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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3863). All of your documentation changes will be reflected on that endpoint." ]

"2022-03-08T15:34:43Z"

"2022-03-09T16:45:30Z"

"2022-03-09T16:45:29Z"

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[ "looks good now :) wdyt @yjernite ?", "Looks good to merge for me, can edit the dataset card later if required. Merging" ]

"2020-12-03T05:05:11Z"

"2020-12-03T18:29:52Z"

"2020-12-03T18:29:39Z"

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[ "Not sure why the AWS test is failing - perhaps I made too many concurrent CI builds 😢. Can someone please rerun the CI to check the error is not on my end?", "I pushed an improved docstring and the unit tests now pass, which suggests the previous failure on AWS was simply a timeout error. \r\n\r\nFor some reason the docs are now failing to build, but does not seem related to my changes:\r\n```\r\nWarning, treated as error:\r\n/home/circleci/nlp/src/nlp/dataset_dict.py:docstring of nlp.DatasetDict.filter:27:Inline interpreted text or phrase reference start-string without end-string.\r\nmake: *** [Makefile:20: html] Error 2\r\n```\r\n\r\nAny ideas what's going wrong?", "The build_doc fail has been fixed on master.\r\nIt was due to the latest update of sphinx that has some issues, so I pinned the previous version for now.", "I noticed that you also changed the Apache Beam `open` to also use utf-8. However it doesn't have an `encoding` parameter.\r\nTherefore you should ignore lines like\r\n\r\n```python\r\nbeam.io.filesystems.FileSystems.open(filepath)\r\n```\r\n\r\nI guess you could add a rule to your regex to only include the `open` call that have a space right before it.", "Good catch @lhoestq! Your suggestion to match on `open(...)` with a whitespace was a great idea - it allowed me to simplify the regexp considerably 😄.\r\n\r\nI fixed the Apache Beam false positives and also caught a few problems in `json.load()`, e.g.\r\n```python\r\nrelation_name_map = json.load(open(rel_info), encoding='utf-8')\r\n```\r\n\r\nI've tested that the new regexp doesn't reintroduce these false positives, so I think the PR is ready for another review.", "Ok to merge this @lhoestq ?" ]

"2020-08-06T20:02:09Z"

"2020-08-20T08:16:08Z"

"2020-08-20T08:16:08Z"

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[ "Is there a dataset which uses these four datatypes for tests purposes?\r\n", "@severo Not yet. I'll let you know if that changes." ]

"2022-01-18T13:46:05Z"

"2022-01-31T18:29:34Z"

"2022-01-20T17:37:33Z"

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"2021-09-30T15:31:57Z"

"2021-10-01T14:17:39Z"

"2021-10-01T14:17:38Z"

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[ "Great thanks ! I think we can fix the CI by copying the NQ folder on gcs to 0.0.3. Does that sound good ?", "I've copied the 0.0.2 folder content to 0.0.3, as suggested.\r\n\r\nI'm updating the dataset card..." ]

"2022-02-10T14:42:24Z"

"2022-02-11T09:50:22Z"

"2022-02-11T09:50:21Z"

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[ "_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) | 0.007351 / 0.011353 (-0.004002) | 0.005025 / 0.011008 (-0.005983) | 0.095978 / 0.038508 (0.057470) | 0.033486 / 0.023109 (0.010377) | 0.294427 / 0.275898 (0.018529) | 0.325157 / 0.323480 (0.001677) | 0.005671 / 0.007986 (-0.002315) | 0.005284 / 0.004328 (0.000955) | 0.073159 / 0.004250 (0.068909) | 0.045162 / 0.037052 (0.008110) | 0.294004 / 0.258489 (0.035515) | 0.343545 / 0.293841 (0.049704) | 0.036857 / 0.128546 (-0.091689) | 0.012245 / 0.075646 (-0.063401) | 0.332258 / 0.419271 (-0.087014) | 0.051909 / 0.043533 (0.008377) | 0.295701 / 0.255139 (0.040562) | 0.315247 / 0.283200 (0.032048) | 0.102363 / 0.141683 (-0.039320) | 1.441944 / 1.452155 (-0.010211) | 1.527161 / 1.492716 (0.034445) |\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) | 0.211769 / 0.018006 (0.193763) | 0.452015 / 0.000490 (0.451525) | 0.004041 / 0.000200 (0.003841) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027396 / 0.037411 (-0.010015) | 0.108318 / 0.014526 (0.093793) | 0.116851 / 0.176557 (-0.059706) | 0.172658 / 0.737135 (-0.564478) | 0.122876 / 0.296338 (-0.173462) |\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) | 0.406484 / 0.215209 (0.191275) | 4.053849 / 2.077655 (1.976194) | 1.842947 / 1.504120 (0.338827) | 1.649473 / 1.541195 (0.108278) | 1.728629 / 1.468490 (0.260139) | 0.699519 / 4.584777 (-3.885258) | 3.730823 / 3.745712 (-0.014889) | 2.139624 / 5.269862 (-3.130237) | 1.487839 / 4.565676 (-3.077837) | 0.086699 / 0.424275 (-0.337576) | 0.012815 / 0.007607 (0.005208) | 0.514014 / 0.226044 (0.287969) | 5.153315 / 2.268929 (2.884387) | 2.324431 / 55.444624 (-53.120193) | 1.971533 / 6.876477 (-4.904944) | 2.074480 / 2.142072 (-0.067592) | 0.842419 / 4.805227 (-3.962808) | 0.169140 / 6.500664 (-6.331524) | 0.065206 / 0.075469 (-0.010263) |\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) | 1.180887 / 1.841788 (-0.660901) | 14.627401 / 8.074308 (6.553093) | 14.382699 / 10.191392 (4.191307) | 0.143986 / 0.680424 (-0.536438) | 0.017460 / 0.534201 (-0.516741) | 0.422100 / 0.579283 (-0.157183) | 0.417474 / 0.434364 (-0.016890) | 0.493712 / 0.540337 (-0.046625) | 0.589744 / 1.386936 (-0.797193) |\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) | 0.007538 / 0.011353 (-0.003815) | 0.005122 / 0.011008 (-0.005887) | 0.073858 / 0.038508 (0.035350) | 0.034561 / 0.023109 (0.011451) | 0.341250 / 0.275898 (0.065352) | 0.373063 / 0.323480 (0.049583) | 0.005785 / 0.007986 (-0.002200) | 0.005393 / 0.004328 (0.001065) | 0.072354 / 0.004250 (0.068104) | 0.047005 / 0.037052 (0.009953) | 0.341179 / 0.258489 (0.082690) | 0.386299 / 0.293841 (0.092458) | 0.038315 / 0.128546 (-0.090231) | 0.012200 / 0.075646 (-0.063446) | 0.086132 / 0.419271 (-0.333140) | 0.049873 / 0.043533 (0.006340) | 0.337985 / 0.255139 (0.082846) | 0.354806 / 0.283200 (0.071607) | 0.103557 / 0.141683 (-0.038126) | 1.445682 / 1.452155 (-0.006473) | 1.551008 / 1.492716 (0.058291) |\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) | 0.235873 / 0.018006 (0.217867) | 0.448445 / 0.000490 (0.447955) | 0.001307 / 0.000200 (0.001108) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029809 / 0.037411 (-0.007603) | 0.108833 / 0.014526 (0.094307) | 0.123289 / 0.176557 (-0.053268) | 0.176516 / 0.737135 (-0.560620) | 0.127186 / 0.296338 (-0.169153) |\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) | 0.422037 / 0.215209 (0.206828) | 4.188073 / 2.077655 (2.110418) | 1.999295 / 1.504120 (0.495175) | 1.809229 / 1.541195 (0.268034) | 1.930798 / 1.468490 (0.462308) | 0.694371 / 4.584777 (-3.890406) | 3.833432 / 3.745712 (0.087719) | 3.235600 / 5.269862 (-2.034262) | 1.867822 / 4.565676 (-2.697854) | 0.085734 / 0.424275 (-0.338541) | 0.012727 / 0.007607 (0.005120) | 0.542261 / 0.226044 (0.316217) | 5.289366 / 2.268929 (3.020437) | 2.469636 / 55.444624 (-52.974988) | 2.139392 / 6.876477 (-4.737084) | 2.193305 / 2.142072 (0.051233) | 0.846747 / 4.805227 (-3.958481) | 0.168965 / 6.500664 (-6.331699) | 0.064463 / 0.075469 (-0.011006) |\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) | 1.263818 / 1.841788 (-0.577970) | 15.254642 / 8.074308 (7.180334) | 14.428111 / 10.191392 (4.236719) | 0.164770 / 0.680424 (-0.515654) | 0.017476 / 0.534201 (-0.516725) | 0.420198 / 0.579283 (-0.159085) | 0.443250 / 0.434364 (0.008886) | 0.496904 / 0.540337 (-0.043434) | 0.596541 / 1.386936 (-0.790395) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4db8e33eb9cf6cd4453cdfa246c065e0eedf170c \"CML watermark\")\n" ]

