Datasets:

BanUrsus

/

github-issues_chapter5_section4

like 0

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[ "Hi ! Thanks for reporting.\r\nThe same issue was reported in #730 (but with the encodings instead of the delimiter). It was fixed by #770 .\r\nThe fix will be available in the next release :)", "Thanks for the prompt reply and terribly sorry for the spam! \r\nLooking forward to the new release! " ]

"2020-10-29T16:06:10Z"

"2020-10-29T21:21:27Z"

"2020-10-29T21:21:27Z"

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"2021-12-20T17:44:01Z"

"2021-12-21T06:24:02Z"

"2021-12-21T06:24:01Z"

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[ "the `ms_terms` formatting CI fails is fixed on master", "merging since the CI is fixed on master" ]

"2020-12-04T15:19:36Z"

"2020-12-04T17:05:56Z"

"2020-12-04T17:05:56Z"

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[ "Yes, we decided to replace the encoded label with the corresponding label when possible in the dataset viewer. But\r\n1. maybe it's the wrong default\r\n2. we could find a way to show both (with a switch, or showing both ie. `0 (neutral)`).\r\n", "Hi @severo,\r\n\r\nThanks for clarifying. \r\n\r\nI think this default is a bit counterintuitive for the user. However, this is a personal opinion that might not be general. I think it is nice to have the actual (non-encoded) labels in the viewer. On the other hand, it would be nice to match what the user sees with what they get when they download a dataset. I don't know - I can see the difficulty of choosing a default :)\r\nMaybe having non-encoded labels as a default can be useful?\r\n\r\nAnyway, I think the issue has been addressed. Thanks a lot for your super-quick answer!\r\n\r\n ", "Thanks for the 👍 in https://github.com/huggingface/datasets/issues/3673#issuecomment-1029008349 @mariosasko @gary149 @pietrolesci, but as I proposed various solutions, it's not clear to me which you prefer. Could you write your preferences as a comment?\r\n\r\n_(note for myself: one idea per comment in the future)_", "As I am working with seq2seq, I prefer having the label in string form rather than numeric. So the viewer is fine and the underlying dataset should be \"decoded\" (from int to str). In this way, the user does not have to search for a mapping `int -> original name` (even though is trivial to find, I reckon). Also, encoding labels is rather easy.\r\n\r\nI hope this is useful", "I like the idea of \"0 (neutral)\". The label name can even be greyed to make it clear that it's not part of the actual item in the dataset, it's just the meaning.", "I like @lhoestq's idea of having grayed-out labels.", "Proposals by @gary149. Which one do you prefer? Please vote with the thumbs\r\n\r\n- 👍 \r\n\r\n \r\n\r\n- 👎 \r\n\r\n \r\n\r\n", "I like Option 1 better as it shows clearly what the user is downloading", "Thanks! ", "It's [live](https://huggingface.co/datasets/glue/viewer/cola/train):\r\n\r\n<img width=\"1126\" alt=\"Capture d’écran 2022-02-14 à 10 26 03\" src=\"https://user-images.githubusercontent.com/1676121/153836716-25f6205b-96af-42d8-880a-7c09cb24c420.png\">\r\n\r\nThanks all for the help to improve the UI!", "Love it ! thanks :)" ]

"2022-02-03T12:10:43Z"

"2022-02-16T11:22:31Z"

"2022-02-11T17:01:21Z"

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[ "OK, I removed Glottolog codes and only added ISO 639-3 ones. The former are for the moment in corpus card description, language details, and in subcorpora names.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4880). All of your documentation changes will be reflected on that endpoint." ]

"2022-08-23T19:32:38Z"

"2022-08-24T12:52:46Z"

"2022-08-24T12:52:46Z"

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"2020-12-13T00:21:14Z"

"2020-12-13T19:14:24Z"

"2020-12-13T19:14:24Z"

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[ "Hi @muelletm, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nUntil our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```" ]

"2022-03-03T09:01:10Z"

"2022-03-03T09:24:58Z"

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

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3926). All of your documentation changes will be reflected on that endpoint." ]

"2022-03-15T17:00:46Z"

"2022-03-15T19:27:15Z"

"2022-03-15T19:27:12Z"

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[ "@patrickvonplaten \r\nI removed the mp3 files, dummy_data is much smaller now!" ]

"2021-02-23T16:28:16Z"

"2021-02-24T18:09:32Z"

"2021-02-24T18:05:09Z"

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"2021-11-26T16:06:14Z"

"2021-11-26T16:31:23Z"

"2021-11-26T16:31:23Z"

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[ "Thank you! I just marked this as a draft PR. It probably would be better to create specific Array2D and Array3D classes as needed instead of a generic MultiArray for now, it should simplify the code a lot too so, I'll update it as such. Also i was meaning to reply earlier, but I wanted to thank you for the testing script you sent me earlier since it ended up being tremendously helpful. ", "Okay, I just converted the MultiArray class to Array2D, and got rid of all those \"globals()\"! \r\n\r\nThe main issues I had were that when including a \"pa.ExtensionType\" as a column, the ordinary methods to batch the data would not work and it would throw me some mysterious error, so I first cleaned up my code to order the row to match the schema (because when including extension types the row is disordered ) and then made each row a pa.Table and then concatenated all the tables. Also each n-dimensional vector class we implement will be size invariant which is some good news. ", "Okay awesome! I just added your suggestions and changed up my recursive functions. \r\n\r\nHere is the traceback for the when I use the original code in the write_on_file method:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 33, in <module>\r\n File \"/home/eltoto/nlp/src/nlp/arrow_writer.py\", line 214, in finalize\r\n self.write_on_file()\r\n File \"/home/eltoto/nlp/src/nlp/arrow_writer.py\", line 134, in write_on_file\r\n pa_array = pa.array(self.current_rows, type=self._type)\r\n File \"pyarrow/array.pxi\", line 269, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 38, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 106, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>\r\n\r\nshell returned 1\r\n```\r\n\r\nI think when trying to cast an extension array within a list of dictionaries, some method gets called that bugs out Arrow and somehow doesn't get called when adding a single row to a a table and then appending multiple tables together. I tinkered with this for a while but could not find any workaround. \r\n\r\nIn the case that this new method causes bad compression/worse performance, we can explicitly set the batch size in the pa.Table.to_batches(***batch_size***) method, which will return a list of batches. Perhaps, we can check that the batch size is not too large converting the table to batches after X many rows are appended to it by following the batch_size check below.", "> I think when trying to cast an extension array within a list of dictionaries, some method gets called that bugs out Arrow and somehow doesn't get called when adding a single row to a a table and then appending multiple tables together. I tinkered with this for a while but could not find any workaround.\r\n\r\nIndeed that's weird.\r\n\r\n> In the case that this new method causes bad compression/worse performance, we can explicitly set the batch size in the pa.Table.to_batches(batch_size) method, which will return a list of batches. Perhaps, we can check that the batch size is not too large converting the table to batches after X many rows are appended to it by following the batch_size check below.\r\n\r\nThe argument of `pa.Table.to_batches` is not `batch_size` but `max_chunksize`, which means that right now it would have no effects (each chunk is of length 1).\r\n\r\nWe can fix that just by doing `entries.combine_chunks().to_batches(batch_size)`. In that case it would write by chunk of 1000 which is what we want. I don't think it will slow down the writing by much, but we may have to do a benchmark just to make sure. If speed is ok we could even replace the original code to always write chunks this way.\r\n\r\nDo you still have errors that need to be fixed ?", "@lhoestq Nope all should be good! \r\n\r\nWould you like me to add the entries.combine_chunks().to_batch_size() code + benchmark?", "> @lhoestq Nope all should be good!\r\n\r\nAwesome :)\r\n\r\nI think it would be good to start to add some tests then.\r\nYou already have `test_multi_array.py` which is a good start, maybe you can place it in /tests and make it a `unittest.TestCase` ?\r\n\r\n> Would you like me to add the entries.combine_chunks().to_batch_size() code + benchmark?\r\n\r\nThat would be interesting. We don't want reading/writing to be the bottleneck of dataset processing for example in terms of speed. Maybe we could test the write + read speed of different datasets:\r\n- write speed + read speed a dataset with `nlp.Array2D` features\r\n- write speed + read speed a dataset with `nlp.Sequence(nlp.Sequence(nlp.Value(\"float32\")))` features\r\n- write speed + read speed a dataset with `nlp.Sequence(nlp.Value(\"float32\"))` features (same data but flatten)\r\nIt will be interesting to see the influence of `.combine_chunks()` on the `Array2D` test too.\r\n\r\nWhat do you think ?", "Well actually it looks like we're still having the `print(dataset[0])` error no ?", "I just tested your code to try to understand better.\r\n\r\n\r\n- First thing you must know is that we've switched from `dataset._data.to_pandas` to `dataset._data.to_pydict` by default when we call `dataset[0]` in #423 . Right now it raises an error but it can be fixed by adding this method to `ExtensionArray2D`:\r\n\r\n```python\r\n def to_pylist(self):\r\n return self.to_numpy().tolist()\r\n```\r\n\r\n- Second, I noticed that `ExtensionArray2D.to_numpy()` always return a (5, 5) shape in your example. I thought `ExtensionArray` was for possibly multiple examples and so I was expecting a shape like (1, 5, 5) for example. Did I miss something ?\r\nTherefore when I apply the fix I mentioned (adding to_pylist), it returns one example per row in each image (in your example of 2 images of shape 5x5, I get `len(dataset._data.to_pydict()[\"image\"]) == 10 # True`)\r\n\r\n[EDIT] I changed the reshape step in `ExtensionArray2D.to_numpy()` by\r\n```python\r\nnumpy_arr = numpy_arr.reshape(len(self), *ExtensionArray2D._construct_shape(self.storage))\r\n```\r\nand it did the job: `len(dataset._data.to_pydict()[\"image\"]) == 2 # True`\r\n\r\n- Finally, I was able to make `to_pandas` work though, by implementing custom array dtype in pandas with arrow conversion (I got inspiration from [here](https://gist.github.com/Eastsun/a59fb0438f65e8643cd61d8c98ec4c08) and [here](https://pandas.pydata.org/pandas-docs/version/1.0.0/development/extending.html#compatibility-with-apache-arrow))\r\n\r\nMaybe you could add me in your repo so I can open a PR to add these changes to your branch ?", "`combine_chunks` doesn't seem to work btw:\r\n`ArrowNotImplementedError: concatenation of extension<arrow.py_extension_type>`", "> > @lhoestq Nope all should be good!\r\n> \r\n> Awesome :)\r\n> \r\n> I think it would be good to start to add some tests then.\r\n> You already have `test_multi_array.py` which is a good start, maybe you can place it in /tests and make it a `unittest.TestCase` ?\r\n> \r\n> > Would you like me to add the entries.combine_chunks().to_batch_size() code + benchmark?\r\n> \r\n> That would be interesting. We don't want reading/writing to be the bottleneck of dataset processing for example in terms of speed. Maybe we could test the write + read speed of different datasets:\r\n> \r\n> * write speed + read speed a dataset with `nlp.Array2D` features\r\n> * write speed + read speed a dataset with `nlp.Sequence(nlp.Sequence(nlp.Value(\"float32\")))` features\r\n> * write speed + read speed a dataset with `nlp.Sequence(nlp.Value(\"float32\"))` features (same data but flatten)\r\n> It will be interesting to see the influence of `.combine_chunks()` on the `Array2D` test too.\r\n> \r\n> What do you think ?\r\n\r\nYa! that should be no problem at all, Ill use the timeit module and get back to you with the results sometime over the weekend.", "Thank you for all your help getting the pandas and row indexing for the dataset to work! For `print(dataset[0])`, I considered the workaround of doing `print(dataset[\"col_name\"][0])` a temporary solution, but ya, I was never able to figure out how to previously get it to work. I'll add you to my repo right now, let me know if you do not see the invite. Also lastly, it is strange how the to_batches method is not working, so I can check that out while I add some speed tests + add the multi dim test under the unit tests this weekend. ", "I created the PR :)\r\nI also tested `to_batches` and it works on my side", "Sorry for the bit of delay! I just added the tests, the PR into my fork, and some speed tests. It should be fairly easy to add more tests if we need. Do you think there is anything else to checkout?", "Cool thanks for adding the tests :) \r\n\r\nNext step is merge master into this branch.\r\nNot sure I understand what you did in your last commit, but it looks like you discarded all the changes from master ^^'\r\n\r\nWe've done some changes in the features logic on master, so let me know if you need help merging it.\r\n\r\nAs soon as we've merged from master, we'll have to make sure that we have extensive tests and we'll be good to do !\r\nAbout the lxmert dataset, we can probably keep it for another PR as soon as we have working 2d features. What do you think ?", "We might want to merge this after tomorrow's release though to avoid potential side effects @lhoestq ", "Yep I'm sure we can have it not for tomorrow's release but for the next one ;)", "haha, when I tried to rebase, I ran into some conflicts. In that last commit, I restored the features.py from the previous commit on the branch in my fork because upon updating to master, the pandasdtypemanger and pandas extension types disappeared. If you actually could help me with merging in what is needed, that would actually help a lot. \r\n\r\nOther than that, ya let me go ahead and move the dataloader code out of this PR. Perhaps we could discuss in the slack channelk soon about what to do with that because we can either just support the pretraining corpus for lxmert or try to implement the full COCO and visual genome datasets (+VQA +GQA) which im sure people would be pretty happy about. \r\n\r\nAlso we can talk more tests soon too when you are free. \r\n\r\nGoodluck on the release tomorrow guys!", "Not sure why github thinks there are conflicts here, as I just rebased from the current master branch.\r\nMerging into master locally works on my side without conflicts\r\n```\r\ngit checkout master\r\ngit reset --hard origin/master\r\ngit merge --no-ff eltoto1219/support_multi_dim_tensors_for_images\r\nMerge made by the 'recursive' strategy.\r\n datasets/lxmert_pretraining_beta/lxmert_pretraining_beta.py | 89 +++++++++++++++++++++++++++++++++++++\r\n datasets/lxmert_pretraining_beta/test_multi_array.py | 45 +++++++++++++++++++\r\n datasets/lxmert_pretraining_beta/to_arrow_data.py | 371 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n src/nlp/arrow_dataset.py | 24 +++++-----\r\n src/nlp/arrow_writer.py | 22 ++++++++--\r\n src/nlp/features.py | 229 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++---\r\n tests/test_array_2d.py | 210 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n 7 files changed, 969 insertions(+), 21 deletions(-)\r\n create mode 100644 datasets/lxmert_pretraining_beta/lxmert_pretraining_beta.py\r\n create mode 100644 datasets/lxmert_pretraining_beta/test_multi_array.py\r\n create mode 100644 datasets/lxmert_pretraining_beta/to_arrow_data.py\r\n create mode 100644 tests/test_array_2d.py\r\n```", "I put everything inside one commit from the master branch but the merge conflicts on github'side were still there for some reason.\r\nClosing and re-opening the PR fixed the conflict check on github's side.", "Almost done ! It still needs a pass on the docs/comments and maybe a few more tests.\r\n\r\nI had to do several changes for type inference in the ArrowWriter to make it support custom types.", "Ok this is now ready for review ! Thanks for your awesome work in this @eltoto1219 \r\n\r\nSummary of the changes:\r\n- added new feature type `Array2D`, that can be instantiated like `Array2D(\"float32\")` for example\r\n- added pyarrow extension type `Array2DExtensionType` and array `Array2DExtensionArray` that take care of converting from and to arrow. `Array2DExtensionType`'s storage is a list of list of any pyarrow array.\r\n- added pandas extension type `PandasArrayExtensionType` and array `PandasArrayExtensionArray` for conversion from and to arrow/python objects\r\n- refactor of the `ArrowWriter` write and write_batch functions to support extension types while preserving the type inference behavior.\r\n- added a utility object `TypedSequence` that is helpful to combine extension arrays and type inference inside the writer's methods.\r\n- added speed test for sequences writing (printed as warnings in pytest)\r\n- breaking: set disable_nullable to False by default as pyarrow's type inference returns nullable fields\r\n\r\nAnd there are plenty of new tests, mainly in `test_array2d.py` and `test_arrow_writer.py`.\r\n\r\nNote that there are some collisions in `arrow_dataset.py` with #513 so let's be careful when we'll merge this one.\r\n\r\nI know this is a big PR so feel free to ask questions", "I'll add Array3D, 4D.. tomorrow but it should take only a few lines. The rest won't change", "I took your comments into account and I added Array[3-5]D.\r\nI changed the storage type to fixed lengths lists. I had to update the `to_numpy` function because of that. Indeed slicing a FixedLengthListArray returns a view a of the original array, while in the previous case slicing a ListArray copies the storage.\r\n" ]

"2020-07-09T07:10:30Z"

"2020-08-24T09:59:35Z"

"2020-08-24T09:59:35Z"

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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==6.0.0\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) | 0.008227 / 0.011353 (-0.003126) | 0.004496 / 0.011008 (-0.006512) | 0.099319 / 0.038508 (0.060811) | 0.029929 / 0.023109 (0.006820) | 0.296686 / 0.275898 (0.020788) | 0.355372 / 0.323480 (0.031892) | 0.006864 / 0.007986 (-0.001122) | 0.003458 / 0.004328 (-0.000871) | 0.077234 / 0.004250 (0.072983) | 0.037072 / 0.037052 (0.000020) | 0.311675 / 0.258489 (0.053186) | 0.338965 / 0.293841 (0.045124) | 0.033562 / 0.128546 (-0.094985) | 0.011399 / 0.075646 (-0.064248) | 0.322406 / 0.419271 (-0.096865) | 0.043034 / 0.043533 (-0.000499) | 0.298083 / 0.255139 (0.042944) | 0.323661 / 0.283200 (0.040462) | 0.089380 / 0.141683 (-0.052303) | 1.479363 / 1.452155 (0.027208) | 1.518337 / 1.492716 (0.025620) |\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) | 0.177822 / 0.018006 (0.159816) | 0.400806 / 0.000490 (0.400317) | 0.002121 / 0.000200 (0.001921) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021986 / 0.037411 (-0.015426) | 0.096749 / 0.014526 (0.082223) | 0.101443 / 0.176557 (-0.075113) | 0.137519 / 0.737135 (-0.599616) | 0.105558 / 0.296338 (-0.190780) |\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) | 0.418983 / 0.215209 (0.203774) | 4.189579 / 2.077655 (2.111924) | 1.877831 / 1.504120 (0.373711) | 1.666213 / 1.541195 (0.125019) | 1.680735 / 1.468490 (0.212245) | 0.693033 / 4.584777 (-3.891744) | 3.420553 / 3.745712 (-0.325160) | 1.819647 / 5.269862 (-3.450214) | 1.144934 / 4.565676 (-3.420743) | 0.082209 / 0.424275 (-0.342066) | 0.012433 / 0.007607 (0.004826) | 0.526781 / 0.226044 (0.300737) | 5.273689 / 2.268929 (3.004760) | 2.323468 / 55.444624 (-53.121156) | 1.960508 / 6.876477 (-4.915969) | 2.035338 / 2.142072 (-0.106735) | 0.812789 / 4.805227 (-3.992438) | 0.148429 / 6.500664 (-6.352235) | 0.064727 / 0.075469 (-0.010742) |\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) | 1.253218 / 1.841788 (-0.588569) | 13.303426 / 8.074308 (5.229118) | 13.651074 / 10.191392 (3.459682) | 0.135178 / 0.680424 (-0.545246) | 0.028483 / 0.534201 (-0.505717) | 0.393284 / 0.579283 (-0.185999) | 0.401957 / 0.434364 (-0.032407) | 0.457136 / 0.540337 (-0.083201) | 0.535835 / 1.386936 (-0.851101) |\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) | 0.006335 / 0.011353 (-0.005017) | 0.004454 / 0.011008 (-0.006554) | 0.097565 / 0.038508 (0.059057) | 0.026917 / 0.023109 (0.003808) | 0.350779 / 0.275898 (0.074881) | 0.391979 / 0.323480 (0.068499) | 0.004648 / 0.007986 (-0.003337) | 0.003204 / 0.004328 (-0.001124) | 0.076987 / 0.004250 (0.072737) | 0.035257 / 0.037052 (-0.001796) | 0.347193 / 0.258489 (0.088704) | 0.391462 / 0.293841 (0.097621) | 0.031244 / 0.128546 (-0.097302) | 0.011460 / 0.075646 (-0.064186) | 0.321606 / 0.419271 (-0.097665) | 0.041218 / 0.043533 (-0.002315) | 0.341884 / 0.255139 (0.086745) | 0.374920 / 0.283200 (0.091720) | 0.086383 / 0.141683 (-0.055300) | 1.501750 / 1.452155 (0.049595) | 1.565060 / 1.492716 (0.072344) |\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) | 0.165447 / 0.018006 (0.147441) | 0.401885 / 0.000490 (0.401395) | 0.000975 / 0.000200 (0.000775) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024494 / 0.037411 (-0.012917) | 0.097334 / 0.014526 (0.082808) | 0.105324 / 0.176557 (-0.071232) | 0.142430 / 0.737135 (-0.594705) | 0.107249 / 0.296338 (-0.189089) |\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) | 0.441632 / 0.215209 (0.226423) | 4.407729 / 2.077655 (2.330074) | 2.078167 / 1.504120 (0.574047) | 1.864210 / 1.541195 (0.323015) | 1.885948 / 1.468490 (0.417458) | 0.693974 / 4.584777 (-3.890803) | 3.386837 / 3.745712 (-0.358875) | 1.840291 / 5.269862 (-3.429571) | 1.150524 / 4.565676 (-3.415153) | 0.082240 / 0.424275 (-0.342035) | 0.012488 / 0.007607 (0.004881) | 0.537589 / 0.226044 (0.311545) | 5.404007 / 2.268929 (3.135078) | 2.537467 / 55.444624 (-52.907157) | 2.190775 / 6.876477 (-4.685702) | 2.224746 / 2.142072 (0.082674) | 0.799524 / 4.805227 (-4.005703) | 0.150639 / 6.500664 (-6.350025) | 0.066473 / 0.075469 (-0.008997) |\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) | 1.258559 / 1.841788 (-0.583228) | 13.773583 / 8.074308 (5.699275) | 13.964322 / 10.191392 (3.772930) | 0.156295 / 0.680424 (-0.524129) | 0.016824 / 0.534201 (-0.517377) | 0.377476 / 0.579283 (-0.201807) | 0.390163 / 0.434364 (-0.044201) | 0.442541 / 0.540337 (-0.097796) | 0.529404 / 1.386936 (-0.857532) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f500a5c554b213aafe87293bd593920567742c3 \"CML watermark\")\n" ]

"2023-01-20T12:53:17Z"

"2023-01-20T13:32:50Z"

"2023-01-20T13:26:03Z"

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[ "Hi @lhoestq ,\r\n\r\nI am running into an issue now when trying to run RAG. Running exactly as described [here](https://huggingface.co/facebook/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage) I get the error below. Wondering if it's related to this.\r\n\r\nRunning Transformers 4.3.2 with datasets installed from source from `master` branch.\r\n\r\n```bash\r\n(venv) sergey_mkrtchyan datasets (master) $ python\r\nPython 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)\r\n[Clang 6.0 (clang-600.0.57)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n>>> tokenizer = RagTokenizer.from_pretrained(\"facebook/rag-token-nq\")\r\n>>> retriever = RagRetriever.from_pretrained(\"facebook/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=True)\r\nUsing custom data configuration dummy.psgs_w100.nq.no_index-dummy=True,with_index=False\r\nReusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.no_index-dummy=True,with_index=False/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\nUsing custom data configuration dummy.psgs_w100.nq.exact-50b6cda57ff32ab4\r\nReusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.exact-50b6cda57ff32ab4/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\n 0%| | 0/10 [00:00<?, ?it/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 425, in from_pretrained\r\n return cls(\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 387, in __init__\r\n self.init_retrieval()\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 458, in init_retrieval\r\n self.index.init_index()\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 284, in init_index\r\n self.dataset = load_dataset(\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/load.py\", line 750, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py\", line 734, in as_dataset\r\n datasets = utils.map_nested(\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/utils/py_utils.py\", line 195, in map_nested\r\n return function(data_struct)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py\", line 769, in _build_single_dataset\r\n post_processed = self._post_process(ds, resources_paths)\r\n File \"/Users/sergey_mkrtchyan/.cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb/wiki_dpr.py\", line 205, in _post_process\r\n dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/arrow_dataset.py\", line 2516, in add_faiss_index\r\n super().add_faiss_index(\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py\", line 416, in add_faiss_index\r\n faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=faiss_verbose)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py\", line 281, in add_vectors\r\n self.faiss_index.add(vecs)\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/__init__.py\", line 104, in replacement_add\r\n self.add_c(n, swig_ptr(x))\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/swigfaiss.py\", line 3263, in add\r\n return _swigfaiss.IndexHNSW_add(self, n, x)\r\nRuntimeError: Error in virtual void faiss::IndexHNSW::add(faiss::Index::idx_t, const float *) at /Users/runner/work/faiss-wheels/faiss-wheels/faiss/faiss/IndexHNSW.cpp:356: Error: 'is_trained' failed\r\n>>>\r\n```\r\n\r\nThe error message is hinting that it could be related to this, but I might be wrong. Any ideas?\r\n\r\n\r\nEdit: Can confirm it's working fine with datasets==1.2.0\r\n\r\nDouble Edit: Did some further digging. The issue is related to this commit: 8c5220307c33f00e01c3bf7b8. I opened a separate issue #1941 for proper tracking." ]

"2021-02-22T15:23:46Z"

"2021-02-25T01:33:48Z"

"2021-02-22T15:36:08Z"

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[ "@lhoestq I can take this issue. Please can you point out to me where I can find the other positional arguments?", "Hi @RohitRathore1 :)\r\n\r\nYou can find all the YAML tags in the tagging app here: https://hf.co/spaces/huggingface/datasets-tagging). They're all passed as arguments to a DatasetMetadata object used to validate the tags." ]

"2022-05-16T13:18:16Z"

"2022-05-30T15:36:52Z"

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[ "Seems to work now, perhaps it was something internal with our university's network." ]

"2023-01-28T15:18:26Z"

"2023-01-29T08:09:49Z"

"2023-01-29T08:09:49Z"

