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"2024-04-26T14:11:44"

"2024-04-26T14:11:44"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6844). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@Wauplin This PR is indeed not needed as explained in https://github.com/huggingface/datasets/issues/6843#issuecomment-2079630389. \r\n\r\nSo, I'm closing it." ]

"2024-04-26T14:09:04"

"2024-04-26T15:37:42"

"2024-04-26T15:37:42"

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[ "Thanks for reporting! I've opened a PR with a fix.", "Thanks, @mariosasko! Related question (although I guess this is a feature request): could we have some kind of exponential back-off for these retries? Here's my reasoning:\r\n- If a one-time accidental error happens, you should retry immediately and will succeed immediately.\r\n- If the Hub has a small outage on the order of minutes, you don't want to retry on the order of hours. \r\n- If the Hub has a prologned outage of several hours, we don't want to keep retrying on the order of minutes.\r\n\r\nThere actually already exists an implementation for (clipped) exponential backoff in the HuggingFace suite ([here](https://github.com/huggingface/huggingface_hub/blob/61b156a4f2e5fe1a492ed8712b26803e2122bde0/src/huggingface_hub/utils/_http.py#L306)), but I don't think it is used here.\r\n\r\nThe requirements are basically that you have an initial minimum waiting time and a maximum waiting time, and with each retry, the waiting time is doubled. We don't want to overload your servers with needless retries, especially when they're down :sweat_smile:", "Oh, I've just remembered that we added retries to the `HfFileSystem` in `huggingface_hub` 0.21.0 (see [this](https://github.com/huggingface/huggingface_hub/blob/61b156a4f2e5fe1a492ed8712b26803e2122bde0/src/huggingface_hub/hf_file_system.py#L703)), so I'll close the linked PR as we don't want to retry the retries :).\r\n\r\nI agree with the exponential backoff suggestion, so I'll open another PR.", "@mariosasko The call you linked indeed points to the implementation I linked in my previous comment, yes, but it has no configurability. Arguably, you want to have this hidden backoff under the hood that catches small network disturbances on the time scale of seconds -- perhaps even with hardcoded limits as is the case currently -- but you also still want to have a separate backoff on top of that with the configurability as suggested by @lhoestq in [the comment I linked](https://github.com/huggingface/datasets/issues/6172#issuecomment-1794876229).\r\n\r\nMy particular use-case is that I'm streaming a dataset while training on a university cluster with a very long scheduling queue. This means that when the backoff runs out of retries (which happens in under 30 seconds with the call you linked), I lose my spot on the cluster and have to queue for a whole day or more. Ideally, I should be able to specify that I want to retry for 2 to 3 hours but with more and more time between requests, so that I can smooth over hours-long outages without a setback of days." ]

"2024-04-26T10:00:43"

"2024-04-26T16:57:31"

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"2024-04-26T00:14:16"

"2024-04-26T00:14:16"

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[ "Hi! I've opened a [PR](https://huggingface.co/datasets/GEM/wiki_auto_asset_turk/discussions/5) with a fix. While waiting for it to be merged, you can load the dataset from the PR branch with `datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\")`", "Thanks Mario. Still getting the same issue though with the suggested fix\r\n\r\n#cat gem_sari.py\r\nimport datasets\r\nprint (datasets.__version__)\r\ndataset =datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\")\r\n\r\nEnd up with \r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/load.py\", line 2582, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1767, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1100, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1565, in _prepare_split\r\n split_info = self.info.splits[split_generator.name]\r\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/splits.py\", line 532, in __getitem__\r\n instructions = make_file_instructions(\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_reader.py\", line 121, in make_file_instructions\r\n info.name: filenames_for_dataset_split(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/naming.py\", line 72, in filenames_for_dataset_split\r\n prefix = os.path.join(path, prefix)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"<frozen posixpath>\", line 76, in join\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType", "Hmm, that's weird. Maybe try deleting the cache with `!rm -rf ~/.cache/huggingface/datasets` and then re-download.", "Tried that a couple of time. It does download the data fresh but end up with same error. Is there a way to see if its using the right version ?", "You can check the version with `python -c \"import datasets; print(datasets.__version__)\"`", "the datasets version is 2.18. \r\n\r\nI wanted to see if the command datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\") is using the right revision (refs/pr/5). \r\n\r\n\r\n\r\n\r\n\r\n " ]

"2024-04-26T00:08:47"

"2024-04-26T17:22:58"

"2024-04-26T16:12:29"

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"2024-04-25T22:33:57"

