Datasets:

T-202

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[ "Hello @kyleclo! Currently, we are getting the data from your bucket, so if you remove it the HF script won't work anymore :) \r\n\r\nUntil you solve things on your end, @lhoestq suggested we just return a warning message when people try to load that dataset from HF. What would you like it to say?", "Hi @kyleclo, as of today, you have not removed your bucket data yet, and therefore HuggingFace can download it from there.\r\n\r\nIs it OK? Are you planning to eventually delete it? Thank you.", "Hi! Sorry I missed @yjernite 's previous message, thanks for responding! \r\n\r\nIs there an option where we can keep our data in our bucket, but the HF script no longer pulls data from it? " ]

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[ "Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`.\r\n\r\nTo use `wiki_movies`, please update `datasets` with\r\n```\r\npip install --upgrade datasets\r\n```", "Thanks a lot! That solved it and I was able to upload a model trained on it as well :)" ]

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[ "Thanks for reporting :)\r\nMaybe we can concatenate fields only if they are different.\r\n\r\nCurrently this is done here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/info.py#L180-L196\r\n\r\nThis can be a good first contribution to the library.\r\nPlease comment if you'd like to improve this and open a PR :)" ]

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[ "Hi !\r\nLooks like there's a unicode error in the new citation in the miam.py file.\r\nCould you try to fix it ? Not sure from which character it comes from though\r\n\r\nYou can test if it works on your side with\r\n```\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_miam\r\n```", "Unicode error resolved!" ]

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[ "I have a question: what about `dictionary_encode_column_`?\r\n- It is deprecated in Dataset, but it recommends using a non-existing method instead: `Dataset.dictionary_encode_column` does not exist.\r\n- It is NOT deprecated in DatasetDict.", "`dictionary_encode_column_ ` should be deprecated since it never worked correctly. It will be removed in a major release.\r\nThis has to be deprecated in `DatasetDict` as well.\r\nAnd `Dataset.dictionary_encode_column` doesn't exist indeed.", "Thanks @lhoestq. I have fixed deprecated for `dictionary_encode_column_`." ]

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[ "Hi !\r\nCan you share more information about the features of your dataset ? You can get them by printing `my_dataset.features`\r\nCan you also share the code of your `map` function ?", "It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a list of integers. The `text` column is removed during tokenization.\r\n\r\n```\r\ndef add_len_and_seq(example):\r\n end_idx = example['input_ids'].index(SEP)\r\n example['actual_len'] = end_idx-1\r\n seq_len = len(example['input_ids'])\r\n \r\n\r\n example['seq'] = [PAD_ID] + [np.uint8(example['some_integer'])]*(end_idx-1) + [PAD_ID]*(seq_len-end_idx)\r\n \r\n return example\r\n```\r\n", "Is `PAD_ID` a python integer ? You need all the integers in `example['seq']` to have the same type.\r\nDoes this work if you remove the `np.uint8` and use python integers instead ?", "yup I casted it to `np.uint8` outside the function where it was defined. It was originally using python integers.", "Strangely, even when I manually created `np.arrays` of specific `dtypes`, the types in the final `dataset_info.json` that gets written are still `int64`.\r\n\r\nUpdate: I tried creating lists of `int8`s and got the same result.", "Yes this is a known issue: #625 \r\nWe're working on making the precision kept for numpy :)\r\nTo specify the precision of the integers, currently one needs to specify the output features with `.map(..., features=output_features)`", "Do you know what step is taking forever in the code ?\r\nWhat happens if you interrupt the execution of the dataset loading ?", "After a synchronous discussion, we found that the cache file sizes have an enormous effect on the loading speed: smaller cache files result in faster load times. `num_proc` controls the number of cache files that are being written and is inversely proportional to the individual file size. In other words, increase `num_proc` for smaller cache files :)\r\n\r\nMaybe this can be highlighted somewhere in the docs." ]

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[ "Hi ! This is 1.1 as specified by the download urls here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/squad/squad.py#L50-L55", "Got it. Thank you~" ]

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[ "Nice thank you !\r\nThis looks like a pretty simple yet effective fix ;)\r\nCould you just add a test in `test_features.py` to make sure that you can create `features` with a `_type` field and that it is possible to convert it as a dict and reload it ?\r\n```python\r\nfrom datasets import Features, Value\r\n\r\n# We usually use `asdict` on a `DatasetInfo` object which is a dataclass instance that contains the features.\r\n# So we need the conversion of features to dict to work.\r\n# You can test that using `dataclasses._asdict_inner`.\r\n# This is the function used by `dataclasses.asdict` to convert a dataclass instance attribute to a dict\r\nfrom dataclasses import _asdict_inner \r\n\r\nf = Features({\"_type\": Value(\"string\")})\r\nreloaded_f = Features.from_dict(_asdict_inner(f, dict))\r\nassert reloaded_f == f\r\n```", "Sure, i will add a test. \r\nOne question: are the posted benchmarks reliable? The extra type check seems to add quite some overhead judging by the relative differences. Do you think this is an issue?", "The benchmark has a bit of noise, the values are fine ;)\r\nespecially in the change you did since the overhead added is negligible.", "Ok, i added the test you described above. \r\n\r\nI avoided importing the private `_asdict_inner` method and directly used the `DatasetInfo` class, if this is ok with you. Thanks a lot for your support during this PR!" ]

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[ "Hi ! We plan to add streaming features in the future.\r\n\r\nThis should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead.\r\nWhat do you think about this ?\r\n\r\nIf you have ideas or suggestions of what you expect from such features as a user, feel free to share them, this is really valuable to us !", "People mainly want this feature either because it takes too much time too make arrow tables, or they occupy too much memory on the disk. I think both the problem can be solved if we provide arrow tables themselves on datasets hub. Can we do this currently @lhoestq ? \r\n", "@lhoestq I think the ```try_from_hf_gcs``` provide the same functionality. What all datasets are available on HF GCS? Are all the datasets on huggingFace datasets hub are made available on GCS, automatically?", "Only datasets like wikipedia, wiki40b, wiki_dpr and natural questions are available already processed on the HF google storage. This is used to download directly the arrow file instead of building it from the original data files.", "@lhoestq How can we make sure that the data we upload on HuggingFace hub is available in form of preprocessed arrow files ?", "We're still working on this :) This will be available soon\r\nUsers will be able to put their processed arrow files on the Hub" ]

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[ "Hello dear maintainer, are there any comments or questions about this PR?", "@iamollas thanks for the feedback. I did not see the template.\r\nI improved it...", "Should be clean for merge IMO.", "@lhoestq CI is green. ;-)", "Thanks again ! this is awesome :)", "Thanks for merging. :-)" ]

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[ "Hi ! We are using the [datasets-tagging app](https://github.com/huggingface/datasets-tagging) to select the tags to add.\r\n\r\nWe are also adding the full list of tags in #2107 \r\nThis covers multilinguality, language_creators, licenses, size_categories and task_categories.\r\n\r\nIn general if you want to add a tag that doesn't exist (for example for a custom license) you must make it start with `other-` and then a custom tag name.\r\n\r\nedit (@theo-m) if you ever find yourself resorting to adding an `other-*` tag, please do ping us somewhere so we can think about adding it to the \"official\" list :)", "@lhoestq hmm - ok thanks for the answer.\r\nTo be honest I am not sure if this issue can be closed now.\r\nI just wanted to point out that this should either be documented or linked in the documentation.\r\nIf you feel like it is (will be) please just close this.", "We're still working on the validation+documentation in this.\r\nFeel free to keep this issue open till we've added them", "@lhoestq what is the status on this? Did you add documentation?", "Hi ! There's the tagging app at https://huggingface.co/datasets/tagging/ that you can use.\r\nIt shows the list of all the tags you can use.\r\n\r\nIt is based on all the tag sets defined in this folder:\r\nhttps://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources", "@lhoestq is there something like this form Models?", "I don't think so. Feel free to take a look at the tags of other models (example [here](https://huggingface.co/bert-base-uncased/blob/main/README.md)). But we should definitely have some docs or an app to write the tags. Feel free to open an issue in the `transformers` repo or in the `huggingface_hub` repo so we can discuss this" ]

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[ "Trailing whitespace was removed. So more changes in diff than just this fix." ]

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[ "@lhoestq I'll try to add a test later if you think this approach with the wrapper is good.", "Awesome thank you !\r\nYes this approach with a wrapper is good :)", "@lhoestq Added a test. To verify that this change fixes the problem, replace:\r\n```\r\n!pip install datasets==1.5\r\n```\r\nwith:\r\n```\r\n!pip install git+https://github.com/mariosasko/datasets-1.git@update-metadata\r\n```\r\nin the first cell of the notebook that is attached to the linked issue.\r\n\r\nThe CI failure is unrelated I think (building the docs locally doesn't throw an error).", "The CI fail for the docs has been fixed on master.\r\nMerging :)" ]

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[ "I tried this with `ade_corpus_v2` dataset. `ade_corpus_v2-train.arrow` (downloaded dataset) and `cache-25d41a4d3c2d8a25.arrow` (ran a mapping function on the dataset) both had file permission with octal value of `0644`. " ]

