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"2023-04-10T23:21:12"

"2023-04-10T23:21:12"

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"2023-04-10T15:22:14"

"2023-04-10T15:22:56"

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"2023-04-10T08:41:43"

"2023-04-10T08:41:43"

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"2023-04-09T16:58:44"

"2023-04-09T16:58:44"

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[ "Hmm the error doesn't seem related to data loading.\r\n\r\nRegarding `split_dataset_by_node`: it's generally used to split an iterable dataset (e.g. when streaming) in pytorch DDP. It's not needed if you use a regular dataset since the pytorch DataLoader already assigns a subset of the dataset indices to each node." ]

"2023-04-09T11:04:59"

"2023-04-09T16:33:00"

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"2023-04-08T23:55:12"

"2023-04-08T23:55:12"

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[ "Edit: This behavior is true even without `.take/.set`" ]

"2023-04-08T18:45:48"

"2023-04-09T16:38:48"

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"2023-04-07T21:04:08"

"2023-04-07T21:07:34"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5718). All of your documentation changes will be reflected on that endpoint.", "After this CI error: https://github.com/huggingface/datasets/actions/runs/4639528358/jobs/8210492953?pr=5718\r\n```\r\nFAILED tests/test_data_files.py::test_get_data_files_patterns[data_file_per_split4] - AssertionError: assert ['random', 'train'] == ['train', 'random']\r\n At index 0 diff: 'random' != 'train'\r\n Full diff:\r\n - ['train', 'random']\r\n + ['random', 'train']\r\n```\r\nI have checked locally and found out that the data split order is nondeterministic. I am addressing this in a separate issue.\r\n\r\nSee:\r\n- #5728 \r\n- #5729" ]

"2023-04-07T16:01:26"

"2023-04-07T16:05:29"

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[ "Looks like as long as the number of shards makes a batch lower than 1000 images it works. In my training set I have 40K images. If I use `num_shards=40` (batch of 1000 images) I get the error, but if I update it to `num_shards=50` (batch of 800 images) it works.\r\n\r\nI will be happy to share my dataset privately if it can help to better debug." ]

"2023-04-07T11:59:17"

"2023-04-07T16:27:26"

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"2023-04-07T09:51:40"

"2023-04-07T09:51:40"

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[ "Hi! \r\n\r\nYou can use [`.set_format(\"np\")`](https://huggingface.co/docs/datasets/process#format) to get NumPy arrays (or Pytorch tensors with `.set_format(\"torch\")`) in `__getitem__`.\r\n\r\nAlso, have you been able to reproduce the linked PyTorch issue with a HF dataset?\r\n " ]

"2023-04-06T13:57:48"

"2023-04-07T14:38:06"

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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) | 0.006498 / 0.011353 (-0.004855) | 0.004406 / 0.011008 (-0.006602) | 0.097136 / 0.038508 (0.058628) | 0.027711 / 0.023109 (0.004601) | 0.303092 / 0.275898 (0.027194) | 0.336804 / 0.323480 (0.013324) | 0.004838 / 0.007986 (-0.003148) | 0.004533 / 0.004328 (0.000204) | 0.075062 / 0.004250 (0.070812) | 0.035105 / 0.037052 (-0.001947) | 0.310245 / 0.258489 (0.051756) | 0.347086 / 0.293841 (0.053245) | 0.030867 / 0.128546 (-0.097679) | 0.011436 / 0.075646 (-0.064211) | 0.320728 / 0.419271 (-0.098544) | 0.042303 / 0.043533 (-0.001230) | 0.308177 / 0.255139 (0.053038) | 0.333673 / 0.283200 (0.050473) | 0.084736 / 0.141683 (-0.056947) | 1.477391 / 1.452155 (0.025237) | 1.530399 / 1.492716 (0.037682) |\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) | 0.212698 / 0.018006 (0.194692) | 0.409098 / 0.000490 (0.408608) | 0.004202 / 0.000200 (0.004002) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022725 / 0.037411 (-0.014686) | 0.095866 / 0.014526 (0.081340) | 0.104153 / 0.176557 (-0.072404) | 0.162964 / 0.737135 (-0.574171) | 0.106505 / 0.296338 (-0.189834) |\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) | 0.431336 / 0.215209 (0.216127) | 4.283290 / 2.077655 (2.205635) | 1.982418 / 1.504120 (0.478298) | 1.762104 / 1.541195 (0.220909) | 1.807528 / 1.468490 (0.339038) | 0.695507 / 4.584777 (-3.889270) | 3.376299 / 3.745712 (-0.369413) | 1.856642 / 5.269862 (-3.413219) | 1.154258 / 4.565676 (-3.411419) | 0.082749 / 0.424275 (-0.341526) | 0.012289 / 0.007607 (0.004682) | 0.525842 / 0.226044 (0.299798) | 5.285764 / 2.268929 (3.016835) | 2.389926 / 55.444624 (-53.054698) | 2.021830 / 6.876477 (-4.854646) | 2.107460 / 2.142072 (-0.034612) | 0.808118 / 4.805227 (-3.997109) | 0.150791 / 6.500664 (-6.349873) | 0.065825 / 0.075469 (-0.009644) |\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) | 1.206939 / 1.841788 (-0.634849) | 13.795902 / 8.074308 (5.721594) | 14.107950 / 10.191392 (3.916558) | 0.144300 / 0.680424 (-0.536124) | 0.016478 / 0.534201 (-0.517723) | 0.379395 / 0.579283 (-0.199888) | 0.388437 / 0.434364 (-0.045927) | 0.451443 / 0.540337 (-0.088894) | 0.523142 / 1.386936 (-0.863794) |\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) | 0.006503 / 0.011353 (-0.004850) | 0.004578 / 0.011008 (-0.006430) | 0.076278 / 0.038508 (0.037770) | 0.028052 / 0.023109 (0.004943) | 0.337873 / 0.275898 (0.061975) | 0.371368 / 0.323480 (0.047888) | 0.005086 / 0.007986 (-0.002899) | 0.003354 / 0.004328 (-0.000975) | 0.076876 / 0.004250 (0.072625) | 0.039146 / 0.037052 (0.002093) | 0.340299 / 0.258489 (0.081810) | 0.381209 / 0.293841 (0.087368) | 0.031771 / 0.128546 (-0.096775) | 0.011670 / 0.075646 (-0.063976) | 0.085156 / 0.419271 (-0.334116) | 0.041990 / 0.043533 (-0.001543) | 0.338644 / 0.255139 (0.083505) | 0.362461 / 0.283200 (0.079262) | 0.089772 / 0.141683 (-0.051911) | 1.480341 / 1.452155 (0.028187) | 1.562815 / 1.492716 (0.070099) |\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) | 0.205700 / 0.018006 (0.187694) | 0.402206 / 0.000490 (0.401716) | 0.001212 / 0.000200 (0.001012) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025172 / 0.037411 (-0.012240) | 0.100959 / 0.014526 (0.086433) | 0.108464 / 0.176557 (-0.068093) | 0.161321 / 0.737135 (-0.575814) | 0.114245 / 0.296338 (-0.182093) |\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) | 0.437425 / 0.215209 (0.222216) | 4.362212 / 2.077655 (2.284557) | 2.068815 / 1.504120 (0.564695) | 1.864089 / 1.541195 (0.322894) | 1.909038 / 1.468490 (0.440548) | 0.696097 / 4.584777 (-3.888680) | 3.358628 / 3.745712 (-0.387084) | 2.999085 / 5.269862 (-2.270777) | 1.533917 / 4.565676 (-3.031760) | 0.083010 / 0.424275 (-0.341266) | 0.012372 / 0.007607 (0.004765) | 0.539926 / 0.226044 (0.313882) | 5.438326 / 2.268929 (3.169397) | 2.498581 / 55.444624 (-52.946043) | 2.153359 / 6.876477 (-4.723117) | 2.177891 / 2.142072 (0.035819) | 0.803169 / 4.805227 (-4.002059) | 0.151079 / 6.500664 (-6.349585) | 0.065981 / 0.075469 (-0.009489) |\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) | 1.336682 / 1.841788 (-0.505106) | 14.133055 / 8.074308 (6.058747) | 14.033972 / 10.191392 (3.842580) | 0.152109 / 0.680424 (-0.528315) | 0.016475 / 0.534201 (-0.517726) | 0.387808 / 0.579283 (-0.191475) | 0.378347 / 0.434364 (-0.056017) | 0.484732 / 0.540337 (-0.055606) | 0.569907 / 1.386936 (-0.817029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1c4ec00511868bd881e84a6f7e0333648d833b8e \"CML watermark\")\n" ]