"2024-04-25T22:33:57"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6839). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005311 / 0.011353 (-0.006042) | 0.003691 / 0.011008 (-0.007317) | 0.063714 / 0.038508 (0.025206) | 0.030875 / 0.023109 (0.007766) | 0.251210 / 0.275898 (-0.024688) | 0.280539 / 0.323480 (-0.042941) | 0.004262 / 0.007986 (-0.003724) | 0.002723 / 0.004328 (-0.001606) | 0.049487 / 0.004250 (0.045237) | 0.045655 / 0.037052 (0.008603) | 0.264399 / 0.258489 (0.005910) | 0.306613 / 0.293841 (0.012772) | 0.028513 / 0.128546 (-0.100033) | 0.010726 / 0.075646 (-0.064921) | 0.210601 / 0.419271 (-0.208670) | 0.036918 / 0.043533 (-0.006614) | 0.257872 / 0.255139 (0.002733) | 0.278951 / 0.283200 (-0.004249) | 0.017900 / 0.141683 (-0.123783) | 1.096749 / 1.452155 (-0.355406) | 1.152603 / 1.492716 (-0.340113) |\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) | 0.095193 / 0.018006 (0.077187) | 0.303919 / 0.000490 (0.303429) | 0.000226 / 0.000200 (0.000026) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018558 / 0.037411 (-0.018853) | 0.061106 / 0.014526 (0.046580) | 0.076233 / 0.176557 (-0.100323) | 0.122402 / 0.737135 (-0.614734) | 0.075579 / 0.296338 (-0.220760) |\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) | 0.283586 / 0.215209 (0.068377) | 2.766179 / 2.077655 (0.688524) | 1.481069 / 1.504120 (-0.023051) | 1.355004 / 1.541195 (-0.186191) | 1.392940 / 1.468490 (-0.075550) | 0.578878 / 4.584777 (-4.005899) | 2.432890 / 3.745712 (-1.312822) | 2.837912 / 5.269862 (-2.431949) | 1.762803 / 4.565676 (-2.802873) | 0.063339 / 0.424275 (-0.360937) | 0.005392 / 0.007607 (-0.002215) | 0.340271 / 0.226044 (0.114227) | 3.388371 / 2.268929 (1.119443) | 1.862622 / 55.444624 (-53.582002) | 1.543209 / 6.876477 (-5.333268) | 1.569858 / 2.142072 (-0.572215) | 0.651487 / 4.805227 (-4.153740) | 0.119048 / 6.500664 (-6.381616) | 0.042309 / 0.075469 (-0.033160) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.991161 / 1.841788 (-0.850627) | 11.778857 / 8.074308 (3.704549) | 9.586019 / 10.191392 (-0.605373) | 0.148093 / 0.680424 (-0.532331) | 0.014301 / 0.534201 (-0.519900) | 0.287983 / 0.579283 (-0.291301) | 0.266070 / 0.434364 (-0.168293) | 0.328261 / 0.540337 (-0.212076) | 0.417908 / 1.386936 (-0.969028) |\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) | 0.005252 / 0.011353 (-0.006100) | 0.003740 / 0.011008 (-0.007268) | 0.049622 / 0.038508 (0.011114) | 0.030040 / 0.023109 (0.006931) | 0.262224 / 0.275898 (-0.013674) | 0.312216 / 0.323480 (-0.011264) | 0.004213 / 0.007986 (-0.003773) | 0.002737 / 0.004328 (-0.001592) | 0.049159 / 0.004250 (0.044908) | 0.041060 / 0.037052 (0.004008) | 0.275826 / 0.258489 (0.017337) | 0.301879 / 0.293841 (0.008038) | 0.029364 / 0.128546 (-0.099182) | 0.010453 / 0.075646 (-0.065193) | 0.058095 / 0.419271 (-0.361176) | 0.032898 / 0.043533 (-0.010635) | 0.263876 / 0.255139 (0.008737) | 0.281686 / 0.283200 (-0.001514) | 0.018711 / 0.141683 (-0.122971) | 1.126056 / 1.452155 (-0.326098) | 1.185125 / 1.492716 (-0.307591) |\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) | 0.094153 / 0.018006 (0.076147) | 0.300719 / 0.000490 (0.300229) | 0.000207 / 0.000200 (0.000007) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022610 / 0.037411 (-0.014801) | 0.075502 / 0.014526 (0.060977) | 0.088858 / 0.176557 (-0.087699) | 0.129421 / 0.737135 (-0.607714) | 0.089331 / 0.296338 (-0.207007) |\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) | 0.291595 / 0.215209 (0.076386) | 2.864377 / 2.077655 (0.786722) | 1.543387 / 1.504120 (0.039267) | 1.404273 / 1.541195 (-0.136922) | 1.421964 / 1.468490 (-0.046526) | 0.579275 / 4.584777 (-4.005502) | 0.979212 / 3.745712 (-2.766500) | 2.822043 / 5.269862 (-2.447818) | 1.745015 / 4.565676 (-2.820661) | 0.064626 / 0.424275 (-0.359649) | 0.005006 / 0.007607 (-0.002601) | 0.345509 / 0.226044 (0.119464) | 3.410369 / 2.268929 (1.141440) | 1.875930 / 55.444624 (-53.568694) | 1.600841 / 6.876477 (-5.275636) | 1.611818 / 2.142072 (-0.530254) | 0.662277 / 4.805227 (-4.142950) | 0.117861 / 6.500664 (-6.382803) | 0.041061 / 0.075469 (-0.034408) |\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) | 1.007834 / 1.841788 (-0.833954) | 12.345653 / 8.074308 (4.271345) | 9.775237 / 10.191392 (-0.416155) | 0.135166 / 0.680424 (-0.545258) | 0.016799 / 0.534201 (-0.517402) | 0.289235 / 0.579283 (-0.290048) | 0.126196 / 0.434364 (-0.308168) | 0.382905 / 0.540337 (-0.157432) | 0.435248 / 1.386936 (-0.951688) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#22bf5388748611a9255d8e17218d36d2f799f182 \"CML watermark\")\n" ]

"2024-04-25T14:36:58"

"2024-04-26T17:03:51"

"2024-04-26T16:57:40"

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"2024-04-25T14:00:38"

"2024-04-26T16:57:41"

"2024-04-26T16:57:41"

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[ "There are 2 workarounds, tho:\r\n1. Download datasets from web and just load them locally\r\n2. Use metadata directly (temporal solution, since metadata can change)\r\n```\r\nimport datasets\r\nfrom datasets.data_files import DataFilesDict, DataFilesList\r\n\r\ndata_files_list = DataFilesList(\r\n [\r\n \"hf://datasets/allenai/c4@1588ec454efa1a09f29cd18ddd04fe05fc8653a2/en/c4-train.00000-of-01024.json.gz\"\r\n ],\r\n [(\"allenai/c4\", \"1588ec454efa1a09f29cd18ddd04fe05fc8653a2\")],\r\n)\r\ndata_files = DataFilesDict({\"train\": data_files_list})\r\nc4_dataset = datasets.load_dataset(\r\n path=\"allenai/c4\",\r\n data_files=data_files,\r\n split=\"train\",\r\n cache_dir=\"/datesets/cache\",\r\n download_mode=\"reuse_cache_if_exists\",\r\n token=False,\r\n)\r\n```\r\nSecond solution also shows where to find the bug. I suggest that the hashing functions should always use only original parameter `data_files`, and not the one they get after connecting to the server and creating `DataFilesDict`", "Hi! You need to set the `HF_DATASETS_OFFLINE` env variable to `1` to load cached datasets offline, as explained in the docs [here](https://huggingface.co/docs/datasets/v2.19.0/en/loading#offline).", "Just tested. It doesn't work, because of the exact problem I described above: hash of dataset config is different.\r\nThe only error difference is the reason why it cannot connect to HuggingFace (now it's 'offline mode is enabled')\r\n\r\n" ]

