Datasets:

Francesco-A

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github-issues_huggingface-datasets

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[ "I've opened a PR with a fix. In the meantime, you can avoid the error by deleting `task_templates` with `dataset.info.task_templates = None` before the `interleave_datasets` call.\r\n` " ]

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[ "> Cool thanks !\r\n> \r\n> Also cc @patrickvonplaten @anton-l it means that when calling push_to_hub, the audio bytes are embedded in the parquet files (we don't upload the audio files themselves)\r\n\r\nJust to verify quickly the size of the dataset doesn't change in this case no? E.g. if a dataset has say 20GB in size when stored in `.mp3` format it could have up to 100GB when stored in WAV. But since we are just taking the bytes here a 20GB .mp3 dataset would also have 20GB when stored in parquet no?", "@lhoestq I've addressed your comments. Additionally, I've modified `cast_storage` to account for possible null (`None`) values.\r\n\r\n@patrickvonplaten Yes, the dataset size stays the same (at least because Parquet files are compressed).", "Feel free to merge if it's all good to you :)" ]

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[ "Update on this: I'm computing the checksums of the data files. It will be available soon", "Added in #2575 :)" ]

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[ "reported by @abidlabs ", "related to https://github.com/huggingface/datasets/pull/3843?", "`datasets` 1.18.4 fixes the issue when you load the dataset with `load_dataset`.\r\n\r\nWhen loading in streaming mode, the fix is indeed on https://github.com/huggingface/datasets/pull/3843 which will be merged soon :)", "That is. The PR #3843 was just opened a bit later we had made our 1.18.4 patch release...\r\nOnce merged, that will fix this issue. ", "OK. Should fix the viewer for 50 datasets\r\n\r\n<img width=\"148\" alt=\"Capture d’écran 2022-03-14 à 11 51 02\" src=\"https://user-images.githubusercontent.com/1676121/158157853-6c544a47-2d6d-4ac4-964a-6f10951ec36b.png\">\r\n" ]

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[ "Looks good to me! :)\r\n\r\nMaybe we can mention that users can also set the `private` argument if they want to keep their dataset private? It would lead nicely into the next section on Privacy.", "Thanks for your comments, I fixed the capitalization for consistency and added an passage to mention the `private` parameter and to have a nice transition to the Privacy section :)\r\n\r\nI also added the login instruction that was missing before the user can actually upload a dataset." ]

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[ "Readme added\r\n", "@lhoestq is it looking alright ? " ]

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

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[ "It was fixed in 2ddd18d139d3047c9c3abe96e1e7d05bb360132c.\r\nCould you pull the latest changes from master @morganmcg1 ?", "Thanks @lhoestq, works fine now!" ]

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[ "I will take a look :-) " ]

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[ "#self-assign", "The Enwik8 pipeline is not present in this codebase, and is hosted elsewhere. I have opened a PR [there](https://huggingface.co/datasets/enwik8/discussions/4) instead. " ]

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[ "Also, some downloads and dataset processing can be quite long for large datasets like wikipedia/pg19/etc. We probably don't want to user to think that the library is hanging. Happy to reorganize logging between DEBUG/INFO/WARNING to make it less verbose by default though.", "The problem is that `transformers` imports `datasets` and the latter starts logging on `import`: at least 3 info messages - apache beam/torch/tf available - so it injects noise whether one uses the library or not - i.e. no choice given to the user.\r\n\r\nWould you be open for me to changing this PR, to keep the initial level at INFO, but to keep the `DATASETS_VERBOSITY` env var it introduces, to let the user control the verbosity?\r\n\r\n", "> Also, some downloads and dataset processing can be quite long for large datasets like wikipedia/pg19/etc. We probably don't want to user to think that the library is hanging.\r\n\r\nIf you're referring to tqdm progress reports, it's not affected by changing the logging levels. It's not using logging.", "> The problem is that `transformers` imports `datasets` and the latter starts logging on `import`: at least 3 info messages - apache beam/torch/tf available - so it injects noise whether one uses the library or not - i.e. no choice given to the user.\r\n> \r\n> Would you be open for me to changing this PR, to keep the initial level at INFO, but to keep the `DATASETS_VERBOSITY` env var it introduces, to let the user control the verbosity?\r\n\r\nFor now we can do that, then I'll change some messages to warnings and set the default verbosity at warning as well at that point. Does it sound good to you ?\r\n\r\n> If you're referring to tqdm progress reports, it's not affected by changing the logging levels. It's not using logging.\r\n\r\nActually we configured some progress bars to be disabled depending on the logging level ^^'\r\n", "> For now we can do that, then I'll change some messages to warnings and set the default verbosity at warning as well at that point. Does it sound good to you ?\r\n\r\nIf it is logical then by all means. \r\n\r\n> > If you're referring to tqdm progress reports, it's not affected by changing the logging levels. It's not using logging.\r\n> \r\n> Actually we configured some progress bars to be disabled depending on the logging level ^^'\r\n\r\nThis is very smart!\r\n\r\nI reverted s/WARNINGS/INFO/.\r\n\r\nThank you!", "Note that it’s the same in `transformers` @stas00, tdqm are also controlled by the logging level there.", "> Note that it’s the same in `transformers` @stas00, tdqm are also controlled by the logging level there.\r\n\r\nThat's good to know, @thomwolf - thank you!\r\n\r\nI see that it's controlled in `trainer.py`, but in `examples` it's not - since that's where I usually see the progressbars (and they are great!). But I suppose they aren't API, so `examples` can behave differently.", "BTW, this is what I'm talking about:\r\n```\r\npython -c \"import transformers\"\r\n2020-09-14 21:00:58.032658: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\nPyTorch version 1.7.0.dev20200910 available.\r\nTensorFlow version 2.3.0 available.\r\nApache Beam available.\r\n```\r\nwhy does the user need to see this? Especially, if they aren't even using `datasets` directly?", "Yes you are right, we should re-think the logging level of various elements.\r\nI also think that the `set_format` messages are confusing when they are the results of our internal operations (as mentioned [here](https://discuss.huggingface.co/t/pipeline-with-custom-dataset-tokenizer-when-to-save-load-manually/1084/7?u=thomwolf))", "Actually I continued this PR in #635 to set the level to warning and update the logging level of some messages.\r\n\r\nLet me know if it sounds good to you", "Closing this one sice #635 got merged", "Awesome! Thank you!\r\n\r\nAny ideas how to eliminate this remaining log line from tensorflow (I know it's not `datasets` related, but perhaps you know).\r\n```\r\npython -c \"import transformers\"\r\n2020-09-17 08:38:34.718410: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n```" ]

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[ "Hi @lhoestq \r\n\r\nI have added the readme file as well, please if you could check it once \r\n\r\nThank you " ]

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[ "@lhoestq I have refactorized the logic. Instead of the previous hierarchy call (local temp file opening -> remote call -> use again temp local file logic but from within the remote caller scope), now it is flattened. Schematically:\r\n```python\r\nwith src.open() as src_file, dst.open() as dst_file:\r\n src_file.fetch(dst_file)\r\n```\r\n\r\nI have created `RemotePath` (analogue to Path) with method `.open()` that returns `FtpFile`/`HttpFile` (analogue to file-like).\r\n\r\nNow I am going to implement `RemotePath.exists()` method (analogue to the Path's method) to check if remote resource is accessible, using `Ftp/Http.head()`.", "Quick update on this one:\r\nwe discussed offline with @albertvillanova on this PR and I think using `fsspec` can help a lot, since it already implements many parts of the abstraction we need to have nice download tools for both http and ftp (and others !)" ]