"2023-03-22T00:12:18Z"

"2023-03-24T16:43:34Z"

"2023-03-24T16:36:21Z"

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[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]

[]

[ "Hi @yjernite I am interested in adding this dataset. \r\nIn the repo they have also added a code mixed MT task from English to Hinglish [here](https://github.com/microsoft/GLUECoS#code-mixed-machine-translation-task). I think this could be a good dataset addition in itself and then I can add the rest of the GLUECoS tasks as one dataset. What do you think?" ]

"2021-08-26T17:47:39Z"

"2021-10-20T18:41:20Z"

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[ "Transferring repos as this is a datasets issue " ]

"2023-12-11T15:06:01Z"

"2023-12-11T15:34:23Z"

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[ "Hi @thomwolf, for simplification purposes, I think you could remove the \"`pip install ...`\" steps from this commit, 'cause these deps (black, isort, flake8) are already installed on `pip install -e \".[dev]\"` on the [Start by preparing your environment](https://github.com/huggingface/datasets/blob/704107f924e74445f6f0fbde69a218b72178b588/ADD_NEW_DATASET.md#start-by-preparing-your-environment)\r\n" ]

"2020-12-10T09:30:59Z"

"2020-12-10T12:18:47Z"

"2020-12-10T09:31:14Z"

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

"2022-11-14T18:05:47Z"

"2022-11-15T16:11:30Z"

"2022-11-15T16:09:31Z"

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[ "It not always practical to use nested `Sequence`. If you have troubles with sequence you can use lists instead. \r\n\r\nFor example\r\n```python\r\n\r\nfeatures=datasets.Features(\r\n {\r\n \"dial_id\": datasets.Value(\"string\"),\r\n \"doc_id\": datasets.Value(\"string\"),\r\n \"domain\": datasets.Value(\"string\"),\r\n \"turns\": [\r\n {\r\n \"turn_id\": datasets.Value(\"int32\"),\r\n \"role\": datasets.Value(\"string\"),\r\n \"da\": datasets.Value(\"string\"),\r\n \"reference\": [\r\n {\r\n \"keys\" : datasets.Value(\"string\"),\r\n \"values\": datasets.Value(\"string\"), \r\n }\r\n\r\n ],\r\n \"utterance\": datasets.Value(\"string\"),\r\n }\r\n ],\r\n }\r\n),\r\n```\r\n\r\nthis way `turns` will be a list of dict, and the \"reference\" key of `turns` will be a list of dict as well", "No problem thanks for all your help getting this to the final stages! Added .gitignore, removed .lock and applied the changes you asked for." ]

"2020-12-07T12:39:09Z"

"2020-12-14T16:17:14Z"

"2020-12-14T16:17:14Z"

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"2020-12-08T08:10:19Z"

"2020-12-08T18:09:28Z"

"2020-12-08T18:07:00Z"

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[ "Actually, I found the answer [here](https://github.com/facebookresearch/faiss/wiki/FAQ#what-does-it-mean-when-a-search-returns--1-ids). \r\n\r\nSo we have to do some modifications to the code for instances where the index doesn't retrieve any IDs.", "@lhoestq @patrickvonplaten \r\n\r\nI also found another short bug in the retrieval part. Especially, when retrieving documents. If Faiss returns the -1 as the index, the retriever will always use the last element in the dataset.\r\n\r\nplease check [def get_doc_dicts function](https://github.com/huggingface/transformers/blob/master/src/transformers/models/rag/retrieval_rag.py#L222)\r\n\r\n\r\nDoes the use of the HNSW guarantee to retrieve valid indexes always? \r\n\r\n", "Hi !\r\nNo it happens sometimes to return -1, especially if your dataset is small.\r\nIf your dataset is big enough it shouldn't happen in my experience.\r\n\r\nIdeally we should ignore all the -1 that are returned. It should be possible to change that in RAG's code ", "I also checked with some indexes it returns more -1s. Specially with IVF\nwhen nprobr is very low. It doesn't happen when using HNSW though. But at\nthe moment if it happens, dataset will always return the last element.\nMaybe we should change it to repeat the most last valid retrieved doc id.\nWhat do you think?\n\nOn Wed, Apr 7, 2021, 21:09 Quentin Lhoest ***@***.***> wrote:\n\n> Hi !\n> No it happens sometimes to return -1, especially if your dataset is small.\n> If your dataset is big enough it shouldn't happen.\n>\n> Ideally we should ignore all the -1 that are returned. It should be\n> possible to change that in RAG's code\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/2175#issuecomment-814746509>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AEA4FGTENOTLBEZTXEO2RS3THQOMPANCNFSM42PRVYDA>\n> .\n>\n", "That would be an easy way to workaround this issue. Feel free to open a PR on `transformers` and ping me ! :)", "Sure. Will push everything together with RAG end to end. :) thanks a lot.\n\nOn Wed, Apr 7, 2021, 21:16 Quentin Lhoest ***@***.***> wrote:\n\n> That would be an easy way to workaround this issue. Feel free to open a PR\n> on transformers and ping me ! :)\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/2175#issuecomment-814752589>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AEA4FGWLROCGARKN7WOJYSTTHQPH5ANCNFSM42PRVYDA>\n> .\n>\n" ]

"2021-04-06T21:50:49Z"

"2021-04-16T12:21:16Z"

"2021-04-16T12:21:15Z"

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[ "Hi @zidingz, thanks for your contribution.\r\n\r\nHowever I am closing it because it is a duplicate of a previous PR:\r\n - #2958\r\n\r\n" ]

"2021-10-21T10:03:03Z"

"2021-10-21T14:33:28Z"

"2021-10-21T14:31:50Z"

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[ "@lhoestq I ran `make style` but CI code quality still failing and I don't have access to logs", "It's also likely that due to the previous placement of the field initialization, much of the data about topics etc was simply wrong and carried over from previous entries. Model scores seem to improve significantly with this PR." ]

"2021-01-17T07:58:49Z"

"2021-01-18T11:26:09Z"

"2021-01-18T11:26:09Z"

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[ "Hi, thanks for reporting! A PR (https://github.com/huggingface/datasets/pull/4614) has already been opened to address this issue." ]

"2022-07-15T22:56:03Z"

"2022-07-19T13:36:24Z"

"2022-07-19T13:36:24Z"

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"2021-09-25T20:58:15Z"

"2021-10-03T20:34:22Z"

"2021-10-03T20:34:22Z"

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[ "Same error here!\r\n", "Same here! My kaggle notebook stopped working like yesterday. It's strange because I have fixed version of datasets==1.1.2", "I'm looking at it right now", "I couldn't reproduce unfortunately. I tried\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"cnn_dailymail\", \"3.0.0\", download_mode=\"force_redownload\")\r\n```\r\nand it worked fine on both my env (python 3.7.2) and colab (python 3.6.9)\r\n\r\nMaybe there was an issue with the google drive download link of the dataset ?\r\nAre you still having the issue ? If so could your give me more info about your python and requests version ?", "No, It's working fine now. Very strange. Here are my python and request versions\r\n\r\nrequests 2.24.0\r\nPython 3.8.2", "It's working as expected. Closing the issue \r\n\r\nThanks everybody." ]

"2020-11-18T04:38:02Z"

"2020-11-20T05:22:11Z"

"2020-11-20T05:22:10Z"

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"2022-11-01T13:18:29Z"

"2022-11-02T11:57:50Z"

"2022-11-02T11:57:50Z"

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[ "If we agree to have data files in a dedicated directory \"data/\" then we should be fine. You're right we should not try to edit a dataset script from the repository directly, but from github, in order to avoid conflicts" ]

"2022-02-11T19:23:20Z"

"2022-02-16T14:02:28Z"

"2022-02-16T14:02:27Z"

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"2021-05-28T07:10:51Z"

"2021-06-10T12:11:35Z"

"2021-06-10T12:11:35Z"

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

"2022-05-03T09:24:07Z"

"2022-05-06T08:38:09Z"

"2022-05-03T11:16:19Z"

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"2021-12-13T15:30:12Z"

"2021-12-14T14:38:30Z"

"2021-12-14T14:38:29Z"

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[ "> Hi @tianjianjiang, thanks for the fix.\r\n> The links should also be updated in the `dataset_infos.json` file.\r\n> The failing tests are due to the missing tag in the header of the `README.md` file:\r\n\r\nHi @albertvillanova, thank you for the info! My apologies for the messy PR.\r\n" ]

"2021-11-30T17:13:30Z"

"2021-12-14T14:50:00Z"

"2021-12-14T14:50:00Z"

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[ "Yes indeed ! Thanks for reporting", "Fixed by:\r\n- #3241" ]

"2020-11-03T10:24:51Z"

"2022-02-14T15:46:21Z"

"2022-02-14T15:46:21Z"

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[ "Awesome!", "Thanks for adding a new task template - great work @nateraw 🚀 !" ]