"2024-04-25T10:48:20"

"2024-04-26T14:27:15"

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"2024-04-24T21:52:35"

"2024-04-24T21:52:35"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6835). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Fixed the conversion from `pyarrow` to `python` `Sequence` features. \r\n\r\nThere is still an issue that if `features` are passed the `Sequence` always forces conversion to `ListArray`.\r\nThis probably causes issues if the `LargeListArray` is actually needed.\r\n\r\nThere doesn't seem to be a great solution since this list is created solely on the `schema` for `Sequence`.\r\nOne solution would be to always use `LargeListArray` instead.\r\n" ]

"2024-04-24T11:34:24"

"2024-04-24T13:54:03"

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"2024-04-24T11:33:43"

"2024-04-24T12:06:37"

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"2024-04-23T20:40:59"

"2024-04-23T20:40:59"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6832). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

"2024-04-23T12:32:27"

"2024-04-25T17:05:42"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6831). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Concretely, the docs about convert_to_parquet are here: https://moon-ci-docs.huggingface.co/docs/datasets/pr_6831/en/cli#convert-to-parquet", "There is an issue with the example snippet when copy/pasting it: the leading shell dollar sign is also copied. I guess they will not like to fix it in the backend: currently they only support Python code snippets (with leading `>>>` or `...`), as they appear in the IPython interactive console.\r\n\r\nWhat do you suggest, @severo?" ]

"2024-04-23T10:41:03"

"2024-04-26T16:51:09"

"2024-04-25T10:44:10"

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"2024-04-23T09:49:04"

"2024-04-25T10:44:11"

"2024-04-25T10:44:11"

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"2024-04-23T09:44:45"

"2024-04-23T09:44:46"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6828). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

"2024-04-23T09:42:38"

"2024-04-23T11:05:52"

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"2024-04-19T21:11:58"

"2024-04-19T21:13:42"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6826). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.004893 / 0.011353 (-0.006460) | 0.003238 / 0.011008 (-0.007771) | 0.063143 / 0.038508 (0.024635) | 0.029770 / 0.023109 (0.006661) | 0.229052 / 0.275898 (-0.046846) | 0.254534 / 0.323480 (-0.068945) | 0.003083 / 0.007986 (-0.004903) | 0.002615 / 0.004328 (-0.001714) | 0.049684 / 0.004250 (0.045434) | 0.043745 / 0.037052 (0.006693) | 0.248985 / 0.258489 (-0.009504) | 0.275957 / 0.293841 (-0.017884) | 0.027323 / 0.128546 (-0.101223) | 0.010372 / 0.075646 (-0.065275) | 0.206494 / 0.419271 (-0.212778) | 0.035230 / 0.043533 (-0.008303) | 0.234235 / 0.255139 (-0.020904) | 0.252395 / 0.283200 (-0.030805) | 0.019442 / 0.141683 (-0.122240) | 1.130677 / 1.452155 (-0.321478) | 1.161721 / 1.492716 (-0.330996) |\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) | 0.091659 / 0.018006 (0.073653) | 0.301323 / 0.000490 (0.300833) | 0.000212 / 0.000200 (0.000012) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018360 / 0.037411 (-0.019051) | 0.061101 / 0.014526 (0.046575) | 0.072383 / 0.176557 (-0.104174) | 0.117656 / 0.737135 (-0.619479) | 0.073903 / 0.296338 (-0.222436) |\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) | 0.272768 / 0.215209 (0.057558) | 2.655714 / 2.077655 (0.578059) | 1.446254 / 1.504120 (-0.057866) | 1.330543 / 1.541195 (-0.210652) | 1.352527 / 1.468490 (-0.115964) | 0.561428 / 4.584777 (-4.023349) | 2.368182 / 3.745712 (-1.377530) | 2.746508 / 5.269862 (-2.523353) | 1.713972 / 4.565676 (-2.851705) | 0.062046 / 0.424275 (-0.362229) | 0.005427 / 0.007607 (-0.002180) | 0.321652 / 0.226044 (0.095607) | 3.181812 / 2.268929 (0.912883) | 1.766778 / 55.444624 (-53.677846) | 1.492502 / 6.876477 (-5.383975) | 1.534658 / 2.142072 (-0.607415) | 0.640372 / 4.805227 (-4.164856) | 0.118180 / 6.500664 (-6.382484) | 0.042698 / 0.075469 (-0.032771) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.993262 / 1.841788 (-0.848525) | 11.512827 / 8.074308 (3.438518) | 9.602140 / 10.191392 (-0.589252) | 0.144723 / 0.680424 (-0.535701) | 0.014122 / 0.534201 (-0.520079) | 0.302211 / 0.579283 (-0.277072) | 0.268026 / 0.434364 (-0.166338) | 0.326524 / 0.540337 (-0.213813) | 0.423781 / 1.386936 (-0.963155) |\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) | 0.005388 / 0.011353 (-0.005965) | 0.003535 / 0.011008 (-0.007473) | 0.050139 / 0.038508 (0.011631) | 0.031813 / 0.023109 (0.008704) | 0.269501 / 0.275898 (-0.006397) | 0.294355 / 0.323480 (-0.029125) | 0.004128 / 0.007986 (-0.003858) | 0.002684 / 0.004328 (-0.001644) | 0.049295 / 0.004250 (0.045045) | 0.040129 / 0.037052 (0.003077) | 0.282406 / 0.258489 (0.023917) | 0.309822 / 0.293841 (0.015981) | 0.028506 / 0.128546 (-0.100040) | 0.010434 / 0.075646 (-0.065213) | 0.057890 / 0.419271 (-0.361382) | 0.032487 / 0.043533 (-0.011046) | 0.270631 / 0.255139 (0.015492) | 0.288734 / 0.283200 (0.005534) | 0.018710 / 0.141683 (-0.122973) | 1.151571 / 1.452155 (-0.300583) | 1.195222 / 1.492716 (-0.297494) |\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) | 0.090939 / 0.018006 (0.072932) | 0.300278 / 0.000490 (0.299788) | 0.000202 / 0.000200 (0.000002) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022036 / 0.037411 (-0.015376) | 0.075131 / 0.014526 (0.060605) | 0.087775 / 0.176557 (-0.088782) | 0.125719 / 0.737135 (-0.611416) | 0.088491 / 0.296338 (-0.207848) |\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) | 0.300363 / 0.215209 (0.085154) | 2.931852 / 2.077655 (0.854197) | 1.633688 / 1.504120 (0.129568) | 1.512641 / 1.541195 (-0.028554) | 1.527703 / 1.468490 (0.059213) | 0.572781 / 4.584777 (-4.011996) | 2.445950 / 3.745712 (-1.299762) | 2.883667 / 5.269862 (-2.386195) | 1.761396 / 4.565676 (-2.804280) | 0.064422 / 0.424275 (-0.359853) | 0.005332 / 0.007607 (-0.002275) | 0.346730 / 0.226044 (0.120686) | 3.443815 / 2.268929 (1.174886) | 1.988677 / 55.444624 (-53.455948) | 1.707688 / 6.876477 (-5.168789) | 1.694216 / 2.142072 (-0.447856) | 0.634834 / 4.805227 (-4.170393) | 0.115044 / 6.500664 (-6.385620) | 0.040853 / 0.075469 (-0.034616) |\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) | 1.009382 / 1.841788 (-0.832405) | 12.327511 / 8.074308 (4.253203) | 10.123296 / 10.191392 (-0.068097) | 0.130770 / 0.680424 (-0.549654) | 0.015548 / 0.534201 (-0.518653) | 0.286650 / 0.579283 (-0.292633) | 0.270267 / 0.434364 (-0.164097) | 0.333485 / 0.540337 (-0.206852) | 0.428288 / 1.386936 (-0.958648) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f96e74d5c633cd5435dd526adb4a74631eb05c43 \"CML watermark\")\n" ]

