Datasets:

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MDU6SXNzdWU5MjQ1OTc0NzA=

datasets.map pickle issue resulting in invalid mapping function

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NONE

I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` / `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts. The following reproduces the issue - most likely I'm missing something A simulated tokeniser which can be pickled ``` class CustomTokenizer: def __init__(self): self.state = "init" def __getstate__(self): print("__getstate__ called") out = self.__dict__.copy() self.state = "pickled" return out def __setstate__(self, d): print("__setstate__ called") self.__dict__ = d self.state = "restored" tokenizer = CustomTokenizer() ``` Test that it actually works - prints "__getstate__ called" and "__setstate__ called" ``` import pickle serialized = pickle.dumps(tokenizer) restored = pickle.loads(serialized) assert restored.state == "restored" ``` Simulate a function that tokenises examples, when dataset.map is called, this function ``` def tokenize_function(examples): assert tokenizer.state == "restored" # this shouldn't fail but it does output = tokenizer(examples) # this will fail as tokenizer isn't really a tokenizer return output ``` Use map to simulate tokenization ``` import glob from datasets import load_dataset assert tokenizer.state == "restored" train_files = glob.glob('train*.csv') validation_files = glob.glob('validation*.csv') datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files)) tokenized_datasets = datasets.map( tokenize_function, batched=True, ) ``` What's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ / __setstate__. I'm not sure if there's another hook I'm supposed to implement as well? --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-22-a2aef4f74aaa> in <module> 8 tokenized_datasets = datasets.map( 9 tokenize_function, ---> 10 batched=True, 11 ) ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc) 487 desc=desc, 488 ) --> 489 for k, dataset in self.items() 490 } 491 ) ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0) 487 desc=desc, 488 ) --> 489 for k, dataset in self.items() 490 } 491 ) ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 1633 fn_kwargs=fn_kwargs, 1634 new_fingerprint=new_fingerprint, -> 1635 desc=desc, 1636 ) 1637 else: ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 184 } 185 # apply actual function --> 186 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 187 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 188 # re-apply format to the output ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 395 # Call actual function 396 --> 397 out = func(self, *args, **kwargs) 398 399 # Update fingerprint of in-place transforms + update in-place history of transforms ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc) 1961 indices, 1962 check_same_num_examples=len(input_dataset.list_indexes()) > 0, -> 1963 offset=offset, 1964 ) 1965 except NumExamplesMismatch: ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 1853 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset 1854 processed_inputs = ( -> 1855 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) 1856 ) 1857 if update_data is None: <ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples) 1 def tokenize_function(examples): ----> 2 assert tokenizer.state == "restored" 3 tokenizer(examples) 4 return examples

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MDExOlB1bGxSZXF1ZXN0NjczMDc3NTIx

CRD3 dataset card

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CONTRIBUTOR

This PR adds additional information to the CRD3 dataset card.

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MDU6SXNzdWU5MjQ0MTcxNzI=

Can datasets remove duplicated rows?

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NONE

**Is your feature request related to a problem? Please describe.** i find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that.. **Describe the solution you'd like** have a functionality of " remove duplicated rows" **Describe alternatives you've considered** convert dataset to pandas, remove duplicate, and convert back... **Additional context** no

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MDU6SXNzdWU5MjQxNzQ0MTM=

Corelation should be Correlation

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NONE

https://github.com/huggingface/datasets/blob/0e87e1d053220e8ecddfa679bcd89a4c7bc5af62/metrics/matthews_correlation/matthews_correlation.py#L66

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MDU6SXNzdWU5MjQwNjkzNTM=

seqeval metric does not work with a recent version of sklearn: classification_report() got an unexpected keyword argument 'output_dict'

closed

NONE

## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric seqeval = load_metric("seqeval") seqeval.compute(predictions=[['A']], references=[['A']]) ``` ## Expected results The function computes a dict with metrics ## Actual results ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-39-69a57f5cf06f> in <module> 1 from datasets import load_dataset, load_metric 2 seqeval = load_metric("seqeval") ----> 3 seqeval.compute(predictions=[['A']], references=[['A']]) ~/p3/lib/python3.7/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/81eda1ff004361d4fa48754a446ec69bb7aa9cf4d14c7215f407d1475941c5ff/seqeval.py in _compute(self, predictions, references, suffix) 95 96 def _compute(self, predictions, references, suffix=False): ---> 97 report = classification_report(y_true=references, y_pred=predictions, suffix=suffix, output_dict=True) 98 report.pop("macro avg") 99 report.pop("weighted avg") TypeError: classification_report() got an unexpected keyword argument 'output_dict' ``` ## Environment info sklearn=0.24 datasets=1.1.3

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MDU6SXNzdWU5MjM3NjIxMzM=

Add C4

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## Adding a Dataset - **Name:** *C4* - **Description:** *https://github.com/allenai/allennlp/discussions/5056* - **Paper:** *https://arxiv.org/abs/1910.10683* - **Data:** *https://huggingface.co/datasets/allenai/c4* - **Motivation:** *Used a lot for pretraining* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Should fix https://github.com/huggingface/datasets/issues/1710

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MDExOlB1bGxSZXF1ZXN0NjcyNDY3MzY3