"2021-07-12T17:41:03Z"

"2021-07-13T15:44:28Z"

"2021-07-13T15:28:16Z"

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

"2022-07-19T13:25:06Z"

"2022-07-22T13:42:27Z"

"2022-07-22T13:29:43Z"

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[]

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[ "I just made the dummy data smaller :)\r\nOnce github refreshes the change I think we can merge !", "Thank you so much for reviewing and accepting my pull request!! :)\r\n\r\nI created these rather large dummy data sets to cover all different cases for the row structure. E.g. in the web configuration, it's possible that a row has evidence from wikipedia (\"EntityPages\") and the web (\"SearchResults\"). But it also might happen that either EntityPages or SearchResults is empty. Probably, I will add this thought to the dataset description in the future.", "Ok I see ! Yes feel free to mention it in the dataset card, this can be useful.\r\n\r\nFor the dummy data though we can keep the small ones, as the tests are mainly about testing the parsing from the dataset script rather than the actual content of the dataset." ]

"2021-09-20T14:17:23Z"

"2021-10-05T13:20:52Z"

"2021-10-01T15:39:29Z"

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"2021-08-30T10:14:39Z"

"2021-08-30T10:14:39Z"

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"2020-05-13T08:51:29Z"

"2020-05-13T09:23:34Z"

"2020-05-13T09:23:33Z"

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"2021-05-19T10:10:07Z"

"2021-05-19T10:45:32Z"

"2021-05-19T10:45:31Z"

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[ "A few things that aren't clear for me:\r\n- \"Because it performs better on individual sentence pairs as compared to BLEU, Google BLEU has also been used in RL experiments.\" -- why is this the case? why would that make it more usable for RL? (also, you should put \"Reinforcement Learning\" explicitly, not just the acronym)\r\n- (Minor issue) -- I put inputs before the first example code, I think that's clearer somehow\r\n\r\nOtherwise, it looks great, good job @emibaylor !\r\n" ]

"2022-03-16T19:27:17Z"

"2022-03-21T16:04:26Z"

"2022-03-21T16:04:25Z"

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[ "Hi ! To be able to run the tests, you need to install all the test dependencies and additional ones with\r\n```\r\npip install -e .[tests]\r\npip install -r additional-tests-requirements.txt --no-deps\r\n```\r\n\r\nIn particular, you probably need to `sacrebleu`. It looks like it wasn't able to instantiate `sacrebleu.TER` properly." ]

"2022-03-21T19:07:37Z"

"2023-07-25T15:18:40Z"

"2023-07-25T15:18:40Z"

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[ "The tagger also doesn't insert the value for the `size_categories` field automatically, so this should be fixed too", "Thanks for reporting. Indeed the `extended` tag doesn't exist. Not sure why we had that in the tagger.\r\nThe repo of the tagger is here if someone wants to give this a try: https://github.com/huggingface/datasets-tagging\r\nOtherwise I can probably fix it next week", "I've opened a PR on `datasets-tagging` to fix the issue 🚀 ", "thanks ! this is fixed now" ]

"2021-06-01T17:18:42Z"

"2021-06-09T09:06:31Z"

"2021-06-09T09:06:30Z"

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"2021-06-24T09:10:39Z"

"2021-06-24T14:57:08Z"

"2021-06-24T14:57:08Z"

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[ "What type of dataset are you using in this script? `torch.utils.data.Dataset` or `datasets.Dataset`? Please share the `datasets` package version if it's the latter. Otherwise, it's better to move this issue to the `accelerate` repo.", "Very sorry, I thought I had a repo in `accelerate!`\r\nI will close this issue and repo the issue in the appropriate place." ]

"2023-09-14T05:30:30Z"

"2023-09-14T23:54:42Z"

"2023-09-14T23:54:42Z"

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"2023-04-25T07:40:02Z"

"2023-04-25T07:40:03Z"

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[ "@lhoestq question about public API: `keep_in_memory` or just `in_memory`?", "For consistence I'd say `keep_in_memory`, but no strong opinion.", "@lhoestq done!" ]

"2021-02-25T15:10:13Z"

"2021-03-12T09:42:48Z"

"2021-03-12T09:42:48Z"

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"2021-10-19T14:48:38Z"

"2021-10-25T14:07:04Z"

"2021-10-25T14:07:04Z"

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[ "Awesome!" ]

"2020-04-24T15:32:06Z"

"2020-04-29T09:27:08Z"

"2020-04-25T21:18:45Z"

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"2021-05-09T14:17:33Z"

"2021-05-11T08:58:33Z"

"2021-05-11T08:58:33Z"

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[ "fixed by https://github.com/huggingface/datasets/pull/4472" ]

"2022-06-10T12:26:06Z"

"2022-06-10T13:24:53Z"

"2022-06-10T13:24:53Z"

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"2020-11-03T08:03:45Z"

"2020-11-03T08:16:41Z"

"2020-11-03T08:16:40Z"