"2024-04-19T08:51:42"

"2024-04-19T09:05:25"

"2024-04-19T08:52:14"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6825). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.004945 / 0.011353 (-0.006407) | 0.003290 / 0.011008 (-0.007718) | 0.062404 / 0.038508 (0.023896) | 0.040056 / 0.023109 (0.016946) | 0.246574 / 0.275898 (-0.029324) | 0.275074 / 0.323480 (-0.048406) | 0.004118 / 0.007986 (-0.003867) | 0.002604 / 0.004328 (-0.001724) | 0.048618 / 0.004250 (0.044367) | 0.044088 / 0.037052 (0.007035) | 0.263059 / 0.258489 (0.004570) | 0.294602 / 0.293841 (0.000761) | 0.027425 / 0.128546 (-0.101121) | 0.010263 / 0.075646 (-0.065383) | 0.205925 / 0.419271 (-0.213346) | 0.048917 / 0.043533 (0.005384) | 0.264227 / 0.255139 (0.009088) | 0.273339 / 0.283200 (-0.009860) | 0.017783 / 0.141683 (-0.123900) | 1.137526 / 1.452155 (-0.314629) | 1.179551 / 1.492716 (-0.313165) |\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) | 0.096809 / 0.018006 (0.078802) | 0.303854 / 0.000490 (0.303364) | 0.000207 / 0.000200 (0.000007) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017756 / 0.037411 (-0.019655) | 0.061005 / 0.014526 (0.046479) | 0.072986 / 0.176557 (-0.103571) | 0.119851 / 0.737135 (-0.617284) | 0.074733 / 0.296338 (-0.221605) |\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) | 0.278270 / 0.215209 (0.063061) | 2.737874 / 2.077655 (0.660219) | 1.460658 / 1.504120 (-0.043462) | 1.337695 / 1.541195 (-0.203499) | 1.364376 / 1.468490 (-0.104114) | 0.565622 / 4.584777 (-4.019155) | 2.365167 / 3.745712 (-1.380546) | 2.694544 / 5.269862 (-2.575317) | 1.699689 / 4.565676 (-2.865987) | 0.062564 / 0.424275 (-0.361712) | 0.005296 / 0.007607 (-0.002311) | 0.340122 / 0.226044 (0.114077) | 3.382133 / 2.268929 (1.113204) | 1.816907 / 55.444624 (-53.627718) | 1.530825 / 6.876477 (-5.345652) | 1.533266 / 2.142072 (-0.608807) | 0.638215 / 4.805227 (-4.167012) | 0.116227 / 6.500664 (-6.384437) | 0.041548 / 0.075469 (-0.033921) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971031 / 1.841788 (-0.870757) | 11.117905 / 8.074308 (3.043597) | 9.358159 / 10.191392 (-0.833233) | 0.127954 / 0.680424 (-0.552470) | 0.013634 / 0.534201 (-0.520567) | 0.285399 / 0.579283 (-0.293885) | 0.267980 / 0.434364 (-0.166383) | 0.320219 / 0.540337 (-0.220119) | 0.416035 / 1.386936 (-0.970901) |\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) | 0.005177 / 0.011353 (-0.006176) | 0.003078 / 0.011008 (-0.007930) | 0.049650 / 0.038508 (0.011142) | 0.030897 / 0.023109 (0.007787) | 0.271186 / 0.275898 (-0.004712) | 0.296050 / 0.323480 (-0.027430) | 0.004204 / 0.007986 (-0.003781) | 0.002755 / 0.004328 (-0.001574) | 0.049550 / 0.004250 (0.045300) | 0.039801 / 0.037052 (0.002749) | 0.283243 / 0.258489 (0.024753) | 0.310932 / 0.293841 (0.017091) | 0.029136 / 0.128546 (-0.099410) | 0.010278 / 0.075646 (-0.065368) | 0.059300 / 0.419271 (-0.359971) | 0.032965 / 0.043533 (-0.010568) | 0.272646 / 0.255139 (0.017507) | 0.293697 / 0.283200 (0.010497) | 0.018330 / 0.141683 (-0.123353) | 1.144251 / 1.452155 (-0.307904) | 1.209660 / 1.492716 (-0.283056) |\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) | 0.091020 / 0.018006 (0.073014) | 0.298294 / 0.000490 (0.297804) | 0.000214 / 0.000200 (0.000014) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021879 / 0.037411 (-0.015532) | 0.074728 / 0.014526 (0.060202) | 0.085499 / 0.176557 (-0.091057) | 0.125743 / 0.737135 (-0.611392) | 0.086130 / 0.296338 (-0.210208) |\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) | 0.292311 / 0.215209 (0.077102) | 2.861240 / 2.077655 (0.783585) | 1.590426 / 1.504120 (0.086306) | 1.472288 / 1.541195 (-0.068907) | 1.472901 / 1.468490 (0.004411) | 0.574924 / 4.584777 (-4.009853) | 2.450817 / 3.745712 (-1.294895) | 2.781903 / 5.269862 (-2.487959) | 1.747110 / 4.565676 (-2.818566) | 0.064680 / 0.424275 (-0.359595) | 0.005376 / 0.007607 (-0.002231) | 0.356846 / 0.226044 (0.130802) | 3.457851 / 2.268929 (1.188922) | 1.952678 / 55.444624 (-53.491946) | 1.670824 / 6.876477 (-5.205653) | 1.655872 / 2.142072 (-0.486200) | 0.655874 / 4.805227 (-4.149353) | 0.117098 / 6.500664 (-6.383566) | 0.040230 / 0.075469 (-0.035239) |\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) | 1.007423 / 1.841788 (-0.834365) | 11.818228 / 8.074308 (3.743920) | 10.153699 / 10.191392 (-0.037693) | 0.132073 / 0.680424 (-0.548351) | 0.015101 / 0.534201 (-0.519100) | 0.286555 / 0.579283 (-0.292728) | 0.281953 / 0.434364 (-0.152411) | 0.323647 / 0.540337 (-0.216691) | 0.418698 / 1.386936 (-0.968238) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d3c7462bc67407c42d3ad102b7f9d5914219d9d \"CML watermark\")\n" ]