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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) | 0.007809 / 0.011353 (-0.003544) | 0.004573 / 0.011008 (-0.006435) | 0.101201 / 0.038508 (0.062693) | 0.089703 / 0.023109 (0.066594) | 0.416502 / 0.275898 (0.140604) | 0.463352 / 0.323480 (0.139872) | 0.006101 / 0.007986 (-0.001885) | 0.003783 / 0.004328 (-0.000545) | 0.076531 / 0.004250 (0.072281) | 0.064017 / 0.037052 (0.026964) | 0.422453 / 0.258489 (0.163964) | 0.485926 / 0.293841 (0.192085) | 0.036797 / 0.128546 (-0.091749) | 0.010172 / 0.075646 (-0.065474) | 0.344442 / 0.419271 (-0.074829) | 0.062240 / 0.043533 (0.018707) | 0.422685 / 0.255139 (0.167546) | 0.451457 / 0.283200 (0.168257) | 0.027831 / 0.141683 (-0.113852) | 1.737187 / 1.452155 (0.285033) | 1.847631 / 1.492716 (0.354915) |\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) | 0.270336 / 0.018006 (0.252330) | 0.500540 / 0.000490 (0.500050) | 0.017042 / 0.000200 (0.016842) | 0.000704 / 0.000054 (0.000650) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033450 / 0.037411 (-0.003962) | 0.100314 / 0.014526 (0.085788) | 0.117216 / 0.176557 (-0.059340) | 0.182352 / 0.737135 (-0.554784) | 0.114903 / 0.296338 (-0.181436) |\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) | 0.458562 / 0.215209 (0.243353) | 4.570492 / 2.077655 (2.492837) | 2.230286 / 1.504120 (0.726167) | 2.032229 / 1.541195 (0.491034) | 2.130431 / 1.468490 (0.661941) | 0.563254 / 4.584777 (-4.021523) | 4.108455 / 3.745712 (0.362743) | 3.994059 / 5.269862 (-1.275802) | 2.424589 / 4.565676 (-2.141087) | 0.067534 / 0.424275 (-0.356741) | 0.008774 / 0.007607 (0.001167) | 0.546356 / 0.226044 (0.320312) | 5.527772 / 2.268929 (3.258843) | 2.934410 / 55.444624 (-52.510215) | 2.536871 / 6.876477 (-4.339605) | 2.598704 / 2.142072 (0.456632) | 0.676721 / 4.805227 (-4.128506) | 0.155904 / 6.500664 (-6.344760) | 0.073274 / 0.075469 (-0.002195) |\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) | 1.559170 / 1.841788 (-0.282618) | 23.228524 / 8.074308 (15.154216) | 16.743246 / 10.191392 (6.551854) | 0.184113 / 0.680424 (-0.496310) | 0.021804 / 0.534201 (-0.512397) | 0.466158 / 0.579283 (-0.113125) | 0.539911 / 0.434364 (0.105547) | 0.544377 / 0.540337 (0.004040) | 0.765779 / 1.386936 (-0.621157) |\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) | 0.008249 / 0.011353 (-0.003104) | 0.004734 / 0.011008 (-0.006275) | 0.077083 / 0.038508 (0.038575) | 0.096959 / 0.023109 (0.073850) | 0.497501 / 0.275898 (0.221603) | 0.530687 / 0.323480 (0.207207) | 0.006379 / 0.007986 (-0.001607) | 0.003899 / 0.004328 (-0.000430) | 0.076165 / 0.004250 (0.071915) | 0.069406 / 0.037052 (0.032354) | 0.515847 / 0.258489 (0.257358) | 0.540639 / 0.293841 (0.246798) | 0.038334 / 0.128546 (-0.090213) | 0.010112 / 0.075646 (-0.065534) | 0.084918 / 0.419271 (-0.334353) | 0.056866 / 0.043533 (0.013333) | 0.495555 / 0.255139 (0.240416) | 0.518988 / 0.283200 (0.235789) | 0.028556 / 0.141683 (-0.113127) | 1.799320 / 1.452155 (0.347165) | 1.874647 / 1.492716 (0.381931) |\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) | 0.264283 / 0.018006 (0.246277) | 0.510278 / 0.000490 (0.509788) | 0.015219 / 0.000200 (0.015019) | 0.000160 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038462 / 0.037411 (0.001051) | 0.115420 / 0.014526 (0.100894) | 0.124250 / 0.176557 (-0.052306) | 0.187724 / 0.737135 (-0.549411) | 0.126674 / 0.296338 (-0.169664) |\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) | 0.499345 / 0.215209 (0.284136) | 4.983924 / 2.077655 (2.906269) | 2.705099 / 1.504120 (1.200980) | 2.516344 / 1.541195 (0.975149) | 2.621103 / 1.468490 (1.152613) | 0.583254 / 4.584777 (-4.001523) | 4.231215 / 3.745712 (0.485503) | 4.028326 / 5.269862 (-1.241536) | 2.459171 / 4.565676 (-2.106505) | 0.069194 / 0.424275 (-0.355081) | 0.008850 / 0.007607 (0.001243) | 0.593878 / 0.226044 (0.367834) | 5.926478 / 2.268929 (3.657549) | 3.287435 / 55.444624 (-52.157189) | 2.902104 / 6.876477 (-3.974372) | 3.151307 / 2.142072 (1.009234) | 0.696922 / 4.805227 (-4.108306) | 0.161140 / 6.500664 (-6.339524) | 0.073728 / 0.075469 (-0.001741) |\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) | 1.636456 / 1.841788 (-0.205331) | 23.884606 / 8.074308 (15.810298) | 17.180875 / 10.191392 (6.989483) | 0.176782 / 0.680424 (-0.503642) | 0.023731 / 0.534201 (-0.510470) | 0.475191 / 0.579283 (-0.104092) | 0.506603 / 0.434364 (0.072239) | 0.571976 / 0.540337 (0.031638) | 0.826935 / 1.386936 (-0.560002) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b19f6b30f49e09b0d1f0c4a38b10d76f35ac483 \"CML watermark\")\n" ]

"2023-10-19T16:24:35Z"

"2023-10-19T17:18:00Z"

"2023-10-19T17:07:35Z"

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[ "Hi! \r\n\r\nYes, this makes sense for numeric values, but first I have to finish https://github.com/huggingface/datasets/pull/3336 because currently ArrayXD only allows the first dimension to be dynamic." ]

"2021-12-27T02:16:02Z"

"2022-01-05T13:54:03Z"

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[ "@lhoestq Can you confirm if this format is fine? I will update cards based on your feedback.", "On HuggingFace side we also have a mapping of hf user => github user (GitHub info used to be required when signing up until not long ago – cc @gary149 @beurkinger) so we can also add a link to HF profile", "All the dataset cards have been updated with GitHub ids :)" ]

"2021-02-01T03:49:19Z"

"2021-02-03T10:09:52Z"

"2021-02-03T10:06:30Z"

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[ "Hi ! The audio data don't always exist as files on disk - the blobs are often stored in the Arrow files. For now I'd suggest disabling decoding with `.cast_column(\"audio\", Audio(decode=False))` and apply your own decoding that handles corrupted files (maybe to filter them out ?)\r\n\r\ncc @sanchit-gandhi since it's related to our discussion about allowing users to make decoding return `None` and show a warning when there are corrupted files", "Thanks @lhoestq, I wasn't aware of the decode flag. It makes more sense as you say to show a warning when there are corrupted files together with some metadata of the file that allows to filter them from the dataset.\r\n\r\nMy workaround was to catch the LibsndfileError and generate a dummy audio with an unsual sample rate to filter it later. However returning `None` seems better. \r\n\r\n`try:\r\n array, sampling_rate = sf.read(file)\r\nexcept sf.LibsndfileError:\r\n print(\"bad file\")\r\n array = np.array([0.0])\r\n sampling_rate = 99.000` \r\n\r\n" ]

"2023-06-13T08:44:09Z"

"2023-06-14T12:45:01Z"

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[ "Looks like this change\r\n```\r\npa_table[pa_table.column_names[0]].to_pandas(types_mapper=pandas_types_mapper)\r\n```\r\ndoesn't return a Series with the correct type.\r\nThis is related to https://issues.apache.org/jira/browse/ARROW-9664\r\n\r\nSince the types_mapper isn't taken into account, the ArrayXD types are not converted to the correct pandas extension dtype", "@lhoestq I think I found a workaround... 😉 ", "For some reason the benchmarks are not run Oo", "Anyway, merging.\r\nWe'll see on master how much speed ups we got" ]

"2021-06-18T13:24:41Z"

"2021-06-21T09:06:06Z"

"2021-06-21T09:06:06Z"

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[ "You can cast the `input_image` column to the `Image` type to fix the issue:\r\n```python\r\nds.cast_column(\"input_image\", datasets.Image())\r\n```" ]

"2023-08-26T12:15:57Z"

"2023-08-29T14:49:58Z"

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[ "Hi ! Can you try setting `datasets.config.MAX_TABLE_NBYTES_FOR_PICKLING` to a smaller value than `4 << 30` (4GiB), for example `500 << 20` (500MiB) ? It should reduce the maximum size of the arrow table being pickled during multiprocessing.\r\n\r\nIf it fixes the issue, we can consider lowering the default value for everyone.", "@lhoestq I tried that just now but didn't seem to help." ]

"2021-12-21T03:29:01Z"

"2022-11-21T19:55:11Z"

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[ "@albertvillanova here's a separate issue for a bug similar to #3792", "Hi @afcruzs-ms, thanks for opening this separate issue for your problem.\r\n\r\nThe root problem in the other issue (#3792) was a change in the service of Google Drive.\r\n\r\nBut in your case, the `xcopa` dataset is not hosted on Google Drive. Therefore, the root cause should be a different one.\r\n\r\nLet me look at it... ", "@afcruzs-ms, I'm not able to reproduce the issue you reported:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"xcopa\", \"it\")\r\nDownloading builder script: 5.21kB [00:00, 2.75MB/s] \r\nDownloading metadata: 28.6kB [00:00, 14.5MB/s] \r\nDownloading and preparing dataset xcopa/it (download: 627.09 KiB, generated: 76.43 KiB, post-processed: Unknown size, total: 703.52 KiB) to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6...\r\nDownloading data: 642kB [00:00, 5.42MB/s]\r\nDataset xcopa downloaded and prepared to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6. Subsequent calls will reuse this data. \r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 733.27it/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n test: Dataset({\r\n features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n num_rows: 500\r\n })\r\n validation: Dataset({\r\n features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n num_rows: 100\r\n })\r\n})\r\n```\r\n\r\nMaybe you have some issue with your cached data... Could you please try to force the redownload of the data?\r\n```python\r\ndataset = load_dataset(\"xcopa\", \"it\", download_mode=\"force_redownload\")\r\n```", "It works indeed, thanks! ", "unfortunately, i am having a similar problem with the irc_disentaglement dataset :/\r\nmy code:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"irc_disentangle\", download_mode=\"force_redownload\")\r\n```\r\n\r\nhowever, it produces the same error as @afcruzs-ms \r\n```\r\n[38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0:\r\n [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master']\r\n```\r\n\r\nI attempted to use the `ignore_verifications' as such:\r\n```\r\nds = datasets.load_dataset('irc_disentangle', download_mode=\"force_redownload\", ignore_verifications=True)\r\n\r\n```\r\n```\r\nDownloading builder script: 12.0kB [00:00, 5.92MB/s] \r\nDownloading metadata: 7.58kB [00:00, 3.48MB/s] \r\nNo config specified, defaulting to: irc_disentangle/ubuntu\r\nDownloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5...\r\nDownloading data: 118MB [00:09, 12.1MB/s] \r\n \r\nDataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data.\r\n100%|██████████| 3/3 [00:00<00:00, 675.38it/s]\r\n```\r\nbut, this returns an empty set?\r\n\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n test: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n validation: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n})\r\n```\r\n\r\nnot sure what else to try at this point?\r\nThanks in advanced🤗", "Thanks @labouz for reporting: yes, better opening a new GitHub issue as you did. I'm addressing it:\r\n- #4376" ]

"2022-03-02T18:10:19Z"

"2022-05-20T06:00:42Z"

"2022-03-03T17:40:31Z"

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[ "> Did you generate the dummy data with the cli or manually ?\r\n\r\nIt was generated by the cli. Do you want me to make it smaller keep it like this?\r\n\r\n" ]

"2020-11-30T17:40:19Z"

"2020-12-03T14:07:11Z"

"2020-12-03T14:07:10Z"

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"2022-06-28T16:26:03Z"

"2022-06-29T06:26:28Z"

"2022-06-29T06:25:08Z"

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

"2022-05-11T17:36:20Z"

"2022-05-12T13:29:51Z"

"2022-05-12T13:22:19Z"

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"2020-12-28T16:09:09Z"

"2021-01-07T10:08:14Z"

"2021-01-07T10:08:13Z"

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[ "Logging looks like\r\n\r\n```\r\nDone writing 21900 indices in 3854400 bytes .\r\nProcess #0 will write at playground/tmp_00000_of_00004.arrow\r\nDone writing 21900 indices in 3854400 bytes .\r\nProcess #1 will write at playground/tmp_00001_of_00004.arrow\r\nDone writing 21900 indices in 3854400 bytes .\r\nProcess #2 will write at playground/tmp_00002_of_00004.arrow\r\nDone writing 21899 indices in 3854224 bytes .\r\nProcess #3 will write at playground/tmp_00003_of_00004.arrow\r\nSpawning 4 processes\r\n#3: 100%|████████████████████████████████████████████████| 21899/21899 [00:02<00:00, 8027.41ex/s]\r\n#0: 100%|████████████████████████████████████████████████| 21900/21900 [00:02<00:00, 7982.87ex/s]\r\n#1: 100%|████████████████████████████████████████████████| 21900/21900 [00:02<00:00, 7923.89ex/s]\r\n#2: 100%|████████████████████████████████████████████████| 21900/21900 [00:02<00:00, 7920.04ex/s]\r\nConcatenating 4 shards from multiprocessing\r\n```", "I added tests and improved logging.\r\nBoth `map` and `filter` support multiprocessing", "A bit strange that the benchmarks on map/filter are worth than `master`.\r\n(maybe because they are not done on the same machine)", "The benchmark also got worse in other PRs (see [here](https://github.com/huggingface/nlp/pull/550#commitcomment-41931609) for example, where we have 16sec for `map fast-tokenizer batched` and 18 sec for `map identity`)", "Hi,\r\n\r\nwhen I use the multiprocessing in ```.map```:\r\n```\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, num_proc=16)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nI get the following error:\r\n```\r\nTraceback (most recent call last):\r\n File \"src/run.py\", line 373, in <module>\r\n main()\r\n File \"src/run.py\", line 295, in main\r\n get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n File \"src/run.py\", line 153, in get_dataset\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1287, in map\r\n transformed_shards = [r.get() for r in results]\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1287, in <listcomp>\r\n transformed_shards = [r.get() for r in results]\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/multiprocessing/pool.py\", line 771, in get\r\n raise self._value\r\n put(task)\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/multiprocessing/connection.py\", line 206, in send\r\n self._send_bytes(_ForkingPickler.dumps(obj))\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/multiprocessing/reduction.py\", line 51, in dumps\r\n cls(buf, protocol).dump(obj)\r\nAttributeError: Can't pickle local object 'get_dataset.<locals>.<lambda>'\r\n```\r\nI think you should use [pathos](https://github.com/uqfoundation/pathos) to pickle the lambda function and some others!\r\nI change the 30 line of src/datasets/arrow_dataset.py as following:\r\n```\r\n# 30 line: from multiprocessing import Pool, RLock\r\nimport pathos\r\nfrom pathos.multiprocessing import Pool\r\nfrom multiprocessing import RLock\r\n```\r\nand it works!", "That's very cool indeed !\r\nShall we condiser adding this dependency @thomwolf ?", "We already use `dill` so that's definitely a very interesting option indeed!", "it gets stuck on debian 9 when num_proc > 1\r\n", "Are you using a tokenizer ?\r\nDid you try to set `TOKENIZERS_PARALLELISM=false` ?\r\n\r\nFeel free to discuss it in #620 , we're discussing this issue", "I set `TOKENIZERS_PARALLELISM=false`. Just the warning went away. The program was still stuck\r\n" ]

"2020-09-01T11:56:17Z"

"2020-09-22T15:11:56Z"

"2020-09-02T10:01:25Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6266). All of your documentation changes will be reflected on that endpoint.", "On Ubuntu, if `libyaml-dev` is installed, you can install PyYAML 6.0.1 with LibYAML with the following command (as it's automatically detected):\r\n\r\n```bash\r\npip install git+https://github.com/yaml/[email protected]\r\n```", "Are the failing tests flaky?", "We use `huggingface_hub`'s RepoCard API instead of these modules to parse the YAML block (notice the deprecations), so the `huggingface_hub` repo is the right place to suggest these changes.\r\n\r\nPersonally, I'm not a fan of these changes, as a single non-standard usage of the `ClassLabel` type is not a sufficient reason to merge them. Also, the dataset in question stores data in a single Parquet file, with the features info embedded in its (schema) metadata, which means the YAML parsing can be skipped while preserving the features by directly loading the Parquet file:\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"parquet\", data_files=\"https://huggingface.co/datasets/HuggingFaceM4/SugarCrepe_swap_obj/resolve/main/data/test-00000-of-00001-ca2ae6017a2336d7.parquet\")\r\n```\r\n\r\nPS: Yes, these tests are flaky. We are working on fixing them.", "Oh, I didn't realize they were deprecated. Thanks for the tip on how to work around this issue!\r\n\r\nFor future reference, the places to change the code in `huggingface_hub` would be:\r\n\r\nhttps://github.com/huggingface/huggingface_hub/blob/89cc69105074f1d071e0471144605f3cdfe1dab3/src/huggingface_hub/repocard.py#L506\r\n\r\nhttps://github.com/huggingface/huggingface_hub/blob/89cc69105074f1d071e0471144605f3cdfe1dab3/src/huggingface_hub/utils/_fixes.py#L34" ]

"2023-09-27T21:13:36Z"

"2023-09-28T14:29:24Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3866). All of your documentation changes will be reflected on that endpoint.", "I think we just need to keep `datasets_logo_name.jpg` and `course_banner.png` because they appear in the README.md of the forks of `datasets`. The other images can be removed", "Force pushed those two imgs only" ]

"2022-03-08T16:01:31Z"

"2022-03-08T17:37:02Z"

"2022-03-08T17:37:01Z"

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[ "Thank you for contributing a Thai dataset, @mapmeld ! I'm super hyped. \r\nOne comment I may add is that wongnai-corpus has two datasets: review classification (this) and word tokenization (https://github.com/wongnai/wongnai-corpus/blob/master/search/labeled_queries_by_judges.txt).\r\nWould it be possible for you to rename this one something along the line of `wongnai-reviews` so that when/if we include the word tokenization dataset, we will know which is which.\r\n\r\nThis helps solve my check_code_quality issue.\r\n```\r\nmake style\r\nblack --line-length 119 --target-version py36 datasets/wongnai\r\nflake8 datasets/wongnai\r\nisort datasets/wongnai/wongnai.py\r\n```", "@cstorm125 thanks! following your suggestions on formatting and on naming the dataset\r\n\r\nI am writing a blog post about Thai NLP and transformers (example: mBERT does 1-2 character tokens instead of doing word segmentation), started adding this dataset to use as an example, and then saw you were adding other datasets. Great work! And if you know any Thai BERT models beyond https://github.com/ThAIKeras/bert we should maybe talk over email!" ]

"2020-12-02T03:20:08Z"

"2020-12-02T15:34:41Z"

"2020-12-02T15:30:05Z"

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[ "Thanks for approving @lhoestq!\r\nWill apply these changes for the other datasets I've added too." ]

"2021-01-31T20:01:59Z"

"2021-02-03T05:01:45Z"

"2021-02-02T22:49:26Z"

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"2021-09-28T19:18:37Z"

"2021-10-01T19:51:23Z"

"2021-10-01T12:21:00Z"

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[ "closing since it's been added in #957 " ]

"2020-12-01T02:04:32Z"

"2020-12-01T23:34:36Z"

"2020-12-01T23:34:36Z"

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[ "Also - I took a stab at updating the docs, but I'm not sure how to actually check the outputs to see if it's formatted properly." ]

"2021-02-18T19:15:31Z"

"2021-02-20T22:01:30Z"

"2021-02-20T22:01:30Z"

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[ "Thank you @lhoestq. This logging configuration makes more sense to me.\r\n\r\nOnce propagation is allowed, the end-user can customize logging behavior and add custom handlers to the proper top logger in the hierarchy.\r\n\r\nAnd I also agree with following the best practices and removing any custom handlers:\r\n- it is the end user who has to implement any custom handlers\r\n- indeed, the previous logging problem with TensorFlow was due to the fact that absl did not follow best practices and had implemented a custom handler\r\n\r\nOur errors/warnings will be displayed anyway, even if we do not implement any custom handler. Since Python 3.2, logging has a built-in \"default\" handler (logging.lastResort) with the expected default behavior (sending error/warning messages to sys.stderr), which is used only if the end user has not configured any custom handler." ]

"2021-02-08T16:22:13Z"

"2021-02-09T14:22:38Z"

"2021-02-09T14:22:37Z"

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[ "@lhoestq reduced the dummy data size to around 19MB in total and added the dataset card.", "@lhoestq so I ended up removing all the nested JSON objects in the gz datafile and keep only one object with minimal content: `{\"publication_number\": \"US-8230922-B2\", \"abstract\": \"dummy abstract\", \"application_number\": \"US-201113163519-A\", \"description\": \"dummy description\"}`. \r\n\r\nThey're reduced to 35KB in total (2.5KB per domain and 17.5KB for all domains), hopefully, they're small enough." ]

"2020-12-04T04:37:30Z"

"2020-12-06T17:21:00Z"

"2020-12-06T17:20:59Z"

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"2021-10-13T05:52:26Z"

"2021-10-13T08:05:49Z"

"2021-10-13T06:49:48Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks ! I updated the two remaining files" ]

"2022-06-23T15:47:52Z"

"2022-06-26T15:49:46Z"

"2022-06-26T15:39:11Z"

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"2022-02-17T18:16:11Z"

"2022-02-17T18:16:11Z"

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[]

[ "@merveenoyan Do you want different files to be splits or configurations?\r\n\r\nFrom [what you specified in `Readme.md`](https://huggingface.co/datasets/inria-soda/tabular-benchmark/commit/fb4575853772c62a20203bdd6cc0202f5db4ce4e) I hypothesize that you want to have 4 **configs** corresponding to directories: `\"clf_cat\", \"clf_num\", \"reg_cat\", \"reg_num\"`. And inside each config you require to have as many splits as there are `csv` files\r\nso if you run \r\n```python\r\nload_dataset(\"inria-soda/tabular-benchmark\", \"clf_cat\", split=\"compass\")\r\n```\r\nyou will generate the data only from `compass.csv` file.\r\nIn this case, running `load_dataset(\"inria-soda/tabular-benchmark\", \"clf_cat\"`) without split parameter will return `DatasetDict` object with `\"KDDCup09_upselling\", \"cat_compass\", \"cat_covertype\", ... \"road_safety\"` keys (which values are splits - `Dataset` objects)\r\n\r\n**or**\r\ndo you want each file to be a separate config? Like:\r\n```python\r\nload_dataset(\"inria-soda/tabular-benchmark\", \"clf_cat_compass\") # returns DatasetDict with a single \"train\" split\r\n```\r\n**or**\r\nmaybe smth completely different? :smile: \r\n\r\nAnyway, now I have an impression that this is probably rather a matter of automatically inferring configs from repository structure rather than providing parameters in metadata yaml.\r\n", "@polinaeterna I want the latter where you can think of every CSV file as a config, like MNLI from GLUE.", "@merveenoyan @lhoestq I see two solutions to this case. \r\n1. Parse configurations automatically from directories names. That is, if you have data structure like:\r\n```\r\ntabular-benchmark\r\n └─clf_cat_compass\r\n └─compass.csv\r\n └─clf_cat_cat_covertype\r\n └─covertype.csv\r\n ...\r\n └─reg_cat_house_sales\r\n └─house_sales.csv\r\n```\r\nyou'll get \"clf_cat_compass\", \"clf_cat_cat_covertype\", ... \"reg_cat_house_sales\" configurations that would contain **only files from corresponding directories**. \r\n**\\+** this is a requested change and needed in general and would solve other problems, see https://github.com/huggingface/datasets/issues/4578, would also help with https://github.com/huggingface/datasets/pull/5213 which I'm working on currently\r\n**\\+** would allow users to do just `load_dataset(“inria-soda/tabular-benchmark”, “clf_cat_compass”)`, no `data_files` param required\r\n**\\-** in this specific case it would require restructuring of the data - putting each file in a directory named as a config name (to me personally it doesn't seem to be a big deal) \r\n\r\n2. More or less what we discussed before - add support for manually specifying parameters in the metadata. We can add new metadata yaml field (say, `\"custom_configs_info\"`), so that we can provide smth like:\r\n```yaml\r\n---\r\n...\r\ndataset_info:\r\n ... \r\ncustom_configs_info:\r\n- config_name: reg_cat_house_sales\r\n data_files:\r\n - reg_cat/house_sales.csv\r\n- config_name: clf_cat_compass\r\n data_files:\r\n - clf_cat/compass.csv\r\n...\r\n---\r\n```\r\n**\\+** Would be useful not only for tabular data and not only for `data_files` parameter - any packaged dataset’s viewer can be customized to use specific, non-default parameters. @merveenoyan do you maybe have any other examples/use cases in mind where you want to provide any specific parameters to the viewer? \r\n**\\-** I'm not sure here but assume that it might require changes in interaction with the viewer on the hub side - to parse these configurations, as they not default configurations (not in `BUILDER_CONFIGS` list). cc @severo But probably this can be solved on the `datasets` side too.\r\n\r\nOverall, I would start from implementing the first solution since it's related to what I'm doing now and is super useful for `datasets` in general. And then if we agree that having more flexibility in providing parameters to the viewer is required, I can implement the second one. Let me know what you think :) ", "> We can add new metadata yaml field (say, \"custom_configs_info\"), so that we can provide smth like:\r\n\r\nLove it ! Some other ideas to name the \"custom_configs_info\" field: \"configs\", \"parameters\", \"config_args\", \"configurations\"\r\n\r\n> it might require changes in interaction with the viewer on the hub side - to parse these configurations, as they not default configurations (not in BUILDER_CONFIGS list)\r\n\r\nIf we update the `get_dataset_config_names()` function in `datasets` in inspect.py we should be fine - that's what the viewer is using\r\n\r\n> Overall, I would start from implementing the first solution since it's related to what I'm doing now and is super useful for datasets in general. And then if we agree that having more flexibility in providing parameters to the viewer is required, I can implement the second one. Let me know what you think :)\r\n\r\nActually I feel like the second solution includes the first use case you mentioned. If you implement the second solution, then users would just have to add a few lines of YAML and their directories would be considered configurations no ? Maybe there's no need to implement two different logics to do the same thing", "is there any update on this? 🕵🏻", "@merveenoyan I haven't started working on this yet, working on adding configs to packaged datasets instead: https://github.com/huggingface/datasets/pull/5213 because this both would allow you to solve your issue and is a frequently requested feature.\r\n\r\nadding arbitrary parameters to yaml would be my next task i think!", "@merveenoyan ignore my comment above, I'm switching to this task now :D", "I want to be able to create folders in a model.", "Addressed in #5331 " ]