Add align_labels_with_mapping to DatasetDict

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https://github.com/huggingface/datasets/pull/2457 added the `Dataset.align_labels_with_mapping` method. In this PR I also added `DatasetDict.align_labels_with_mapping`

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MDExOlB1bGxSZXF1ZXN0NjcxNjcyMzU5

Fix fingerprint when moving cache dir

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The fingerprint of a dataset changes if the cache directory is moved. I fixed that by setting the fingerprint to be the hash of: - the relative cache dir (dataset_name/version/config_id) - the requested split Close #2496 I had to fix an issue with the filelock filename that was too long (>255). It prevented the tests to run on my machine. I just added `hash_filename_if_too_long` in case this happens, to not get filenames longer than 255. We usually have long filenames for filelocks because they are named after the path that is being locked. In case the path is a cache directory that has long directory names, then the filelock filename could en up being very long.

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MDU6SXNzdWU5MjE4NjMxNzM=

Load Image Classification Dataset from Local

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NONE

**Is your feature request related to a problem? Please describe.** Yes - we would like to load an image classification dataset with datasets without having to write a custom data loader. **Describe the solution you'd like** Given a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like "cifar10". **Describe alternatives you've considered** Implement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path) Write custom data loader logic **Additional context** We're training ViT on custom dataset

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MDExOlB1bGxSZXF1ZXN0NjcwNDQ0MDgz

Rearrange JSON field names to match passed features schema field names

closed

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This PR depends on PR #2453 (which must be merged first). Close #2366.

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MDExOlB1bGxSZXF1ZXN0NjcwNDM4NTgx

Add course banner

closed

MEMBER

This PR adds a course banner similar to the one you can now see in the [Transformers repo](https://github.com/huggingface/transformers) that links to the course. Let me know if placement seems right to you or not, I can move it just below the badges too.

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MDExOlB1bGxSZXF1ZXN0NjcwMjY2NjQy

Make numpy arrow extractor faster

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I changed the NumpyArrowExtractor to call directly to_numpy and see if it can lead to speed-ups as discussed in https://github.com/huggingface/datasets/issues/2498 This could make the numpy/torch/tf/jax formatting faster

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MDU6SXNzdWU5MjA2MzYxODY=

SubjQA wrong boolean values in entries

open

NONE

## Describe the bug SubjQA seems to have a boolean that's consistently wrong. It defines: - question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective). - is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective) However, `is_ques_subjective` seems to have wrong values in the entire dataset. For instance, in the example in the dataset card, we have: - "question_subj_level": 2 - "is_ques_subjective": false However, according to the description, the question should be subjective since the `question_subj_level` is below 4

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MDExOlB1bGxSZXF1ZXN0NjY5NzQ1MDA5

JAX integration

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MEMBER

Hi ! I just added the "jax" formatting, as we already have for pytorch, tensorflow, numpy (and also pandas and arrow). It does pretty much the same thing as the pytorch formatter except it creates jax.numpy.ndarray objects. ```python from datasets import Dataset d = Dataset.from_dict({"foo": [[0., 1., 2.]]}) d = d.with_format("jax") d[0] # {'foo': DeviceArray([0., 1., 2.], dtype=float32)} ``` A few details: - The default integer precision for jax depends on the jax configuration `jax_enable_x64` (see [here](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision)), I took that into account. Unless `jax_enable_x64` is specified, it is int32 by default - AFAIK it's not possible to do a full conversion from arrow data to jax data. We are doing arrow -> numpy -> jax but the numpy -> jax part doesn't do zero copy unfortutanely (see [here](https://github.com/google/jax/issues/4486)) - the env var for disabling JAX is `USE_JAX`. However I noticed that in `transformers` it is `USE_FLAX`. This is not an issue though IMO I also updated `convert_to_python_objects` to allow users to pass jax.numpy.ndarray objects to build a dataset. Since the `convert_to_python_objects` method became slow because it's the time when pytorch, tf (and now jax) are imported, I fixed it by checking the `sys.modules` to avoid unecessary import of pytorch, tf or jax. Close #2495

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MDExOlB1bGxSZXF1ZXN0NjY5NzA3Nzc0

Add Zenodo metadata file with license

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This Zenodo metadata file fixes the name of the `Datasets` license appearing in the DOI as `"Apache-2.0"`, which otherwise by default is `"other-open"`. Close #2472.

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MDExOlB1bGxSZXF1ZXN0NjY5NjE2MjQ1

Add load_dataset_builder

closed

CONTRIBUTOR

Adds the `load_dataset_builder` function. The good thing is that we can reuse this function to load the dataset info without downloading the dataset itself. TODOs: - [x] Add docstring and entry in the docs - [x] Add tests Closes #2484

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MDU6SXNzdWU5MjA0MTMwMjE=

Python Programming Puzzles

open

MEMBER

## Adding a Dataset - **Name:** Python Programming Puzzles - **Description:** Programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis - **Paper:** https://arxiv.org/pdf/2106.05784.pdf - **Data:** https://github.com/microsoft/PythonProgrammingPuzzles ([Scrolling through the data](https://github.com/microsoft/PythonProgrammingPuzzles/blob/main/problems/README.md)) - **Motivation:** Spans a large range of difficulty, problems, and domains. A useful resource for evaluation as we don't have a clear understanding of the abilities and skills of extremely large LMs. Note: it's a growing dataset (contributions are welcome), so we'll need careful versioning for this dataset. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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Improve torch formatting performance

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** It would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. A bit more background. I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs. The current performance is about 30% slower than NVidia optimized BERT [examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. **Describe the solution you'd like** Using profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.  As you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. Digging a bit deeper into format_batch we can see the following profiler data:  Once again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. **Describe alternatives you've considered** I am not familiar with pyarrow and have not yet considered the alternatives to the current approach. Most of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement. Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%.