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[ "+1 on this request" ]

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[ "Hi,\r\n\r\nthis bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit:\r\n```python\r\ncommon_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'])\r\n\r\n``` \r\nThe order is important because the resulting dataset inherits the schema metadata of the first dataset passed to the `concatenate_datasets(...)` function (`pa.concat_tables` [docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html)). I'll try to fix this ASAP." ]

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[ "Hi !\r\n\r\nThis is actually supported ! but not yet in `from_pandas`.\r\nYou can use `from_dict` for now instead:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n 'bbox': [\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n ],\r\n 'input_ids': [1, 2, 3, 4]\r\n}\r\ndataset = Dataset.from_dict(dataset)\r\n```\r\n\r\nThis will work but to use it with the torch formatter you must specify the `Array2D` feature type in order to tell the shape:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n 'bbox': [\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n ],\r\n 'input_ids': [1, 2, 3, 4]\r\n}\r\ndataset = Dataset.from_dict(dataset, features=Features({\r\n \"bbox\": Array2D(shape=(3, 4), dtype=\"int64\"),\r\n \"input_ids\": Value(\"int64\")\r\n}))\r\ndataset.set_format(\"torch\")\r\nprint(dataset[0]['bbox'])\r\n# tensor([[1, 2, 3, 4],\r\n# [1, 2, 3, 4],\r\n# [1, 2, 3, 4]])\r\n```\r\nIf you don't specify the `Array2D` feature type, then the inferred type will be Sequence(Sequence(Value(\"int64\"))) and therefore the torch formatter will return list of tensors", "Thanks for the explanation. \r\nWith my original DataFrame, I did\r\n```\r\ndataset = dataset.to_dict(\"list\")\r\n```\r\nand then the rest of the transformation from dictionary works just fine." ]

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[ "Hi ! Thanks for reporting.\r\nWe're indeed using `jiwer` to compute the WER.\r\n\r\nMaybe instead of calling `jiwer.wer` once for all the preditions/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible.\r\n\r\nCurrently the code to compute the WER is defined here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/metrics/wer/wer.py#L93-L94", "Hi,\r\n\r\nI've just pushed a pull request that is related to this issue https://github.com/huggingface/datasets/pull/2169. It's not iterative, but it should avoid memory errors. It's based on the editdistance python library. An iterative implementation should be as easy as storing scores and words stepwise and dividing at the end. ", "I see, this was solved by other thread. Ok, let me know if you want to switch the implementation for any reason :)", "Thanks for diving into this anyway ^^'\r\nAs you said this actually got solved a few days ago", "Someone created an issue https://github.com/jitsi/jiwer/issues/40 at jiwer which shows that this is still a problem in the current version. Would be curious to figure out how this can be fixed by jiwer... :) I assume that it runs of out memory because it's trying to compute the WER over (too many) test samples?", "Hi !\r\n\r\nIt's computed iteratively so not sure what could go wrong\r\n\r\nhttps://github.com/huggingface/datasets/blob/8afd0ba8c27800a55ea69d9fcd702dc97d9c16d8/metrics/wer/wer.py#L100-L106\r\n\r\n@NiklasHoltmeyer what version of `datasets` are you running ?\r\n", "One possible explanation might be that it is the user who is passing all the sentences in a single element to `wer.compute`?\r\n\r\nAs current implementation iterates over the elements of `predictions` and `references`, this can be problematic if `predictions` and `references` contain a single huge element each. \r\n\r\nThis could be the case, for example, with a single string with all sentences:\r\n```python\r\nresult[\"predicted\"] = \"One sentence. Other sentence.\"\r\n```\r\nor with a __double__ nested list of sentence lists\r\n```python\r\nresult[\"predicted\"] = [[ [\"One sentence.\"], [\"Other sentence\"] ]]\r\n```\r\n\r\nThe user should check the dimensions of the data structure passed to `predictions` and `references`.", "Hi all,\r\n\r\nin my case I was using and older version of datasets and, as @albertvillanova points out, passing the full list of sentences for the metric calculation. The problem was in the way jiwer implements WER, as it tries to compute WER for the full list at once instead of doing it element-wise. I think that with the latest implementation of datasets, or by using the alternative WER function that I've contributed on this [pull request](https://github.com/huggingface/datasets/pull/2169) there shouldn't be memory errors.", "@lhoestq i was using Datasets==1.5.0 with 1.6.1 it worked (atleast the first run) but 1.5.0 is not compatible with my preprocessing. i cant save my dataset to a parquet file while using the latest datasets version\r\n\r\n-> \r\n```\r\n File \"../preprocess_dataset.py\", line 132, in <module>\r\n pq.write_table(train_dataset.data, f'{resampled_data_dir}/{data_args.dataset_config_name}.train.parquet')\r\n File \"/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py\", line 1674, in write_table\r\n writer.write_table(table, row_group_size=row_group_size)\r\n File \"/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py\", line 588, in write_table\r\n self.writer.write_table(table, row_group_size=row_group_size)\r\nTypeError: Argument 'table' has incorrect type (expected pyarrow.lib.Table, got ConcatenationTable)\r\n``` \r\n\r\nif i do \r\n```\r\nimport pyarrow.parquet as pq\r\n...\r\n...\r\npq.write_table(train_dataset.data, 'train.parquet')\r\npq.write_table(eval_dataset.data, 'eval.parquet')\r\n```\r\n\r\nwhile using 1.6.1. and its working with 1.5.0\r\n", "Hi ! You can pass dataset.data.table instead of dataset.data to pq.write_table", "This seems to be working so far! Thanks!" ]

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[ "Hi @XuhuiZhou, thanks for reporting this issue. \r\n\r\nIndeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.", "It would be nice to update the urls indeed !\r\n\r\nTo do this, you just need to replace the urls in `iwslt2017.py` and then update the dataset_infos.json file with\r\n```\r\ndatasets-cli test ./datasets/iwslt2017 --all_configs --save_infos --ignore_verifications\r\n```", "Is this a command to update my local files or fix the file Github repo in general? (I am not so familiar with the datasets-cli command here)\r\n\r\nI also took a brief look at the **Sharing your dataset** section, looks like I could fix that locally and push it to the repo? I guess we are \"canonical\" category?", "This command will update your local file. Then you can open a Pull Request to push your fix to the github repo :)\r\nAnd yes you are right, it is a \"canonical\" dataset, i.e. a dataset script defined in this github repo (as opposed to dataset repositories of users on the huggingface hub)", "Hi, thanks for the answer. \r\n\r\nI gave a try to the problem today. But I encountered an upload error: \r\n\r\n```\r\ngit push -u origin fix_link_iwslt\r\nEnter passphrase for key '/home2/xuhuizh/.ssh/id_rsa': \r\nERROR: Permission to huggingface/datasets.git denied to XuhuiZhou.\r\nfatal: Could not read from remote repository.\r\n\r\nPlease make sure you have the correct access rights\r\nand the repository exists.\r\n```\r\n\r\nAny insight here? \r\n\r\nBy the way, when I run the datasets-cli command, it shows the following error, but does not seem to be the error coming from `iwslt.py`\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home2/xuhuizh/anaconda3/envs/UMT/bin/datasets-cli\", line 33, in <module>\r\n sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/commands/datasets_cli.py\", line 35, in main\r\n service.run()\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/commands/test.py\", line 141, in run\r\n try_from_hf_gcs=False,\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/builder.py\", line 579, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/builder.py\", line 639, in _download_and_prepare\r\n self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/utils/info_utils.py\", line 32, in verify_checksums\r\n raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz'}\r\n```", "Hi ! To create a PR on this repo your must fork it and create a branch on your fork. See how to fork the repo [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#start-by-preparing-your-environment).\r\nAnd to make the command work without the `ExpectedMoreDownloadedFiles` error, you just need to use the `--ignore_verifications` flag.", "Hi @XuhuiZhou,\r\n\r\nAs @lhoestq has well explained, you need to fork HF's repository, create a feature branch in your fork, push your changes to it and then open a Pull Request to HF's upstream repository. This is so because at HuggingFace Datasets we follow a development model called \"Fork and Pull Model\". You can find more information here:\r\n- [Understanding the GitHub flow](https://guides.github.com/introduction/flow/)\r\n- [Forking Projects](https://guides.github.com/activities/forking/)\r\n\r\nAlternatively, if you find all these steps too complicated, you can use the GitHub official command line tool: [GitHub CLI](https://cli.github.com/). Once installed, in order to create a Pull Request, you only need to use this command:\r\n```shell\r\ngh pr create --web\r\n```\r\nThis utility will automatically create the fork, push your changes and open a Pull Request, under the hood." ]

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[ "Hi @LifaSun, thanks for reporting this issue.\r\n\r\nSometimes, GitHub has some connectivity problems. Could you confirm that the problem persists?", "@albertvillanova Thanks! It works well now. " ]

