Datasets:

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[ "Looks like we have a nice speed up in some benchmarks. For example:\r\n- `read_formatted numpy 5000`: 4.584777 sec -> 0.487113 sec\r\n- `read_formatted torch 5000`: 4.565676 sec -> 1.289514 sec", "Can we convert this draft to PR @lhoestq ?", "Ready for review ! cc @vblagoje", "@lhoestq I tried the branch and it works for me. Although performance trace now shows a speedup, the overall pre-training speed up is minimal. But that's on my plate to explore further. ", "Thanks for investigating @vblagoje \r\n\r\n@albertvillanova , do you have any comments on this PR ? Otherwise I think we can merge it" ]

"2021-06-15T10:11:32Z"

"2021-06-28T09:53:39Z"

"2021-06-28T09:53:38Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-03-30T16:40:30Z"

"2022-03-31T17:12:10Z"

"2022-03-31T17:07:50Z"

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[ "This seems to be related to your environment and not the `datasets` code (e.g., this could happen when exposing the Python 3.9 site packages to a lower Python version (interpreter))" ]

"2023-08-25T08:35:14Z"

"2023-09-27T17:33:39Z"

"2023-09-27T17:33:39Z"

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[ "Hi :) \r\nIn your dummy data zip file you can just have `subset000.xz` as directories instead of compressed files.\r\nLet me know if it helps", "Thanks for your comment @lhoestq ,\r\nJust for confirmation, changing dummy data like this won't make dummy test test the functionality to extract `subsetxxx.xz` but actually kind of circumvent it. But since we will test the real data so it is ok ?", "Yes it's fine for now. We plan to add a job for slow tests.\r\nAnd at one point we'll also do another pass on the dummy data handling and consider extracting files.", "Thanks for the confirmation.\r\nAlso the suggestion works. Thank you." ]

"2020-09-19T11:07:03Z"

"2020-09-22T11:54:10Z"

"2020-09-22T11:54:09Z"

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"2020-12-29T11:20:40Z"

"2020-12-30T17:15:16Z"

"2020-12-30T17:15:16Z"

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"2020-12-04T15:04:33Z"

"2020-12-04T15:16:49Z"

"2020-12-04T15:16:49Z"

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[ "Hi ! When `batched=True`, you filter function must take a batch as input, and return a list of booleans.\r\n\r\nIn your case, something like\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n\r\nds_mc4_ja = load_dataset(\"mc4\", \"ja\") # This will take 6+ hours... perhaps test it with a toy dataset instead?\r\nds_mc4_ja_2020 = ds_mc4_ja.filter(\r\n lambda batch: [timestamp[:4] == \"2020\" for timestamp in batch[\"timestamp\"]],\r\n batched=True,\r\n)\r\n```\r\n\r\nLet me know if it helps !", "> Hi ! When `batched=True`, you filter function must take a batch as input, and return a list of booleans.\r\n> [...]\r\n> Let me know if it helps !\r\n\r\nHi @lhoestq,\r\n\r\nAh, my bad, I totally forgot that part...\r\nSorry for the trouble and thank you for the kind help!" ]

"2022-09-06T09:47:48Z"

"2022-09-06T11:44:27Z"

"2022-09-06T11:44:27Z"

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"2020-05-13T10:39:00Z"

"2020-05-13T10:40:15Z"

"2020-05-13T10:40:14Z"

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[ "Can you reopen with the correct dataset name (if relevant)?\r\n\r\nThanks", "The dataset viewer was down tonight. It works again." ]

"2022-01-03T02:03:23Z"

"2022-01-03T08:41:26Z"

"2022-01-03T08:23:07Z"

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[ "Amazing! ", "Small poll @thomwolf @yjernite @lhoestq @JetRunner @qiweizhen .\r\n\r\nAs stated in the XGLUE paper: https://arxiv.org/pdf/2004.01401.pdf , for each of the 11 down-stream tasks training data is only available in English, whereas development and test data is available in multiple different language *cf.* here: \r\n\r\n\r\n\r\nSo, I'd suggest to have exactly 11 \"language-independent\" configs: \"ner\", \"pos\", ... and give the sample in each dataset in the config a \"language\" label being one of \"ar\", \"bg\", .... => To me this makes more sense than making languaga specific config, *e.g.* \"ner-de\", ...especially because training data is only available in English. Do you guys agree? ", "In this case we should have named splits, so config `ner` has splits `train`, `validation`, `test-en`, `test-ar`, `test-bg`, etc...\r\n\r\nThis is more in the spirit of the task afaiu, and will avoid making users do the filtering step themselves when testing different models or different configurations of the same model.", "I see your point! \r\n\r\nI think this would be quite feasible to do and makes sense to me as well! In the paper results are reported per language, so it seems more natural to do it this way. \r\n\r\nGood for me @yjernite ! What do the others think? @lhoestq \r\n", "I agree with Yacine on this!", "Okey actually not that easy to add things like `test-de` to `datasets` => this would be the first dataset to have this.\r\nSee: https://github.com/huggingface/datasets/pull/802", "IMO we should have one config per language. That's what we're doing for xnli, xtreme etc.\r\nHaving split names that depend on the language seems wrong. We should try to avoid split names that are not train/val/test.\r\nSorry for late response on this one", "@lhoestq agreed on having one config per language, but we also need to be able to have different split names and people are going to want to use hyphens, so we should at the very least warn them why it's failing :) E.g. for ANLI with different stages of data (currently using underscores) or https://www.tau-nlp.org/commonsenseqa with their train-sanity or dev-sanity splits", "Yes sure ! Could you open a separate issue for that ?", "Really cool dataset 👍 btw. does Transformers support all 11 tasks 🤔 would be awesome to have a xglue script (like the \"normal\" glue one)", "Just to make sure this is what we want here. If we add one config per language, \r\n\r\nthis means that this dataset ends up with well over 100 different configs most of which will have the same `train` split. The train split is always in English. Also, I'm not sure whether it's better for the user to be honest. \r\n\r\nI think it could be quite confusing for the user to have\r\n\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner-de\", split=\"train\")\r\n```\r\n\r\nin English even though it's `ner-de`.\r\n\r\nTo be honest, I'd prefer:\r\n\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner\", split=\"train\")\r\ntest_dataset_de = load_dataset(\"xglue\", \"ner\", split=\"test-de\")\r\ntest_dataset_fr = load_dataset(\"xglue\", \"ner\", split=\"test-fr\")\r\n```\r\n\r\nhere", "Oh yes right I didn't notice the train set was always in english sorry.\r\nMoreover it seems that the way this dataset is used is to pick a pretrained multilingual model, fine-tune it on the english train set and then evaluate on each test set (one per language).\r\nSo to better fit the usual usage of this dataset, I agree that it's better to have one test split per language. \r\n\r\nSomething like your latest example patrick is fine imo :\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner\", split=\"train\")\r\ntest_dataset_de = load_dataset(\"xglue\", \"ner\", split=\"test.de\")\r\n```\r\n\r\nI just replace test-de with test.de since `-` is not allowed for split names (it has to follow the `\\w+` regex), and usually we specify the language after a point. ", "Closing since XGLUE has been added in #802 , thanks patrick :) ", "I need xglue Urdu summarization dataset so how can i get it?", "According to the table in https://huggingface.co/datasets/xglue, Urdu only exists for POS and XNLI in XGLUE - not for summarization" ]

"2020-10-21T10:51:36Z"

"2022-09-30T11:35:30Z"

"2021-01-06T10:02:55Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-03-31T18:34:34Z"

"2022-04-12T20:43:45Z"

"2022-04-12T20:37:38Z"

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[ "I think it's more likely that this issue is related to PyTorch than Datasets, as PyTorch (on import) registers functions to execute when forking a process. Maybe this is the culprit: https://github.com/pytorch/pytorch/issues/99625", "From reading that ticket, it may be down in mkl? Is it worth hotfixing in the meantime, with the express intention of turning it off? I know that's a horribly crufty solution, but it's also deeply frustrating to be limited to 2 cores for operations as simple as filtration.", "This is too specific and unrelated to `datasets`, so this shouldn't be fixed here.", "@mariosasko @mmr-crexi I had the exact same problem on my kubernetes cluster. the datasets subprocess only user 1 and 17 core" ]

"2023-06-22T19:57:31Z"

"2023-10-30T06:17:40Z"

"2023-07-24T11:54:52Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Some links are still missing I think :)", "This is probably quite close to being ready, so cc some TF people @gante @amyeroberts @merveenoyan just so they see it! No need for a full review, but if you have any comments or suggestions feel free.", "Thanks ! We plan to make a new release later today for `to_tf_dataset` FYI, so I think we can merge it soon and include this documentation in the new release" ]

"2022-06-07T16:06:48Z"

"2022-06-14T16:08:41Z"

"2022-06-14T15:59:08Z"

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[ "This is super cool, I love that ❤️ " ]

"2021-05-27T16:16:17Z"