"2022-11-07T13:27:16Z"

"2023-07-21T14:36:02Z"

"2023-07-21T14:36:01Z"

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[ "@patrickvonplaten Could you please help me with how the output text has to be represented in the data? TIMIT has Words, Phonemes and texts. Also has lot on info on the speaker and the dialect. Could you please help me? An example of how to arrange it would be super helpful!\r\n\r\n", "Hey @vrindaprabhu - sure I'll help you :-) Could you open a first PR for TIMIT where you copy-paste more or less the `librispeech_asr` script: https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L93 (obviously replacing all the naming and links correctly...) and then you can list all possible outputs in the features dict: https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L104 (words, phonemes should probably be of kind `datasets.Sequence(datasets.Value(\"string\"))` and texts I think should be of type `\"text\": datasets.Value(\"string\")`.\r\n\r\nWhen you've opened a first PR, I think it'll be much easier for us to take a look together :-) ", "I am sorry! I created the PR [#1903](https://github.com/huggingface/datasets/pull/1903#). Requesting your comments! CircleCI tests are failing, will address them along with your comments!" ]

"2021-02-09T07:29:41Z"

"2021-03-15T05:59:37Z"

"2021-03-15T05:59:37Z"

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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) | 0.006374 / 0.011353 (-0.004979) | 0.004100 / 0.011008 (-0.006909) | 0.104031 / 0.038508 (0.065523) | 0.035186 / 0.023109 (0.012076) | 0.328904 / 0.275898 (0.053006) | 0.361409 / 0.323480 (0.037929) | 0.003855 / 0.007986 (-0.004130) | 0.004140 / 0.004328 (-0.000189) | 0.080406 / 0.004250 (0.076156) | 0.045658 / 0.037052 (0.008606) | 0.341133 / 0.258489 (0.082644) | 0.372688 / 0.293841 (0.078847) | 0.032025 / 0.128546 (-0.096521) | 0.008877 / 0.075646 (-0.066769) | 0.354784 / 0.419271 (-0.064488) | 0.068874 / 0.043533 (0.025341) | 0.335441 / 0.255139 (0.080302) | 0.356498 / 0.283200 (0.073298) | 0.113367 / 0.141683 (-0.028316) | 1.522458 / 1.452155 (0.070304) | 1.608046 / 1.492716 (0.115329) |\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) | 0.231653 / 0.018006 (0.213647) | 0.446678 / 0.000490 (0.446188) | 0.003246 / 0.000200 (0.003046) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025299 / 0.037411 (-0.012112) | 0.111440 / 0.014526 (0.096914) | 0.118758 / 0.176557 (-0.057799) | 0.175037 / 0.737135 (-0.562098) | 0.124583 / 0.296338 (-0.171755) |\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) | 0.418694 / 0.215209 (0.203484) | 4.174695 / 2.077655 (2.097041) | 1.890323 / 1.504120 (0.386203) | 1.683300 / 1.541195 (0.142106) | 1.781954 / 1.468490 (0.313464) | 0.546131 / 4.584777 (-4.038645) | 3.768055 / 3.745712 (0.022343) | 1.839878 / 5.269862 (-3.429983) | 1.111877 / 4.565676 (-3.453800) | 0.068568 / 0.424275 (-0.355707) | 0.011950 / 0.007607 (0.004343) | 0.527469 / 0.226044 (0.301425) | 5.274887 / 2.268929 (3.005958) | 2.391274 / 55.444624 (-53.053351) | 2.063837 / 6.876477 (-4.812640) | 2.140627 / 2.142072 (-0.001445) | 0.681508 / 4.805227 (-4.123719) | 0.148203 / 6.500664 (-6.352461) | 0.064456 / 0.075469 (-0.011013) |\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) | 1.221478 / 1.841788 (-0.620310) | 14.713705 / 8.074308 (6.639397) | 14.674184 / 10.191392 (4.482792) | 0.148411 / 0.680424 (-0.532012) | 0.017858 / 0.534201 (-0.516343) | 0.436166 / 0.579283 (-0.143117) | 0.437290 / 0.434364 (0.002926) | 0.521994 / 0.540337 (-0.018343) | 0.635488 / 1.386936 (-0.751448) |\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) | 0.006108 / 0.011353 (-0.005245) | 0.003888 / 0.011008 (-0.007120) | 0.078424 / 0.038508 (0.039916) | 0.033618 / 0.023109 (0.010509) | 0.376284 / 0.275898 (0.100386) | 0.396957 / 0.323480 (0.073477) | 0.003799 / 0.007986 (-0.004187) | 0.003160 / 0.004328 (-0.001168) | 0.078358 / 0.004250 (0.074107) | 0.045597 / 0.037052 (0.008545) | 0.386396 / 0.258489 (0.127907) | 0.412985 / 0.293841 (0.119144) | 0.031610 / 0.128546 (-0.096936) | 0.008720 / 0.075646 (-0.066926) | 0.085944 / 0.419271 (-0.333328) | 0.050780 / 0.043533 (0.007247) | 0.378099 / 0.255139 (0.122960) | 0.381894 / 0.283200 (0.098694) | 0.098926 / 0.141683 (-0.042756) | 1.513842 / 1.452155 (0.061688) | 1.595040 / 1.492716 (0.102323) |\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) | 0.208169 / 0.018006 (0.190163) | 0.431653 / 0.000490 (0.431163) | 0.000935 / 0.000200 (0.000735) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029600 / 0.037411 (-0.007812) | 0.116936 / 0.014526 (0.102410) | 0.125603 / 0.176557 (-0.050953) | 0.177007 / 0.737135 (-0.560129) | 0.130602 / 0.296338 (-0.165736) |\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) | 0.457158 / 0.215209 (0.241949) | 4.563254 / 2.077655 (2.485599) | 2.303549 / 1.504120 (0.799429) | 2.107269 / 1.541195 (0.566074) | 2.130861 / 1.468490 (0.662371) | 0.548931 / 4.584777 (-4.035846) | 3.745578 / 3.745712 (-0.000134) | 1.820372 / 5.269862 (-3.449490) | 1.099316 / 4.565676 (-3.466361) | 0.068218 / 0.424275 (-0.356057) | 0.012336 / 0.007607 (0.004728) | 0.569721 / 0.226044 (0.343676) | 5.691312 / 2.268929 (3.422384) | 2.797483 / 55.444624 (-52.647141) | 2.422621 / 6.876477 (-4.453855) | 2.426187 / 2.142072 (0.284115) | 0.674777 / 4.805227 (-4.130451) | 0.144855 / 6.500664 (-6.355809) | 0.065805 / 0.075469 (-0.009664) |\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) | 1.305078 / 1.841788 (-0.536709) | 14.874315 / 8.074308 (6.800007) | 14.541301 / 10.191392 (4.349909) | 0.175818 / 0.680424 (-0.504606) | 0.018169 / 0.534201 (-0.516032) | 0.435836 / 0.579283 (-0.143447) | 0.458397 / 0.434364 (0.024033) | 0.506232 / 0.540337 (-0.034106) | 0.605306 / 1.386936 (-0.781630) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e0c1ceab96821c7c6557482d25a9bd2078d716a \"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) | 0.006138 / 0.011353 (-0.005215) | 0.003792 / 0.011008 (-0.007216) | 0.099417 / 0.038508 (0.060908) | 0.028739 / 0.023109 (0.005630) | 0.302835 / 0.275898 (0.026937) | 0.336397 / 0.323480 (0.012918) | 0.003537 / 0.007986 (-0.004449) | 0.002973 / 0.004328 (-0.001355) | 0.077461 / 0.004250 (0.073211) | 0.039493 / 0.037052 (0.002440) | 0.302367 / 0.258489 (0.043878) | 0.344936 / 0.293841 (0.051095) | 0.027813 / 0.128546 (-0.100733) | 0.008591 / 0.075646 (-0.067055) | 0.318975 / 0.419271 (-0.100297) | 0.045971 / 0.043533 (0.002438) | 0.301672 / 0.255139 (0.046533) | 0.328202 / 0.283200 (0.045003) | 0.091400 / 0.141683 (-0.050282) | 1.487215 / 1.452155 (0.035060) | 1.557730 / 1.492716 (0.065014) |\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) | 0.208343 / 0.018006 (0.190336) | 0.426764 / 0.000490 (0.426275) | 0.001196 / 0.000200 (0.000996) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024332 / 0.037411 (-0.013079) | 0.101861 / 0.014526 (0.087335) | 0.108669 / 0.176557 (-0.067888) | 0.172042 / 0.737135 (-0.565093) | 0.113048 / 0.296338 (-0.183290) |\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) | 0.421419 / 0.215209 (0.206210) | 4.200816 / 2.077655 (2.123162) | 1.913516 / 1.504120 (0.409396) | 1.712167 / 1.541195 (0.170972) | 1.762129 / 1.468490 (0.293639) | 0.561616 / 4.584777 (-4.023161) | 3.398122 / 3.745712 (-0.347590) | 1.744323 / 5.269862 (-3.525538) | 1.036023 / 4.565676 (-3.529653) | 0.067658 / 0.424275 (-0.356617) | 0.011145 / 0.007607 (0.003538) | 0.522803 / 0.226044 (0.296759) | 5.226245 / 2.268929 (2.957317) | 2.355148 / 55.444624 (-53.089476) | 2.014939 / 6.876477 (-4.861538) | 2.140028 / 2.142072 (-0.002044) | 0.695049 / 4.805227 (-4.110178) | 0.138428 / 6.500664 (-6.362236) | 0.066721 / 0.075469 (-0.008748) |\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) | 1.219610 / 1.841788 (-0.622177) | 14.239576 / 8.074308 (6.165268) | 14.381955 / 10.191392 (4.190563) | 0.131208 / 0.680424 (-0.549216) | 0.016698 / 0.534201 (-0.517503) | 0.361373 / 0.579283 (-0.217910) | 0.382560 / 0.434364 (-0.051804) | 0.419427 / 0.540337 (-0.120911) | 0.508314 / 1.386936 (-0.878622) |\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) | 0.006174 / 0.011353 (-0.005179) | 0.003893 / 0.011008 (-0.007115) | 0.079614 / 0.038508 (0.041106) | 0.028685 / 0.023109 (0.005576) | 0.368627 / 0.275898 (0.092729) | 0.411599 / 0.323480 (0.088119) | 0.003573 / 0.007986 (-0.004413) | 0.002989 / 0.004328 (-0.001340) | 0.078653 / 0.004250 (0.074402) | 0.041146 / 0.037052 (0.004094) | 0.362387 / 0.258489 (0.103898) | 0.417234 / 0.293841 (0.123393) | 0.027958 / 0.128546 (-0.100589) | 0.008695 / 0.075646 (-0.066952) | 0.084637 / 0.419271 (-0.334635) | 0.044188 / 0.043533 (0.000655) | 0.358514 / 0.255139 (0.103375) | 0.392314 / 0.283200 (0.109114) | 0.093986 / 0.141683 (-0.047697) | 1.535366 / 1.452155 (0.083212) | 1.605978 / 1.492716 (0.113262) |\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) | 0.196215 / 0.018006 (0.178209) | 0.429403 / 0.000490 (0.428913) | 0.003736 / 0.000200 (0.003536) | 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) | 0.025281 / 0.037411 (-0.012130) | 0.104325 / 0.014526 (0.089799) | 0.111548 / 0.176557 (-0.065009) | 0.162326 / 0.737135 (-0.574809) | 0.113853 / 0.296338 (-0.182486) |\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) | 0.447600 / 0.215209 (0.232391) | 4.463422 / 2.077655 (2.385767) | 2.168028 / 1.504120 (0.663908) | 1.968699 / 1.541195 (0.427504) | 2.035531 / 1.468490 (0.567041) | 0.564575 / 4.584777 (-4.020202) | 3.435338 / 3.745712 (-0.310374) | 2.981930 / 5.269862 (-2.287932) | 1.492172 / 4.565676 (-3.073505) | 0.067981 / 0.424275 (-0.356294) | 0.011254 / 0.007607 (0.003647) | 0.544385 / 0.226044 (0.318340) | 5.441694 / 2.268929 (3.172765) | 2.650168 / 55.444624 (-52.794456) | 2.333974 / 6.876477 (-4.542503) | 2.383424 / 2.142072 (0.241351) | 0.669814 / 4.805227 (-4.135414) | 0.135456 / 6.500664 (-6.365209) | 0.067067 / 0.075469 (-0.008402) |\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) | 1.313275 / 1.841788 (-0.528513) | 14.527636 / 8.074308 (6.453328) | 14.470957 / 10.191392 (4.279565) | 0.144361 / 0.680424 (-0.536063) | 0.016847 / 0.534201 (-0.517354) | 0.365158 / 0.579283 (-0.214125) | 0.393809 / 0.434364 (-0.040555) | 0.428527 / 0.540337 (-0.111810) | 0.515816 / 1.386936 (-0.871120) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7845d4c3c301226b3f8941ac90aaa123bfd7c69e \"CML watermark\")\n" ]

"2023-06-14T10:33:10Z"

"2023-06-14T12:33:27Z"

"2023-06-14T12:26:31Z"

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[]

[ "Got solved it with push_to_hub, closing" ]

"2021-12-06T18:22:49Z"

"2021-12-06T20:22:05Z"

"2021-12-06T20:22:05Z"

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[ "Awesome :) \r\n\r\nDon't pay attention to the RemoteDatasetTest error, I'm fixing it right now", "You can rebase from master to fix the CI test :)", "If we're lucky we can even include this dataset in today's release", "Just thinking since `type` can only be `comparison` or `bridge` and `level` can only be `easy`, `medium`, `hard` should they be `ClassLabel`?", "> Just thinking since `type` can only be `comparison` or `bridge` and `level` can only be `easy`, `medium`, `hard` should they be `ClassLabel`?\r\n\r\nI think it's more a tag than a label. I guess a string is fine\r\n" ]

"2020-10-02T11:44:28Z"

"2020-10-02T12:54:41Z"

"2020-10-02T12:54:41Z"

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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) | 0.007683 / 0.011353 (-0.003670) | 0.004159 / 0.011008 (-0.006849) | 0.097017 / 0.038508 (0.058509) | 0.074216 / 0.023109 (0.051107) | 0.323115 / 0.275898 (0.047217) | 0.412836 / 0.323480 (0.089356) | 0.005151 / 0.007986 (-0.002834) | 0.004037 / 0.004328 (-0.000292) | 0.067881 / 0.004250 (0.063631) | 0.051395 / 0.037052 (0.014342) | 0.356391 / 0.258489 (0.097901) | 0.386744 / 0.293841 (0.092903) | 0.043571 / 0.128546 (-0.084975) | 0.012844 / 0.075646 (-0.062803) | 0.369440 / 0.419271 (-0.049832) | 0.056944 / 0.043533 (0.013411) | 0.316159 / 0.255139 (0.061020) | 0.435530 / 0.283200 (0.152330) | 0.033622 / 0.141683 (-0.108061) | 1.379602 / 1.452155 (-0.072553) | 1.766400 / 1.492716 (0.273683) |\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) | 0.304151 / 0.018006 (0.286145) | 0.616365 / 0.000490 (0.615875) | 0.013588 / 0.000200 (0.013389) | 0.000441 / 0.000054 (0.000387) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032812 / 0.037411 (-0.004600) | 0.100914 / 0.014526 (0.086388) | 0.124004 / 0.176557 (-0.052552) | 0.195087 / 0.737135 (-0.542048) | 0.124388 / 0.296338 (-0.171951) |\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) | 0.575649 / 0.215209 (0.360440) | 5.665461 / 2.077655 (3.587806) | 2.474892 / 1.504120 (0.970773) | 2.142687 / 1.541195 (0.601492) | 2.254962 / 1.468490 (0.786472) | 0.816635 / 4.584777 (-3.768141) | 5.044279 / 3.745712 (1.298567) | 4.566728 / 5.269862 (-0.703134) | 2.867146 / 4.565676 (-1.698531) | 0.092994 / 0.424275 (-0.331281) | 0.008395 / 0.007607 (0.000788) | 0.680346 / 0.226044 (0.454302) | 6.909875 / 2.268929 (4.640946) | 3.275602 / 55.444624 (-52.169022) | 2.556000 / 6.876477 (-4.320477) | 2.581337 / 2.142072 (0.439264) | 0.997883 / 4.805227 (-3.807344) | 0.204109 / 6.500664 (-6.296555) | 0.069705 / 0.075469 (-0.005764) |\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) | 1.504573 / 1.841788 (-0.337215) | 22.219363 / 8.074308 (14.145055) | 19.078040 / 10.191392 (8.886648) | 0.234970 / 0.680424 (-0.445454) | 0.027324 / 0.534201 (-0.506877) | 0.427960 / 0.579283 (-0.151323) | 0.570258 / 0.434364 (0.135894) | 0.502335 / 0.540337 (-0.038003) | 0.788078 / 1.386936 (-0.598858) |\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) | 0.008370 / 0.011353 (-0.002982) | 0.004573 / 0.011008 (-0.006435) | 0.073080 / 0.038508 (0.034572) | 0.068752 / 0.023109 (0.045643) | 0.439648 / 0.275898 (0.163750) | 0.499700 / 0.323480 (0.176220) | 0.006119 / 0.007986 (-0.001866) | 0.004300 / 0.004328 (-0.000028) | 0.073173 / 0.004250 (0.068923) | 0.055676 / 0.037052 (0.018624) | 0.464152 / 0.258489 (0.205663) | 0.476954 / 0.293841 (0.183113) | 0.046335 / 0.128546 (-0.082211) | 0.013373 / 0.075646 (-0.062274) | 0.092006 / 0.419271 (-0.327265) | 0.054802 / 0.043533 (0.011269) | 0.456594 / 0.255139 (0.201455) | 0.491931 / 0.283200 (0.208732) | 0.034021 / 0.141683 (-0.107662) | 1.575200 / 1.452155 (0.123045) | 1.689742 / 1.492716 (0.197026) |\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) | 0.299432 / 0.018006 (0.281426) | 0.605643 / 0.000490 (0.605153) | 0.006280 / 0.000200 (0.006080) | 0.000120 / 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) | 0.028414 / 0.037411 (-0.008997) | 0.085812 / 0.014526 (0.071286) | 0.109142 / 0.176557 (-0.067414) | 0.163458 / 0.737135 (-0.573677) | 0.100837 / 0.296338 (-0.195501) |\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) | 0.615557 / 0.215209 (0.400348) | 6.051599 / 2.077655 (3.973944) | 2.872353 / 1.504120 (1.368234) | 2.508322 / 1.541195 (0.967128) | 2.550073 / 1.468490 (1.081583) | 0.835793 / 4.584777 (-3.748983) | 5.208484 / 3.745712 (1.462772) | 4.361846 / 5.269862 (-0.908016) | 2.776164 / 4.565676 (-1.789513) | 0.090831 / 0.424275 (-0.333444) | 0.007320 / 0.007607 (-0.000287) | 0.725533 / 0.226044 (0.499488) | 7.051321 / 2.268929 (4.782393) | 3.515464 / 55.444624 (-51.929160) | 2.798193 / 6.876477 (-4.078284) | 3.022512 / 2.142072 (0.880440) | 0.986744 / 4.805227 (-3.818484) | 0.198050 / 6.500664 (-6.302615) | 0.069200 / 0.075469 (-0.006269) |\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) | 1.623759 / 1.841788 (-0.218029) | 22.269700 / 8.074308 (14.195392) | 19.577429 / 10.191392 (9.386037) | 0.215990 / 0.680424 (-0.464434) | 0.033005 / 0.534201 (-0.501196) | 0.436848 / 0.579283 (-0.142435) | 0.591442 / 0.434364 (0.157078) | 0.547701 / 0.540337 (0.007364) | 0.741695 / 1.386936 (-0.645241) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e17e139b1323aca3321a5d2c2da40d82c458bae \"CML watermark\")\n", "CI failures are unrelated", "<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) | 0.009027 / 0.011353 (-0.002326) | 0.006118 / 0.011008 (-0.004890) | 0.118939 / 0.038508 (0.080431) | 0.089979 / 0.023109 (0.066869) | 0.412425 / 0.275898 (0.136527) | 0.455706 / 0.323480 (0.132227) | 0.006762 / 0.007986 (-0.001224) | 0.004409 / 0.004328 (0.000080) | 0.088002 / 0.004250 (0.083751) | 0.063708 / 0.037052 (0.026656) | 0.417373 / 0.258489 (0.158884) | 0.489582 / 0.293841 (0.195741) | 0.050222 / 0.128546 (-0.078324) | 0.014386 / 0.075646 (-0.061260) | 0.435363 / 0.419271 (0.016092) | 0.069375 / 0.043533 (0.025842) | 0.410242 / 0.255139 (0.155103) | 0.436439 / 0.283200 (0.153239) | 0.039318 / 0.141683 (-0.102365) | 1.857574 / 1.452155 (0.405419) | 1.919402 / 1.492716 (0.426686) |\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) | 0.343916 / 0.018006 (0.325910) | 0.633639 / 0.000490 (0.633150) | 0.014756 / 0.000200 (0.014557) | 0.000707 / 0.000054 (0.000652) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031983 / 0.037411 (-0.005429) | 0.097222 / 0.014526 (0.082697) | 0.114644 / 0.176557 (-0.061912) | 0.187787 / 0.737135 (-0.549348) | 0.120595 / 0.296338 (-0.175743) |\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) | 0.605861 / 0.215209 (0.390652) | 6.039318 / 2.077655 (3.961664) | 2.699251 / 1.504120 (1.195132) | 2.436398 / 1.541195 (0.895203) | 2.493653 / 1.468490 (1.025163) | 0.889423 / 4.584777 (-3.695354) | 5.384769 / 3.745712 (1.639056) | 5.033033 / 5.269862 (-0.236829) | 3.056894 / 4.565676 (-1.508783) | 0.100683 / 0.424275 (-0.323592) | 0.009103 / 0.007607 (0.001495) | 0.737066 / 0.226044 (0.511021) | 7.370485 / 2.268929 (5.101556) | 3.422670 / 55.444624 (-52.021954) | 2.830392 / 6.876477 (-4.046084) | 2.985789 / 2.142072 (0.843717) | 0.999239 / 4.805227 (-3.805989) | 0.203506 / 6.500664 (-6.297158) | 0.076135 / 0.075469 (0.000666) |\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) | 1.697001 / 1.841788 (-0.144787) | 24.653975 / 8.074308 (16.579667) | 22.241622 / 10.191392 (12.050230) | 0.257075 / 0.680424 (-0.423349) | 0.029159 / 0.534201 (-0.505041) | 0.493329 / 0.579283 (-0.085954) | 0.596661 / 0.434364 (0.162297) | 0.569431 / 0.540337 (0.029094) | 0.812231 / 1.386936 (-0.574705) |\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) | 0.009815 / 0.011353 (-0.001538) | 0.005136 / 0.011008 (-0.005872) | 0.078224 / 0.038508 (0.039716) | 0.103276 / 0.023109 (0.080166) | 0.512742 / 0.275898 (0.236844) | 0.544010 / 0.323480 (0.220530) | 0.007957 / 0.007986 (-0.000029) | 0.004629 / 0.004328 (0.000300) | 0.074983 / 0.004250 (0.070733) | 0.071831 / 0.037052 (0.034778) | 0.542752 / 0.258489 (0.284262) | 0.573176 / 0.293841 (0.279335) | 0.053939 / 0.128546 (-0.074607) | 0.015007 / 0.075646 (-0.060640) | 0.085389 / 0.419271 (-0.333882) | 0.063587 / 0.043533 (0.020055) | 0.509580 / 0.255139 (0.254441) | 0.563374 / 0.283200 (0.280174) | 0.037575 / 0.141683 (-0.104108) | 1.840740 / 1.452155 (0.388585) | 1.836414 / 1.492716 (0.343698) |\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) | 0.310188 / 0.018006 (0.292182) | 0.641478 / 0.000490 (0.640988) | 0.011057 / 0.000200 (0.010857) | 0.000173 / 0.000054 (0.000119) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043280 / 0.037411 (0.005869) | 0.109256 / 0.014526 (0.094730) | 0.126701 / 0.176557 (-0.049856) | 0.199172 / 0.737135 (-0.537963) | 0.123584 / 0.296338 (-0.172755) |\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) | 0.649272 / 0.215209 (0.434063) | 6.487501 / 2.077655 (4.409846) | 3.170330 / 1.504120 (1.666210) | 2.960912 / 1.541195 (1.419718) | 3.024531 / 1.468490 (1.556041) | 0.905112 / 4.584777 (-3.679665) | 5.560961 / 3.745712 (1.815249) | 4.920463 / 5.269862 (-0.349399) | 3.158989 / 4.565676 (-1.406687) | 0.095444 / 0.424275 (-0.328831) | 0.008264 / 0.007607 (0.000657) | 0.819292 / 0.226044 (0.593247) | 7.982695 / 2.268929 (5.713767) | 4.098704 / 55.444624 (-51.345921) | 3.442330 / 6.876477 (-3.434147) | 3.763426 / 2.142072 (1.621354) | 1.065464 / 4.805227 (-3.739763) | 0.215089 / 6.500664 (-6.285575) | 0.085280 / 0.075469 (0.009811) |\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) | 1.881770 / 1.841788 (0.039983) | 25.671479 / 8.074308 (17.597171) | 22.367019 / 10.191392 (12.175627) | 0.241377 / 0.680424 (-0.439047) | 0.033555 / 0.534201 (-0.500646) | 0.501786 / 0.579283 (-0.077497) | 0.596376 / 0.434364 (0.162012) | 0.579674 / 0.540337 (0.039337) | 0.855534 / 1.386936 (-0.531402) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c9c1166e1cf81d38534020f9c167b326585339e5 \"CML watermark\")\n" ]

"2023-10-31T17:57:17Z"

"2023-10-31T18:35:53Z"

"2023-10-31T18:24:02Z"

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

"2022-10-27T15:58:33Z"

"2022-10-28T16:03:05Z"

"2022-10-28T16:00:51Z"

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[ { "color": "0e8a16", "default": false, "description": "Contribution to a dataset script", "id": 4564477500, "name": "dataset contribution", "node_id": "LA_kwDODunzps8AAAABEBBmPA", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20contribution" } ]