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[ "> It would be great if there was a way to make the task categories consistent too. For this, the streamlit app can look into all the datasets and check for existing categories and show them in the list. This may add some consistency.\r\n\r\nWe can also update the task lists here: https://github.com/huggingface/datasets-tagging/blob/main/task_set.json", "Hi @lhoestq,\r\n\r\nThanks for approving.\r\nHow do I add the new categories to the tagging app? What I have added is till `1T` and not `1M`.\r\n\r\nI'll also check the task list :)\r\n\r\nThanks,\r\nGunjan", "I think you can change it here: https://github.com/huggingface/datasets-tagging/blob/main/tagging_app.py#L412-L423", "Hi @lhoestq,\r\n\r\nI have made a PR for size categories on `datasets-tagging`\r\n\r\nFor tags, I have thought of adding more tags and categories, based on what I know about the existing datasets, any list will not be exhaustive because the contributors can be very specific or very general. Hence, there could be a continuous process of evaluating existing tags and adding more and more.\r\n\r\n```json\r\n{\r\n \"image-classification\": {\r\n \"description\": \"image classification tasks\",\r\n \"options\": [\r\n \"multi-class-classification\",\r\n \"multi-label-classification\",\r\n \"other\"\r\n ]\r\n },\r\n \"conditional-text-generation\": {\r\n \"description\": \"data-to-text and text transduction tasks such as translation or summarization\",\r\n \"options\": [\r\n \"machine-translation\",\r\n \"sentence-splitting-fusion\",\r\n \"extractive-and-abstractive-summarization\",\r\n \"abstractive-summarization\",\r\n \"extractive-summarization\",\r\n \"multi-document-summarization\",\r\n \"table-to-text\",\r\n \"text-simplification\",\r\n \"explanation-generation\",\r\n \"stuctured-to-text\",\r\n \"other\"\r\n ]\r\n },\r\n \"conditional-speech-generation\": {\r\n \"description\": \"speech generation tasks\",\r\n \"options\": [\r\n \"text-to-speech\",\r\n \"speech-translation\",\r\n \"other\"\r\n ]\r\n },\r\n\r\n \"conditional-structure-generation\":{\r\n \"description\": \"text or speech to structured data\",\r\n \"options\":[\r\n \"knowlege-graph-mining\",\r\n \"code-generation\",\r\n ]\r\n },\r\n \"question-answering\": {\r\n \"description\": \"question answering tasks\",\r\n \"options\": [\r\n \"open-domain-qa\",\r\n \"closed-domain-qa\",\r\n \"multiple-choice-qa\",\r\n \"extractive-qa\",\r\n \"abstractive-qa\",\r\n \"conversational-qa\",\r\n \"multi-document-qa\",\r\n \"other\"\r\n ]\r\n },\r\n \"speech-classification\": {\r\n \"description\": \"speech to label tasks\",\r\n \"options\": [\r\n \"other\"\r\n ]\r\n },\r\n \"sequence-modeling\": {\r\n \"description\": \"such as language, speech or dialogue modeling\",\r\n \"options\": [\r\n \"dialogue-modeling\",\r\n \"language-modeling\",\r\n \"speech-modeling\",\r\n \"multi-turn\",\r\n \"slot-filling\",\r\n \"other\"\r\n ]\r\n },\r\n \"speech-recognition\": {\r\n \"description\": \"speech to text tasks\",\r\n \"options\": [\r\n \"automatic-speech-recognition\",\r\n \"other\"\r\n ]\r\n },\r\n \"structure-prediction\": {\r\n \"description\": \"predicting structural properties of the text, such as syntax\",\r\n \"options\": [\r\n \"coreference-resolution\",\r\n \"named-entity-recognition\",\r\n \"part-of-speech-tagging\",\r\n \"parsing\",\r\n \"sentence-segmentation\",\r\n \"single-span-prediction\",\r\n \"multi-span-prediction\",\r\n \"clause-or-phrase-segmentation\",\r\n \"dependency-parsing\",\r\n \"constituency-parsing\",\r\n \"other\"\r\n ]\r\n },\r\n\r\n \"text-classification\": {\r\n \"description\": \"predicting a class index or boolean value\",\r\n \"options\": [\r\n \"acceptability-classification\",\r\n \"entity-linking-classification\",\r\n \"relation-extraction\",\r\n \"common-sense-reasoning\",\r\n \"fact-checking\",\r\n \"intent-classification\",\r\n \"multi-class-classification\",\r\n \"multi-label-classification\",\r\n \"natural-language-inference\",\r\n \"semantic-similarity-classification\",\r\n \"sentiment-classification\",\r\n \"topic-classification\",\r\n \"emotion-classification\",\r\n \"token-classification\",\r\n \"word-sense-disambiguation\",\r\n \"offense-classification\",\r\n \"hate-speech-classification\",\r\n \"language-classification\",\r\n \"bias-classification\",\r\n \"other\"\r\n ]\r\n },\r\n \"text-retrieval\": {\r\n \"description\": \"information or text retrieval tasks\",\r\n \"options\": [\r\n \"document-retrieval\",\r\n \"utterance-retrieval\",\r\n \"entity-linking-retrieval\",\r\n \"fact-checking-retrieval\",\r\n \"other\"\r\n ]\r\n },\r\n \"text-scoring\": {\r\n \"description\": \"text scoring tasks, predicting a real valued score for some text\",\r\n \"options\": [\r\n \"semantic-similarity-scoring\",\r\n \"sentiment-scoring\",\r\n \"other\"\r\n ]\r\n },\r\n \"other\": {\r\n \"description\": \"raw data or other task families\",\r\n \"options\": [\r\n \"data-mining\",\r\n \"raw-text\",\r\n \"raw-speech\",\r\n \"raw-image\",\r\n \"other\"\r\n ]\r\n }\r\n}\r\n```\r\nI'll sort this when adding it to the .json. Also, I'll change categories according to this if this seems okay to you and commit it to this PR.\r\n\r\nI'll also fix spelling others, and some categories which are partially correct, for e.g. `other-machine-translation` to the correct tag.\r\nLastly, with the options also we can add a description to make it easier for the users to understand what we mean by each option. Example, for \"emotion-classification\", we can explain what kinds of data we are talking about, or what we mean by \"single-span-prediction\", etc.", "Good idea thank you ! Can you open a PR on datasets-tagging for the tasks as well ?\r\nAlso you can update the dataset card with the new tasks categories in another PR if you don't mind", "Hi @lhoestq,\r\n\r\nThanks, what all do I need to add to merge this PR?", "We can merge this one once the PR on dataset sizes is merged on `datasets-tagging` ;)", "Hi @lhoestq,\r\n\r\nOne problem with this approach is that for datasets like `ccaligned_multilingual`, the infos won't be complete because we don't have all configs. In that case, people might face trouble finding the datatset using the tag. Although, they probably won't be checking the size tag for a dataset like that.\r\n\r\nWhat do you think?\r\n\r\nCC @theo-m ", "For datasets like `ccaligned_multilingual` it's important to have all the tags for users to search and find it. Currently is has the full list of tags (without the config names). So you can actually find the dataset, but you don't know what tag correspond to what configuration. " ]

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[ "I think I will stop pushing to this PR, so that it can me merged for today release. \r\n\r\nI will open another PR for further fixing docs.\r\n\r\nDo you agree, @lhoestq ?", "Sounds good thanks !" ]

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[ "dupe of #1992" ]

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[ "Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column.\r\n\r\nIndeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a batch.\r\n\r\nHowever it seems like `tokenized_examples` doesn't have the same number of elements in each field. One field seems to have `1180` elements while `candidate_attention_mask` only has `1178`." ]

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[ "Maybe we should add some other split classes?" ]

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[ "cc @philschmid ", "Hey @sivakhno,\r\n\r\nhow does your `requirements.txt` look like to install the `datasets` library and which version of it are you running? Can you try to install `datasets>=1.4.0`", "Hi @philschmid - thanks for suggestion. I am using `datasets==1.4.1`. \r\nI have also tried using `torch=1.6.0` (docker `763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.6.0-gpu-py3 `), but the error is the same. ", "Could paste the code you use the start your training job and the fine-tuning script you run? ", "@sivakhno this should be now fixed in `datasets>=1.5.0`. ", "@philschmid Recently released tensorflow-macos seems to be missing. ", "I've created a PR to add this. " ]