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[ "And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`.", "Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs\r\n\r\n> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.\r\n\r\nThat's unexpected, can you share the code you're running ?", "here is the code snippet\r\n\r\n```python\r\n\r\n# add faiss index\r\nsource_dataset = load_dataset(source_path)\r\nqueries = load_dataset(query_path)\r\ngpu = [0,1,2,3]\r\nsource_dataset.add_faiss_index(\r\n \"embedding\",\r\n device=gpu,\r\n )\r\n\r\n\r\n# batch query\r\nbatch_size = 32\r\nfor i in tqdm(range(0, len(queries), batch_size)):\r\n if i + batch_size >= len(queries):\r\n batched_queries = queries[i:]\r\n else:\r\n batched_queries = queries[i:i+batch_size]\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n scores, candidates = source_dataset.get_nearest_examples_batch(\r\n \"embedding\",\r\n batched_query_embeddings,\r\n k=5\r\n )\r\n```", "My version of datasets is `2.4.1.dev0`.", "The code looks all good to me, do you see all the GPUs being utilized ? What version of faiss are you using ?", "I can see the memory usage of all the GPUs.\r\nMy version of `faiss-gpu` is `1.7.2`", "It looks all good to me then ^^ though you said you didn't experienced speed improvements by adding more GPUs ? What size is your source dataset and what time differences did you experience ?", "query set: 1e6\r\nsource dataset: 1e6\r\nembedding size: 768\r\nindex: Flat\r\ntopk: 20\r\nGPU: V100\r\n\r\nThe time taken to traverse the query set once is about 1.5h, which is almost not influenced by the value of query batch size or the number of GPUs according to my experiments.", "Hmmm the number of GPUs should divide the time, something is going wrong. Can you check that adding more GPU does divide the memory used per GPU ? Maybe it can be worth looking at similar issues in the FAISS repository or create a noew issue over there to understand what's going on", "> Can you check that adding more GPU does divide the memory used per GPU \r\n\r\nThe memory used per GPU is unchanged while adding more GPU. Is this unexpected?\r\n\r\nI used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆 ", "> I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆\r\n\r\nOh indeed that's possible, I wasn't sure. Anyway you can check that calling get_nearest_examples_batch simply calls search under the hood: \r\n\r\nhttps://github.com/huggingface/datasets/blob/f90f71fbbb33889fe75a3ffc101cdf16a88a3453/src/datasets/search.py#L375", "Here is a runnable script. \r\nMulti-GPU searching still does not work in my experiments.\r\n\r\n\r\n```python\r\nimport os\r\nfrom tqdm import tqdm\r\nimport numpy as np\r\nimport datasets\r\nfrom datasets import Dataset\r\n\r\nclass DPRSelector:\r\n\r\n def __init__(self, source, target, index_name, gpu=None):\r\n self.source = source\r\n self.target = target\r\n self.index_name = index_name\r\n\r\n cache_path = 'embedding.faiss'\r\n\r\n if not os.path.exists(cache_path):\r\n self.source.add_faiss_index(\r\n column=\"embedding\",\r\n index_name=index_name,\r\n device=gpu,\r\n )\r\n self.source.save_faiss_index(index_name, cache_path)\r\n else:\r\n self.source.load_faiss_index(\r\n index_name,\r\n cache_path,\r\n device=gpu\r\n )\r\n print('index builded!')\r\n\r\n def build_dataset(self, top_k, batch_size):\r\n print('start search')\r\n\r\n for i in tqdm(range(0, len(self.target), batch_size)):\r\n if i + batch_size >= len(self.target):\r\n batched_queries = self.target[i:]\r\n else:\r\n batched_queries = self.target[i:i+batch_size]\r\n\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n search_res = self.source.get_nearest_examples_batch(\r\n self.index_name,\r\n batched_query_embeddings,\r\n k=top_k\r\n )\r\n \r\n print('finish search')\r\n\r\n\r\ndef get_pseudo_dataset():\r\n pseudo_dict = {\"embedding\": np.zeros((1000000, 768), dtype=np.float32)}\r\n print('generate pseudo data')\r\n\r\n dataset = Dataset.from_dict(pseudo_dict)\r\n def list_to_array(data):\r\n return {\"embedding\": [np.array(vector, dtype=np.float32) for vector in data[\"embedding\"]]} \r\n dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)\r\n\r\n print('build dataset')\r\n return dataset\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n\r\n np.random.seed(42)\r\n\r\n\r\n source_dataset = get_pseudo_dataset()\r\n target_dataset = get_pseudo_dataset()\r\n\r\n gpu = [0,1,2,3,4,5,6,7]\r\n selector = DPRSelector(source_dataset, target_dataset, \"embedding\", gpu=gpu)\r\n\r\n selector.build_dataset(top_k=20, batch_size=32)\r\n```", "@lhoestq Hi, could you please test the code above if you have time? 😄 ", "Maybe @albertvillanova you can take a look ? I won't be available in the following days", "@albertvillanova Hi, can you help with this issue?", "Hi @xwwwwww I'm investigating it, but I'm not an expert in Faiss. In principle, it is weird that your code does not work properly because it seems right...", "Have you tried passing `gpu=-1` and check if there is a speedup?", "> Have you tried passing `gpu=-1` and check if there is a speedup?\r\n\r\nyes, there is a speed up using GPU compared with CPU. ", "When passing `device=-1`, ALL existing GPUs are used (multi GPU): this is the maximum speedup you can get. To know the number of total GPUs:\r\n```\r\nimport faiss\r\n\r\nngpus = faiss.get_num_gpus()\r\nprint(ngpus)\r\n```\r\n\r\nWhen passing a list of integers to `device`, then only that number of GPUs are used (multi GPU as well)\r\n- the speedup should be proportional (more or less) to the ratio of the number of elements passed to `device` over `ngpus`\r\n- if this is not the case, then there is an issue in the implementation of this use case (however, I have reviewed the code and in principle I can't find any evident bug)\r\n\r\nWhen passing a positive integer to `device`, then only a single GPU is used.\r\n- this time should be more or less proportional to the time when passing `device=-1` over `ngpus`", "Thanks for your help!\r\nHave you run the code and replicated the same experimental results (i.e., no speedup while increasing the number of GPUs)?", "@albertvillanova @lhoestq Sorry for the bother, is there any progress on this issue? 😃 ", "I can confirm `add_faiss_index` calls `index = faiss.index_cpu_to_gpus_list(index, gpus=list(device))`.\r\n\r\nCould this be an issue with your environment ? Could you try running with 1 and 8 GPUs with a code similar to[ this one from the FAISS examples](https://github.com/facebookresearch/faiss/blob/main/tutorial/python/5-Multiple-GPUs.py) but using `gpu_index = faiss.index_cpu_to_gpus_list(cpu_index, gpus=list(device))`, and see if the speed changes ?", "Hi, I test the FAISS example and the speed indeed changes. I set `nb=1000000`, `nq=1000000` and `d=64`\r\n\r\n| num GPUS | time cost |\r\n| -------- | --------- |\r\n| 1 | 28.53 |\r\n| 5 | 7.16 |\r\n\r\n\r\n\r\n", "Ok the benchmark is great, not sure why it doesn't speed up the index in your case though. You can try running the benchmark with the same settings as your actual dataset\r\n```\r\nquery set: 1e6\r\nsource dataset: 1e6\r\nembedding size: 768\r\nindex: Flat\r\ntopk: 20\r\nGPU: V100\r\n```\r\n\r\nNote that you can still pass a FAISS index you built yourself to a dataset using https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index_from_external_arrays", "> Here is a runnable script. Multi-GPU searching still does not work in my experiments.\r\n> \r\n> ```python\r\n> import os\r\n> from tqdm import tqdm\r\n> import numpy as np\r\n> import datasets\r\n> from datasets import Dataset\r\n> \r\n> class DPRSelector:\r\n> \r\n> def __init__(self, source, target, index_name, gpu=None):\r\n> self.source = source\r\n> self.target = target\r\n> self.index_name = index_name\r\n> \r\n> cache_path = 'embedding.faiss'\r\n> \r\n> if not os.path.exists(cache_path):\r\n> self.source.add_faiss_index(\r\n> column=\"embedding\",\r\n> index_name=index_name,\r\n> device=gpu,\r\n> )\r\n> self.source.save_faiss_index(index_name, cache_path)\r\n> else:\r\n> self.source.load_faiss_index(\r\n> index_name,\r\n> cache_path,\r\n> device=gpu\r\n> )\r\n> print('index builded!')\r\n> \r\n> def build_dataset(self, top_k, batch_size):\r\n> print('start search')\r\n> \r\n> for i in tqdm(range(0, len(self.target), batch_size)):\r\n> if i + batch_size >= len(self.target):\r\n> batched_queries = self.target[i:]\r\n> else:\r\n> batched_queries = self.target[i:i+batch_size]\r\n> \r\n> \r\n> batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n> search_res = self.source.get_nearest_examples_batch(\r\n> self.index_name,\r\n> batched_query_embeddings,\r\n> k=top_k\r\n> )\r\n> \r\n> print('finish search')\r\n> \r\n> \r\n> def get_pseudo_dataset():\r\n> pseudo_dict = {\"embedding\": np.zeros((1000000, 768), dtype=np.float32)}\r\n> print('generate pseudo data')\r\n> \r\n> dataset = Dataset.from_dict(pseudo_dict)\r\n> def list_to_array(data):\r\n> return {\"embedding\": [np.array(vector, dtype=np.float32) for vector in data[\"embedding\"]]} \r\n> dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)\r\n> \r\n> print('build dataset')\r\n> return dataset\r\n> \r\n> \r\n> \r\n> if __name__==\"__main__\":\r\n> \r\n> np.random.seed(42)\r\n> \r\n> \r\n> source_dataset = get_pseudo_dataset()\r\n> target_dataset = get_pseudo_dataset()\r\n> \r\n> gpu = [0,1,2,3,4,5,6,7]\r\n> selector = DPRSelector(source_dataset, target_dataset, \"embedding\", gpu=gpu)\r\n> \r\n> selector.build_dataset(top_k=20, batch_size=32)\r\n> ```\r\n\r\nBy the way, have you run this toy example and replicated my experiment results? I think it is a more direct way to figure this out :)", "Hi,\r\n\r\nI have a similar question and would like to know if there's any progress in this issue. \r\n\r\n`dataset.add_faiss_index(column=\"embedding\")`, this takes around 5minutes to add the index.\r\n\r\n`dataset.add_faiss_index(column=\"embedding\", device=-1)`, this ran for more than 10minutes and still didn't complete execution. \r\n\r\nNow, I don't understand why that's the case as I expected for GPU the indexing should be faster" ]

"2022-07-28T14:57:03Z"

"2023-07-21T02:07:10Z"