"2024-04-19T08:29:02"

"2024-04-19T08:50:57"

"2024-04-19T08:44:57"

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[ "Hi ! Do you mean 2.18 ? Can you try to update `fsspec` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U fsspec huggingface_hub\r\n```", "Yes I meant 2.18, and it works after updating `fsspec` and `huggingface_hub`. Thanks!" ]

"2024-04-18T16:11:04"

"2024-04-19T09:53:15"

"2024-04-19T09:52:33"

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"2024-04-18T13:59:00"

"2024-04-18T17:51:08"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6822). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005084 / 0.011353 (-0.006269) | 0.003658 / 0.011008 (-0.007351) | 0.063369 / 0.038508 (0.024860) | 0.030739 / 0.023109 (0.007630) | 0.244335 / 0.275898 (-0.031564) | 0.271731 / 0.323480 (-0.051749) | 0.004133 / 0.007986 (-0.003853) | 0.002798 / 0.004328 (-0.001530) | 0.048790 / 0.004250 (0.044540) | 0.044054 / 0.037052 (0.007002) | 0.261514 / 0.258489 (0.003025) | 0.292155 / 0.293841 (-0.001686) | 0.027971 / 0.128546 (-0.100575) | 0.010723 / 0.075646 (-0.064923) | 0.207328 / 0.419271 (-0.211944) | 0.035928 / 0.043533 (-0.007605) | 0.245320 / 0.255139 (-0.009819) | 0.268774 / 0.283200 (-0.014426) | 0.017119 / 0.141683 (-0.124564) | 1.107052 / 1.452155 (-0.345103) | 1.151752 / 1.492716 (-0.340965) |\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) | 0.089941 / 0.018006 (0.071935) | 0.299788 / 0.000490 (0.299298) | 0.000211 / 0.000200 (0.000012) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018159 / 0.037411 (-0.019252) | 0.061876 / 0.014526 (0.047350) | 0.074733 / 0.176557 (-0.101824) | 0.122070 / 0.737135 (-0.615065) | 0.076100 / 0.296338 (-0.220238) |\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) | 0.282209 / 0.215209 (0.067000) | 2.758098 / 2.077655 (0.680444) | 1.482454 / 1.504120 (-0.021666) | 1.372649 / 1.541195 (-0.168546) | 1.373171 / 1.468490 (-0.095319) | 0.563606 / 4.584777 (-4.021171) | 2.406760 / 3.745712 (-1.338952) | 2.796322 / 5.269862 (-2.473540) | 1.732327 / 4.565676 (-2.833350) | 0.063623 / 0.424275 (-0.360652) | 0.005338 / 0.007607 (-0.002269) | 0.337562 / 0.226044 (0.111518) | 3.345225 / 2.268929 (1.076296) | 1.844353 / 55.444624 (-53.600271) | 1.551003 / 6.876477 (-5.325474) | 1.570623 / 2.142072 (-0.571449) | 0.644843 / 4.805227 (-4.160385) | 0.118811 / 6.500664 (-6.381853) | 0.041731 / 0.075469 (-0.033738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970469 / 1.841788 (-0.871319) | 11.775531 / 8.074308 (3.701222) | 9.757852 / 10.191392 (-0.433540) | 0.130187 / 0.680424 (-0.550237) | 0.013654 / 0.534201 (-0.520547) | 0.328387 / 0.579283 (-0.250896) | 0.268181 / 0.434364 (-0.166183) | 0.325230 / 0.540337 (-0.215107) | 0.421055 / 1.386936 (-0.965881) |\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) | 0.005846 / 0.011353 (-0.005507) | 0.003606 / 0.011008 (-0.007402) | 0.050787 / 0.038508 (0.012279) | 0.031635 / 0.023109 (0.008526) | 0.277040 / 0.275898 (0.001142) | 0.300544 / 0.323480 (-0.022936) | 0.004200 / 0.007986 (-0.003786) | 0.002749 / 0.004328 (-0.001580) | 0.049449 / 0.004250 (0.045198) | 0.041616 / 0.037052 (0.004564) | 0.289570 / 0.258489 (0.031081) | 0.316138 / 0.293841 (0.022297) | 0.029578 / 0.128546 (-0.098969) | 0.010582 / 0.075646 (-0.065064) | 0.058284 / 0.419271 (-0.360988) | 0.033078 / 0.043533 (-0.010455) | 0.277964 / 0.255139 (0.022825) | 0.295008 / 0.283200 (0.011808) | 0.017753 / 0.141683 (-0.123930) | 1.128635 / 1.452155 (-0.323519) | 1.190142 / 1.492716 (-0.302575) |\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) | 0.091504 / 0.018006 (0.073498) | 0.303875 / 0.000490 (0.303385) | 0.000221 / 0.000200 (0.000021) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021413 / 0.037411 (-0.015998) | 0.074825 / 0.014526 (0.060299) | 0.086329 / 0.176557 (-0.090228) | 0.125632 / 0.737135 (-0.611503) | 0.087918 / 0.296338 (-0.208420) |\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) | 0.297914 / 0.215209 (0.082705) | 2.922885 / 2.077655 (0.845230) | 1.625758 / 1.504120 (0.121638) | 1.500174 / 1.541195 (-0.041021) | 1.517162 / 1.468490 (0.048672) | 0.576885 / 4.584777 (-4.007892) | 2.458723 / 3.745712 (-1.286989) | 2.798471 / 5.269862 (-2.471391) | 1.762499 / 4.565676 (-2.803178) | 0.064736 / 0.424275 (-0.359539) | 0.005325 / 0.007607 (-0.002282) | 0.351697 / 0.226044 (0.125652) | 3.496223 / 2.268929 (1.227294) | 1.977535 / 55.444624 (-53.467090) | 1.695223 / 6.876477 (-5.181254) | 1.689692 / 2.142072 (-0.452381) | 0.656404 / 4.805227 (-4.148823) | 0.123106 / 6.500664 (-6.377558) | 0.040980 / 0.075469 (-0.034489) |\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) | 1.036972 / 1.841788 (-0.804816) | 12.163931 / 8.074308 (4.089623) | 10.297927 / 10.191392 (0.106535) | 0.144087 / 0.680424 (-0.536337) | 0.015553 / 0.534201 (-0.518648) | 0.286225 / 0.579283 (-0.293058) | 0.275567 / 0.434364 (-0.158797) | 0.332717 / 0.540337 (-0.207620) | 0.423804 / 1.386936 (-0.963132) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0bc709af303c8dc64c973a17016bd5aa5db2f3d5 \"CML watermark\")\n" ]

"2024-04-18T10:21:41"

"2024-04-18T11:15:41"

"2024-04-18T11:09:13"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6820). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "This is ready for review, @huggingface/datasets.", "I am adding a test...", "@lhoestq I am getting an error in the test and I think it happens because the CI endpoint does not have the /preupload functionality:\r\n```\r\nhuggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-662a4de9-7134df595e29e4c073ac1298;332ff6e3-597a-4dfc-89df-4e9ac64215ad)\r\n\r\nRepository Not Found for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-6c54e2-17140484441915/preupload/main?create_pr=1.\r\nPlease make sure you specified the correct `repo_id` and `repo_type`.\r\nIf you are trying to access a private or gated repo, make sure you are authenticated.\r\nInvalid username or password.\r\nNote: Creating a commit assumes that the repo already exists on the Huggingface Hub. Please use `create_repo` if it's not the case.\r\n```" ]

"2024-04-17T14:41:12"

"2024-04-25T14:41:13"

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"2024-04-17T11:19:47"