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[ "Hi ! Thanks for reporting.\r\nThis looks like a bug, could you try to provide a minimal code example that reproduces the issue ? This would be very helpful !\r\n\r\nOtherwise I can try to run the wav2vec2 code above on my side but probably not this week..", "```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\n\r\n\r\nupdated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4)\r\n\r\n```", "\r\n\r\n\r\n\r\n\r\nI was able to copy some of the shell \r\nThis is repeating every half second\r\nWin 10, Anaconda with python 3.8, datasets installed from main branche\r\n```\r\n\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 287, in _fixup_main_from_path\r\n _check_not_importing_main()\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 116, in spawn_main\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 134, in _check_not_importing_main\r\n main_content = runpy.run_path(main_path,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 265, in run_path\r\n exitcode = _main(fd, parent_sentinel)\r\n raise RuntimeError('''\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 125, in _main\r\nRuntimeError:\r\n An attempt has been made to start a new process before the\r\n current process has finished its bootstrapping phase.\r\n\r\n This probably means that you are not using fork to start your\r\n child processes and you have forgotten to use the proper idiom\r\n in the main module:\r\n\r\n if __name__ == '__main__':\r\n freeze_support()\r\n ...\r\n\r\n The \"freeze_support()\" line can be omitted if the program\r\n is not going to be frozen to produce an executable. return _run_module_code(code, init_globals, run_name,\r\n prepare(preparation_data)\r\n\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 97, in _run_module_code\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 236, in prepare\r\n _run_code(code, mod_globals, init_globals,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 87, in _run_code\r\n _fixup_main_from_path(data['init_main_from_path'])\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 287, in _fixup_main_from_path\r\n exec(code, run_globals)\r\n File \"F:\\Codes\\Python Apps\\asr\\test.py\", line 6, in <module>\r\n updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4)\r\n main_content = runpy.run_path(main_path,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1370, in map\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 265, in run_path\r\n with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool:\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\context.py\", line 119, in Pool\r\n return _run_module_code(code, init_globals, run_name,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 97, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n return Pool(processes, initializer, initargs, maxtasksperchild,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 87, in _run_code\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 212, in __init__\r\n exec(code, run_globals)\r\n File \"F:\\Codes\\Python Apps\\asr\\test.py\", line 6, in <module>\r\n self._repopulate_pool()\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 303, in _repopulate_pool\r\n updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1370, in map\r\n return self._repopulate_pool_static(self._ctx, self.Process,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 326, in _repopulate_pool_static\r\n with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool:\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\context.py\", line 119, in Pool\r\n w.start()\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\process.py\", line 121, in start\r\n return Pool(processes, initializer, initargs, maxtasksperchild,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 212, in __init__\r\n self._popen = self._Popen(self)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\context.py\", line 327, in _Popen\r\n self._repopulate_pool()\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 303, in _repopulate_pool\r\n return Popen(process_obj)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\popen_spawn_win32.py\", line 45, in __init__\r\n return self._repopulate_pool_static(self._ctx, self.Process,\r\n prep_data = spawn.get_preparation_data(process_obj._name)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 326, in _repopulate_pool_static\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 154, in get_preparation_data\r\n _check_not_importing_main()\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 134, in _check_not_importing_main\r\n w.start()\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\process.py\", line 121, in start\r\n raise RuntimeError('''\r\nRuntimeError:\r\n An attempt has been made to start a new process before the\r\n current process has finished its bootstrapping phase.\r\n\r\n This probably means that you are not using fork to start your\r\n child processes and you have forgotten to use the proper idiom\r\n in the main module:\r\n\r\n if __name__ == '__main__':\r\n freeze_support()\r\n ...\r\n```", "Thanks this is really helpful !\r\nI'll try to reproduce on my side and come back to you", "if __name__ == '__main__':\r\n\r\n\r\nThis line before calling the map function stops the error but the script still repeats endless", "Indeed you needed `if __name__ == '__main__'` since accoding to [this stackoverflow post](https://stackoverflow.com/a/18205006):\r\n\r\n> On Windows the subprocesses will import (i.e. execute) the main module at start. You need to insert an if __name__ == '__main__': guard in the main module to avoid creating subprocesses recursively.\r\n\r\nRegarding the hanging issue, can you try to update `dill` and `multiprocess` ?", "It's already on the newest version", "```\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 791, in move\r\n os.rename(src, real_dst)\r\nFileExistsError: [WinError 183] Eine Datei kann nicht erstellt werden, wenn sie bereits vorhanden ist: 'D:\\\\huggingfacecache\\\\common_voice\\\\de\\\\6.1.0\\\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\\\tmpx9fl_jg8' -> 'D:\\\\huggingfacecache\\\\common_voice\\\\de\\\\6.1.0\\\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\\\cache-9b4f203a63742dfc.arrow'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 116, in spawn_main\r\n exitcode = _main(fd, parent_sentinel)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 125, in _main\r\n prepare(preparation_data)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 236, in prepare\r\n _fixup_main_from_path(data['init_main_from_path'])\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 287, in _fixup_main_from_path\r\n main_content = runpy.run_path(main_path,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 265, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 97, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 87, in _run_code\r\n exec(code, run_globals)\r\n File \"F:\\Codes\\Python Apps\\asr\\cvtrain.py\", line 243, in <module>\r\n common_voice_train = common_voice_train.map(remove_special_characters, remove_columns=[\"sentence\"])\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1339, in map\r\n return self._map_single(\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 203, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\fingerprint.py\", line 337, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1646, in _map_single\r\n shutil.move(tmp_file.name, cache_file_name)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 805, in move\r\n copy_function(src, real_dst)\r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 435, in copy2\r\n copyfile(src, dst, follow_symlinks=follow_symlinks)\r\n 0%| | 0/27771 [00:00<?, ?ex/s] \r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 264, in copyfile\r\n with open(src, 'rb') as fsrc, open(dst, 'wb') as fdst:\r\nOSError: [Errno 22] Invalid argument: 'D:\\\\huggingfacecache\\\\common_voice\\\\de\\\\6.1.0\\\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\\\cache-9b4f203a63742dfc.arrow'\r\n```\r\n\r\nI was adding freeze support before calling the mapping function like this\r\nif __name__ == '__main__':\r\n freeze_support()\r\n dataset.map(....)", "Usually OSError of an arrow file on windows means that the file is currently opened as a dataset object, so you can't overwrite it until the dataset object falls out of scope.\r\nCan you make sure that there's no dataset object that loaded the `cache-9b4f203a63742dfc.arrow` file ?", "Now I understand\r\nThe error occures because the script got restarted in another thread, so the object is already loaded.\r\nStill don't have an idea why a new thread starts the whole script again" ]

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[ "Hi ! Thanks for reporting.\r\n\r\nCurrently there's no way to specify this.\r\n\r\nWhen loading/processing a dataset, the arrow file is written using a temporary file. Then once writing is finished, it's moved to the cache directory (using `shutil.move` [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1646))\r\n\r\nThat means it keeps the permissions specified by the `tempfile.NamedTemporaryFile` object, i.e. `-rw-------` instead of `-rw-r--r--`. Improving this could be a nice first contribution to the library :)", "Hi @lhoestq,\r\nI looked into this and yes you're right. The `NamedTemporaryFile` is always created with mode 0600, which prevents group from reading the file. Should we change the permissions of `tmp_file.name` [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1871) and [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1590), post creation to 0644 inorder for group and others to read it?", "Good idea :) we could even update the permissions after the file has been moved by shutil.move [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1899) and [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1646) actually.\r\nApparently they set the default 0600 for temporary files for security reasons, so let's update the umask only after the file has been moved", "Would it be possible to actually set the umask based on a user provided argument? For example, a popular usecase my team has is using a shared file-system for processing datasets. This may involve writing/deleting other files, or changing filenames, which a -rw-r--r-- wouldn't fix. ", "Note that you can get the cache files of a dataset with the `cache_files` attributes.\r\nThen you can `chmod` those files and all the other cache files in the same directory.\r\n\r\nMoreover we can probably keep the same permissions after each transform. This way you just need to set the permissions once after doing `load_dataset` for example, and then all the new transformed cached files will have the same permissions.\r\nWhat do you think ?", "This means we'll check the permissions of other `cache_files` already created for a dataset before setting permissions for new `cache_files`?", "You can just check the permission of `dataset.cache_files[0]` imo", "> This way you just need to set the permissions once after doing load_dataset for example, and then all the new transformed cached files will have the same permissions.\r\n\r\nI was referring to this. Ensuring that newly generated `cache_files` have the same permissions", "Yes exactly\r\n\r\nI imagine users can first do `load_dataset`, then chmod on the arrow files. After that all the new cache files could have the same permissions as the first arrow files. Opinions on this ?", "Sounds nice but I feel this is a sub-part of the approach mentioned by @siddk. Instead of letting the user set new permissions by itself first and then making sure newly generated files have same permissions why don't we ask the user initially only what they want? What are your thoughts?", "Yes sounds good. Should this be a parameter in `load_dataset` ? Or an env variable ? Or use the value of `os.umask` ?", "Ideally it should be a parameter in `load_dataset` but I'm not sure how important it is for the users (considering only important things should go into `load_dataset` parameters)", "I think it's fairly important; for context, our team uses a shared file-system where many folks run experiments based on datasets that are cached by other users.\r\n\r\nFor example, I might start a training run, downloading a dataset. Then, a couple of days later, a collaborator using the same repository might want to use the same dataset on the same shared filesystem, but won't be able to under the default permissions.\r\n\r\nBeing able to specify directly in the top-level `load_dataset()` call seems important, but an equally valid option would be to just inherit from the running user's `umask` (this should probably be the default anyway).\r\n\r\nSo basically, argument that takes a custom set of permissions, and by default, use the running user's umask!", "Maybe let's start by defaulting to the user's umask !\r\nDo you want to give it a try @bhavitvyamalik ?", "Yeah sure! Instead of using default `0o644` should I first extract umask of current user and then use `os.umask` on it? We can do it inside `Dataset` class so that all folders/files created during the call use running user's umask\r\n\r\n", "You can get the umask using `os.umask` and then I guess you can just use `os.chmod` as in your previous PR, but with the right permissions depending on the umask.", "FWIW, we have this issue with other caches - e.g. `transformers` model files. So probably will need to backport this into `transformers` as well.\r\n\r\nthanks @thomwolf for the pointer.", "Hi @stas00,\r\nFor this should we use the same umask code in the respective model directory inside `TRANSFORMERS_CACHE`?", "That sounds very right to me, @bhavitvyamalik " ]