"2023-04-06T13:01:45"

"2023-04-07T09:23:54"

"2023-04-07T09:16:57"

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[ "Hi Julien ! This sounds related to https://github.com/huggingface/datasets/issues/5695 - TL;DR: you need to have shards smaller than 2GB to avoid this issue\r\n\r\nThe number of rows per shard is computed using an estimated size of the full dataset, which can sometimes lead to shards bigger than `max_shard_size`. The estimation is currently done using the first samples of the dataset (which can surely be improved). We should probably open an issue to fix this once and for all.\r\n\r\nAnyway for your specific dataset I'd suggest you to pass `num_shards` instead of `max_shard_size` for now, and make sure to have enough shards to end up with shards smaller than 2GB", "Hi Quentin! Thanks a lot! Using `num_shards` instead of `max_shard_size` works as expected.\r\n\r\nIndeed the way you describe how the size is computed cannot really work with the dataset I'm building as all the image doesn't have the same resolution and then size. Opening an issue on this might be a good idea." ]

"2023-04-06T10:27:22"

"2023-04-06T13:06:22"

"2023-04-06T13:06:21"

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[ "Closing since this is a duplicate of #5711", "> Closing since this is a duplicate of #5711\r\n\r\nSorry @mariosasko , my internet went down went submitting the issue, and somehow it ended up creating a duplicate" ]

"2023-04-05T16:47:10"

"2023-04-06T08:32:37"

"2023-04-05T17:17:44"

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[ "It seems like https://github.com/huggingface/datasets/pull/5626 has introduced this error. \r\n\r\ncc @albertvillanova \r\n\r\nI think replacing:\r\nhttps://github.com/huggingface/datasets/blob/0803a006db1c395ac715662cc6079651f77c11ea/src/datasets/download/streaming_download_manager.py#L777-L778\r\nwith:\r\n```python\r\nreturn np.load(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), *args, **kwargs)\r\n```\r\nshould fix the issue.\r\n\r\n(Maybe this is also worth doing a patch release afterward)", "Thanks for reporting, @rcasero.\r\n\r\nI can have a look..." ]

"2023-04-05T16:46:49"

"2023-04-07T09:16:59"

"2023-04-07T09:16:59"

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[ "Hi! This error means that PyArrow's internal [`mmap`](https://man7.org/linux/man-pages/man2/mmap.2.html) call failed to allocate memory, which can be tricky to debug. Since this error is more related to PyArrow than us, I think it's best to report this issue in their [repo](https://github.com/apache/arrow) (they are more experienced on this matter). Also, googling \"mmap cannot allocate memory\" returns some approaches to solving this problem." ]

"2023-04-05T14:11:26"

"2023-04-05T17:09:28"

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[ "hi @jplu ! Did I understand you correctly that you create the dataset, push it to the Hub with `.push_to_hub` and you see a `dataset_infos.json` file there, then you edit this file, load the dataset with `load_dataset` and you don't see any changes in `.info` attribute of a dataset object? \r\n\r\nThis is actually weird that when you push your dataset to the Hub, a `dataset_infos.json` file is created, because this file is deprecated and it should create `README.md` with the `dataset_info` field instead. Some keys are also deprecated, like \"supervised_keys\" and \"task_templates\".\r\n\r\nCan you please provide a toy reproducible example of how you create and push the dataset? And also why do you want to change this file, especially the number of bytes and examples?", "Hi @polinaeterna Yes I have created the dataset with `Dataset.from_dict` applied some updates afterward and when I pushed to the hub I had a `dataset_infos.json` file and there was a `README.md` file as well.\r\n\r\nI didn't know that the JSON file was deprecated. So I have built my dataset with `ImageBuilder` instead and now it works like a charm without having to touch anything.\r\n\r\nI haven't succeed to reproduce the creation of the JSON file with a toy example, hence, I certainly did some mistakes when I have manipulated my dataset manually at first. My bad." ]

"2023-04-05T11:15:17"

"2023-04-06T08:52:20"

"2023-04-06T08:52:19"

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[ "Example of bulk edit: https://huggingface.co/datasets/aeslc/discussions/5", "looks great! \r\n\r\nDo you encode the fact that you've already converted a dataset? (to not convert it twice) or do you base yourself on the info contained in `dataset_info`", "I am only looping trough the dataset cards, assuming that all of them were created with MiB.\r\n\r\nI agree we should only run the bulk edit once for all canonical datasets: I'm using a for-loop over canonical datasets.", "yes, worst case, we have this in structured data:\r\n\r\n<img width=\"337\" alt=\"image\" src=\"https://user-images.githubusercontent.com/326577/230037051-06caddcb-08c8-4953-a710-f3d122917db3.png\">\r\n", "I have just included as well the conversion from MB to GB if necessary. See: \r\n- https://huggingface.co/datasets/bookcorpus/discussions/2/files\r\n- https://huggingface.co/datasets/asnq/discussions/2/files", "Nice. Is it another loop? Because in https://huggingface.co/datasets/amazon_us_reviews/discussions/2/files we have `32377.29 MB` for example", "First, I tested some batches to check the changes made. Then I incorporated the MB to GB conversion. Now I'm running the rest.", "The bulk edit parsed 751 canonical datasets and updated 166.", "Thanks a lot!\r\n\r\nThe sizes now match as expected!\r\n\r\n<img width=\"1446\" alt=\"Capture d’écran 2023-04-05 à 16 10 15\" src=\"https://user-images.githubusercontent.com/1676121/230107044-ac2a76ea-a4fe-4e81-a925-f464b85f5edd.png\">\r\n", "I made another bulk edit of ancient canonical datasets that were moved to community organization. I have parsed 11 datasets and opened a PR on 3 of them:\r\n- [ ] \"allenai/scicite\": https://huggingface.co/datasets/allenai/scicite/discussions/3\r\n- [ ] \"allenai/scifact\": https://huggingface.co/datasets/allenai/scifact/discussions/2\r\n- [ ] \"dair-ai/emotion\": https://huggingface.co/datasets/dair-ai/emotion/discussions/6" ]

"2023-04-05T06:36:03"

"2023-04-07T06:20:32"

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[ "Hi ! We could definitely a type that holds the categories and uses a DictionaryType storage. There's a ClassLabel type that is similar with a 'names' parameter (similar to a id2label in deep learning frameworks) that uses an integer array as storage.\r\n\r\nIt can be added in `features.py`. Here are some pointers:\r\n- the conversion from HF type to PyArrow type is done in `get_nested_type`\r\n- the conversion from Pyarrow type to HF type is done in `generate_from_arrow_type`\r\n- `encode_nested_example` and `decode_nested_example` are used to do user's value (what users see) <-> storage value (what is in the pyarrow.array) if there's any conversion to do" ]

"2023-04-04T09:45:35"

"2023-04-04T16:37:25"

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[ "Hi! It can take some time to iterate over Parquet files as big as yours, convert the samples to Python, and find the first one that matches a filter predicate before yielding it...", "Thanks @mariosasko, I figured it was the filter operation. I'm closing this issue because it is not a bug, it is the expected beheaviour." ]

"2023-04-04T09:16:17"

"2023-04-05T23:35:41"

"2023-04-05T23:35:41"

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[ "Awesome ! cc @mariosasko :)", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5704). All of your documentation changes will be reflected on that endpoint.", "Hi, thanks for working on this!\r\n\r\nYour solution only works if the `root` is `\"\"`, e.g., this would yield an incorrect result:\r\n```python\r\ndset = load_dataset(\"user/hf-dataset-repo\", data_dir=\"path/to/data_dir\")\r\n```\r\n\r\nAlso, the `HfFileSystem` implementation in `datasets` will be replaced with the more powerful [one](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/hf_file_system.py) from `huggingface_hub` soon (I plan to open a PR that makes `find` much faster in the coming days). \r\n\r\nSo I don't think we want to merge this PR in the current state, but thanks again for the effort.\r\n\r\n (I'll comment on the original issue to propose a cleaner solution)", "Ooof. Sorry, I should have checked that more thoroughly then! I would say we could just add that check and only use my approach if the root is \"\", which would still be faster in many cases, but it sounds like you have a better solution on the way. Thanks for the feedback Mario." ]