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[ "I am facing probably facing similar issues with \r\n\r\n`wiki40b_en_100_0`", "Could you try to run `dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")` once before calling the script ?\r\n\r\nIt looks like several processes try to create the dataset in arrow format at the same time. If the dataset is already created it should be fine", "Thanks! I tested on 328MB text data on `n1-standard-8 (8 vCPUs, 30 GB memory)`. The main script ran without any issue, but it seems to require a huge space in the drive.\r\n\r\nAs suggested, I ran the following script before running the pre-training command with `xla_spawn.py`.\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n\r\nfile_path=\"your_file_name\"\r\nload_dataset(\"text\", data_files=file_path, split=\"train\")\r\n```\r\nThis will create `text-train.arrow` under the default cache directory. Then, I run the script with `xla_spawn.py`. It will load data from the cached file. My understanding is that there's no other way but to do this two-step process with the current version (0.4) of `nlp`.\r\n\r\nDuring another caching process that happens in the main script:\r\n\r\n```\r\n08/26/2020 09:19:51 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 09:19:53 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-f90f341e5308a7469\r\n8d872bcc88f9c0e.arrow\r\n```\r\n\r\n`nlp` generates a temporary file per core, each of which is three times larger than the original text data. If each process is actually writing on the disk, you will need a huge amount of space in your drive. (Maybe I'm missing something.)\r\n\r\n```\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp0k43sazw\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp7sxs9mj5\r\n-rw------- 1 ***** ***** 939M Aug 26 09:31 tmpbbiqw2vp\r\n-rw------- 1 ***** ***** 937M Aug 26 09:31 tmpjxb5ptyu\r\n-rw------- 1 ***** ***** 933M Aug 26 09:31 tmpk3hkdh0e\r\n-rw------- 1 ***** ***** 944M Aug 26 09:31 tmpnoalwftz\r\n-rw------- 1 ***** ***** 931M Aug 26 09:31 tmpuxdr_dz3\r\n-rw------- 1 ***** ***** 945M Aug 26 09:31 tmpxjyuy6dk\r\n```\r\nAfter the caching process, they seem to be merged into one file.\r\n\r\n```\r\n-rw------- 1 ***** ***** 989M Aug 26 09:32 cache-f90f341e5308a74698d872bcc88f9c0e.arrow\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n```", "Again it looks like every process tries to tokenize the full dataset at the same time.\r\nIf you do the tokenization before calling `xla_spawn.py` once, then each process will then use the tokenized cached file `cache-f90f341e5308a74698d872bcc88f9c0e.arrow` and not recompute it.\r\n\r\nNot sure if there's a better way to do that cc @julien-c @thomwolf ", "I wrote a separate script just for preparing a cached file, including tokenization. Each process did use the tokenized cached file.\r\n\r\nCurrently I'm testing the pipeline on 24GB text data. It took about 1.5 hour to create a cached file on `n1-highmem-16 (16 vCPUs, 104 GB memory)`. I assume loading this cached file in the main script with `xla_spawn.py` won't be an issue (even if there are 8 processes).\r\n\r\n```\r\ntotal 98G\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 13:38 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 12:24 ..\r\n-rw------- 1 ***** ***** 74G Aug 26 13:38 cache-a7aa04134ba7b1aff5d9710f14a4e334.arrow\r\n-rw-r--r-- 1 ***** ***** 681 Aug 26 12:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 12:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 25G Aug 26 12:24 text-train.arrow\r\n```", "Yes loading the cached file should be fine from different processes", "Sorry, I thought it was working, but actually the second call doesn't use the cached file that was generated separately, and it will generate another cache-****.arrorw file with a different name. If I run the training script again (with `xla_spawn.py`), it will use the second cached file, which was generated by the training script itself in the previous run.\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:35 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:29 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:35 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:29 cache-69633651476e943b93c89ace715f9487.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:33 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:33 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:29 text-train.arrow\r\n```", "So if I understand correctly it means that the cached file generated by your separated script is different by the one used by the training script ?", "Yes.\r\n\r\n1. `cache-69633651476e943b93c89ace715f9487.arrow` was generated with a separate script. \r\n2. I ran the entire script with `xla_spawn.py`.\r\n3. `cache-69633651476e943b93c89ace715f9487.arrow` is not used.\r\n4. `cache-0d77dfce704493dbe63f071eed6a5431.arrow` is created.\r\n5. training starts...\r\n\r\nNow, if I kill the process at step 5, and do the step 2 again, it will use `cache-0d77dfce704493dbe63f071eed6a5431.arrow` (cached file created at step 4) without any issue.\r\n\r\nI used the following to generate the first cached file.\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```", "1. Here's the log from the first step.\r\n```\r\nDownloading and preparing dataset text/default-e84dd29acc4ad9ef (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDataset text downloaded and prepared to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d. Subsequent calls will reuse this data.\r\n```\r\nThere's a file named `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow`, so it did create a cached file.\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:59 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:58 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:58 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n2. Ideally, `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow` should be used in `run_language_modeling.py` (modified version using `nlp`) with `xla_spawn.py`. But it looks like it's creating a new cached file.\r\n\r\n```\r\n08/26/2020 16:13:03 - INFO - filelock - Lock 139635836351096 released on /home/*****/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.202fa4f84f552bff1f5400ae012663839c61efb3de068c6c8722d34ac0ea6192\r\n.py.lock\r\n08/26/2020 16:13:03 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce704493dbe\r\n63f071eed6a5431.arrow\r\n^M 0%| | 0/100 [00:00<?, ?it/s]08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6\r\nfe661fe4d070d380d/cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n```\r\n\r\nThere are two cached files in the directory:\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 16:14 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 16:14 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 16:13 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 16:13 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n\r\nIf I kill the process, and run it again, it will use the second cached file.\r\n\r\n```\r\n08/26/2020 16:19:52 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:19:52 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Loading cached processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce70\r\n4493dbe63f071eed6a5431.arrow\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Set __getitem__(key) output type to torch for ['input_ids'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```", "Thanks for all the details.\r\nThe two cached files are supposed to be the same. I suspect that the caching has a problem with the tokenizer.\r\nWhich tokenizer did you use ?", "I trained a byte-level BPE tokenizer on my data with `tokenziers` library following this [example](https://github.com/huggingface/tokenizers/blob/master/bindings/python/examples/train_bytelevel_bpe.py).\r\n\r\nAnd I put these model files in a directory named `\"model_name\"`. I also put config.json, which is the original RoBERTa config file.\r\n\r\n```bash\r\n%ls model_name\r\nconfig.json merges.txt vocab.json\r\n```\r\n\r\n[This](https://github.com/huggingface/transformers/blob/4bd7be9a4268221d2a0000c7e8033aaeb365c03b/examples/language-modeling/run_language_modeling.py#L196) is the line where `run_language_modeling.py` loads the tokenier.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\n\r\nI use `\"model_name\"` for `model_args.tokenizer_name`. I don't specify `model_args.cache_dir`. It is 'None' by default.", "In my separated script for caching, I'm using `use_fast=True` when initializing a tokenizer.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(args.config_name, use_fast=True)\r\n```\r\nI wasn't using that option in the main script. That could be the reason...", "Yea it could definitely explain why you have two different cache files.\r\nLet me know if using the same tokenizers on both sides fixes the issue", "It still creates a new file even if I remove `use_fast=True`... \r\n\r\nHere's the script used to create a cached file.\r\n```python \r\n#!/usr/bin/env python3\r\n\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\n\r\nfrom nlp import load_dataset\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--config_name', type=str, help='Pretrained config name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(args.config_name)\r\n\r\n dataset = load_dataset(\"text\", data_files=args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nHere's how the data is loaded in the modified `run_language_modeling.py`. [[original function](https://github.com/huggingface/transformers/blob/971d1802d009d9996b36a34a34477cee849ef39f/examples/language-modeling/run_language_modeling.py#L128-L135)]\r\n\r\n```python\r\ndef get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):\r\n file_path = args.eval_data_file if evaluate else args.train_data_file\r\n split = \"validation\" if evaluate else \"train\"\r\n if args.line_by_line:\r\n # return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n return dataset\r\n\r\n else:\r\n return TextDataset(\r\n tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache\r\n )\r\n```\r\n\r\nProbably I don't need this part in the main script,\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nand simply do this?\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\nreturn dataset\r\n```", "You need this part in the main script or it will use the dataset that is not tokenized\r\n\r\n", "I can see that the tokenizer in `run_language_modeling.py` is not instantiated the same way as in your separated script.\r\nIndeed we can see L196:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\nCould you try to make it so they are instantiated the exact same way please ?", "I updated my separated script, but it's creating a cached file again. If I don't use the `model_args.cache_dir`, both will get `None`, so they should be the same.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n\r\n model_args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n\r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nIs there a way to specify the cache file to load, and skip the re-computation?", "Could you also check that the `args.block_size` used in the lambda function is the same as well ?", "Here's a minimal working example to reproduce this issue.\r\n\r\nAssumption:\r\n- You have access to TPU.\r\n- You have installed `transformers` and `nlp`.\r\n- You have tokenizer files (`config.json`, `merges.txt`, `vocab.json`) under the directory named `model_name`.\r\n- You have `xla_spawn.py` (Download from https://github.com/huggingface/transformers/blob/master/examples/xla_spawn.py).\r\n- You have saved the following script as `prepare_cached_dataset.py`.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n parser.add_argument('--tpu_num_cores', type=int, default=1, help='Number of TPU cores to use (1 or 8). For xla_apwan.py')\r\n model_args = parser.parse_args()\r\n \r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=True)\r\n \r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\ndef _mp_fn(index):\r\n # For xla_spawn (TPUs)\r\n main()\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\n- Run the following command. Replace `your_training_data` with some text file.\r\n\r\n```bash\r\nexport TRAIN_DATA=your_training_data\r\n\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:08 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:08 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n\r\n- Run the same script again. (The output should be just `Using custom data configuration default`.)\r\n```\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:20 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:20 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n- The cached file (`cache-bfc7cb0702426d19242db5e8c079f04b.arrow`) is reused.\r\n- Now, run this script with `xla_spawn.py`. Ideally, it should reuse the cached file, however, you will see each process is creating a cache file again.\r\n\r\n```bash\r\npython xla_spawn.py --num_cores 8 \\\r\nprepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n\r\n- Check the cached directory. There are two arrrow files.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 230M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:25 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw------- 1 ***** ***** 99M Aug 28 13:25 cache-e0e2313e49c8a110aafcc8133154c19a.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n", "I ended up specifying the `cache_file_name` argument when I call `map` function.\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size),\r\n batched=True,\r\n cache_file_name=cache_file_name)\r\n```\r\n\r\nNote:\r\n- `text` dataset in `nlp` does not strip `\"\\n\"`. If you want the same output as in [`LineByLineTextDataset`](https://github.com/huggingface/transformers/blob/afc4ece462ad83a090af620ff4da099a0272e171/src/transformers/data/datasets/language_modeling.py#L88-L111), you would need to create your own dataset class where you replace `line` to `line.strip()` [here](https://github.com/huggingface/nlp/blob/master/datasets/text/text.py#L35).\r\n" ]

