could you implement automatic data loading of squad-like data using squad JSON format, to read it from JSON files and view it in the correct squad structure. The dataset structure should be like this: https://huggingface.co/datasets/squad Columns:id,title,context,question,answers ### Motivation Dataset repo requires arbitrary Python code execution ### Your contribution The dataset structure should be like this: https://huggingface.co/datasets/squad Columns:id,title,context,question,answers train and dev sets in squad structure JSON files	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6345/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 1 }	https://api.github.com/repos/huggingface/datasets/issues/6345/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6344	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6344/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6344/comments	https://api.github.com/repos/huggingface/datasets/issues/6344/events	https://github.com/huggingface/datasets/pull/6344	1,957,412,169	PR_kwDODunzps5diyd5	6,344	set dev version	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6344). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008237 / 0.011353 (-0.003116) \| 0.004658 / 0.011008 (-0.006351) \| 0.105902 / 0.038508 (0.067394) \| 0.082690 / 0.023109 (0.059581) \| 0.471745 / 0.275898 (0.195847) \| 0.464772 / 0.323480 (0.141292) \| 0.006373 / 0.007986 (-0.001613) \| 0.003823 / 0.004328 (-0.000505) \| 0.077721 / 0.004250 (0.073471) \| 0.068371 / 0.037052 (0.031318) \| 0.457004 / 0.258489 (0.198515) \| 0.500989 / 0.293841 (0.207148) \| 0.036688 / 0.128546 (-0.091858) \| 0.010004 / 0.075646 (-0.065643) \| 0.363398 / 0.419271 (-0.055874) \| 0.065354 / 0.043533 (0.021821) \| 0.440326 / 0.255139 (0.185187) \| 0.475314 / 0.283200 (0.192115) \| 0.029024 / 0.141683 (-0.112659) \| 1.851005 / 1.452155 (0.398851) \| 1.939997 / 1.492716 (0.447281) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269739 / 0.018006 (0.251732) \| 0.510411 / 0.000490 (0.509922) \| 0.013423 / 0.000200 (0.013223) \| 0.000513 / 0.000054 (0.000458) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032912 / 0.037411 (-0.004499) \| 0.097497 / 0.014526 (0.082971) \| 0.111945 / 0.176557 (-0.064612) \| 0.179264 / 0.737135 (-0.557871) \| 0.111901 / 0.296338 (-0.184437) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.480994 / 0.215209 (0.265785) \| 4.800969 / 2.077655 (2.723314) \| 2.467390 / 1.504120 (0.963270) \| 2.283219 / 1.541195 (0.742024) \| 2.407735 / 1.468490 (0.939245) \| 0.573862 / 4.584777 (-4.010915) \| 4.213394 / 3.745712 (0.467682) \| 4.120092 / 5.269862 (-1.149770) \| 2.479549 / 4.565676 (-2.086128) \| 0.077204 / 0.424275 (-0.347071) \| 0.009165 / 0.007607 (0.001558) \| 0.583887 / 0.226044 (0.357842) \| 5.760759 / 2.268929 (3.491830) \| 3.089220 / 55.444624 (-52.355404) \| 2.652330 / 6.876477 (-4.224146) \| 2.746255 / 2.142072 (0.604182) \| 0.689010 / 4.805227 (-4.116217) \| 0.158042 / 6.500664 (-6.342622) \| 0.072789 / 0.075469 (-0.002680) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.658877 / 1.841788 (-0.182911) \| 22.928756 / 8.074308 (14.854448) \| 17.231823 / 10.191392 (7.040431) \| 0.201475 / 0.680424 (-0.478949) \| 0.025533 / 0.534201 (-0.508668) \| 0.467023 / 0.579283 (-0.112260) \| 0.470779 / 0.434364 (0.036415) \| 0.643192 / 0.540337 (0.102855) \| 0.822006 / 1.386936 (-0.564930) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008096 / 0.011353 (-0.003257) \| 0.004708 / 0.011008 (-0.006300) \| 0.076607 / 0.038508 (0.038099) \| 0.086278 / 0.023109 (0.063168) \| 0.478027 / 0.275898 (0.202129) \| 0.533121 / 0.323480 (0.209641) \| 0.006331 / 0.007986 (-0.001654) \| 0.004005 / 0.004328 (-0.000324) \| 0.076018 / 0.004250 (0.071767) \| 0.067240 / 0.037052 (0.030188) \| 0.484882 / 0.258489 (0.226393) \| 0.536924 / 0.293841 (0.243083) \| 0.045064 / 0.128546 (-0.083482) \| 0.010071 / 0.075646 (-0.065575) \| 0.084319 / 0.419271 (-0.334953) \| 0.066267 / 0.043533 (0.022734) \| 0.479283 / 0.255139 (0.224144) \| 0.507832 / 0.283200 (0.224633) \| 0.026436 / 0.141683 (-0.115247) \| 1.820043 / 1.452155 (0.367889) \| 1.954663 / 1.492716 (0.461947) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292672 / 0.018006 (0.274666) \| 0.495523 / 0.000490 (0.495033) \| 0.020836 / 0.000200 (0.020636) \| 0.000143 / 0.000054 (0.000088) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038326 / 0.037411 (0.000915) \| 0.114629 / 0.014526 (0.100103) \| 0.126036 / 0.176557 (-0.050521) \| 0.191498 / 0.737135 (-0.545638) \| 0.128763 / 0.296338 (-0.167575) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.507657 / 0.215209 (0.292448) \| 5.062056 / 2.077655 (2.984401) \| 2.765895 / 1.504120 (1.261775) \| 2.590335 / 1.541195 (1.049141) \| 2.790912 / 1.468490 (1.322422) \| 0.582819 / 4.584777 (-4.001958) \| 4.350034 / 3.745712 (0.604322) \| 3.899466 / 5.269862 (-1.370396) \| 2.499655 / 4.565676 (-2.066021) \| 0.068909 / 0.424275 (-0.355366) \| 0.008633 / 0.007607 (0.001026) \| 0.593597 / 0.226044 (0.367553) \| 5.934398 / 2.268929 (3.665470) \| 3.358549 / 55.444624 (-52.086075) \| 3.145686 / 6.876477 (-3.730791) \| 3.232153 / 2.142072 (1.090080) \| 0.753039 / 4.805227 (-4.052188) \| 0.164043 / 6.500664 (-6.336621) \| 0.072084 / 0.075469 (-0.003385) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.632702 / 1.841788 (-0.209086) \| 23.411084 / 8.074308 (15.336776) \| 17.035726 / 10.191392 (6.844334) \| 0.223460 / 0.680424 (-0.456964) \| 0.023723 / 0.534201 (-0.510478) \| 0.474160 / 0.579283 (-0.105124) \| 0.538638 / 0.434364 (0.104274) \| 0.595591 / 0.540337 (0.055254) \| 0.803324 / 1.386936 (-0.583612) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84855c8ddc8d3e33b516f04b687e01d498d0906e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008300 / 0.011353 (-0.003053) \| 0.004667 / 0.011008 (-0.006341) \| 0.101028 / 0.038508 (0.062520) \| 0.100269 / 0.023109 (0.077160) \| 0.418651 / 0.275898 (0.142752) \| 0.459061 / 0.323480 (0.135581) \| 0.006786 / 0.007986 (-0.001199) \| 0.003926 / 0.004328 (-0.000403) \| 0.076682 / 0.004250 (0.072432) \| 0.066173 / 0.037052 (0.029120) \| 0.430644 / 0.258489 (0.172155) \| 0.466244 / 0.293841 (0.172403) \| 0.040601 / 0.128546 (-0.087946) \| 0.009856 / 0.075646 (-0.065790) \| 0.351467 / 0.419271 (-0.067805) \| 0.068727 / 0.043533 (0.025194) \| 0.419527 / 0.255139 (0.164388) \| 0.431245 / 0.283200 (0.148045) \| 0.028933 / 0.141683 (-0.112750) \| 1.749540 / 1.452155 (0.297386) \| 1.829076 / 1.492716 (0.336360) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282248 / 0.018006 (0.264242) \| 0.587293 / 0.000490 (0.586803) \| 0.014497 / 0.000200 (0.014297) \| 0.000383 / 0.000054 (0.000329) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031861 / 0.037411 (-0.005550) \| 0.097395 / 0.014526 (0.082869) \| 0.113610 / 0.176557 (-0.062946) \| 0.181208 / 0.737135 (-0.555927) \| 0.115340 / 0.296338 (-0.180999) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.459746 / 0.215209 (0.244537) \| 4.582387 / 2.077655 (2.504733) \| 2.247968 / 1.504120 (0.743848) \| 2.032340 / 1.541195 (0.491145) \| 2.151766 / 1.468490 (0.683276) \| 0.567664 / 4.584777 (-4.017113) \| 4.491732 / 3.745712 (0.746020) \| 4.000651 / 5.269862 (-1.269211) \| 2.429113 / 4.565676 (-2.136564) \| 0.067052 / 0.424275 (-0.357223) \| 0.009095 / 0.007607 (0.001488) \| 0.546461 / 0.226044 (0.320417) \| 5.473524 / 2.268929 (3.204595) \| 2.902091 / 55.444624 (-52.542533) \| 2.517510 / 6.876477 (-4.358966) \| 2.572537 / 2.142072 (0.430464) \| 0.683499 / 4.805227 (-4.121728) \| 0.154863 / 6.500664 (-6.345801) \| 0.071298 / 0.075469 (-0.004171) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.625236 / 1.841788 (-0.216552) \| 23.531541 / 8.074308 (15.457233) \| 16.762514 / 10.191392 (6.571122) \| 0.215922 / 0.680424 (-0.464502) \| 0.021928 / 0.534201 (-0.512273) \| 0.466055 / 0.579283 (-0.113228) \| 0.553036 / 0.434364 (0.118672) \| 0.590063 / 0.540337 (0.049725) \| 0.789959 / 1.386936 (-0.596977) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008240 / 0.011353 (-0.003113) \| 0.004151 / 0.011008 (-0.006858) \| 0.077988 / 0.038508 (0.039479) \| 0.092865 / 0.023109 (0.069756) \| 0.468238 / 0.275898 (0.192340) \| 0.512882 / 0.323480 (0.189402) \| 0.006632 / 0.007986 (-0.001354) \| 0.003879 / 0.004328 (-0.000450) \| 0.076238 / 0.004250 (0.071988) \| 0.069372 / 0.037052 (0.032319) \| 0.481040 / 0.258489 (0.222550) \| 0.526332 / 0.293841 (0.232491) \| 0.036768 / 0.128546 (-0.091778) \| 0.009891 / 0.075646 (-0.065756) \| 0.084426 / 0.419271 (-0.334846) \| 0.062382 / 0.043533 (0.018849) \| 0.480667 / 0.255139 (0.225528) \| 0.509001 / 0.283200 (0.225802) \| 0.029215 / 0.141683 (-0.112468) \| 1.776075 / 1.452155 (0.323920) \| 1.948558 / 1.492716 (0.455841) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257879 / 0.018006 (0.239873) \| 0.471038 / 0.000490 (0.470548) \| 0.009273 / 0.000200 (0.009073) \| 0.000208 / 0.000054 (0.000154) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039249 / 0.037411 (0.001838) \| 0.133281 / 0.014526 (0.118755) \| 0.138261 / 0.176557 (-0.038296) \| 0.191051 / 