"2021-05-28T13:10:10Z"

"2021-05-28T13:09:16Z"

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"2021-08-03T15:48:59Z"

"2021-08-04T09:43:25Z"

"2021-08-04T09:43:25Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-05-02T18:38:08Z"

"2022-05-06T08:38:13Z"

"2022-05-02T19:03:49Z"

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"2021-04-29T11:52:38Z"

"2021-04-29T12:54:34Z"

"2021-04-29T12:54:34Z"

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[ "Nice catch! Yes I didn't check the actual data, instead I was just looking for the `if line.startswith(\"-DOCSTART-\")` pattern." ]

"2021-03-06T19:08:29Z"

"2021-03-11T02:20:07Z"

"2021-03-11T02:20:07Z"

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"2021-06-08T09:45:14Z"

"2021-06-08T17:41:37Z"

"2021-06-08T17:41:36Z"

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[ "Yes that would be awesome. Not only the download speeds are awful, but also some files are missing.\r\nWe list all the URLs in the datasets/wmt19/wmt_utils.py so we can make a script to download them all and host on S3.\r\nAlso I think most of the materials are under the CC BY-NC-SA 3.0 license (must double check) so it should be possible to redistribute the data with no issues.\r\n\r\ncc @patrickvonplaten who knows more about the wmt scripts", "Yeah, the scripts are pretty ugly! A big refactor would make sense here...and I also remember that the datasets were veeery slow to download", "I'm downloading them.\r\nI'm starting with the ones hosted on http://data.statmt.org which are the slowest ones", "@lhoestq better to use our new git-based system than just raw S3, no? (that way we have built-in CDN etc.)", "Closing since the urls were changed to mirror urls in #1912 ", "Hi there! What about mirroring other datasets like [CCAligned](http://www.statmt.org/cc-aligned/) as well? All of them are really slow to download..." ]

"2021-02-16T18:36:11Z"

"2021-10-26T06:55:42Z"

"2021-03-25T11:53:23Z"

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[ "Hi @ChewKokWah,\r\n\r\nWe're working on it! In the meantime, you can still find the dataset explorer at the following URL: https://huggingface.co/datasets/viewer/", "Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. \r\nHope similar feature can be retain in the future version." ]

"2021-01-26T20:39:02Z"

"2021-02-01T13:58:44Z"

"2021-02-01T13:58:44Z"

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"2020-11-03T18:38:58Z"

"2020-11-03T18:44:14Z"

"2020-11-03T18:44:10Z"

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[ "Hi ! Yes this is definitely something we'll explore, since optimizing processing pipelines can be challenging and because performance is key here: we want anyone to be able to play with large-scale datasets more easily.\r\n\r\nI think we'll start by documenting the performance of the dataset transforms we provide, and then we can have some tools to help debugging/optimizing them" ]

"2022-03-05T00:28:06Z"

"2022-03-10T16:24:27Z"

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[ "Hi ! It runs on my side without issues. I tried\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"boolq\")\r\n```\r\n\r\nWhat version of datasets and tensorflow are your runnning ?\r\nAlso if you manage to get a minimal reproducible script (on google colab for example) that would be useful.", "hey\ni do the exact same commands. for me it fails i guess might be issues with\ncaching maybe?\nthanks\nbest\nrabeeh\n\nOn Tue, Nov 24, 2020, 10:24 AM Quentin Lhoest <[email protected]>\nwrote:\n\n> Hi ! It runs on my side without issues. I tried\n>\n> from datasets import load_datasetload_dataset(\"boolq\")\n>\n> What version of datasets and tensorflow are your runnning ?\n> Also if you manage to get a minimal reproducible script (on google colab\n> for example) that would be useful.\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/879#issuecomment-732769114>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABP4ZCGGDR2FUMRKZTIY5CTSRN3VXANCNFSM4T7R3U6A>\n> .\n>\n", "Could you check if it works on the master branch ?\r\nYou can use `load_dataset(\"boolq\", script_version=\"master\")` to do so.\r\nWe did some changes recently in boolq to remove the TF dependency and we changed the way the data files are downloaded in https://github.com/huggingface/datasets/pull/881" ]

"2020-11-23T14:28:28Z"

"2022-10-05T12:23:32Z"

"2022-10-05T12:23:32Z"

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"2023-02-06T14:25:55Z"

"2023-02-28T18:19:18Z"

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"2021-06-21T07:04:25Z"

"2021-06-21T10:01:13Z"

"2021-06-21T08:57:33Z"

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[ "@SBrandeis, What exactly is it that makes the tests fail? Can you help me please?", "These errors\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ajgt_twitter_ar\r\nFAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_chr_en\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_ajgt_twitter_ar\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_chr_en\r\n```\r\nappeared on master 3 hours ago and are now fixed.\r\n(it was due to today's update of `xlrd` that broke two datasets)\r\n\r\nYou can ignore them, they're not related to your dataset", "> These errors\r\n> \r\n> ```\r\n> =========================== short test summary info ============================\r\n> FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ajgt_twitter_ar\r\n> FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_chr_en\r\n> FAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_ajgt_twitter_ar\r\n> FAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_chr_en\r\n> ```\r\n> \r\n> appeared on master 3 hours ago and are now fixed.\r\n> (it was due to today's update of `xlrd` that broke two datasets)\r\n> \r\n> You can ignore them, they're not related to your dataset\r\n\r\n**If it is not a problem caused by us, we have already completed the review notes. You can then check it out and confirm?**", "merging since the CI is fixed on master" ]

"2020-12-10T19:41:12Z"

"2020-12-11T14:27:15Z"

"2020-12-11T14:27:15Z"

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[ "Hi,\r\n\r\nthere is a difference in how the `data_dir` is zipped between the `classla/janes_tag` and the `classla/reldi_hr` dataset. After unzipping, for the former, the data files (`*.conllup`) are in the root directory (root -> data files), and for the latter, they are inside the `data` directory (root -> `data` -> data files).\r\n\r\nThis can be fixed by removing the `os.path.join` call in https://huggingface.co/datasets/classla/janes_tag/blob/main/janes_tag.py#L86\r\n\r\nLet me know if this works for you.", "Hi Mario,\r\n\r\nI had already tried that before, but it didn't work. I have now recreated the `classla/janes_tag` zip file so that it also contains the `data` directory, but I am still getting the same error.", "Hi,\r\n\r\nI just tried to download the `classla/janes_tag` dataset, and this time the zip file is extracted correctly. However, the script is now throwing the IndexError, probably due to a bug in the `_generate_examples`.\r\n\r\nLet me know if you are still getting the same error.", "I am still getting the same error.", "Hi, \r\n\r\ncould you try to download the dataset with a different `cache_dir` like so:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('classla/janes_tag', split='validation', cache_dir=\"path/to/different/cache/dir\")\r\n```\r\nIf this works, then most likely the cached extracted data is causing issues. This data is stored at `~/.cache/huggingface/datasets/downloads/extracted` and needs to be deleted, and then it should work (you can easily locate the directory with the path given in the `OSError` message). Additionally, I'd suggest you to update `datasets` to the newest version with:\r\n```\r\npip install -U datasets\r\n```", "Thank you, deleting the `~/.cache/huggingface/datasets/downloads/extracted` directory helped. However, I am still having problems.\r\n\r\nThere was indeed a bug in the script that was throwing an `IndexError`, which I have now corrected (added the condition to skip the lines starting with '# text') and it is working locally, but still throws an error when I try to load the dataset from HuggingFace. I literally copied and pasted the `_generate_examples` function and ran it on the `dev_all.conllup` file, which I even re-downloaded from the repository to be certain that the files are exactly the same. I also deleted everything again just in case, but it didn't help. The code works locally, but throws an `IndexError` when loading from `datasets.`", "Hi,\r\n\r\nDid some investigation.\r\n\r\nTo fix the dataset script on the Hub, append the following labels to the `names` list of the `upos_tags` field:\r\n```'INTJ NOUN', 'AUX PRON', 'PART ADV', 'PRON ADP', 'INTJ INTJ', 'VERB NOUN', 'NOUN AUX'```.\r\n\r\nThis step is required to avoid an error due to missing labels in the following step which is:\r\n```python\r\nload_dataset(\"classla/janes_tag\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\nThis will generate and cache the dataset, so specifying `download_mode` will not be required anymore unless you update the script/data on the Hub.", "It works now, thank you!" ]

"2021-10-20T20:08:39Z"

"2021-11-23T09:55:38Z"

"2021-11-23T09:55:38Z"

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"2023-11-14T04:21:01Z"

"2023-11-22T16:42:09Z"

"2023-11-22T16:42:09Z"