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[ "Actually it looks like the old URL is still working, and it's also the one that is mentioned in https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md\r\n\r\nWhat makes you think we should use the new URL ?", "@lhoestq Sorry! I might have jumped to conclusions a bit too fast here... \r\n\r\nI was working in Google Colab and got an error that it was unable to use the URL. I then forked the project, updated the URL, ran it locally and it worked. I therefore assumed that my URL update fixed the issue, however, I see now that it might rather be a Google Colab issue... \r\n\r\nStill - this seems to be the official URL for downloading the dataset, and I think that it will be most beneficial to use. :-) ", "It looks like they're using these new urls for their new datasets. Maybe let's change to the new URL in case the old one stops working at one point. Thanks" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4582). All of your documentation changes will be reflected on that endpoint." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "changes made! @lhoestq let me know what you think ", "The CI fail is unrelated to this PR and fixed on master, feel free to merge :)" ]

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[ "Hi so there are indeed two configurations in the datasets as you can see [here](https://github.com/huggingface/nlp/blob/master/datasets/scientific_papers/scientific_papers.py#L81-L82).\r\n\r\nYou can load either one with:\r\n```python\r\ndataset = nlp.load_dataset('scientific_papers', 'pubmed')\r\ndataset = nlp.load_dataset('scientific_papers', 'arxiv')\r\n```\r\n\r\nThis issues is actually related to a similar user-experience issue with GLUE. When several configurations are available and the first configuration is loaded by default (see issue #152 and #130), it seems to be unexpected for users.\r\n\r\nI think we should maybe raise a (very explicit) error when there are several configurations available and the user doesn't specify one.\r\n\r\nWhat do you think @lhoestq @patrickvonplaten @mariamabarham ?", "Yes, it looks like the right thing to do ", "Now if you don't specify which part you want, it raises an error:\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['pubmed', 'arxiv']\r\nExample of usage:\r\n\t`load_dataset('scientific_papers', 'pubmed')`\r\n```" ]

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[ "Great! LGTM :-) ", "> Ok, really clean!\r\n> I like the logic (not a huge fan of using `_asdict_inner` but it makes sense).\r\n> I think it's a nice improvement!\r\n> \r\n> How should we update the files in the repo? Run a big job on a server or on somebody's computer who has most of the datasets already downloaded?\r\n\r\nMaybe we can split the updates among us...IMO most datasets run very quickly. \r\nI think I've downloaded 50 datasets and 80% are loaded in <5min, 15% in <1h and then `wmt` which is still downloading (since 12h). \r\nI deleted my cache because the `wmt` downloads require quite a lot of space, so I only have parts of the `wmt` datasets on my computer. \r\n\r\n@mariamabarham I guess you have downloaded most of the datasets no? " ]

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[ "I am done adding the dataset. Requesting to review and advise.", "looks like this PR has changes about many other files than the ones for WIki Lingua \r\n\r\nCan you create another branch and another PR please ?", "Any reason to have english as the default config over the other languages ?", "> looks like this PR has changes about many other files than the ones for WIki Lingua\r\n> \r\n> Can you create another branch and another PR please ?\r\n\r\nOk, I will create another branch and submit a fresh PR.", "> Any reason to have english as the default config over the other languages ?\r\n\r\nThe data for all other languages has a direct reference to English article. Thus, I kept English as default.", "closing in favor of #1470 " ]

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[ "Thanks for reporting, @wangyuxinwhy. " ]

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[ "Ok this is starting to be really big so it's probably good to merge this first version of the doc and continue in another PR :)\r\n\r\nThis first version of the doc can be explored here: https://2219-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html" ]

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[ "Hi @margotwagner, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your bug: in an environment with datasets-2.3.2 and pyarrow-8.0.0, I can load the datasets without any problem:\r\n```python\r\n>>> ds = load_dataset(\"glue\", \"cola\")\r\n>>> ds\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 8551\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1043\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1063\r\n })\r\n})\r\n\r\n>>> import pyarrow\r\n>>> pyarrow.__version__\r\n8.0.0\r\n>>> from pyarrow.lib import IpcReadOptions\r\n>>> IpcReadOptions\r\npyarrow.lib.IpcReadOptions\r\n```\r\n\r\nI think you may have a problem in your Python environment: maybe you have also an old version of pyarrow that has precedence when importing it.\r\n\r\nCould you please check this (just after you tried to load the dataset and got the error)?\r\n```python\r\n>>> import pyarrow\r\n>>> pyarrow.__version__\r\n``` " ]