"2020-08-25T14:36:38Z"

"2020-09-01T12:14:56Z"

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[ "Hi ! Oh good catch. I think the features should be passed to `IterableDataset.from_generator()` in `to_iterable_dataset()` indeed.\r\n\r\nSetting this as a good first issue if someone would like to contribute, otherwise we can take care of it :)", "#self-assign", "seems like the feature parameter is missing from `return IterableDataset.from_generator(Dataset._iter_shards, gen_kwargs={\"shards\": shards})` hence it defaults to None." ]

"2023-02-23T13:45:33Z"

"2023-02-24T13:22:36Z"

"2023-02-24T13:22:36Z"

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[ "@albertvillanova Is there a specific reason you’re adding the Pile under “the” instead of under “pile”? That does not appear to be consistent with other datasets.", "Hi @StellaAthena,\r\n\r\nI asked myself the same question, but at the end I decided to be consistent with previously added Pile subsets:\r\n- #2817\r\n\r\nI guess the reason is to stress that the definite article is always used before the name of the dataset (your site says: \"The Pile. An 800GB Dataset of Diverse Text for Language Modeling\"). Other datasets are not usually preceded by the definite article, like \"the SQuAD\" or \"the GLUE\" or \"the Common Voice\"...\r\n\r\nCC: @lhoestq ", "> I guess the reason is to stress that the definite article is always used before the name of the dataset (your site says: \"The Pile. An 800GB Dataset of Diverse Text for Language Modeling\").\r\n\r\nYes that's because of this that it starts with \"the\"" ]

"2021-12-01T16:46:04Z"

"2021-12-06T10:12:17Z"

"2021-12-01T17:30:44Z"

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[ "Hi, it depends on what it is in your `dataset_builder.py` file. Can you share it?\r\n\r\nIf you are just loading `json` files, you can also directly use the `json` script (which will find the schema/features from your JSON structure):\r\n\r\n```python\r\nfrom nlp import load_dataset\r\nds = load_dataset(\"json\", data_files=rel_datafiles)\r\n```", "The behavior I'm seeing is from the `json` script. \r\nI hacked this together to overcome the error with the `JSON` dataloader\r\n\r\n```\r\nclass DatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n def _info(self):\r\n return DatasetInfo()\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\" We handle string, list and dicts in datafiles\r\n \"\"\"\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n splits = []\r\n for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n if split_name in self.config.data_files:\r\n files = self.config.data_files[split_name]\r\n if isinstance(files, str):\r\n files = [files]\r\n splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n return splits\r\n\r\n def _prepare_split(self, split_generator):\r\n fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n fpath = os.path.join(self._cache_dir, fname)\r\n\r\n writer = ArrowWriter(path=fpath)\r\n\r\n generator = self._generate_tables(**split_generator.gen_kwargs)\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n writer.write_table(table)\r\n num_examples, num_bytes = writer.finalize()\r\n\r\n split_generator.split_info.num_examples = num_examples\r\n split_generator.split_info.num_bytes = num_bytes\r\n # this is where the error is coming from\r\n # def parse_schema(schema, schema_dict):\r\n # for field in schema:\r\n # if pa.types.is_struct(field.type):\r\n # schema_dict[field.name] = {}\r\n # parse_schema(field.type, schema_dict[field.name])\r\n # elif pa.types.is_list(field.type) and pa.types.is_struct(field.type.value_type):\r\n # schema_dict[field.name] = {}\r\n # parse_schema(field.type.value_type, schema_dict[field.name])\r\n # else:\r\n # schema_dict[field.name] = Value(str(field.type))\r\n # \r\n # parse_schema(writer.schema, features)\r\n # self.info.features = Features(features)\r\n\r\n def _generate_tables(self, files):\r\n for i, file in enumerate(files):\r\n pa_table = paj.read_json(\r\n file\r\n )\r\n yield i, pa_table\r\n```\r\n\r\nSo I basically just don't populate the `self.info.features` though this doesn't seem to cause any problems in my downstream applications. \r\n\r\nThe other workaround I was doing was to just use pyarrow.json to build a table and then to create the Dataset with its constructor or from_table methods. `load_dataset` has nice split logic, so I'd prefer to use that.\r\n\r\n", "Also noticed that if you for example in a loader script\r\n\r\n```\r\nfrom nlp import ArrowBasedBuilder\r\n\r\nclass MyBuilder(ArrowBasedBuilder):\r\n...\r\n\r\n```\r\nand use that in the subclass, it will be on the module's __dict__ and will be selected before the `MyBuilder` subclass, and it will raise `NotImplementedError` on its `_generate_examples` method... In the code it check for abstract classes but Builder and ArrowBasedBuilder aren't abstract classes, they're regular classes with `@abstract_methods`.", "Indeed this is part of a more general limitation which is the fact that we should generate and update the `features` from the auto-inferred Arrow schema when they are not provided (also happen when a user change the schema using `map()`, the features should be auto-generated and guessed as much as possible to keep the `features` synced with the underlying Arrow table schema).\r\n\r\nWe will try to solve this soon." ]

"2020-07-08T23:24:05Z"

"2020-07-10T14:52:06Z"

"2020-07-10T14:52:06Z"

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"2021-01-11T17:23:56Z"

"2021-01-11T18:45:03Z"

"2021-01-11T18:45:02Z"

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[ "Hi! This should be fixed in the latest (patch) release (run `pip install -U datasets` to install it). This behavior was due to a bug in our authentication logic." ]

"2023-08-02T20:20:26Z"

"2023-08-21T18:18:27Z"

"2023-08-21T18:18:27Z"

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"2023-07-20T09:08:52Z"

"2023-07-20T09:09:37Z"

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[ "A duplicate of https://github.com/huggingface/datasets/issues/1774" ]

"2023-05-19T07:21:10Z"

"2023-05-23T16:02:14Z"

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[ "Thanks for reporting !\r\nYou can override the directory in which cache file are stored using for example\r\n```\r\nENV HF_HOME=\"/root/cache/hf_cache_home\"\r\n```\r\n\r\nThis way both `transformers` and `datasets` will use this directory instead of the default `.cache`", "Great, thanks. I didn't see documentation about than ENV variable, looks like an obvious solution. ", "> Thanks for reporting !\r\n> You can override the directory in which cache file are stored using for example\r\n> \r\n> ```\r\n> ENV HF_HOME=\"/root/cache/hf_cache_home\"\r\n> ```\r\n> \r\n> This way both `transformers` and `datasets` will use this directory instead of the default `.cache`\r\n\r\ncan we disable caching directly?", "Hi ! Unfortunately no since we need this directory to load datasets.\r\nWhen you load a dataset, it downloads the raw data files in the cache directory inside <cache_dir>/downloads. Then it builds the dataset and saves it as arrow data inside <cache_dir>/<dataset_name>.\r\n\r\nHowever you can specify the directory of your choice, and it can be a temporary directory if you want to clean everything up at one point.", "I'm closing this to keep issues a bit cleaner" ]

"2020-12-16T00:02:21Z"

"2021-06-17T15:40:45Z"