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[ "Hi ! Note that it does `block_size *= 2` until `block_size > len(batch)`, so it doesn't loop indefinitely. What do you mean by \"get stuck indefinitely\" then ? Is this the actual call to `paj.read_json` that hangs ?\r\n\r\n> increasing the `chunksize` argument decreases the chance of getting stuck\r\n\r\nCould you share the values of chunksize that you're using to observe this ? And maybe the order of magnitude of number of bytes per line of JSON ?", "To clarify, I don't think it loops indefinitely but the `paj.read_json` gets stuck after the first try. That's why I think it could be an issue with a lock somewhere. \r\n\r\nUsing `load_dataset(..., chunksize=40<<20)` worked without errors.", "@lhoestq I encountered another related issue. I use load_dataset() for my json data and set_transform() for preprocessing. But it hangs at the end of the epoch if `dataloader_num_workers>=1`. It appears to be working fine with num_worker=0, but it's slow.\r\n```\r\ntrain_dataset = datasets.load_dataset(\"json\", \r\n data_files=corpus_jsonl_path,\r\n keep_in_memory=False,\r\n cache_dir=model_args.cache_dir,\r\n streaming=False)\r\ntrain_dataset.set_transform(psg_parse_fn)\r\n```\r\n", "I couldn't I think your problem is unrelated to this issue @memray\r\nIndeed this issue discusses a bug when doing `load_dataset`, while your case has to do with the dataloader in a multiprocessing setup. Can you open a new issue and provide more details (share your env and what psg_parse_fn does) ?", "I also encountered a similar issue when loading a 190GB dataset of jsonl files (255 files with less than 1Gb) where it got stuck for over 20h at tables generation (fig below), increasing the `chunksize` with `load_dataset(..., chunksize=40<<20)` fixed the issue\r\n\r\n<img width=\"560\" alt=\"image\" src=\"https://user-images.githubusercontent.com/44069155/195605603-548a106e-7ad3-4269-8cdd-2ad3e975bf16.png\">\r\n", "> @lhoestq I encountered another related issue. I use load_dataset() for my json data and set_transform() for preprocessing. But it hangs at the end of the epoch if `dataloader_num_workers>=1`. It appears to be working fine with num_worker=0, but it's slow.\r\n> \r\n> ```\r\n> train_dataset = datasets.load_dataset(\"json\", \r\n> data_files=corpus_jsonl_path,\r\n> keep_in_memory=False,\r\n> cache_dir=model_args.cache_dir,\r\n> streaming=False)\r\n> train_dataset.set_transform(psg_parse_fn)\r\n> ```\r\n\r\nIn case people also get this problem, I found a way to fix it by adding `persistent_workers=True` when initializing DataLoader, like:\r\n`train_loader = DataLoader(\r\n train_dataset,\r\n batch_size=self._train_batch_size,\r\n sampler=train_sampler,\r\n collate_fn=data_collator,\r\n num_workers=self.args.dataloader_num_workers,\r\n persistent_workers=True\r\n )`\r\n\r\nThe error was `CUDA error: initialization error Exception raised from insert_events at ../c10/cuda/CUDACachingAllocator.cpp:1266` after the 1st epoch, I guess it's because the data_loader worker is killed after each epoch and the data supply is cut off. This error only occurs when num_workers>1.\r\n\r\n\r\n", "I can confirm the issue using datasets (2.12.0) with the following code and Accelerate (0.20.3) env:\r\n\r\n````\r\ntrainDataloader = DataLoader(trainSplit, batch_size=args.train_batch_size, shuffle=True)\r\nevalDataloader = DataLoader(validSplit, batch_size=args.valid_batch_size) // Here is where it gets stuck.\r\n````\r\n````\r\n- `Accelerate` version: 0.20.3\r\n- Platform: Linux-5.4.0-150-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- Numpy version: 1.24.3\r\n- PyTorch version (GPU?): 2.0.1+cu117 (True)\r\n- PyTorch XPU available: False\r\n- System RAM: 503.28 GB\r\n- GPU type: Tesla V100-SXM2-32GB\r\n- `Accelerate` default config:\r\n\t- compute_environment: LOCAL_MACHINE\r\n\t- distributed_type: MULTI_GPU\r\n\t- mixed_precision: fp16\r\n\t- use_cpu: False\r\n\t- num_processes: 2\r\n\t- machine_rank: 0\r\n\t- num_machines: 1\r\n\t- gpu_ids: 0,1\r\n\t- rdzv_backend: static\r\n\t- same_network: True\r\n\t- main_training_function: main\r\n\t- downcast_bf16: no\r\n\t- tpu_use_cluster: False\r\n\t- tpu_use_sudo: False\r\n\t- tpu_env: []\r\n````\r\n\r\nNotable that with Accelerate configured for one GPU only, **it doesn't get stuck.** \r\n\r\nThe suggestion made by @memray worked in my case. This is how it was applied: \r\n````\r\ntrainDataloader = DataLoader(trainSplit, batch_size=args.train_batch_size, shuffle=True, num_workers=2, persistent_workers=True)\r\nevalDataloader = DataLoader(validSplit, batch_size=args.valid_batch_size, num_workers=2, persistent_workers=True)\r\n````\r\n", "I think your issue is related to `accelerate`, feel free to open an issue there: https://github.com/huggingface/accelerate/issues\r\n\r\n`Dataset` objects generally work fine with the torch DataLoader, idk what `accelerate` does that could make it get stuck." ]

"2022-02-11T18:50:48Z"

"2023-06-16T11:24:12Z"

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"2023-05-11T14:15:01Z"

"2023-05-11T14:15:01Z"

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"2021-09-15T17:12:11Z"

"2021-09-15T17:21:45Z"

"2021-09-15T17:21:45Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6509). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I created `DatatasetGenerationCastError` in `exceptions.py` that inherits from `DatasetGenerationError` (for backward compatibility) that inherits from `DatasetsError`.\r\n\r\nI also added a help message at the end of the error:\r\n\r\n```\r\nPlease either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004991 / 0.011353 (-0.006361) | 0.003362 / 0.011008 (-0.007646) | 0.062093 / 0.038508 (0.023585) | 0.051533 / 0.023109 (0.028424) | 0.247508 / 0.275898 (-0.028390) | 0.275593 / 0.323480 (-0.047886) | 0.003828 / 0.007986 (-0.004158) | 0.002573 / 0.004328 (-0.001755) | 0.047727 / 0.004250 (0.043477) | 0.037029 / 0.037052 (-0.000023) | 0.250359 / 0.258489 (-0.008130) | 0.282640 / 0.293841 (-0.011201) | 0.027853 / 0.128546 (-0.100693) | 0.010247 / 0.075646 (-0.065400) | 0.206826 / 0.419271 (-0.212445) | 0.035837 / 0.043533 (-0.007695) | 0.251795 / 0.255139 (-0.003344) | 0.275654 / 0.283200 (-0.007545) | 0.017722 / 0.141683 (-0.123960) | 1.120287 / 1.452155 (-0.331868) | 1.203087 / 1.492716 (-0.289630) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092320 / 0.018006 (0.074314) | 0.300079 / 0.000490 (0.299589) | 0.000211 / 0.000200 (0.000011) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018193 / 0.037411 (-0.019218) | 0.061310 / 0.014526 (0.046784) | 0.072433 / 0.176557 (-0.104124) | 0.119092 / 0.737135 (-0.618043) | 0.074044 / 0.296338 (-0.222294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297184 / 0.215209 (0.081975) | 2.805197 / 2.077655 (0.727543) | 1.521326 / 1.504120 (0.017206) | 1.374321 / 1.541195 (-0.166874) | 1.388767 / 1.468490 (-0.079723) | 0.571865 / 4.584777 (-4.012912) | 2.385213 / 3.745712 (-1.360499) | 2.726840 / 5.269862 (-2.543021) | 1.725352 / 4.565676 (-2.840325) | 0.063012 / 0.424275 (-0.361263) | 0.004911 / 0.007607 (-0.002697) | 0.336430 / 0.226044 (0.110385) | 3.390616 / 2.268929 (1.121688) | 1.846398 / 55.444624 (-53.598227) | 1.576797 / 6.876477 (-5.299680) | 1.579445 / 2.142072 (-0.562627) | 0.652515 / 4.805227 (-4.152712) | 0.118393 / 6.500664 (-6.382271) | 0.042155 / 0.075469 (-0.033314) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942269 / 1.841788 (-0.899518) | 11.318258 / 8.074308 (3.243950) | 10.299948 / 10.191392 (0.108556) | 0.136088 / 0.680424 (-0.544336) | 0.013682 / 0.534201 (-0.520519) | 0.287549 / 0.579283 (-0.291734) | 0.258346 / 0.434364 (-0.176018) | 0.337146 / 0.540337 (-0.203191) | 0.443922 / 1.386936 (-0.943014) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005302 / 0.011353 (-0.006051) | 0.003234 / 0.011008 (-0.007774) | 0.049159 / 0.038508 (0.010651) | 0.050459 / 0.023109 (0.027350) | 0.273718 / 0.275898 (-0.002180) | 0.296997 / 0.323480 (-0.026483) | 0.003948 / 0.007986 (-0.004038) | 0.002590 / 0.004328 (-0.001739) | 0.048129 / 0.004250 (0.043879) | 0.039369 / 0.037052 (0.002317) | 0.276469 / 0.258489 (0.017980) | 0.306359 / 0.293841 (0.012519) | 0.028864 / 0.128546 (-0.099682) | 0.010253 / 0.075646 (-0.065394) | 0.058264 / 0.419271 (-0.361008) | 0.032451 / 0.043533 (-0.011082) | 0.277336 / 0.255139 (0.022197) | 0.296137 / 0.283200 (0.012937) | 0.018094 / 0.141683 (-0.123589) | 1.119539 / 1.452155 (-0.332615) | 1.163116 / 1.492716 (-0.329600) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092578 / 0.018006 (0.074572) | 0.300756 / 0.000490 (0.300267) | 0.000222 / 0.000200 (0.000022) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022333 / 0.037411 (-0.015078) | 0.076632 / 0.014526 (0.062107) | 0.087829 / 0.176557 (-0.088727) | 0.127686 / 0.737135 (-0.609449) | 0.091314 / 0.296338 (-0.205024) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297499 / 0.215209 (0.082290) | 2.889775 / 2.077655 (0.812120) | 1.598976 / 1.504120 (0.094856) | 1.478805 / 1.541195 (-0.062389) | 1.481818 / 1.468490 (0.013328) | 0.557972 / 4.584777 (-4.026804) | 2.453248 / 3.745712 (-1.292464) | 2.771823 / 5.269862 (-2.498039) | 1.721527 / 4.565676 (-2.844150) | 0.062786 / 0.424275 (-0.361489) | 0.005298 / 0.007607 (-0.002309) | 0.346660 / 0.226044 (0.120615) | 3.412262 / 2.268929 (1.143334) | 1.940240 / 55.444624 (-53.504384) | 1.654015 / 6.876477 (-5.222461) | 1.652039 / 2.142072 (-0.490034) | 0.636870 / 4.805227 (-4.168357) | 0.116213 / 6.500664 (-6.384451) | 0.040937 / 0.075469 (-0.034532) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.001605 / 1.841788 (-0.840183) | 11.986592 / 8.074308 (3.912284) | 10.231288 / 10.191392 (0.039896) | 0.130242 / 0.680424 (-0.550182) | 0.015764 / 0.534201 (-0.518437) | 0.289257 / 0.579283 (-0.290026) | 0.275996 / 0.434364 (-0.158368) | 0.323089 / 0.540337 (-0.217248) | 0.556383 / 1.386936 (-0.830553) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#773324159ad4afd7931588a710839b76670ddf87 \"CML watermark\")\n" ]