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MDExOlB1bGxSZXF1ZXN0NjY5NDI3OTU3

Use default cast for sliced list arrays if pyarrow >= 4

closed

MEMBER

From pyarrow version 4, it is supported to cast sliced lists. This PR uses default pyarrow cast in Datasets to cast sliced list arrays if pyarrow version is >= 4. In relation with PR #2461 and #2490. cc: @lhoestq, @abhi1thakur, @SBrandeis

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MDU6SXNzdWU5MjAyMTYzMTQ=

Dataset fingerprint changes after moving the cache directory, which prevent cache reload when using `map`

closed

MEMBER

`Dataset.map` uses the dataset fingerprint (a hash) for caching. However the fingerprint seems to change when someone moves the cache directory of the dataset. This is because it uses the default fingerprint generation: 1. the dataset path is used to get the fingerprint 2. the modification times of the arrow file is also used to get the fingerprint To fix that we could set the fingerprint of the dataset to be a hash of (<dataset_name>, <config_name>, <version>, <script_hash>), i.e. a hash of the the cache path relative to the cache directory.

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MDU6SXNzdWU5MjAxNzAwMzA=

JAX formatting

closed

MEMBER

We already support pytorch, tensorflow, numpy, pandas and arrow dataset formatting. Let's add jax as well

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MDU6SXNzdWU5MjAxNDkxODM=

Improve docs on Enhancing performance

open

MEMBER

In the ["Enhancing performance"](https://huggingface.co/docs/datasets/loading_datasets.html#enhancing-performance) section of docs, add specific use cases: - How to make datasets the fastest - How to make datasets take the less RAM - How to make datasets take the less hard drive mem cc: @thomwolf

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MDExOlB1bGxSZXF1ZXN0NjY5MDc4OTcw

add tensorflow-macos support

closed

CONTRIBUTOR

ref - https://github.com/huggingface/datasets/issues/2068

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MDExOlB1bGxSZXF1ZXN0NjY4OTkxODk4

Eduge

closed

CONTRIBUTOR

Hi, awesome folks behind the huggingface! Here is my PR for the text classification dataset in Mongolian. Please do let me know in case you have anything to clarify. Thanks & Regards, Enod

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MDExOlB1bGxSZXF1ZXN0NjY4OTg5MTUw

add eduge classification dataset

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NjY4ODc4NDA3

Allow latest pyarrow version

closed

MEMBER

Allow latest pyarrow version, once that version 4.0.1 fixes the segfault bug introduced in version 4.0.0. Close #2489.

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MDU6SXNzdWU5MTk1Njk3NDk=

Allow latest pyarrow version once segfault bug is fixed

closed

MEMBER

As pointed out by @symeneses (see https://github.com/huggingface/datasets/pull/2268#issuecomment-860048613), pyarrow has fixed the segfault bug present in version 4.0.0 (see https://issues.apache.org/jira/browse/ARROW-12568): - it was fixed on 3 May 2021 - version 4.0.1 was released on 19 May 2021 with the bug fix

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MDExOlB1bGxSZXF1ZXN0NjY4ODIwNDA1

Set configurable downloaded datasets path

closed

MEMBER

Part of #2480.

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MDExOlB1bGxSZXF1ZXN0NjY4Nzc5Mjk0

Set configurable extracted datasets path

closed

MEMBER

Part of #2480.

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MDExOlB1bGxSZXF1ZXN0NjY4NTI2Njg3

Add Rico Dataset

open

CONTRIBUTOR

Hi there! I'm wanting to add the Rico datasets for software engineering type data to y'alls awesome library. However, as I have started coding, I've ran into a few hiccups so I thought it best to open the PR early to get a bit of discussion on how the Rico datasets should be added to the `datasets` lib. 1) There are 7 different datasets under Rico and so I was wondering, should I make a folder for each or should I put them as different configurations of a single dataset? You can see the datasets available for Rico here: http://interactionmining.org/rico 2) As of right now, I have a semi working version of the first dataset which has pairs of screenshots and hierarchies from android applications. However, these screenshots are very large (1440, 2560, 3) and there are 66,000 of them so I am not able to perform the processing that the `datasets` lib does after downloading and extracting the dataset since I run out of memory very fast. Is there a way to have `datasets` lib not put everything into memory while it is processing the dataset? 2.1) If there is not a way, would it be better to just return the path to the screenshots instead of the actual image? 3) The hierarchies are JSON objects and looking through the documentation of `datasets`, I didn't see any feature that I could use for this type of data. So, for now I just have it being read in as a string, is this okay or should I be doing it differently? 4) One of the Rico datasets is a bunch of animations (GIFs), is there a `datasets` feature that I can put this type of data into or should I just return the path as a string? I appreciate any and all help I can get for this PR, I think the Rico datasets will be an awesome addition to the library :nerd_face: !