"2023-04-04T08:58:14"

"2023-04-07T16:10:55"

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[ "`multiprocess` uses `dill` instead of `pickle` for pickling shared objects and, as such, can pickle more types than `multiprocessing`. And I don't think this is something we want to change :).", "That makes sense to me, and I don't think you should merge this change. I was only curious about the performance impact. I saw the benchmarks that was produced in other PRs, and wanted to get a better understanding of it. I created this PR to see if it got automatically added here.\r\n\r\nIs there a way I can generate those benchmarks myself?", "You can find some speed comparisons between dill and pickle on SO if you google \"dill vs pickle speed\".\r\n\r\nAnd for the benchmarks, you can generate them locally with DVC running this code from the repo root: https://github.com/huggingface/datasets/blob/0803a006db1c395ac715662cc6079651f77c11ea/.github/workflows/benchmarks.yaml#L23-L47." ]

"2023-04-04T04:37:49"

"2023-04-05T12:59:41"

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[ "Hi ! `datasets` uses Apache Arrow as backend to store the data, and it requires each column to have a fixed type. Therefore a column can't have a mix of dicts/lists/strings.\r\n\r\nThough it's possible to have one (nullable) field for each type:\r\n```python\r\nfeatures = Features({\r\n \"text_alone\": Value(\"string\"),\r\n \"text_with_idxes\": {\r\n \"text\": Value(\"string\"),\r\n \"idxes\": Value(\"int64\")\r\n }\r\n})\r\n```\r\n\r\nbut you'd have to reformat your data fiels or define a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) to apply the appropriate parsing.\r\n\r\nAlternatively we could explore supporting the Arrow [Union](https://arrow.apache.org/docs/python/generated/pyarrow.UnionType.html) type which could solve this issue, but I don't know if it's well supported in python and with the rest of the ecosystem like Parquet", "@lhoestq Thank you! I further wonder if it's possible to use list subscripts as keys of a feature? Like\r\n```python\r\nfeatures = Features({\r\n 0: Value(\"string\"),\r\n 1: {\r\n \"text\": Value(\"string\"),\r\n \"idxes\": [Value(\"int64\")]\r\n },\r\n 2: Value(\"string\"),\r\n # ...\r\n})\r\n```", "Column names need to be strings, so you could use \"1\", \"2\", etc. or give appropriate column names", "@lhoestq Got it. Thank you!" ]

"2023-04-04T03:20:43"

"2023-04-05T14:15:18"

"2023-04-05T14:15:17"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5701). All of your documentation changes will be reflected on that endpoint.", "@mariosasko Would you or another HF datasets maintainer be able to review this, please?", "Amazing ! Great job @maddiedawson \r\n\r\nDo you know if it's possible to also support writing to Parquet using the HF ParquetWriter if `file_format=\"parquet\"` ?\r\n\r\nParquet is often used when people want to stream the data to train models - which is suitable for big datasets. On the other hand Arrow is generally used for local memory mapping with random access.\r\n\r\n> Please note there was a previous PR adding this functionality\r\n\r\nAm I right to say that it uses the spark workers to prepare the Arrow files ? If so this should make the data preparation fast and won't fill up the executor's memory as in the previously proposed PR", "Thanks for taking a look! Unlike the previous PR's approach, this implementation takes advantage of Spark mapping to distribute file writing over multiple tasks. (Also it doesn't load the entire dataset into memory :) )\r\n\r\nSupporting Parquet here sgtm; I'll modify the PR.\r\n\r\nI also updated the PR description with a common Spark-HF use case that we want to improve." ]

"2023-04-03T23:51:29"

"2023-04-10T04:47:15"

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[ "Have you tried to set the cache file names if `keep_in_memory`is True ?\r\n\r\n```diff\r\n- if self.cache_files:\r\n+ if self.cache_files and not keep_in_memory:\r\n```\r\n\r\nThis way it doesn't change the indice cache arguments and leave them as `None`", "@lhoestq \r\nRegarding what you suggest:\r\nThe thing is, if cached files already exist and do correspond to the split that we are currently trying to perform, then it would be a shame not to use them, would it not? So I don't think that we should necessarily bypass this step in the method (corresponding to the reading of already existing data), if 'keep_in_memory' = True. For me, 'keep_in_memory' = True is supposed to mean \"don't cache the output of this method\", but it should say nothing regarding what to do with potentially already existing cached data, should it?\r\nBesides, even if we do what you suggest, and do only that (so, not the modifs that I suggested), then, assuming that 'keep_in_memory' = False and that there exist cached files, if the following check on the existence of cached files with specific name fails, we will still have ended up modifying an input value which will be then used in the remaining of the method, potentially altering the behavior that the user intended the method's call to have. Basically, the issue with what you suggest is that we can't guaranty that we won't continue with the remaining of the method even if this condition is met. Because of that, in my opinion, the best way to not have to worry about potential, unwanted side effects in the rest of the code is to not modify those variables in place, and so, here, to use other variables.\r\nSo, I'm sorry, but for those two reasons, I don't think that what you are suggesting addresses the problems which are described in the opened issue.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5700). All of your documentation changes will be reflected on that endpoint.", "Makes sense ! Therefore removing the ValueError messages sounds good to me, thanks for detailing.\r\n\r\nThen I think it's fine to keep using the same variables for the cache file names is enough instead of defining new ones - it doesn't alter the behavior of the function. Otherwise it would feel a bit confusing to have similar variables with slightly modified names just for that", "Ok for the removing the ValueError exceptions, thanks.\r\n\r\nThat said, it seems to me like we should still find a way not to modify the values input by the user, insofar as they can be used elsewhere down the line in the program. Sure, here, by removing the raising of those ValueError exceptions, we have fixed one use cases were allowing this modification actually caused an issue, but maybe there are other use cases where this would also caused an issue? Also, maybe in the future we will add other functionalities which will depend on the values of those input parameters, with then new risks of such an issue occurring?\r\nThat's why, in order not to have to worry about that, and in order to make the code a bit more future -proof, I suggest that make sure those input values are not modified.\r\n\r\nOne way that I did this is to create different but similar looking variable names. If you find this confusing, we can always add a comment.\r\nAnother way would be to not store the result of the conditional definition of the values (the '\\_cache_file_name = (... if condition else ...)' in my proposition of code), and to use it every time we need. But since we use those new variables at least twice, that creates code redundancy, which is not great either.\r\nFinally, a third way that I can imagine would be to put all this logic into its own method, which would then encapsulate it, and protect the remaining of the 'train_test_split' code from all unintended side effect that this logic can currently cause. This one is probably best. Also, maybe it could be used to remove some code redundancy elsewhere in the definition of the Dataset class? I have not checked if such a code redundancy exists.", "We're already replacing the user's input by default values automatically in other methods, it's fine to do it here as well and actually fits the library's style.\r\n\r\nNote that the case where it would reload the cache even if `keep_in_memory=True` is not implemented though, but it should be easy to add in `_select_with_indices_mapping`:\r\n- add keep_in_memory in `_new_dataset_with_indices` that uses InMemoryTable.from_file\r\n- inside `_select_with_indices_mapping` return the dataset from `_new_dataset_with_indices` if:\r\n - `keep_in_memory=True`\r\n - and `indices_cache_file_name` is not None and exists \r\n - and `is_caching_enabled()`\r\n\r\nBecause if we let it this way it would recreate the cache file unfortunately", "> We're already replacing the user's input by default values automatically in other methods, it's fine to do it here as well and actually fits the library's style.\r\n\r\nI think the fact that it's a style of the library is not really an argument in itself; however, after thinking through it several times, I think I know see why your solution is acceptable: as soon as the user specifies that 'keep_in_memory=True', they should not care anymore about the value of the '\\_indices_cache_file_name' variables, since from their point of view those are now irrelevant. So it's \"fine\" if we allow ourselves to modify the value of those variables, if it helps the internal code being more concise.\r\nStill, I find that it's a bit unintuitive, and a risk as far as future evolution of the method / of the code is concerned; someone tasked with doing that would need to have the knowledge of a lot of, if not all, the other methods of the class, in order to understand the potentially far-reaching impact of some modifications made to this portion of the code. But I guess that's a choice which is the library's owners to make. Also, if we use your proposed solution, as I explained, we can't get the benefit of potentially reusing possibly already existing cached data.\r\nOn that note...\r\n\r\n> Note that the case where it would reload the cache even if `keep_in_memory=True` is not implemented though\r\n\r\nI'm not sure what you mean here:\r\nWithin the current code trying to load up the potentially already existing split data, there is no trace of the 'keep_in_memory' variable. So why do you say that 'the case where it would reload the cache even if keep_in_memory=True is not implemented' (I assume that you mean 'currently implemented')? Surely, currently, this bit of code works regardless of the value of the 'keep_in_memory' variable', does it not?" ]