"2023-12-18T13:57:24Z"

"2023-12-18T17:17:54Z"

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[ "Ok I tried to add the dummy dataset (I actually modified the dummy_data command to generate them for me because it was too painful to do that manually).\r\n\r\nThe dummy_data test seems to work:\r\n```bash\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_iwslt2017\r\n```\r\n\r\nHowever the test on the full data fails, because the `**config_kwargs` don't include `pair, multilingual`.\r\nI could add a default parameter for the Config (but that feels broken, how can one config be the \"default\" ?). If I do I still have errors, saying that something within the downloader is a directory so I'm not sure where that comes from.\r\n\r\nI can share my auto_zip dummy data code if you want (I tried to keep it clean). [Edit: it's [here](https://github.com/Narsil/nlp/tree/auto_zip)]. \r\nThe way it works is that it just keeps X line from the beginning of the original files, and Y lines at the end. It's good enough for my usage, but I guess it could work for most data files out there (as long as they're real text and not binary format)", "The slow test doesn't support dataset that require config parameters that don't have default values.\r\n\r\nTo improve that we can replace it by two tests:\r\n- one test that loads the default config (it can simply be the first config of the config lists for example)\r\n- one tests that iterate over all configs and load them all one by one\r\n\r\nBy using the configs inside the builder config lists, there is no need to instantiate new configs, so the missing parameter error doesn't happen.\r\n\r\nDoes that sound good to you ?", "Seems fair.\r\nHowever I'm unsure what I should do ?\r\n\r\nShould I wait for #527 to pass and rebase and the command will be the same ?\r\nShould I update something ?", "I think everything is fine on your side. Thanks for adding this dataset :)\r\n\r\nI think it's better to wait for the slow test to be updated if you don't mind.\r\n", "Sure ! :)", "Thanks for fixing the isort/black changes :)\r\nFeel free to merge if it's good for you @Narsil " ]

"2020-08-03T09:52:39Z"

"2020-09-07T12:33:30Z"

"2020-09-07T12:33:30Z"

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[ "Fixed on master (see https://github.com/huggingface/datasets/pull/3719).", "Awesome, I did not find that one! Thanks." ]

"2022-02-18T11:30:52Z"

"2022-02-18T11:38:23Z"

"2022-02-18T11:38:23Z"

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"2021-12-13T19:32:02Z"

"2021-12-14T14:39:51Z"

"2021-12-14T14:39:50Z"

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"2021-10-05T08:55:22Z"

"2021-10-05T09:58:28Z"

"2021-10-05T09:58:27Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-03-28T07:01:25Z"

"2022-03-28T07:49:39Z"

"2022-03-28T07:44:24Z"

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[ "May eventually be solved in #5883 ", "#self-assign" ]

"2023-05-22T12:03:06Z"

"2023-06-09T16:04:56Z"

"2023-06-09T16:04:55Z"