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MDU6SXNzdWU5MTkwOTkyMTg=

Implement layered building

open

MEMBER

As discussed with @stas00 and @lhoestq (see also here https://github.com/huggingface/datasets/issues/2481#issuecomment-859712190): > My suggestion for this would be to have this enabled by default. > > Plus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is: > > 1. uncompress a handful of files via a generator enough to generate one arrow file > 2. process arrow file 1 > 3. delete all the files that went in and aren't needed anymore. > > rinse and repeat. > > 1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project > 2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing > 3. It would already include deleting temp files this issue is talking about > > I wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders.

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MDU6SXNzdWU5MTkwOTI2MzU=

Implement loading a dataset builder

closed

MEMBER

As discussed with @stas00 and @lhoestq, this would allow things like: ```python from datasets import load_dataset_builder dataset_name = "openwebtext" builder = load_dataset_builder(dataset_name) print(builder.cache_dir) ```

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MDExOlB1bGxSZXF1ZXN0NjY4MjU1Mjg1

Use gc.collect only when needed to avoid slow downs

closed

MEMBER

In https://github.com/huggingface/datasets/commit/42320a110d9d072703814e1f630a0d90d626a1e6 we added a call to gc.collect to resolve some issues on windows (see https://github.com/huggingface/datasets/pull/2482) However calling gc.collect too often causes significant slow downs (the CI run time doubled). So I just moved the gc.collect call to the exact place where it's actually needed: when post-processing a dataset

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MDExOlB1bGxSZXF1ZXN0NjY4MjMyMzI5

Allow to use tqdm>=4.50.0

closed

MEMBER

We used to have permission errors on windows whith the latest versions of tqdm (see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/6365/workflows/24f7c960-3176-43a5-9652-7830a23a981e/jobs/39232)) They were due to open arrow files not properly closed by pyarrow. Since https://github.com/huggingface/datasets/commit/42320a110d9d072703814e1f630a0d90d626a1e6 gc.collect is called each time we don't need an arrow file to make sure that the files are closed. close https://github.com/huggingface/datasets/issues/2471 cc @lewtun

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MDU6SXNzdWU5MTg2ODAxNjg=

Delete extracted files to save disk space

closed

MEMBER

As discussed with @stas00 and @lhoestq, allowing the deletion of extracted files would save a great amount of disk space to typical user.

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MDU6SXNzdWU5MTg2Nzg1Nzg=

Set download/extracted paths configurable

open

MEMBER

As discussed with @stas00 and @lhoestq, setting these paths configurable may allow to overcome disk space limitation on different partitions/drives. TODO: - [x] Set configurable extracted datasets path: #2487 - [x] Set configurable downloaded datasets path: #2488 - [ ] Set configurable "incomplete" datasets path?

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MDExOlB1bGxSZXF1ZXN0NjY4MDc3NTI4

❌ load_datasets ❌

closed

MEMBER

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MDU6SXNzdWU5MTg1MDc1MTA=

Create release script

open

MEMBER

Create a script so that releases can be done automatically (as done in `transformers`).

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MDExOlB1bGxSZXF1ZXN0NjY3NzczMTY0

Fix docs custom stable version

closed

MEMBER

Currently docs default version is 1.5.0. This PR fixes this and sets the latest version instead.

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MDExOlB1bGxSZXF1ZXN0NjY3MTg3OTk1

Add TimeDial

closed

CONTRIBUTOR

Dataset: https://github.com/google-research-datasets/TimeDial To-Do: Update README.md and add YAML tags

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MDU6SXNzdWU5MTc2NTA4ODI=

Issue in timit_asr database

closed

NONE

## Describe the bug I am trying to load the timit_asr dataset however only the first record is shown (duplicated over all the rows). I am using the next code line dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10)) The above code result with the same sentence duplicated ten times. It also happens when I use the dataset viewer at Streamlit . ## Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10)) data = dataset.to_pandas() # Sample code to reproduce the bug ``` ## Expected results table with different row information ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.4.1 (also occur in the latest version) - Platform: Linux-4.15.0-143-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyTorch version (GPU?): 1.8.1+cu102 (False) - Tensorflow version (GPU?): 1.15.3 (False) - Using GPU in script?: No - Using distributed or parallel set-up in script?: No - `datasets` version: - Platform: - Python version: - PyArrow version:

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MDU6SXNzdWU5MTc2MjIwNTU=

cache_dir parameter for load_from_disk ?

closed

NONE

**Is your feature request related to a problem? Please describe.** When using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk: ` from datasets import load_from_disk myPreprocessedData = load_from_disk("/content/gdrive/MyDrive/ASR_data/myPreprocessedData") ` I know that chaching on google drive could slow down learning. But at least it would run. **Describe the solution you'd like** Add cache_Dir parameter to the load_from_disk function. **Describe alternatives you've considered** It looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?