[]

[ "Thanks for your contribution, @figenfikri.\r\n\r\nWe 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 you create this dataset there. Please, feel free to tell us if you need some help." ]

"2022-04-12T18:10:05Z"

"2022-10-03T09:36:25Z"

"2022-10-03T09:36:25Z"

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[ "Hi ! Can you try adding the `s3://` prefix ?\r\n```python\r\nf\"s3://{s3_bucket}/{s3_dir}/{dataset_name}\"\r\n```", "Ugh, yeah that was it. Thank you!" ]

"2023-05-05T05:22:59Z"

"2023-05-05T15:01:18Z"

"2023-05-05T15:01:17Z"

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[]

"2021-01-08T06:14:03Z"

"2021-01-08T09:00:08Z"

"2021-01-08T09:00:08Z"

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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) | 0.006410 / 0.011353 (-0.004943) | 0.003995 / 0.011008 (-0.007013) | 0.083585 / 0.038508 (0.045076) | 0.074285 / 0.023109 (0.051176) | 0.307163 / 0.275898 (0.031265) | 0.344691 / 0.323480 (0.021212) | 0.004277 / 0.007986 (-0.003708) | 0.004192 / 0.004328 (-0.000136) | 0.065156 / 0.004250 (0.060905) | 0.056774 / 0.037052 (0.019721) | 0.315483 / 0.258489 (0.056994) | 0.361911 / 0.293841 (0.068070) | 0.030454 / 0.128546 (-0.098092) | 0.008600 / 0.075646 (-0.067047) | 0.286692 / 0.419271 (-0.132579) | 0.052354 / 0.043533 (0.008821) | 0.308997 / 0.255139 (0.053858) | 0.337847 / 0.283200 (0.054647) | 0.022459 / 0.141683 (-0.119224) | 1.482758 / 1.452155 (0.030604) | 1.572853 / 1.492716 (0.080137) |\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) | 0.288603 / 0.018006 (0.270597) | 0.632903 / 0.000490 (0.632413) | 0.013702 / 0.000200 (0.013502) | 0.000284 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028448 / 0.037411 (-0.008964) | 0.082441 / 0.014526 (0.067916) | 0.099048 / 0.176557 (-0.077508) | 0.154370 / 0.737135 (-0.582765) | 0.146143 / 0.296338 (-0.150195) |\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) | 0.399250 / 0.215209 (0.184040) | 3.986683 / 2.077655 (1.909028) | 1.962606 / 1.504120 (0.458486) | 1.782653 / 1.541195 (0.241459) | 1.830251 / 1.468490 (0.361761) | 0.492498 / 4.584777 (-4.092278) | 3.549581 / 3.745712 (-0.196131) | 3.200056 / 5.269862 (-2.069806) | 2.028109 / 4.565676 (-2.537568) | 0.058222 / 0.424275 (-0.366053) | 0.007629 / 0.007607 (0.000022) | 0.482083 / 0.226044 (0.256039) | 4.824728 / 2.268929 (2.555800) | 2.448772 / 55.444624 (-52.995852) | 2.079629 / 6.876477 (-4.796848) | 2.267739 / 2.142072 (0.125667) | 0.586712 / 4.805227 (-4.218515) | 0.134073 / 6.500664 (-6.366591) | 0.060565 / 0.075469 (-0.014904) |\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) | 1.263244 / 1.841788 (-0.578544) | 18.964498 / 8.074308 (10.890190) | 14.125062 / 10.191392 (3.933670) | 0.167635 / 0.680424 (-0.512789) | 0.018469 / 0.534201 (-0.515732) | 0.390395 / 0.579283 (-0.188888) | 0.406055 / 0.434364 (-0.028309) | 0.460717 / 0.540337 (-0.079620) | 0.642746 / 1.386936 (-0.744190) |\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) | 0.006637 / 0.011353 (-0.004716) | 0.003972 / 0.011008 (-0.007036) | 0.064569 / 0.038508 (0.026061) | 0.075450 / 0.023109 (0.052341) | 0.405250 / 0.275898 (0.129352) | 0.433530 / 0.323480 (0.110050) | 0.005625 / 0.007986 (-0.002361) | 0.004118 / 0.004328 (-0.000211) | 0.065092 / 0.004250 (0.060842) | 0.057979 / 0.037052 (0.020927) | 0.413732 / 0.258489 (0.155243) | 0.451983 / 0.293841 (0.158142) | 0.032170 / 0.128546 (-0.096377) | 0.008690 / 0.075646 (-0.066957) | 0.071792 / 0.419271 (-0.347479) | 0.048560 / 0.043533 (0.005027) | 0.410312 / 0.255139 (0.155173) | 0.427294 / 0.283200 (0.144095) | 0.023006 / 0.141683 (-0.118677) | 1.496319 / 1.452155 (0.044164) | 1.566744 / 1.492716 (0.074027) |\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) | 0.266812 / 0.018006 (0.248805) | 0.540277 / 0.000490 (0.539788) | 0.008998 / 0.000200 (0.008799) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032496 / 0.037411 (-0.004915) | 0.091387 / 0.014526 (0.076861) | 0.107516 / 0.176557 (-0.069041) | 0.160019 / 0.737135 (-0.577116) | 0.107686 / 0.296338 (-0.188652) |\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) | 0.433321 / 0.215209 (0.218111) | 4.330221 / 2.077655 (2.252566) | 2.367215 / 1.504120 (0.863095) | 2.192464 / 1.541195 (0.651269) | 2.200204 / 1.468490 (0.731714) | 0.488057 / 4.584777 (-4.096720) | 3.625429 / 3.745712 (-0.120283) | 3.282859 / 5.269862 (-1.987003) | 2.038716 / 4.565676 (-2.526960) | 0.057968 / 0.424275 (-0.366307) | 0.007753 / 0.007607 (0.000146) | 0.509133 / 0.226044 (0.283089) | 5.086445 / 2.268929 (2.817516) | 2.846017 / 55.444624 (-52.598607) | 2.469546 / 6.876477 (-4.406931) | 2.673218 / 2.142072 (0.531145) | 0.591228 / 4.805227 (-4.213999) | 0.131920 / 6.500664 (-6.368744) | 0.059967 / 0.075469 (-0.015502) |\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) | 1.375634 / 1.841788 (-0.466153) | 19.506752 / 8.074308 (11.432444) | 14.677876 / 10.191392 (4.486484) | 0.165071 / 0.680424 (-0.515353) | 0.020614 / 0.534201 (-0.513587) | 0.395967 / 0.579283 (-0.183316) | 0.424358 / 0.434364 (-0.010006) | 0.469954 / 0.540337 (-0.070384) | 0.643169 / 1.386936 (-0.743767) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#887a854f03c4ac6d2e99b9ef4d89e6fe8c46d6f1 \"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) | 0.006072 / 0.011353 (-0.005281) | 0.003691 / 0.011008 (-0.007318) | 0.081683 / 0.038508 (0.043175) | 0.059114 / 0.023109 (0.036005) | 0.317053 / 0.275898 (0.041155) | 0.357672 / 0.323480 (0.034192) | 0.003577 / 0.007986 (-0.004408) | 0.003890 / 0.004328 (-0.000438) | 0.063667 / 0.004250 (0.059417) | 0.048233 / 0.037052 (0.011181) | 0.322854 / 0.258489 (0.064365) | 0.368014 / 0.293841 (0.074173) | 0.027750 / 0.128546 (-0.100796) | 0.008137 / 0.075646 (-0.067509) | 0.263906 / 0.419271 (-0.155366) | 0.045402 / 0.043533 (0.001870) | 0.315414 / 0.255139 (0.060275) | 0.340906 / 0.283200 (0.057707) | 0.023475 / 0.141683 (-0.118208) | 1.443922 / 1.452155 (-0.008233) | 1.550332 / 1.492716 (0.057616) |\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) | 0.211914 / 0.018006 (0.193908) | 0.423577 / 0.000490 (0.423088) | 0.003436 / 0.000200 (0.003236) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024675 / 0.037411 (-0.012737) | 0.072550 / 0.014526 (0.058024) | 0.084533 / 0.176557 (-0.092024) | 0.146106 / 0.737135 (-0.591029) | 0.085523 / 0.296338 (-0.210816) |\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) | 0.403498 / 0.215209 (0.188289) | 4.019000 / 2.077655 (1.941345) | 1.984821 / 1.504120 (0.480701) | 1.805071 / 1.541195 (0.263876) | 1.860906 / 1.468490 (0.392416) | 0.499570 / 4.584777 (-4.085207) | 3.088424 / 3.745712 (-0.657288) | 2.833693 / 5.269862 (-2.436169) | 1.869731 / 4.565676 (-2.695945) | 0.057606 / 0.424275 (-0.366669) | 0.006960 / 0.007607 (-0.000647) | 0.476085 / 0.226044 (0.250040) | 4.774063 / 2.268929 (2.505134) | 2.458079 / 55.444624 (-52.986545) | 2.106075 / 6.876477 (-4.770402) | 2.248373 / 2.142072 (0.106301) | 0.589767 / 4.805227 (-4.215460) | 0.124382 / 6.500664 (-6.376282) | 0.060705 / 0.075469 (-0.014764) |\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) | 1.287031 / 1.841788 (-0.554756) | 17.662455 / 8.074308 (9.588147) | 14.288812 / 10.191392 (4.097420) | 0.156168 / 0.680424 (-0.524256) | 0.016795 / 0.534201 (-0.517406) | 0.333726 / 0.579283 (-0.245557) | 0.362327 / 0.434364 (-0.072037) | 0.387773 / 0.540337 (-0.152564) | 0.547232 / 1.386936 (-0.839704) |\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) | 0.006494 / 0.011353 (-0.004859) | 0.003762 / 0.011008 (-0.007247) | 0.062373 / 0.038508 (0.023864) | 0.066357 / 0.023109 (0.043247) | 0.448687 / 0.275898 (0.172789) | 0.482445 / 0.323480 (0.158965) | 0.004990 / 0.007986 (-0.002996) | 0.002945 / 0.004328 (-0.001384) | 0.062444 / 0.004250 (0.058194) | 0.051381 / 0.037052 (0.014329) | 0.449310 / 0.258489 (0.190821) | 0.483188 / 0.293841 (0.189347) | 0.029078 / 0.128546 (-0.099468) | 0.008146 / 0.075646 (-0.067501) | 0.067369 / 0.419271 (-0.351903) | 0.041732 / 0.043533 (-0.001801) | 0.451675 / 0.255139 (0.196536) | 0.470445 / 0.283200 (0.187246) | 0.021053 / 0.141683 (-0.120630) | 1.483627 / 1.452155 (0.031472) | 1.541594 / 1.492716 (0.048878) |\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) | 0.210247 / 0.018006 (0.192240) | 0.424663 / 0.000490 (0.424173) | 0.005394 / 0.000200 (0.005194) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026894 / 0.037411 (-0.010517) | 0.081324 / 0.014526 (0.066798) | 0.091362 / 0.176557 (-0.085195) | 0.145602 / 0.737135 (-0.591533) | 0.091896 / 0.296338 (-0.204443) |\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) | 0.469662 / 0.215209 (0.254453) | 4.689495 / 2.077655 (2.611840) | 2.596462 / 1.504120 (1.092342) | 2.422584 / 1.541195 (0.881389) | 2.476710 / 1.468490 (1.008220) | 0.507049 / 4.584777 (-4.077728) | 3.185519 / 3.745712 (-0.560193) | 2.879842 / 5.269862 (-2.390019) | 1.882643 / 4.565676 (-2.683034) | 0.058046 / 0.424275 (-0.366229) | 0.006797 / 0.007607 (-0.000811) | 0.545245 / 0.226044 (0.319201) | 5.449248 / 2.268929 (3.180319) | 3.057341 / 55.444624 (-52.387283) | 2.728385 / 6.876477 (-4.148092) | 2.898945 / 2.142072 (0.756873) | 0.600035 / 4.805227 (-4.205192) | 0.126337 / 6.500664 (-6.374327) | 0.061333 / 0.075469 (-0.014136) |\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) | 1.332966 / 1.841788 (-0.508821) | 17.960805 / 8.074308 (9.886497) | 14.978838 / 10.191392 (4.787446) | 0.148852 / 0.680424 (-0.531572) | 0.018307 / 0.534201 (-0.515894) | 0.335234 / 0.579283 (-0.244050) | 0.389659 / 0.434364 (-0.044704) | 0.393259 / 0.540337 (-0.147078) | 0.549237 / 1.386936 (-0.837699) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#278a5673172c30b915a9ebf64cc7aff9667b58fd \"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) | 0.008808 / 0.011353 (-0.002545) | 0.005001 / 0.011008 (-0.006008) | 0.110022 / 0.038508 (0.071514) | 0.078015 / 0.023109 (0.054906) | 0.384724 / 0.275898 (0.108826) | 0.441354 / 0.323480 (0.117874) | 0.005116 / 0.007986 (-0.002870) | 0.004308 / 0.004328 (-0.000020) | 0.081679 / 0.004250 (0.077429) | 0.061386 / 0.037052 (0.024333) | 0.398149 / 0.258489 (0.139660) | 0.464859 / 0.293841 (0.171018) | 0.047443 / 0.128546 (-0.081104) | 0.014693 / 0.075646 (-0.060954) | 0.365438 / 0.419271 (-0.053833) | 0.081689 / 0.043533 (0.038156) | 0.400458 / 0.255139 (0.145319) | 0.449958 / 0.283200 (0.166758) | 0.038266 / 0.141683 (-0.103417) | 1.795043 / 1.452155 (0.342888) | 1.908819 / 1.492716 (0.416102) |\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) | 0.297911 / 0.018006 (0.279905) | 0.601640 / 0.000490 (0.601150) | 0.015406 / 0.000200 (0.015206) | 0.000163 / 0.000054 (0.000108) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034520 / 0.037411 (-0.002891) | 0.092657 / 0.014526 (0.078131) | 0.113992 / 0.176557 (-0.062564) | 0.189075 / 0.737135 (-0.548061) | 0.106602 / 0.296338 (-0.189736) |\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) | 0.585838 / 0.215209 (0.370629) | 5.719281 / 2.077655 (3.641627) | 2.525914 / 1.504120 (1.021794) | 2.231908 / 1.541195 (0.690713) | 2.215272 / 1.468490 (0.746782) | 0.814425 / 4.584777 (-3.770352) | 5.243406 / 3.745712 (1.497694) | 4.476642 / 5.269862 (-0.793220) | 2.929438 / 4.565676 (-1.636239) | 0.092070 / 0.424275 (-0.332205) | 0.009358 / 0.007607 (0.001751) | 0.713975 / 0.226044 (0.487931) | 6.948846 / 2.268929 (4.679918) | 3.361125 / 55.444624 (-52.083500) | 2.575224 / 6.876477 (-4.301253) | 2.783082 / 2.142072 (0.641009) | 1.016205 / 4.805227 (-3.789022) | 0.202578 / 6.500664 (-6.298086) | 0.076696 / 0.075469 (0.001227) |\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) | 1.650889 / 1.841788 (-0.190898) | 23.358273 / 8.074308 (15.283965) | 19.882450 / 10.191392 (9.691058) | 0.228971 / 0.680424 (-0.451453) | 0.027736 / 0.534201 (-0.506465) | 0.472405 / 0.579283 (-0.106878) | 0.581799 / 0.434364 (0.147435) | 0.533000 / 0.540337 (-0.007338) | 0.815588 / 1.386936 (-0.571348) |\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) | 0.009151 / 0.011353 (-0.002202) | 0.005074 / 0.011008 (-0.005934) | 0.078709 / 0.038508 (0.040201) | 0.077696 / 0.023109 (0.054586) | 0.522356 / 0.275898 (0.246458) | 0.562345 / 0.323480 (0.238865) | 0.006411 / 0.007986 (-0.001575) | 0.004379 / 0.004328 (0.000051) | 0.082402 / 0.004250 (0.078151) | 0.064223 / 0.037052 (0.027170) | 0.518184 / 0.258489 (0.259695) | 0.566221 / 0.293841 (0.272380) | 0.046796 / 0.128546 (-0.081750) | 0.013987 / 0.075646 (-0.061659) | 0.094925 / 0.419271 (-0.324346) | 0.058810 / 0.043533 (0.015277) | 0.520252 / 0.255139 (0.265113) | 0.566403 / 0.283200 (0.283203) | 0.034720 / 0.141683 (-0.106963) | 1.796809 / 1.452155 (0.344654) | 1.913787 / 1.492716 (0.421070) |\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) | 0.317449 / 0.018006 (0.299443) | 0.620154 / 0.000490 (0.619664) | 0.007066 / 0.000200 (0.006866) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035252 / 0.037411 (-0.002160) | 0.111648 / 0.014526 (0.097122) | 0.120692 / 0.176557 (-0.055864) | 0.193202 / 0.737135 (-0.543933) | 0.127905 / 0.296338 (-0.168434) |\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) | 0.661012 / 0.215209 (0.445803) | 6.626680 / 2.077655 (4.549026) | 3.243065 / 1.504120 (1.738945) | 2.904053 / 1.541195 (1.362858) | 2.880516 / 1.468490 (1.412026) | 0.875650 / 4.584777 (-3.709127) | 5.381993 / 3.745712 (1.636281) | 4.743997 / 5.269862 (-0.525864) | 3.020736 / 4.565676 (-1.544940) | 0.106573 / 0.424275 (-0.317702) | 0.011151 / 0.007607 (0.003544) | 0.821990 / 0.226044 (0.595946) | 8.225383 / 2.268929 (5.956454) | 3.963232 / 55.444624 (-51.481392) | 3.288916 / 6.876477 (-3.587561) | 3.579435 / 2.142072 (1.437363) | 1.043379 / 4.805227 (-3.761848) | 0.207508 / 6.500664 (-6.293156) | 0.085109 / 0.075469 (0.009640) |\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) | 1.723798 / 1.841788 (-0.117990) | 24.709848 / 8.074308 (16.635540) | 22.484674 / 10.191392 (12.293282) | 0.260357 / 0.680424 (-0.420067) | 0.033539 / 0.534201 (-0.500662) | 0.487814 / 0.579283 (-0.091469) | 0.610171 / 0.434364 (0.175807) | 0.585012 / 0.540337 (0.044674) | 0.803764 / 1.386936 (-0.583172) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f611e5815ce1bdcb4fa8556f55d85a6739cba0ea \"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) | 0.006661 / 0.011353 (-0.004692) | 0.004022 / 0.011008 (-0.006987) | 0.084269 / 0.038508 (0.045760) | 0.070707 / 0.023109 (0.047598) | 0.315035 / 0.275898 (0.039137) | 0.339830 / 0.323480 (0.016350) | 0.003994 / 0.007986 (-0.003991) | 0.004129 / 0.004328 (-0.000199) | 0.065383 / 0.004250 (0.061133) | 0.055493 / 0.037052 (0.018441) | 0.320521 / 0.258489 (0.062032) | 0.354301 / 0.293841 (0.060460) | 0.031177 / 0.128546 (-0.097370) | 0.008724 / 0.075646 (-0.066922) | 0.288298 / 0.419271 (-0.130974) | 0.052418 / 0.043533 (0.008885) | 0.319122 / 0.255139 (0.063983) | 0.335859 / 0.283200 (0.052659) | 0.026260 / 0.141683 (-0.115423) | 1.450039 / 1.452155 (-0.002115) | 1.545172 / 1.492716 (0.052455) |\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) | 0.234232 / 0.018006 (0.216226) | 0.454983 / 0.000490 (0.454493) | 0.007590 / 0.000200 (0.007390) | 0.000550 / 0.000054 (0.000495) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028714 / 0.037411 (-0.008698) | 0.083686 / 0.014526 (0.069160) | 0.162986 / 0.176557 (-0.013570) | 0.167574 / 0.737135 (-0.569561) | 0.273158 / 0.296338 (-0.023180) |\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) | 0.388275 / 0.215209 (0.173066) | 3.862034 / 2.077655 (1.784379) | 1.843561 / 1.504120 (0.339441) | 1.675224 / 1.541195 (0.134029) | 1.730394 / 1.468490 (0.261904) | 0.495259 / 4.584777 (-4.089518) | 3.627155 / 3.745712 (-0.118557) | 3.290590 / 5.269862 (-1.979272) | 2.032432 / 4.565676 (-2.533245) | 0.058212 / 0.424275 (-0.366063) | 0.007815 / 0.007607 (0.000208) | 0.460625 / 0.226044 (0.234580) | 4.616845 / 2.268929 (2.347916) | 2.339280 / 55.444624 (-53.105344) | 1.957216 / 6.876477 (-4.919261) | 2.129511 / 2.142072 (-0.012562) | 0.591782 / 4.805227 (-4.213446) | 0.136391 / 6.500664 (-6.364273) | 0.059627 / 0.075469 (-0.015842) |\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) | 1.278998 / 1.841788 (-0.562789) | 18.485496 / 8.074308 (10.411188) | 14.161273 / 10.191392 (3.969881) | 0.164346 / 0.680424 (-0.516078) | 0.018144 / 0.534201 (-0.516057) | 0.391601 / 0.579283 (-0.187682) | 0.424391 / 0.434364 (-0.009973) | 0.458209 / 0.540337 (-0.082129) | 0.645124 / 1.386936 (-0.741812) |\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) | 0.006799 / 0.011353 (-0.004554) | 0.004023 / 0.011008 (-0.006985) | 0.065206 / 0.038508 (0.026698) | 0.074386 / 0.023109 (0.051277) | 0.437399 / 0.275898 (0.161501) | 0.467382 / 0.323480 (0.143903) | 0.005467 / 0.007986 (-0.002519) | 0.003324 / 0.004328 (-0.001005) | 0.064289 / 0.004250 (0.060039) | 0.057257 / 0.037052 (0.020205) | 0.440035 / 0.258489 (0.181546) | 0.477138 / 0.293841 (0.183298) | 0.032171 / 0.128546 (-0.096375) | 0.008400 / 0.075646 (-0.067247) | 0.070877 / 0.419271 (-0.348395) | 0.048180 / 0.043533 (0.004648) | 0.441274 / 0.255139 (0.186135) | 0.461386 / 0.283200 (0.178187) | 0.022576 / 0.141683 (-0.119106) | 1.520914 / 1.452155 (0.068759) | 1.575593 / 1.492716 (0.082877) |\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) | 0.221551 / 0.018006 (0.203545) | 0.447213 / 0.000490 (0.446723) | 0.004435 / 0.000200 (0.004235) | 0.000099 / 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) | 0.032123 / 0.037411 (-0.005288) | 0.091809 / 0.014526 (0.077283) | 0.103938 / 0.176557 (-0.072618) | 0.156878 / 0.737135 (-0.580258) | 0.105071 / 0.296338 (-0.191268) |\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) | 0.430389 / 0.215209 (0.215180) | 4.293496 / 2.077655 (2.215841) | 2.292801 / 1.504120 (0.788681) | 2.135320 / 1.541195 (0.594126) | 2.195720 / 1.468490 (0.727229) | 0.493277 / 4.584777 (-4.091500) | 3.685617 / 3.745712 (-0.060096) | 3.278897 / 5.269862 (-1.990965) | 2.036939 / 4.565676 (-2.528737) | 0.058766 / 0.424275 (-0.365509) | 0.007783 / 0.007607 (0.000176) | 0.511165 / 0.226044 (0.285120) | 5.126757 / 2.268929 (2.857829) | 2.756690 / 55.444624 (-52.687935) | 2.421745 / 6.876477 (-4.454732) | 2.597249 / 2.142072 (0.455177) | 0.647206 / 4.805227 (-4.158021) | 0.143392 / 6.500664 (-6.357273) | 0.060110 / 0.075469 (-0.015359) |\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) | 1.340289 / 1.841788 (-0.501499) | 19.057620 / 8.074308 (10.983312) | 14.832892 / 10.191392 (4.641500) | 0.167730 / 0.680424 (-0.512694) | 0.020178 / 0.534201 (-0.514023) | 0.394060 / 0.579283 (-0.185223) | 0.433976 / 0.434364 (-0.000388) | 0.474417 / 0.540337 (-0.065921) | 0.640653 / 1.386936 (-0.746283) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d0519c6a1988a3344ecae37f7348c208bcbc99d6 \"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) | 0.007661 / 0.011353 (-0.003692) | 0.004541 / 0.011008 (-0.006467) | 0.100547 / 0.038508 (0.062039) | 0.084257 / 0.023109 (0.061148) | 0.377627 / 0.275898 (0.101729) | 0.433764 / 0.323480 (0.110284) | 0.005995 / 0.007986 (-0.001990) | 0.003810 / 0.004328 (-0.000518) | 0.076409 / 0.004250 (0.072158) | 0.063411 / 0.037052 (0.026359) | 0.382504 / 0.258489 (0.124015) | 0.449721 / 0.293841 (0.155880) | 0.036499 / 0.128546 (-0.092047) | 0.009942 / 0.075646 (-0.065705) | 0.343839 / 0.419271 (-0.075433) | 0.062147 / 0.043533 (0.018614) | 0.383244 / 0.255139 (0.128105) | 0.415606 / 0.283200 (0.132406) | 0.027475 / 0.141683 (-0.114207) | 1.740413 / 1.452155 (0.288258) | 1.862210 / 1.492716 (0.369493) |\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) | 0.260064 / 0.018006 (0.242058) | 0.499001 / 0.000490 (0.498511) | 0.015811 / 0.000200 (0.015611) | 0.000119 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033599 / 0.037411 (-0.003812) | 0.099354 / 0.014526 (0.084828) | 0.114693 / 0.176557 (-0.061864) | 0.180231 / 0.737135 (-0.556904) | 0.114715 / 0.296338 (-0.181623) |\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) | 0.459884 / 0.215209 (0.244675) | 4.580806 / 2.077655 (2.503151) | 2.270770 / 1.504120 (0.766650) | 2.077127 / 1.541195 (0.535932) | 2.167175 / 1.468490 (0.698685) | 0.570593 / 4.584777 (-4.014184) | 4.120926 / 3.745712 (0.375214) | 3.817595 / 5.269862 (-1.452267) | 2.404782 / 4.565676 (-2.160894) | 0.067972 / 0.424275 (-0.356304) | 0.009378 / 0.007607 (0.001771) | 0.549642 / 0.226044 (0.323597) | 5.490369 / 2.268929 (3.221440) | 2.905264 / 55.444624 (-52.539361) | 2.452935 / 6.876477 (-4.423542) | 2.700760 / 2.142072 (0.558688) | 0.700407 / 4.805227 (-4.104820) | 0.159349 / 6.500664 (-6.341315) | 0.074605 / 0.075469 (-0.000864) |\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) | 1.517803 / 1.841788 (-0.323985) | 22.343700 / 8.074308 (14.269392) | 16.411639 / 10.191392 (6.220247) | 0.169816 / 0.680424 (-0.510608) | 0.021532 / 0.534201 (-0.512668) | 0.470161 / 0.579283 (-0.109122) | 0.473412 / 0.434364 (0.039048) | 0.539690 / 0.540337 (-0.000647) | 0.774011 / 1.386936 (-0.612925) |\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) | 0.007629 / 0.011353 (-0.003724) | 0.004651 / 0.011008 (-0.006357) | 0.075162 / 0.038508 (0.036654) | 0.085365 / 0.023109 (0.062256) | 0.493272 / 0.275898 (0.217374) | 0.535776 / 0.323480 (0.212296) | 0.006323 / 0.007986 (-0.001663) | 0.003785 / 0.004328 (-0.000544) | 0.076161 / 0.004250 (0.071911) | 0.065982 / 0.037052 (0.028929) | 0.513355 / 0.258489 (0.254866) | 0.549219 / 0.293841 (0.255378) | 0.038052 / 0.128546 (-0.090494) | 0.010055 / 0.075646 (-0.065592) | 0.083744 / 0.419271 (-0.335527) | 0.056708 / 0.043533 (0.013175) | 0.496273 / 0.255139 (0.241135) | 0.523709 / 0.283200 (0.240509) | 0.026502 / 0.141683 (-0.115181) | 1.793032 / 1.452155 (0.340877) | 1.870534 / 1.492716 (0.377817) |\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) | 0.252288 / 0.018006 (0.234281) | 0.490380 / 0.000490 (0.489890) | 0.005884 / 0.000200 (0.005684) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038238 / 0.037411 (0.000827) | 0.110010 / 0.014526 (0.095485) | 0.125497 / 0.176557 (-0.051059) | 0.188154 / 0.737135 (-0.548981) | 0.126112 / 0.296338 (-0.170227) |\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) | 0.515837 / 0.215209 (0.300628) | 5.135153 / 2.077655 (3.057498) | 2.761740 / 1.504120 (1.257620) | 2.552718 / 1.541195 (1.011523) | 2.636425 / 1.468490 (1.167935) | 0.588442 / 4.584777 (-3.996335) | 4.220833 / 3.745712 (0.475120) | 3.874637 / 5.269862 (-1.395225) | 2.424668 / 4.565676 (-2.141009) | 0.069979 / 0.424275 (-0.354296) | 0.009349 / 0.007607 (0.001742) | 0.608936 / 0.226044 (0.382891) | 6.081209 / 2.268929 (3.812280) | 3.348067 / 55.444624 (-52.096557) | 2.919130 / 6.876477 (-3.957347) | 3.159093 / 2.142072 (1.017020) | 0.704059 / 4.805227 (-4.101169) | 0.158417 / 6.500664 (-6.342247) | 0.071321 / 0.075469 (-0.004148) |\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) | 1.595287 / 1.841788 (-0.246501) | 23.096619 / 8.074308 (15.022311) | 17.258041 / 10.191392 (7.066649) | 0.186197 / 0.680424 (-0.494227) | 0.023633 / 0.534201 (-0.510567) | 0.472181 / 0.579283 (-0.107102) | 0.493817 / 0.434364 (0.059453) | 0.567657 / 0.540337 (0.027320) | 0.793789 / 1.386936 (-0.593147) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e0bd8444689c5d82344a62ddf79e5dc103fc67b8 \"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) | 0.007084 / 0.011353 (-0.004268) | 0.004093 / 0.011008 (-0.006915) | 0.086395 / 0.038508 (0.047887) | 0.087734 / 0.023109 (0.064625) | 0.356936 / 0.275898 (0.081038) | 0.386413 / 0.323480 (0.062933) | 0.005531 / 0.007986 (-0.002454) | 0.003462 / 0.004328 (-0.000866) | 0.065503 / 0.004250 (0.061252) | 0.058973 / 0.037052 (0.021920) | 0.354151 / 0.258489 (0.095662) | 0.398812 / 0.293841 (0.104971) | 0.031508 / 0.128546 (-0.097038) | 0.008537 / 0.075646 (-0.067109) | 0.290942 / 0.419271 (-0.128329) | 0.053537 / 0.043533 (0.010004) | 0.352067 / 0.255139 (0.096928) | 0.375142 / 0.283200 (0.091943) | 0.025658 / 0.141683 (-0.116025) | 1.468496 / 1.452155 (0.016341) | 1.556926 / 1.492716 (0.064210) |\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) | 0.238858 / 0.018006 (0.220852) | 0.460018 / 0.000490 (0.459528) | 0.009613 / 0.000200 (0.009414) | 0.000326 / 0.000054 (0.000272) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030333 / 0.037411 (-0.007078) | 0.088431 / 0.014526 (0.073905) | 0.098130 / 0.176557 (-0.078427) | 0.155160 / 0.737135 (-0.581975) | 0.099963 / 0.296338 (-0.196375) |\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) | 0.385769 / 0.215209 (0.170560) | 3.836723 / 2.077655 (1.759069) | 1.861065 / 1.504120 (0.356945) | 1.685159 / 1.541195 (0.143965) | 1.780679 / 1.468490 (0.312189) | 0.491865 / 4.584777 (-4.092912) | 3.581139 / 3.745712 (-0.164573) | 3.366278 / 5.269862 (-1.903584) | 2.093094 / 4.565676 (-2.472583) | 0.058063 / 0.424275 (-0.366212) | 0.007903 / 0.007607 (0.000296) | 0.464866 / 0.226044 (0.238821) | 4.647754 / 2.268929 (2.378825) | 2.316466 / 55.444624 (-53.128158) | 1.984079 / 6.876477 (-4.892398) | 2.235020 / 2.142072 (0.092948) | 0.592591 / 4.805227 (-4.212636) | 0.135586 / 6.500664 (-6.365078) | 0.061434 / 0.075469 (-0.014035) |\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) | 1.282940 / 1.841788 (-0.558848) | 19.635975 / 8.074308 (11.561667) | 14.426135 / 10.191392 (4.234743) | 0.166732 / 0.680424 (-0.513692) | 0.018438 / 0.534201 (-0.515763) | 0.393173 / 0.579283 (-0.186110) | 0.417291 / 0.434364 (-0.017073) | 0.459188 / 0.540337 (-0.081149) | 0.632568 / 1.386936 (-0.754368) |\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) | 0.007166 / 0.011353 (-0.004187) | 0.004254 / 0.011008 (-0.006754) | 0.064667 / 0.038508 (0.026159) | 0.085142 / 0.023109 (0.062033) | 0.410081 / 0.275898 (0.134183) | 0.445803 / 0.323480 (0.122323) | 0.005600 / 0.007986 (-0.002385) | 0.003520 / 0.004328 (-0.000809) | 0.064148 / 0.004250 (0.059897) | 0.059869 / 0.037052 (0.022817) | 0.407439 / 0.258489 (0.148950) | 0.451169 / 0.293841 (0.157329) | 0.032619 / 0.128546 (-0.095927) | 0.008706 / 0.075646 (-0.066940) | 0.071230 / 0.419271 (-0.348041) | 0.048499 / 0.043533 (0.004966) | 0.416401 / 0.255139 (0.161262) | 0.430737 / 0.283200 (0.147537) | 0.022511 / 0.141683 (-0.119172) | 1.517296 / 1.452155 (0.065141) | 1.581704 / 1.492716 (0.088988) |\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) | 0.220738 / 0.018006 (0.202732) | 0.454026 / 0.000490 (0.453536) | 0.004695 / 0.000200 (0.004495) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033202 / 0.037411 (-0.004209) | 0.097506 / 0.014526 (0.082980) | 0.106661 / 0.176557 (-0.069896) | 0.160554 / 0.737135 (-0.576581) | 0.109203 / 0.296338 (-0.187135) |\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) | 0.432013 / 0.215209 (0.216804) | 4.310399 / 2.077655 (2.232744) | 2.296529 / 1.504120 (0.792409) | 2.139929 / 1.541195 (0.598734) | 2.227432 / 1.468490 (0.758942) | 0.493697 / 4.584777 (-4.091080) | 3.639877 / 3.745712 (-0.105835) | 3.323165 / 5.269862 (-1.946697) | 2.084527 / 4.565676 (-2.481150) | 0.058812 / 0.424275 (-0.365463) | 0.007813 / 0.007607 (0.000206) | 0.512366 / 0.226044 (0.286321) | 5.119660 / 2.268929 (2.850732) | 2.783819 / 55.444624 (-52.660806) | 2.490669 / 6.876477 (-4.385808) | 2.696653 / 2.142072 (0.554581) | 0.627161 / 4.805227 (-4.178066) | 0.137032 / 6.500664 (-6.363632) | 0.064040 / 0.075469 (-0.011429) |\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) | 1.369578 / 1.841788 (-0.472210) | 20.421182 / 8.074308 (12.346873) | 15.719347 / 10.191392 (5.527955) | 0.166150 / 0.680424 (-0.514274) | 0.020262 / 0.534201 (-0.513939) | 0.395645 / 0.579283 (-0.183638) | 0.430363 / 0.434364 (-0.004001) | 0.477843 / 0.540337 (-0.062494) | 0.638501 / 1.386936 (-0.748435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c89e60cc50563dfc41ea039c6d3a1f6e43033e8e \"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) | 0.006141 / 0.011353 (-0.005211) | 0.003683 / 0.011008 (-0.007325) | 0.081127 / 0.038508 (0.042618) | 0.064285 / 0.023109 (0.041176) | 0.323038 / 0.275898 (0.047140) | 0.347280 / 0.323480 (0.023800) | 0.003518 / 0.007986 (-0.004467) | 0.002958 / 0.004328 (-0.001370) | 0.063093 / 0.004250 (0.058843) | 0.050682 / 0.037052 (0.013629) | 0.321222 / 0.258489 (0.062733) | 0.359266 / 0.293841 (0.065425) | 0.027515 / 0.128546 (-0.101032) | 0.007964 / 0.075646 (-0.067682) | 0.261305 / 0.419271 (-0.157966) | 0.044897 / 0.043533 (0.001365) | 0.320684 / 0.255139 (0.065545) | 0.335722 / 0.283200 (0.052522) | 0.023378 / 0.141683 (-0.118305) | 1.418211 / 1.452155 (-0.033943) | 1.523728 / 1.492716 (0.031011) |\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) | 0.222316 / 0.018006 (0.204310) | 0.426943 / 0.000490 (0.426454) | 0.008785 / 0.000200 (0.008585) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024716 / 0.037411 (-0.012695) | 0.075341 / 0.014526 (0.060816) | 0.089532 / 0.176557 (-0.087024) | 0.147638 / 0.737135 (-0.589498) | 0.085697 / 0.296338 (-0.210641) |\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) | 0.396395 / 0.215209 (0.181186) | 3.947280 / 2.077655 (1.869625) | 1.894762 / 1.504120 (0.390642) | 1.712094 / 1.541195 (0.170899) | 1.779049 / 1.468490 (0.310559) | 0.509206 / 4.584777 (-4.075571) | 3.073951 / 3.745712 (-0.671761) | 2.886826 / 5.269862 (-2.383035) | 1.894444 / 4.565676 (-2.671232) | 0.059519 / 0.424275 (-0.364756) | 0.006951 / 0.007607 (-0.000656) | 0.468213 / 0.226044 (0.242169) | 4.667134 / 2.268929 (2.398206) | 2.342516 / 55.444624 (-53.102108) | 1.992047 / 6.876477 (-4.884430) | 2.142059 / 2.142072 (-0.000014) | 0.600507 / 4.805227 (-4.204720) | 0.128982 / 6.500664 (-6.371682) | 0.062100 / 0.075469 (-0.013369) |\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) | 1.234500 / 1.841788 (-0.607288) | 17.951646 / 8.074308 (9.877338) | 13.862763 / 10.191392 (3.671371) | 0.143133 / 0.680424 (-0.537291) | 0.016643 / 0.534201 (-0.517558) | 0.333174 / 0.579283 (-0.246109) | 0.366956 / 0.434364 (-0.067408) | 0.384569 / 0.540337 (-0.155769) | 0.546830 / 1.386936 (-0.840106) |\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) | 0.006146 / 0.011353 (-0.005207) | 0.003725 / 0.011008 (-0.007283) | 0.062099 / 0.038508 (0.023591) | 0.064117 / 0.023109 (0.041008) | 0.456100 / 0.275898 (0.180202) | 0.490794 / 0.323480 (0.167314) | 0.005652 / 0.007986 (-0.002334) | 0.002897 / 0.004328 (-0.001432) | 0.061909 / 0.004250 (0.057659) | 0.050634 / 0.037052 (0.013582) | 0.454422 / 0.258489 (0.195933) | 0.493208 / 0.293841 (0.199367) | 0.028822 / 0.128546 (-0.099724) | 0.008115 / 0.075646 (-0.067531) | 0.067214 / 0.419271 (-0.352058) | 0.041529 / 0.043533 (-0.002004) | 0.458016 / 0.255139 (0.202877) | 0.476059 / 0.283200 (0.192859) | 0.019926 / 0.141683 (-0.121757) | 1.465345 / 1.452155 (0.013190) | 1.533518 / 1.492716 (0.040802) |\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) | 0.218830 / 0.018006 (0.200823) | 0.418869 / 0.000490 (0.418380) | 0.005154 / 0.000200 (0.004954) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027648 / 0.037411 (-0.009763) | 0.083842 / 0.014526 (0.069316) | 0.092300 / 0.176557 (-0.084257) | 0.146098 / 0.737135 (-0.591037) | 0.093441 / 0.296338 (-0.202898) |\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) | 0.464426 / 0.215209 (0.249217) | 4.632705 / 2.077655 (2.555051) | 2.642091 / 1.504120 (1.137971) | 2.461768 / 1.541195 (0.920573) | 2.535554 / 1.468490 (1.067064) | 0.507506 / 4.584777 (-4.077271) | 3.095485 / 3.745712 (-0.650227) | 2.884261 / 5.269862 (-2.385601) | 1.908943 / 4.565676 (-2.656734) | 0.058622 / 0.424275 (-0.365653) | 0.006892 / 0.007607 (-0.000715) | 0.536045 / 0.226044 (0.310001) | 5.377448 / 2.268929 (3.108519) | 3.076023 / 55.444624 (-52.368602) | 2.745586 / 6.876477 (-4.130890) | 2.939582 / 2.142072 (0.797510) | 0.595639 / 4.805227 (-4.209589) | 0.125086 / 6.500664 (-6.375578) | 0.061075 / 0.075469 (-0.014394) |\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) | 1.342820 / 1.841788 (-0.498968) | 18.326240 / 8.074308 (10.251932) | 15.007094 / 10.191392 (4.815702) | 0.133037 / 0.680424 (-0.547387) | 0.018702 / 0.534201 (-0.515499) | 0.330245 / 0.579283 (-0.249038) | 0.381494 / 0.434364 (-0.052870) | 0.393705 / 0.540337 (-0.146633) | 0.533676 / 1.386936 (-0.853260) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45291275d84448c235829fb62aa951070aa4061d \"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) | 0.007644 / 0.011353 (-0.003709) | 0.004759 / 0.011008 (-0.006249) | 0.100569 / 0.038508 (0.062061) | 0.089645 / 0.023109 (0.066536) | 0.376679 / 0.275898 (0.100781) | 0.413214 / 0.323480 (0.089735) | 0.006087 / 0.007986 (-0.001899) | 0.003832 / 0.004328 (-0.000496) | 0.075892 / 0.004250 (0.071641) | 0.064635 / 0.037052 (0.027582) | 0.376874 / 0.258489 (0.118385) | 0.436756 / 0.293841 (0.142915) | 0.036372 / 0.128546 (-0.092174) | 0.010047 / 0.075646 (-0.065599) | 0.345073 / 0.419271 (-0.074198) | 0.062092 / 0.043533 (0.018559) | 0.380503 / 0.255139 (0.125364) | 0.414800 / 0.283200 (0.131600) | 0.028274 / 0.141683 (-0.113409) | 1.732463 / 1.452155 (0.280308) | 1.859049 / 1.492716 (0.366333) |\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) | 0.267129 / 0.018006 (0.249123) | 0.509109 / 0.000490 (0.508619) | 0.012329 / 0.000200 (0.012130) | 0.000432 / 0.000054 (0.000377) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033773 / 0.037411 (-0.003638) | 0.102800 / 0.014526 (0.088274) | 0.114256 / 0.176557 (-0.062300) | 0.182048 / 0.737135 (-0.555087) | 0.118225 / 0.296338 (-0.178113) |\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) | 0.457553 / 0.215209 (0.242344) | 4.588212 / 2.077655 (2.510557) | 2.184138 / 1.504120 (0.680018) | 2.003570 / 1.541195 (0.462375) | 2.093217 / 1.468490 (0.624727) | 0.585679 / 4.584777 (-3.999098) | 4.175319 / 3.745712 (0.429607) | 3.914168 / 5.269862 (-1.355693) | 2.452992 / 4.565676 (-2.112684) | 0.068363 / 0.424275 (-0.355912) | 0.009314 / 0.007607 (0.001707) | 0.543640 / 0.226044 (0.317595) | 5.440853 / 2.268929 (3.171925) | 2.782415 / 55.444624 (-52.662210) | 2.332359 / 6.876477 (-4.544118) | 2.628520 / 2.142072 (0.486448) | 0.696838 / 4.805227 (-4.108389) | 0.160653 / 6.500664 (-6.340012) | 0.075599 / 0.075469 (0.000130) |\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) | 1.545305 / 1.841788 (-0.296483) | 23.073174 / 8.074308 (14.998866) | 16.974977 / 10.191392 (6.783585) | 0.183719 / 0.680424 (-0.496705) | 0.021633 / 0.534201 (-0.512568) | 0.471202 / 0.579283 (-0.108081) | 0.479385 / 0.434364 (0.045021) | 0.550872 / 0.540337 (0.010535) | 0.766825 / 1.386936 (-0.620111) |\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) | 0.007918 / 0.011353 (-0.003435) | 0.004793 / 0.011008 (-0.006215) | 0.077273 / 0.038508 (0.038765) | 0.092079 / 0.023109 (0.068969) | 0.483269 / 0.275898 (0.207371) | 0.524919 / 0.323480 (0.201439) | 0.006273 / 0.007986 (-0.001713) | 0.004018 / 0.004328 (-0.000310) | 0.077188 / 0.004250 (0.072937) | 0.067891 / 0.037052 (0.030839) | 0.478531 / 0.258489 (0.220042) | 0.526956 / 0.293841 (0.233115) | 0.038309 / 0.128546 (-0.090237) | 0.010133 / 0.075646 (-0.065513) | 0.083892 / 0.419271 (-0.335379) | 0.057369 / 0.043533 (0.013836) | 0.509427 / 0.255139 (0.254288) | 0.506574 / 0.283200 (0.223374) | 0.027987 / 0.141683 (-0.113696) | 1.897469 / 1.452155 (0.445314) | 1.893102 / 1.492716 (0.400385) |\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) | 0.243003 / 0.018006 (0.224997) | 0.500267 / 0.000490 (0.499777) | 0.007442 / 0.000200 (0.007242) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039266 / 0.037411 (0.001855) | 0.114438 / 0.014526 (0.099912) | 0.124528 / 0.176557 (-0.052029) | 0.189399 / 0.737135 (-0.547736) | 0.126703 / 0.296338 (-0.169635) |\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) | 0.518139 / 0.215209 (0.302930) | 5.162058 / 2.077655 (3.084403) | 2.835111 / 1.504120 (1.330991) | 2.640919 / 1.541195 (1.099724) | 2.736800 / 1.468490 (1.268310) | 0.582813 / 4.584777 (-4.001964) | 4.246269 / 3.745712 (0.500557) | 3.891161 / 5.269862 (-1.378701) | 2.445392 / 4.565676 (-2.120285) | 0.068943 / 0.424275 (-0.355332) | 0.009248 / 0.007607 (0.001641) | 0.604859 / 0.226044 (0.378815) | 6.030660 / 2.268929 (3.761731) | 3.409778 / 55.444624 (-52.034846) | 2.990488 / 6.876477 (-3.885988) | 3.281317 / 2.142072 (1.139245) | 0.697705 / 4.805227 (-4.107523) | 0.159502 / 6.500664 (-6.341162) | 0.072471 / 0.075469 (-0.002999) |\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) | 1.625428 / 1.841788 (-0.216360) | 23.602509 / 8.074308 (15.528201) | 18.091474 / 10.191392 (7.900082) | 0.172816 / 0.680424 (-0.507608) | 0.023708 / 0.534201 (-0.510493) | 0.473768 / 0.579283 (-0.105515) | 0.493713 / 0.434364 (0.059349) | 0.566326 / 0.540337 (0.025989) | 0.788670 / 1.386936 (-0.598266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ee2359c17ccb35b57e195f2bfe8478f49630039 \"CML watermark\")\n", "> Thanks. Any comment on my comment below?\r\n> \r\n> >Maybe we should update the docstring of get_data_patterns accordingly? Currently it only gives examples of outputs with ** not in a single path segment (i.e. not with a / as prefix or suffix).\r\n\r\nYea right we need to update it indeed, the outputs are the ones from older versions of fsspec, and from older patterns that we don't use anymore.\r\n\r\nIn general in docstrings I also think we should encourage users to use `**/*` instead of `**` (which has a behavior that is unique to fsspec)", "Also just noticed that `KEYWORDS_IN_DIR_NAME_BASE_PATTERNS` seems to include `KEYWORDS_IN_FILENAME_BASE_PATTERNS`. I guess we can try to remove the filename one in another PR to remove this redundancy \r\n\r\n(noticed this by checking that the data pattern is the same for both the dir name and filename examples in the get_data_patterns docstring)", "<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) | 0.006922 / 0.011353 (-0.004431) | 0.004459 / 0.011008 (-0.006549) | 0.084742 / 0.038508 (0.046234) | 0.089002 / 0.023109 (0.065893) | 0.310886 / 0.275898 (0.034988) | 0.340518 / 0.323480 (0.017038) | 0.007011 / 0.007986 (-0.000975) | 0.004566 / 0.004328 (0.000237) | 0.067260 / 0.004250 (0.063009) | 0.066349 / 0.037052 (0.029297) | 0.324029 / 0.258489 (0.065540) | 0.373785 / 0.293841 (0.079944) | 0.031780 / 0.128546 (-0.096766) | 0.009208 / 0.075646 (-0.066438) | 0.288871 / 0.419271 (-0.130401) | 0.054548 / 0.043533 (0.011015) | 0.313344 / 0.255139 (0.058205) | 0.336430 / 0.283200 (0.053231) | 0.029037 / 0.141683 (-0.112646) | 1.483797 / 1.452155 (0.031642) | 1.581884 / 1.492716 (0.089167) |\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) | 0.370520 / 0.018006 (0.352514) | 0.796720 / 0.000490 (0.796230) | 0.009329 / 0.000200 (0.009129) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033002 / 0.037411 (-0.004410) | 0.083442 / 0.014526 (0.068916) | 0.106468 / 0.176557 (-0.070088) | 0.165315 / 0.737135 (-0.571820) | 0.103048 / 0.296338 (-0.193291) |\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) | 0.386800 / 0.215209 (0.171591) | 3.843312 / 2.077655 (1.765658) | 1.848953 / 1.504120 (0.344834) | 1.679508 / 1.541195 (0.138313) | 1.733578 / 1.468490 (0.265088) | 0.488455 / 4.584777 (-4.096322) | 3.613594 / 3.745712 (-0.132118) | 3.533334 / 5.269862 (-1.736528) | 2.176216 / 4.565676 (-2.389460) | 0.056915 / 0.424275 (-0.367360) | 0.007349 / 0.007607 (-0.000258) | 0.465132 / 0.226044 (0.239088) | 4.638479 / 2.268929 (2.369550) | 2.354741 / 55.444624 (-53.089883) | 1.991777 / 6.876477 (-4.884700) | 2.249823 / 2.142072 (0.107751) | 0.582748 / 4.805227 (-4.222480) | 0.133829 / 6.500664 (-6.366835) | 0.060949 / 0.075469 (-0.014520) |\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) | 1.252027 / 1.841788 (-0.589760) | 20.660234 / 8.074308 (12.585926) | 14.328496 / 10.191392 (4.137104) | 0.164872 / 0.680424 (-0.515552) | 0.018867 / 0.534201 (-0.515334) | 0.392850 / 0.579283 (-0.186433) | 0.425684 / 0.434364 (-0.008679) | 0.461776 / 0.540337 (-0.078562) | 0.663688 / 1.386936 (-0.723248) |\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) | 0.007010 / 0.011353 (-0.004343) | 0.004791 / 0.011008 (-0.006217) | 0.064738 / 0.038508 (0.026230) | 0.088648 / 0.023109 (0.065539) | 0.418106 / 0.275898 (0.142208) | 0.446767 / 0.323480 (0.123287) | 0.006761 / 0.007986 (-0.001224) | 0.004649 / 0.004328 (0.000320) | 0.066345 / 0.004250 (0.062094) | 0.068326 / 0.037052 (0.031274) | 0.423426 / 0.258489 (0.164937) | 0.463160 / 0.293841 (0.169319) | 0.032689 / 0.128546 (-0.095858) | 0.009299 / 0.075646 (-0.066347) | 0.071321 / 0.419271 (-0.347951) | 0.048752 / 0.043533 (0.005219) | 0.418932 / 0.255139 (0.163793) | 0.440673 / 0.283200 (0.157473) | 0.027898 / 0.141683 (-0.113785) | 1.531860 / 1.452155 (0.079705) | 1.620456 / 1.492716 (0.127739) |\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) | 0.354917 / 0.018006 (0.336911) | 0.792432 / 0.000490 (0.791943) | 0.006626 / 0.000200 (0.006426) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036190 / 0.037411 (-0.001222) | 0.093052 / 0.014526 (0.078526) | 0.111927 / 0.176557 (-0.064629) | 0.165571 / 0.737135 (-0.571564) | 0.112159 / 0.296338 (-0.184180) |\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) | 0.437798 / 0.215209 (0.222589) | 4.367166 / 2.077655 (2.289511) | 2.343292 / 1.504120 (0.839172) | 2.169298 / 1.541195 (0.628103) | 2.224471 / 1.468490 (0.755981) | 0.487317 / 4.584777 (-4.097460) | 3.627825 / 3.745712 (-0.117887) | 3.500914 / 5.269862 (-1.768947) | 2.175862 / 4.565676 (-2.389815) | 0.057975 / 0.424275 (-0.366300) | 0.007509 / 0.007607 (-0.000098) | 0.517389 / 0.226044 (0.291345) | 5.169694 / 2.268929 (2.900766) | 2.850993 / 55.444624 (-52.593631) | 2.473111 / 6.876477 (-4.403366) | 2.746731 / 2.142072 (0.604659) | 0.586597 / 4.805227 (-4.218630) | 0.134082 / 6.500664 (-6.366582) | 0.061035 / 0.075469 (-0.014434) |\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) | 1.375186 / 1.841788 (-0.466602) | 20.960817 / 8.074308 (12.886509) | 15.035071 / 10.191392 (4.843679) | 0.169494 / 0.680424 (-0.510930) | 0.020654 / 0.534201 (-0.513547) | 0.398047 / 0.579283 (-0.181236) | 0.438117 / 0.434364 (0.003753) | 0.483896 / 0.540337 (-0.056441) | 0.690728 / 1.386936 (-0.696208) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e7fc64af912e5fcdcf949ed09d954332f0ae94a \"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) | 0.006892 / 0.011353 (-0.004461) | 0.004087 / 0.011008 (-0.006921) | 0.084695 / 0.038508 (0.046187) | 0.078084 / 0.023109 (0.054975) | 0.322976 / 0.275898 (0.047078) | 0.355332 / 0.323480 (0.031852) | 0.004235 / 0.007986 (-0.003750) | 0.003450 / 0.004328 (-0.000879) | 0.065355 / 0.004250 (0.061104) | 0.058593 / 0.037052 (0.021541) | 0.335761 / 0.258489 (0.077272) | 0.370392 / 0.293841 (0.076551) | 0.031720 / 0.128546 (-0.096827) | 0.008611 / 0.075646 (-0.067036) | 0.288213 / 0.419271 (-0.131059) | 0.053374 / 0.043533 (0.009842) | 0.321863 / 0.255139 (0.066724) | 0.341587 / 0.283200 (0.058387) | 0.025694 / 0.141683 (-0.115989) | 1.470502 / 1.452155 (0.018348) | 1.565068 / 1.492716 (0.072352) |\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) | 0.231063 / 0.018006 (0.213057) | 0.464996 / 0.000490 (0.464506) | 0.007316 / 0.000200 (0.007116) | 0.000288 / 0.000054 (0.000233) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029244 / 0.037411 (-0.008167) | 0.086303 / 0.014526 (0.071777) | 0.097281 / 0.176557 (-0.079276) | 0.153552 / 0.737135 (-0.583583) | 0.098488 / 0.296338 (-0.197850) |\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) | 0.382753 / 0.215209 (0.167544) | 3.826503 / 2.077655 (1.748848) | 1.848439 / 1.504120 (0.344319) | 1.688519 / 1.541195 (0.147324) | 1.787867 / 1.468490 (0.319377) | 0.489708 / 4.584777 (-4.095069) | 3.576780 / 3.745712 (-0.168932) | 3.341536 / 5.269862 (-1.928325) | 2.108787 / 4.565676 (-2.456889) | 0.057409 / 0.424275 (-0.366866) | 0.007325 / 0.007607 (-0.000282) | 0.459536 / 0.226044 (0.233492) | 4.590609 / 2.268929 (2.321681) | 2.313005 / 55.444624 (-53.131620) | 1.972389 / 6.876477 (-4.904087) | 2.218511 / 2.142072 (0.076439) | 0.613817 / 4.805227 (-4.191410) | 0.133846 / 6.500664 (-6.366818) | 0.062190 / 0.075469 (-0.013279) |\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) | 1.279860 / 1.841788 (-0.561928) | 19.549777 / 8.074308 (11.475469) | 14.225844 / 10.191392 (4.034452) | 0.164682 / 0.680424 (-0.515741) | 0.018321 / 0.534201 (-0.515880) | 0.389874 / 0.579283 (-0.189409) | 0.408597 / 0.434364 (-0.025767) | 0.454327 / 0.540337 (-0.086011) | 0.645571 / 1.386936 (-0.741365) |\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) | 0.007021 / 0.011353 (-0.004332) | 0.004119 / 0.011008 (-0.006889) | 0.065393 / 0.038508 (0.026885) | 0.085005 / 0.023109 (0.061896) | 0.412221 / 0.275898 (0.136323) | 0.438266 / 0.323480 (0.114786) | 0.005594 / 0.007986 (-0.002392) | 0.003499 / 0.004328 (-0.000829) | 0.065053 / 0.004250 (0.060802) | 0.060608 / 0.037052 (0.023555) | 0.413938 / 0.258489 (0.155449) | 0.446192 / 0.293841 (0.152351) | 0.032232 / 0.128546 (-0.096314) | 0.008617 / 0.075646 (-0.067029) | 0.071296 / 0.419271 (-0.347976) | 0.048756 / 0.043533 (0.005223) | 0.404977 / 0.255139 (0.149838) | 0.426801 / 0.283200 (0.143602) | 0.023650 / 0.141683 (-0.118033) | 1.526928 / 1.452155 (0.074773) | 1.627504 / 1.492716 (0.134787) |\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) | 0.224318 / 0.018006 (0.206312) | 0.469717 / 0.000490 (0.469227) | 0.005539 / 0.000200 (0.005339) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034240 / 0.037411 (-0.003171) | 0.096449 / 0.014526 (0.081923) | 0.107309 / 0.176557 (-0.069247) | 0.160246 / 0.737135 (-0.576889) | 0.107595 / 0.296338 (-0.188743) |\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) | 0.434266 / 0.215209 (0.219057) | 4.325571 / 2.077655 (2.247916) | 2.324066 / 1.504120 (0.819946) | 2.140238 / 1.541195 (0.599044) | 2.244593 / 1.468490 (0.776103) | 0.486259 / 4.584777 (-4.098518) | 3.644120 / 3.745712 (-0.101592) | 3.372330 / 5.269862 (-1.897531) | 2.074779 / 4.565676 (-2.490897) | 0.057154 / 0.424275 (-0.367121) | 0.007304 / 0.007607 (-0.000303) | 0.516944 / 0.226044 (0.290899) | 5.174300 / 2.268929 (2.905372) | 2.816269 / 55.444624 (-52.628356) | 2.462943 / 6.876477 (-4.413534) | 2.735851 / 2.142072 (0.593779) | 0.589028 / 4.805227 (-4.216200) | 0.131804 / 6.500664 (-6.368860) | 0.060173 / 0.075469 (-0.015296) |\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) | 1.354540 / 1.841788 (-0.487248) | 20.436511 / 8.074308 (12.362203) | 15.541981 / 10.191392 (5.350589) | 0.168399 / 0.680424 (-0.512025) | 0.020716 / 0.534201 (-0.513485) | 0.396275 / 0.579283 (-0.183008) | 0.427232 / 0.434364 (-0.007132) | 0.475121 / 0.540337 (-0.065216) | 0.648579 / 1.386936 (-0.738357) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4fa138fc0d9aa1536194fd46566840e698ccde03 \"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) | 0.009071 / 0.011353 (-0.002282) | 0.005820 / 0.011008 (-0.005188) | 0.119974 / 0.038508 (0.081466) | 0.092145 / 0.023109 (0.069036) | 0.445349 / 0.275898 (0.169451) | 0.442488 / 0.323480 (0.119008) | 0.005352 / 0.007986 (-0.002634) | 0.004332 / 0.004328 (0.000003) | 0.084397 / 0.004250 (0.080147) | 0.064624 / 0.037052 (0.027572) | 0.430938 / 0.258489 (0.172448) | 0.503574 / 0.293841 (0.209733) | 0.047900 / 0.128546 (-0.080647) | 0.014237 / 0.075646 (-0.061409) | 0.366145 / 0.419271 (-0.053127) | 0.066344 / 0.043533 (0.022811) | 0.424582 / 0.255139 (0.169443) | 0.451845 / 0.283200 (0.168646) | 0.041409 / 0.141683 (-0.100274) | 1.886998 / 1.452155 (0.434843) | 2.011676 / 1.492716 (0.518960) |\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) | 0.301008 / 0.018006 (0.283001) | 0.608670 / 0.000490 (0.608180) | 0.011963 / 0.000200 (0.011763) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031996 / 0.037411 (-0.005415) | 0.102274 / 0.014526 (0.087748) | 0.121437 / 0.176557 (-0.055120) | 0.181647 / 0.737135 (-0.555489) | 0.121634 / 0.296338 (-0.174704) |\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) | 0.597070 / 0.215209 (0.381861) | 5.973808 / 2.077655 (3.896154) | 2.486345 / 1.504120 (0.982225) | 2.125395 / 1.541195 (0.584201) | 2.270864 / 1.468490 (0.802374) | 0.880031 / 4.584777 (-3.704746) | 5.396522 / 3.745712 (1.650809) | 4.702005 / 5.269862 (-0.567857) | 3.023087 / 4.565676 (-1.542589) | 0.097093 / 0.424275 (-0.327182) | 0.008457 / 0.007607 (0.000850) | 0.712164 / 0.226044 (0.486120) | 7.112867 / 2.268929 (4.843938) | 3.364509 / 55.444624 (-52.080115) | 2.646953 / 6.876477 (-4.229524) | 2.795967 / 2.142072 (0.653894) | 1.067182 / 4.805227 (-3.738046) | 0.218297 / 6.500664 (-6.282368) | 0.071720 / 0.075469 (-0.003750) |\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) | 1.640477 / 1.841788 (-0.201311) | 24.875163 / 8.074308 (16.800855) | 22.125706 / 10.191392 (11.934314) | 0.247267 / 0.680424 (-0.433157) | 0.033717 / 0.534201 (-0.500484) | 0.492422 / 0.579283 (-0.086862) | 0.578323 / 0.434364 (0.143959) | 0.579503 / 0.540337 (0.039165) | 0.816721 / 1.386936 (-0.570215) |\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) | 0.009372 / 0.011353 (-0.001981) | 0.005449 / 0.011008 (-0.005559) | 0.095371 / 0.038508 (0.056863) | 0.086320 / 0.023109 (0.063211) | 0.539573 / 0.275898 (0.263675) | 0.580338 / 0.323480 (0.256858) | 0.007028 / 0.007986 (-0.000958) | 0.004196 / 0.004328 (-0.000133) | 0.082710 / 0.004250 (0.078460) | 0.064336 / 0.037052 (0.027284) | 0.521490 / 0.258489 (0.263001) | 0.567942 / 0.293841 (0.274101) | 0.049659 / 0.128546 (-0.078887) | 0.017297 / 0.075646 (-0.058350) | 0.093874 / 0.419271 (-0.325398) | 0.061664 / 0.043533 (0.018131) | 0.524476 / 0.255139 (0.269337) | 0.563255 / 0.283200 (0.280055) | 0.039990 / 0.141683 (-0.101693) | 1.854438 / 1.452155 (0.402283) | 1.819321 / 1.492716 (0.326605) |\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) | 0.298817 / 0.018006 (0.280811) | 0.629381 / 0.000490 (0.628891) | 0.006259 / 0.000200 (0.006059) | 0.000690 / 0.000054 (0.000635) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.041009 / 0.037411 (0.003598) | 0.123845 / 0.014526 (0.109319) | 0.138606 / 0.176557 (-0.037951) | 0.215042 / 0.737135 (-0.522093) | 0.129572 / 0.296338 (-0.166767) |\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) | 0.668823 / 0.215209 (0.453614) | 6.596762 / 2.077655 (4.519108) | 3.275429 / 1.504120 (1.771309) | 2.921747 / 1.541195 (1.380553) | 2.963748 / 1.468490 (1.495258) | 0.897588 / 4.584777 (-3.687188) | 5.683618 / 3.745712 (1.937906) | 5.051102 / 5.269862 (-0.218760) | 3.178855 / 4.565676 (-1.386822) | 0.107446 / 0.424275 (-0.316829) | 0.008967 / 0.007607 (0.001360) | 0.785577 / 0.226044 (0.559532) | 8.236556 / 2.268929 (5.967628) | 3.914725 / 55.444624 (-51.529899) | 3.129068 / 6.876477 (-3.747409) | 3.368383 / 2.142072 (1.226310) | 1.004307 / 4.805227 (-3.800920) | 0.204788 / 6.500664 (-6.295876) | 0.078250 / 0.075469 (0.002780) |\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) | 1.778574 / 1.841788 (-0.063213) | 25.583659 / 8.074308 (17.509351) | 23.505866 / 10.191392 (13.314474) | 0.228759 / 0.680424 (-0.451665) | 0.038348 / 0.534201 (-0.495853) | 0.468980 / 0.579283 (-0.110303) | 0.630194 / 0.434364 (0.195830) | 0.587535 / 0.540337 (0.047198) | 0.831761 / 1.386936 (-0.555175) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#68f4f847f3248f02fc99458310d9d786906d7a6f \"CML watermark\")\n", "I've addressed the comments. Let me know if it looks all good now :)", "Actually just found out that the current `**/*[-._ 0-9/]train[-._ 0-9/]**` doesn't match `data/train.csv` in bash (but does match in fsspec right now).\r\n\r\nSo there might be a risk that this pattern breaks in the future no ?", "@lhoestq `fsspec` has tests to check their specific (non-posix) behavior, so I think merging in the current state is fine. And if they make a breaking change in the future, we can align the patterns once again :) ", "Yea after more thoughts I also think it's fine. Feel free to merge !", "<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) | 0.006920 / 0.011353 (-0.004433) | 0.004182 / 0.011008 (-0.006826) | 0.084629 / 0.038508 (0.046121) | 0.086052 / 0.023109 (0.062943) | 0.326062 / 0.275898 (0.050164) | 0.344190 / 0.323480 (0.020710) | 0.005393 / 0.007986 (-0.002593) | 0.003410 / 0.004328 (-0.000918) | 0.064327 / 0.004250 (0.060076) | 0.056556 / 0.037052 (0.019504) | 0.319255 / 0.258489 (0.060766) | 0.357943 / 0.293841 (0.064102) | 0.032097 / 0.128546 (-0.096450) | 0.008778 / 0.075646 (-0.066868) | 0.291057 / 0.419271 (-0.128215) | 0.053225 / 0.043533 (0.009692) | 0.307713 / 0.255139 (0.052574) | 0.350058 / 0.283200 (0.066858) | 0.024380 / 0.141683 (-0.117303) | 1.459482 / 1.452155 (0.007328) | 1.555711 / 1.492716 (0.062994) |\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) | 0.239487 / 0.018006 (0.221480) | 0.467604 / 0.000490 (0.467114) | 0.010742 / 0.000200 (0.010542) | 0.000285 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029394 / 0.037411 (-0.008018) | 0.087404 / 0.014526 (0.072879) | 0.098701 / 0.176557 (-0.077855) | 0.154145 / 0.737135 (-0.582990) | 0.099726 / 0.296338 (-0.196612) |\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) | 0.389008 / 0.215209 (0.173799) | 3.873165 / 2.077655 (1.795510) | 1.860676 / 1.504120 (0.356556) | 1.679668 / 1.541195 (0.138474) | 1.782347 / 1.468490 (0.313857) | 0.489469 / 4.584777 (-4.095308) | 3.678706 / 3.745712 (-0.067006) | 3.404076 / 5.269862 (-1.865785) | 2.110972 / 4.565676 (-2.454704) | 0.057478 / 0.424275 (-0.366797) | 0.007443 / 0.007607 (-0.000164) | 0.464780 / 0.226044 (0.238736) | 4.643606 / 2.268929 (2.374678) | 2.355744 / 55.444624 (-53.088881) | 1.993992 / 6.876477 (-4.882485) | 2.245520 / 2.142072 (0.103447) | 0.592773 / 4.805227 (-4.212454) | 0.135369 / 6.500664 (-6.365295) | 0.062478 / 0.075469 (-0.012991) |\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) | 1.257537 / 1.841788 (-0.584251) | 19.828010 / 8.074308 (11.753702) | 14.709260 / 10.191392 (4.517868) | 0.168359 / 0.680424 (-0.512065) | 0.018907 / 0.534201 (-0.515294) | 0.397223 / 0.579283 (-0.182060) | 0.421760 / 0.434364 (-0.012604) | 0.464597 / 0.540337 (-0.075740) | 0.665905 / 1.386936 (-0.721031) |\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) | 0.007247 / 0.011353 (-0.004106) | 0.004104 / 0.011008 (-0.006904) | 0.065008 / 0.038508 (0.026500) | 0.083485 / 0.023109 (0.060376) | 0.399808 / 0.275898 (0.123910) | 0.433374 / 0.323480 (0.109894) | 0.005453 / 0.007986 (-0.002532) | 0.003479 / 0.004328 (-0.000850) | 0.065126 / 0.004250 (0.060876) | 0.059945 / 0.037052 (0.022893) | 0.402018 / 0.258489 (0.143529) | 0.437927 / 0.293841 (0.144086) | 0.032654 / 0.128546 (-0.095892) | 0.008717 / 0.075646 (-0.066929) | 0.071737 / 0.419271 (-0.347534) | 0.048903 / 0.043533 (0.005370) | 0.402107 / 0.255139 (0.146968) | 0.417602 / 0.283200 (0.134402) | 0.024821 / 0.141683 (-0.116862) | 1.474471 / 1.452155 (0.022316) | 1.559571 / 1.492716 (0.066855) |\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) | 0.232010 / 0.018006 (0.214003) | 0.460768 / 0.000490 (0.460278) | 0.005250 / 0.000200 (0.005050) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033839 / 0.037411 (-0.003573) | 0.101617 / 0.014526 (0.087091) | 0.107984 / 0.176557 (-0.068573) | 0.160923 / 0.737135 (-0.576212) | 0.110367 / 0.296338 (-0.185971) |\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) | 0.433087 / 0.215209 (0.217878) | 4.324100 / 2.077655 (2.246445) | 2.312937 / 1.504120 (0.808817) | 2.159903 / 1.541195 (0.618708) | 2.240235 / 1.468490 (0.771745) | 0.500659 / 4.584777 (-4.084118) | 3.743801 / 3.745712 (-0.001911) | 3.441350 / 5.269862 (-1.828512) | 2.141370 / 4.565676 (-2.424306) | 0.059078 / 0.424275 (-0.365197) | 0.007468 / 0.007607 (-0.000139) | 0.508108 / 0.226044 (0.282064) | 5.076738 / 2.268929 (2.807809) | 2.825939 / 55.444624 (-52.618685) | 2.467762 / 6.876477 (-4.408715) | 2.705079 / 2.142072 (0.563006) | 0.603363 / 4.805227 (-4.201864) | 0.136267 / 6.500664 (-6.364397) | 0.062887 / 0.075469 (-0.012582) |\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) | 1.359344 / 1.841788 (-0.482443) | 20.581510 / 8.074308 (12.507202) | 15.534489 / 10.191392 (5.343097) | 0.192068 / 0.680424 (-0.488356) | 0.020831 / 0.534201 (-0.513370) | 0.403330 / 0.579283 (-0.175953) | 0.429536 / 0.434364 (-0.004828) | 0.479906 / 0.540337 (-0.060431) | 0.674170 / 1.386936 (-0.712766) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33ac74c2df928dece49ca2cf25e14172896b442e \"CML watermark\")\n" ]