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[ "Hi !\r\n\r\nTo fix 1, an you try to run this code ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"squad\", download_mode=\"force_redownload\")\r\n```\r\nMaybe the file your downloaded was corrupted, in this case redownloading this way should fix your issue 1.\r\n\r\nRegarding your 2nd point, you're right that loading the raw json this way doesn't give you a dataset with the column \"context\", \"question\" and \"answers\". Indeed the squad format is a very nested format so you have to preprocess the data. You can do it this way:\r\n```python\r\ndef process_squad(examples):\r\n \"\"\"\r\n Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n \"\"\"\r\n out = {\"context\": [], \"question\": [], \"answers\":[]} \r\n for paragraphs in examples[\"paragraphs\"]: \r\n for paragraph in paragraphs: \r\n for qa in paragraph[\"qas\"]: \r\n answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n out[\"context\"].append(paragraph[\"context\"].strip()) \r\n out[\"question\"].append(qa[\"question\"].strip()) \r\n out[\"answers\"].append(answers) \r\n return out\r\n\r\ndatasets = load_dataset(extension, data_files=data_files, field=\"data\")\r\ncolumn_names = datasets[\"train\"].column_names\r\n\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n```\r\n\r\nHope that helps :)", "Thks for quickly answering！\r\n### 1 I try the first way,but seems not work \r\n```\r\nTraceback (most recent call last):\r\n File \"examples/question-answering/run_qa.py\", line 503, in <module>\r\n main()\r\n File \"examples/question-answering/run_qa.py\", line 218, in main\r\n datasets = load_dataset(data_args.dataset_name, download_mode=\"force_redownload\")\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/load.py\", line 746, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/builder.py\", line 573, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/builder.py\", line 633, in _download_and_prepare\r\n self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 39, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json']\r\n```\r\n### 2 I try the second way,and run the examples/question-answering/run_qa.py,it lead to another bug orz..\r\n```\r\nTraceback (most recent call last):\r\n File \"examples/question-answering/run_qa.py\", line 523, in <module>\r\n main()\r\n File \"examples/question-answering/run_qa.py\", line 379, in main\r\n load_from_cache_file=not data_args.overwrite_cache,\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/arrow_dataset.py\", line 1120, in map\r\n update_data = does_function_return_dict(test_inputs, test_indices)\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/arrow_dataset.py\", line 1091, in does_function_return_dict\r\n function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n File \"examples/question-answering/run_qa.py\", line 339, in prepare_train_features\r\n if len(answers[\"answer_start\"]) == 0:\r\nTypeError: list indices must be integers or slices, not str\r\n```\r\n## may be the function prepare_train_features in run_qa.py need to fix,I think is that the prep\r\n```python\r\nfor i, offsets in enumerate(offset_mapping):\r\n # We will label impossible answers with the index of the CLS token.\r\n input_ids = tokenized_examples[\"input_ids\"][i]\r\n cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n # One example can give several spans, this is the index of the example containing this span of text.\r\n sample_index = sample_mapping[i]\r\n answers = examples[answer_column_name][sample_index]\r\n print(examples,answers)\r\n # If no answers are given, set the cls_index as answer.\r\n if len(answers[\"answer_start\"]) == 0:\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Start/end character index of the answer in the text.\r\n start_char = answers[\"answer_start\"][0]\r\n end_char = start_char + len(answers[\"text\"][0])\r\n\r\n # Start token index of the current span in the text.\r\n token_start_index = 0\r\n while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n token_start_index += 1\r\n\r\n # End token index of the current span in the text.\r\n token_end_index = len(input_ids) - 1\r\n while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n token_end_index -= 1\r\n\r\n # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n # Note: we could go after the last offset if the answer is the last word (edge case).\r\n while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n token_start_index += 1\r\n tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n while offsets[token_end_index][1] >= end_char:\r\n token_end_index -= 1\r\n tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n return tokenized_examples\r\n``` ", "## I have fixed it, @lhoestq \r\n### the first section change as you said and add [\"id\"]\r\n```python\r\ndef process_squad(examples):\r\n \"\"\"\r\n Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n \"\"\"\r\n # print(examples)\r\n out = {\"context\": [], \"question\": [], \"answers\":[],\"id\":[]} \r\n for paragraphs in examples[\"paragraphs\"]: \r\n for paragraph in paragraphs: \r\n for qa in paragraph[\"qas\"]: \r\n answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n out[\"context\"].append(paragraph[\"context\"].strip()) \r\n out[\"question\"].append(qa[\"question\"].strip()) \r\n out[\"answers\"].append(answers) \r\n out[\"id\"].append(qa[\"id\"]) \r\n return out\r\ncolumn_names = datasets[\"train\"].column_names if training_args.do_train else datasets[\"validation\"].column_names\r\n# print(datasets[\"train\"].column_names)\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n# Preprocessing the datasets.\r\n# Preprocessing is slighlty different for training and evaluation.\r\nif training_args.do_train:\r\n column_names = datasets[\"train\"].column_names\r\nelse:\r\n column_names = datasets[\"validation\"].column_names\r\n# print(column_names)\r\nquestion_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\ncontext_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\nanswer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n```\r\n### the second section\r\n```python\r\ndef prepare_train_features(examples):\r\n # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n # in one example possible giving several features when a context is long, each of those features having a\r\n # context that overlaps a bit the context of the previous feature.\r\n tokenized_examples = tokenizer(\r\n examples[question_column_name if pad_on_right else context_column_name],\r\n examples[context_column_name if pad_on_right else question_column_name],\r\n truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n max_length=data_args.max_seq_length,\r\n stride=data_args.doc_stride,\r\n return_overflowing_tokens=True,\r\n return_offsets_mapping=True,\r\n padding=\"max_length\" if data_args.pad_to_max_length else False,\r\n )\r\n\r\n # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n # its corresponding example. This key gives us just that.\r\n sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n # The offset mappings will give us a map from token to character position in the original context. This will\r\n # help us compute the start_positions and end_positions.\r\n offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n # Let's label those examples!\r\n tokenized_examples[\"start_positions\"] = []\r\n tokenized_examples[\"end_positions\"] = []\r\n\r\n for i, offsets in enumerate(offset_mapping):\r\n # We will label impossible answers with the index of the CLS token.\r\n input_ids = tokenized_examples[\"input_ids\"][i]\r\n cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n # One example can give several spans, this is the index of the example containing this span of text.\r\n sample_index = sample_mapping[i]\r\n answers = examples[answer_column_name][sample_index]\r\n # print(examples,answers,offset_mapping,tokenized_examples)\r\n # If no answers are given, set the cls_index as answer.\r\n if len(answers) == 0:#len(answers[\"answer_start\"]) == 0:\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Start/end character index of the answer in the text.\r\n start_char = answers[0][\"answer_start\"]\r\n end_char = start_char + len(answers[0][\"text\"])\r\n\r\n # Start token index of the current span in the text.\r\n token_start_index = 0\r\n while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n token_start_index += 1\r\n\r\n # End token index of the current span in the text.\r\n token_end_index = len(input_ids) - 1\r\n while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n token_end_index -= 1\r\n\r\n # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n # Note: we could go after the last offset if the answer is the last word (edge case).\r\n while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n token_start_index += 1\r\n tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n while offsets[token_end_index][1] >= end_char:\r\n token_end_index -= 1\r\n tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n return tokenized_examples\r\n```", "I'm glad you managed to fix run_qa.py for your case :)\r\n\r\nRegarding the checksum error, I'm not able to reproduce on my side.\r\nThis errors says that the downloaded file doesn't match the expected file.\r\n\r\nCould you try running this and let me know if you get the same output as me ?\r\n```python\r\nfrom datasets.utils.info_utils import get_size_checksum_dict\r\nfrom datasets import cached_path\r\n\r\nget_size_checksum_dict(cached_path(\"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json\"))\r\n# {'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```", "I run the code,and it show below:\r\n```\r\n>>> from datasets.utils.info_utils import get_size_checksum_dict\r\n>>> from datasets import cached_path\r\n>>> get_size_checksum_dict(cached_path(\"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json\"))\r\nDownloading: 30.3MB [04:13, 120kB/s]\r\n{'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```", "Alright ! So in this case redownloading the file with `download_mode=\"force_redownload\"` should fix it. Can you try using `download_mode=\"force_redownload\"` again ?\r\n\r\nNot sure why it didn't work for you the first time though :/" ]

"2021-03-01T08:41:31Z"

"2022-10-05T13:09:47Z"

"2022-10-05T13:09:47Z"

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[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]

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[ "Thank you for creating this feature request, Albert.\r\n\r\nFor context this is the datatest where Albert has been helping me to switch to on-the-fly split config https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing\r\n\r\nand the attempt to switch on-the-fly splits was here: https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing/discussions/2/files\r\n\r\nbut which I had to revert since providing no split breaks at run time.\r\n" ]

"2022-10-04T19:49:35Z"

"2022-10-06T14:40:26Z"

"2022-10-06T14:40:26Z"

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"2021-11-04T09:15:32Z"

"2021-11-05T10:57:37Z"

"2021-11-05T10:57:37Z"

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"2023-10-20T12:59:55Z"

"2023-10-20T12:59:55Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I think it's also reasonable to add the same note to the AudioFolder decription", "Thank you ! I think \"regular\" is more appropriate than \"small\". It can easily scale to a few thousands of images - just not millions x)", "Replaced \"small\" with \"several thousand\" since what is considered \"regular\" and even \"small\" can be kind of vague!" ]

"2022-12-01T21:47:29Z"

"2022-12-06T17:20:04Z"

"2022-12-06T17:16:53Z"

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"2020-12-02T03:05:42Z"

"2020-12-02T16:07:16Z"

"2020-12-02T16:07:16Z"

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[ "I just take a look at all the outputs produced by `datasets` using the different log-levels.\r\nAs far as i can tell using `datasets==1.17.0` they overall issue seems to be fixed.\r\n\r\nHowever, I noticed that there is one tqdm based progress indicator appearing on STDERR that I can simply not suppress.\r\n```\r\nResolving data files: 100%|██████████| 652/652 [00:00<00:00, 1604.52it/s]\r\n```\r\n\r\nAccording to _get_origin_metadata_locally_or_by_urls it shold be supressable by using the `NOTSET` log-level\r\nhttps://github.com/huggingface/datasets/blob/1406a04c3e911cec2680d8bc513653e0cafcaaa4/src/datasets/data_files.py#L491-L501\r\nSadly when specifiing the log-level `NOTSET` it seems to has no effect.\r\n\r\nBut appart from it not having any effect I must admit that it seems unintuitive to me.\r\nI would suggest changing this such that it is only shown when the log-level is greater or equal to INFO.\r\n\r\nThis would conform better to INFO according to the [documentation](https://huggingface.co/docs/datasets/v1.0.0/package_reference/logging_methods.html#datasets.logging.set_verbosity_info).\r\n> This will display most of the logging information and tqdm bars.\r\n\r\nAny inputs on this?\r\nI will be happy to supply a PR if desired 👍 ", "Hi! This should disable the tqdm output:\r\n```python\r\nimport datasets\r\ndatasets.set_progress_bar_enabled(False)\r\n```\r\n\r\nOn a side note: I believe the issue with logging (not tqdm) is still relevant on master." ]

"2021-08-24T11:50:41Z"

"2023-07-12T17:19:30Z"

"2023-07-12T17:19:29Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-10-19T07:58:27Z"

"2022-10-20T08:12:21Z"

"2022-10-20T08:10:00Z"

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[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/altlex)." ]

"2022-07-07T02:23:02Z"

"2022-07-14T02:12:39Z"

"2022-07-14T02:12:39Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-04-25T16:17:59Z"

"2022-04-26T17:00:24Z"

"2022-04-26T13:38:26Z"

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[ "Hi ! Unfortunately I couldn't reproduce on my side locally and with datasets 2.11 and python 3.10.11 on colab.\r\nWhat version of `multiprocess` are you using ?", "I've got `multiprocess` version `0.70.14`.\r\n\r\nI've done some more testing and the error only occurs in PyCharm's Python Console. It seems to be [this PyCharm bug](https://youtrack.jetbrains.com/issue/PY-51922/Multiprocessing-bug.-Can-only-run-in-debugger.), I'll close this.", "For other users facing this, my workaround is to conditionally set `num_proc` so I can work interactively in the PyCharm Python Console while developing, then when I'm ready to run on the whole dataset, run it as a script and use multiprocessing.\r\n\r\n```py\r\nmapped_ds = ds.map(\r\n my_map_function,\r\n batched=True,\r\n remove_columns=ds.column_names,\r\n num_proc=1 if \"PYCHARM_HOSTED\" in os.environ else 8,\r\n)\r\n```" ]