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

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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) | 0.006763 / 0.011353 (-0.004589) | 0.004548 / 0.011008 (-0.006460) | 0.095631 / 0.038508 (0.057123) | 0.034046 / 0.023109 (0.010936) | 0.298064 / 0.275898 (0.022166) | 0.330391 / 0.323480 (0.006911) | 0.006058 / 0.007986 (-0.001928) | 0.004163 / 0.004328 (-0.000165) | 0.073260 / 0.004250 (0.069010) | 0.048885 / 0.037052 (0.011832) | 0.304651 / 0.258489 (0.046162) | 0.345882 / 0.293841 (0.052042) | 0.028061 / 0.128546 (-0.100485) | 0.008823 / 0.075646 (-0.066823) | 0.325620 / 0.419271 (-0.093651) | 0.064480 / 0.043533 (0.020948) | 0.303373 / 0.255139 (0.048234) | 0.321672 / 0.283200 (0.038472) | 0.116353 / 0.141683 (-0.025330) | 1.442327 / 1.452155 (-0.009827) | 1.567553 / 1.492716 (0.074837) |\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) | 0.213042 / 0.018006 (0.195035) | 0.457646 / 0.000490 (0.457156) | 0.003989 / 0.000200 (0.003789) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028068 / 0.037411 (-0.009344) | 0.114791 / 0.014526 (0.100265) | 0.120870 / 0.176557 (-0.055686) | 0.183006 / 0.737135 (-0.554130) | 0.126772 / 0.296338 (-0.169567) |\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) | 0.406438 / 0.215209 (0.191229) | 4.041890 / 2.077655 (1.964235) | 1.839967 / 1.504120 (0.335847) | 1.646857 / 1.541195 (0.105662) | 1.729372 / 1.468490 (0.260882) | 0.525540 / 4.584777 (-4.059237) | 3.809996 / 3.745712 (0.064284) | 1.842598 / 5.269862 (-3.427263) | 1.062815 / 4.565676 (-3.502862) | 0.065301 / 0.424275 (-0.358974) | 0.012027 / 0.007607 (0.004420) | 0.505459 / 0.226044 (0.279415) | 5.051177 / 2.268929 (2.782248) | 2.354368 / 55.444624 (-53.090256) | 2.035482 / 6.876477 (-4.840995) | 2.120493 / 2.142072 (-0.021579) | 0.642233 / 4.805227 (-4.162994) | 0.141690 / 6.500664 (-6.358974) | 0.063933 / 0.075469 (-0.011536) |\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) | 1.186261 / 1.841788 (-0.655527) | 14.919653 / 8.074308 (6.845345) | 14.534003 / 10.191392 (4.342611) | 0.183165 / 0.680424 (-0.497259) | 0.017581 / 0.534201 (-0.516620) | 0.397284 / 0.579283 (-0.181999) | 0.431363 / 0.434364 (-0.003001) | 0.510774 / 0.540337 (-0.029564) | 0.614421 / 1.386936 (-0.772516) |\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) | 0.006682 / 0.011353 (-0.004671) | 0.004558 / 0.011008 (-0.006450) | 0.076272 / 0.038508 (0.037764) | 0.034285 / 0.023109 (0.011176) | 0.395594 / 0.275898 (0.119696) | 0.402702 / 0.323480 (0.079222) | 0.006093 / 0.007986 (-0.001893) | 0.005538 / 0.004328 (0.001209) | 0.075797 / 0.004250 (0.071547) | 0.051638 / 0.037052 (0.014585) | 0.396071 / 0.258489 (0.137582) | 0.409282 / 0.293841 (0.115441) | 0.028193 / 0.128546 (-0.100354) | 0.008827 / 0.075646 (-0.066819) | 0.083182 / 0.419271 (-0.336089) | 0.047605 / 0.043533 (0.004072) | 0.391148 / 0.255139 (0.136009) | 0.386784 / 0.283200 (0.103584) | 0.115303 / 0.141683 (-0.026380) | 1.463666 / 1.452155 (0.011512) | 1.566147 / 1.492716 (0.073431) |\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) | 0.213846 / 0.018006 (0.195839) | 0.454769 / 0.000490 (0.454279) | 0.004767 / 0.000200 (0.004567) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030369 / 0.037411 (-0.007042) | 0.115585 / 0.014526 (0.101059) | 0.125181 / 0.176557 (-0.051376) | 0.179247 / 0.737135 (-0.557888) | 0.129336 / 0.296338 (-0.167003) |\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) | 0.446040 / 0.215209 (0.230831) | 4.462644 / 2.077655 (2.384989) | 2.254511 / 1.504120 (0.750392) | 2.062679 / 1.541195 (0.521484) | 2.180766 / 1.468490 (0.712276) | 0.530928 / 4.584777 (-4.053849) | 3.781392 / 3.745712 (0.035680) | 3.522539 / 5.269862 (-1.747322) | 1.506960 / 4.565676 (-3.058717) | 0.067101 / 0.424275 (-0.357174) | 0.012011 / 0.007607 (0.004404) | 0.546407 / 0.226044 (0.320362) | 5.429894 / 2.268929 (3.160965) | 2.702244 / 55.444624 (-52.742381) | 2.367559 / 6.876477 (-4.508917) | 2.556032 / 2.142072 (0.413960) | 0.639690 / 4.805227 (-4.165538) | 0.144538 / 6.500664 (-6.356126) | 0.067822 / 0.075469 (-0.007647) |\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) | 1.284977 / 1.841788 (-0.556811) | 15.546489 / 8.074308 (7.472181) | 14.747519 / 10.191392 (4.556127) | 0.160044 / 0.680424 (-0.520380) | 0.017746 / 0.534201 (-0.516454) | 0.390140 / 0.579283 (-0.189143) | 0.420342 / 0.434364 (-0.014021) | 0.459788 / 0.540337 (-0.080549) | 0.556360 / 1.386936 (-0.830576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d646afbac7ea3dc0996fa2cb6ffd8a98e158e742 \"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) | 0.006493 / 0.011353 (-0.004860) | 0.004532 / 0.011008 (-0.006476) | 0.096509 / 0.038508 (0.058001) | 0.033084 / 0.023109 (0.009974) | 0.297802 / 0.275898 (0.021904) | 0.345880 / 0.323480 (0.022400) | 0.005461 / 0.007986 (-0.002525) | 0.005282 / 0.004328 (0.000954) | 0.073719 / 0.004250 (0.069469) | 0.045035 / 0.037052 (0.007983) | 0.295504 / 0.258489 (0.037015) | 0.345400 / 0.293841 (0.051559) | 0.027880 / 0.128546 (-0.100666) | 0.008804 / 0.075646 (-0.066842) | 0.328017 / 0.419271 (-0.091255) | 0.050169 / 0.043533 (0.006637) | 0.299642 / 0.255139 (0.044503) | 0.313573 / 0.283200 (0.030374) | 0.103359 / 0.141683 (-0.038323) | 1.482145 / 1.452155 (0.029990) | 1.554584 / 1.492716 (0.061867) |\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) | 0.212860 / 0.018006 (0.194853) | 0.444823 / 0.000490 (0.444334) | 0.003014 / 0.000200 (0.002815) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026906 / 0.037411 (-0.010506) | 0.108056 / 0.014526 (0.093530) | 0.118721 / 0.176557 (-0.057835) | 0.176646 / 0.737135 (-0.560489) | 0.123285 / 0.296338 (-0.173053) |\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) | 0.430157 / 0.215209 (0.214948) | 4.279362 / 2.077655 (2.201707) | 1.999732 / 1.504120 (0.495612) | 1.803787 / 1.541195 (0.262592) | 1.868322 / 1.468490 (0.399832) | 0.529314 / 4.584777 (-4.055463) | 3.785101 / 3.745712 (0.039389) | 2.812608 / 5.269862 (-2.457254) | 1.373460 / 4.565676 (-3.192216) | 0.066208 / 0.424275 (-0.358067) | 0.012173 / 0.007607 (0.004566) | 0.528716 / 0.226044 (0.302672) | 5.295003 / 2.268929 (3.026074) | 2.450188 / 55.444624 (-52.994437) | 2.114560 / 6.876477 (-4.761917) | 2.268468 / 2.142072 (0.126395) | 0.651706 / 4.805227 (-4.153521) | 0.142185 / 6.500664 (-6.358479) | 0.064862 / 0.075469 (-0.010607) |\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) | 1.184933 / 1.841788 (-0.656854) | 14.503903 / 8.074308 (6.429595) | 13.928965 / 10.191392 (3.737573) | 0.156788 / 0.680424 (-0.523636) | 0.017320 / 0.534201 (-0.516881) | 0.391366 / 0.579283 (-0.187918) | 0.416261 / 0.434364 (-0.018103) | 0.461951 / 0.540337 (-0.078387) | 0.553496 / 1.386936 (-0.833440) |\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) | 0.006623 / 0.011353 (-0.004730) | 0.004617 / 0.011008 (-0.006392) | 0.075579 / 0.038508 (0.037071) | 0.033863 / 0.023109 (0.010754) | 0.357097 / 0.275898 (0.081199) | 0.396177 / 0.323480 (0.072697) | 0.005712 / 0.007986 (-0.002274) | 0.004232 / 0.004328 (-0.000097) | 0.074669 / 0.004250 (0.070418) | 0.048253 / 0.037052 (0.011201) | 0.362453 / 0.258489 (0.103964) | 0.405423 / 0.293841 (0.111582) | 0.028709 / 0.128546 (-0.099837) | 0.008884 / 0.075646 (-0.066763) | 0.083042 / 0.419271 (-0.336230) | 0.048074 / 0.043533 (0.004541) | 0.355314 / 0.255139 (0.100175) | 0.372536 / 0.283200 (0.089336) | 0.111548 / 0.141683 (-0.030135) | 1.466353 / 1.452155 (0.014198) | 1.555077 / 1.492716 (0.062361) |\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) | 0.217016 / 0.018006 (0.199010) | 0.450145 / 0.000490 (0.449655) | 0.001910 / 0.000200 (0.001711) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029787 / 0.037411 (-0.007624) | 0.115282 / 0.014526 (0.100756) | 0.121962 / 0.176557 (-0.054595) | 0.173424 / 0.737135 (-0.563711) | 0.127519 / 0.296338 (-0.168819) |\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) | 0.438211 / 0.215209 (0.223002) | 4.346352 / 2.077655 (2.268697) | 2.140197 / 1.504120 (0.636077) | 1.957890 / 1.541195 (0.416696) | 2.044300 / 1.468490 (0.575810) | 0.527958 / 4.584777 (-4.056819) | 3.805079 / 3.745712 (0.059367) | 2.601763 / 5.269862 (-2.668098) | 1.359469 / 4.565676 (-3.206208) | 0.065358 / 0.424275 (-0.358917) | 0.011571 / 0.007607 (0.003964) | 0.538513 / 0.226044 (0.312469) | 5.363508 / 2.268929 (3.094580) | 2.640495 / 55.444624 (-52.804129) | 2.335930 / 6.876477 (-4.540547) | 2.407782 / 2.142072 (0.265710) | 0.641637 / 4.805227 (-4.163590) | 0.142196 / 6.500664 (-6.358468) | 0.065041 / 0.075469 (-0.010428) |\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) | 1.296031 / 1.841788 (-0.545757) | 14.950424 / 8.074308 (6.876115) | 14.371304 / 10.191392 (4.179912) | 0.148157 / 0.680424 (-0.532267) | 0.017506 / 0.534201 (-0.516695) | 0.392037 / 0.579283 (-0.187246) | 0.423238 / 0.434364 (-0.011126) | 0.464608 / 0.540337 (-0.075730) | 0.563876 / 1.386936 (-0.823060) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04b1d0371408beb0c7bc587a69c382bd8d0bec36 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "oops looks like some tests are failing sorry, will fix them tomorrow\r\n\r\nEDIT: not today but asap hopefully", "cc @mariosasko this is ready for review, let me know what you think !" ]