"2023-04-03T18:05:26"

"2023-04-06T17:17:27"

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[ "Hi ! Good catch, this is wrong indeed and thanks for opening a PR :)" ]

"2023-04-03T17:00:07"

"2023-04-04T16:52:42"

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"2023-04-03T14:25:19"

"2023-04-03T14:25:19"

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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) | 0.009644 / 0.011353 (-0.001709) | 0.006407 / 0.011008 (-0.004601) | 0.148353 / 0.038508 (0.109845) | 0.037537 / 0.023109 (0.014428) | 0.379697 / 0.275898 (0.103799) | 0.466260 / 0.323480 (0.142780) | 0.007884 / 0.007986 (-0.000102) | 0.005140 / 0.004328 (0.000812) | 0.111078 / 0.004250 (0.106827) | 0.049429 / 0.037052 (0.012377) | 0.364766 / 0.258489 (0.106277) | 0.453809 / 0.293841 (0.159968) | 0.051918 / 0.128546 (-0.076628) | 0.020081 / 0.075646 (-0.055566) | 0.616041 / 0.419271 (0.196770) | 0.059834 / 0.043533 (0.016301) | 0.373104 / 0.255139 (0.117965) | 0.419304 / 0.283200 (0.136104) | 0.113526 / 0.141683 (-0.028156) | 1.827160 / 1.452155 (0.375006) | 1.912092 / 1.492716 (0.419376) |\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) | 0.269584 / 0.018006 (0.251578) | 0.554100 / 0.000490 (0.553610) | 0.006618 / 0.000200 (0.006418) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025280 / 0.037411 (-0.012131) | 0.123116 / 0.014526 (0.108591) | 0.127674 / 0.176557 (-0.048883) | 0.189106 / 0.737135 (-0.548030) | 0.142072 / 0.296338 (-0.154267) |\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) | 0.602201 / 0.215209 (0.386992) | 5.959610 / 2.077655 (3.881956) | 2.404856 / 1.504120 (0.900736) | 2.175017 / 1.541195 (0.633823) | 2.154360 / 1.468490 (0.685870) | 1.265339 / 4.584777 (-3.319438) | 5.598429 / 3.745712 (1.852716) | 5.130249 / 5.269862 (-0.139612) | 2.764922 / 4.565676 (-1.800754) | 0.143232 / 0.424275 (-0.281043) | 0.014721 / 0.007607 (0.007114) | 0.764734 / 0.226044 (0.538689) | 7.518810 / 2.268929 (5.249882) | 3.344734 / 55.444624 (-52.099890) | 2.601158 / 6.876477 (-4.275319) | 2.726018 / 2.142072 (0.583945) | 1.397918 / 4.805227 (-3.407309) | 0.253277 / 6.500664 (-6.247387) | 0.077772 / 0.075469 (0.002303) |\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) | 1.499535 / 1.841788 (-0.342253) | 17.782490 / 8.074308 (9.708182) | 21.953064 / 10.191392 (11.761672) | 0.248753 / 0.680424 (-0.431671) | 0.029194 / 0.534201 (-0.505007) | 0.529700 / 0.579283 (-0.049583) | 0.618412 / 0.434364 (0.184048) | 0.605062 / 0.540337 (0.064725) | 0.725661 / 1.386936 (-0.661275) |\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) | 0.009489 / 0.011353 (-0.001864) | 0.006423 / 0.011008 (-0.004585) | 0.096789 / 0.038508 (0.058281) | 0.034639 / 0.023109 (0.011530) | 0.403875 / 0.275898 (0.127977) | 0.439368 / 0.323480 (0.115888) | 0.006354 / 0.007986 (-0.001631) | 0.006794 / 0.004328 (0.002466) | 0.095537 / 0.004250 (0.091287) | 0.047749 / 0.037052 (0.010697) | 0.424157 / 0.258489 (0.165668) | 0.487825 / 0.293841 (0.193984) | 0.054675 / 0.128546 (-0.073872) | 0.021349 / 0.075646 (-0.054297) | 0.108917 / 0.419271 (-0.310354) | 0.075891 / 0.043533 (0.032358) | 0.412889 / 0.255139 (0.157750) | 0.464512 / 0.283200 (0.181312) | 0.118832 / 0.141683 (-0.022850) | 1.721215 / 1.452155 (0.269060) | 1.857195 / 1.492716 (0.364478) |\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) | 0.248308 / 0.018006 (0.230302) | 0.559496 / 0.000490 (0.559006) | 0.007136 / 0.000200 (0.006936) | 0.000160 / 0.000054 (0.000106) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031772 / 0.037411 (-0.005639) | 0.123565 / 0.014526 (0.109039) | 0.132660 / 0.176557 (-0.043896) | 0.201428 / 0.737135 (-0.535707) | 0.135238 / 0.296338 (-0.161101) |\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) | 0.646978 / 0.215209 (0.431769) | 6.183477 / 2.077655 (4.105822) | 2.782117 / 1.504120 (1.277997) | 2.294093 / 1.541195 (0.752898) | 2.346932 / 1.468490 (0.878442) | 1.239085 / 4.584777 (-3.345692) | 5.696364 / 3.745712 (1.950652) | 4.980102 / 5.269862 (-0.289759) | 2.278116 / 4.565676 (-2.287560) | 0.157339 / 0.424275 (-0.266936) | 0.014936 / 0.007607 (0.007329) | 0.778001 / 0.226044 (0.551957) | 7.708066 / 2.268929 (5.439138) | 3.412235 / 55.444624 (-52.032389) | 2.670670 / 6.876477 (-4.205806) | 2.731802 / 2.142072 (0.589730) | 1.446516 / 4.805227 (-3.358712) | 0.263689 / 6.500664 (-6.236975) | 0.086359 / 0.075469 (0.010890) |\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) | 1.573169 / 1.841788 (-0.268619) | 17.690842 / 8.074308 (9.616534) | 20.343336 / 10.191392 (10.151944) | 0.231028 / 0.680424 (-0.449396) | 0.025954 / 0.534201 (-0.508247) | 0.570554 / 0.579283 (-0.008729) | 0.610453 / 0.434364 (0.176089) | 0.675830 / 0.540337 (0.135493) | 0.790650 / 1.386936 (-0.596286) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d094ed07823bfb3271f3a9006daa1f92a64967a5 \"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) | 0.007553 / 0.011353 (-0.003800) | 0.005426 / 0.011008 (-0.005582) | 0.096550 / 0.038508 (0.058042) | 0.034393 / 0.023109 (0.011284) | 0.322297 / 0.275898 (0.046399) | 0.340943 / 0.323480 (0.017463) | 0.006350 / 0.007986 (-0.001635) | 0.005700 / 0.004328 (0.001372) | 0.074929 / 0.004250 (0.070678) | 0.054819 / 0.037052 (0.017767) | 0.320151 / 0.258489 (0.061662) | 0.346957 / 0.293841 (0.053116) | 0.036659 / 0.128546 (-0.091887) | 0.012443 / 0.075646 (-0.063204) | 0.332232 / 0.419271 (-0.087040) | 0.051467 / 0.043533 (0.007934) | 0.310952 / 0.255139 (0.055813) | 0.325617 / 0.283200 (0.042417) | 0.104908 / 0.141683 (-0.036775) | 1.446752 / 1.452155 (-0.005403) | 1.558773 / 1.492716 (0.066056) |\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) | 0.300639 / 0.018006 (0.282633) | 0.499901 / 0.000490 (0.499411) | 0.007340 / 0.000200 (0.007140) | 0.000255 / 0.000054 (0.000201) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027206 / 0.037411 (-0.010206) | 0.105603 / 0.014526 (0.091077) | 0.118669 / 0.176557 (-0.057887) | 0.174050 / 0.737135 (-0.563086) | 0.125099 / 0.296338 (-0.171239) |\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) | 0.404285 / 0.215209 (0.189076) | 4.034587 / 2.077655 (1.956933) | 1.812639 / 1.504120 (0.308519) | 1.625745 / 1.541195 (0.084551) | 1.735523 / 1.468490 (0.267033) | 0.709699 / 4.584777 (-3.875078) | 3.802196 / 3.745712 (0.056484) | 3.656984 / 5.269862 (-1.612877) | 1.968470 / 4.565676 (-2.597206) | 0.086612 / 0.424275 (-0.337663) | 0.012368 / 0.007607 (0.004761) | 0.502622 / 0.226044 (0.276577) | 5.017876 / 2.268929 (2.748948) | 2.279794 / 55.444624 (-53.164831) | 1.956938 / 6.876477 (-4.919538) | 2.150430 / 2.142072 (0.008357) | 0.847691 / 4.805227 (-3.957536) | 0.170157 / 6.500664 (-6.330507) | 0.064141 / 0.075469 (-0.011328) |\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) | 1.172246 / 1.841788 (-0.669542) | 15.229444 / 8.074308 (7.155136) | 14.715913 / 10.191392 (4.524521) | 0.192501 / 0.680424 (-0.487923) | 0.017972 / 0.534201 (-0.516229) | 