"2023-05-03T21:46:53Z"

"2023-05-04T21:14:21Z"

"2023-05-04T20:22:25Z"

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[]

[ "I've raised a PR for this. Should work with `dataset = load_dataset(\"web_of_science\", \"WOS11967\", ignore_verifications=True)`once it gets merged into the main branch. Thanks for reporting this! " ]

"2021-05-09T02:02:02Z"

"2021-05-10T13:35:53Z"

"2021-05-10T13:35:53Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004811 / 0.011353 (-0.006542) | 0.002478 / 0.011008 (-0.008530) | 0.062241 / 0.038508 (0.023733) | 0.031153 / 0.023109 (0.008044) | 0.248896 / 0.275898 (-0.027002) | 0.276860 / 0.323480 (-0.046620) | 0.002934 / 0.007986 (-0.005052) | 0.002428 / 0.004328 (-0.001901) | 0.048507 / 0.004250 (0.044257) | 0.044567 / 0.037052 (0.007515) | 0.253570 / 0.258489 (-0.004919) | 0.280762 / 0.293841 (-0.013079) | 0.023549 / 0.128546 (-0.104997) | 0.006985 / 0.075646 (-0.068661) | 0.206227 / 0.419271 (-0.213044) | 0.054027 / 0.043533 (0.010494) | 0.257655 / 0.255139 (0.002516) | 0.273498 / 0.283200 (-0.009702) | 0.018997 / 0.141683 (-0.122685) | 1.111732 / 1.452155 (-0.340422) | 1.162078 / 1.492716 (-0.330639) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091816 / 0.018006 (0.073810) | 0.299428 / 0.000490 (0.298938) | 0.000211 / 0.000200 (0.000012) | 0.000048 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018503 / 0.037411 (-0.018908) | 0.062933 / 0.014526 (0.048407) | 0.076349 / 0.176557 (-0.100208) | 0.123291 / 0.737135 (-0.613844) | 0.077491 / 0.296338 (-0.218847) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280770 / 0.215209 (0.065561) | 2.762185 / 2.077655 (0.684530) | 1.429124 / 1.504120 (-0.074996) | 1.303162 / 1.541195 (-0.238033) | 1.307523 / 1.468490 (-0.160967) | 0.405593 / 4.584777 (-4.179184) | 2.396992 / 3.745712 (-1.348721) | 2.550968 / 5.269862 (-2.718894) | 1.557358 / 4.565676 (-3.008318) | 0.046149 / 0.424275 (-0.378126) | 0.004808 / 0.007607 (-0.002799) | 0.341870 / 0.226044 (0.115825) | 3.362478 / 2.268929 (1.093550) | 1.786360 / 55.444624 (-53.658264) | 1.483419 / 6.876477 (-5.393058) | 1.493463 / 2.142072 (-0.648609) | 0.470605 / 4.805227 (-4.334623) | 0.098372 / 6.500664 (-6.402292) | 0.041722 / 0.075469 (-0.033748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.938148 / 1.841788 (-0.903640) | 11.219184 / 8.074308 (3.144876) | 10.454439 / 10.191392 (0.263047) | 0.139645 / 0.680424 (-0.540778) | 0.014453 / 0.534201 (-0.519748) | 0.268975 / 0.579283 (-0.310308) | 0.262060 / 0.434364 (-0.172304) | 0.313652 / 0.540337 (-0.226686) | 0.423992 / 1.386936 (-0.962944) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004829 / 0.011353 (-0.006524) | 0.002426 / 0.011008 (-0.008582) | 0.049064 / 0.038508 (0.010555) | 0.049728 / 0.023109 (0.026619) | 0.273263 / 0.275898 (-0.002635) | 0.295645 / 0.323480 (-0.027835) | 0.004156 / 0.007986 (-0.003830) | 0.002397 / 0.004328 (-0.001932) | 0.048902 / 0.004250 (0.044652) | 0.038414 / 0.037052 (0.001362) | 0.276176 / 0.258489 (0.017687) | 0.306844 / 0.293841 (0.013003) | 0.024546 / 0.128546 (-0.104000) | 0.006946 / 0.075646 (-0.068701) | 0.054024 / 0.419271 (-0.365247) | 0.032444 / 0.043533 (-0.011089) | 0.274125 / 0.255139 (0.018986) | 0.293226 / 0.283200 (0.010027) | 0.018003 / 0.141683 (-0.123680) | 1.130402 / 1.452155 (-0.321752) | 1.195969 / 1.492716 (-0.296748) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090043 / 0.018006 (0.072037) | 0.298699 / 0.000490 (0.298209) | 0.000214 / 0.000200 (0.000014) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021284 / 0.037411 (-0.016127) | 0.069954 / 0.014526 (0.055428) | 0.080445 / 0.176557 (-0.096111) | 0.119461 / 0.737135 (-0.617674) | 0.080632 / 0.296338 (-0.215706) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302246 / 0.215209 (0.087037) | 2.991936 / 2.077655 (0.914281) | 1.662969 / 1.504120 (0.158850) | 1.533141 / 1.541195 (-0.008054) | 1.583183 / 1.468490 (0.114693) | 0.402864 / 4.584777 (-4.181913) | 2.424119 / 3.745712 (-1.321593) | 2.489558 / 5.269862 (-2.780303) | 1.502196 / 4.565676 (-3.063481) | 0.045980 / 0.424275 (-0.378295) | 0.004768 / 0.007607 (-0.002839) | 0.356089 / 0.226044 (0.130044) | 3.481333 / 2.268929 (1.212404) | 2.009713 / 55.444624 (-53.434912) | 1.730021 / 6.876477 (-5.146455) | 1.704656 / 2.142072 (-0.437416) | 0.470832 / 4.805227 (-4.334395) | 0.097473 / 6.500664 (-6.403191) | 0.040437 / 0.075469 (-0.035032) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981497 / 1.841788 (-0.860291) | 11.827242 / 8.074308 (3.752933) | 10.888324 / 10.191392 (0.696932) | 0.129249 / 0.680424 (-0.551174) | 0.015812 / 0.534201 (-0.518389) | 0.269657 / 0.579283 (-0.309626) | 0.275585 / 0.434364 (-0.158779) | 0.305698 / 0.540337 (-0.234639) | 0.411497 / 1.386936 (-0.975439) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bcde318293af04fd5044b42ddfcb650f9b092d45 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6425). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005402 / 0.011353 (-0.005951) | 0.003955 / 0.011008 (-0.007053) | 0.064096 / 0.038508 (0.025588) | 0.062330 / 0.023109 (0.039221) | 0.254729 / 0.275898 (-0.021169) | 0.276259 / 0.323480 (-0.047221) | 0.003052 / 0.007986 (-0.004934) | 0.003474 / 0.004328 (-0.000854) | 0.048938 / 0.004250 (0.044687) | 0.038635 / 0.037052 (0.001583) | 0.267953 / 0.258489 (0.009464) | 0.293725 / 0.293841 (-0.000116) | 0.028266 / 0.128546 (-0.100280) | 0.011188 / 0.075646 (-0.064458) | 0.221204 / 0.419271 (-0.198067) | 0.036549 / 0.043533 (-0.006984) | 0.252484 / 0.255139 (-0.002655) | 0.273855 / 0.283200 (-0.009345) | 0.017975 / 0.141683 (-0.123708) | 1.112265 / 1.452155 (-0.339890) | 1.185647 / 1.492716 (-0.307069) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096223 / 0.018006 (0.078217) | 0.305010 / 0.000490 (0.304520) | 0.000227 / 0.000200 (0.000027) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018924 / 0.037411 (-0.018488) | 0.061910 / 0.014526 (0.047384) | 0.073751 / 0.176557 (-0.102806) | 0.120956 / 0.737135 (-0.616179) | 0.075090 / 0.296338 (-0.221249) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293277 / 0.215209 (0.078068) | 2.867468 / 2.077655 (0.789813) | 1.518218 / 1.504120 (0.014098) | 1.393741 / 1.541195 (-0.147454) | 1.424979 / 1.468490 (-0.043511) | 0.579766 / 4.584777 (-4.005011) | 2.434951 / 3.745712 (-1.310761) | 2.909924 / 5.269862 (-2.359937) | 1.838123 / 4.565676 (-2.727554) | 0.064260 / 0.424275 (-0.360015) | 0.005169 / 0.007607 (-0.002438) | 0.348228 / 0.226044 (0.122184) | 3.447558 / 2.268929 (1.178629) | 1.884988 / 55.444624 (-53.559636) | 1.570921 / 6.876477 (-5.305556) | 1.646341 / 2.142072 (-0.495732) | 0.660189 / 4.805227 (-4.145038) | 0.120026 / 6.500664 (-6.380638) | 0.043715 / 0.075469 (-0.031754) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953253 / 1.841788 (-0.888535) | 12.576112 / 8.074308 (4.501804) | 11.132637 / 10.191392 (0.941245) | 0.132870 / 0.680424 (-0.547553) | 0.014720 / 0.534201 (-0.519481) | 0.291866 / 0.579283 (-0.287417) | 0.265456 / 0.434364 (-0.168908) | 0.338629 / 0.540337 (-0.201709) | 0.456323 / 1.386936 (-0.930613) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005644 / 0.011353 (-0.005709) | 0.003624 / 0.011008 (-0.007384) | 0.049043 / 0.038508 (0.010535) | 0.059572 / 0.023109 (0.036463) | 0.277159 / 0.275898 (0.001261) | 0.303933 / 0.323480 (-0.019547) | 0.004294 / 0.007986 (-0.003692) | 0.002744 / 0.004328 (-0.001584) | 0.048187 / 0.004250 (0.043937) | 0.043655 / 0.037052 (0.006603) | 0.282441 / 0.258489 (0.023952) | 0.317130 / 0.293841 (0.023289) | 0.030159 / 0.128546 (-0.098387) | 0.011300 / 0.075646 (-0.064346) | 0.057451 / 0.419271 (-0.361821) | 0.033666 / 0.043533 (-0.009866) | 0.274554 / 0.255139 (0.019415) | 0.292470 / 0.283200 (0.009270) | 0.018757 / 0.141683 (-0.122926) | 1.170094 / 1.452155 (-0.282060) | 1.244626 / 1.492716 (-0.248090) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094920 / 0.018006 (0.076914) | 0.304156 / 0.000490 (0.303666) | 0.000226 / 0.000200 (0.000026) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022297 / 0.037411 (-0.015115) | 0.068908 / 0.014526 (0.054383) | 0.081520 / 0.176557 (-0.095037) | 0.122422 / 0.737135 (-0.614714) | 0.082533 / 0.296338 (-0.213806) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296080 / 0.215209 (0.080871) | 2.883120 / 2.077655 (0.805465) | 1.607950 / 1.504120 (0.103830) | 1.496191 / 1.541195 (-0.045004) | 1.520549 / 1.468490 (0.052059) | 0.562081 / 4.584777 (-4.022696) | 2.453447 / 3.745712 (-1.292265) | 2.943676 / 5.269862 (-2.326186) | 1.820581 / 4.565676 (-2.745096) | 0.064518 / 0.424275 (-0.359757) | 0.005406 / 0.007607 (-0.002201) | 0.349022 / 0.226044 (0.122978) | 3.472117 / 2.268929 (1.203188) | 2.006928 / 55.444624 (-53.437696) | 1.704800 / 6.876477 (-5.171677) | 1.719025 / 2.142072 (-0.423048) | 0.643719 / 4.805227 (-4.161508) | 0.117723 / 6.500664 (-6.382941) | 0.043158 / 0.075469 (-0.032311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981229 / 1.841788 (-0.860559) | 12.637620 / 8.074308 (4.563312) | 10.848775 / 10.191392 (0.657383) | 0.143981 / 0.680424 (-0.536443) | 0.015950 / 0.534201 (-0.518251) | 0.287542 / 0.579283 (-0.291741) | 0.278989 / 0.434364 (-0.155375) | 0.331786 / 0.540337 (-0.208552) | 0.607238 / 1.386936 (-0.779698) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06fb2f9973962ee97d1af7888209819b8ba7de37 \"CML watermark\")\n" ]