"2023-09-15T17:58:25Z"

"2023-09-26T15:41:38Z"

"2023-09-26T15:32:51Z"

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[ "Hi ! Maybe we'd need to align with `transformers` and other libraries that implement `push_to_hub` to agree on what it should return.\r\n\r\ne.g. in `transformers` the typing says it returns a string, but in practice it returns a `CommitInfo`.\r\n\r\nTherefore I'd not add an output to `push_to_hub` here unless we had a chance to discuss more broadly.\r\n\r\nAnyway in my opinion it should no just return the URL of the repository, but ideally the URL at the revision where the data were pushed", "Perhaps a mixin or something similar could be defined on the `hfh` side to ensure the `push_to_hub` API is aligned across our projects. \r\n\r\nPS: this would also mean that the PRs such as https://github.com/huggingface/datasets/pull/5528 would no longer be our responsibility\r\n\r\ncc @Wauplin ", "I agree, with universability and the idea is more about returning at least something that references where to find the uploaded file/model or otherwise. \r\n\r\nIdeally, the referenced PR would work.", "imo this would be a good use case to just use `huggingface_hub` and align to what we do there :)", "@mariosasko, can you give me some pointers to where I might help implementing this for the `huggingface-hub`?", "> @mariosasko: Perhaps a mixin or something similar could be defined on the hfh side to ensure the push_to_hub API is aligned across our projects.\r\n\r\n> @julien-c: imo this would be a good use case to just use huggingface_hub and align to what we do there :)\r\n\r\nI (finally) opened a PR to harmonize return types: https://github.com/huggingface/huggingface_hub/pull/1921. It should hopefully be shipped in next release later this week (:crossed_fingers:). " ]

"2023-02-18T11:26:25Z"

"2023-12-18T16:57:56Z"

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"2021-06-23T16:20:25Z"

"2021-06-24T14:41:15Z"

"2021-06-24T13:34:03Z"

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"2022-01-13T13:56:59Z"

"2022-01-13T14:03:02Z"

"2022-01-13T14:03:01Z"

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"2021-03-31T03:21:19Z"

"2021-04-01T10:20:37Z"

"2021-04-01T10:20:36Z"

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[ "Haha it looks like you got the chance of being reviewed twice at the same time and got the same suggestion twice x)\r\nAnyway it's all good now so we can merge !", "Thanks for the feedback. " ]

"2021-11-12T14:05:36Z"

"2021-11-12T15:09:33Z"

"2021-11-12T14:59:32Z"

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[ "Hi! Yes, the docs are outdated. Expect this to be fixed soon. \r\n\r\nIn the meantime, you can try to fix the issue yourself.\r\n\r\nThese are the configs/language pairs supported by `wmt15` from which you can choose:\r\n* `cs-en` (Czech - English)\r\n* `de-en` (German - English)\r\n* `fi-en` (Finnish- English)\r\n* `fr-en` (French - English)\r\n* `ru-en` (Russian - English)\r\n\r\nAnd the current implementation always uses all the subsets available for a language, so to define custom subsets, you'll have to clone the repo from the Hub and replace the line https://huggingface.co/datasets/wmt15/blob/main/wmt_utils.py#L688 with:\r\n`for split, ss_names in (self._subsets if self.config.subsets is None else self.config.subsets).items()`\r\n\r\nThen, you can load the dataset as follows:\r\n```python\r\nfrom datasets import load_dataset\r\ndset = load_dataset(\"path/to/local/wmt15_folder\", \"<one of 5 available configs>\", subsets=...)", "@mariosasko thanks a lot for the suggested fix! ", "Hi @mariosasko \r\n\r\nAre the docs updated? If not, I would like to get on it. I am new around here, would we helpful, if you can guide.\r\n\r\nThanks", "Hi @khushmeeet! The docs haven't been updated, so feel free to work on this issue. This is a tricky issue, so I'll give the steps you can follow to fix this:\r\n\r\nFirst, this code:\r\nhttps://github.com/huggingface/datasets/blob/7cff5b9726a223509dbd6224de3f5f452c8d924f/src/datasets/load.py#L113-L118\r\n\r\nneeds to be replaced with (makes the dataset builder search more robust and allows us to remove the ABC stuff from `wmt_utils.py`):\r\n```python\r\n for name, obj in module.__dict__.items():\r\n if inspect.isclass(obj) and issubclass(obj, main_cls_type):\r\n if inspect.isabstract(obj):\r\n continue\r\n module_main_cls = obj\r\n obj_module = inspect.getmodule(obj)\r\n if obj_module is not None and module == obj_module:\r\n break\r\n```\r\n\r\nThen, all the `wmt_utils.py` scripts need to be updated as follows (these are the diffs with the requiered changes):\r\n````diff\r\n import os\r\n import re\r\n import xml.etree.cElementTree as ElementTree\r\n-from abc import ABC, abstractmethod\r\n\r\n import datasets\r\n````\r\n\r\n````diff\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\n _DESCRIPTION = \"\"\"\\\r\n-Translate dataset based on the data from statmt.org.\r\n+Translation dataset based on the data from statmt.org.\r\n\r\n-Versions exists for the different years using a combination of multiple data\r\n-sources. The base `wmt_translate` allows you to create your own config to choose\r\n-your own data/language pair by creating a custom `datasets.translate.wmt.WmtConfig`.\r\n+Versions exist for different years using a combination of data\r\n+sources. The base `wmt` allows you to create a custom dataset by choosing\r\n+your own data/language pair. This can be done as follows:\r\n\r\n ```\r\n-config = datasets.wmt.WmtConfig(\r\n- version=\"0.0.1\",\r\n+from datasets import inspect_dataset, load_dataset_builder\r\n+\r\n+inspect_dataset(\"<insert the dataset name\", \"path/to/scripts\")\r\n+builder = load_dataset_builder(\r\n+ \"path/to/scripts/wmt_utils.py\",\r\n language_pair=(\"fr\", \"de\"),\r\n subsets={\r\n datasets.Split.TRAIN: [\"commoncrawl_frde\"],\r\n datasets.Split.VALIDATION: [\"euelections_dev2019\"],\r\n },\r\n )\r\n-builder = datasets.builder(\"wmt_translate\", config=config)\r\n-```\r\n\r\n+# Standard version\r\n+builder.download_and_prepare()\r\n+ds = builder.as_dataset()\r\n+\r\n+# Streamable version\r\n+ds = builder.as_streaming_dataset()\r\n+```\r\n \"\"\"\r\n````\r\n\r\n````diff\r\n+class Wmt(datasets.GeneratorBasedBuilder):\r\n \"\"\"WMT translation dataset.\"\"\"\r\n+\r\n+ BUILDER_CONFIG_CLASS = WmtConfig\r\n\r\n def __init__(self, *args, **kwargs):\r\n- if type(self) == Wmt and \"config\" not in kwargs: # pylint: disable=unidiomatic-typecheck\r\n- raise ValueError(\r\n- \"The raw `wmt_translate` can only be instantiated with the config \"\r\n- \"kwargs. You may want to use one of the `wmtYY_translate` \"\r\n- \"implementation instead to get the WMT dataset for a specific year.\"\r\n- )\r\n super(Wmt, self).__init__(*args, **kwargs)\r\n\r\n @property\r\n- @abstractmethod\r\n def _subsets(self):\r\n \"\"\"Subsets that make up each split of the dataset.\"\"\"\r\n````\r\n```diff\r\n \"\"\"Subsets that make up each split of the dataset for the language pair.\"\"\"\r\n source, target = self.config.language_pair\r\n filtered_subsets = {}\r\n- for split, ss_names in self._subsets.items():\r\n+ subsets = self._subsets if self.config.subsets is None else self.config.subsets\r\n+ for split, ss_names in subsets.items():\r\n filtered_subsets[split] = []\r\n for ss_name in ss_names:\r\n dataset = DATASET_MAP[ss_name]\r\n```\r\n\r\n`wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t` have this script, so all of them need to be updated. Also, the dataset summaries from the READMEs of these datasets need to be updated to match the new `_DESCRIPTION` string. And that's it! Let me know if you need additional help.", "Hi @mariosasko ,\r\n\r\nI have made the changes as suggested by you and have opened a PR #4537.\r\n\r\nThanks", "Resolved via #4554 " ]