0.423834 / 0.579283 (-0.155449) | 0.423019 / 0.434364 (-0.011345) | 0.493298 / 0.540337 (-0.047039) | 0.589833 / 1.386936 (-0.797103) |\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) | 0.007773 / 0.011353 (-0.003580) | 0.005449 / 0.011008 (-0.005560) | 0.075180 / 0.038508 (0.036672) | 0.035221 / 0.023109 (0.012111) | 0.338169 / 0.275898 (0.062271) | 0.374002 / 0.323480 (0.050522) | 0.006391 / 0.007986 (-0.001595) | 0.004406 / 0.004328 (0.000078) | 0.074925 / 0.004250 (0.070675) | 0.056527 / 0.037052 (0.019475) | 0.338071 / 0.258489 (0.079582) | 0.391882 / 0.293841 (0.098041) | 0.037241 / 0.128546 (-0.091305) | 0.012546 / 0.075646 (-0.063100) | 0.087331 / 0.419271 (-0.331940) | 0.049851 / 0.043533 (0.006318) | 0.335264 / 0.255139 (0.080125) | 0.354813 / 0.283200 (0.071614) | 0.110614 / 0.141683 (-0.031069) | 1.432782 / 1.452155 (-0.019372) | 1.548800 / 1.492716 (0.056083) |\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) | 0.307892 / 0.018006 (0.289886) | 0.518809 / 0.000490 (0.518319) | 0.004058 / 0.000200 (0.003858) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029155 / 0.037411 (-0.008256) | 0.111706 / 0.014526 (0.097180) | 0.122964 / 0.176557 (-0.053592) | 0.170939 / 0.737135 (-0.566196) | 0.128538 / 0.296338 (-0.167801) |\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) | 0.426529 / 0.215209 (0.211320) | 4.254218 / 2.077655 (2.176563) | 2.011455 / 1.504120 (0.507335) | 1.817397 / 1.541195 (0.276202) | 1.952915 / 1.468490 (0.484425) | 0.705052 / 4.584777 (-3.879725) | 3.844458 / 3.745712 (0.098746) | 3.592754 / 5.269862 (-1.677107) | 1.573567 / 4.565676 (-2.992109) | 0.086834 / 0.424275 (-0.337441) | 0.012389 / 0.007607 (0.004782) | 0.541695 / 0.226044 (0.315650) | 5.224492 / 2.268929 (2.955564) | 2.473648 / 55.444624 (-52.970976) | 2.167458 / 6.876477 (-4.709019) | 2.253319 / 2.142072 (0.111246) | 0.836322 / 4.805227 (-3.968905) | 0.168680 / 6.500664 (-6.331984) | 0.065699 / 0.075469 (-0.009770) |\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) | 1.281886 / 1.841788 (-0.559902) | 15.451741 / 8.074308 (7.377433) | 14.906870 / 10.191392 (4.715478) | 0.168554 / 0.680424 (-0.511870) | 0.017365 / 0.534201 (-0.516836) | 0.434183 / 0.579283 (-0.145100) | 0.421891 / 0.434364 (-0.012473) | 0.538993 / 0.540337 (-0.001344) | 0.636212 / 1.386936 (-0.750724) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f428b8172319a6bfe95d7a4356b1d14a8d386d8 \"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) | 0.007362 / 0.011353 (-0.003991) | 0.004992 / 0.011008 (-0.006016) | 0.098730 / 0.038508 (0.060222) | 0.033673 / 0.023109 (0.010563) | 0.296334 / 0.275898 (0.020436) | 0.328208 / 0.323480 (0.004728) | 0.005658 / 0.007986 (-0.002327) | 0.004130 / 0.004328 (-0.000199) | 0.074596 / 0.004250 (0.070346) | 0.048230 / 0.037052 (0.011178) | 0.295631 / 0.258489 (0.037142) | 0.347176 / 0.293841 (0.053335) | 0.036359 / 0.128546 (-0.092187) | 0.011889 / 0.075646 (-0.063758) | 0.332889 / 0.419271 (-0.086382) | 0.049708 / 0.043533 (0.006175) | 0.291207 / 0.255139 (0.036068) | 0.311066 / 0.283200 (0.027867) | 0.098418 / 0.141683 (-0.043265) | 1.415450 / 1.452155 (-0.036705) | 1.526928 / 1.492716 (0.034212) |\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) | 0.212636 / 0.018006 (0.194630) | 0.432337 / 0.000490 (0.431847) | 0.006839 / 0.000200 (0.006639) | 0.000205 / 0.000054 (0.000150) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026045 / 0.037411 (-0.011366) | 0.107427 / 0.014526 (0.092901) | 0.114634 / 0.176557 (-0.061922) | 0.169943 / 0.737135 (-0.567192) | 0.123290 / 0.296338 (-0.173048) |\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) | 0.409432 / 0.215209 (0.194223) | 4.097910 / 2.077655 (2.020255) | 1.857177 / 1.504120 (0.353057) | 1.672355 / 1.541195 (0.131160) | 1.740130 / 1.468490 (0.271640) | 0.706520 / 4.584777 (-3.878257) | 3.773606 / 3.745712 (0.027893) | 2.101635 / 5.269862 (-3.168226) | 1.326295 / 4.565676 (-3.239382) | 0.085672 / 0.424275 (-0.338604) | 0.012142 / 0.007607 (0.004534) | 0.501168 / 0.226044 (0.275123) | 5.049784 / 2.268929 (2.780855) | 2.322477 / 55.444624 (-53.122148) | 1.990105 / 6.876477 (-4.886372) | 2.115003 / 2.142072 (-0.027070) | 0.837518 / 4.805227 (-3.967709) | 0.168457 / 6.500664 (-6.332207) | 0.064622 / 0.075469 (-0.010847) |\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) | 1.188152 / 1.841788 (-0.653635) | 14.991585 / 8.074308 (6.917276) | 14.635187 / 10.191392 (4.443795) | 0.183708 / 0.680424 (-0.496716) | 0.017452 / 0.534201 (-0.516749) | 0.418963 / 0.579283 (-0.160320) | 0.428893 / 0.434364 (-0.005471) | 0.502108 / 0.540337 (-0.038229) | 0.596345 / 1.386936 (-0.790591) |\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) | 0.007404 / 0.011353 (-0.003949) | 0.005148 / 0.011008 (-0.005860) | 0.074785 / 0.038508 (0.036277) | 0.033815 / 0.023109 (0.010706) | 0.332752 / 0.275898 (0.056854) | 0.368018 / 0.323480 (0.044538) | 0.005642 / 0.007986 (-0.002344) | 0.004041 / 0.004328 (-0.000287) | 0.073455 / 0.004250 (0.069205) | 0.047380 / 0.037052 (0.010328) | 0.337017 / 0.258489 (0.078528) | 0.384185 / 0.293841 (0.090344) | 0.036592 / 0.128546 (-0.091954) | 0.012109 / 0.075646 (-0.063537) | 0.086862 / 0.419271 (-0.332410) | 0.049030 / 0.043533 (0.005497) | 0.336542 / 0.255139 (0.081403) | 0.350295 / 0.283200 (0.067096) | 0.100998 / 0.141683 (-0.040685) | 1.469749 / 1.452155 (0.017594) | 1.588355 / 1.492716 (0.095639) |\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) | 0.227552 / 0.018006 (0.209546) | 0.438087 / 0.000490 (0.437598) | 0.000394 / 0.000200 (0.000194) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030575 / 0.037411 (-0.006836) | 0.111914 / 0.014526 (0.097388) | 0.124583 / 0.176557 (-0.051973) | 0.175471 / 0.737135 (-0.561665) | 0.129535 / 0.296338 (-0.166803) |\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) | 0.425625 / 0.215209 (0.210416) | 4.228328 / 2.077655 (2.150673) | 2.021087 / 1.504120 (0.516967) | 1.832550 / 1.541195 (0.291355) | 1.925572 / 1.468490 (0.457082) | 0.690772 / 4.584777 (-3.894005) | 3.724900 / 3.745712 (-0.020813) | 2.080286 / 5.269862 (-3.189576) | 1.316854 / 4.565676 (-3.248822) | 0.085123 / 0.424275 (-0.339152) | 0.012078 / 0.007607 (0.004471) | 0.525802 / 0.226044 (0.299758) | 5.242598 / 2.268929 (2.973670) | 2.491596 / 55.444624 (-52.953028) | 2.125156 / 6.876477 (-4.751320) | 2.185922 / 2.142072 (0.043850) | 0.823116 / 4.805227 (-3.982111) | 0.165188 / 6.500664 (-6.335476) | 0.063970 / 0.075469 (-0.011499) |\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) | 1.256948 / 1.841788 (-0.584840) | 14.981990 / 8.074308 (6.907682) | 14.565266 / 10.191392 (4.373874) | 0.175064 / 0.680424 (-0.505360) | 0.017628 / 0.534201 (-0.516573) | 0.429979 / 0.579283 (-0.149304) | 0.422509 / 0.434364 (-0.011855) | 0.546262 / 0.540337 (0.005924) | 0.647103 / 1.386936 (-0.739833) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0803a006db1c395ac715662cc6079651f77c11ea \"CML watermark\")\n" ]