"2023-11-15T18:00:11Z"

"2023-12-13T14:22:30Z"

"2023-12-13T14:16:00Z"

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[]

[]

[ "Better will be if you close this PR and make a fresh PR", "Feel free to ping me if you also have questions about the dummy data", "also it looks like this PR includes changes about dummy_data.zip files in the ./datasets//un_pc folder. Can you remove them ?", "Thanks for all the advice @lhoestq. I've implemented the changes you kindly highlighted and have made sure the scripts pass all the test. I've also marked this as ready for review as I believe it's in a good place to be merged now.", "Great suggestion I fixed the dummy data and the file paths." ]

"2020-12-12T11:55:29Z"

"2020-12-28T14:01:12Z"

"2020-12-28T14:01:12Z"

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"2021-07-26T05:22:16Z"

"2021-07-26T09:30:55Z"

"2021-07-26T09:30:55Z"

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[ "Thanks for working on this! The [issue](https://bugs.python.org/issue24658) with pickling objects larger than 4GB seems to be patched in Python 3.8 (the minimal supported version was 3.6 at the time of implementing this), so a simple solution would be removing the `Table.__setstate__` and `Table.__getstate__` overrides.", "@mariosasko \r\nCool!\r\nI removed these overrides, and it worked.\r\n\r\nAll modifications are committed. Ready for review!", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005251 / 0.011353 (-0.006102) | 0.003804 / 0.011008 (-0.007204) | 0.063143 / 0.038508 (0.024635) | 0.059409 / 0.023109 (0.036300) | 0.255319 / 0.275898 (-0.020579) | 0.279194 / 0.323480 (-0.044285) | 0.004643 / 0.007986 (-0.003343) | 0.002560 / 0.004328 (-0.001768) | 0.047490 / 0.004250 (0.043240) | 0.039034 / 0.037052 (0.001982) | 0.257352 / 0.258489 (-0.001137) | 0.293029 / 0.293841 (-0.000812) | 0.027548 / 0.128546 (-0.100998) | 0.011307 / 0.075646 (-0.064339) | 0.210325 / 0.419271 (-0.208946) | 0.035161 / 0.043533 (-0.008372) | 0.253491 / 0.255139 (-0.001648) | 0.272085 / 0.283200 (-0.011115) | 0.018924 / 0.141683 (-0.122759) | 1.111148 / 1.452155 (-0.341007) | 1.178076 / 1.492716 (-0.314641) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092447 / 0.018006 (0.074441) | 0.303680 / 0.000490 (0.303190) | 0.000208 / 0.000200 (0.000008) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019087 / 0.037411 (-0.018325) | 0.062663 / 0.014526 (0.048137) | 0.074651 / 0.176557 (-0.101905) | 0.121334 / 0.737135 (-0.615802) | 0.076703 / 0.296338 (-0.219636) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286505 / 0.215209 (0.071295) | 2.804942 / 2.077655 (0.727287) | 1.481930 / 1.504120 (-0.022190) | 1.369485 / 1.541195 (-0.171710) | 1.424467 / 1.468490 (-0.044023) | 0.556810 / 4.584777 (-4.027967) | 2.416338 / 3.745712 (-1.329374) | 2.901869 / 5.269862 (-2.367992) | 1.827007 / 4.565676 (-2.738669) | 0.062252 / 0.424275 (-0.362024) | 0.005076 / 0.007607 (-0.002531) | 0.343850 / 0.226044 (0.117805) | 3.377611 / 2.268929 (1.108683) | 1.860214 / 55.444624 (-53.584410) | 1.595146 / 6.876477 (-5.281331) | 1.627234 / 2.142072 (-0.514838) | 0.651027 / 4.805227 (-4.154200) | 0.119214 / 6.500664 (-6.381450) | 0.043342 / 0.075469 (-0.032127) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942863 / 1.841788 (-0.898924) | 12.484633 / 8.074308 (4.410324) | 10.560668 / 10.191392 (0.369276) | 0.144647 / 0.680424 (-0.535777) | 0.014734 / 0.534201 (-0.519466) | 0.286575 / 0.579283 (-0.292708) | 0.270913 / 0.434364 (-0.163451) | 0.323792 / 0.540337 (-0.216545) | 0.419186 / 1.386936 (-0.967750) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005315 / 0.011353 (-0.006038) | 0.003548 / 0.011008 (-0.007460) | 0.049271 / 0.038508 (0.010763) | 0.055198 / 0.023109 (0.032089) | 0.275940 / 0.275898 (0.000042) | 0.307637 / 0.323480 (-0.015843) | 0.003997 / 0.007986 (-0.003988) | 0.002544 / 0.004328 (-0.001785) | 0.050381 / 0.004250 (0.046130) | 0.041158 / 0.037052 (0.004105) | 0.281519 / 0.258489 (0.023030) | 0.308085 / 0.293841 (0.014244) | 0.030464 / 0.128546 (-0.098083) | 0.010690 / 0.075646 (-0.064957) | 0.057458 / 0.419271 (-0.361814) | 0.032814 / 0.043533 (-0.010719) | 0.282435 / 0.255139 (0.027296) | 0.301342 / 0.283200 (0.018142) | 0.017556 / 0.141683 (-0.124127) | 1.159423 / 1.452155 (-0.292732) | 1.177344 / 1.492716 (-0.315372) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091086 / 0.018006 (0.073079) | 0.305316 / 0.000490 (0.304826) | 0.000218 / 0.000200 (0.000019) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021832 / 0.037411 (-0.015579) | 0.071055 / 0.014526 (0.056529) | 0.082982 / 0.176557 (-0.093574) | 0.119966 / 0.737135 (-0.617169) | 0.083539 / 0.296338 (-0.212800) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302501 / 0.215209 (0.087292) | 2.936347 / 2.077655 (0.858692) | 1.601658 / 1.504120 (0.097538) | 1.467267 / 1.541195 (-0.073928) | 1.514656 / 1.468490 (0.046166) | 0.563934 / 4.584777 (-4.020843) | 2.513715 / 3.745712 (-1.231997) | 2.813014 / 5.269862 (-2.456847) | 1.773243 / 4.565676 (-2.792433) | 0.063208 / 0.424275 (-0.361067) | 0.004979 / 0.007607 (-0.002628) | 0.360694 / 0.226044 (0.134650) | 3.520578 / 2.268929 (1.251650) | 1.975369 / 55.444624 (-53.469255) | 1.691257 / 6.876477 (-5.185220) | 1.730872 / 2.142072 (-0.411200) | 0.655366 / 4.805227 (-4.149861) | 0.146043 / 6.500664 (-6.354621) | 0.041386 / 0.075469 (-0.034083) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979840 / 1.841788 (-0.861948) | 12.456924 / 8.074308 (4.382616) | 10.938595 / 10.191392 (0.747203) | 0.133853 / 0.680424 (-0.546571) | 0.015744 / 0.534201 (-0.518457) | 0.289585 / 0.579283 (-0.289698) | 0.291143 / 0.434364 (-0.143221) | 0.328109 / 0.540337 (-0.212228) | 0.561897 / 1.386936 (-0.825039) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05ec66cc1abc20bd13d02c681b7be372ae084a4f \"CML watermark\")\n" ]