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[ "@lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n\r\nThe bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.", "Hi ! Thanks for reporting @adzcodez \r\n\r\n\r\n> @lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n> \r\n> The bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.\r\n\r\nYou're right: \"_\" should be added to the list of labels, and the examples must be sequences of tokens, not singles tokens.\r\n", "@lhoestq Can you please label this issue with the \"good first issue\" label? I'm not sure I'll find time to fix this.\r\n\r\nTo resolve it, the user should:\r\n1. add `\"_\"` to the list of labels\r\n2. transform the udpos subset to the conll format (I think the preprocessing logic can be borrowed from [the original repo](https://github.com/google-research/xtreme/blob/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de/utils_preprocess.py#L187-L204))\r\n3. update the dummy data\r\n4. update the dataset info\r\n5. [optional] add info about the data fields structure of the udpos subset to the dataset readme", "I tried fixing this issue, but its working fine in the dev version : \"1.6.2.dev0\"\r\n\r\nI think somebody already fixed it. ", "Hi,\r\n\r\nafter #2326, the lines with pos tags equal to `\"_\"` are filtered out when generating the dataset, so this fixes the KeyError described above. However, the udpos subset should be in the conll format i.e. it should yield sequences of tokens and not single tokens, so it would be great to see this fixed (feel free to borrow the logic from [here](https://github.com/google-research/xtreme/blob/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de/utils_preprocess.py#L187-L204) if you decide to work on this). ", "Closed by #2466." ]

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[ "I thought at first that the multiproc test was not relevant now that we do stuff only in memory, but I think there's something that's actually broken, my tiny benchmark on bookcorpus runs forever (2hrs+) when I add `num_proc=4` as a kwarg, will investigate 👀 \r\n\r\nI'm not familiar with the caching you describe for `.map`, I'll look it up.", "turns out the multi proc issue is also on master, I won't fix it in this PR but opened #2071 to track the problem.", "tracemalloc outputs from this script:\r\n\r\n```python\r\nimport logging\r\nimport sys\r\nimport time\r\nimport tracemalloc\r\n\r\nfrom datasets import load_dataset, set_caching_enabled\r\n\r\n\r\nif __name__ == \"__main__\":\r\n set_caching_enabled(False)\r\n logging.basicConfig(level=logging.DEBUG)\r\n\r\n tracemalloc.start()\r\n bc = load_dataset(\"bookcorpus\")\r\n\r\n now = time.time()\r\n try:\r\n snapshot1 = tracemalloc.take_snapshot()\r\n bc[\"train\"].filter(lambda x: len(x[\"text\"]) < 64, num_proc=int(sys.argv[1]))\r\n except Exception as e:\r\n print(f\"cancelled: {e}\")\r\n exit(1)\r\n snapshot2 = tracemalloc.take_snapshot()\r\n tracemalloc.stop()\r\n elapsed = time.time() - now\r\n\r\n print(elapsed)\r\n top_stats = snapshot2.compare_to(snapshot1, \"lineno\")\r\n\r\n print(\"[ Top 10 differences ]\")\r\n for stat in top_stats[:10]:\r\n print(stat)\r\n\r\n```\r\n\r\n\r\nThis branch:\r\n\r\n```\r\n ssh://[email protected]:22/home/theo/.local/share/miniconda3/envs/datasets/bin/python -u benchmark_filter.py 1\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.amazonaws.com:443\r\n DEBUG:urllib3.connectionpool:https://s3.amazonaws.com:443 \"HEAD /datasets.huggingface.co/datasets/datasets/bookcorpus/bookcorpus.py HTTP/1.1\" 200 0\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): raw.githubusercontent.com:443\r\n DEBUG:urllib3.connectionpool:https://raw.githubusercontent.com:443 \"HEAD /huggingface/datasets/master/datasets/bookcorpus/bookcorpus.py HTTP/1.1\" 200 0\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): raw.githubusercontent.com:443\r\n DEBUG:urllib3.connectionpool:https://raw.githubusercontent.com:443 \"HEAD /huggingface/datasets/master/datasets/bookcorpus/dataset_infos.json HTTP/1.1\" 200 0\r\n WARNING:datasets.builder:Reusing dataset bookcorpus (/home/theo/.cache/huggingface/datasets/bookcorpus/plain_text/1.0.0/af844be26c089fb64810e9f2cd841954fd8bd596d6ddd26326e4c70e2b8c96fc)\r\n 0%| | 0/74005 [00:00<?, ?ba/s]2021-03-23 10:23:20.051255: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n 2021-03-23 10:23:20.051304: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n DEBUG:tensorflow:Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.\r\n 100%|████████████████████████████████████| 74005/74005 [12:16<00:00, 100.54ba/s]\r\n 815.6356580257416\r\n [ Top 10 differences ]\r\n <frozen importlib._bootstrap_external>:580: size=38.0 MiB (+33.7 MiB), count=326226 (+307928), average=122 B\r\n <frozen importlib._bootstrap>:219: size=7643 KiB (+7553 KiB), count=26372 (+25473), average=297 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/torch/__init__.py:427: size=1291 KiB (+1291 KiB), count=5924 (+5924), average=223 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/abc.py:85: size=1039 KiB (+1026 KiB), count=3428 (+3384), average=310 B\r\n <frozen importlib._bootstrap_external>:64: size=917 KiB (+891 KiB), count=5300 (+5132), average=177 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/collections/__init__.py:456: size=720 KiB (+709 KiB), count=3403 (+3349), average=217 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/tf_export.py:346: size=607 KiB (+607 KiB), count=3962 (+3962), average=157 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/linecache.py:137: size=998 KiB (+487 KiB), count=9551 (+4517), average=107 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/tf_decorator.py:241: size=367 KiB (+367 KiB), count=5225 (+5225), average=72 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/decorator_utils.py:114: size=359 KiB (+359 KiB), count=330 (+330), average=1114 B\r\n```\r\n\r\nOn master:\r\n```\r\n ssh://[email protected]:22/home/theo/.local/share/miniconda3/envs/datasets/bin/python -u benchmark_filter.py 1\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.amazonaws.com:443\r\n DEBUG:urllib3.connectionpool:https://s3.amazonaws.com:443 \"HEAD /datasets.huggingface.co/datasets/datasets/bookcorpus/bookcorpus.py HTTP/1.1\" 200 0\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): raw.githubusercontent.com:443\r\n DEBUG:urllib3.connectionpool:https://raw.githubusercontent.com:443 \"HEAD /huggingface/datasets/master/datasets/bookcorpus/bookcorpus.py HTTP/1.1\" 200 0\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): raw.githubusercontent.com:443\r\n DEBUG:urllib3.connectionpool:https://raw.githubusercontent.com:443 \"HEAD /huggingface/datasets/master/datasets/bookcorpus/dataset_infos.json HTTP/1.1\" 200 0\r\n WARNING:datasets.builder:Reusing dataset bookcorpus (/home/theo/.cache/huggingface/datasets/bookcorpus/plain_text/1.0.0/af844be26c089fb64810e9f2cd841954fd8bd596d6ddd26326e4c70e2b8c96fc)\r\n 0%| | 0/74005 [00:00<?, ?ba/s]2021-03-23 12:26:47.219622: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n 2021-03-23 12:26:47.219669: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n DEBUG:tensorflow:Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.\r\n 100%|███████████████████████████████████| 74005/74005 [1:02:17<00:00, 19.80ba/s]\r\n 3738.870892047882\r\n [ Top 10 differences ]\r\n <frozen importlib._bootstrap_external>:580: size=38.0 MiB (+33.7 MiB), count=326221 (+307919), average=122 B\r\n <frozen importlib._bootstrap>:219: size=7648 KiB (+7557 KiB), count=26455 (+25555), average=296 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/torch/__init__.py:427: size=1291 KiB (+1291 KiB), count=5924 (+5924), average=223 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/abc.py:85: size=1039 KiB (+1026 KiB), count=3429 (+3385), average=310 B\r\n <frozen importlib._bootstrap_external>:64: size=917 KiB (+891 KiB), count=5300 (+5132), average=177 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/collections/__init__.py:456: size=720 KiB (+709 KiB), count=3403 (+3349), average=217 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/tf_export.py:346: size=607 KiB (+607 KiB), count=3962 (+3962), average=157 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/linecache.py:137: size=1000 KiB (+489 KiB), count=9569 (+4535), average=107 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/tf_decorator.py:241: size=367 KiB (+367 KiB), count=5225 (+5225), average=72 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/decorator_utils.py:114: size=359 KiB (+359 KiB), count=330 (+330), average=1114 B\r\n```\r\n\r\nI'm not concluding much, it seems nothing is really happening to memory on `pyarrow::Table.filter`? ", "Cool ! Maybe it increases the memory a bit but what's brought in memory is not the resulting Table but something else (not sure what though).\r\nWhat's the length of the resulting dataset ?\r\nYou can also take a look at `pyarrow.total_allocated_memory()` to show how much memory is being used by pyarrow", "```diff\r\ndiff --git a/benchmarks/benchmark_filter.py b/benchmarks/benchmark_filter.py\r\nindex 4b9efd4e..a862c204 100644\r\n--- a/benchmarks/benchmark_filter.py\r\n+++ b/benchmarks/benchmark_filter.py\r\n@@ -1,6 +1,9 @@\r\n import logging\r\n import sys\r\n import time\r\n+import tracemalloc\r\n+\r\n+import pyarrow as pa\r\n \r\n from datasets import load_dataset, set_caching_enabled\r\n \r\n@@ -9,13 +12,28 @@ if __name__ == \"__main__\":\r\n set_caching_enabled(False)\r\n logging.basicConfig(level=logging.DEBUG)\r\n \r\n+ tracemalloc.start()\r\n bc = load_dataset(\"bookcorpus\")\r\n \r\n now = time.time()\r\n try:\r\n+ snapshot1 = tracemalloc.take_snapshot()\r\n+ pamem1 = pa.total_allocated_bytes()\r\n bc[\"train\"].filter(lambda x: len(x[\"text\"]) < 64, num_proc=int(sys.argv[1]))\r\n+ pamem2 = pa.total_allocated_bytes()\r\n+ snapshot2 = tracemalloc.take_snapshot()\r\n except Exception as e:\r\n print(f\"cancelled: {e}\")\r\n+ exit(1)\r\n+ tracemalloc.stop()\r\n elapsed = time.time() - now\r\n \r\n print(elapsed)\r\n+ top_stats = snapshot2.compare_to(snapshot1, \"lineno\")\r\n+\r\n+ print(\"[ Top 10 differences ]\")\r\n+ for stat in top_stats[:10]:\r\n+ print(stat)\r\n+\r\n+ print(\"[ pyarrow reporting ]\")\r\n+ print(f\"before: ({pamem1}) after: ({pamem2})\")\r\n```\r\n\r\nthis yields 0-0, does not seem like a good tool 😛 and the documentation is [quite mysterious.](https://arrow.apache.org/docs/python/generated/pyarrow.total_allocated_bytes.html)", "Personally if I use your script to benchmark on this branch\r\n```python\r\nbc = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nbc = bc.filter(lambda x: len(x[\"text\"]) < 64)\r\n```\r\n\r\nthen I get\r\n```\r\n[ pyarrow reporting ]\r\nbefore: (0) after: (15300672)\r\n```\r\n\r\nMaybe you got 0-0 because the filter output is directly garbage collected, since you didn't do\r\n```python\r\nbc[\"train\"] = bc[\"train\"].filter(...)\r\n```\r\nCan you try again on your side just to make sure ?\r\n\r\nEven if the documentation doesn't say much, `pa.total_allocated_bytes` if pretty useful, and also very consistent.\r\nIt tracks the number of bytes used for arrow data.", "> Maybe you got 0-0 because the filter output is directly garbage collected, since you didn't do\r\n> \r\n> ```python\r\n> bc[\"train\"] = bc[\"train\"].filter(...)\r\n> ```\r\nNice catch! I get 1.74GB for this branch", "Looks like we may need to write the filtered table on the disk then.\r\n\r\nThe other option is to slice the table to keep only the good rows and concatenate them but this is too slow at the moment since slicing is O(n) until #1803 is fixed. I'll work on this issue this afternoon", "From investigation it looks like the lib's `Table.filter` cannot send its output to memorymap, asked a question on the mailing list, see [here](https://lists.apache.org/thread.html/r8cd8591ce83a967eb0097a7f31785ac2f3ee95ea371c8c5beb0720ad%40%3Cuser.arrow.apache.org%3E)", "closing in favor of #2836 " ]