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[ "Sounds good ! Can I help with anything ?" ]

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[ "In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.\r\nCould you try to update `nlp` ?\r\n\r\nAlso, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.", "Or maybe the assertion comes from elsewhere ?", "I'm using the master branch. The assertion failure comes from the underlying `pa.concat_tables()`, which is in the pyarrow package. That method does check schemas.\r\n\r\nSince `features.type` does not contain information about nullable vs non-nullable features, the `cast_()` method won't resolve the schema mismatch. There is information in a schema which is not stored in features.", "I'm doing a refactor of type inference in #363 . Both text fields should match after that", "By default nullable will be set to True", "It should be good now. I was able to run\r\n\r\n```python\r\n>>> from nlp import concatenate_datasets, load_dataset\r\n>>>\r\n>>> bookcorpus = load_dataset(\"bookcorpus\", split=\"train\")\r\n>>> wiki = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\")\r\n>>> wiki.remove_columns_(\"title\") # only keep the text\r\n>>>\r\n>>> assert bookcorpus.features.type == wiki.features.type\r\n>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])\r\n```", "Thanks!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hey :) Have you had a chance to continue this PR ? Let me know if you have questions or if I can help", "Hey @lhoestq, let me wrap this up soon. I will resolve your comments in next push." ]

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[ "PR is already open here : #348 \r\nThe only thing remaining is to compute the metadata of each subdataset (one per language + shuffled/unshuffled).\r\nAs soon as #863 is merged we can start computing them. This will take a bit of time though", "Grand, thanks for this!" ]

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[ "That's the way!" ]

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[ "Closing since we don't need to update the tags of those three datasets (for each one of them there is only one tag set)" ]

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[ "Hi ! Good idea :)\r\nIf you are interested in contributing, feel free to give it a try and open a Pull Request. Also let me know if I can help you with this or if you have questions" ]

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[ "Wait until #2567 is merged so we can benefit from the tagger :)", "thanks for the feedback @lhoestq! i've added the new language codes and this PR should be ready for a merge :)" ]

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[ "Thanks for cleaning up the extracted dummy data folders! Instead of changing the file_utils we could also just put these folders under `.gitignore` (or maybe already done?).", "Awesome! I guess you might have to add the changes for the MockDLManager now in a different file though because of my last PR - sorry!" ]

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[ "If possible, a neat API could be something like `Dataset.to_polars()`, as well as `Dataset.set_format(\"polars\")`", "Note they use a \"custom\" implementation of Arrow: [Arrow2](https://github.com/jorgecarleitao/arrow2).", "Polars has grown rapidly in popularity over the last year - could you consider integrating the Polars functionality again?\r\n\r\nI don't think the \"custom\" implementation should be a barrier, it still conforms to the Arrow specification " ]

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[ "could you tell me how I can avoid download, by pre-downloading the data first, put them in a folder so the code does not try to redownload? could you tell me the path to put the downloaded data, and how to do it? thanks\r\n@lhoestq ", "Does your instance have an internet connection ?\r\n\r\nIf you don't have an internet connection you'll need to have the dataset on the instance disk.\r\nTo do so first download the dataset on another machine using `load_dataset` and then you can save it in a folder using `my_dataset.save_to_disk(\"path/to/folder\")`. Once the folder is copied on your instance you can reload the dataset with `datasets.load_from_disk(\"path/to/folder\")`" ]

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[ "Closing this PR because of messed up history and opening another one after discussion with Quentin Lhoest.\r\n" ]

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[ "Hi @Bharat123rox ! It looks like some of the dummy data is broken or missing. Did you auto-generate it? Does the local test pass for you?", "@yjernite requesting you to have a look as to why the tests are failing only on Windows, there seems to be a backslash error somewhere, could it be the result of `os.path.join` and what should be the fix for this?", "This is the `black` output locally:\r\n```\r\n(datasets_env) datasets (openpi) > black --check --line-length 119 --target-version py36 datasets/openpi/\r\nAll done! ✨ 🍰 ✨\r\n1 file would be left unchanged.\r\n```", "Can you check your version of black (should be `20.8b1`) and run `make style again`? (And don't forget to rebase before pushing ;) )\r\n\r\nThe other test was a time-out error so should be good on the next commit", "Thanks @yjernite the CI tests finally passed!!", "Hi @Bharat123rox did you manage to join the different config into one using the IDs ?\r\n\r\nFeel free to ping me when you're ready for the next review :) ", "> Hi @Bharat123rox did you manage to join the different config into one using the IDs ?\n> \n> Feel free to ping me when you're ready for the next review :) \n\nNot yet @lhoestq still working on this! Meanwhile please review #1507 where I added the SelQA dataset :)", "Ok ! Let me review SelQA then :) \r\nThanks for your help !", "Apologies for the very late response. Here is the openpi dataset file with a single file per partition after merging `id_answers, answers.jsonl, question.jsonl , question_metadata.jsonl`\r\n\r\nhttps://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl", "Nice thank you @nikett !", "Hi @Bharat123rox , when you get a chance, please feel free to use the dataset from the repo ( [Link](https://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl) ) . Please let me know if any file is missing! Thank you ", "Hi @Bharat123rox are you working on this? ", "@nikett Sorry I'm no longer working on this as I'm out of time for it, please feel free to raise a new PR for this\r\n\r\n", "We are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest to create this dataset there. Please, feel free to tell us if you need some help." ]