"2023-11-22T17:55:10Z"

"2023-11-23T15:19:43Z"

"2023-11-23T15:13:28Z"

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"2023-07-27T17:55:35Z"

"2023-07-27T17:55:35Z"

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[ "Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`/`Audio` feature.", "> Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`/`Audio` feature.\r\n\r\nThanks, I'll keep an eye out for #3575 getting merged. I managed to use `push_to_hub` sucesfully with images when they were loaded via `map` - something like `ds.map(lambda example: {\"img\": load_image_function(example['fname']})`, this only pushed the images to the hub if the `load_image_function` return a PIL Image without the filename attribute though. I guess this might often be the prefered behaviour though. \r\n", "Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?", "> Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?\r\n\r\nThanks for checking. There is no longer an error when calling `select` but it appears the cast value isn't preserved. Before `select`\r\n\r\n```python\r\ndataset.features\r\n{'url': Image(id=None)}\r\n```\r\n\r\nafter select:\r\n```\r\n{'url': Value(dtype='string', id=None)}\r\n```\r\n\r\nUpdated Colab example [here](https://colab.research.google.com/gist/davanstrien/4e88f55a3675c279b5c2f64299ae5c6f/potential_casting_bug.ipynb) ", "Hmmm, if I re-run your google colab I'm getting the right type at the end:\r\n```\r\nsample.features\r\n# {'url': Image(id=None)}\r\n```", "Appolgies - I've just run again and also got this output. I have also sucesfully used the `push_to_hub` method. I think this is fixed now so will close this issue. ", "Fixed in #3575 " ]

"2022-01-18T20:44:01Z"

"2022-01-21T18:07:28Z"

"2022-01-21T18:07:28Z"

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"2023-06-09T14:01:34Z"

"2023-06-12T12:19:34Z"

"2023-06-12T12:19:19Z"

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[ "Thanks for reporting, @catalys1.\r\n\r\nThis seems a duplicate of:\r\n- #3310 \r\n\r\nThe source of the problem is in PyArrow:\r\n- [ARROW-15141: [C++] Fatal error condition occurred in aws_thread_launch](https://issues.apache.org/jira/browse/ARROW-15141)\r\n- [ARROW-17501: [C++] Fatal error condition occurred in aws_thread_launch](https://issues.apache.org/jira/browse/ARROW-17501)\r\n\r\nThe bug in their dependency is still unresolved:\r\n- https://github.com/aws/aws-sdk-cpp/issues/1809\r\n\r\nApparently, the `aws-sdk-cpp` PyArrow dependency needs to be pinned at version `1.8.186` if using conda. Have you updated it after installing PyArrow?\r\n```shell\r\nconda list aws-sdk-cpp\r\n```\r\n\r\nMaybe you should try to downgrade it to that version:\r\n```shell\r\nconda install -c conda-forge aws-sdk-cpp=1.8.186\r\n```" ]

"2022-10-10T20:29:04Z"

"2022-10-11T06:56:01Z"

"2022-10-11T06:56:00Z"

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[]

[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-03-16T15:46:26Z"

"2022-03-17T17:50:54Z"

"2022-03-17T17:47:05Z"

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[]

[]

[ "Also cc @madlag since I recall you wanted to work on CodeXGlue as well ?", "Yes, sorry I did not see earlier your message. I added 34 on the 35 datasets in CodeXGlue, tomorrow I will wrap it up, and so I will remove my version for code_refinement. Maybe we can just have a renaming after the merge, to have a consistent naming with all the other codexglue datasets ? What do you think @reshinthadithyan ?", "> Yes, sorry I did not see earlier your message. I added 34 on the 35 datasets in CodeXGlue, tomorrow I will wrap it up, and so I will remove my version for code_refinement. Maybe we can just have a renaming after the merge, to have a consistent naming with all the other codexglue datasets ? What do you think @reshinthadithyan ?\r\n\r\nHello @madlag, I think you can retain that in your script. Let's stick onto the same file like how Glue is maintained.", "Hi @reshinthadithyan ! Are you still working on this version of the dataset or are we going with @madlag 's only ?", "> Hi @reshinthadithyan ! Are you still working on this version of the dataset or are we going with @madlag 's only ?\r\n\r\nHello, yes. We are going with Madlag's" ]

"2020-12-02T01:29:58Z"

"2020-12-07T01:52:58Z"

"2020-12-07T01:52:58Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-09-21T14:14:25Z"

"2022-09-22T10:16:14Z"

"2022-09-22T10:14:06Z"

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[ "You should use `datasets.load_dataset` instead of `nlp.load_dataset`, as the `nlp` package is outdated.\r\n\r\nIf switching to `datasets.load_dataset` doesn't fix the issue, sharing the JSON file (feel free to replace the data with dummy data) would be nice so that we can reproduce it ourselves." ]

"2023-11-17T14:28:50Z"

"2023-11-22T17:34:58Z"

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[ "@lhoestq yes. I asked the authors for direct link but unfortunately we need to fill a form (captcha)" ]

"2020-12-09T14:13:19Z"

"2020-12-10T16:58:22Z"

"2020-12-10T16:58:21Z"

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[ "Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix" ]

"2023-02-28T12:18:09Z"

"2023-02-28T18:17:26Z"

"2023-02-28T17:21:59Z"

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[ "In the framework of BigScience:\r\n- bigscience-workshop/data_tooling#121\r\n\r\nI have created this dataset as a community dataset: https://huggingface.co/datasets/albertvillanova/pmc_open_access\r\n\r\nHowever, I was wondering that it may be more appropriate to move it under an org namespace: `pubmed_central` or `pmc`\r\nThis way, we could add other datasets I'm also working on: Author Manuscript Dataset, Historical OCR Dataset, LitArch Open Access Subset.\r\n\r\nWhat do you think? @lhoestq @mariosasko ", "Why not ! Having them under such namespaces would also help people searching for this kind of datasets.\r\nWe can also invite people from pubmed at one point", "DONE: https://huggingface.co/datasets/pmc/open_access" ]

"2022-01-04T06:54:35Z"

"2022-01-17T15:25:57Z"

"2022-01-17T15:25:57Z"

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"2021-11-26T16:08:07Z"

"2021-11-26T16:20:37Z"

"2021-11-26T16:20:36Z"

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[ "I transferred from datasets-server, since the issue is more about `datasets` and the integration with `huggingface_hub`." ]

"2023-12-14T11:24:54Z"

"2023-12-14T12:22:21Z"

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[ "Hi :) Good catch ! Feel free to open a PR if you want to contribute, this would be very welcome ;)", "Have raised the PR [here](https://github.com/huggingface/datasets/pull/2717)", "Fixed by #2717." ]

"2021-07-26T13:24:59Z"

"2021-07-26T18:04:43Z"

"2021-07-26T18:04:43Z"

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"2020-11-26T13:05:58Z"

"2020-11-26T13:28:27Z"

"2020-11-26T13:28:26Z"

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