0.737135 (-0.546084) \| 0.134493 / 0.296338 (-0.161845) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.507165 / 0.215209 (0.291955) \| 5.081018 / 2.077655 (3.003364) \| 2.747633 / 1.504120 (1.243513) \| 2.558265 / 1.541195 (1.017070) \| 2.710839 / 1.468490 (1.242348) \| 0.579913 / 4.584777 (-4.004864) \| 4.843657 / 3.745712 (1.097945) \| 3.942503 / 5.269862 (-1.327358) \| 2.529641 / 4.565676 (-2.036036) \| 0.068826 / 0.424275 (-0.355449) \| 0.008847 / 0.007607 (0.001240) \| 0.605332 / 0.226044 (0.379287) \| 6.039574 / 2.268929 (3.770646) \| 3.437291 / 55.444624 (-52.007333) \| 3.086631 / 6.876477 (-3.789846) \| 3.189340 / 2.142072 (1.047267) \| 0.702650 / 4.805227 (-4.102578) \| 0.157403 / 6.500664 (-6.343261) \| 0.074637 / 0.075469 (-0.000832) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.816532 / 1.841788 (-0.025256) \| 24.526675 / 8.074308 (16.452367) \| 17.371691 / 10.191392 (7.180299) \| 0.236044 / 0.680424 (-0.444380) \| 0.024759 / 0.534201 (-0.509442) \| 0.530578 / 0.579283 (-0.048705) \| 0.527424 / 0.434364 (0.093060) \| 0.620267 / 0.540337 (0.079929) \| 0.791159 / 1.386936 (-0.595777) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78cfce823b98b6cce79a9297fe6fa9e8f80a869c \"CML watermark\")\n" ]	2023-10-23T15:13:28	2023-10-23T15:24:31	2023-10-23T15:13:38	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6344/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6344/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6344", "html_url": "https://github.com/huggingface/datasets/pull/6344", "diff_url": "https://github.com/huggingface/datasets/pull/6344.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6344.patch", "merged_at": "2023-10-23T15:13:38" }	true
https://api.github.com/repos/huggingface/datasets/issues/6343	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6343/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6343/comments	https://api.github.com/repos/huggingface/datasets/issues/6343/events	https://github.com/huggingface/datasets/pull/6343	1,957,370,711	PR_kwDODunzps5dipeb	6,343	Remove unused argument in `_get_data_files_patterns`	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006584 / 0.011353 (-0.004769) \| 0.004197 / 0.011008 (-0.006812) \| 0.083598 / 0.038508 (0.045090) \| 0.075502 / 0.023109 (0.052392) \| 0.312986 / 0.275898 (0.037088) \| 0.344630 / 0.323480 (0.021150) \| 0.005394 / 0.007986 (-0.002591) \| 0.003485 / 0.004328 (-0.000843) \| 0.064529 / 0.004250 (0.060279) \| 0.055003 / 0.037052 (0.017950) \| 0.320522 / 0.258489 (0.062033) \| 0.362623 / 0.293841 (0.068782) \| 0.030900 / 0.128546 (-0.097646) \| 0.008459 / 0.075646 (-0.067187) \| 0.286986 / 0.419271 (-0.132285) \| 0.052310 / 0.043533 (0.008777) \| 0.315873 / 0.255139 (0.060734) \| 0.333962 / 0.283200 (0.050762) \| 0.023836 / 0.141683 (-0.117847) \| 1.481806 / 1.452155 (0.029651) \| 1.567926 / 1.492716 (0.075209) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268188 / 0.018006 (0.250182) \| 0.520542 / 0.000490 (0.520052) \| 0.017617 / 0.000200 (0.017417) \| 0.000631 / 0.000054 (0.000577) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028828 / 0.037411 (-0.008584) \| 0.083028 / 0.014526 (0.068502) \| 0.099808 / 0.176557 (-0.076748) \| 0.154282 / 0.737135 (-0.582853) \| 0.098590 / 0.296338 (-0.197748) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.407548 / 0.215209 (0.192339) \| 4.066128 / 2.077655 (1.988474) \| 2.036757 / 1.504120 (0.532637) \| 1.870130 / 1.541195 (0.328935) \| 1.949031 / 1.468490 (0.480541) \| 0.489263 / 4.584777 (-4.095514) \| 3.506269 / 3.745712 (-0.239443) \| 3.457232 / 5.269862 (-1.812629) \| 2.060097 / 4.565676 (-2.505580) \| 0.057252 / 0.424275 (-0.367024) \| 0.007727 / 0.007607 (0.000120) \| 0.480229 / 0.226044 (0.254185) \| 4.807064 / 2.268929 (2.538135) \| 2.495438 / 55.444624 (-52.949186) \| 2.186194 / 6.876477 (-4.690283) \| 2.243372 / 2.142072 (0.101300) \| 0.580550 / 4.805227 (-4.224678) \| 0.135398 / 6.500664 (-6.365266) \| 0.061878 / 0.075469 (-0.013591) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.305635 / 1.841788 (-0.536152) \| 19.194421 / 8.074308 (11.120113) \| 14.531699 / 10.191392 (4.340307) \| 0.167144 / 0.680424 (-0.513280) \| 0.018270 / 0.534201 (-0.515931) \| 0.393702 / 0.579283 (-0.185581) \| 0.406518 / 0.434364 (-0.027846) \| 0.458126 / 0.540337 (-0.082211) \| 0.639839 / 1.386936 (-0.747097) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006742 / 0.011353 (-0.004611) \| 0.004092 / 0.011008 (-0.006916) \| 0.065547 / 0.038508 (0.027039) \| 0.076293 / 0.023109 (0.053184) \| 0.389701 / 0.275898 (0.113803) \| 0.429158 / 0.323480 (0.105678) \| 0.005606 / 0.007986 (-0.002380) \| 0.003491 / 0.004328 (-0.000837) \| 0.065903 / 0.004250 (0.061653) \| 0.057346 / 0.037052 (0.020293) \| 0.393233 / 0.258489 (0.134744) \| 0.433106 / 0.293841 (0.139265) \| 0.032612 / 0.128546 (-0.095934) \| 0.008777 / 0.075646 (-0.066869) \| 0.073135 / 0.419271 (-0.346137) \| 0.048167 / 0.043533 (0.004635) \| 0.389309 / 0.255139 (0.134170) \| 0.416442 / 0.283200 (0.133242) \| 0.022839 / 0.141683 (-0.118844) \| 1.531607 / 1.452155 (0.079453) \| 1.598950 / 1.492716 (0.106234) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254856 / 0.018006 (0.236850) \| 0.528186 / 0.000490 (0.527697) \| 0.006975 / 0.000200 (0.006775) \| 0.000102 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032377 / 0.037411 (-0.005034) \| 0.092706 / 0.014526 (0.078180) \| 0.107618 / 0.176557 (-0.068939) \| 0.160103 / 0.737135 (-0.577032) \| 0.107226 / 0.296338 (-0.189112) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430922 / 0.215209 (0.215713) \| 4.312556 / 2.077655 (2.234901) \| 2.287686 / 1.504120 (0.783567) \| 2.111103 / 1.541195 (0.569908) \| 2.284105 / 1.468490 (0.815614) \| 0.485987 / 4.584777 (-4.098790) \| 3.557320 / 3.745712 (-0.188392) \| 3.341150 / 5.269862 (-1.928711) \| 2.056705 / 4.565676 (-2.508972) \| 0.057265 / 0.424275 (-0.367010) \| 0.007264 / 0.007607 (-0.000344) \| 0.505191 / 0.226044 (0.279146) \| 5.045379 / 2.268929 (2.776450) \| 2.732357 / 55.444624 (-52.712267) \| 2.390256 / 6.876477 (-4.486220) \| 2.643676 / 2.142072 (0.501604) \| 0.584630 / 4.805227 (-4.220597) \| 0.132402 / 6.500664 (-6.368262) \| 0.061387 / 0.075469 (-0.014082) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.340721 / 1.841788 (-0.501066) \| 19.744145 / 8.074308 (11.669837) \| 14.694482 / 10.191392 (4.503090) \| 0.166294 / 0.680424 (-0.514129) \| 0.020691 / 0.534201 (-0.513510) \| 0.398359 / 0.579283 (-0.180924) \| 0.423831 / 0.434364 (-0.010533) \| 0.474365 / 0.540337 (-0.065972) \| 0.649410 / 1.386936 (-0.737526) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b29bc9cef6237eb0d18f77c56686705f468bed25 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004369 / 0.011353 (-0.006984) \| 0.002728 / 0.011008 (-0.008280) \| 0.063754 / 0.038508 (0.025246) \| 0.029396 / 0.023109 (0.006287) \| 0.269409 / 0.275898 (-0.006489) \| 0.287654 / 0.323480 (-0.035826) \| 0.003926 / 0.007986 (-0.004060) \| 0.002366 / 0.004328 (-0.001963) \| 0.048910 / 0.004250 (0.044660) \| 0.043126 / 0.037052 (0.006074) \| 0.260774 / 0.258489 (0.002285) \| 0.299996 / 0.293841 (0.006155) \| 0.023359 / 0.128546 (-0.105187) \| 0.007259 / 0.075646 (-0.068388) \| 0.211412 / 0.419271 (-0.207860) \| 0.053883 / 0.043533 (0.010350) \| 0.268946 / 0.255139 (0.013807) \| 0.287664 / 0.283200 (0.004465) \| 0.017600 / 0.141683 (-0.124083) \| 1.096478 / 1.452155 (-0.355676) \| 1.193063 / 1.492716 (-0.299653) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090985 / 0.018006 (0.072979) \| 0.287168 / 0.000490 (0.286678) \| 0.000208 / 0.000200 (0.000009) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019238 / 0.037411 (-0.018173) \| 0.062660 / 0.014526 (0.048134) \| 0.073414 / 0.176557 (-0.103143) \| 0.120842 / 0.737135 (-0.616294) \| 0.077658 / 0.296338 (-0.218681) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280285 / 0.215209 (0.065076) \| 2.729807 / 2.077655 (0.652152) \| 1.430686 / 1.504120 (-0.073434) \| 1.307260 / 1.541195 (-0.233935) \| 1.321013 / 1.468490 (-0.147477) \| 0.387253 / 4.584777 (-4.197524) \| 2.415635 / 3.745712 (-1.330077) \| 2.557206 / 5.269862 (-2.712656) \| 1.553224 / 4.565676 (-3.012453) \| 0.045402 / 0.424275 (-0.378873) \| 0.004798 / 0.007607 (-0.002809) \| 0.330493 / 0.226044 (0.104449) \| 3.226835 / 2.268929 (0.957906) \| 1.739068 / 55.444624 (-53.705557) \| 1.494841 / 6.876477 (-5.381636) \| 1.528253 / 2.142072 (-0.613820) \| 0.451525 / 4.805227 (-4.353702) \| 0.096620 / 6.500664 (-6.404044) \| 0.041176 / 0.075469 (-0.034293) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.930892 / 1.841788 (-0.910896) \| 11.343351 / 8.074308 (3.269043) \| 10.420327 / 10.191392 (0.228935) \| 0.137629 / 0.680424 (-0.542795) \| 0.013907 / 0.534201 (-0.520293) \| 0.267778 / 0.579283 (-0.311505) \| 0.260774 / 0.434364 (-0.173590) \| 0.308213 / 0.540337 (-0.232124) \| 0.419659 / 1.386936 (-0.967277) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004867 / 0.011353 (-0.006486) \| 0.002830 / 0.011008 (-0.008178) \| 0.048506 / 0.038508 (0.009998) \| 0.048190 / 0.023109 (0.025080) \| 0.279995 / 0.275898 (0.004097) \| 0.296396 / 0.323480 (-0.027083) \| 0.004700 / 0.007986 (-0.003285) \| 0.003546 / 0.004328 (-0.000782) \| 0.048237 / 0.004250 (0.043987) \| 0.037102 / 0.037052 (0.000050) \| 0.284582 / 0.258489 (0.026093) \| 0.315896 / 0.293841 (0.022055) \| 0.024699 / 0.128546 (-0.103848) \| 0.007077 / 0.075646 (-0.068569) \| 0.054471 / 0.419271 (-0.364800) \| 0.032537 / 0.043533 (-0.010996) \| 0.276761 / 0.255139 (0.021622) \| 0.294741 / 0.283200 (0.011542) \| 0.017766 / 0.141683 (-0.123917) \| 1.118377 / 1.452155 (-0.333778) \| 1.186617 / 1.492716 (-0.306100) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088981 / 0.018006 (0.070975) \| 0.297793 / 0.000490 (0.297303) \| 0.000220 / 0.000200 (0.000020) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021300 / 0.037411 (-0.016111) \| 0.070059 / 0.014526 (0.055533) \| 0.080452 / 0.176557 (-0.096104) \| 0.118461 / 0.737135 (-0.618674) \| 0.081099 / 0.296338 (-0.215240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300560 / 0.215209 (0.085351) \| 2.951461 / 2.077655 (0.873806) \| 1.621978 / 1.504120 (0.117858) \| 1.478871 / 1.541195 (-0.062324) \| 1.520732 / 1.468490 (0.052242) \| 0.408625 / 4.584777 (-4.176152) \| 2.407253 / 3.745712 (-1.338459) \| 2.546000 / 5.269862 (-2.723861) \| 1.525920 / 4.565676 (-3.039757) \| 0.046817 / 0.424275 (-0.377458) \| 0.004880 / 0.007607 (-0.002727) \| 0.350866 / 0.226044 (0.124821) \| 3.489379 / 2.268929 (1.220451) \| 1.967197 / 55.444624 (-53.477427) \| 1.686083 / 6.876477 (-5.190394) \| 1.699307 / 2.142072 (-0.442766) \| 0.479659 / 4.805227 (-4.325568) \| 0.098853 / 6.500664 (-6.401811) \| 0.040718 / 0.075469 (-0.034751) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.018352 / 1.841788 (-0.823436) \| 12.022551 / 8.074308 (3.948243) \| 10.841890 / 10.191392 (0.650498) \| 0.130732 / 0.680424 (-0.549692) \| 0.016334 / 0.534201 (-0.517867) \| 0.271984 / 0.579283 (-0.307299) \| 0.276733 / 0.434364 (-0.157631) \| 0.308049 / 0.540337 (-0.232289) \| 0.415428 / 1.386936 (-0.971508) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31d95717e4e5fc6dd7699878720f063d51f1d595 \"CML watermark\")\n" ]	2023-10-23T14:54:18	2023-11-16T09:09:42	2023-11-16T09:03:39	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6343/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6343/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6343", "html_url": "https://github.com/huggingface/datasets/pull/6343", "diff_url": "https://github.com/huggingface/datasets/pull/6343.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6343.patch", "merged_at": "2023-11-16T09:03:39" }	true
https://api.github.com/repos/huggingface/datasets/issues/6342	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6342/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6342/comments	https://api.github.com/repos/huggingface/datasets/issues/6342/events	https://github.com/huggingface/datasets/pull/6342	1,957,344,445	PR_kwDODunzps5dijxt	6,342	Release: 2.14.6	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007051 / 0.011353 (-0.004302) \| 0.004291 / 0.011008 (-0.006717) \| 0.085557 / 0.038508 (0.047048) \| 0.087919 / 0.023109 (0.064810) \| 0.356912 / 0.275898 (0.081014) \| 0.394835 / 0.323480 (0.071355) \| 0.004464 / 0.007986 (-0.003522) \| 0.003688 / 0.004328 (-0.000640) \| 0.065437 / 0.004250 (0.061186) \| 0.060156 / 0.037052 (0.023103) \| 0.361807 / 0.258489 (0.103318) \| 0.420917 / 0.293841 (0.127076) \| 0.031704 / 0.128546 (-0.096842) \| 0.008921 / 0.075646 (-0.066726) \| 0.287828 / 0.419271 (-0.131443) \| 0.053600 / 0.043533 (0.010067) \| 0.361833 / 0.255139 (0.106694) \| 0.396732 / 0.283200 (0.113532) \| 0.025874 / 0.141683 (-0.115809) \| 1.474926 / 1.452155 (0.022771) \| 1.563186 / 1.492716 (0.070469) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.316823 / 0.018006 (0.298817) \| 0.604085 / 0.000490 (0.603595) \| 0.020828 / 0.000200 (0.020628) \| 0.000351 / 0.000054 (0.000297) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030468 / 0.037411 (-0.006943) \| 0.083904 / 0.014526 (0.069378) \| 0.103019 / 0.176557 (-0.073537) \| 0.159018 / 0.737135 (-0.578117) \| 0.102737 / 0.296338 (-0.193602) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405311 / 0.215209 (0.190102) \| 4.029060 / 2.077655 (1.951406) \| 2.046590 / 1.504120 (0.542470) \| 1.919335 / 1.541195 (0.378140) \| 2.030371 / 1.468490 (0.561881) \| 0.484209 / 4.584777 (-4.100568) \| 3.486888 / 3.745712 (-0.258824) \| 3.390777 / 5.269862 (-1.879084) \| 2.110744 / 4.565676 (-2.454933) \| 0.056587 / 0.424275 (-0.367688) \| 0.007766 / 0.007607 (0.000159) \| 0.488217 / 0.226044 (0.262173) \| 4.853904 / 2.268929 (2.584976) \| 2.595122 / 55.444624 (-52.849502) \| 2.217712 / 6.876477 (-4.658765) \| 2.500368 / 2.142072 (0.358296) \| 0.580843 / 4.805227 (-4.224384) \| 0.132719 / 6.500664 (-6.367945) \| 0.060202 / 0.075469 (-0.015267) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.260748 / 1.841788 (-0.581040) \| 20.148848 / 8.074308 (12.074540) \| 14.738779 / 10.191392 (4.547387) \| 0.167562 / 0.680424 (-0.512862) \| 0.018944 / 0.534201 (-0.515257) \| 0.394314 / 0.579283 (-0.184969) \| 0.409345 / 0.434364 (-0.025019) \| 0.458743 / 0.540337 (-0.081594) \| 0.638175 / 1.386936 (-0.748761) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007097 / 0.011353 (-0.004256) \| 0.004304 / 0.011008 (-0.006705) \| 0.065539 / 0.038508 (0.027030) \| 0.094078 / 0.023109 (0.070969) \| 0.412411 / 0.275898 (0.136513) \| 0.441900 / 0.323480 (0.118420) \| 0.006038 / 0.007986 (-0.001948) \| 0.003647 / 0.004328 (-0.000682) \| 0.065298 / 0.004250 (0.061048) \| 0.062571 / 0.037052 (0.025518) \| 0.405156 / 0.258489 (0.146667) \| 0.443779 / 0.293841 (0.149938) \| 0.034470 / 0.128546 (-0.094077) \| 0.008858 / 0.075646 (-0.066789) \| 0.071840 / 0.419271 (-0.347431) \| 0.050468 / 0.043533 (0.006935) \| 0.404198 / 0.255139 (0.149059) \| 0.430196 / 0.283200 (0.146997) \| 0.025710 / 0.141683 (-0.115973) \| 1.525374 / 1.452155 (0.073219) \| 1.591830 / 1.492716 (0.099114) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294330 / 0.018006 (0.276324) \| 0.516943 / 0.000490 (0.516453) \| 0.004807 / 0.000200 (0.004607) \| 0.000103 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034505 / 0.037411 (-0.002907) \| 0.096645 / 0.014526 (0.082119) \| 0.111926 / 0.176557 (-0.064630) \| 0.165241 / 0.737135 (-0.571894) \| 0.111834 / 0.296338 (-0.184504) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436370 / 0.215209 (0.221161) \| 4.357568 / 2.077655 (2.279913) \| 2.360529 / 1.504120 (0.856409) \| 2.196375 / 1.541195 (0.655180) \| 2.307481 / 1.468490 (0.838991) \| 0.494072 / 4.584777 (-4.090705) \| 3.565078 / 3.745712 (-0.180634) \| 3.405174 / 5.269862 (-1.864688) \| 2.203307 / 4.565676 (-2.362369) \| 0.058582 / 0.424275 (-0.365693) \| 0.007410 / 0.007607 (-0.000197) \| 0.514323 / 0.226044 (0.288279) \| 5.139834 / 2.268929 (2.870905) \| 2.884111 / 55.444624 (-52.560513) \| 2.589021 / 6.876477 (-4.287456) \| 2.787577 / 2.142072 (0.645504) \| 0.590765 / 4.805227 (-4.214462) \| 0.135237 / 6.500664 (-6.365427) \| 0.061078 / 0.075469 (-0.014391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.346938 / 1.841788 (-0.494850) \| 21.009948 / 8.074308 (12.935640) \| 15.203281 / 10.191392 (5.011889) \| 0.166208 / 0.680424 (-0.514216) \| 0.020634 / 0.534201 (-0.513567) \| 0.413825 / 0.579283 (-0.165458) \| 0.416477 / 0.434364 (-0.017887) \| 0.485888 / 0.540337 (-0.054449) \| 0.664941 / 1.386936 (-0.721995) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#395b30ee2c0f6088e28fe78a3e61b591e40a4668 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005927 / 0.011353 (-0.005425) \| 0.003622 / 0.011008 (-0.007386) \| 0.081414 / 0.038508 (0.042906) \| 0.061031 / 0.023109 (0.037922) \| 0.358323 / 0.275898 (0.082425) \| 0.394192 / 0.323480 (0.070712) \| 0.003471 / 0.007986 (-0.004515) \| 0.002930 / 0.004328 (-0.001399) \| 0.064215 / 0.004250 (0.059964) \| 0.048678 / 0.037052 (0.011625) \| 0.367966 / 0.258489 (0.109477) \| 0.412618 / 0.293841 (0.118777) \| 0.027192 / 0.128546 (-0.101355) \| 0.007921 / 0.075646 (-0.067725) \| 0.262213 / 0.419271 (-0.157059) \| 0.044750 / 0.043533 (0.001217) \| 0.351573 / 0.255139 (0.096434) \| 0.389000 / 0.283200 (0.105800) \| 0.020842 / 0.141683 (-0.120840) \| 1.448925 / 1.452155 (-0.003229) \| 1.530478 / 1.492716 (0.037761) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227787 / 0.018006 (0.209780) \| 0.423161 / 0.000490 (0.422671) \| 0.007557 / 0.000200 (0.007357) \| 0.000205 / 0.000054 (0.000150) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024703 / 0.037411 (-0.012709) \| 0.074044 / 0.014526 (0.059518) \| 0.085520 / 0.176557 (-0.091037) \| 0.146132 / 0.737135 (-0.591003) \| 0.085637 / 0.296338 (-0.210701) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.393177 / 0.215209 (0.177968) \| 3.926740 / 2.077655 (1.849085) \| 1.892420 / 1.504120 (0.388300) \| 1.716844 / 1.541195 (0.175650) \| 1.784040 / 1.468490 (0.315550) \| 0.499570 / 4.584777 (-4.085207) \| 3.057764 / 3.745712 (-0.687948) \| 2.885463 / 5.269862 (-2.384399) \| 1.905206 / 4.565676 (-2.660471) \| 0.058216 / 0.424275 (-0.366059) \| 0.006805 / 0.007607 (-0.000802) \| 0.465406 / 0.226044 (0.239361) \| 4.658569 / 2.268929 (2.389641) \| 2.461737 / 55.444624 (-52.982887) \| 2.170620 / 6.876477 (-4.705856) \| 2.373715 / 2.142072 (0.231643) \| 0.592818 / 4.805227 (-4.212409) \| 0.127960 / 6.500664 (-6.372704) \| 0.061696 / 0.075469 (-0.013773) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.229073 / 1.841788 (-0.612715) \| 17.832087 / 8.074308 (9.757778) \| 13.889485 / 10.191392 (3.698093) \| 0.142237 / 0.680424 (-0.538187) \| 0.016752 / 0.534201 (-0.517449) \| 0.338342 / 0.579283 (-0.240941) \| 0.383933 / 0.434364 (-0.050431) \| 0.393017 / 0.540337 (-0.147320) \| 0.557621 / 1.386936 (-0.829315) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006218 / 0.011353 (-0.005135) \| 0.003679 / 0.011008 (-0.007329) \| 0.062934 / 0.038508 (0.024426) \| 0.066764 / 0.023109 (0.043655) \| 0.482737 / 0.275898 (0.206839) \| 0.483241 / 0.323480 (0.159761) \| 0.004828 / 0.007986 (-0.003158) \| 0.002880 / 0.004328 (-0.001448) \| 0.063111 / 0.004250 (0.058861) \| 0.049500 / 0.037052 (0.012448) \| 0.453155 / 0.258489 (0.194666) \| 0.488776 / 0.293841 (0.194935) \| 0.028568 / 0.128546 (-0.099978) \| 0.008490 / 0.075646 (-0.067157) \| 0.068202 / 0.419271 (-0.351069) \| 0.040695 / 0.043533 (-0.002838) \| 0.457473 / 0.255139 (0.202334) \| 0.471968 / 0.283200 (0.188768) \| 0.021261 / 0.141683 (-0.120422) \| 1.476304 / 1.452155 (0.024150) \| 1.503433 / 1.492716 (0.010716) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227108 / 0.018006 (0.209102) \| 0.428330 / 0.000490 (0.427840) \| 0.004637 / 0.000200 (0.004437) \| 0.000074 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027253 / 0.037411 (-0.010158) \| 0.081990 / 0.014526 (0.067464) \| 0.092763 / 0.176557 (-0.083794) \| 0.146155 / 0.737135 (-0.590981) \| 0.093175 / 0.296338 (-0.203164) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.464585 / 0.215209 (0.249376) \| 4.630704 / 2.077655 (2.553050) \| 2.583272 / 1.504120 (1.079152) \| 2.393810 / 1.541195 (0.852615) \| 2.463255 / 1.468490 (0.994765) \| 0.507045 / 4.584777 (-4.077732) \| 3.181972 / 3.745712 (-0.563740) \| 2.902321 / 5.269862 (-2.367541) \| 1.905431 / 4.565676 (-2.660246) \| 0.059427 / 0.424275 (-0.364848) \| 0.006387 / 0.007607 (-0.001220) \| 0.542247 / 0.226044 (0.316203) \| 5.426868 / 2.268929 (3.157939) \| 3.073489 / 55.444624 (-52.371136) \| 2.719620 / 6.876477 (-4.156857) \| 2.861865 / 2.142072 (0.719793) \| 0.593757 / 4.805227 (-4.211471) \| 0.125439 / 6.500664 (-6.375225) \| 0.060901 / 0.075469 (-0.014568) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.359938 / 1.841788 (-0.481850) \| 18.484867 / 8.074308 (10.410559) \| 14.685645 / 10.191392 (4.494253) \| 0.164098 / 0.680424 (-0.516325) \| 0.018090 / 0.534201 (-0.516111) \| 0.339760 / 0.579283 (-0.239523) \| 0.376668 / 0.434364 (-0.057696) \| 0.396963 / 0.540337 (-0.143374) \| 0.549305 / 1.386936 (-0.837631) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0c896f4195ec8a91e09f8bb9a57950bcec8b8450 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006052 / 0.011353 (-0.005301) \| 0.003715 / 0.011008 (-0.007293) \| 0.079646 / 0.038508 (0.041138) \| 0.059053 / 0.023109 (0.035944) \| 0.393016 / 0.275898 (0.117118) \| 0.424758 / 0.323480 (0.101278) \| 0.005407 / 0.007986 (-0.002578) \| 0.002920 / 0.004328 (-0.001408) \| 0.062145 / 0.004250 (0.057894) \| 0.047289 / 0.037052 (0.010237) \| 0.399848 / 0.258489 (0.141359) \| 0.434239 / 0.293841 (0.140398) \| 0.027388 / 0.128546 (-0.101158) \| 0.007967 / 0.075646 (-0.067680) \| 0.262546 / 0.419271 (-0.156725) \| 0.045014 / 0.043533 (0.001482) \| 0.398086 / 0.255139 (0.142947) \| 0.414615 / 0.283200 (0.131415) \| 0.020410 / 0.141683 (-0.121272) \| 1.447276 / 1.452155 (-0.004879) \| 1.512390 / 1.492716 (0.019673) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224854 / 0.018006 (0.206847) \| 0.434173 / 0.000490 (0.433683) \| 0.010091 / 0.000200 (0.009891) \| 0.000259 / 0.000054 (0.000205) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025316 / 0.037411 (-0.012095) \| 0.073284 / 0.014526 (0.058758) \| 0.085177 / 0.176557 (-0.091379) \| 0.148905 / 0.737135 (-0.588230) \| 0.084696 / 0.296338 (-0.211642) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438259 / 0.215209 (0.223050) \| 4.380679 / 2.077655 (2.303025) \| 2.310329 / 1.504120 (0.806209) \| 2.144002 / 1.541195 (0.602807) \| 2.203761 / 1.468490 (0.735270) \| 0.500559 / 4.584777 (-4.084218) \| 3.031172 / 3.745712 (-0.714540) \| 2.839425 / 5.269862 (-2.430436) \| 1.878391 / 4.565676 (-2.687285) \| 0.057325 / 0.424275 (-0.366950) \| 0.006719 / 0.007607 (-0.000888) \| 0.510122 / 0.226044 (0.284078) \| 5.108632 / 2.268929 (2.839704) \| 2.805716 / 55.444624 (-52.638909) \| 2.422183 / 6.876477 (-4.454293) \| 2.635280 / 2.142072 (0.493207) \| 0.589351 / 4.805227 (-4.215876) \| 0.125416 / 6.500664 (-6.375248) \| 0.061142 / 0.075469 (-0.014327) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.234997 / 1.841788 (-0.606791) \| 17.731828 / 8.074308 (9.657520) \| 13.858081 / 10.191392 (3.666689) \| 0.145975 / 0.680424 (-0.534449) \| 0.016827 / 0.534201 (-0.517374) \| 0.335701 / 0.579283 (-0.243582) \| 0.361867 / 0.434364 (-0.072497) \| 0.394620 / 0.540337 (-0.145718) \| 0.532146 / 1.386936 (-0.854790) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006091 / 0.011353 (-0.005262) \| 0.003663 / 0.011008 (-0.007345) \| 0.062596 / 0.038508 (0.024088) \| 0.061649 / 0.023109 (0.038539) \| 0.440647 / 0.275898 (0.164749) \| 0.472974 / 0.323480 (0.149494) \| 0.005009 / 0.007986 (-0.002976) \| 0.002879 / 0.004328 (-0.001449) \| 0.062815 / 0.004250 (0.058565) \| 0.049000 / 0.037052 (0.011947) \| 0.442990 / 0.258489 (0.184501) \| 0.477622 / 0.293841 (0.183781) \| 0.028512 / 0.128546 (-0.100034) \| 0.008031 / 0.075646 (-0.067615) \| 0.067853 / 0.419271 (-0.351418) \| 0.040823 / 0.043533 (-0.002710) \| 0.437811 / 0.255139 (0.182672) \| 0.464615 / 0.283200 (0.181416) \| 0.021348 / 0.141683 (-0.120334) \| 1.479230 / 1.452155 (0.027075) \| 1.544053 / 1.492716 (0.051337) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210697 / 0.018006 (0.192691) \| 0.436450 / 0.000490 (0.435960) \| 0.003413 / 0.000200 (0.003213) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027190 / 0.037411 (-0.010222) \| 0.083254 / 0.014526 (0.068728) \| 0.092936 / 0.176557 (-0.083620) \| 0.147261 / 0.737135 (-0.589874) \| 0.092910 / 0.296338 (-0.203429) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.454195 / 0.215209 (0.238986) \| 4.569122 / 2.077655 (2.491468) \| 2.497198 / 1.504120 (0.993079) \| 2.314337 / 1.541195 (0.773142) \| 2.378471 / 1.468490 (0.909981) \| 0.515402 / 4.584777 (-4.069375) \| 3.199374 / 3.745712 (-0.546338) \| 2.899300 / 5.269862 (-2.370562) \| 1.873314 / 4.565676 (-2.692362) \| 0.058820 / 0.424275 (-0.365455) \| 0.006651 / 0.007607 (-0.000957) \| 0.526681 / 0.226044 (0.300636) \| 5.275232 / 2.268929 (3.006303) \| 2.969107 / 55.444624 (-52.475517) \| 2.600959 / 6.876477 (-4.275518) \| 2.762930 / 2.142072 (0.620858) \| 0.605726 / 4.805227 (-4.199501) \| 0.127618 / 6.500664 (-6.373046) \| 0.062840 / 0.075469 (-0.012629) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.367276 / 1.841788 (-0.474512) \| 18.069385 / 8.074308 (9.995077) \| 14.691945 / 10.191392 (4.500553) \| 0.147203 / 0.680424 (-0.533221) \| 0.018484 / 0.534201 (-0.515717) \| 0.333759 / 0.579283 (-0.245524) \| 0.395503 / 0.434364 (-0.038861) \| 0.387031 / 0.540337 (-0.153306) \| 0.550428 / 1.386936 (-0.836508) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c8f7eb79dff66dd03211321dcb55f7a7a05ef38 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007675 / 0.011353 (-0.003678) \| 0.004532 / 0.011008 (-0.006476) \| 0.088176 / 0.038508 (0.049668) \| 0.103257 / 0.023109 (0.080148) \| 0.314785 / 0.275898 (0.038887) \| 0.354280 / 0.323480 (0.030800) \| 0.004638 / 0.007986 (-0.003348) \| 0.003736 / 0.004328 (-0.000592) \| 0.066744 / 0.004250 (0.062493) \| 0.064647 / 0.037052 (0.027595) \| 0.320227 / 0.258489 (0.061738) \| 0.369581 / 0.293841 (0.075740) \| 0.032347 / 0.128546 (-0.096199) \| 0.009226 / 0.075646 (-0.066421) \| 0.292966 / 0.419271 (-0.126306) \| 0.055738 / 0.043533 (0.012206) \| 0.316537 / 0.255139 (0.061398) \| 0.334699 / 0.283200 (0.051499) \| 0.027401 / 0.141683 (-0.114282) \| 1.482390 / 1.452155 (0.030236) \| 1.594771 / 1.492716 (0.102055) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.322181 / 0.018006 (0.304175) \| 0.577701 / 0.000490 (0.577212) \| 0.014565 / 0.000200 (0.014365) \| 0.000393 / 0.000054 (0.000338) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033255 / 0.037411 (-0.004156) \| 0.094271 / 0.014526 (0.079745) \| 0.105360 / 0.176557 (-0.071197) \| 0.163699 / 0.737135 (-0.573436) \| 0.105620 / 0.296338 (-0.190719) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383449 / 0.215209 (0.168240) \| 3.824292 / 2.077655 (1.746637) \| 1.861809 / 1.504120 (0.357689) \| 1.698153 / 1.541195 (0.156958) \| 1.819460 / 1.468490 (0.350970) \| 0.488277 / 4.584777 (-4.096500) \| 3.622772 / 3.745712 (-0.122940) \| 3.486041 / 