"2023-04-03T10:44:58"

"2023-04-04T15:05:24"

"2023-04-04T14:58:16"

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"2023-04-03T09:40:03"

"2023-04-04T14:58:18"

"2023-04-04T14:58:18"

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[ "Hi ! It looks like an issue with PyArrow: https://issues.apache.org/jira/browse/ARROW-5030\r\n\r\nIt appears it can happen when you have parquet files with row groups larger than 2GB.\r\nI can see that your parquet files are around 10GB. It is usually advised to keep a value around the default value 500MB to avoid these issues.\r\n\r\nNote that currently the row group size is simply defined by the number of rows `datasets.config.DEFAULT_MAX_BATCH_SIZE`, so reducing this value could let you have parquet files bigger than 2GB and with row groups lower than 2GB.\r\n\r\nWould it be possible for you to re-upload the dataset with the default shard size 500MB ?", "Hey, thanks for the reply! I've since switched to working with the locally-saved dataset (which works).\r\nMaybe it makes sense to show a warning for uploads with large shard sizes? Since the functionality completely breaks (due to the PyArrow bug).", "Just tried uploading the same dataset with 500MB shards, I get an errors 4 hours in:\r\n\r\n```\r\nPushing dataset shards to the dataset hub: 25%|██▍ | 358/1453 [4:40:31<14:18:00, 47.01s/it]\r\nTraceback (most recent call last):\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 344, in _inner_upload_lfs_object\r\n return _upload_lfs_object(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _upload_lfs_object\r\n lfs_upload(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 254, in lfs_upload\r\n _upload_multi_part(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 374, in _upload_multi_part\r\n hf_raise_for_status(part_upload_res)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 301, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 46, in __init__\r\n server_data = response.json()\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/requests/models.py\", line 899, in json\r\n return complexjson.loads(\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/json/__init__.py\", line 357, in loads\r\n return _default_decoder.decode(s)\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/json/decoder.py\", line 337, in decode\r\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/json/decoder.py\", line 355, in raw_decode\r\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"process_wit.py\", line 146, in <module>\r\n dataset.push_to_hub(FINAL_PATH, max_shard_size=\"500MB\", private=False)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1534, in push_to_hub\r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 4804, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 281, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 120, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 2593, in upload_file\r\n commit_info = self.create_commit(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 120, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 2411, in create_commit\r\n upload_lfs_files(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 120, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 351, in upload_lfs_files\r\n thread_map(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map\r\n return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py\", line 51, in _executor_map\r\n return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/tqdm/std.py\", line 1178, in __iter__\r\n for obj in iterable:\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/_base.py\", line 619, in result_iterator\r\n yield fs.pop().result()\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/_base.py\", line 444, in result\r\n return self.__get_result()\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/_base.py\", line 389, in __get_result\r\n raise self._exception\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/thread.py\", line 57, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 346, in _inner_upload_lfs_object\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'data/train-00358-of-01453-22a5cc8b3eb12be3.parquet' to the Hub.\r\n```\r\nLocal saves do work, however.", "Hmmm that was probably an intermitent bug, you can resume the upload by re-running push_to_hub", "Leaving this other error here for the record, which occurs when I load the +700GB dataset from the hub with shard sizes of 500MB:\r\n\r\n```\r\n Traceback (most recent call last): \r\n File \"/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py\", line 1860, in _prepare_split_single\r\n for _, table in generator:\r\n File \"/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 69, in _generate_tables\r\n for batch_idx, record_batch in enumerate(\r\n File \"pyarrow/_parquet.pyx\", line 1323, in iter_batches\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\nOSError: Corrupt snappy compressed data.\r\n```\r\nI will probably switch back to the local big dataset or shrink it." ]

"2023-04-02T14:42:44"

"2023-04-10T08:04:04"

"2023-04-10T08:04:04"

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[ "Originally we also though about adding it to the YAML part of the README.md:\r\n\r\n```yaml\r\nbuilder_config:\r\n data_dir: data\r\n data_files:\r\n - split: train\r\n pattern: \"train-[0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*\"\r\n```\r\n\r\nHaving it in the README.md could make it easier to modify it in the UI on HF, and for validation on commit", "From internal discussions we agreed to go with the YAML approach, since it's the one that seems more appropriate to be modified by a human on the Hub or locally (while JSON e.g. for models are usually created programmatically).", "Current format:\r\n```yaml\r\nbuilder_config:\r\n data_files:\r\n - split: train\r\n pattern: data/train-*\r\n```" ]

"2023-04-01T13:08:05"

"2023-04-04T14:54:37"