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MDExOlB1bGxSZXF1ZXN0NjY3MDU5MjI5

Add Disfl-QA

closed

CONTRIBUTOR

Dataset: https://github.com/google-research-datasets/disfl-qa To-Do: Update README.md and add YAML tags

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MDU6SXNzdWU5MTc0NjM4MjE=

Fix automatic generation of Zenodo DOI

closed

MEMBER

After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as "Received", instead of in green as "Published". I have contacted Zenodo support to fix this issue. TODO: - [x] Check with Zenodo to fix the issue - [x] Check BibTeX entry is right

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MDU6SXNzdWU5MTcwNjcxNjU=

Fix PermissionError on Windows when using tqdm >=4.50.0

closed

MEMBER

See: https://app.circleci.com/pipelines/github/huggingface/datasets/235/workflows/cfb6a39f-68eb-4802-8b17-2cd5e8ea7369/jobs/1111 ``` PermissionError: [WinError 32] The process cannot access the file because it is being used by another process ```

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MDU6SXNzdWU5MTY3MjQyNjA=

Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`.

closed

NONE

## Describe the bug Crash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`. I believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose. ## Steps to reproduce the bug ```python # this function will be applied with map() def tokenize_function(examples): return tokenizer( examples["text"], padding=PaddingStrategy.DO_NOT_PAD, truncation=True, ) # data_files is a Dict[str, str] mapping name -> path datasets = load_dataset("text", data_files={...}) # this is where the error happens if num_proc = 16, # but is fine if num_proc = 1 tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=num_workers, ) ``` ## Expected results The `map()` function succeeds with `num_proc` > 1. ## Actual results   ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31 - Python version: 3.9.5 - PyTorch version (GPU?): 1.8.1+cu111 (True) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: Yes, but I think N/A for this issue - Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N/A for this issue

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MDExOlB1bGxSZXF1ZXN0NjY2MTA1OTk1

Bump tqdm version

closed

MEMBER

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MDExOlB1bGxSZXF1ZXN0NjY2MDk0ODI5

Implement ClassLabel encoding in JSON loader

closed

MEMBER

Close #2365.

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MDExOlB1bGxSZXF1ZXN0NjY1NjY1MjQy

change udpos features structure

closed

CONTRIBUTOR

The structure is change such that each example is a sentence The change is done for issues: #2061 #2444 Close #2061 , close #2444.

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MDExOlB1bGxSZXF1ZXN0NjY1MzMxMDMz

adding masahaner dataset

closed

CONTRIBUTOR

Adding Masakhane dataset https://github.com/masakhane-io/masakhane-ner @lhoestq , can you please review

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MDExOlB1bGxSZXF1ZXN0NjY1Mjk1MDE5

fix: adjusting indexing for the labels.

closed

CONTRIBUTOR

The labels index were mismatching the actual ones used in the dataset. Specifically `0` is used for `SUPPORTS` and `1` is used for `REFUTES` After this change, the `README.md` now reflects the content of `dataset_infos.json`. Signed-off-by: Matteo Manica <[email protected]>

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MDExOlB1bGxSZXF1ZXN0NjY1MjY3NTA2

Fix proto_qa download link

closed

CONTRIBUTOR

Fixes #2459 Instead of updating the path, this PR fixes a commit hash as suggested by @lhoestq.

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MDU6SXNzdWU5MTUzODQ2MTM=

Merge DatasetDict and Dataset

open

MEMBER

As discussed in #2424 and #2437 (please see there for detailed conversation): - It would be desirable to improve UX with respect the confusion between DatasetDict and Dataset. - The difference between Dataset and DatasetDict is an additional abstraction complexity that confuses "typical" end users. - A user expects a "Dataset" (whatever it contains multiple or a single split) and maybe it could be interesting to try to simplify the user-facing API as much as possible to hide this complexity from the end user. Here is a proposal for discussion and refined (and potential abandon if it's not good enough): - let's consider that a DatasetDict is also a Dataset with the various split concatenated one after the other - let's disallow the use of integers in split names (probably not a very big breaking change) - when you index with integers you access the examples progressively in split after the other is finished (in a deterministic order) - when you index with strings/split name you have the same behavior as now (full backward compat) - let's then also have all the methods of a Dataset on the DatasetDict The end goal would be to merge both Dataset and DatasetDict object in a single object that would be (pretty much totally) backward compatible with both. There are a few things that we could discuss if we want to merge Dataset and DatasetDict: 1. what happens if you index by a string ? Does it return the column or the split ? We could disallow conflicts between column names and split names to avoid ambiguities. It can be surprising to be able to get a column or a split using the same indexing feature ``` from datasets import load_dataset dataset = load_dataset(...) dataset["train"] dataset["input_ids"] ``` 2. what happens when you iterate over the object ? I guess it should iterate over the examples as a Dataset object, but a DatasetDict used to iterate over the splits as they are the dictionary keys. This is a breaking change that we can discuss. Moreover regarding your points: - integers are not allowed as split names already - it's definitely doable to have all the methods. Maybe some of them like train_test_split that is currently only available for Dataset can be tweaked to work for a split dataset cc: @thomwolf @lhoestq

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MDExOlB1bGxSZXF1ZXN0NjY1MTE3MTY4