5.269862 (-1.783821) \| 2.211679 / 4.565676 (-2.353998) \| 0.057637 / 0.424275 (-0.366638) \| 0.008028 / 0.007607 (0.000421) \| 0.461917 / 0.226044 (0.235873) \| 4.626493 / 2.268929 (2.357565) \| 2.374846 / 55.444624 (-53.069779) \| 1.976003 / 6.876477 (-4.900473) \| 2.325342 / 2.142072 (0.183269) \| 0.582538 / 4.805227 (-4.222689) \| 0.133575 / 6.500664 (-6.367089) \| 0.061696 / 0.075469 (-0.013773) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.271846 / 1.841788 (-0.569941) \| 20.944702 / 8.074308 (12.870394) \| 15.438119 / 10.191392 (5.246727) \| 0.167334 / 0.680424 (-0.513090) \| 0.019538 / 0.534201 (-0.514663) \| 0.401467 / 0.579283 (-0.177816) \| 0.428222 / 0.434364 (-0.006142) \| 0.466108 / 0.540337 (-0.074229) \| 0.645326 / 1.386936 (-0.741610) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007096 / 0.011353 (-0.004257) \| 0.004398 / 0.011008 (-0.006610) \| 0.066253 / 0.038508 (0.027745) \| 0.089415 / 0.023109 (0.066306) \| 0.395760 / 0.275898 (0.119862) \| 0.436058 / 0.323480 (0.112579) \| 0.005944 / 0.007986 (-0.002042) \| 0.003821 / 0.004328 (-0.000507) \| 0.065286 / 0.004250 (0.061036) \| 0.060990 / 0.037052 (0.023937) \| 0.394674 / 0.258489 (0.136185) \| 0.437672 / 0.293841 (0.143831) \| 0.032370 / 0.128546 (-0.096177) \| 0.009025 / 0.075646 (-0.066622) \| 0.071365 / 0.419271 (-0.347906) \| 0.048232 / 0.043533 (0.004699) \| 0.395677 / 0.255139 (0.140538) \| 0.415869 / 0.283200 (0.132669) \| 0.024632 / 0.141683 (-0.117051) \| 1.511386 / 1.452155 (0.059231) \| 1.604475 / 1.492716 (0.111759) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.312864 / 0.018006 (0.294858) \| 0.535432 / 0.000490 (0.534943) \| 0.005195 / 0.000200 (0.004995) \| 0.000101 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035827 / 0.037411 (-0.001584) \| 0.099353 / 0.014526 (0.084827) \| 0.110796 / 0.176557 (-0.065761) \| 0.165224 / 0.737135 (-0.571911) \| 0.112111 / 0.296338 (-0.184228) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428873 / 0.215209 (0.213664) \| 4.284264 / 2.077655 (2.206609) \| 2.303966 / 1.504120 (0.799847) \| 2.153868 / 1.541195 (0.612674) \| 2.275669 / 1.468490 (0.807179) \| 0.495452 / 4.584777 (-4.089325) \| 3.706773 / 3.745712 (-0.038939) \| 3.471988 / 5.269862 (-1.797874) \| 2.194851 / 4.565676 (-2.370825) \| 0.058998 / 0.424275 (-0.365277) \| 0.007522 / 0.007607 (-0.000085) \| 0.511222 / 0.226044 (0.285177) \| 5.097058 / 2.268929 (2.828130) \| 2.856793 / 55.444624 (-52.587832) \| 2.521907 / 6.876477 (-4.354569) \| 2.783133 / 2.142072 (0.641060) \| 0.600511 / 4.805227 (-4.204717) \| 0.134130 / 6.500664 (-6.366534) \| 0.061726 / 0.075469 (-0.013743) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.385272 / 1.841788 (-0.456516) \| 21.149260 / 8.074308 (13.074952) \| 15.548746 / 10.191392 (5.357354) \| 0.167506 / 0.680424 (-0.512918) \| 0.020494 / 0.534201 (-0.513707) \| 0.400697 / 0.579283 (-0.178586) \| 0.427386 / 0.434364 (-0.006978) \| 0.478514 / 0.540337 (-0.061824) \| 0.655753 / 1.386936 (-0.731183) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c8f7eb79dff66dd03211321dcb55f7a7a05ef38 \"CML watermark\")\n" ]	2023-10-23T14:43:26	2023-10-23T15:21:54	2023-10-23T15:07:25	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6342/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6342/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6342", "html_url": "https://github.com/huggingface/datasets/pull/6342", "diff_url": "https://github.com/huggingface/datasets/pull/6342.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6342.patch", "merged_at": "2023-10-23T15:07:25" }	true
https://api.github.com/repos/huggingface/datasets/issues/6340	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6340/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6340/comments	https://api.github.com/repos/huggingface/datasets/issues/6340/events	https://github.com/huggingface/datasets/pull/6340	1,956,917,893	PR_kwDODunzps5dhGpW	6,340	Release 2.14.5	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6340). All of your documentation changes will be reflected on that endpoint." ]	2023-10-23T11:10:22	2023-10-23T14:20:46	2023-10-23T11:12:40	MEMBER	null	(wrong release number - I was continuing the 2.14 branch but 2.14.5 was released from `main`)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6340/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6340/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6340", "html_url": "https://github.com/huggingface/datasets/pull/6340", "diff_url": "https://github.com/huggingface/datasets/pull/6340.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6340.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6339	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6339/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6339/comments	https://api.github.com/repos/huggingface/datasets/issues/6339/events	https://github.com/huggingface/datasets/pull/6339	1,956,912,627	PR_kwDODunzps5dhFfg	6,339	minor release step improvement	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006572 / 0.011353 (-0.004780) \| 0.004019 / 0.011008 (-0.006989) \| 0.084080 / 0.038508 (0.045572) \| 0.070111 / 0.023109 (0.047002) \| 0.340440 / 0.275898 (0.064542) \| 0.358839 / 0.323480 (0.035359) \| 0.005254 / 0.007986 (-0.002732) \| 0.003296 / 0.004328 (-0.001032) \| 0.064368 / 0.004250 (0.060117) \| 0.054549 / 0.037052 (0.017497) \| 0.343817 / 0.258489 (0.085328) \| 0.369871 / 0.293841 (0.076030) \| 0.030621 / 0.128546 (-0.097925) \| 0.008457 / 0.075646 (-0.067189) \| 0.287839 / 0.419271 (-0.131432) \| 0.051700 / 0.043533 (0.008167) \| 0.331602 / 0.255139 (0.076463) \| 0.339836 / 0.283200 (0.056636) \| 0.023224 / 0.141683 (-0.118459) \| 1.494597 / 1.452155 (0.042443) \| 1.578640 / 1.492716 (0.085924) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236985 / 0.018006 (0.218979) \| 0.506153 / 0.000490 (0.505664) \| 0.009753 / 0.000200 (0.009553) \| 0.000345 / 0.000054 (0.000291) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028355 / 0.037411 (-0.009056) \| 0.082104 / 0.014526 (0.067578) \| 0.095141 / 0.176557 (-0.081415) \| 0.151054 / 0.737135 (-0.586081) \| 0.095139 / 0.296338 (-0.201200) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403773 / 0.215209 (0.188564) \| 4.025567 / 2.077655 (1.947912) \| 2.024641 / 1.504120 (0.520521) \| 1.857039 / 1.541195 (0.315845) \| 1.957346 / 1.468490 (0.488856) \| 0.481486 / 4.584777 (-4.103291) \| 3.574463 / 3.745712 (-0.171249) \| 3.399311 / 5.269862 (-1.870551) \| 1.996806 / 4.565676 (-2.568870) \| 0.056644 / 0.424275 (-0.367631) \| 0.007503 / 0.007607 (-0.000104) \| 0.479480 / 0.226044 (0.253435) \| 4.793686 / 2.268929 (2.524757) \| 2.481011 / 55.444624 (-52.963613) \| 2.176473 / 6.876477 (-4.700004) \| 2.203192 / 2.142072 (0.061120) \| 0.574071 / 4.805227 (-4.231156) \| 0.131852 / 6.500664 (-6.368812) \| 0.058883 / 0.075469 (-0.016586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249945 / 1.841788 (-0.591842) \| 18.439267 / 8.074308 (10.364959) \| 14.100934 / 10.191392 (3.909542) \| 0.164191 / 0.680424 (-0.516233) \| 0.018086 / 0.534201 (-0.516115) \| 0.390821 / 0.579283 (-0.188462) \| 0.414166 / 0.434364 (-0.020198) \| 0.460073 / 0.540337 (-0.080265) \| 0.636299 / 1.386936 (-0.750637) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006606 / 0.011353 (-0.004747) \| 0.003987 / 0.011008 (-0.007021) \| 0.064616 / 0.038508 (0.026108) \| 0.070830 / 0.023109 (0.047721) \| 0.397340 / 0.275898 (0.121442) \| 0.426823 / 0.323480 (0.103343) \| 0.005345 / 0.007986 (-0.002641) \| 0.003264 / 0.004328 (-0.001065) \| 0.064728 / 0.004250 (0.060477) \| 0.055763 / 0.037052 (0.018711) \| 0.405347 / 0.258489 (0.146858) \| 0.433163 / 0.293841 (0.139322) \| 0.032394 / 0.128546 (-0.096153) \| 0.008474 / 0.075646 (-0.067172) \| 0.071583 / 0.419271 (-0.347689) \| 0.048424 / 0.043533 (0.004892) \| 0.400582 / 0.255139 (0.145443) \| 0.418111 / 0.283200 (0.134911) \| 0.022257 / 0.141683 (-0.119426) \| 1.495521 / 1.452155 (0.043366) \| 1.554626 / 1.492716 (0.061910) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218249 / 0.018006 (0.200242) \| 0.438527 / 0.000490 (0.438037) \| 0.005406 / 0.000200 (0.005206) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031600 / 0.037411 (-0.005812) \| 0.090836 / 0.014526 (0.076310) \| 0.105000 / 0.176557 (-0.071556) \| 0.157648 / 0.737135 (-0.579487) \| 0.103827 / 0.296338 (-0.192512) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426428 / 0.215209 (0.211219) \| 4.259435 / 2.077655 (2.181780) \| 2.300795 / 1.504120 (0.796675) \| 2.121302 / 1.541195 (0.580108) \| 2.145602 / 1.468490 (0.677112) \| 0.486856 / 4.584777 (-4.097921) \| 3.673568 / 3.745712 (-0.072144) \| 3.278619 / 5.269862 (-1.991243) \| 2.037760 / 4.565676 (-2.527917) \| 0.057699 / 0.424275 (-0.366576) \| 0.007269 / 0.007607 (-0.000338) \| 0.499549 / 0.226044 (0.273505) \| 4.996214 / 2.268929 (2.727285) \| 2.766480 / 55.444624 (-52.678144) \| 2.417308 / 6.876477 (-4.459168) \| 2.581026 / 2.142072 (0.438953) \| 0.589463 / 4.805227 (-4.215765) \| 0.134820 / 6.500664 (-6.365844) \| 0.061699 / 0.075469 (-0.013770) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353704 / 1.841788 (-0.488084) \| 19.104167 / 8.074308 (11.029859) \| 14.652166 / 10.191392 (4.460774) \| 0.171885 / 0.680424 (-0.508539) \| 0.020222 / 0.534201 (-0.513978) \| 0.396777 / 0.579283 (-0.182506) \| 0.426304 / 0.434364 (-0.008060) \| 0.471347 / 0.540337 (-0.068991) \| 0.635887 / 1.386936 (-0.751049) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce19ec527c581eddec306a03ad1db554223cc94a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004686 / 0.011353 (-0.006667) \| 0.002998 / 0.011008 (-0.008010) \| 0.063604 / 0.038508 (0.025096) \| 0.048927 / 0.023109 (0.025818) \| 0.247238 / 0.275898 (-0.028660) \| 0.272409 / 0.323480 (-0.051071) \| 0.003909 / 0.007986 (-0.004077) \| 0.002469 / 0.004328 (-0.001859) \| 0.048473 / 0.004250 (0.044223) \| 0.037514 / 0.037052 (0.000462) \| 0.257292 / 0.258489 (-0.001197) \| 0.285203 / 0.293841 (-0.008638) \| 0.023131 / 0.128546 (-0.105415) \| 0.006803 / 0.075646 (-0.068843) \| 0.202920 / 0.419271 (-0.216351) \| 0.035653 / 0.043533 (-0.007880) \| 0.254791 / 0.255139 (-0.000348) \| 0.272973 / 0.283200 (-0.010226) \| 0.017707 / 0.141683 (-0.123976) \| 1.091606 / 1.452155 (-0.360549) \| 1.151453 / 1.492716 (-0.341263) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093701 / 0.018006 (0.075695) \| 0.304199 / 0.000490 (0.303709) \| 0.000223 / 0.000200 (0.000023) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019291 / 0.037411 (-0.018120) \| 0.062168 / 0.014526 (0.047642) \| 0.073273 / 0.176557 (-0.103284) \| 0.119497 / 0.737135 (-0.617638) \| 0.075008 / 0.296338 (-0.221331) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279983 / 0.215209 (0.064774) \| 2.774413 / 2.077655 (0.696758) \| 1.476678 / 1.504120 (-0.027441) \| 1.336273 / 1.541195 (-0.204922) \| 1.332349 / 1.468490 (-0.136142) \| 0.403150 / 4.584777 (-4.181627) \| 2.390026 / 3.745712 (-1.355686) \| 2.619151 / 5.269862 (-2.650711) \| 1.578607 / 4.565676 (-2.987069) \| 0.046632 / 0.424275 (-0.377643) \| 0.007352 / 0.007607 (-0.000255) \| 0.333419 / 0.226044 (0.107375) \| 3.288734 / 2.268929 (1.019805) \| 1.843677 / 55.444624 (-53.600947) \| 1.536746 / 6.876477 (-5.339731) \| 1.573005 / 2.142072 (-0.569067) \| 0.475699 / 4.805227 (-4.329529) \| 0.104742 / 6.500664 (-6.395922) \| 0.042450 / 0.075469 (-0.033019) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949039 / 1.841788 (-0.892749) \| 11.895928 / 8.074308 (3.821620) \| 10.650521 / 10.191392 (0.459129) \| 0.142308 / 0.680424 (-0.538116) \| 0.014207 / 0.534201 (-0.519994) \| 0.274011 / 0.579283 (-0.305272) \| 0.288259 / 0.434364 (-0.146105) \| 0.327729 / 0.540337 (-0.212609) \| 0.395728 / 1.386936 (-0.991208) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004830 / 0.011353 (-0.006523) \| 0.002978 / 0.011008 (-0.008030) \| 0.048623 / 0.038508 (0.010114) \| 0.055040 / 0.023109 (0.031930) \| 0.276436 / 0.275898 (0.000538) \| 0.302403 / 0.323480 (-0.021076) \| 0.004080 / 0.007986 (-0.003905) \| 0.002479 / 0.004328 (-0.001849) \| 0.048078 / 0.004250 (0.043827) \| 0.039680 / 0.037052 (0.002627) \| 0.279095 / 0.258489 (0.020606) \| 0.307399 / 0.293841 (0.013558) \| 0.024533 / 0.128546 (-0.104013) \| 0.007196 / 0.075646 (-0.068450) \| 0.053879 / 0.419271 (-0.365393) \| 0.032545 / 0.043533 (-0.010988) \| 0.275501 / 0.255139 (0.020362) \| 0.298530 / 0.283200 (0.015330) \| 0.017992 / 0.141683 (-0.123691) \| 1.144191 / 1.452155 (-0.307963) \| 1.208309 / 1.492716 (-0.284408) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095690 / 0.018006 (0.077684) \| 0.304932 / 0.000490 (0.304442) \| 0.000223 / 0.000200 (0.000023) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021409 / 0.037411 (-0.016003) \| 0.069861 / 0.014526 (0.055335) \| 0.080959 / 0.176557 (-0.095597) \| 0.119432 / 0.737135 (-0.617703) \| 0.083649 / 0.296338 (-0.212690) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297243 / 0.215209 (0.082034) \| 2.909288 / 2.077655 (0.831634) \| 1.571512 / 1.504120 (0.067392) \| 1.452403 / 1.541195 (-0.088792) \| 1.481290 / 1.468490 (0.012800) \| 0.405795 / 4.584777 (-4.178982) \| 2.452923 / 3.745712 (-1.292789) \| 2.513371 / 5.269862 (-2.756490) \| 1.593216 / 4.565676 (-2.972460) \| 0.048073 / 0.424275 (-0.376202) \| 0.005312 / 0.007607 (-0.002296) \| 0.355783 / 0.226044 (0.129738) \| 3.494062 / 2.268929 (1.225133) \| 1.947388 / 55.444624 (-53.497236) \| 1.651724 / 6.876477 (-5.224753) \| 1.789007 / 2.142072 (-0.353065) \| 0.487073 / 4.805227 (-4.318154) \| 0.100271 / 6.500664 (-6.400393) \| 0.041571 / 0.075469 (-0.033898) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983766 / 1.841788 (-0.858021) \| 12.384778 / 8.074308 (4.310469) \| 10.669519 / 10.191392 (0.478127) \| 0.133105 / 0.680424 (-0.547318) \| 0.016665 / 0.534201 (-0.517536) \| 0.269479 / 0.579283 (-0.309804) \| 0.276498 / 0.434364 (-0.157866) \| 0.302105 / 0.540337 (-0.238233) \| 0.391204 / 1.386936 (-0.995732) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12ebe695b4748c5a26e08b44ed51955f74f5801d \"CML watermark\")\n" ]	2023-10-23T11:07:04	2023-11-07T10:38:54	2023-11-07T10:32:41	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6339/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6339/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6339", "html_url": "https://github.com/huggingface/datasets/pull/6339", "diff_url": "https://github.com/huggingface/datasets/pull/6339.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6339.patch", "merged_at": "2023-11-07T10:32:41" }	true
https://api.github.com/repos/huggingface/datasets/issues/6338	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6338/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6338/comments	https://api.github.com/repos/huggingface/datasets/issues/6338/events	https://github.com/huggingface/datasets/pull/6338	1,956,886,072	PR_kwDODunzps5dg_sb	6,338	pin fsspec before it switches to glob.glob	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "closing in favor of https://github.com/huggingface/datasets/pull/6337", "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6338). All of your documentation changes will be reflected on that endpoint." ]	2023-10-23T10:50:54	2023-10-23T10:57:07	2023-10-23T10:51:52	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6338/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6338/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6338", "html_url": "https://github.com/huggingface/datasets/pull/6338", "diff_url": "https://github.com/huggingface/datasets/pull/6338.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6338.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6337	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6337/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6337/comments	https://api.github.com/repos/huggingface/datasets/issues/6337/events	https://github.com/huggingface/datasets/pull/6337	1,956,875,259	PR_kwDODunzps5dg9Uu	6,337	Pin supported upper version of fsspec	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006915 / 0.011353 (-0.004438) \| 0.004110 / 0.011008 (-0.006898) \| 0.084392 / 0.038508 (0.045884) \| 0.079649 / 0.023109 (0.056540) \| 0.305760 / 0.275898 (0.029862) \| 0.343968 / 0.323480 (0.020488) \| 0.005402 / 0.007986 (-0.002584) \| 0.003342 / 0.004328 (-0.000986) \| 0.064774 / 0.004250 (0.060523) \| 0.055919 / 0.037052 (0.018866) \| 0.315194 / 0.258489 (0.056705) \| 0.355014 / 0.293841 (0.061173) \| 0.032140 / 0.128546 (-0.096406) \| 0.008865 / 0.075646 (-0.066781) \| 0.287684 / 0.419271 (-0.131588) \| 0.053504 / 0.043533 (0.009971) \| 0.306852 / 0.255139 (0.051713) \| 0.331125 / 0.283200 (0.047925) \| 0.023476 / 0.141683 (-0.118207) \| 1.506590 / 1.452155 (0.054435) \| 1.574508 / 1.492716 (0.081792) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239987 / 0.018006 (0.221981) \| 0.459144 / 0.000490 (0.458654) \| 0.008509 / 0.000200 (0.008309) \| 0.000335 / 0.000054 (0.000280) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028353 / 0.037411 (-0.009058) \| 0.082345 / 0.014526 (0.067819) \| 0.499524 / 0.176557 (0.322967) \| 0.152896 / 0.737135 (-0.584239) \| 0.096978 / 0.296338 (-0.199360) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404855 / 0.215209 (0.189646) \| 4.053103 / 2.077655 (1.975448) \| 2.069638 / 1.504120 (0.565518) \| 1.917354 / 1.541195 (0.376159) \| 2.035816 / 1.468490 (0.567326) \| 0.480358 / 4.584777 (-4.104419) \| 3.594316 / 3.745712 (-0.151396) \| 3.582952 / 5.269862 (-1.686910) \| 2.101142 / 4.565676 (-2.464535) \| 0.057004 / 0.424275 (-0.367271) \| 0.007715 / 0.007607 (0.000108) \| 0.487417 / 0.226044 (0.261372) \| 4.863100 / 2.268929 (2.594172) \| 2.569038 / 55.444624 (-52.875587) \| 2.187167 / 6.876477 (-4.689310) \| 2.270034 / 2.142072 (0.127962) \| 0.578095 / 4.805227 (-4.227132) \| 0.133283 / 6.500664 (-6.367381) \| 0.060164 / 0.075469 (-0.015305) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.269120 / 1.841788 (-0.572667) \| 19.493072 / 8.074308 (11.418764) \| 14.560576 / 10.191392 (4.369184) \| 0.167440 / 0.680424 (-0.512984) \| 0.018493 / 0.534201 (-0.515708) \| 0.392774 / 0.579283 (-0.186509) \| 0.420903 / 0.434364 (-0.013461) \| 0.461904 / 0.540337 (-0.078433) \| 0.643104 / 1.386936 (-0.743832) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006985 / 0.011353 (-0.004368) \| 0.004246 / 0.011008 (-0.006762) \| 0.066246 / 0.038508 (0.027738) \| 0.080757 / 0.023109 (0.057648) \| 0.391774 / 0.275898 (0.115876) \| 0.424957 / 0.323480 (0.101478) \| 0.005575 / 0.007986 (-0.002411) \| 0.003447 / 0.004328 (-0.000881) \| 0.066565 / 0.004250 (0.062315) \| 0.057597 / 0.037052 (0.020544) \| 0.394663 / 0.258489 (0.136174) \| 0.430310 / 0.293841 (0.136469) \| 0.032746 / 0.128546 (-0.095800) \| 0.008783 / 0.075646 (-0.066863) \| 0.071940 / 0.419271 (-0.347331) \| 0.048877 / 0.043533 (0.005344) \| 0.390269 / 0.255139 (0.135130) \| 0.411867 / 0.283200 (0.128668) \| 0.024101 / 0.141683 (-0.117582) \| 1.507370 / 1.452155 (0.055215) \| 1.585810 / 1.492716 (0.093093) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222796 / 0.018006 (0.204790) \| 0.459035 / 0.000490 (0.458546) \| 0.005322 / 0.000200 (0.005122) \| 0.000099 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033237 / 0.037411 (-0.004174) \| 0.098244 / 0.014526 (0.083718) \| 0.106654 / 0.176557 (-0.069903) \| 0.159675 / 0.737135 (-0.577460) \| 0.108470 / 0.296338 (-0.187869) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429085 / 0.215209 (0.213876) \| 4.281206 / 2.077655 (2.203551) \| 2.320492 / 1.504120 (0.816372) \| 2.153218 / 1.541195 (0.612024) \| 2.287122 / 1.468490 (0.818632) \| 0.497307 / 4.584777 (-4.087470) \| 3.799541 / 3.745712 (0.053828) \| 3.380053 / 5.269862 (-1.889809) \| 2.100009 / 4.565676 (-2.465667) \| 0.057988 / 0.424275 (-0.366287) \| 0.007381 / 0.007607 (-0.000226) \| 0.506843 / 0.226044 (0.280798) \| 5.071286 / 2.268929 (2.802357) \| 2.750487 / 55.444624 (-52.694137) \| 2.415613 / 6.876477 (-4.460864) \| 2.667144 / 2.142072 (0.525072) \| 0.624889 / 4.805227 (-4.180338) \| 0.134191 / 6.500664 (-6.366473) \| 0.060704 / 0.075469 (-0.014765) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353074 / 1.841788 (-0.488714) \| 20.507074 / 8.074308 (12.432766) \| 14.911788 / 10.191392 (4.720396) \| 0.149248 / 0.680424 (-0.531176) \| 0.020593 / 0.534201 (-0.513608) \| 0.398458 / 0.579283 (-0.180825) \| 0.434846 / 0.434364 (0.000482) \| 0.478853 / 0.540337 (-0.061484) \| 0.648072 / 1.386936 (-0.738864) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b30c72a2d3d9c191a590e0f0a6b3a6363ab15e8f \"CML watermark\")\n", "In particular I expect fsspec to do another breaking change in the next release (switch to glob.glob)", "_The documentation is not available anymore as the PR was closed or merged._", "see https://github.com/huggingface/datasets/pull/6338", "Yes, unfortunately breaking changes are quite usual from their part.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006099 / 0.011353 (-0.005253) \| 0.003672 / 0.011008 (-0.007336) \| 0.083095 / 0.038508 (0.044587) \| 0.059607 / 0.023109 (0.036498) \| 0.319591 / 0.275898 (0.043693) \| 0.351945 / 0.323480 (0.028465) \| 0.004785 / 0.007986 (-0.003201) \| 0.002965 / 0.004328 (-0.001364) \| 0.062907 / 0.004250 (0.058657) \| 0.049122 / 0.037052 (0.012070) \| 0.344641 / 0.258489 (0.086152) \| 0.361519 / 0.293841 (0.067678) \| 0.027254 / 0.128546 (-0.101292) \| 0.008081 / 0.075646 (-0.067565) \| 0.261569 / 0.419271 (-0.157702) \| 0.045101 / 0.043533 (0.001568) \| 0.313645 / 0.255139 (0.058506) \| 0.337843 / 0.283200 (0.054644) \| 0.020968 / 0.141683 (-0.120715) \| 1.438450 / 1.452155 (-0.013705) \| 1.507567 / 1.492716 (0.014850) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230826 / 0.018006 (0.212820) \| 0.434363 / 0.000490 (0.433873) \| 0.008210 / 0.000200 (0.008010) \| 0.000212 / 0.000054 (0.000157) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025278 / 0.037411 (-0.012133) \| 0.073659 / 0.014526 (0.059133) \| 0.085147 / 0.176557 (-0.091409) \| 0.145451 / 0.737135 (-0.591684) \| 0.086400 / 0.296338 (-0.209939) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429887 / 0.215209 (0.214678) \| 4.292626 / 2.077655 (2.214971) \| 2.266824 / 1.504120 (0.762704) \| 2.090472 / 1.541195 (0.549277) \| 2.186477 / 1.468490 (0.717987) \| 0.503684 / 4.584777 (-4.081093) \| 3.100791 / 3.745712 (-0.644921) \| 3.008938 / 5.269862 (-2.260923) \| 1.885559 / 4.565676 (-2.680118) \| 0.057434 / 0.424275 (-0.366841) \| 0.006639 / 0.007607 (-0.000969) \| 0.506579 / 0.226044 (0.280535) \| 5.058905 / 2.268929 (2.789977) \| 2.708321 / 55.444624 (-52.736304) \| 2.367388 / 6.876477 (-4.509089) \| 2.422660 / 2.142072 (0.280587) \| 0.587562 / 4.805227 (-4.217665) \| 0.125260 / 6.500664 (-6.375404) \| 0.061856 / 0.075469 (-0.013613) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.280495 / 1.841788 (-0.561292) \| 17.968873 / 8.074308 (9.894565) \| 13.922838 / 10.191392 (3.731446) \| 0.149907 / 0.680424 (-0.530517) \| 0.016736 / 0.534201 (-0.517465) \| 0.333417 / 0.579283 (-0.245866) \| 0.367710 / 0.434364 (-0.066654) \| 0.389648 / 0.540337 (-0.150690) \| 0.535625 / 1.386936 (-0.851311) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006237 / 0.011353 (-0.005116) \| 0.003787 / 0.011008 (-0.007221) \| 0.062536 / 0.038508 (0.024028) \| 0.062335 / 0.023109 (0.039226) \| 0.455209 / 0.275898 (0.179311) \| 0.488961 / 0.323480 (0.165482) \| 0.004875 / 0.007986 (-0.003111) \| 0.002961 / 0.004328 (-0.001368) \| 0.063045 / 0.004250 (0.058795) \| 0.048624 / 0.037052 (0.011571) \| 0.455743 / 0.258489 (0.197254) \| 0.494024 / 0.293841 (0.200183) \| 0.028690 / 0.128546 (-0.099856) \| 0.008147 / 0.075646 (-0.067499) \| 0.069479 / 0.419271 (-0.349792) \| 0.041613 / 0.043533 (-0.001919) \| 0.460472 / 0.255139 (0.205333) \| 0.475606 / 0.283200 (0.192406) \| 0.020600 / 0.141683 (-0.121083) \| 1.464960 / 1.452155 (0.012805) \| 1.540942 / 1.492716 (0.048226) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214558 / 0.018006 (0.196552) \| 0.410482 / 0.000490 (0.409992) \| 0.005539 / 0.000200 (0.005339) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027044 / 0.037411 (-0.010367) \| 0.081512 / 0.014526 (0.066986) \| 0.101963 / 0.176557 (-0.074593) \| 0.146686 / 0.737135 (-0.590449) \| 0.092676 / 0.296338 (-0.203663) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468766 / 0.215209 (0.253557) \| 4.680514 / 2.077655 (2.602859) \| 2.562454 / 1.504120 (1.058334) \| 2.383692 / 1.541195 (0.842497) \| 2.481820 / 1.468490 (1.013330) \| 0.509122 / 4.584777 (-4.075655) \| 3.201597 / 3.745712 (-0.544115) \| 2.853539 / 5.269862 (-2.416323) \| 1.891535 / 4.565676 (-2.674141) \| 0.058594 / 0.424275 (-0.365681) \| 0.006448 / 0.007607 (-0.001159) \| 0.535950 / 0.226044 (0.309906) \| 5.388239 / 2.268929 (3.119311) \| 2.999986 / 55.444624 (-52.444638) \| 2.733291 / 6.876477 (-4.143186) \| 2.841548 / 2.142072 (0.699475) \| 0.602388 / 4.805227 (-4.202840) \| 0.126369 / 6.500664 (-6.374295) \| 0.061519 / 0.075469 (-0.013951) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.322746 / 1.841788 (-0.519042) \| 17.940825 / 8.074308 (9.866517) \| 14.679559 / 10.191392 (4.488167) \| 0.146481 / 0.680424 (-0.533943) \| 0.018060 / 0.534201 (-0.516141) \| 0.334924 / 0.579283 (-0.244359) \| 0.384735 / 0.434364 (-0.049629) \| 0.391834 / 0.540337 (-0.148503) \| 0.540011 / 1.386936 (-0.846925) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d82f3c2264436ef60fac8c397fb11c80175c5132 \"CML watermark\")\n" ]	2023-10-23T10:44:16	2023-10-23T12:13:20	2023-10-23T12:04:36	MEMBER	null	Pin upper version of `fsspec` to avoid disruptions introduced by breaking changes (and the need of urgent patch releases with hotfixes) on each release on their side. See: - #6331 - #6210 - #5731 - #5617 - #5447 I propose that we explicitly test, introduce fixes and support each new `fsspec` version release. CC: @LysandreJik	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6337/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6337/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6337", "html_url": "https://github.com/huggingface/datasets/pull/6337", "diff_url": "https://github.com/huggingface/datasets/pull/6337.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6337.patch", "merged_at": "2023-10-23T12:04:36" }	true