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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) | 0.007841 / 0.011353 (-0.003512) | 0.005640 / 0.011008 (-0.005368) | 0.096465 / 0.038508 (0.057957) | 0.036476 / 0.023109 (0.013367) | 0.306431 / 0.275898 (0.030533) | 0.339545 / 0.323480 (0.016065) | 0.006064 / 0.007986 (-0.001922) | 0.004404 / 0.004328 (0.000076) | 0.073130 / 0.004250 (0.068879) | 0.052765 / 0.037052 (0.015713) | 0.309895 / 0.258489 (0.051406) | 0.354037 / 0.293841 (0.060196) | 0.037127 / 0.128546 (-0.091420) | 0.012387 / 0.075646 (-0.063260) | 0.333503 / 0.419271 (-0.085769) | 0.059799 / 0.043533 (0.016266) | 0.305496 / 0.255139 (0.050358) | 0.324122 / 0.283200 (0.040922) | 0.107007 / 0.141683 (-0.034676) | 1.416743 / 1.452155 (-0.035411) | 1.520772 / 1.492716 (0.028055) |\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) | 0.261233 / 0.018006 (0.243227) | 0.573806 / 0.000490 (0.573316) | 0.000390 / 0.000200 (0.000190) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027672 / 0.037411 (-0.009740) | 0.112803 / 0.014526 (0.098278) | 0.121085 / 0.176557 (-0.055471) | 0.176056 / 0.737135 (-0.561080) | 0.127171 / 0.296338 (-0.169167) |\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) | 0.414756 / 0.215209 (0.199547) | 4.148743 / 2.077655 (2.071088) | 1.883940 / 1.504120 (0.379820) | 1.698771 / 1.541195 (0.157576) | 1.811926 / 1.468490 (0.343436) | 0.708293 / 4.584777 (-3.876484) | 3.780456 / 3.745712 (0.034744) | 2.098556 / 5.269862 (-3.171306) | 1.323512 / 4.565676 (-3.242164) | 0.086253 / 0.424275 (-0.338022) | 0.012587 / 0.007607 (0.004980) | 0.514824 / 0.226044 (0.288779) | 5.157415 / 2.268929 (2.888487) | 2.382519 / 55.444624 (-53.062105) | 2.014539 / 6.876477 (-4.861938) | 2.215239 / 2.142072 (0.073166) | 0.847178 / 4.805227 (-3.958049) | 0.170053 / 6.500664 (-6.330611) | 0.066461 / 0.075469 (-0.009008) |\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) | 1.199056 / 1.841788 (-0.642732) | 15.244999 / 8.074308 (7.170691) | 14.661593 / 10.191392 (4.470201) | 0.168855 / 0.680424 (-0.511569) | 0.017889 / 0.534201 (-0.516312) | 0.424961 / 0.579283 (-0.154322) | 0.428632 / 0.434364 (-0.005732) | 0.502680 / 0.540337 (-0.037658) | 0.597827 / 1.386936 (-0.789109) |\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) | 0.007749 / 0.011353 (-0.003604) | 0.005527 / 0.011008 (-0.005482) | 0.074774 / 0.038508 (0.036266) | 0.035367 / 0.023109 (0.012258) | 0.340594 / 0.275898 (0.064696) | 0.373970 / 0.323480 (0.050490) | 0.006094 / 0.007986 (-0.001892) | 0.004428 / 0.004328 (0.000100) | 0.074120 / 0.004250 (0.069869) | 0.054852 / 0.037052 (0.017800) | 0.357173 / 0.258489 (0.098684) | 0.388877 / 0.293841 (0.095036) | 0.037002 / 0.128546 (-0.091545) | 0.012337 / 0.075646 (-0.063309) | 0.086962 / 0.419271 (-0.332310) | 0.050370 / 0.043533 (0.006837) | 0.342989 / 0.255139 (0.087850) | 0.358065 / 0.283200 (0.074865) | 0.111063 / 0.141683 (-0.030620) | 1.516704 / 1.452155 (0.064549) | 1.634359 / 1.492716 (0.141643) |\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) | 0.261493 / 0.018006 (0.243487) | 0.566288 / 0.000490 (0.565799) | 0.000439 / 0.000200 (0.000239) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030426 / 0.037411 (-0.006985) | 0.114606 / 0.014526 (0.100080) | 0.126134 / 0.176557 (-0.050423) | 0.175324 / 0.737135 (-0.561812) | 0.132766 / 0.296338 (-0.163573) |\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) | 0.426785 / 0.215209 (0.211576) | 4.243555 / 2.077655 (2.165900) | 2.089631 / 1.504120 (0.585511) | 1.994562 / 1.541195 (0.453367) | 2.140284 / 1.468490 (0.671794) | 0.698645 / 4.584777 (-3.886132) | 3.807471 / 3.745712 (0.061759) | 3.275343 / 5.269862 (-1.994519) | 1.796756 / 4.565676 (-2.768921) | 0.085986 / 0.424275 (-0.338289) | 0.012213 / 0.007607 (0.004606) | 0.536815 / 0.226044 (0.310771) | 5.344611 / 2.268929 (3.075683) | 2.498578 / 55.444624 (-52.946047) | 2.153260 / 6.876477 (-4.723217) | 2.251310 / 2.142072 (0.109237) | 0.839104 / 4.805227 (-3.966123) | 0.169639 / 6.500664 (-6.331025) | 0.065880 / 0.075469 (-0.009589) |\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) | 1.268610 / 1.841788 (-0.573178) | 15.624915 / 8.074308 (7.550606) | 15.163684 / 10.191392 (4.972292) | 0.172992 / 0.680424 (-0.507432) | 0.018154 / 0.534201 (-0.516047) | 0.440485 / 0.579283 (-0.138798) | 0.431949 / 0.434364 (-0.002415) | 0.547935 / 0.540337 (0.007597) | 0.662442 / 1.386936 (-0.724494) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5c8a6ba43c4aaa0ca0665d8dadd87ef33e28e8e4 \"CML watermark\")\n" ]

"2023-03-31T19:51:38"

"2023-04-03T18:43:30"

"2023-04-03T18:29:58"

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[ "Hi! The link pointing to the code that generated the dataset is broken. Can you please fix it to make debugging easier?", "> Hi! The link pointing to the code that generated the dataset is broken. Can you please fix it to make debugging easier?\r\n\r\nSorry about that, it's fixed now.\r\n" ]

"2023-03-31T18:19:40"

"2023-04-04T14:38:30"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5691). All of your documentation changes will be reflected on that endpoint.", "[Confirmed](https://huggingface.slack.com/archives/C02EMARJ65P/p1680541667004199) with the Hub team the file size limit for the Hugging Face Hub is 10MB :)" ]

"2023-03-31T17:17:26"

"2023-04-03T18:00:10"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5689). All of your documentation changes will be reflected on that endpoint.", "```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"wikipedia\", \"20220301.en\", split=\"train\", streaming=True); item = next(iter(ds)); item\r\nOut[2]: \r\n{'id': '12',\r\n 'url': 'https://en.wikipedia.org/wiki/Anarchism',\r\n 'title': 'Anarchism',\r\n 'text': 'Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, placed on the farthest left of the political spectrum, it is usually described alongside communalism and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement,...}\r\n```", "I love your example 🏴‍🅰️" ]

"2023-03-31T08:44:24"

"2023-04-07T18:15:34"

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[ "Hi @wccccp, thanks for reporting. \r\nThat's weird since `huggingface_hub` _has_ a module called `hf_api` and you are using a recent version of it. \r\n\r\nWhich version of `datasets` are you using? And is it a bug that you experienced only recently? (cc @lhoestq can it be somehow related to the recent release of `datasets`?)\r\n\r\n~@wccccp what I can suggest you is to uninstall and reinstall completely huggingface_hub and datasets? My first guess is that there is a discrepancy somewhere in your setup 😕~", "@wccccp Actually I have also been able to reproduce the error so it's not an issue with your setup.\r\n\r\n@huggingface/datasets I found this issue quite weird. Is this a module that is not used very often?\r\nThe problematic line is [this one](https://github.com/huggingface/datasets/blame/c33e8ce68b5000988bf6b2e4bca27ffaa469acea/src/datasets/data_files.py#L476) where `huggingface_hub.hf_api.DatasetInfo` is used. `huggingface_hub` is imported [here](https://github.com/huggingface/datasets/blame/c33e8ce68b5000988bf6b2e4bca27ffaa469acea/src/datasets/data_files.py#L6) as `import huggingface_hub`. However since modules are lazy-loaded in `hfh` you need to explicitly import them (i.e. `import huggingface_hub.hf_api`).\r\n\r\nWhat's weird is that nothing has changed for months. Datasets code seems that it didn't change for 2 years when I git-blame this part. And lazy-loading was introduced 1 year ago in `huggingface_hub`. Could it be that `data_files.py` is a file almost never used?\r\n", "For context, I tried to run `import huggingface_hub; huggingface_hub.hf_api.DatasetInfo` in the terminal with different versions of `hfh` and I need to go back to `huggingface_hub==0.7.0` to make it work (latest is 0.13.3).", "Before the error happens at line 120 in `data_files.py`, `datasets.filesystems.hffilesystem` is imported at the top of `data_files.py` and this file does `from huggingface_hub.hf_api import DatasetInfo` - so `huggingface_hub.hf_api` is imported. Not sure how the error could happen, what version of `datasets` are you using @wccccp ?" ]