Support sliced list arrays in cast

closed

MEMBER

There is this issue in pyarrow: ```python import pyarrow as pa arr = pa.array([[i * 10] for i in range(4)]) arr.cast(pa.list_(pa.int32())) # works arr = arr.slice(1) arr.cast(pa.list_(pa.int32())) # fails # ArrowNotImplementedError("Casting sliced lists (non-zero offset) not yet implemented") ``` However in `Dataset.cast` we slice tables to cast their types (it's memory intensive), so we have the same issue. Because of this it is currently not possible to cast a Dataset with a Sequence feature type (unless the table is small enough to not be sliced). In this PR I fixed this by resetting the offset of `pyarrow.ListArray` arrays to zero in the table before casting. I used `pyarrow.compute.subtract` function to update the offsets of the ListArray. cc @abhi1thakur @SBrandeis

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MDExOlB1bGxSZXF1ZXN0NjY1MTAyMjA4

Revert default in-memory for small datasets

closed

MEMBER

Close #2458

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MDU6SXNzdWU5MTUyMjIwMTU=

`Proto_qa` hosting seems to be broken

closed

MEMBER

## Describe the bug The hosting (on Github) of the `proto_qa` dataset seems broken. I haven't investigated more yet, just flagging it for now. @zaidalyafeai if you want to dive into it, I think it's just a matter of changing the links in `proto_qa.py` ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("proto_qa") ``` ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset use_auth_token=use_auth_token, File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 630, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/hf/.cache/huggingface/modules/datasets_modules/datasets/proto_qa/445346efaad5c5f200ecda4aa7f0fb50ff1b55edde3003be424a2112c3e8102e/proto_qa.py", line 131, in _split_generators train_fpath = dl_manager.download(_URLs[self.config.name]["train"]) File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 199, in download num_proc=download_config.num_proc, File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 195, in map_nested return function(data_struct) File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 218, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 291, in cached_path use_auth_token=download_config.use_auth_token, File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/iesl/protoqa-data/master/data/train/protoqa_train.jsonl ```

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MDU6SXNzdWU5MTUxOTk2OTM=

Revert default in-memory for small datasets

closed

MEMBER

Users are reporting issues and confusion about setting default in-memory to True for small datasets. We see 2 clear use cases of Datasets: - the "canonical" way, where you can work with very large datasets, as they are memory-mapped and cached (after every transformation) - some edge cases (speed benchmarks, interactive/exploratory analysis,...), where default in-memory can explicitly be enabled, and no caching will be done After discussing with @lhoestq we have agreed to: - revert this feature (implemented in #2182) - explain in the docs how to optimize speed/performance by setting default in-memory cc: @stas00 https://github.com/huggingface/datasets/pull/2409#issuecomment-856210552

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MDExOlB1bGxSZXF1ZXN0NjY0OTQwMzQ0

Add align_labels_with_mapping function

closed

MEMBER

This PR adds a helper function to align the `label2id` mapping between a `datasets.Dataset` and a classifier (e.g. a transformer with a `PretrainedConfig.label2id` dict), with the alignment performed on the dataset itself. This will help us with the Hub evaluation, where we won't know in advance whether a model that is fine-tuned on say MNLI has the same mappings as the MNLI dataset we load from `datasets`. An example where this is needed is if we naively try to evaluate `microsoft/deberta-base-mnli` on `mnli` because the model config has the following mappings: ```python "id2label": { "0": "CONTRADICTION", "1": "NEUTRAL", "2": "ENTAILMENT" }, "label2id": { "CONTRADICTION": 0, "ENTAILMENT": 2, "NEUTRAL": 1 } ``` while the `mnli` dataset has the `contradiction` and `neutral` labels swapped: ```python id2label = {0: 'entailment', 1: 'neutral', 2: 'contradiction'} label2id = {'contradiction': 2, 'entailment': 0, 'neutral': 1} ``` As a result, we get a much lower accuracy during evaluation: ```python from datasets import load_dataset from transformers.trainer_utils import EvalPrediction from transformers import AutoModelForSequenceClassification, Trainer # load dataset for evaluation mnli = load_dataset("glue", "mnli", split="test") # load model model_ckpt = "microsoft/deberta-base-mnli" model = AutoModelForSequenceClassification.from_pretrained(checkpoint) # preprocess, create trainer ... mnli_enc = ... trainer = Trainer(model, args=args, tokenizer=tokenizer) # generate preds preds = trainer.predict(mnli_enc) # preds.label_ids misalinged with model.config => returns wrong accuracy (too low)! compute_metrics(EvalPrediction(preds.predictions, preds.label_ids)) ``` The fix is to use the helper function before running the evaluation to make sure the label IDs are aligned: ```python mnli_enc_aligned = mnli_enc.align_labels_with_mapping(label2id=config.label2id, label_column="label") # preds now aligned and everyone is happy :) preds = trainer.predict(mnli_enc_aligned) ``` cc @thomwolf @lhoestq

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MDExOlB1bGxSZXF1ZXN0NjY0NjAwOTk1

Fix cross-reference typos in documentation

closed

MEMBER

Fix some minor typos in docs that avoid the creation of cross-reference links.

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MDExOlB1bGxSZXF1ZXN0NjY0MTEzNjg2

Update version in xor_tydi_qa.py

closed

CONTRIBUTOR

Fix #2449 @lhoestq Should I revert to the old `dummy/1.0.0` or delete it and keep only `dummy/1.1.0`?