"2022-05-15T20:58:26Z"

"2022-07-11T14:54:02Z"

"2022-07-11T14:54:01Z"

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"2020-07-15T15:05:52Z"

"2020-07-16T07:39:52Z"

"2020-07-16T07:39:51Z"

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

"2022-09-21T09:27:03Z"

"2022-09-21T16:24:01Z"

"2022-09-21T16:20:59Z"

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

"2022-07-06T15:43:21Z"

"2022-07-06T15:56:21Z"

"2022-07-06T15:45:05Z"

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[ "There is an exception when running `pip install .[tests]`:\r\n```\r\nProcessing /home/circleci/datasets\r\nCollecting numpy>=1.17 (from datasets==1.12.2.dev0)\r\n Downloading https://files.pythonhosted.org/packages/45/b2/6c7545bb7a38754d63048c7696804a0d947328125d81bf12beaa692c3ae3/numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl (13.4MB)\r\n 100% |████████████████████████████████| 13.4MB 3.9MB/s eta 0:00:011\r\n\r\n...\r\n\r\nCollecting faiss-cpu (from datasets==1.12.2.dev0)\r\n Downloading https://files.pythonhosted.org/packages/87/91/bf8ea0d42733cbb04f98d3bf27808e4919ceb5ec71102e21119398a97237/faiss-cpu-1.7.1.post2.tar.gz (41kB)\r\n 100% |████████████████████████████████| 51kB 30.9MB/s ta 0:00:01\r\n Complete output from command python setup.py egg_info:\r\n Traceback (most recent call last):\r\n File \"/home/circleci/.pyenv/versions/3.6.14/lib/python3.6/site-packages/setuptools/sandbox.py\", line 154, in save_modules\r\n yield saved\r\n File \"/home/circleci/.pyenv/versions/3.6.14/lib/python3.6/site-packages/setuptools/sandbox.py\", line 195, in setup_context\r\n yield\r\n File \"/home/circleci/.pyenv/versions/3.6.14/lib/python3.6/site-packages/setuptools/sandbox.py\", line 250, in run_setup\r\n _execfile(setup_script, ns)\r\n File \"/home/circleci/.pyenv/versions/3.6.14/lib/python3.6/site-packages/setuptools/sandbox.py\", line 45, in _execfile\r\n exec(code, globals, locals)\r\n File \"/tmp/easy_install-1pop4blm/numpy-1.21.2/setup.py\", line 34, in <module>\r\n method can be invoked.\r\n RuntimeError: Python version >= 3.7 required.\r\n```\r\n\r\nApparently, `numpy-1.21.2` tries to be installed in the temporary directory `/tmp/easy_install-1pop4blm` instead of the downloaded `numpy-1.19.5` (requirement of `datasets`).\r\n\r\nThis is caused because `pip` downloads the `.tar.gz` (instead of the `.whl`) and tries to build it in a tmp dir." ]

"2021-09-22T08:25:27Z"

"2021-09-24T10:36:05Z"

"2021-09-24T10:36:05Z"

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[ "I have to admit I'm not a fan of this idea, as this would result in a non-consistent behavior between tabular and non-tabular datasets, which is confusing if done without the context you provided. Instead, we could consider returning a `Dataset` object rather than `DatasetDict` if there is only one split in the generated dataset. But then again, I think this lib is a bit too old to make such changes. @lhoestq @albertvillanova WDYT?\r\n\r\n", "We can brainstorm here to see how we could make it happen ? And then depending on the options we see if it's a change we can do.\r\n\r\nI'm starting with a first reasoning\r\n\r\nCurrently not passing `split=` in `load_dataset` means \"return a dict with each split\".\r\n\r\nNow what would happen if a dataset has no split ? Ideally it should return one Dataset. And passing `split=` would have no sense. So depending on the dataset content, not passing `split=` should return a dict or a Dataset. In particular, those two cases should work:\r\n```python\r\n# case 1: dataset without split\r\nds = load_dataset(\"dataset_without_split\")\r\nds[0], ds[\"column_name\"], list(ds) # we want this\r\n\r\n# case 2: dataset with splits\r\nds = load_dataset(\"dataset_with_splits\")\r\nds[\"train\"] # this works and can't be changed\r\nds = load_dataset(\"dataset_with_splits\", split=\"train\")\r\nds[0], ds[\"column_name\"], list(ds) # this works and can't be changed\r\n```\r\n\r\nI can see several ideas:\r\n1. allowing `load_dataset` to return a different object based on the dataset content - either a Dataset or a DatasetDict\r\n - we can update `get_dataset_split_names` to return None or a list if users want to know in advance what object will be returned. They can also use `isinstance` _a posteriori_\r\n - but in this case we expect users to be careful when loading datasets and always to extra steps to check if they got a Dataset or DatasetDict\r\n2. merge Dataset and DatasetDict objects\r\n - they already share many functions: map, filter, push_to_hub etc.\r\n - we can define `ds[0]` to be the first item of the first split, and consider that the uses accesses rows from the full table of all the splits concatenated\r\n - however there is a collision when doing `ds[\"column_name\"]` or `ds[\"train\"]` that we need to address: the first returns a list, while the other returns a Dataset.\r\n\r\nWhat are your opinions on those two ideas ? Do you have other ideas in mind ?", "I like the first idea more (concatenating splits doesn't seem useful, no?). This is a significant breaking change, so I think we should do a poll (or something similar) to gather more info on the actual \"expected behavior\" and wait for Datasets 3.0 if we decide to implement it.\r\n\r\nPS: @thomwolf also suggested the same thing a while ago (https://github.com/huggingface/datasets/issues/743#issuecomment-746074641).", "I think it's an interesting improvement to the user experience for a case that comes often (no split) so I would definitively support it.\r\n\r\nI would be more in favor of option 2 rather than returning various types of objects from load_dataset and handling carefully the possible collisions indeed", "Related: if a dataset only has one split, we don't show the splits select control in the dataset viewer on the Hub, eg. compare https://huggingface.co/datasets/hf-internal-testing/fixtures_image_utils/viewer/image/test with https://huggingface.co/datasets/glue/viewer/mnli/test.\r\n\r\nSee https://github.com/huggingface/moon-landing/pull/3858 for more details (internal)", "I feel like the second idea is a bit more overkill. \r\n@severo I would say it's a bit irrelevant to the problem we have but is a separate problem @polinaeterna is solving at the moment. 😅 (also discussed on slack)", "OK, sorry for polluting the thread. The relation I saw with the dataset viewer is that from a UX point of view, we hide the concepts of split and configuration whenever possible -> this issue feels like doing the same in the datasets library.", "I would agree that returning different types based on the content of the dataset might be confusing.\r\n\r\nWe can do something similar to what `fetch_*` or `load_*` from `sklearn.datasets` do, which is to have an arg which changes the type of the returned type. For instance, `load_iris` would return a dict, but `load_iris(..., return_X_y=True)` would return a tuple.\r\n\r\nHere we can have a similar arg such as `return_X` which would then only return a single `DataSet` or an array.", "> I feel like the second idea is a bit more overkill.\r\n\r\nOverkill in what sense ?\r\n\r\n> Here we can have a similar arg such as return_X which would then only return a single DataSet or an array.\r\n\r\nRight now one can already pass `split=\"all\"` to get one `Dataset` object with all the data in it (unsplit). We could also have something like `return_all=True` so make the API clearer.\r\n\r\n> I would be more in favor of option 2 rather than returning various types of objects from load_dataset and handling carefully the possible collisions indeed\r\n\r\nI think it would be ok to handle the collision by allowing both `ds[\"train\"]` and `ds[\"column_name\"]` (and maybe adding something like `ds.splits` for those who want to iterate over the splits or add new ones)", "Would it make sense to remove the notion of \"split\" in `load_dataset`? I feel a lof of it comes from the want to have some sort of group of more or less similar dataset. \"train\"/\"test\"/\"validation\" are the traditional ones, but there are some datasets that have much more splits.\r\n\r\nWould it make sense to force `load_dataset` to only load a single `Dataset` object, and fail if it doesn't point to one. And have another method that's like `load_dataset_group_info` that can return a very arbitrary info class (Dict, List whatever), but you need to pass individual infos to `load_dataset` to run anything? Typically I don't think `DatasetDict.map` is really that helpful, but that's my personal opinion. This would help make things more readable (typically knowing if an object is a `Dataset` or a `DatasetDict`)", "> Would it make sense to remove the notion of \"split\" in load_dataset?\r\n\r\nI think we need to keep it - though in practice people can name the splits whatever they want anyway.\r\n\r\n> Would it make sense to force load_dataset to only load a single Dataset object, and fail if it doesn't point to one.\r\n\r\nWe need to keep backward compatibility ideally - in particular the load_dataset + ds[\"train\"] one", "> I think we need to keep it - though in practice people can name the splits whatever they want anyway.\r\n\r\nIt was my understanding that the whole issue was that `load_dataset` returned multiple types of objects.\r\n\r\n> We need to keep backward compatibility ideally - in particular the load_dataset + ds[\"train\"] one\r\n\r\nYeah sorry I meant ideally. One can always start developing `load_dataset_v2` can deprecate the first one and remove it in the longer term.", "> It was my understanding that the whole issue was that load_dataset returned multiple types of objects.\r\n\r\nYes indeed, but we still want to keep a way to load the train/val/test/whatever splits alone ;)", "@thomasw21's solution is good but it will break backwards compatibility. 😅", "Started to experiment with merging Dataset and DatasetDict. My plan is to define the splits of a Dataset in Dataset.info.splits (already exists, but never used). A Dataset would then be the concatenation of its splits if they exist.\r\n\r\nNot sure yet this is the way to go. My plan is to play with it and see and share it with you, so we can see if it makes sense from a UX point of view.", "So just to make sure that I understand the current direction, people will have to be extra careful when handling splits right?\r\nImagine \"potato\" a dataset containing train/validation split:\r\n```\r\nload_dataset(\"potato\") # returns the concatenation of all the splits\r\n```\r\nPreviously the design would force you to choose a split (it would raise otherwise), or manually concat them if you really wanted to play with concatenated splits. Now it would potentially run without raising for a bit of time until you figure out that you've been training on both train and validation split.\r\n\r\nWould it make sense to use a dataset specific default instead of using the concatenation, typically \"potato\" dataset's default would be train?\r\n```\r\nload_dataset(\"potato\") # returns \"train\" split\r\nload_dataset(\"potato\", split=\"train\") # returns \"train\" split\r\nload_dataset(\"potato\", split=\"validation\") # returns \"validation\" split\r\nconcatenate_datasets([load_dataset(\"potato\", split=\"train\"), load_dataset(\"potato\", split=\"validation\")]) # returns concatenation\r\n```", "> load_dataset(\"potato\") # returns \"train\" split\r\n\r\nTo avoid a breaking change we need to be able to do `load_dataset(\"potato\")[\"validation\"]` as well.\r\n\r\nIn that case I'd wonder where the validation split comes from, since the rows of the dataset wouldn't contain the validation split according to your example. That's why I'm more in favor of concatenating.\r\n\r\nA dataset is one table, that optionally has some split info about subsets (e.g. for training an evaluation)\r\n\r\nThis also allows anyone to re-split the dataset the way they want if they're not happy with the default:\r\n\r\n```python\r\nds = load_dataset(\"potato\").train_test_split(test_size=0.2)\r\ntrain_ds = ds[\"train\"]\r\ntest_ds = ds[\"test\"]\r\n```", "Just thinking about this, we could just have `to_dataframe()` as `load_dataset(\"blah\").to_dataframe()` to get the whole dataset, and not change anything else.", "I have a first implementation of option 2 (merging Dataset and DatasetDict) in this PR: https://github.com/huggingface/datasets/pull/5301/\r\n\r\nFeel free to play with it if you're interested, and let me know what you think. In this PR, a dataset is one table that optionally has some split info about subsets.", "@adrinjalali we already have [to_pandas](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_pandas) AFAIK that essentially does the same thing (for a dataset, not for a dataset dict), I was wondering if it makes sense to have this as I don't know portion of people who load non-tabular datasets into dataframes. @lhoestq I saw your PR and it will break a lot of things imo, WDYT of this option? ", "> we already have [to_pandas](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_pandas) AFAIK that essentially does the same thing (for a dataset, not for a dataset dict)\r\n\r\nyes correct :)\r\n\r\n> I saw your PR and it will break a lot of things imo\r\n\r\nDo you have concrete examples you can share ?\r\n\r\n> WDYT of this option?\r\n\r\nThe to_dataframe option ? I think it not enough, since you'd still get a `DatasetDict({\"train\": Dataset()})` if you load a dataset with no splits (e.g. one CSV), and this doesn't really make sense.\r\n\r\nNote that in the PR I opened you can do\r\n```python\r\nds = load_dataset(\"dataset_with_just_one_csv\") # Dataset type\r\ndf = load_dataset(\"dataset_with_just_one_csv\").to_pandas() # DataFrame type\r\n```", "@lhoestq no I think @adrinjalali and I meant when user calls `to_dataframe` if there's only train split in `DatasetDict` we could directly load that into dataframe. This might cause a confusion given there's to_pandas but I think it's more intuitive and least breaking change. (given people -who use `datasets` for tabular workflows- will eventually call `to_pandas` anyway) ", "So in that case it would be fine to still end up with a dataset dict with a \"train\" split ?", "yeah what I mean is this:\r\n\r\n```py\r\ndataset = load_dataset(\"blah\")\r\n\r\n# deal with a split of the dataset\r\ntrain = dataset[\"train\"]\r\ntrain_df = dataset[\"train\"].to_dataframe()\r\n\r\n# deal with the whole dataset\r\ndataset_df = dataset.to_dataframe()\r\n```\r\n\r\nSo we do two things to improve tabular experience:\r\n- allow datasets to have a single split\r\n- add `to_dataframe` to the root dict level so that users can simply call `df = load_dataset(\"blah\").to_dataframe()` and have it in their `pandas.DataFrame` object.", "Ok ! Note that we already have `Dataset.to_pandas()` so for consistency I'd call it `DatasetDict.to_pandas()` as well, does it sound good to you ? This is something we can add pretty easily", "yeah that sounds perfect @lhoestq !", "> So just to make sure that I understand the current direction, people will have to be extra careful when handling splits right?\r\n\r\nWe can raise an error if someone does `load_dataset(...)[0]` if the dataset is made of several splits, and return the first example if there's one or zero splits (i.e. when it's not ambiguous). Had this idea from the dicussions in #5312 WDYT @thomasw21 ?", "> We can raise an error if someone does load_dataset(...)[0] if the dataset is made of several splits,\r\n\r\nBut then how is that different to have the distinction between DatasetDict and Dataset then? Is it just that \"default behaviour when there are no splits or single split, it returns directly the split when there's no ambiguity\".\r\n\r\nAlso I was wondering how the concatenation could have heavy impacts when running mapping functions/filtering in batch? Typically can batch be somehow mixed?", "> But then how is that different to have the distinction between DatasetDict and Dataset then?\r\n\r\nBecause it doesn't make sense to be able to do `example = ds[0]` or `examples = list(ds)` on a class named `DatasetDict` of type `Dict[str, Dataset]`.\r\n\r\n> Also I was wondering how the concatenation could have heavy impacts when running mapping functions/filtering in batch? Typically can batch be somehow mixed?\r\n\r\nNo, we run each function on each split separated", "> Because it doesn't make sense to be able to do example = ds[0] or examples = list(ds) on a class named DatasetDict of type Dict[str, Dataset].\r\n\r\nHum but you're still going to raise an exception in both those cases with your current change no? (actually list(ds) would return the name of the splits no?)\r\n\r\n> No, we run each function on each split separated\r\n\r\nNice!" ]

"2022-11-02T09:15:02Z"

"2022-12-06T12:13:17Z"

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[]

[ "Beware of cases like: `http://ufal.ms.mff.cuni.cz/umc/005-en-ur/download.php?f=umc005-corpus.zip` or `gzip://bg-cs.xml::https://opus.nlpl.eu/download.php?f=Europarl/v8/xml/bg-cs.xml.gz`. I see these URLs in the errors (https://observablehq.com/@huggingface/quality-assessment-of-datasets-loading?collection=@huggingface/datasets), but not in the `Extraction protocol for file at xxx is not implemented yet` error, so I'm not sure if they would break now or not.\r\n\r\nMaybe: first try to find an extension, and if none, try to remove the `?...` part and retry to find the extension.\r\n\r\nBy the way, here is the list of URLs for errors of this type, with a '?' in the URL:\r\n\r\n```\r\nhttps://dl.orangedox.com/WyaCpL?dl=1\r\nhttps://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download\r\nhttps://drive.google.com/u/0/uc?id=1-CaP3xHgZxOGjQ3pXC5tr9YnIajmel-t&export=download\r\nhttps://drive.google.com/u/0/uc?id=11EBGHMAswT5JDO60xh7gnZfYjpMQs7h7&export=download\r\nhttps://drive.google.com/u/0/uc?id=13JCCr-IjZK7uhbLXeufptr_AxvsKinVl&export=download\r\nhttps://drive.google.com/u/0/uc?id=13ZyFc2qepAYSg9WIFaeJ9y402gblsl2e&export=download\r\nhttps://drive.google.com/u/0/uc?id=15auwrFAlq52JJ61u7eSfnhT9rZtI5sjk&export=download\r\nhttps://drive.google.com/u/0/uc?id=16OgJ_OrfzUF_i3ftLjFn9kpcyoi7UJeO&export=download\r\nhttps://drive.google.com/u/0/uc?id=1BFYF05rx-DK9Eb5hgoIgd6EcB8zOI-zu&export=download\r\nhttps://drive.google.com/u/0/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download\r\nhttps://drive.google.com/u/0/uc?id=1H7FphKVVCYoH49sUXl79CuztEfJLaKoF&export=download\r\nhttps://drive.google.com/u/0/uc?id=1NAeuWLgYBzLwU5jCdkrtj4_PRUocuvlb&export=download\r\nhttps://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\r\nhttps://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/\r\nhttps://drive.google.com/u/0/uc?id=1R1jR4DcH2UEaM1ZwDSRHdfTGvkCNu6NW&export=download\r\nhttps://drive.google.com/u/0/uc?id=1hDHeoFIfQzJec1NgZNXh3CTNbchiIvuG&export=download\r\nhttps://drive.google.com/u/0/uc?id=1wxwqnWGRzwvc_-ugRoFX8BPgpO3Q7sch&export=download\r\nhttps://drive.google.com/u/0/uc?id=1ydsOTvBZXKqcRvXawOuePrJ99slOEbkk&export=download\r\nhttps://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ\r\nhttps://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k\r\nhttps://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0\r\nhttps://drive.google.com/uc?export=download&id=0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU\r\nhttps://drive.google.com/uc?export=download&id=1-w-0uqaC6hnRn1F_3XqJEvi09zlcTIhX\r\nhttps://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff\r\nhttps://drive.google.com/uc?export=download&id=17FGi8KI9N9SuGe7elM8qU8_3fx4sfgTr\r\nhttps://drive.google.com/uc?export=download&id=1AHUm1-_V9GCtGuDcc8XrMUCJE8B-HHoL\r\nhttps://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U\r\nhttps://drive.google.com/uc?export=download&id=1Ev4RqWcPsLI9rgOGAKh-_dFKqcEZ1u-G\r\nhttps://drive.google.com/uc?export=download&id=1GTHUJxxmjLmG2lnF9dwRgIDRFZaOY3-F\r\nhttps://drive.google.com/uc?export=download&id=1GcUN6mytEcOMBBOvjJOQzBmEkc-LdgQg\r\nhttps://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa\r\nhttps://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P\r\nhttps://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil\r\nhttps://drive.google.com/uc?export=download&id=1M1M5yIOyjKWGprc3LUeVVwxgKXxgpqxm\r\nhttps://drive.google.com/uc?export=download&id=1Nug7-Sri50mkJL4GrWw6C2ZIbfeU-6Am\r\nhttps://drive.google.com/uc?export=download&id=1PGa8j1_IqxiGTc3SU6NMB38sAzxCPS34\r\nhttps://drive.google.com/uc?export=download&id=1QsV8C5EPJrQl37mwva_5-IJOrCaOi2tH\r\nhttps://drive.google.com/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ\r\nhttps://drive.google.com/uc?export=download&id=1TuWH7uwu6V90QWmZn25qhou1rm97Egmn\r\nhttps://drive.google.com/uc?export=download&id=1U7WdBpd9kJ85S7BbBhWUSiy9NnXrKdO6\r\nhttps://drive.google.com/uc?export=download&id=1USoQ8lJgN8kAWnUnRrupMGrPMLlDVqlV\r\nhttps://drive.google.com/uc?export=download&id=1Uit4Og1pk-br_0UJIO5sdhApyhTuHzqo\r\nhttps://drive.google.com/uc?export=download&id=1Z2ty5hU0tIGRZRDlFQZLO7b5vijRfvo0\r\nhttps://drive.google.com/uc?export=download&id=1ZyFGufe4puX3vjGPbp4xg9Hca3Gwq22g\r\nhttps://drive.google.com/uc?export=download&id=1ZzlIQvw1KNBG97QQCfdatvVrrbeLaM1u\r\nhttps://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0\r\nhttps://drive.google.com/uc?export=download&id=1__EjA6oZsgXQpggPm-h54jZu3kP6Y6zu\r\nhttps://drive.google.com/uc?export=download&id=1aHPVfC5TrlnUjehtagVZoDfq4VccgaNT\r\nhttps://drive.google.com/uc?export=download&id=1cqu_YAgvlyVSzzjcUyP1Cz7q0k8Pw7vN\r\nhttps://drive.google.com/uc?export=download&id=1dUIqVwvoZAtbX_-z5axCoe97XNcFo1No\r\nhttps://drive.google.com/uc?export=download&id=1eTtRs5cUlBP5dXsx-FTAlmXuB6JQi2qj\r\nhttps://drive.google.com/uc?export=download&id=1fUR3MqJ8jTMka6owA0S-Fe6aHmiophc_\r\nhttps://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF\r\nhttps://drive.google.com/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx\r\nhttps://drive.google.com/uc?export=download&id=1meSNZHxd_0TZLKCRCYGN-Ke3IA5c1qOE\r\nhttps://drive.google.com/uc?export=download&id=1okwGJiOZmTpNRNgJLCnjFF4Q0H1z4l6_\r\nhttps://drive.google.com/uc?export=download&id=1phryJg4FjCFkn0mSCqIOP2-FscAeKGV0\r\nhttps://drive.google.com/uc?export=download&id=1s8NSFT4Kz0caKZ4VybPNzt88F8ZanprY\r\nhttps://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\r\nhttps://drive.google.com/uc?export=download&id=1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA\r\nhttps://drive.google.com/uc?id=12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X\r\nhttps://drive.google.com/uc?id=1PGH5H_oW7wUvMw_5xaXvbEN7DFll-wDX\r\nhttps://github.com/MaazAmjad/Datasets-for-Urdu-news/blob/master/Urdu%20Fake%20News%20Dataset.zip?raw=true\r\nhttps://github.com/TevenLeScao/glucose/blob/master/GLUCOSE_training_data.zip?raw=true\r\nhttps://github.com/TevenLeScao/what-time-is-it/blob/master/gutenberg_time_phrases.zip?raw=true\r\nhttps://github.com/aviaefrat/cryptonite/blob/main/data/cryptonite-official-split.zip?raw=true\r\nhttps://github.com/facebookresearch/Imppres/blob/master/dataset/IMPPRES.zip?raw=true\r\nhttps://github.com/ljos/navnkjenner/blob/master/data/bokmaal/no_bokmaal-ud-train.bioes?raw=true\r\nhttps://github.com/ljos/navnkjenner/blob/master/data/nynorsk/no_nynorsk-ud-train.bioes?raw=true\r\nhttps://github.com/ljos/navnkjenner/blob/master/data/samnorsk/no_samnorsk-ud-train.bioes?raw=true\r\nhttps://github.com/mirfan899/Urdu/blob/master/sentiment/imdb_urdu_reviews.csv.tar.gz?raw=true\r\nhttps://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew/blob/master/data/morph_train.tsv?raw=true\r\nhttps://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew/blob/master/data/token_train.tsv?raw=true\r\nhttps://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11858/00-097C-0000-0023-625F-0/hindencorp05.plaintext.gz?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/299/nchlt_afrikaans_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/312/nchlt_isixhosa_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/319/nchlt_isizulu_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/328/nchlt_sepedi_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/334/nchlt_sesotho_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/341/nchlt_setswana_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/346/nchlt_siswati_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz?dl=1\r\nhttps://zenodo.org/record/1043504/files/corpus-webis-tldr-17.zip?download=1\r\nhttps://zenodo.org/record/1489920/files/articles-training-byarticle-20181122.zip?download=1\r\nhttps://zenodo.org/record/1489920/files/articles-training-bypublisher-20181122.zip?download=1\r\nhttps://zenodo.org/record/2787612/files/SICK.zip?download=1\r\nhttps://zenodo.org/record/3553423/files/Swahili%20data.zip?download=1\r\nhttps://zenodo.org/record/3707949/files/tapaco_v1.0.zip?download=1\r\nhttps://zenodo.org/record/4300294/files/train.csv?download=1\r\n```\r\n\r\n", "Hi @severo, I just saw your comment. Thank you.\r\n\r\nFinally I just swapped the 2 parsings: first I extract extension and then I remove query parameters. 😉 ", "OK :) Maybe we should add some unit tests to ensure we improve the detection without regressions (it's Friday afternoon, I trust the unit tests more than my analysis of the code)", "Great! For the tests, I think we should also add some URLs in the form: `http://ufal.ms.mff.cuni.cz/umc/005-en-ur/download.php?f=umc005-corpus.zip` to be sure they are still correctly detected." ]

"2021-10-01T12:05:34Z"

"2021-10-04T08:48:13Z"

"2021-10-04T08:48:13Z"