"2021-06-17T15:40:45Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007561 / 0.011353 (-0.003792) | 0.004824 / 0.011008 (-0.006184) | 0.110372 / 0.038508 (0.071864) | 0.076767 / 0.023109 (0.053657) | 0.357094 / 0.275898 (0.081196) | 0.420566 / 0.323480 (0.097086) | 0.004753 / 0.007986 (-0.003232) | 0.004734 / 0.004328 (0.000405) | 0.072926 / 0.004250 (0.068675) | 0.058045 / 0.037052 (0.020992) | 0.401109 / 0.258489 (0.142620) | 0.444585 / 0.293841 (0.150744) | 0.046492 / 0.128546 (-0.082055) | 0.013948 / 0.075646 (-0.061698) | 0.305188 / 0.419271 (-0.114083) | 0.063112 / 0.043533 (0.019579) | 0.384711 / 0.255139 (0.129572) | 0.411375 / 0.283200 (0.128175) | 0.048147 / 0.141683 (-0.093536) | 1.632357 / 1.452155 (0.180202) | 1.661021 / 1.492716 (0.168304) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281104 / 0.018006 (0.263098) | 0.567152 / 0.000490 (0.566662) | 0.007178 / 0.000200 (0.006978) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029337 / 0.037411 (-0.008075) | 0.081644 / 0.014526 (0.067118) | 0.103326 / 0.176557 (-0.073230) | 0.155299 / 0.737135 (-0.581836) | 0.093518 / 0.296338 (-0.202821) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517979 / 0.215209 (0.302769) | 5.250052 / 2.077655 (3.172397) | 2.220543 / 1.504120 (0.716424) | 1.901087 / 1.541195 (0.359892) | 1.920564 / 1.468490 (0.452073) | 0.766289 / 4.584777 (-3.818488) | 5.130968 / 3.745712 (1.385256) | 4.561874 / 5.269862 (-0.707988) | 2.702808 / 4.565676 (-1.862868) | 0.078929 / 0.424275 (-0.345346) | 0.007834 / 0.007607 (0.000226) | 0.636628 / 0.226044 (0.410583) | 6.309391 / 2.268929 (4.040463) | 2.942180 / 55.444624 (-52.502445) | 2.369557 / 6.876477 (-4.506920) | 2.347528 / 2.142072 (0.205456) | 0.911110 / 4.805227 (-3.894117) | 0.189102 / 6.500664 (-6.311562) | 0.068012 / 0.075469 (-0.007457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.494431 / 1.841788 (-0.347356) | 22.161476 / 8.074308 (14.087168) | 19.426403 / 10.191392 (9.235011) | 0.211154 / 0.680424 (-0.469270) | 0.030655 / 0.534201 (-0.503546) | 0.440449 / 0.579283 (-0.138834) | 0.526522 / 0.434364 (0.092158) | 0.517494 / 0.540337 (-0.022844) | 0.727387 / 1.386936 (-0.659549) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008354 / 0.011353 (-0.002999) | 0.006108 / 0.011008 (-0.004900) | 0.069079 / 0.038508 (0.030571) | 0.080402 / 0.023109 (0.057292) | 0.452166 / 0.275898 (0.176268) | 0.440264 / 0.323480 (0.116784) | 0.005942 / 0.007986 (-0.002043) | 0.003397 / 0.004328 (-0.000932) | 0.079856 / 0.004250 (0.075606) | 0.056329 / 0.037052 (0.019276) | 0.424261 / 0.258489 (0.165772) | 0.464362 / 0.293841 (0.170521) | 0.051968 / 0.128546 (-0.076578) | 0.015204 / 0.075646 (-0.060442) | 0.085940 / 0.419271 (-0.333332) | 0.066673 / 0.043533 (0.023140) | 0.436481 / 0.255139 (0.181342) | 0.445285 / 0.283200 (0.162085) | 0.035188 / 0.141683 (-0.106495) | 1.579442 / 1.452155 (0.127288) | 1.686120 / 1.492716 (0.193404) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319039 / 0.018006 (0.301032) | 0.655080 / 0.000490 (0.654591) | 0.005445 / 0.000200 (0.005245) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028566 / 0.037411 (-0.008845) | 0.092131 / 0.014526 (0.077605) | 0.103654 / 0.176557 (-0.072902) | 0.158082 / 0.737135 (-0.579054) | 0.107520 / 0.296338 (-0.188819) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.573479 / 0.215209 (0.358270) | 5.629751 / 2.077655 (3.552096) | 2.501722 / 1.504120 (0.997602) | 2.156255 / 1.541195 (0.615061) | 2.251296 / 1.468490 (0.782805) | 0.767686 / 4.584777 (-3.817091) | 5.080866 / 3.745712 (1.335154) | 4.353351 / 5.269862 (-0.916510) | 2.818707 / 4.565676 (-1.746970) | 0.082617 / 0.424275 (-0.341658) | 0.008045 / 0.007607 (0.000438) | 0.665462 / 0.226044 (0.439417) | 6.961380 / 2.268929 (4.692452) | 3.308717 / 55.444624 (-52.135907) | 2.664239 / 6.876477 (-4.212238) | 2.782790 / 2.142072 (0.640718) | 0.919567 / 4.805227 (-3.885660) | 0.186731 / 6.500664 (-6.313933) | 0.063437 / 0.075469 (-0.012032) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.668076 / 1.841788 (-0.173712) | 22.720187 / 8.074308 (14.645879) | 19.803359 / 10.191392 (9.611967) | 0.237201 / 0.680424 (-0.443223) | 0.041156 / 0.534201 (-0.493045) | 0.458974 / 0.579283 (-0.120309) | 0.620276 / 0.434364 (0.185912) | 0.544079 / 0.540337 (0.003741) | 0.722715 / 1.386936 (-0.664221) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ed9306b6c512befb721b681fba3222221c8468e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006882 / 0.011353 (-0.004471) | 0.004238 / 0.011008 (-0.006770) | 0.084042 / 0.038508 (0.045534) | 0.074175 / 0.023109 (0.051065) | 0.308771 / 0.275898 (0.032873) | 0.346300 / 0.323480 (0.022820) | 0.005455 / 0.007986 (-0.002530) | 0.003638 / 0.004328 (-0.000690) | 0.065326 / 0.004250 (0.061076) | 0.056080 / 0.037052 (0.019028) | 0.326324 / 0.258489 (0.067834) | 0.360133 / 0.293841 (0.066292) | 0.031577 / 0.128546 (-0.096969) | 0.008675 / 0.075646 (-0.066971) | 0.288051 / 0.419271 (-0.131221) | 0.052769 / 0.043533 (0.009236) | 0.308689 / 0.255139 (0.053550) | 0.328270 / 0.283200 (0.045070) | 0.025028 / 0.141683 (-0.116655) | 1.520670 / 1.452155 (0.068515) | 1.585229 / 1.492716 (0.092513) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284078 / 0.018006 (0.266072) | 0.558134 / 0.000490 (0.557644) | 0.015042 / 0.000200 (0.014842) | 0.000429 / 0.000054 (0.000375) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028747 / 0.037411 (-0.008664) | 0.083816 / 0.014526 (0.069290) | 0.207467 / 0.176557 (0.030911) | 0.163527 / 0.737135 (-0.573608) | 0.100148 / 0.296338 (-0.196190) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.376109 / 0.215209 (0.160900) | 3.749639 / 2.077655 (1.671984) | 1.827081 / 1.504120 (0.322961) | 1.662021 / 1.541195 (0.120827) | 1.734655 / 1.468490 (0.266165) | 0.483701 / 4.584777 (-4.101075) | 3.454772 / 3.745712 (-0.290941) | 3.465079 / 5.269862 (-1.804783) | 2.070874 / 4.565676 (-2.494802) | 0.056714 / 0.424275 (-0.367561) | 0.007786 / 0.007607 (0.000179) | 0.455980 / 0.226044 (0.229936) | 4.530612 / 2.268929 (2.261683) | 2.345757 / 55.444624 (-53.098867) | 2.030289 / 6.876477 (-4.846188) | 2.068440 / 2.142072 (-0.073632) | 0.576502 / 4.805227 (-4.228725) | 0.131787 / 6.500664 (-6.368878) | 0.060038 / 0.075469 (-0.015431) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272225 / 1.841788 (-0.569563) | 19.373635 / 8.074308 (11.299327) | 14.167831 / 10.191392 (3.976439) | 0.166336 / 0.680424 (-0.514088) | 0.018420 / 0.534201 (-0.515781) | 0.387878 / 0.579283 (-0.191405) | 0.413105 / 0.434364 (-0.021259) | 0.458618 / 0.540337 (-0.081720) | 0.639031 / 1.386936 (-0.747905) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007122 / 0.011353 (-0.004230) | 0.004193 / 0.011008 (-0.006815) | 0.066194 / 0.038508 (0.027686) | 0.077775 / 0.023109 (0.054666) | 0.349780 / 0.275898 (0.073882) | 0.383417 / 0.323480 (0.059937) | 0.006416 / 0.007986 (-0.001570) | 0.003651 / 0.004328 (-0.000677) | 0.064837 / 0.004250 (0.060587) | 0.058012 / 0.037052 (0.020959) | 0.351085 / 0.258489 (0.092596) | 0.387302 / 0.293841 (0.093462) | 0.032447 / 0.128546 (-0.096099) | 0.008636 / 0.075646 (-0.067011) | 0.071962 / 0.419271 (-0.347309) | 0.047839 / 0.043533 (0.004306) | 0.349508 / 0.255139 (0.094369) | 0.361892 / 0.283200 (0.078693) | 0.024129 / 0.141683 (-0.117554) | 1.523828 / 1.452155 (0.071673) | 1.607371 / 1.492716 (0.114655) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245928 / 0.018006 (0.227922) | 0.567708 / 0.000490 (0.567218) | 0.003789 / 0.000200 (0.003589) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034107 / 0.037411 (-0.003304) | 0.092539 / 0.014526 (0.078014) | 0.110735 / 0.176557 (-0.065821) | 0.163251 / 0.737135 (-0.573884) | 0.110353 / 0.296338 (-0.185985) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399992 / 0.215209 (0.184783) | 3.976526 / 2.077655 (1.898872) | 2.056182 / 1.504120 (0.552062) | 1.856624 / 1.541195 (0.315429) | 1.941540 / 1.468490 (0.473050) | 0.484662 / 4.584777 (-4.100115) | 3.548228 / 3.745712 (-0.197484) | 3.352900 / 5.269862 (-1.916962) | 2.056310 / 4.565676 (-2.509366) | 0.056952 / 0.424275 (-0.367323) | 0.007284 / 0.007607 (-0.000323) | 0.473749 / 0.226044 (0.247704) | 4.736510 / 2.268929 (2.467581) | 2.570711 / 55.444624 (-52.873913) | 2.204237 / 6.876477 (-4.672239) | 2.438512 / 2.142072 (0.296439) | 0.575542 / 4.805227 (-4.229685) | 0.129260 / 6.500664 (-6.371404) | 0.057704 / 0.075469 (-0.017765) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316659 / 1.841788 (-0.525128) | 20.103340 / 8.074308 (12.029032) | 14.488385 / 10.191392 (4.296993) | 0.171841 / 0.680424 (-0.508583) | 0.020148 / 0.534201 (-0.514053) | 0.398456 / 0.579283 (-0.180828) | 0.443516 / 0.434364 (0.009152) | 0.479597 / 0.540337 (-0.060741) | 0.643665 / 1.386936 (-0.743271) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#370be814b0c18769ea8e699e3647fadcf431e6df \"CML watermark\")\n" ]