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[ "Thanks for noticing ! This looks more reasonable indeed.\r\nFeel free to open a PR", "Hi @lhoestq \r\nI made the proposed changes to the `xtreme.py` script. I noticed that I also need to change the schema in the `dataset_infos.json` file. More specifically the `\"features\"` part of the PAN-X.LANG dataset:\r\n\r\n```json\r\n\"features\":{\r\n \"word\":{\r\n \"dtype\":\"string\",\r\n \"id\":null,\r\n \"_type\":\"Value\"\r\n },\r\n \"ner_tag\":{\r\n \"dtype\":\"string\",\r\n \"id\":null,\r\n \"_type\":\"Value\"\r\n },\r\n \"lang\":{\r\n \"dtype\":\"string\",\r\n \"id\":null,\r\n \"_type\":\"Value\"\r\n }\r\n}\r\n```\r\nTo fit the code above the fields `\"word\"`, `\"ner_tag\"`, and `\"lang\"` would become `\"words\"`, `ner_tags\"` and `\"langs\"`. In addition the `dtype` should be changed from `\"string\"` to `\"list\"`.\r\n\r\n I made this changes but when trying to test this locally with `dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='./data')` I face the issue that the `dataset_info.json` file is always overwritten by a downloaded version with the old settings, which then throws an error because the schema does not match. This makes it hard to test the changes locally. Do you have any suggestions on how to deal with that?\r\n", "Hi !\r\n\r\nYou have to point to your local script.\r\nFirst clone the repo and then:\r\n\r\n```python\r\ndataset = load_dataset(\"./datasets/xtreme\", \"PAN-X.en\")\r\n```\r\nThe \"xtreme\" directory contains \"xtreme.py\".\r\n\r\nYou also have to change the features definition in the `_info` method. You could use:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n \"words\": [nlp.Value(\"string\")],\r\n \"ner_tags\": [nlp.Value(\"string\")],\r\n \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nHope this helps !\r\nLet me know if you have other questions.", "Thanks, I am making progress. I got a new error `NonMatchingSplitsSizesError ` (see traceback below), which I suspect is due to the fact that number of rows in the dataset changed (one row per word --> one row per sentence) as well as the number of bytes due to the slightly updated data structure. \r\n\r\n```python\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=1756492, num_examples=80536, dataset_name='xtreme'), 'recorded': SplitInfo(name='validation', num_bytes=1837109, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='test', num_bytes=1752572, num_examples=80326, dataset_name='xtreme'), 'recorded': SplitInfo(name='test', num_bytes=1833214, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='train', num_bytes=3496832, num_examples=160394, dataset_name='xtreme'), 'recorded': SplitInfo(name='train', num_bytes=3658428, num_examples=20000, dataset_name='xtreme')}]\r\n```\r\nI can fix the error by replacing the values in the `datasets_infos.json` file, which I tested for English. However, to update this for all 40 datasets manually is slightly painful. Is there a better way to update the expected values for all datasets?", "You can update the json file by calling\r\n```\r\nnlp-cli test ./datasets/xtreme --save_infos --all_configs\r\n```", "One more thing about features. I mentioned\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n \"words\": [nlp.Value(\"string\")],\r\n \"ner_tags\": [nlp.Value(\"string\")],\r\n \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nbut it's actually not consistent with the way we write datasets. Something like this is simpler to read and more consistent with the way we define datasets:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n \"words\": nlp.Sequence(nlp.Value(\"string\")),\r\n \"ner_tags\": nlp.Sequence(nlp.Value(\"string\")),\r\n \"langs\": nlp.Sequence(nlp.Value(\"string\")),\r\n})\r\n```\r\n\r\nSorry about that", "Closing this since PR #437 fixed the problem and has been merged to `master`. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "& thank you!" ]

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[ "@lhoestq @albertvillanova - It might be a good idea to have a patch release after this gets merged (presumably tomorrow morning when you're around). The Colab issue linked to this PR is a pretty big blocker. " ]

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[ "Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)" ]

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[ "There is one more thing I would love to see. Let's say we iteratively keep updating a data source that loaded from **load_dataset** or **load_from_disk**. Now we need to save it to the same location by overriding the previous file inorder to save the disk space. At the moment **save_to_disk** can not assign a name. So I do not see an easy way to override the previous files. @lhoestq is this possible?\r\n\r\n\r\n\r\np.s one last thing?\r\n\r\nIs there a way to flush out any connection to a data source loaded from **load_from_disk** or **load_dataset** methods? At the moment I suspect when we use any of those functions, it will always keep a pointer although we override it again with a new version of the dataset source. This is really useful in an iterative process. \r\n\r\n", "> There is one more thing I would love to see. Let's say we iteratively keep updating a data source that loaded from **load_dataset** or **load_from_disk**. Now we need to save it to the same location by overriding the previous file inorder to save the disk space. At the moment **save_to_disk** can not assign a name. So I do not see an easy way to override the previous files. @lhoestq is this possible?\r\n\r\nIn the new save_to_disk, the filename of the arrow file is fixed: `dataset.arrow`.\r\nThis way is will be overwritten if you save your dataset again\r\n\r\n> Is there a way to flush out any connection to a data source loaded from **load_from_disk** or **load_dataset** methods? At the moment I suspect when we use any of those functions, it will always keep a pointer although we override it again with a new version of the dataset source. This is really useful in an iterative process.\r\n\r\nIf you update an arrow file, then you must reload it with `load_from_disk` for example in order to have the updated data.\r\nDoes that answer the question ? How does this \"pointer\" behavior manifest exactly on your side ?", "Apparently the usage of the compute layer of pyarrow requires pyarrow>=1.0.0 (otherwise there are some issues on windows with file permissions when doing dataset concatenation).\r\n\r\nI'll bump the pyarrow requirement from, 0.17.1 to 1.0.0", "\r\n> If you update an arrow file, then you must reload it with `load_from_disk` for example in order to have the updated data.\r\n> Does that answer the question? How does this \"pointer\" behavior manifest exactly on your side?\r\n\r\nYes, I checked this behavior.. if we update the .arrow file it kind of flushes out the previous one. So your solution is perfect <3. ", "Sorry for spamming, there's a a bug that only happens on the CI so I have to re-run it several times", "Alright I finally added all the tests I wanted !\r\nI also fixed all the bugs and now all the tests are passing :)\r\n\r\nLet me know if you have comments.\r\n\r\nI also noticed that two methods in pyarrow seem to bring some data in memory even for a memory mapped table: filter and cast:\r\n- for filter I took a look at the C++ code on the arrow's side and found [this part](https://github.com/apache/arrow/blob/55c8d74d5556b25238fb2028e9fb97290ea24684/cpp/src/arrow/compute/kernels/vector_selection.cc#L93-L160) that \"builds\" the array during filter. It seems to indicate that it allocates new memory for the filtered array but not 100% sure.\r\n- regarding cast I noticed that it happens when changing the precision of an array of integers. Not sure if there are other cases.\r\n\r\n\r\nMaybe we'll need to investigate this a bit for your PR on improving `filter` @theo-m , since we don't want to fill the users memory.", "> Maybe we'll need to investigate this a bit for your PR on improving `filter` @theo-m , since we don't want to fill the users memory.\r\n\r\nI'm a bit unclear on this, I thought the point of the refactor was to use `Table.filter` to speed up our own `.filter` and stop using `.map` that offloaded too much stuff on disk. \r\nAt some point I recall we decided to use `keep_in_memory=True` as the expectations were that it would be hard to fill the memory?", "> I'm a bit unclear on this, I thought the point of the refactor was to use Table.filter to speed up our own .filter and stop using .map that offloaded too much stuff on disk.\r\n> At some point I recall we decided to use keep_in_memory=True as the expectations were that it would be hard to fill the memory?\r\n\r\nYes it's ok to have the mask in memory, but not the full table. I was not aware that the table returned by filter could actually be in memory (it's not part of the pyarrow documentation afaik).\r\nTo be more specific I noticed that every time you call `filter`, the pyarrow total allocated memory increases.\r\nI haven't checked on a big dataset though, but it would be nice to see how much memory it uses with respect to the size of the dataset.", "I have addressed your comments @theo-m @albertvillanova ! Thanks for the suggestions", "I totally agree with you. I would have loved to use inheritance instead.\r\nHowever because `pa.Table` is a cython class without proper initialization methods (you can't call `__init__` for example): you can't instantiate a subclass of `pa.Table` in python.\r\nTo be more specific, you actually can try to instantiate a subclass of `pa.Table` with no data BUT this is not a valid table so you get an error.\r\nAnd since `pa.Table` objects are immutable you can't even set the data in `__new__` or `__init__`.\r\n\r\nEDIT: one could make a new cython class that inherits from `pa.Table` with proper initialization methods, so that we can inherit from this class instead in python. We can do that in the future if we plan to use cython in `datasets`.\r\n(see: https://arrow.apache.org/docs/python/extending.html)", "@lhoestq, but in which cases you would like to instantiate directly either `InMemoryTable` or `MemoryMappedTable`? You normally use one of their `from_xxx` class methods...", "Yes I was thinking of these cases. The issue is that they return `pa.Table` objects even from a subclass of `pa.Table`", "That is indeed a weird behavior...", "I guess that in this case, the best approach is as you did, using composition over inheritance...\r\n\r\nhttps://github.com/apache/arrow/pull/5322", "@lhoestq I think you forgot to add the new classes to the docs?", "Yes you're right, let me add them" ]