"2024-04-17T11:19:47"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6817). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005464 / 0.011353 (-0.005889) | 0.004174 / 0.011008 (-0.006834) | 0.064252 / 0.038508 (0.025744) | 0.033305 / 0.023109 (0.010196) | 0.245831 / 0.275898 (-0.030067) | 0.275575 / 0.323480 (-0.047905) | 0.003359 / 0.007986 (-0.004626) | 0.004196 / 0.004328 (-0.000132) | 0.049961 / 0.004250 (0.045710) | 0.048940 / 0.037052 (0.011888) | 0.261037 / 0.258489 (0.002548) | 0.295329 / 0.293841 (0.001488) | 0.028570 / 0.128546 (-0.099976) | 0.010747 / 0.075646 (-0.064900) | 0.216021 / 0.419271 (-0.203251) | 0.036885 / 0.043533 (-0.006648) | 0.251169 / 0.255139 (-0.003970) | 0.286233 / 0.283200 (0.003034) | 0.021253 / 0.141683 (-0.120429) | 1.150669 / 1.452155 (-0.301485) | 1.187577 / 1.492716 (-0.305140) |\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) | 0.094443 / 0.018006 (0.076436) | 0.304410 / 0.000490 (0.303920) | 0.000213 / 0.000200 (0.000013) | 0.000041 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019568 / 0.037411 (-0.017844) | 0.065734 / 0.014526 (0.051208) | 0.076042 / 0.176557 (-0.100515) | 0.123624 / 0.737135 (-0.613511) | 0.078047 / 0.296338 (-0.218291) |\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) | 0.295725 / 0.215209 (0.080515) | 2.752501 / 2.077655 (0.674846) | 1.461856 / 1.504120 (-0.042264) | 1.353692 / 1.541195 (-0.187503) | 1.391777 / 1.468490 (-0.076713) | 0.563423 / 4.584777 (-4.021354) | 2.384620 / 3.745712 (-1.361092) | 2.876092 / 5.269862 (-2.393769) | 1.803913 / 4.565676 (-2.761763) | 0.062678 / 0.424275 (-0.361597) | 0.005428 / 0.007607 (-0.002179) | 0.333797 / 0.226044 (0.107753) | 3.304458 / 2.268929 (1.035530) | 1.801768 / 55.444624 (-53.642856) | 1.569406 / 6.876477 (-5.307070) | 1.614535 / 2.142072 (-0.527538) | 0.650178 / 4.805227 (-4.155049) | 0.119693 / 6.500664 (-6.380971) | 0.042832 / 0.075469 (-0.032637) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982035 / 1.841788 (-0.859753) | 12.390006 / 8.074308 (4.315698) | 10.127018 / 10.191392 (-0.064374) | 0.131963 / 0.680424 (-0.548461) | 0.013926 / 0.534201 (-0.520275) | 0.289587 / 0.579283 (-0.289696) | 0.270302 / 0.434364 (-0.164062) | 0.327231 / 0.540337 (-0.213107) | 0.422522 / 1.386936 (-0.964414) |\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) | 0.005666 / 0.011353 (-0.005687) | 0.003914 / 0.011008 (-0.007094) | 0.050315 / 0.038508 (0.011807) | 0.032367 / 0.023109 (0.009257) | 0.271732 / 0.275898 (-0.004166) | 0.297248 / 0.323480 (-0.026231) | 0.005101 / 0.007986 (-0.002884) | 0.002882 / 0.004328 (-0.001447) | 0.049651 / 0.004250 (0.045401) | 0.043773 / 0.037052 (0.006721) | 0.288011 / 0.258489 (0.029522) | 0.311863 / 0.293841 (0.018023) | 0.029147 / 0.128546 (-0.099399) | 0.010722 / 0.075646 (-0.064925) | 0.058832 / 0.419271 (-0.360440) | 0.033092 / 0.043533 (-0.010441) | 0.274686 / 0.255139 (0.019547) | 0.294174 / 0.283200 (0.010975) | 0.019196 / 0.141683 (-0.122486) | 1.126615 / 1.452155 (-0.325540) | 1.193107 / 1.492716 (-0.299609) |\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) | 0.097547 / 0.018006 (0.079541) | 0.316018 / 0.000490 (0.315529) | 0.000330 / 0.000200 (0.000130) | 0.000073 / 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) | 0.022336 / 0.037411 (-0.015076) | 0.077092 / 0.014526 (0.062566) | 0.088873 / 0.176557 (-0.087684) | 0.128517 / 0.737135 (-0.608619) | 0.094061 / 0.296338 (-0.202278) |\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) | 0.300100 / 0.215209 (0.084891) | 2.893114 / 2.077655 (0.815460) | 1.570541 / 1.504120 (0.066421) | 1.453538 / 1.541195 (-0.087657) | 1.505325 / 1.468490 (0.036835) | 0.567955 / 4.584777 (-4.016822) | 2.458547 / 3.745712 (-1.287166) | 2.969181 / 5.269862 (-2.300680) | 1.850082 / 4.565676 (-2.715594) | 0.063811 / 0.424275 (-0.360464) | 0.005378 / 0.007607 (-0.002229) | 0.348219 / 0.226044 (0.122175) | 3.443986 / 2.268929 (1.175057) | 1.943005 / 55.444624 (-53.501620) | 1.686541 / 6.876477 (-5.189935) | 1.715552 / 2.142072 (-0.426520) | 0.641361 / 4.805227 (-4.163866) | 0.116652 / 6.500664 (-6.384012) | 0.042216 / 0.075469 (-0.033253) |\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) | 1.020102 / 1.841788 (-0.821686) | 12.966127 / 8.074308 (4.891819) | 10.748397 / 10.191392 (0.557005) | 0.132601 / 0.680424 (-0.547823) | 0.016643 / 0.534201 (-0.517558) | 0.289422 / 0.579283 (-0.289861) | 0.275524 / 0.434364 (-0.158840) | 0.332835 / 0.540337 (-0.207503) | 0.427867 / 1.386936 (-0.959069) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5eb93f61f9f6e7fefba5d800defe21e50ddf8c58 \"CML watermark\")\n" ]

"2024-04-16T17:41:27"

"2024-04-16T18:27:44"

"2024-04-16T18:17:29"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6816). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Hi! This is a breaking change. A better solution is to check for \"indexable\" types in `__getitem__` to support keys such as `np.int64`:\r\n```python\r\nimport operator\r\n\r\ndef _query_table_with_indices_mapping(...): # or _query_table\r\n ...\r\n try:\r\n operator.index(key)\r\n except TypeError:\r\n pass\r\n \r\n _raise_bad_key_type(key)\r\n```", "Sounds good! We should still update type annotations for SearchResult in my opinion." ]

"2024-04-16T14:53:39"

"2024-04-16T15:54:10"