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[ "@skyprince999 as you authored the PR for this dataset, any comments?", "This has been fixed in #2064 by @mariosasko (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)" ]

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[ "@lhoestq I also deleted the cache and redownload the file and still the same issue, I appreciate any help on this. thanks ", "Here please find the minimal code to reproduce the issue @lhoestq note this only happens with MT5TokenizerFast\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import MT5TokenizerFast\r\n\r\ndef get_tokenized_dataset(dataset_name, dataset_config_name, tokenizer):\r\n datasets = load_dataset(dataset_name, dataset_config_name, script_version=\"master\")\r\n column_names = datasets[\"train\"].column_names\r\n text_column_name = \"translation\"\r\n def process_dataset(datasets):\r\n def process_function(examples):\r\n lang = \"fr\"\r\n return {\"src_texts\": [example[lang] for example in examples[text_column_name]]}\r\n datasets = datasets.map(\r\n process_function,\r\n batched=True,\r\n num_proc=None,\r\n remove_columns=column_names,\r\n load_from_cache_file=True,\r\n )\r\n return datasets\r\n datasets = process_dataset(datasets)\r\n text_column_name = \"src_texts\"\r\n column_names = [\"src_texts\"]\r\n def tokenize_function(examples):\r\n return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n tokenized_datasets = datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=None,\r\n remove_columns=column_names,\r\n load_from_cache_file=True\r\n )\r\n\r\nif __name__ == \"__main__\":\r\n tokenizer_kwargs = {\r\n \"cache_dir\": None,\r\n \"use_fast\": True,\r\n \"revision\": \"main\",\r\n \"use_auth_token\": None\r\n }\r\n tokenizer = MT5TokenizerFast.from_pretrained(\"google/mt5-small\", **tokenizer_kwargs)\r\n get_tokenized_dataset(dataset_name=\"opus100\", dataset_config_name=\"en-fr\", tokenizer=tokenizer)\r\n~ \r\n```", "as per https://github.com/huggingface/tokenizers/issues/626 this looks like to be the tokenizer bug, I therefore, reported it there https://github.com/huggingface/tokenizers/issues/626 and I am closing this one." ]

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[ "Hi\r\nYou can rename the arrow file and update the name in `state.json`", "I tried this way, but when there is a mapping process to the dataset, it again uses a random cache name. atm, I am trying to use the following method by setting an exact cache file,\r\n\r\n```\r\n dataset_with_embedding =csv_dataset.map(\r\n partial(self.embed, ctx_encoder=ctx_encoder, ctx_tokenizer=self.context_tokenizer),\r\n batched=True,\r\n batch_size=1,\r\n features=new_features,\r\n cache_file_name=cache_arrow_path,\r\n load_from_cache_file=False\r\n )\r\n```\r\nSo here we set a cache_file_name , after this it uses the same file name when saving again and again. ", "I'm not sure I understand your issue, can you elaborate ?\r\n\r\n`cache_file_name` is indeed an argument you can set to specify the cache file that will be used for the processed dataset. By default the file is named with something like `cache-<fingerprint>.arrow` where the fingerprint is a hash.", "Let's say I am updating a set of embedding in a dataset that is around 40GB inside a training loop every 500 steps (Ex: calculating the embeddings in updated ctx_encoder in RAG and saving it to the passage path). So when we use **dataset_object.save_to_disk('passage_path_directory')** it will save the new dataset object every time with a random file name, especially when we do some transformations to dataset objects such as map or shards. This way, we keep collecting unwanted files that will eventually eat up all the disk space. \r\n\r\nBut if we can save the dataset object every time by a single name like **data_shard_1.arrow**, it will automatically remove the previous file and save the new one in the same directory. I found the above-mentioned code snippet useful to complete this task. \r\n\r\nIs this clear?" ]

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[ "The zest dataset url was changed (allenai/zest#3) and #2057 should resolve this.", "This has been fixed in #2057 by @matt-peters (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)", "Thanks @lhoestq and @matt-peters ", "I am closing this issue since its fixed!" ]