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[ "I found a solution \r\n\r\nIf you specifically install datasets==1.18 and then run\r\n\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.en')\r\nthen this should work (it worked for me.)" ]

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[ "@lhoestq I tried manually creating the dummy files. But unfortunately it was raising an error during testing the dummy data (regarding JSON parsing).\r\n\r\nThe JSONs are pretty big so I cannot actually open it without crashing the text editor.\r\n\r\n Do you have any suggestions?", "@lhoestq I have made all the changes you mentioned." ]

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[ "`load_dataset` first checks for a local directory before checking for the Hub.\r\n\r\nTo make it explicit that it has to fetch the Hub, we could support the `hffs` syntax:\r\n```python\r\nload_dataset(\"hf://datasets/glue\")\r\n```\r\n\r\nwould that work for you ? Also cc @mariosasko who's leading the `hffs` project", "yeah, that would be a fine solution.", "This still has no proper solution in 2.11\r\n\r\nperhaps have a `download_config=\"force_remote\"` or just backtrack once you reach `EmptyDatasetError` locally and then try to load it from the hub (or a local cache, as that only gets checked if there is no local folder...?)" ]

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[ "Hi, thanks for reporting! This is a duplicate of https://github.com/huggingface/datasets/issues/3240. We are working on a fix.\r\n\r\n" ]