https://api.github.com/repos/huggingface/datasets/issues/6336	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6336/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6336/comments	https://api.github.com/repos/huggingface/datasets/issues/6336/events	https://github.com/huggingface/datasets/pull/6336	1,956,827,232	PR_kwDODunzps5dgy0w	6,336	unpin-fsspec	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_6336). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006202 / 0.011353 (-0.005151) \| 0.003627 / 0.011008 (-0.007381) \| 0.080643 / 0.038508 (0.042135) \| 0.057135 / 0.023109 (0.034026) \| 0.315853 / 0.275898 (0.039955) \| 0.348503 / 0.323480 (0.025023) \| 0.004762 / 0.007986 (-0.003224) \| 0.002884 / 0.004328 (-0.001445) \| 0.063208 / 0.004250 (0.058958) \| 0.046777 / 0.037052 (0.009725) \| 0.321426 / 0.258489 (0.062937) \| 0.362128 / 0.293841 (0.068287) \| 0.027494 / 0.128546 (-0.101052) \| 0.007931 / 0.075646 (-0.067715) \| 0.262262 / 0.419271 (-0.157009) \| 0.044330 / 0.043533 (0.000797) \| 0.310504 / 0.255139 (0.055366) \| 0.339409 / 0.283200 (0.056209) \| 0.021030 / 0.141683 (-0.120652) \| 1.405333 / 1.452155 (-0.046822) \| 1.493497 / 1.492716 (0.000781) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225431 / 0.018006 (0.207425) \| 0.451723 / 0.000490 (0.451233) \| 0.007763 / 0.000200 (0.007563) \| 0.000310 / 0.000054 (0.000256) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023381 / 0.037411 (-0.014031) \| 0.074183 / 0.014526 (0.059657) \| 0.084003 / 0.176557 (-0.092553) \| 0.143628 / 0.737135 (-0.593507) \| 0.084543 / 0.296338 (-0.211796) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.393062 / 0.215209 (0.177853) \| 3.905649 / 2.077655 (1.827994) \| 1.923155 / 1.504120 (0.419035) \| 1.751554 / 1.541195 (0.210359) \| 1.816141 / 1.468490 (0.347651) \| 0.502789 / 4.584777 (-4.081988) \| 3.006149 / 3.745712 (-0.739564) \| 2.979645 / 5.269862 (-2.290216) \| 1.877408 / 4.565676 (-2.688269) \| 0.057544 / 0.424275 (-0.366731) \| 0.006733 / 0.007607 (-0.000874) \| 0.468469 / 0.226044 (0.242425) \| 4.695595 / 2.268929 (2.426667) \| 2.367238 / 55.444624 (-53.077387) \| 2.041035 / 6.876477 (-4.835442) \| 2.087396 / 2.142072 (-0.054676) \| 0.586866 / 4.805227 (-4.218361) \| 0.125616 / 6.500664 (-6.375049) \| 0.060535 / 0.075469 (-0.014934) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244753 / 1.841788 (-0.597035) \| 17.652902 / 8.074308 (9.578594) \| 13.733195 / 10.191392 (3.541803) \| 0.143741 / 0.680424 (-0.536683) \| 0.016775 / 0.534201 (-0.517426) \| 0.335487 / 0.579283 (-0.243797) \| 0.350292 / 0.434364 (-0.084072) \| 0.388744 / 0.540337 (-0.151594) \| 0.536630 / 1.386936 (-0.850306) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006008 / 0.011353 (-0.005345) \| 0.003708 / 0.011008 (-0.007301) \| 0.062504 / 0.038508 (0.023996) \| 0.058570 / 0.023109 (0.035461) \| 0.450549 / 0.275898 (0.174651) \| 0.467768 / 0.323480 (0.144288) \| 0.004955 / 0.007986 (-0.003031) \| 0.002903 / 0.004328 (-0.001426) \| 0.062778 / 0.004250 (0.058528) \| 0.048750 / 0.037052 (0.011698) \| 0.439848 / 0.258489 (0.181359) \| 0.471780 / 0.293841 (0.177939) \| 0.028472 / 0.128546 (-0.100074) \| 0.008221 / 0.075646 (-0.067425) \| 0.068325 / 0.419271 (-0.350946) \| 0.040612 / 0.043533 (-0.002921) \| 0.435530 / 0.255139 (0.180391) \| 0.458992 / 0.283200 (0.175792) \| 0.020143 / 0.141683 (-0.121539) \| 1.479101 / 1.452155 (0.026947) \| 1.507408 / 1.492716 (0.014692) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207723 / 0.018006 (0.189717) \| 0.406596 / 0.000490 (0.406106) \| 0.004431 / 0.000200 (0.004231) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027037 / 0.037411 (-0.010374) \| 0.081576 / 0.014526 (0.067050) \| 0.091177 / 0.176557 (-0.085379) \| 0.146191 / 0.737135 (-0.590944) \| 0.092485 / 0.296338 (-0.203854) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456676 / 0.215209 (0.241467) \| 4.556214 / 2.077655 (2.478559) \| 2.500146 / 1.504120 (0.996026) \| 2.325175 / 1.541195 (0.783981) \| 2.421023 / 1.468490 (0.952533) \| 0.512135 / 4.584777 (-4.072641) \| 3.167070 / 3.745712 (-0.578642) \| 2.897697 / 5.269862 (-2.372165) \| 1.881974 / 4.565676 (-2.683702) \| 0.058453 / 0.424275 (-0.365823) \| 0.006515 / 0.007607 (-0.001092) \| 0.530742 / 0.226044 (0.304698) \| 5.304943 / 2.268929 (3.036014) \| 2.928824 / 55.444624 (-52.515800) \| 2.598023 / 6.876477 (-4.278454) \| 2.758496 / 2.142072 (0.616423) \| 0.601777 / 4.805227 (-4.203450) \| 0.126701 / 6.500664 (-6.373964) \| 0.061808 / 0.075469 (-0.013661) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.357844 / 1.841788 (-0.483943) \| 17.887666 / 8.074308 (9.813358) \| 14.561904 / 10.191392 (4.370512) \| 0.146788 / 0.680424 (-0.533636) \| 0.018277 / 0.534201 (-0.515924) \| 0.343168 / 0.579283 (-0.236115) \| 0.382220 / 0.434364 (-0.052144) \| 0.401234 / 0.540337 (-0.139104) \| 0.546246 / 1.386936 (-0.840690) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0980a74d58098b8b1738e2411f1212161a211b8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008919 / 0.011353 (-0.002434) \| 0.006110 / 0.011008 (-0.004898) \| 0.110554 / 0.038508 (0.072046) \| 0.075705 / 0.023109 (0.052596) \| 0.391235 / 0.275898 (0.115336) \| 0.458331 / 0.323480 (0.134851) \| 0.007489 / 0.007986 (-0.000497) \| 0.003744 / 0.004328 (-0.000585) \| 0.078124 / 0.004250 (0.073874) \| 0.057244 / 0.037052 (0.020192) \| 0.393251 / 0.258489 (0.134762) \| 0.460153 / 0.293841 (0.166312) \| 0.047245 / 0.128546 (-0.081301) \| 0.014086 / 0.075646 (-0.061560) \| 0.421272 / 0.419271 (0.002001) \| 0.067668 / 0.043533 (0.024135) \| 0.397325 / 0.255139 (0.142186) \| 0.432683 / 0.283200 (0.149483) \| 0.039086 / 0.141683 (-0.102596) \| 1.764898 / 1.452155 (0.312744) \| 1.848820 / 1.492716 (0.356104) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.258163 / 0.018006 (0.240156) \| 0.498655 / 0.000490 (0.498165) \| 0.014959 / 0.000200 (0.014759) \| 0.000465 / 0.000054 (0.000410) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028889 / 0.037411 (-0.008522) \| 0.091568 / 0.014526 (0.077042) \| 0.102700 / 0.176557 (-0.073857) \| 0.173580 / 0.737135 (-0.563555) \| 0.108763 / 0.296338 (-0.187576) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.610147 / 0.215209 (0.394938) \| 5.851239 / 2.077655 (3.773584) \| 2.467471 / 1.504120 (0.963351) \| 2.117189 / 1.541195 (0.575995) \| 2.197947 / 1.468490 (0.729457) \| 0.851736 / 4.584777 (-3.733041) \| 5.163183 / 3.745712 (1.417471) \| 5.039564 / 5.269862 (-0.230297) \| 3.067215 / 4.565676 (-1.498462) \| 0.098593 / 0.424275 (-0.325682) \| 0.008646 / 0.007607 (0.001038) \| 0.788397 / 0.226044 (0.562352) \| 7.340837 / 2.268929 (5.071909) \| 3.511611 / 55.444624 (-51.933013) \| 2.767479 / 6.876477 (-4.108998) \| 2.687368 / 2.142072 (0.545296) \| 1.046387 / 4.805227 (-3.758841) \| 0.215902 / 6.500664 (-6.284763) \| 0.072939 / 0.075469 (-0.002530) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.512795 / 1.841788 (-0.328992) \| 22.086131 / 8.074308 (14.011823) \| 20.235550 / 10.191392 (10.044158) \| 0.240381 / 0.680424 (-0.440043) \| 0.029171 / 0.534201 (-0.505030) \| 0.465123 / 0.579283 (-0.114160) \| 0.569260 / 0.434364 (0.134896) \| 0.540967 / 0.540337 (0.000629) \| 0.764006 / 1.386936 (-0.622930) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011024 / 0.011353 (-0.000329) \| 0.005915 / 0.011008 (-0.005094) \| 0.076455 / 0.038508 (0.037947) \| 0.087842 / 0.023109 (0.064733) \| 0.471732 / 0.275898 (0.195834) \| 0.513666 / 0.323480 (0.190186) \| 0.007062 / 0.007986 (-0.000924) \| 0.004013 / 0.004328 (-0.000315) \| 0.076016 / 0.004250 (0.071766) \| 0.061296 / 0.037052 (0.024244) \| 0.487277 / 0.258489 (0.228788) \| 0.508185 / 0.293841 (0.214344) \| 0.049963 / 0.128546 (-0.078583) \| 0.013774 / 0.075646 (-0.061873) \| 0.089376 / 0.419271 (-0.329895) \| 0.067502 / 0.043533 (0.023969) \| 0.471283 / 0.255139 (0.216144) \| 0.507365 / 0.283200 (0.224165) \| 0.033638 / 0.141683 (-0.108045) \| 1.785544 / 1.452155 (0.333390) \| 1.878765 / 1.492716 (0.386048) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230462 / 0.018006 (0.212456) \| 0.502458 / 0.000490 (0.501968) \| 0.005987 / 0.000200 (0.005787) \| 0.000114 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031588 / 0.037411 (-0.005824) \| 0.113566 / 0.014526 (0.099040) \| 0.115734 / 0.176557 (-0.060822) \| 0.174162 / 0.737135 (-0.562974) \| 0.121574 / 0.296338 (-0.174764) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.662837 / 0.215209 (0.447628) \| 6.420327 / 2.077655 (4.342672) \| 3.033522 / 1.504120 (1.529402) \| 2.728294 / 1.541195 (1.187099) \| 2.790621 / 1.468490 (1.322131) \| 0.852478 / 4.584777 (-3.732299) \| 5.033637 / 3.745712 (1.287925) \| 4.543152 / 5.269862 (-0.726709) \| 2.980261 / 4.565676 (-1.585415) \| 0.102444 / 0.424275 (-0.321831) \| 0.008362 / 0.007607 (0.000755) \| 0.786868 / 0.226044 (0.560823) \| 7.887665 / 2.268929 (5.618737) \| 4.010614 / 55.444624 (-51.434010) \| 3.220715 / 6.876477 (-3.655762) \| 3.317316 / 2.142072 (1.175244) \| 1.098137 / 4.805227 (-3.707090) \| 0.218309 / 6.500664 (-6.282355) \| 0.078182 / 0.075469 (0.002713) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.696740 / 1.841788 (-0.145047) \| 23.762454 / 8.074308 (15.688146) \| 21.802645 / 10.191392 (11.611253) \| 0.233654 / 0.680424 (-0.446770) \| 0.032911 / 0.534201 (-0.501290) \| 0.511760 / 0.579283 (-0.067524) \| 0.586299 / 0.434364 (0.151935) \| 0.583704 / 0.540337 (0.043367) \| 0.780762 / 1.386936 (-0.606174) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0a94aa2f738075bbc08291583f1b153220d5e6e7 \"CML watermark\")\n" ]	2023-10-23T10:16:46	2023-10-23T10:28:46	2023-10-23T10:17:48	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6336/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6336/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6336", "html_url": "https://github.com/huggingface/datasets/pull/6336", "diff_url": "https://github.com/huggingface/datasets/pull/6336.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6336.patch", "merged_at": "2023-10-23T10:17:48" }	true