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[ "Hi @lhoestq,\r\n\r\nShould I remove the 160 configurations? Is it too much?\r\n\r\nEDIT:\r\nCan you also check the task category? I'm not sure if there is an appropriate tag for the same.", "Thanks for the changes !\r\n\r\n> Should I remove the 160 configurations? Is it too much?\r\n\r\nYea 160 configuration is a lot.\r\nMaybe this dataset can work with parameters `type` and `task_no` ?\r\nYou can just remove the configuration in BUILDER_CONFIGS to only keep a few ones.\r\nAlso feel free to add an example in the dataset card of how to load the other configurations\r\n```\r\nload_dataset(\"babi_qa\", type=\"hn\", task_no=\"qa1\")\r\n```\r\nfor example, and with a list of the possible combinations.\r\n\r\n> Can you also check the task category? I'm not sure if there is an appropriate tag for the same.\r\n\r\nIt looks appropriate, thanks :)", "Hi @lhoestq \r\n\r\nI'm unable to test it locally using:\r\n```python\r\nload_dataset(\"datasets/babi_qa\", type=\"hn\", task_no=\"qa1\")\r\n```\r\nIt raises an error:\r\n```python\r\nTypeError: __init__() got an unexpected keyword argument 'type'\r\n```\r\nWill this be possible only after merging? Or am I missing something here?", "Can you try adding this class attribute to `BabiQa` ?\r\n```python\r\nBUILDER_CONFIG_CLASS = BabiQaConfig\r\n```\r\nThis should fix the TypeError issue you got", "My bad. Thanks a lot!", "Hi @lhoestq \r\n\r\nI have added the changes. Only the \"qa1\" task for each category is included. Also, I haven't removed the size categories and other description because I think it will still be useful. I have updated the line in README showing the example.\r\n\r\nThanks,\r\nGunjan", "Hi @lhoestq,\r\n\r\nDoes this look good now?" ]

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[ "Hi,\r\n\r\nthis was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: \r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n```", "Ty!" ]

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[ "Hi @lhoestq,\r\n\r\nI have added changes from review.", "Thanks for approving :)" ]

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[ "@lhoestq - We could simply use the \"general\" json dataset for this no? ", "Sure you can use the json loader\r\n```python\r\ndata_files = {\"train\": \"path/to/your/train_data.json\", \"test\": \"path/to/your/test_data.json\"}\r\ntrain_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\ntest_dataset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n```\r\n\r\nYou just need to make sure that the data contain the paths to the audio files.\r\nIf not, feel free to use `.map()` to add them.", "Many thanks! that was what I was looking for. " ]

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[ "LGTM, thanks for fixing." ]

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[ "Hello. All aforementioned changes have been made. I've also re-run black on miam.py. :-)", "I will run isort again. Hopefully it resolves the current check_code_quality test failure.", "Once the review period is over, feel free to open a PR to add all the missing information ;)", "Hi! I will follow up right now with one more pull request as I have new anonymous citation information to include." ]

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[ "I think faiss automatically sets the number of threads to use to build the index.\r\nCan you check how many CPU cores are being used when you build the index in `use_own_knowleldge_dataset` as compared to this script ? Are there other programs running (maybe for rank>0) ?", "Hi,\r\n I am running the add_faiss_index during the training process of the RAG from the master process (rank 0). But at the exact moment, I do not run any other process since I do it in every 5000 training steps. \r\n \r\n I think what you say is correct. It depends on the number of CPU cores. I did an experiment to compare the time taken to finish the add_faiss_index process on use_own_knowleldge_dataset.py vs the training loop thing. The training loop thing takes 40 mins more. It might be natural right? \r\n \r\n \r\n at the moment it uses around 40 cores of a 96 core machine (I am fine-tuning the entire process). ", "Can you try to set the number of threads manually ?\r\nIf you set the same number of threads for both the `use_own_knowledge_dataset.py` and RAG training, it should take the same amount of time.\r\nYou can see how to set the number of thread in the faiss wiki: https://github.com/facebookresearch/faiss/wiki/Threads-and-asynchronous-calls", "Ok, I will report the details too soon. I am the first one on the list and currently add_index being computed for the 3rd time in the loop. Actually seems like the time is taken to complete each interaction is the same, but around 1 hour more compared to running it without the training loop. A the moment this takes 5hrs and 30 mins. If there is any way to faster the process, an end-to-end rag will be perfect. So I will also try out with different thread numbers too. \r\n\r\n\r\n", "@lhoestq on a different note, I read about using Faiss-GPU, but the documentation says we should use it when the dataset has the ability to fit into the GPU memory. Although this might work, in the long-term this is not that practical for me.\r\n\r\nhttps://github.com/matsui528/faiss_tips", "@lhoestq \r\n\r\nHi, I executed the **use_own_dataset.py** script independently and ask a few of my friends to run their programs in the HPC machine at the same time. \r\n\r\n Once there are so many other processes are running the add_index function gets slows down naturally. So basically the speed of the add_index depends entirely on the number of CPU processes. Then I set the number of threads as you have mentioned and got actually the same time for RAG training and independat running. So you are correct! :) \r\n\r\n \r\n Then I added this [issue in Faiss repostiary](https://github.com/facebookresearch/faiss/issues/1767). I got an answer saying our current **IndexHNSWFlat** can get slow for 30 million vectors and it would be better to use alternatives. What do you think?", "It's a matter of tradeoffs.\r\nHSNW is fast at query time but takes some time to build.\r\nA flat index is flat to build but is \"slow\" at query time.\r\nAn IVF index is probably a good choice for you: fast building and fast queries (but still slower queries than HSNW).\r\n\r\nNote that for an IVF index you would need to have an `nprobe` parameter (number of cells to visit for one query, there are `nlist` in total) that is not too small in order to have good retrieval accuracy, but not too big otherwise the queries will take too much time. From the faiss documentation:\r\n> The nprobe parameter is always a way of adjusting the tradeoff between speed and accuracy of the result. Setting nprobe = nlist gives the same result as the brute-force search (but slower).\r\n\r\nFrom my experience with indexes on DPR embeddings, setting nprobe around 1/4 of nlist gives really good retrieval accuracy and there's no need to have a value higher than that (or you would need to brute-force in order to see a difference).", "@lhoestq \r\n\r\nThanks a lot for sharing all this prior knowledge. \r\n\r\nJust asking what would be a good nlist of parameters for 30 million embeddings?", "When IVF is used alone, nlist should be between `4*sqrt(n)` and `16*sqrt(n)`.\r\nFor more details take a look at [this section of the Faiss wiki](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#how-big-is-the-dataset)", "Thanks a lot. I was lost with calling the index from class and using faiss_index_factory. ", "@lhoestq Thanks a lot for the help you have given to solve this issue. As per my experiments, IVF index suits well for my case and it is a lot faster. The use of this can make the entire RAG end-to-end trainable lot faster. So I will close this issue. Will do the final PR soon. " ]

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[ "Not sure why CI isn't triggered.\r\n\r\n@lhoestq Can you please help me with this? ", "I don't know how to trigger it manually, but an empty commit should do the job" ]

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[ "Hi @lhoestq,\r\n\r\nI have added changes from the review.", "Thanks for approving @lhoestq " ]

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[ "@lhoestq But we don't perform conversion to a `NamedSplit` if `_split` is not a string which means it **will** be a `ReadInstruction` after reloading.", "Yes right ! I read it wrong.\r\nPerfect then" ]

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[ "Hi ! To help me understand the situation, can you print the values of `load_from_disk(PATH_DATA_CLS_A)['train']._indices_data_files` and `load_from_disk(PATH_DATA_CLS_B)['train']._indices_data_files` ?\r\nThey should both have a path to an arrow file\r\n\r\nAlso note that from #2025 concatenating datasets will no longer have such restrictions.", "Sure, thanks for the fast reply!\r\n\r\nFor dataset A: `[{'filename': 'drive/MyDrive/data_target_task/dataset_a/train/cache-4797266bf4db1eb7.arrow'}]`\r\nFor dataset B: `[]`\r\n\r\nNo clue why for B it returns nothing. `PATH_DATA_CLS_B` is exactly the same in `save_to_disk` and `load_from_disk`... Also I can verify that the folder physically exists under 'drive/MyDrive/data_target_task/dataset_b/'", "In the next release you'll be able to concatenate any kinds of dataset (either from memory or from disk).\r\n\r\nFor now I'd suggest you to flatten the indices of the A and B datasets. This will remove the indices mapping and you will be able to concatenate them. You can flatten the indices with\r\n```python\r\ndataset = dataset.flatten_indices()\r\n```", "Indeed this works. Not the most elegant solution, but it does the trick. Thanks a lot! " ]

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[ "Hi ! Thanks for reporting.\r\n\r\nTo update the dataset_infos.json you can run:\r\n```\r\ndatasets-cli test ./datasets/doc2dial --all_configs --save_infos --ignore_verifications\r\n```", "Fixed by #2041, thanks again @songfeng !" ]

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[ "The error you got is minor and appeared in the last version of pyarrow, we'll fix the CI to take this into account. You can ignore it" ]

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[ "Solved!" ]