"2023-11-02T10:25:10Z"

"2023-11-02T15:24:28Z"

"2023-11-02T15:15:44Z"

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"2021-07-05T07:14:01Z"

"2021-07-05T09:04:38Z"

"2021-07-05T09:04:38Z"

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[ "This can happen when a map transform cannot be hashed deterministically (e.g., an object referenced by the transform changes its state after the first call - an issue with fast tokenizers). The solution is to provide `cache_file_name` in the `map` call to check this file for the cached result instead of relying on the default caching mechanism." ]

"2023-06-07T09:03:33Z"

"2023-06-21T16:15:40Z"

"2023-06-21T16:15:40Z"

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[ "merging since the CI is fixed on master" ]

"2020-12-03T11:59:25Z"

"2020-12-04T13:29:57Z"

"2020-12-04T13:29:57Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "CI is failing because some sections are missing in the dataset card, but this is unrelated to this PR - Merging !" ]

"2022-04-29T15:55:46Z"

"2022-04-29T16:24:55Z"

"2022-04-29T16:18:56Z"

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[]

[]

[ "This issue seems to have been resolved, so I'm closing it." ]

"2023-06-07T06:58:23Z"

"2023-06-15T14:49:21Z"

"2023-06-15T14:49:20Z"

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[]

[]

[ "All the datasets that are loaded normally with `load_dataset`, if `dataset_infos.json` exists, have this file in the importable directory. So it's fine if we copy the file instead of moving it but it's not a big deal.\r\n\r\nAny reason to prefer moving it rather than copying it ?", "@lvwerra reported than when generating the `dataset_infos.json` for multiple dataset directories containing only JSONL files, subsequent `dataset_infos.json` files contained all previous directories as configs:\r\n- First generate metadata for dataset in dir `dir1`: dataset_infos.json contains one config for `dir1`\r\n- Then generate metadata for dataset in dir `dir2`: dataset_infos.json contains 2 configs, for `dir1` and `dir2`\r\n\r\nThe reason is that all dataset_infos.json files are first created in the same dir (the one containing the json builder) and then **copied** to the user dir.\r\n\r\nSubsequent calls of TestCommand don't replace the dataset_infos.json already present in the dir of the json builder, but append to it.\r\n\r\nMAYBE: we should just move for this use case, and copy for the other use cases? See this use case here:\r\n- #3680", "@lhoestq aren't you mentioning the case in the else clause?\r\n```python\r\nelse: # in case of a remote dataset\r\n dataset_dir = None\r\n```\r\n\r\nIn that case `dataset_infos.json` is not copied: `dataset_dir = None`", "When using the JSON loader, calling `get_imported_module_dir()` returns a path inside the pip installed packages, so we shouldn't write files in it anyway, and the dataset_infos.json file should be written directly in the user's directory instead (some users don't have write access to the pip installed packages for example).\r\n\r\nMaybe the packaged modules like `json` should override `_save_infos` to save them in the user's directory instead of next to the builder's script. What do you think ?", "Anyway as a hotfix we can just add an exception for the `json` builder for now, if the issue has to be fixed soon", "I'm closing this PR." ]

"2022-02-04T14:01:52Z"

"2023-09-24T10:00:11Z"

"2023-09-24T09:59:55Z"

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[ "Thanks for reporting. The dataset viewer depends on some backend treatments, and for now, they might take some hours to get processed. We're working on improving it.", "It has finally been processed. Thanks for the patience.", "Thanks for the info @severo !" ]

"2022-02-18T13:06:45Z"

"2022-02-19T14:30:28Z"

"2022-02-18T15:41:33Z"

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[]

[]

[ "Hi @ggdupont !\r\nHave you had a chance to take a look at my suggestions ?\r\nFeel free to ping me if you have questions or when you're ready for a review", "> Hi @ggdupont !\r\n> Have you had a chance to take a look at my suggestions ?\r\n> Feel free to ping me if you have questions or when you're ready for a review\r\n\r\nYes I did, just busy period (and no time on weekend right now ;-) )", "With some delay, reduced the dummy data and had t rebase", "Thanks !\r\n\r\nIt looks like the rebase messed up the github diff for this PR (2.000+ files changed)\r\nCould you create another branch and another PR please ?", "Cleaned PR: https://github.com/huggingface/datasets/pull/1850" ]

"2020-12-04T17:03:17Z"

"2021-02-09T10:22:35Z"

"2021-02-09T10:18:06Z"

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[]

[]

[ "This dataset seems to have around 1000 configs. Therefore when creating the dummy data we end up with hundreds of MB of dummy data which we don't want to add in the repo.\r\nLet's make this PR on hold for now and find a solution after the sprint of next week", "Closing in favor of #1566 " ]

"2020-11-27T10:29:18Z"

"2020-12-15T12:54:40Z"

"2020-12-15T12:54:19Z"

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"2021-10-18T13:12:38Z"

"2021-10-22T09:36:10Z"

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[ "Hi @AnzorGozalishvili,\r\n\r\nMaybe their site was temporarily down, but it seems to work fine now.\r\n\r\nCould you please try again and confirm if the problem persists? ", "Hi @albertvillanova \r\nI checked and it works. \r\nIt seems that it was really temporarily down.\r\nThanks!" ]

"2022-01-26T13:35:37Z"

"2022-02-10T06:58:11Z"

"2022-02-10T06:58:11Z"

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[ "Also fix #3125." ]

"2021-10-29T00:59:27Z"

"2021-11-04T16:20:45Z"

"2021-10-29T10:04:09Z"

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[ "Version 2.3.2 is over one year old, so please use the latest release (2.14.0) to get the expected behavior. Version 2.3.2 does not contain some fixes we made to fix resolving hidden files/directories (starting with a dot)." ]

"2023-07-26T11:09:31Z"

"2023-08-29T15:53:59Z"

"2023-08-29T15:53:59Z"

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[ "Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!", "Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.\r\n\r\nWe also tried keeping the 'info.features' and then adding a modified version back after the remove/rename. Unforutunately that leads to a dataset that is not possible to iterate over.", "So if you iterate over the `IterableDataset` as `next(iter(ds))` and then run `rename_columns` when checking that data it will work, but in the end, it's just renaming the column one example/batch at a time, not renaming the column name for all the entries in the dataset, which is the ideal.", "@alvarobartt Thanks. My use case was that I wanted to do multiple things, ie removing all unnecessary columns, renaming some valid columns, and then using cast (in my case checking if the audio is not 16K and casting it). It is just convenient to look into the info.features between each of these operations. Alternatively, I will just plan ahead...;) To me it seems like all the operations are working.\r\n\r\nThanks for the advice. It was very useful.", "If we know the features before renaming, then we know the features after renaming, so we can pass the new features to the returned dataset in `rename_column` indeed ! If anyone is interested in contributing, feel free to open a PR and I'd be happy to help / give some pointers :)", "Sure @lhoestq thanks! I’ll try to work on that", "#self-assign" ]

"2022-11-15T21:04:41Z"

"2022-11-28T12:53:24Z"

"2022-11-28T12:53:24Z"

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[ "Same problem.", "hi! \r\nI think you can run python from `/data/train/raw/` directory and load dataset as `load_dataset(\"code_contests\")` to mitigate this issue as a workaround. \r\n@ericxsun Do you want to open a PR to fix the regex? As you already found the solution :) ", "> hi! I think you can run python from `/data/train/raw/` directory and load dataset as `load_dataset(\"code_contests\")` to mitigate this issue as a workaround. @ericxsun Do you want to open a PR to fix the regex? As you already found the solution :)\r\n\r\nSure, please see https://github.com/huggingface/datasets/pull/5748 @polinaeterna ", "I think `string_to_dict` is ok, and that the issue is that it gets `'/data2/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'` as input instead of `'data/test-00000-of-00001-9c49eeff30aacaa8.parquet'`. The path should be relative to the directory being loaded by `load_dataset`" ]

"2023-04-12T04:51:35Z"

"2023-04-21T14:20:59Z"

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