"2024-04-16T15:54:10"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6815). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005101 / 0.011353 (-0.006252) | 0.003478 / 0.011008 (-0.007531) | 0.063634 / 0.038508 (0.025126) | 0.030670 / 0.023109 (0.007561) | 0.240057 / 0.275898 (-0.035841) | 0.258726 / 0.323480 (-0.064754) | 0.004136 / 0.007986 (-0.003849) | 0.002667 / 0.004328 (-0.001662) | 0.048968 / 0.004250 (0.044718) | 0.043125 / 0.037052 (0.006073) | 0.249033 / 0.258489 (-0.009456) | 0.282630 / 0.293841 (-0.011211) | 0.027528 / 0.128546 (-0.101018) | 0.009987 / 0.075646 (-0.065660) | 0.210614 / 0.419271 (-0.208657) | 0.034965 / 0.043533 (-0.008567) | 0.239199 / 0.255139 (-0.015940) | 0.276891 / 0.283200 (-0.006309) | 0.017781 / 0.141683 (-0.123902) | 1.142795 / 1.452155 (-0.309360) | 1.184171 / 1.492716 (-0.308545) |\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) | 0.092075 / 0.018006 (0.074068) | 0.300709 / 0.000490 (0.300220) | 0.000217 / 0.000200 (0.000017) | 0.000046 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017887 / 0.037411 (-0.019525) | 0.061134 / 0.014526 (0.046608) | 0.077075 / 0.176557 (-0.099482) | 0.118808 / 0.737135 (-0.618327) | 0.074961 / 0.296338 (-0.221377) |\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) | 0.280404 / 0.215209 (0.065194) | 2.759453 / 2.077655 (0.681798) | 1.437552 / 1.504120 (-0.066568) | 1.318703 / 1.541195 (-0.222492) | 1.313075 / 1.468490 (-0.155416) | 0.564876 / 4.584777 (-4.019901) | 2.381595 / 3.745712 (-1.364118) | 2.759171 / 5.269862 (-2.510691) | 1.725878 / 4.565676 (-2.839799) | 0.062627 / 0.424275 (-0.361648) | 0.005295 / 0.007607 (-0.002312) | 0.335245 / 0.226044 (0.109201) | 3.276266 / 2.268929 (1.007337) | 1.843272 / 55.444624 (-53.601353) | 1.519948 / 6.876477 (-5.356529) | 1.519626 / 2.142072 (-0.622447) | 0.637891 / 4.805227 (-4.167336) | 0.116260 / 6.500664 (-6.384404) | 0.041768 / 0.075469 (-0.033701) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981739 / 1.841788 (-0.860049) | 11.354768 / 8.074308 (3.280460) | 9.900585 / 10.191392 (-0.290807) | 0.130683 / 0.680424 (-0.549741) | 0.014122 / 0.534201 (-0.520079) | 0.297451 / 0.579283 (-0.281832) | 0.264786 / 0.434364 (-0.169577) | 0.337559 / 0.540337 (-0.202778) | 0.425131 / 1.386936 (-0.961805) |\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) | 0.005182 / 0.011353 (-0.006171) | 0.003355 / 0.011008 (-0.007653) | 0.049842 / 0.038508 (0.011334) | 0.031094 / 0.023109 (0.007985) | 0.270080 / 0.275898 (-0.005818) | 0.291602 / 0.323480 (-0.031878) | 0.004210 / 0.007986 (-0.003776) | 0.002720 / 0.004328 (-0.001608) | 0.048986 / 0.004250 (0.044736) | 0.055187 / 0.037052 (0.018135) | 0.280085 / 0.258489 (0.021595) | 0.308148 / 0.293841 (0.014308) | 0.029300 / 0.128546 (-0.099246) | 0.009976 / 0.075646 (-0.065670) | 0.057930 / 0.419271 (-0.361341) | 0.032543 / 0.043533 (-0.010990) | 0.277485 / 0.255139 (0.022346) | 0.289345 / 0.283200 (0.006145) | 0.018070 / 0.141683 (-0.123613) | 1.140977 / 1.452155 (-0.311178) | 1.190543 / 1.492716 (-0.302173) |\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) | 0.093416 / 0.018006 (0.075410) | 0.298732 / 0.000490 (0.298242) | 0.000224 / 0.000200 (0.000024) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022167 / 0.037411 (-0.015244) | 0.074970 / 0.014526 (0.060444) | 0.086047 / 0.176557 (-0.090509) | 0.125228 / 0.737135 (-0.611907) | 0.088330 / 0.296338 (-0.208008) |\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) | 0.292016 / 0.215209 (0.076807) | 2.845712 / 2.077655 (0.768057) | 1.576951 / 1.504120 (0.072831) | 1.452298 / 1.541195 (-0.088897) | 1.456918 / 1.468490 (-0.011572) | 0.560529 / 4.584777 (-4.024248) | 2.425333 / 3.745712 (-1.320379) | 2.739416 / 5.269862 (-2.530445) | 1.715779 / 4.565676 (-2.849898) | 0.062568 / 0.424275 (-0.361707) | 0.005327 / 0.007607 (-0.002280) | 0.351376 / 0.226044 (0.125332) | 3.401855 / 2.268929 (1.132927) | 1.921844 / 55.444624 (-53.522780) | 1.648423 / 6.876477 (-5.228054) | 1.642003 / 2.142072 (-0.500069) | 0.640789 / 4.805227 (-4.164438) | 0.114699 / 6.500664 (-6.385965) | 0.040451 / 0.075469 (-0.035018) |\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) | 1.004186 / 1.841788 (-0.837602) | 11.879918 / 8.074308 (3.805609) | 9.981852 / 10.191392 (-0.209540) | 0.141298 / 0.680424 (-0.539126) | 0.015005 / 0.534201 (-0.519196) | 0.291537 / 0.579283 (-0.287746) | 0.272093 / 0.434364 (-0.162271) | 0.331361 / 0.540337 (-0.208977) | 0.422940 / 1.386936 (-0.963996) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed8860faef3e751f3b77c08e09ce723a74d2c2e5 \"CML watermark\")\n" ]

"2024-04-16T14:23:13"

"2024-04-16T16:06:48"

"2024-04-16T15:58:22"

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[ "Hi ! You can try to reduce `writer_batch_size`. It corresponds to the number of samples that stay in RAM before being flushed to disk" ]

"2024-04-16T11:56:03"

"2024-04-19T11:53:41"

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