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[ "Hi @yjernite I'm new but wanted to contribute. Has anyone already taken this problem and do you think it is suitable for newbies?", "Hi @tenjjin! This dataset is still up for grabs! Here's the link with the guide to add it. You should play around with the library first (download and look at a few datasets), then follow the steps here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md", "Closed by #1399 " ]

"2020-11-12T19:55:46Z"

"2020-12-10T21:47:33Z"

"2020-12-10T21:47:33Z"

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[ "Hi @dan-the-meme-man, thanks for reporting.\r\n\r\nWe are investigating a similar issue but with Beam+Dataflow (instead of Beam+Flink): \r\n- #4525\r\n\r\nIn order to go deeper into the root cause, we need as much information as possible: logs from the main process + logs from the workers are very informative.\r\n\r\nIn the case of the issue with Beam+Dataflow, the logs from the workers report an out of memory issue.", "As I continued working on this today, I came to suspect that it is in fact an out of memory issue - I have a few more notebooks that I've left running, and if they produce the same error, I will try to get the logs. In the meantime, if there's any chance that there is a repo out there with those three languages already as .arrow files, or if you know about how much memory would be needed to actually download those sets, please let me know!" ]

"2022-06-18T23:36:45Z"

"2022-06-21T00:38:20Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008505 / 0.011353 (-0.002848) | 0.004445 / 0.011008 (-0.006563) | 0.102197 / 0.038508 (0.063689) | 0.029886 / 0.023109 (0.006776) | 0.305387 / 0.275898 (0.029489) | 0.355986 / 0.323480 (0.032507) | 0.006814 / 0.007986 (-0.001172) | 0.003298 / 0.004328 (-0.001030) | 0.079204 / 0.004250 (0.074954) | 0.035618 / 0.037052 (-0.001434) | 0.320430 / 0.258489 (0.061941) | 0.353330 / 0.293841 (0.059490) | 0.033280 / 0.128546 (-0.095266) | 0.011300 / 0.075646 (-0.064347) | 0.324627 / 0.419271 (-0.094644) | 0.040405 / 0.043533 (-0.003128) | 0.308760 / 0.255139 (0.053621) | 0.331885 / 0.283200 (0.048685) | 0.084605 / 0.141683 (-0.057077) | 1.576598 / 1.452155 (0.124443) | 1.530694 / 1.492716 (0.037977) |\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) | 0.191142 / 0.018006 (0.173136) | 0.404042 / 0.000490 (0.403552) | 0.001185 / 0.000200 (0.000985) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022889 / 0.037411 (-0.014523) | 0.095862 / 0.014526 (0.081336) | 0.104382 / 0.176557 (-0.072175) | 0.139407 / 0.737135 (-0.597728) | 0.106813 / 0.296338 (-0.189525) |\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) | 0.419083 / 0.215209 (0.203874) | 4.188702 / 2.077655 (2.111047) | 1.897854 / 1.504120 (0.393734) | 1.689544 / 1.541195 (0.148350) | 1.714032 / 1.468490 (0.245542) | 0.695541 / 4.584777 (-3.889236) | 3.370584 / 3.745712 (-0.375128) | 3.205549 / 5.269862 (-2.064313) | 1.641202 / 4.565676 (-2.924474) | 0.081849 / 0.424275 (-0.342426) | 0.012043 / 0.007607 (0.004436) | 0.529618 / 0.226044 (0.303574) | 5.314167 / 2.268929 (3.045238) | 2.357271 / 55.444624 (-53.087353) | 1.979684 / 6.876477 (-4.896793) | 2.030057 / 2.142072 (-0.112015) | 0.813013 / 4.805227 (-3.992214) | 0.150165 / 6.500664 (-6.350499) | 0.064595 / 0.075469 (-0.010874) |\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) | 1.237824 / 1.841788 (-0.603964) | 13.552178 / 8.074308 (5.477870) | 14.089433 / 10.191392 (3.898041) | 0.149325 / 0.680424 (-0.531099) | 0.028543 / 0.534201 (-0.505658) | 0.396848 / 0.579283 (-0.182435) | 0.396230 / 0.434364 (-0.038134) | 0.466317 / 0.540337 (-0.074021) | 0.539579 / 1.386936 (-0.847357) |\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) | 0.006224 / 0.011353 (-0.005128) | 0.004429 / 0.011008 (-0.006579) | 0.075740 / 0.038508 (0.037232) | 0.026717 / 0.023109 (0.003608) | 0.341685 / 0.275898 (0.065787) | 0.383671 / 0.323480 (0.060191) | 0.004682 / 0.007986 (-0.003304) | 0.004681 / 0.004328 (0.000352) | 0.076638 / 0.004250 (0.072387) | 0.034577 / 0.037052 (-0.002476) | 0.341160 / 0.258489 (0.082671) | 0.407590 / 0.293841 (0.113749) | 0.031121 / 0.128546 (-0.097425) | 0.011479 / 0.075646 (-0.064167) | 0.085299 / 0.419271 (-0.333973) | 0.042005 / 0.043533 (-0.001528) | 0.339682 / 0.255139 (0.084543) | 0.377669 / 0.283200 (0.094469) | 0.087751 / 0.141683 (-0.053932) | 1.523910 / 1.452155 (0.071756) | 1.607487 / 1.492716 (0.114771) |\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) | 0.225605 / 0.018006 (0.207599) | 0.395851 / 0.000490 (0.395361) | 0.004404 / 0.000200 (0.004204) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024489 / 0.037411 (-0.012922) | 0.099813 / 0.014526 (0.085287) | 0.107392 / 0.176557 (-0.069165) | 0.139567 / 0.737135 (-0.597568) | 0.110080 / 0.296338 (-0.186258) |\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) | 0.449051 / 0.215209 (0.233841) | 4.463098 / 2.077655 (2.385443) | 2.122548 / 1.504120 (0.618428) | 1.913863 / 1.541195 (0.372669) | 1.963988 / 1.468490 (0.495498) | 0.698442 / 4.584777 (-3.886335) | 3.330425 / 3.745712 (-0.415287) | 1.867843 / 5.269862 (-3.402019) | 1.163740 / 4.565676 (-3.401937) | 0.083209 / 0.424275 (-0.341066) | 0.012594 / 0.007607 (0.004987) | 0.547074 / 0.226044 (0.321030) | 5.474779 / 2.268929 (3.205851) | 2.548025 / 55.444624 (-52.896599) | 2.202435 / 6.876477 (-4.674041) | 2.220330 / 2.142072 (0.078257) | 0.810104 / 4.805227 (-3.995124) | 0.151141 / 6.500664 (-6.349523) | 0.066204 / 0.075469 (-0.009265) |\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) | 1.272075 / 1.841788 (-0.569712) | 13.749523 / 8.074308 (5.675215) | 14.270974 / 10.191392 (4.079582) | 0.141285 / 0.680424 (-0.539139) | 0.016526 / 0.534201 (-0.517675) | 0.393175 / 0.579283 (-0.186109) | 0.391577 / 0.434364 (-0.042787) | 0.492824 / 0.540337 (-0.047513) | 0.580069 / 1.386936 (-0.806867) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cda14136c9f79c763c17d49b77eabfb233fbb35 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008901 / 0.011353 (-0.002452) | 0.005017 / 0.011008 (-0.005991) | 0.099340 / 0.038508 (0.060832) | 0.034218 / 0.023109 (0.011109) | 0.295927 / 0.275898 (0.020029) | 0.330087 / 0.323480 (0.006607) | 0.008041 / 0.007986 (0.000056) | 0.005013 / 0.004328 (0.000685) | 0.074255 / 0.004250 (0.070004) | 0.049634 / 0.037052 (0.012582) | 0.299972 / 0.258489 (0.041483) | 0.349879 / 0.293841 (0.056038) | 0.038500 / 0.128546 (-0.090047) | 0.011980 / 0.075646 (-0.063666) | 0.332408 / 0.419271 (-0.086863) | 0.048385 / 0.043533 (0.004852) | 0.300393 / 0.255139 (0.045254) | 0.316972 / 0.283200 (0.033772) | 0.101674 / 0.141683 (-0.040009) | 1.424300 / 1.452155 (-0.027854) | 1.520658 / 1.492716 (0.027942) |\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) | 0.270084 / 0.018006 (0.252078) | 0.538612 / 0.000490 (0.538123) | 0.004439 / 0.000200 (0.004240) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026841 / 0.037411 (-0.010570) | 0.106454 / 0.014526 (0.091928) | 0.118371 / 0.176557 (-0.058186) | 0.155545 / 0.737135 (-0.581590) | 0.125119 / 0.296338 (-0.171220) |\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) | 0.395794 / 0.215209 (0.180585) | 3.958195 / 2.077655 (1.880540) | 1.789010 / 1.504120 (0.284890) | 1.601380 / 1.541195 (0.060186) | 1.641062 / 1.468490 (0.172572) | 0.679547 / 4.584777 (-3.905230) | 3.778018 / 3.745712 (0.032306) | 2.101232 / 5.269862 (-3.168630) | 1.463932 / 4.565676 (-3.101745) | 0.083639 / 0.424275 (-0.340636) | 0.012339 / 0.007607 (0.004732) | 0.498708 / 0.226044 (0.272663) | 4.995178 / 2.268929 (2.726249) | 2.272650 / 55.444624 (-53.171975) | 1.907879 / 6.876477 (-4.968598) | 2.012666 / 2.142072 (-0.129407) | 0.829564 / 4.805227 (-3.975663) | 0.165049 / 6.500664 (-6.335615) | 0.062291 / 0.075469 (-0.013178) |\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) | 1.193977 / 1.841788 (-0.647811) | 14.816939 / 8.074308 (6.742631) | 14.369729 / 10.191392 (4.178337) | 0.156339 / 0.680424 (-0.524084) | 0.029151 / 0.534201 (-0.505050) | 0.449362 / 0.579283 (-0.129921) | 0.451895 / 0.434364 (0.017531) | 0.520324 / 0.540337 (-0.020013) | 0.610716 / 1.386936 (-0.776220) |\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) | 0.007145 / 0.011353 (-0.004207) | 0.005299 / 0.011008 (-0.005710) | 0.074216 / 0.038508 (0.035708) | 0.033015 / 0.023109 (0.009906) | 0.337117 / 0.275898 (0.061219) | 0.367161 / 0.323480 (0.043682) | 0.005898 / 0.007986 (-0.002088) | 0.005283 / 0.004328 (0.000955) | 0.073795 / 0.004250 (0.069544) | 0.049253 / 0.037052 (0.012201) | 0.343327 / 0.258489 (0.084838) | 0.396417 / 0.293841 (0.102576) | 0.037162 / 0.128546 (-0.091384) | 0.012456 / 0.075646 (-0.063191) | 0.086668 / 0.419271 (-0.332604) | 0.049937 / 0.043533 (0.006404) | 0.335138 / 0.255139 (0.079999) | 0.358111 / 0.283200 (0.074912) | 0.107328 / 0.141683 (-0.034355) | 1.482290 / 1.452155 (0.030135) | 1.557872 / 1.492716 (0.065156) |\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) | 0.343759 / 0.018006 (0.325752) | 0.542697 / 0.000490 (0.542207) | 0.025943 / 0.000200 (0.025743) | 0.000264 / 0.000054 (0.000209) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028469 / 0.037411 (-0.008943) | 0.108620 / 0.014526 (0.094094) | 0.123667 / 0.176557 (-0.052890) | 0.168829 / 0.737135 (-0.568306) | 0.125875 / 0.296338 (-0.170464) |\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) | 0.424640 / 0.215209 (0.209431) | 4.227611 / 2.077655 (2.149956) | 2.003605 / 1.504120 (0.499486) | 1.810696 / 1.541195 (0.269501) | 1.882700 / 1.468490 (0.414210) | 0.701361 / 4.584777 (-3.883416) | 3.808054 / 3.745712 (0.062342) | 3.234896 / 5.269862 (-2.034966) | 1.872195 / 4.565676 (-2.693482) | 0.088102 / 0.424275 (-0.336173) | 0.012810 / 0.007607 (0.005203) | 0.551855 / 0.226044 (0.325810) | 5.245654 / 2.268929 (2.976725) | 2.557123 / 55.444624 (-52.887502) | 2.238897 / 6.876477 (-4.637580) | 2.256260 / 2.142072 (0.114187) | 0.849804 / 4.805227 (-3.955424) | 0.170557 / 6.500664 (-6.330107) | 0.064718 / 0.075469 (-0.010751) |\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) | 1.271701 / 1.841788 (-0.570087) | 14.925010 / 8.074308 (6.850702) | 14.966948 / 10.191392 (4.775556) | 0.162966 / 0.680424 (-0.517458) | 0.017618 / 0.534201 (-0.516583) | 0.433484 / 0.579283 (-0.145799) | 0.430047 / 0.434364 (-0.004316) | 0.537356 / 0.540337 (-0.002981) | 0.639237 / 1.386936 (-0.747699) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aba888cb4d225b1a05596f52258a079bda98df70 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012054 / 0.011353 (0.000702) | 0.005923 / 0.011008 (-0.005085) | 0.129531 / 0.038508 (0.091023) | 0.036283 / 0.023109 (0.013173) | 0.374406 / 0.275898 (0.098508) | 0.452538 / 0.323480 (0.129058) | 0.009419 / 0.007986 (0.001434) | 0.004783 / 0.004328 (0.000454) | 0.095292 / 0.004250 (0.091042) | 0.041290 / 0.037052 (0.004238) | 0.403940 / 0.258489 (0.145451) | 0.443091 / 0.293841 (0.149250) | 0.054635 / 0.128546 (-0.073911) | 0.019062 / 0.075646 (-0.056584) | 0.417053 / 0.419271 (-0.002218) | 0.060865 / 0.043533 (0.017332) | 0.378535 / 0.255139 (0.123396) | 0.401036 / 0.283200 (0.117836) | 0.122959 / 0.141683 (-0.018724) | 1.768517 / 1.452155 (0.316362) | 1.794700 / 1.492716 (0.301984) |\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) | 0.246529 / 0.018006 (0.228523) | 0.576887 / 0.000490 (0.576397) | 0.005031 / 0.000200 (0.004831) | 0.000125 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027363 / 0.037411 (-0.010049) | 0.119037 / 0.014526 (0.104511) | 0.148109 / 0.176557 (-0.028447) | 0.179370 / 0.737135 (-0.557765) | 0.145105 / 0.296338 (-0.151234) |\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) | 0.588748 / 0.215209 (0.373539) | 5.934433 / 2.077655 (3.856778) | 2.549811 / 1.504120 (1.045691) | 2.234616 / 1.541195 (0.693421) | 2.268002 / 1.468490 (0.799512) | 1.154643 / 4.584777 (-3.430134) | 5.333935 / 3.745712 (1.588223) | 2.971065 / 5.269862 (-2.298796) | 2.131427 / 4.565676 (-2.434250) | 0.127737 / 0.424275 (-0.296538) | 0.014699 / 0.007607 (0.007091) | 0.735160 / 0.226044 (0.509115) | 7.403838 / 2.268929 (5.134909) | 3.298169 / 55.444624 (-52.146455) | 2.661285 / 6.876477 (-4.215192) | 2.688877 / 2.142072 (0.546805) | 1.344110 / 4.805227 (-3.461118) | 0.242016 / 6.500664 (-6.258648) | 0.077418 / 0.075469 (0.001948) |\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) | 1.566426 / 1.841788 (-0.275362) | 17.144308 / 8.074308 (9.070000) | 19.360598 / 10.191392 (9.169206) | 0.238554 / 0.680424 (-0.441870) | 0.044946 / 0.534201 (-0.489255) | 0.554183 / 0.579283 (-0.025100) | 0.630175 / 0.434364 (0.195811) | 0.630319 / 0.540337 (0.089982) | 0.745060 / 1.386936 (-0.641876) |\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) | 0.009255 / 0.011353 (-0.002098) | 0.006951 / 0.011008 (-0.004057) | 0.092021 / 0.038508 (0.053513) | 0.035588 / 0.023109 (0.012479) | 0.415564 / 0.275898 (0.139666) | 0.446393 / 0.323480 (0.122913) | 0.006532 / 0.007986 (-0.001453) | 0.005099 / 0.004328 (0.000771) | 0.094801 / 0.004250 (0.090550) | 0.044926 / 0.037052 (0.007874) | 0.439125 / 0.258489 (0.180636) | 0.473004 / 0.293841 (0.179163) | 0.057025 / 0.128546 (-0.071522) | 0.018711 / 0.075646 (-0.056935) | 0.110844 / 0.419271 (-0.308427) | 0.058347 / 0.043533 (0.014814) | 0.435721 / 0.255139 (0.180583) | 0.434624 / 0.283200 (0.151424) | 0.114505 / 0.141683 (-0.027178) | 1.722379 / 1.452155 (0.270225) | 1.775836 / 1.492716 (0.283120) |\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) | 0.275893 / 0.018006 (0.257887) | 0.552590 / 0.000490 (0.552100) | 0.007919 / 0.000200 (0.007719) | 0.000122 / 0.000054 (0.000068) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030003 / 0.037411 (-0.007408) | 0.130145 / 0.014526 (0.115619) | 0.131878 / 0.176557 (-0.044678) | 0.194693 / 0.737135 (-0.542442) | 0.137689 / 0.296338 (-0.158650) |\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) | 0.619591 / 0.215209 (0.404382) | 6.324095 / 2.077655 (4.246441) | 2.756563 / 1.504120 (1.252444) | 2.384744 / 1.541195 (0.843549) | 2.450407 / 1.468490 (0.981917) | 1.235391 / 4.584777 (-3.349386) | 5.535383 / 3.745712 (1.789671) | 4.831927 / 5.269862 (-0.437934) | 2.757158 / 4.565676 (-1.808519) | 0.133980 / 0.424275 (-0.290295) | 0.014965 / 0.007607 (0.007358) | 0.731423 / 0.226044 (0.505379) | 7.401850 / 2.268929 (5.132921) | 3.346585 / 55.444624 (-52.098039) | 2.705523 / 6.876477 (-4.170953) | 2.637397 / 2.142072 (0.495324) | 1.347745 / 4.805227 (-3.457482) | 0.248658 / 6.500664 (-6.252006) | 0.077427 / 0.075469 (0.001958) |\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) | 1.520860 / 1.841788 (-0.320928) | 17.153000 / 8.074308 (9.078692) | 19.051393 / 10.191392 (8.860001) | 0.236840 / 0.680424 (-0.443584) | 0.026638 / 0.534201 (-0.507563) | 0.518417 / 0.579283 (-0.060866) | 0.607555 / 0.434364 (0.173191) | 0.637381 / 0.540337 (0.097044) | 0.767109 / 1.386936 (-0.619827) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ee291f2c5e68a782c82f916e250d470a7e285e7 \"CML watermark\")\n", "Great, I merged it. Thanks for the review :)", "<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) | 0.006711 / 0.011353 (-0.004641) | 0.004472 / 0.011008 (-0.006536) | 0.099581 / 0.038508 (0.061073) | 0.028036 / 0.023109 (0.004927) | 0.301197 / 0.275898 (0.025298) | 0.339341 / 0.323480 (0.015861) | 0.005107 / 0.007986 (-0.002879) | 0.003312 / 0.004328 (-0.001017) | 0.075823 / 0.004250 (0.071573) | 0.040861 / 0.037052 (0.003809) | 0.303407 / 0.258489 (0.044918) | 0.350717 / 0.293841 (0.056876) | 0.031657 / 0.128546 (-0.096889) | 0.011627 / 0.075646 (-0.064020) | 0.325465 / 0.419271 (-0.093806) | 0.052671 / 0.043533 (0.009138) | 0.301953 / 0.255139 (0.046814) | 0.327164 / 0.283200 (0.043964) | 0.091264 / 0.141683 (-0.050419) | 1.508947 / 1.452155 (0.056792) | 1.605685 / 1.492716 (0.112968) |\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) | 0.202977 / 0.018006 (0.184971) | 0.400602 / 0.000490 (0.400112) | 0.003253 / 0.000200 (0.003053) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022453 / 0.037411 (-0.014958) | 0.098633 / 0.014526 (0.084107) | 0.105996 / 0.176557 (-0.070561) | 0.162428 / 0.737135 (-0.574707) | 0.107139 / 0.296338 (-0.189199) |\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) | 0.453061 / 0.215209 (0.237852) | 4.530844 / 2.077655 (2.453190) | 2.286394 / 1.504120 (0.782274) | 2.076479 / 1.541195 (0.535284) | 2.143730 / 1.468490 (0.675240) | 0.702540 / 4.584777 (-3.882237) | 3.442688 / 3.745712 (-0.303024) | 1.874429 / 5.269862 (-3.395433) | 1.172331 / 4.565676 (-3.393346) | 0.083643 / 0.424275 (-0.340632) | 0.012519 / 0.007607 (0.004911) | 0.556859 / 0.226044 (0.330814) | 5.582843 / 2.268929 (3.313915) | 2.753734 / 55.444624 (-52.690890) | 2.415771 / 6.876477 (-4.460705) | 2.531428 / 2.142072 (0.389356) | 0.813005 / 4.805227 (-3.992222) | 0.153322 / 6.500664 (-6.347343) | 0.068061 / 0.075469 (-0.007408) |\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) | 1.180481 / 1.841788 (-0.661306) | 13.623933 / 8.074308 (5.549625) | 14.431288 / 10.191392 (4.239896) | 0.127580 / 0.680424 (-0.552844) | 0.016714 / 0.534201 (-0.517487) | 0.394236 / 0.579283 (-0.185047) | 0.381718 / 0.434364 (-0.052646) | 0.486749 / 0.540337 (-0.053589) | 0.565939 / 1.386936 (-0.820997) |\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) | 0.006720 / 0.011353 (-0.004633) | 0.004518 / 0.011008 (-0.006491) | 0.076819 / 0.038508 (0.038311) | 0.027272 / 0.023109 (0.004163) | 0.340890 / 0.275898 (0.064992) | 0.381435 / 0.323480 (0.057955) | 0.004980 / 0.007986 (-0.003005) | 0.003382 / 0.004328 (-0.000947) | 0.076368 / 0.004250 (0.072117) | 0.037365 / 0.037052 (0.000313) | 0.341484 / 0.258489 (0.082995) | 0.388917 / 0.293841 (0.095076) | 0.032004 / 0.128546 (-0.096543) | 0.011612 / 0.075646 (-0.064034) | 0.084929 / 0.419271 (-0.334342) | 0.041861 / 0.043533 (-0.001671) | 0.350392 / 0.255139 (0.095253) | 0.369745 / 0.283200 (0.086546) | 0.088301 / 0.141683 (-0.053382) | 1.587296 / 1.452155 (0.135141) | 1.629761 / 1.492716 (0.137045) |\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) | 0.174825 / 0.018006 (0.156818) | 0.414371 / 0.000490 (0.413881) | 0.001595 / 0.000200 (0.001395) | 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) | 0.025403 / 0.037411 (-0.012009) | 0.099593 / 0.014526 (0.085067) | 0.108819 / 0.176557 (-0.067738) | 0.161613 / 0.737135 (-0.575523) | 0.112302 / 0.296338 (-0.184037) |\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) | 0.439234 / 0.215209 (0.224024) | 4.389073 / 2.077655 (2.311418) | 2.063215 / 1.504120 (0.559095) | 1.852550 / 1.541195 (0.311356) | 1.920014 / 1.468490 (0.451524) | 0.710255 / 4.584777 (-3.874522) | 3.430549 / 3.745712 (-0.315164) | 1.886072 / 5.269862 (-3.383790) | 1.177490 / 4.565676 (-3.388186) | 0.084877 / 0.424275 (-0.339398) | 0.012894 / 0.007607 (0.005287) | 0.544950 / 0.226044 (0.318906) | 5.467347 / 2.268929 (3.198419) | 2.508169 / 55.444624 (-52.936455) | 2.167756 / 6.876477 (-4.708721) | 2.212817 / 2.142072 (0.070744) | 0.824762 / 4.805227 (-3.980465) | 0.154387 / 6.500664 (-6.346277) | 0.068535 / 0.075469 (-0.006934) |\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) | 1.284165 / 1.841788 (-0.557623) | 14.153006 / 8.074308 (6.078697) | 14.152569 / 10.191392 (3.961177) | 0.130083 / 0.680424 (-0.550341) | 0.016556 / 0.534201 (-0.517645) | 0.383828 / 0.579283 (-0.195455) | 0.388241 / 0.434364 (-0.046123) | 0.477982 / 0.540337 (-0.062355) | 0.565583 / 1.386936 (-0.821353) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1e7442d34a059ff377437381542cc762feab057 \"CML watermark\")\n" ]

"2023-03-09T08:46:01Z"

"2023-03-09T15:39:00Z"

"2023-03-09T15:31:59Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the feedback!\r\n\r\nI agree that adding it to `load_hub.mdx` is probably a bit too specific, especially for beginners reading the tutorials. Since this clarification is closely related to loading from the Hub (the only difference being the presence/absence of a loading script), I think it makes the most sense to keep it somewhere in `loading.mdx`. What do you think about adding a Warning in Loading >>> Hugging Face Hub that explains the difference between relative/absolute paths when there is a script?", "What about updating the section about \"manual download\" ? I think it goes there no ?\r\n\r\nhttps://huggingface.co/docs/datasets/v2.4.0/en/loading#manual-download", "Updated the manual download section :)", "Thanks ! Pinging @albertvillanova to review this change, and then I think we're good to merge" ]

"2022-07-29T23:32:21Z"

"2022-08-25T18:36:45Z"

"2022-08-25T18:34:23Z"

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

"2022-06-02T06:45:04Z"

"2022-06-02T09:32:56Z"

"2022-06-02T09:24:27Z"

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

"2022-03-19T18:43:42Z"

"2022-04-12T14:48:23Z"

"2022-04-12T14:41:58Z"

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[ "Note that torchaudio is maybe less practical to use for TF or JAX users.\r\nThis is not in the scope of this PR, but in the future if we manage to find a way to let the user control the decoding it would be nice", "> Note that torchaudio is maybe less practical to use for TF or JAX users. This is not in the scope of this PR, but in the future if we manage to find a way to let the user control the decoding it would be nice\r\n\r\n@lhoestq so maybe don't do this PR? :) if it doesn't work anyway with an opened file, only with path", "Yes as discussed offline there seems to be issues with torchaudio on opened files. Feel free to close this PR if it's better to stick with soundfile because of that", "We should be able to remove torchaudio, which has torch as a hard dependency, soon and use only soundfile for decoding: https://github.com/bastibe/python-soundfile/issues/252#issuecomment-1000246773 (opus + mp3 support is on the way)." ]

"2022-02-02T15:23:14Z"

"2022-02-04T15:29:38Z"

"2022-02-04T15:29:38Z"

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