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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) | 0.006770 / 0.011353 (-0.004583) | 0.004143 / 0.011008 (-0.006865) | 0.098928 / 0.038508 (0.060420) | 0.044893 / 0.023109 (0.021783) | 0.302630 / 0.275898 (0.026732) | 0.368173 / 0.323480 (0.044693) | 0.005631 / 0.007986 (-0.002354) | 0.003397 / 0.004328 (-0.000931) | 0.075748 / 0.004250 (0.071497) | 0.062582 / 0.037052 (0.025530) | 0.329586 / 0.258489 (0.071097) | 0.362625 / 0.293841 (0.068784) | 0.033250 / 0.128546 (-0.095296) | 0.008880 / 0.075646 (-0.066766) | 0.329683 / 0.419271 (-0.089588) | 0.054426 / 0.043533 (0.010893) | 0.297940 / 0.255139 (0.042801) | 0.319796 / 0.283200 (0.036597) | 0.023296 / 0.141683 (-0.118387) | 1.462142 / 1.452155 (0.009987) | 1.495796 / 1.492716 (0.003079) |\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) | 0.201771 / 0.018006 (0.183765) | 0.454514 / 0.000490 (0.454024) | 0.003333 / 0.000200 (0.003133) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028084 / 0.037411 (-0.009327) | 0.109452 / 0.014526 (0.094926) | 0.119200 / 0.176557 (-0.057357) | 0.180302 / 0.737135 (-0.556834) | 0.125653 / 0.296338 (-0.170686) |\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) | 0.409819 / 0.215209 (0.194610) | 4.055117 / 2.077655 (1.977462) | 1.855279 / 1.504120 (0.351159) | 1.655281 / 1.541195 (0.114086) | 1.687938 / 1.468490 (0.219448) | 0.528352 / 4.584777 (-4.056425) | 3.750250 / 3.745712 (0.004538) | 3.386741 / 5.269862 (-1.883121) | 1.572036 / 4.565676 (-2.993640) | 0.065125 / 0.424275 (-0.359150) | 0.011259 / 0.007607 (0.003652) | 0.513449 / 0.226044 (0.287405) | 5.139421 / 2.268929 (2.870492) | 2.316973 / 55.444624 (-53.127651) | 1.984109 / 6.876477 (-4.892368) | 2.127915 / 2.142072 (-0.014158) | 0.653238 / 4.805227 (-4.151989) | 0.142686 / 6.500664 (-6.357978) | 0.063666 / 0.075469 (-0.011803) |\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) | 1.185174 / 1.841788 (-0.656614) | 14.790282 / 8.074308 (6.715974) | 13.089222 / 10.191392 (2.897830) | 0.146055 / 0.680424 (-0.534369) | 0.017835 / 0.534201 (-0.516366) | 0.399598 / 0.579283 (-0.179685) | 0.425296 / 0.434364 (-0.009068) | 0.478552 / 0.540337 (-0.061786) | 0.579702 / 1.386936 (-0.807234) |\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) | 0.006750 / 0.011353 (-0.004603) | 0.004156 / 0.011008 (-0.006853) | 0.074948 / 0.038508 (0.036440) | 0.043368 / 0.023109 (0.020259) | 0.355389 / 0.275898 (0.079491) | 0.429167 / 0.323480 (0.105687) | 0.003911 / 0.007986 (-0.004075) | 0.004340 / 0.004328 (0.000012) | 0.075940 / 0.004250 (0.071689) | 0.054293 / 0.037052 (0.017241) | 0.400317 / 0.258489 (0.141827) | 0.432001 / 0.293841 (0.138160) | 0.032340 / 0.128546 (-0.096206) | 0.008876 / 0.075646 (-0.066770) | 0.082284 / 0.419271 (-0.336987) | 0.050819 / 0.043533 (0.007286) | 0.351994 / 0.255139 (0.096855) | 0.375917 / 0.283200 (0.092717) | 0.022466 / 0.141683 (-0.119217) | 1.538824 / 1.452155 (0.086669) | 1.563995 / 1.492716 (0.071279) |\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) | 0.227330 / 0.018006 (0.209323) | 0.446380 / 0.000490 (0.445890) | 0.000408 / 0.000200 (0.000208) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028534 / 0.037411 (-0.008878) | 0.113467 / 0.014526 (0.098941) | 0.123590 / 0.176557 (-0.052966) | 0.174309 / 0.737135 (-0.562827) | 0.130631 / 0.296338 (-0.165707) |\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) | 0.441020 / 0.215209 (0.225811) | 4.386564 / 2.077655 (2.308909) | 2.100704 / 1.504120 (0.596584) | 1.901484 / 1.541195 (0.360289) | 1.963494 / 1.468490 (0.495004) | 0.536838 / 4.584777 (-4.047939) | 3.739071 / 3.745712 (-0.006642) | 3.278981 / 5.269862 (-1.990881) | 1.515476 / 4.565676 (-3.050201) | 0.066388 / 0.424275 (-0.357887) | 0.011857 / 0.007607 (0.004250) | 0.545507 / 0.226044 (0.319463) | 5.441479 / 2.268929 (3.172550) | 2.602144 / 55.444624 (-52.842480) | 2.235583 / 6.876477 (-4.640894) | 2.293458 / 2.142072 (0.151385) | 0.658535 / 4.805227 (-4.146692) | 0.141327 / 6.500664 (-6.359337) | 0.063726 / 0.075469 (-0.011743) |\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) | 1.247819 / 1.841788 (-0.593968) | 15.234524 / 8.074308 (7.160216) | 14.592700 / 10.191392 (4.401308) | 0.141952 / 0.680424 (-0.538472) | 0.017747 / 0.534201 (-0.516454) | 0.396819 / 0.579283 (-0.182465) | 0.415902 / 0.434364 (-0.018462) | 0.464619 / 0.540337 (-0.075718) | 0.560866 / 1.386936 (-0.826070) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b7f6c59deb868e21f295917548fa2df10dd0158 \"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) | 0.008278 / 0.011353 (-0.003075) | 0.005044 / 0.011008 (-0.005964) | 0.123382 / 0.038508 (0.084874) | 0.054039 / 0.023109 (0.030929) | 0.382338 / 0.275898 (0.106440) | 0.453287 / 0.323480 (0.129807) | 0.006342 / 0.007986 (-0.001644) | 0.003930 / 0.004328 (-0.000398) | 0.094039 / 0.004250 (0.089789) | 0.076525 / 0.037052 (0.039472) | 0.394066 / 0.258489 (0.135577) | 0.445600 / 0.293841 (0.151759) | 0.039348 / 0.128546 (-0.089199) | 0.010485 / 0.075646 (-0.065161) | 0.433730 / 0.419271 (0.014459) | 0.082671 / 0.043533 (0.039138) | 0.375250 / 0.255139 (0.120111) | 0.416269 / 0.283200 (0.133070) | 0.038397 / 0.141683 (-0.103286) | 1.864834 / 1.452155 (0.412680) | 2.010453 / 1.492716 (0.517737) |\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) | 0.240008 / 0.018006 (0.222002) | 0.470975 / 0.000490 (0.470485) | 0.004001 / 0.000200 (0.003801) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031107 / 0.037411 (-0.006304) | 0.129371 / 0.014526 (0.114846) | 0.141559 / 0.176557 (-0.034997) | 0.205571 / 0.737135 (-0.531564) | 0.144611 / 0.296338 (-0.151728) |\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) | 0.506972 / 0.215209 (0.291763) | 5.055951 / 2.077655 (2.978296) | 2.397438 / 1.504120 (0.893318) | 2.170435 / 1.541195 (0.629240) | 2.240296 / 1.468490 (0.771806) | 0.641559 / 4.584777 (-3.943218) | 4.644772 / 3.745712 (0.899060) | 4.064200 / 5.269862 (-1.205662) | 1.946991 / 4.565676 (-2.618685) | 0.086413 / 0.424275 (-0.337862) | 0.015082 / 0.007607 (0.007475) | 0.670413 / 0.226044 (0.444369) | 6.331346 / 2.268929 (4.062418) | 2.965813 / 55.444624 (-52.478812) | 2.547952 / 6.876477 (-4.328524) | 2.718390 / 2.142072 (0.576318) | 0.796657 / 4.805227 (-4.008571) | 0.173229 / 6.500664 (-6.327435) | 0.079606 / 0.075469 (0.004137) |\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) | 1.568761 / 1.841788 (-0.273026) | 18.485432 / 8.074308 (10.411124) | 15.758513 / 10.191392 (5.567121) | 0.170427 / 0.680424 (-0.509997) | 0.021421 / 0.534201 (-0.512780) | 0.518623 / 0.579283 (-0.060660) | 0.525887 / 0.434364 (0.091523) | 0.640331 / 0.540337 (0.099993) | 0.766748 / 1.386936 (-0.620188) |\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) | 0.007680 / 0.011353 (-0.003673) | 0.005289 / 0.011008 (-0.005719) | 0.093773 / 0.038508 (0.055265) | 0.054997 / 0.023109 (0.031888) | 0.456277 / 0.275898 (0.180379) | 0.500642 / 0.323480 (0.177162) | 0.005935 / 0.007986 (-0.002050) | 0.004375 / 0.004328 (0.000047) | 0.094131 / 0.004250 (0.089881) | 0.063399 / 0.037052 (0.026347) | 0.470546 / 0.258489 (0.212057) | 0.504989 / 0.293841 (0.211148) | 0.038541 / 0.128546 (-0.090006) | 0.010403 / 0.075646 (-0.065244) | 0.102469 / 0.419271 (-0.316802) | 0.063105 / 0.043533 (0.019572) | 0.466005 / 0.255139 (0.210866) | 0.458677 / 0.283200 (0.175477) | 0.028407 / 0.141683 (-0.113276) | 1.893829 / 1.452155 (0.441675) | 1.917954 / 1.492716 (0.425238) |\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) | 0.272760 / 0.018006 (0.254754) | 0.476159 / 0.000490 (0.475669) | 0.008467 / 0.000200 (0.008267) | 0.000146 / 0.000054 (0.000091) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035755 / 0.037411 (-0.001656) | 0.145038 / 0.014526 (0.130512) | 0.148322 / 0.176557 (-0.028235) | 0.210193 / 0.737135 (-0.526943) | 0.156547 / 0.296338 (-0.139792) |\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) | 0.541204 / 0.215209 (0.325995) | 5.382746 / 2.077655 (3.305091) | 2.704229 / 1.504120 (1.200109) | 2.468422 / 1.541195 (0.927227) | 2.522672 / 1.468490 (1.054182) | 0.644899 / 4.584777 (-3.939878) | 4.654401 / 3.745712 (0.908689) | 2.159223 / 5.269862 (-3.110638) | 1.280098 / 4.565676 (-3.285578) | 0.080053 / 0.424275 (-0.344222) | 0.014383 / 0.007607 (0.006776) | 0.662770 / 0.226044 (0.436725) | 6.617651 / 2.268929 (4.348722) | 3.234347 / 55.444624 (-52.210277) | 2.861417 / 6.876477 (-4.015059) | 2.888928 / 2.142072 (0.746856) | 0.792854 / 4.805227 (-4.012374) | 0.172553 / 6.500664 (-6.328111) | 0.078402 / 0.075469 (0.002933) |\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) | 1.565351 / 1.841788 (-0.276436) | 18.681916 / 8.074308 (10.607608) | 17.264473 / 10.191392 (7.073081) | 0.168461 / 0.680424 (-0.511963) | 0.021353 / 0.534201 (-0.512848) | 0.517843 / 0.579283 (-0.061440) | 0.519907 / 0.434364 (0.085543) | 0.623687 / 0.540337 (0.083350) | 0.761796 / 1.386936 (-0.625140) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bbf58747f734a46e75937bdbcbc05b06ade0224a \"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) | 0.006750 / 0.011353 (-0.004603) | 0.004268 / 0.011008 (-0.006741) | 0.098644 / 0.038508 (0.060136) | 0.044643 / 0.023109 (0.021534) | 0.309420 / 0.275898 (0.033522) | 0.379294 / 0.323480 (0.055815) | 0.005729 / 0.007986 (-0.002256) | 0.003615 / 0.004328 (-0.000714) | 0.076086 / 0.004250 (0.071835) | 0.068994 / 0.037052 (0.031942) | 0.325653 / 0.258489 (0.067164) | 0.375187 / 0.293841 (0.081347) | 0.032546 / 0.128546 (-0.096000) | 0.009089 / 0.075646 (-0.066557) | 0.329905 / 0.419271 (-0.089366) | 0.066832 / 0.043533 (0.023300) | 0.299247 / 0.255139 (0.044108) | 0.323460 / 0.283200 (0.040260) | 0.034226 / 0.141683 (-0.107457) | 1.475659 / 1.452155 (0.023505) | 1.556234 / 1.492716 (0.063518) |\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) | 0.292305 / 0.018006 (0.274299) | 0.542584 / 0.000490 (0.542094) | 0.003047 / 0.000200 (0.002847) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030096 / 0.037411 (-0.007315) | 0.112341 / 0.014526 (0.097815) | 0.124965 / 0.176557 (-0.051591) | 0.183159 / 0.737135 (-0.553976) | 0.131885 / 0.296338 (-0.164453) |\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) | 0.426437 / 0.215209 (0.211228) | 4.260984 / 2.077655 (2.183330) | 2.078358 / 1.504120 (0.574238) | 1.877644 / 1.541195 (0.336449) | 2.044036 / 1.468490 (0.575546) | 0.532980 / 4.584777 (-4.051797) | 3.749573 / 3.745712 (0.003860) | 1.944155 / 5.269862 (-3.325706) | 1.090307 / 4.565676 (-3.475370) | 0.065445 / 0.424275 (-0.358830) | 0.011237 / 0.007607 (0.003630) | 0.521448 / 0.226044 (0.295403) | 5.213118 / 2.268929 (2.944189) | 2.507829 / 55.444624 (-52.936795) | 2.177179 / 6.876477 (-4.699297) | 2.351161 / 2.142072 (0.209088) | 0.656775 / 4.805227 (-4.148452) | 0.141207 / 6.500664 (-6.359457) | 0.063286 / 0.075469 (-0.012183) |\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) | 1.190281 / 1.841788 (-0.651506) | 15.327424 / 8.074308 (7.253116) | 13.300695 / 10.191392 (3.109303) | 0.190484 / 0.680424 (-0.489939) | 0.017984 / 0.534201 (-0.516217) | 0.405714 / 0.579283 (-0.173569) | 0.435915 / 0.434364 (0.001551) | 0.494083 / 0.540337 (-0.046254) | 0.600616 / 1.386936 (-0.786320) |\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) | 0.006740 / 0.011353 (-0.004613) | 0.004289 / 0.011008 (-0.006719) | 0.076532 / 0.038508 (0.038024) | 0.043305 / 0.023109 (0.020196) | 0.356111 / 0.275898 (0.080213) | 0.434121 / 0.323480 (0.110641) | 0.005599 / 0.007986 (-0.002387) | 0.003461 / 0.004328 (-0.000868) | 0.077097 / 0.004250 (0.072847) | 0.055369 / 0.037052 (0.018317) | 0.367093 / 0.258489 (0.108604) | 0.418801 / 0.293841 (0.124960) | 0.032057 / 0.128546 (-0.096489) | 0.009048 / 0.075646 (-0.066599) | 0.082897 / 0.419271 (-0.336374) | 0.050287 / 0.043533 (0.006754) | 0.352060 / 0.255139 (0.096921) | 0.376278 / 0.283200 (0.093078) | 0.023924 / 0.141683 (-0.117759) | 1.522780 / 1.452155 (0.070626) | 1.578938 / 1.492716 (0.086222) |\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) | 0.287317 / 0.018006 (0.269311) | 0.508490 / 0.000490 (0.508000) | 0.000431 / 0.000200 (0.000231) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031139 / 0.037411 (-0.006272) | 0.113927 / 0.014526 (0.099401) | 0.128147 / 0.176557 (-0.048409) | 0.179712 / 0.737135 (-0.557424) | 0.134364 / 0.296338 (-0.161975) |\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) | 0.452834 / 0.215209 (0.237625) | 4.507944 / 2.077655 (2.430289) | 2.287758 / 1.504120 (0.783638) | 2.091145 / 1.541195 (0.549951) | 2.196228 / 1.468490 (0.727738) | 0.539306 / 4.584777 (-4.045471) | 3.838941 / 3.745712 (0.093228) | 1.908801 / 5.269862 (-3.361060) | 1.139235 / 4.565676 (-3.426442) | 0.066677 / 0.424275 (-0.357599) | 0.011422 / 0.007607 (0.003815) | 0.562966 / 0.226044 (0.336921) | 5.633712 / 2.268929 (3.364784) | 2.788622 / 55.444624 (-52.656002) | 2.438465 / 6.876477 (-4.438012) | 2.523479 / 2.142072 (0.381407) | 0.668730 / 4.805227 (-4.136498) | 0.143977 / 6.500664 (-6.356687) | 0.064661 / 0.075469 (-0.010808) |\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) | 1.291708 / 1.841788 (-0.550080) | 15.573316 / 8.074308 (7.499008) | 14.435099 / 10.191392 (4.243707) | 0.147745 / 0.680424 (-0.532679) | 0.017602 / 0.534201 (-0.516599) | 0.401560 / 0.579283 (-0.177723) | 0.429861 / 0.434364 (-0.004502) | 0.469800 / 0.540337 (-0.070538) | 0.567515 / 1.386936 (-0.819421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c340f5dcfd06340f180f6c6ea2d5ef81f49d98 \"CML watermark\")\n" ]