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[ "Dear @lhoestq for wikipedia dataset I also get the same error, I greatly appreciate if you could have a look into this dataset as well. Below please find the command to reproduce the error:\r\n\r\n```\r\ndataset = load_dataset(\"wikipedia\", \"20200501.bg\")\r\nprint(dataset)\r\n```\r\n\r\nYour library is my only chance to be able training the models at scale and I am grateful for your help.\r\n\r\n", "Hi @dorost1234,\r\nTry installing this library first, `pip install 'apache-beam[gcp]' --use-feature=2020-resolver` followed by loading dataset like this using beam runner.\r\n\r\n`dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')`\r\n\r\n I also read in error stack trace that:\r\n\r\n> Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc.\r\n\r\nWorked perfectly fine after this (Ignore these warnings)\r\n\r\n\r\n\r\n", "For wikipedia dataset, looks like the files it's looking for are no longer available. For `bg`, I checked [here](https://dumps.wikimedia.org/bgwiki/). For this I think `dataset_infos.json` for this dataset has to made again? You'll have to load this dataset also using beam runner.\r\n\r\n", "Hello @dorost1234,\r\n\r\nIndeed, Wikipedia datasets need a lot of preprocessing and this is done using Apache Beam. That is the reason why it is required that you install Apache Beam in order to preform this preprocessing.\r\n\r\nFor some specific default parameters (English Wikipedia), Hugging Face has already preprocessed the dataset for you (and it is stored in the cloud). That is the reason why you do not get the error for English: the preprocessing is already done by HF and you just get the preprocessed dataset; Apache Beam is not required in that case.", "Hi\nI really appreciate if huggingface can kindly provide preprocessed\ndatasets, processing these datasets require sufficiently large resources\nand I do not have unfortunately access to, and perhaps many others too.\nthanks\n\nOn Fri, Mar 12, 2021 at 9:04 AM Albert Villanova del Moral <\n***@***.***> wrote:\n\n> Hello @dorost1234 <https://github.com/dorost1234>,\n>\n> Indeed, Wikipedia datasets need a lot of preprocessing and this is done\n> using Apache Beam. That is the reason why it is required that you install\n> Apache Beam in order to preform this preprocessing.\n>\n> For some specific default parameters (English Wikipedia), Hugging Face has\n> already preprocessed the dataset for you (and it is stored in the cloud).\n> That is the reason why you do not get the error for English: the\n> preprocessing is already done by HF and you just get the preprocessed\n> dataset; Apache Beam is not required in that case.\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2035#issuecomment-797310899>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AS37NMXACFQZAGMK4VGXRETTDHDI3ANCNFSM4ZA5R2UA>\n> .\n>\n", "Hi everyone\r\nthanks for the helpful pointers, I did it as @bhavitvyamalik suggested, for me this freezes on this command for several hours, \r\n\r\n`Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /users/dara/cache/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\n`\r\n\r\nDo you know how long this takes? Any specific requirements the machine should have? like very large memory or so? @lhoestq \r\n\r\nthanks \r\n\r\n\r\n", "HI @dorost1234, \r\nThe dataset size is 631.84 MiB so depending on your internet speed it'll take some time. You can monitor your internet speed meanwhile to see if it's downloading the dataset or not (use `nload` if you're using linux/mac to monitor the same). In my case it took around 3-4 mins. Since they haven't used `download_and_extract` here that's why there's no download progress bar.", "Hi\r\nthanks, my internet speed should be good, but this really freezes for me, this is how I try to get this dataset:\r\n\r\n`from datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')`\r\n\r\nthe output I see if different also from what you see after writing this command:\r\n\r\n`Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /users/dara/cache/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...`\r\n\r\ndo you have any idea why it might get freezed? anything I am missing @lhoestq @bhavitvyamalik. Do I need maybe to set anything special for apache-beam? \r\n\r\nthanks a lot \r\n\r\nOn Tue, Mar 16, 2021 at 9:03 AM Bhavitvya Malik ***@***.***>\r\nwrote:\r\n\r\n> HI @dorost1234 <https://github.com/dorost1234>,\r\n> The dataset size is 631.84 MiB so depending on your internet speed it'll\r\n> take some time. You can monitor your internet speed meanwhile to see if\r\n> it's downloading the dataset or not (use nload if you're using linux/mac\r\n> to monitor the same). In my case it took around 3-4 mins. Since they\r\n> haven't used download_and_extract here that's why there's no download\r\n> progress bar.\r\n>\r\n> —\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/2035#issuecomment-800044303>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AS37NMQIHNNLM2LGG6QKZ73TD4GDJANCNFSM4ZA5R2UA>\r\n> .\r\n>\r\n", "I tried this on another machine (followed the same procedure I've mentioned above). This is what it shows (during the freeze period) for me:\r\n```\r\n>>> dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')\r\nDownloading: 5.26kB [00:00, 1.23MB/s] \r\nDownloading: 1.40kB [00:00, 327kB/s] \r\nDownloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bhavitvya/.cache/huggingface/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nWARNING:apache_beam.internal.gcp.auth:Unable to find default credentials to use: The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials. See https://developers.google.com/accounts/docs/application-default-credentials for more information.\r\nConnecting anonymously.\r\nWARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\r\n```\r\nAfter around 10 minutes, here's the loading of dataset:\r\n```\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:16<00:00, 16.42s/sources]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.12sources/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.14sources/s]\r\nDataset wiki40b downloaded and prepared to /home/bhavitvya/.cache/huggingface/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f. Subsequent calls will reuse this data.\r\n```", "Hi\r\nI honestly also now tried on another machine and nothing shows up after\r\nhours of waiting. Are you sure you have not set any specific setting? maybe\r\ngoogle cloud which seems it is used here, needs some credential setting?\r\nthanks for any suggestions on this\r\n\r\nOn Tue, Mar 16, 2021 at 10:02 AM Bhavitvya Malik ***@***.***>\r\nwrote:\r\n\r\n> I tried this on another machine (followed the same procedure I've\r\n> mentioned above). This is what it shows (during the freeze period) for me:\r\n>\r\n> >>> dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')\r\n> Downloading: 5.26kB [00:00, 1.23MB/s]\r\n> Downloading: 1.40kB [00:00, 327kB/s]\r\n> Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bhavitvya/.cache/huggingface/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\n> WARNING:apache_beam.internal.gcp.auth:Unable to find default credentials to use: The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials. See https://developers.google.com/accounts/docs/application-default-credentials for more information.\r\n> Connecting anonymously.\r\n> WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\r\n>\r\n> After around 10 minutes, here's the loading of dataset:\r\n>\r\n> 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:16<00:00, 16.42s/sources]\r\n> 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.12sources/s]\r\n> 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.14sources/s]\r\n> Dataset wiki40b downloaded and prepared to /home/bhavitvya/.cache/huggingface/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f. Subsequent calls will reuse this data.\r\n>\r\n> —\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/2035#issuecomment-800081772>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AS37NMX6A2ZTRZUIIZVFRCDTD4NC3ANCNFSM4ZA5R2UA>\r\n> .\r\n>\r\n" ]

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[ "Hi @miyamonz \r\nThanks for investigating this issue, good job !\r\nIt would be awesome to integrate your fix in the library, could you open a pull request ?", "OK! I'll send it later." ]

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[ "I am wondering why only one test of \"keep_in_memory=True\" fails, when there are many other tests that test the same and it happens only in pyarrow_1..." ]

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[ "In your code `dataset2` doesn't contain the \"embeddings\" column, since it is created from the pandas DataFrame with columns \"text\" and \"label\".\r\n\r\nTherefore when you call `dataset2[embeddings_name]`, you get a `KeyError`.\r\n\r\nIf you want the \"embeddings\" column back, you can create `dataset2` with\r\n```python\r\ndataset2 = load_from_disk(dataset_filename)\r\n```\r\nwhere `dataset_filename` is the place where you saved you dataset with the embeddings in the first place.", "Ok in that case HF should fix their misleading example at https://huggingface.co/docs/datasets/faiss_and_ea.html#adding-a-faiss-index \r\n\r\nI copy-pasted it here.\r\n\r\n> When you are done with your queries you can save your index on disk:\r\n> \r\n> ```python\r\n> ds_with_embeddings.save_faiss_index('embeddings', 'my_index.faiss')\r\n> ```\r\n> Then reload it later:\r\n> \r\n> ```python\r\n> ds = load_dataset('crime_and_punish', split='train[:100]')\r\n> ds.load_faiss_index('embeddings', 'my_index.faiss')\r\n> ```", "Hi !\r\n\r\nThe code of the example is valid.\r\nAn index is a search engine, it's not considered a column of a dataset.\r\nWhen you do `ds.load_faiss_index(\"embeddings\", 'my_index.faiss')`, it attaches an index named \"embeddings\" to the dataset but it doesn't re-add the \"embeddings\" column. You can list the indexes of a dataset by using `ds.list_indexes()`.\r\n\r\nIf I understand correctly by reading this example you thought that it was re-adding the \"embeddings\" column.\r\nThis looks misleading indeed, and we should add a note to make it more explicit that it doesn't store the column that was used to build the index.\r\n\r\nFeel free to open a PR to suggest an improvement on the documentation if you want to contribute :)", "> If I understand correctly by reading this example you thought that it was re-adding the \"embeddings\" column.\r\nYes. I was trying to use the dataset in RAG and it complained that the dataset didn't have the right columns. No problems when loading the dataset with `load_from_disk` and then doing `load_faiss_index`\r\n\r\nWhat I learned was\r\n1. column and index are different\r\n2. loading the index does not create a column\r\n3. the column is not needed to be able to use the index\r\n4. RAG needs both the embeddings column and the index\r\n\r\nIf I can come up with a way to articulate this in the right spot in the docs, I'll open a PR" ]

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