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MDExOlB1bGxSZXF1ZXN0NjYzODUyODU1

Rename config and environment variable for in memory max size

closed

MEMBER

As discussed in #2409, both config and environment variable have been renamed. cc: @stas00, huggingface/transformers#12056

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MDExOlB1bGxSZXF1ZXN0NjYzNzE3NTk2

Keep original features order

closed

MEMBER

When loading a Dataset from a JSON file whose column names are not sorted alphabetically, we should get the same column name order, whether we pass features (in the same order as in the file) or not. I found this issue while working on #2366.

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MDU6SXNzdWU5MTM2MDM4Nzc=

MRPC test set differences between torch and tensorflow datasets

closed

NONE

## Describe the bug When using `load_dataset("glue", "mrpc")` to load the MRPC dataset, the test set includes the labels. When using `tensorflow_datasets.load('glue/{}'.format('mrpc'))` to load the dataset the test set does not contain the labels. There should be consistency between torch and tensorflow ways of importing the GLUE datasets. ## Steps to reproduce the bug Minimal working code ```python from datasets import load_dataset import tensorflow as tf import tensorflow_datasets # torch dataset = load_dataset("glue", "mrpc") # tf data = tensorflow_datasets.load('glue/{}'.format('mrpc')) data = list(data['test'].as_numpy_iterator()) for i in range(40,50): tf_sentence1 = data[i]['sentence1'].decode("utf-8") tf_sentence2 = data[i]['sentence2'].decode("utf-8") tf_label = data[i]['label'] index = data[i]['idx'] print('Index {}'.format(index)) torch_sentence1 = dataset['test']['sentence1'][index] torch_sentence2 = dataset['test']['sentence2'][index] torch_label = dataset['test']['label'][index] print('Tensorflow: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(tf_sentence1, tf_sentence2, tf_label)) print('Torch: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(torch_sentence1, torch_sentence2, torch_label)) ``` Sample output ``` Index 954 Tensorflow: Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws . Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws . Label -1 Torch: Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws . Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws . Label 1 Index 711 Tensorflow: Sentence1 Others keep records sealed for as little as five years or as much as 30 . Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years . Label -1 Torch: Sentence1 Others keep records sealed for as little as five years or as much as 30 . Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years . Label 0 ``` ## Expected results I would expect the datasets to be independent of whether I am working with torch or tensorflow. ## Actual results Test set labels are provided in the `datasets.load_datasets()` for MRPC. However MRPC is the only task where the test set labels are not -1. ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.10 - PyArrow version: 3.0.0

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MDExOlB1bGxSZXF1ZXN0NjYzMzIwNDY1

Mention that there are no answers in adversarial_qa test set

closed

MEMBER

As mention in issue https://github.com/huggingface/datasets/issues/2447, there are no answers in the test set

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MDU6SXNzdWU5MTI4OTAyOTE=

BLUE file not found

closed

NONE

Hi, I'm having the following issue when I try to load the `blue` metric. ```shell import datasets metric = datasets.load_metric('blue') Traceback (most recent call last): File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 320, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path use_auth_token=download_config.use_auth_token, File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 332, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path use_auth_token=download_config.use_auth_token, File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/metrics/blue/blue.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<input>", line 1, in <module> File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 605, in load_metric dataset=False, File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 343, in prepare_module combined_path, github_file_path FileNotFoundError: Couldn't find file locally at blue/blue.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py. The file is also not present on the master branch on github. ``` Here is dataset installed version info ```shell pip freeze | grep datasets datasets==1.7.0 ```

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MDExOlB1bGxSZXF1ZXN0NjYyODg1ODUz

Update `xor_tydi_qa` url to v1.1

closed

CONTRIBUTOR

The dataset is updated and the old url no longer works. So I updated it. I faced a bug while trying to fix this. Documenting the solution here. Maybe we can add it to the doc (`CONTRIBUTING.md` and `ADD_NEW_DATASET.md`). > And to make the command work without the ExpectedMoreDownloadedFiles error, you just need to use the --ignore_verifications flag. https://github.com/huggingface/datasets/issues/2076#issuecomment-803904366

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MDExOlB1bGxSZXF1ZXN0NjYyNTI2NjA3

Fix flores download link

closed

CONTRIBUTOR

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MDU6SXNzdWU5MTIyOTk1Mjc=

dataset adversarial_qa has no answers in the "test" set

closed

NONE

## Describe the bug When loading the adversarial_qa dataset the 'test' portion has no answers. Only the 'train' and 'validation' portions do. This occurs with all four of the configs ('adversarialQA', 'dbidaf', 'dbert', 'droberta') ## Steps to reproduce the bug ``` from datasets import load_dataset examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['test'] print('Loaded {:,} examples'.format(len(examples))) has_answers = 0 for e in examples: if e['answers']['text']: has_answers += 1 print('{:,} have answers'.format(has_answers)) >>> Loaded 3,000 examples >>> 0 have answers examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['validation'] <...code above...> >>> Loaded 3,000 examples >>> 3,000 have answers ``` ## Expected results If 'test' is a valid dataset, it should have answers. Also note that all of the 'train' and 'validation' sets have answers, there are no "no answer" questions with this set (not sure if this is correct or not). ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.8.0-53-generic-x86_64-with-glibc2.29 - Python version: 3.8.5 - PyArrow version: 1.0.0