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{ "closed_at": "2021-07-09T05:50:07Z", "closed_issues": 12, "created_at": "2021-05-31T16:13:06Z", "creator": { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }, "description": "Next minor release", "due_on": "2021-07-08T07:00:00Z", "html_url": "https://github.com/huggingface/datasets/milestone/5", "id": 6808903, "labels_url": "https://api.github.com/repos/huggingface/datasets/milestones/5/labels", "node_id": "MDk6TWlsZXN0b25lNjgwODkwMw==", "number": 5, "open_issues": 0, "state": "closed", "title": "1.9", "updated_at": "2021-07-12T14:12:00Z", "url": "https://api.github.com/repos/huggingface/datasets/milestones/5" }

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[ "Let me push a small fix... 😉 ", "Thanks !" ]

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[ "Hi @colbym-MM, thanks for reporting. We are fixing it." ]

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[ "Hi ! You can access the faiss index with\r\n```python\r\nfaiss_index = my_dataset.get_index(\"my_index_name\").faiss_index\r\n```\r\nand then do whatever you want with it, e.g. query it using range_search:\r\n```python\r\nthreshold = 0.95\r\nlimits, distances, indices = faiss_index.range_search(x=xq, thresh=threshold)\r\n\r\ntexts = dataset[indices]\r\n```", "wow, that's great, thank you for the explanation. (if that's not already in the documentation, could be worth adding it)\r\n\r\nwhich type of faiss index is Datasets using? I looked into faiss recently and I understand that there are several different types of indexes and the choice is important, e.g. regarding which distance metric you use (euclidian vs. cosine/dot product), the size of my dataset etc. can I chose the type of index somehow as well?", "`Dataset.add_faiss_index` has a `string_factory` parameter, used to set the type of index (see the faiss documentation about [index factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory)). Alternatively, you can pass an index you've defined yourself using faiss with the `custom_index` parameter of `Dataset.add_faiss_index` \r\n\r\nHere is the full documentation of `Dataset.add_faiss_index`: https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index", "great thanks, I will try it out" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3878). All of your documentation changes will be reflected on that endpoint.", "Maybe `NonMatchingSplitsSizesError` errors should also tell the user to try using a more recent version of the dataset to get the fixes ?", "@lhoestq Good idea. Will open a new PR to improve the error messages of NonMatchingSplitsSizesError, NonMatchingChecksumsError, ...", "It seems there is still a problem. I am using datasets version 2.5.1. \r\nI just typed `ds = load_dataset(\"cats_vs_dogs\")` and get the error below.\r\n\r\n```\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=3893603, num_examples=23422, dataset_name='cats_vs_dogs'), 'recorded': SplitInfo(name='train', num_bytes=3891612, num_examples=23410, dataset_name='cats_vs_dogs')}]\r\n```\r\nIt looks like the dataset still only has 23,410 examples....\r\n", "Thanks for reporting, I opened https://github.com/huggingface/datasets/pull/5047" ]

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[ "Could you test to run the test using the changes in #527 and let me know if it fixes the issue ? If so I'll merge it and we'll be good to go :)", "Hello, it does work on the fixing real dataset branch. Merci Quentin :)", "Nice, glad to hear that :)\r\nde rien !" ]

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