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MDU6SXNzdWU5MTE2MzUzOTk=

`yelp_polarity` is broken

closed

MEMBER


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MDExOlB1bGxSZXF1ZXN0NjYxODMzMTky

Fix broken URLs for bn_hate_speech and covid_tweets_japanese

closed

MEMBER

Closes #2388

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MDU6SXNzdWU5MTEyOTcxMzk=

Sentence Boundaries missing in Dataset: xtreme / udpos

closed

CONTRIBUTOR

I was browsing through annotation guidelines, as suggested by the datasets introduction. The guidlines saids "There must be exactly one blank line after every sentence, including the last sentence in the file. Empty sentences are not allowed." in the [Sentence Boundaries and Comments section](https://universaldependencies.org/format.html#sentence-boundaries-and-comments) But the sentence boundaries seems not to be represented by huggingface datasets features well. I found out that multiple sentence are concatenated together as a 1D array, without any delimiter. PAN-x, which is another token classification subset from xtreme do represent the sentence boundary using a 2D array. You may compare in PAN-x.en and udpos.English in the explorer: https://huggingface.co/datasets/viewer/?dataset=xtreme

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MDU6SXNzdWU5MDk5ODM1NzQ=

Some tests hang on Windows

closed

CONTRIBUTOR

Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220. This can be very tricky to debug, so I think now is a good time to address these issues/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.

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MDExOlB1bGxSZXF1ZXN0NjYwMjE1ODY1

add english language tags for ~100 datasets

closed

MEMBER

As discussed on Slack, I have manually checked for ~100 datasets that they have at least one subset in English. This information was missing so adding into the READMEs. Note that I didn't check all the subsets so it's possible that some of the datasets have subsets in other languages than English...

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MDU6SXNzdWU5MDg1NTQ3MTM=

DuplicatedKeysError on personal dataset

closed

NONE

## Describe the bug Ever since today, I have been getting a DuplicatedKeysError while trying to load my dataset from my own script. Error returned when running this line: `dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py')` Note that my script was working fine with earlier versions of the Datasets library. Cannot say with 100% certainty if I have been doing something wrong with my dataset script this whole time or if this is simply a bug with the new version of datasets. ## Steps to reproduce the bug I cannot provide code to reproduce the error as I am working with my own dataset. I can however provide my script if requested. ## Expected results For my data to be loaded. ## Actual results **DuplicatedKeysError** exception is raised ``` Downloading and preparing dataset good_reads_practice_dataset/main_domain (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/good_reads_practice_dataset/main_domain/1.1.0/64ff7c3fee2693afdddea75002eb6887d4fedc3d812ae3622128c8504ab21655... --------------------------------------------------------------------------- DuplicatedKeysError Traceback (most recent call last) <ipython-input-6-c342ea0dae9d> in <module>() ----> 1 dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py') 5 frames /usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs) 749 try_from_hf_gcs=try_from_hf_gcs, 750 base_path=base_path, --> 751 use_auth_token=use_auth_token, 752 ) 753 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 573 if not downloaded_from_gcs: 574 self._download_and_prepare( --> 575 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 576 ) 577 # Sync info /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 650 try: 651 # Prepare split will record examples associated to the split --> 652 self._prepare_split(split_generator, **prepare_split_kwargs) 653 except OSError as e: 654 raise OSError( /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator) 990 writer.write(example, key) 991 finally: --> 992 num_examples, num_bytes = writer.finalize() 993 994 split_generator.split_info.num_examples = num_examples /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in finalize(self, close_stream) 407 # In case current_examples < writer_batch_size, but user uses finalize() 408 if self._check_duplicates: --> 409 self.check_duplicate_keys() 410 # Re-intializing to empty list for next batch 411 self.hkey_record = [] /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self) 347 for hash, key in self.hkey_record: 348 if hash in tmp_record: --> 349 raise DuplicatedKeysError(key) 350 else: 351 tmp_record.add(hash) DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 0 Keys should be unique and deterministic in nature ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.7.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.9 - PyArrow version: 3.0.0

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MDU6SXNzdWU5MDg1MjE5NTQ=

Remove `extended` field from dataset tagger

closed

MEMBER

## Describe the bug While working on #2435 I used the [dataset tagger](https://huggingface.co/datasets/tagging/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included: ``` dataset_name = 'arcd' @pytest.mark.parametrize("dataset_name", get_changed_datasets(repo_path)) def test_changed_dataset_card(dataset_name): card_path = repo_path / "datasets" / dataset_name / "README.md" assert card_path.exists() error_messages = [] try: ReadMe.from_readme(card_path) except Exception as readme_error: error_messages.append(f"The following issues have been found in the dataset cards:\nREADME:\n{readme_error}") try: DatasetMetadata.from_readme(card_path) except Exception as metadata_error: error_messages.append( f"The following issues have been found in the dataset cards:\nYAML tags:\n{metadata_error}" ) if error_messages: > raise ValueError("\n".join(error_messages)) E ValueError: The following issues have been found in the dataset cards: E YAML tags: E __init__() got an unexpected keyword argument 'extended' tests/test_dataset_cards.py:70: ValueError ``` Consider either removing this tag from the tagger or including it as part of the validation step in the CI. cc @yjernite

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