"2023-03-31T08:22:22"

"2023-04-03T16:02:25"

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[ "Hi @adrianfagerland, thanks for reporting.\r\n\r\nPlease note that \"wikipedia\" is a special dataset, with an Apache Beam builder: https://beam.apache.org/\r\nYou can find more info about Beam datasets in our docs: https://huggingface.co/docs/datasets/beam\r\n\r\nIt was implemented to be run in parallel processing, using one of the distributed back-ends supported by Apache Beam: https://beam.apache.org/get-started/beam-overview/#apache-beam-pipeline-runners\r\n\r\nThat is, you are trying to process the source wikipedia data on your machine (not distributed) when passing `beam_runner=\"DirectRunner\"`.\r\n\r\nAs documented in the wikipedia dataset page (https://huggingface.co/datasets/wikipedia):\r\n\r\n Some subsets of Wikipedia have already been processed by HuggingFace, and you can load them just with:\r\n \r\n from datasets import load_dataset\r\n \r\n load_dataset(\"wikipedia\", \"20220301.en\")\r\n\r\n The list of pre-processed subsets is:\r\n - \"20220301.de\"\r\n - \"20220301.en\"\r\n - \"20220301.fr\"\r\n - \"20220301.frr\"\r\n - \"20220301.it\"\r\n - \"20220301.simple\"\r\n\r\nTo download the available processed data (in Arrow format):\r\n```python\r\nbuilder = datasets.load_dataset_builder(\"wikipedia\", \"20220301.en\")\r\nbuilder.download_and_prepare(your_path)\r\n```", "When running this using :\r\n```\r\nimport datasets\r\nfrom apache_beam.options.pipeline_options import PipelineOptions\r\nfrom gcsfs import GCSFileSystem\r\n\r\nstorage_options = {\"project\":\"tdt4310\", \"token\":\"cloud\"}\r\nfs = GCSFileSystem(**storage_options)\r\n\r\noutput_dir = \"gcs://quiz_transformer/\"\r\nbeam_options = PipelineOptions(\r\n region=\"europe-west4\",\r\n project=\"tdt4310\",\r\n temp_location=output_dir+\"tmp/\")\r\n\r\n\r\nbuilder = datasets.load_dataset_builder(\"wikipedia\", \"20220301.en\", beam_runner=\"dataflow\", beam_options=beam_options)\r\nbuilder.download_and_prepare(\r\n output_dir, storage_options=storage_options, file_format=\"parquet\")\r\n```\r\nI now get this error:\r\n```\r\nraise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json\r\nDownloading data files: 0%| | 0/1 [00:00<?, ?it/s]\r\n```\r\n\r\nI get the same error for this:\r\n```\r\nimport datasets\r\nfrom gcsfs import GCSFileSystem\r\n\r\nstorage_options = {\"project\":\"tdt4310\", \"token\":\"cloud\"}\r\nfs = GCSFileSystem(**storage_options)\r\n\r\noutput_dir = \"gcs://quiz_transformer/\"\r\nbuilder = datasets.load_dataset_builder(\"wikipedia\", \"20220301.en\")\r\nbuilder.download_and_prepare(\r\n output_dir, storage_options=storage_options, file_format=\"parquet\")\r\n```\r\n\r\n\r\n\r\n" ]

"2023-03-30T23:43:22"

"2023-03-31T13:31:32"

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[ "Great idea!\r\n\r\nShould we also take this opportunity to include some audio/image file formats? Currently, it still reads very text heavy. Something like:\r\n\r\n> We support many text, audio, and image data extensions such as `.zip`, `.rar`, `.mp3`, and `.jpg` among many others. For data extensions like `.csv`, `.json`, `.jsonl`, and `txt`, we recommend compressing them before uploading to the Hub. These file extensions are not tracked by Git LFS by default, and if they're too large, they will not be committed and uploaded. Take a look at the `.gitattributes` file in your repository for a complete list of supported file extensions.", "Hi @stevhliu, thanks for your suggestion.\r\n\r\nI agree it is a good opportunity to mention that audio/image file formats are also supported.\r\n\r\nNit:\r\nI would not mention .zip, .rar after \"text, audio, and image data extensions\". Those are \"compression\" extensions and not \"text, audio, and image data extensions\".\r\n\r\nWhat about something similar to:\r\n> We support many text, audio, and image data extensions such as `.csv`, `.mp3`, and `.jpg` among many others. For text data extensions like `.csv`, `.json`, `.jsonl`, and `.txt`, we recommend compressing them before uploading to the Hub (to `.zip` or `.gz` file extension for example). \r\n>\r\n> Note that text file extensions are not tracked by Git LFS by default, and if they're too large, they will not be committed and uploaded. Take a look at the `.gitattributes` file in your repository for a complete list of tracked file extensions by default.\r\n\r\nNote that for compressions I have mentioned:\r\n- gz, to compress individual files\r\n- zip, to compress and archive multiple files; zip is preferred rather than tar because it supports streaming out of the box", "Perfect, thanks for making the distinction between compression and data extensions!" ]

"2023-03-30T06:41:07"

"2023-03-31T17:01:58"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5686). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008460 / 0.011353 (-0.002893) | 0.006114 / 0.011008 (-0.004894) | 0.121496 / 0.038508 (0.082987) | 0.035030 / 0.023109 (0.011920) | 0.397778 / 0.275898 (0.121880) | 0.429020 / 0.323480 (0.105540) | 0.007811 / 0.007986 (-0.000174) | 0.006269 / 0.004328 (0.001940) | 0.098895 / 0.004250 (0.094645) | 0.045407 / 0.037052 (0.008355) | 0.413679 / 0.258489 (0.155189) | 0.437491 / 0.293841 (0.143650) | 0.053207 / 0.128546 (-0.075339) | 0.018471 / 0.075646 (-0.057175) | 0.414800 / 0.419271 (-0.004472) | 0.060864 / 0.043533 (0.017332) | 0.398501 / 0.255139 (0.143362) | 0.421142 / 0.283200 (0.137942) | 0.114908 / 0.141683 (-0.026775) | 1.678630 / 1.452155 (0.226475) | 1.782313 / 1.492716 (0.289596) |\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) | 0.280783 / 0.018006 (0.262777) | 0.591573 / 0.000490 (0.591083) | 0.005797 / 0.000200 (0.005597) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030431 / 0.037411 (-0.006981) | 0.117342 / 0.014526 (0.102816) | 0.128456 / 0.176557 (-0.048101) | 0.198782 / 0.737135 (-0.538354) | 0.128501 / 0.296338 (-0.167838) |\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) | 0.603073 / 0.215209 (0.387864) | 6.101354 / 2.077655 (4.023699) | 2.527812 / 1.504120 (1.023692) | 2.101468 / 1.541195 (0.560273) | 2.092813 / 1.468490 (0.624323) | 1.182150 / 4.584777 (-3.402627) | 5.389278 / 3.745712 (1.643566) | 5.041001 / 5.269862 (-0.228860) | 2.650581 / 4.565676 (-1.915095) | 0.138761 / 0.424275 (-0.285514) | 0.014209 / 0.007607 (0.006602) | 0.748596 / 0.226044 (0.522552) | 7.373937 / 2.268929 (5.105008) | 3.245882 / 55.444624 (-52.198742) | 2.523569 / 6.876477 (-4.352908) | 2.581343 / 2.142072 (0.439270) | 1.340436 / 4.805227 (-3.464791) | 0.241388 / 6.500664 (-6.259276) | 0.076634 / 0.075469 (0.001164) |\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) | 1.480237 / 1.841788 (-0.361551) | 16.781338 / 8.074308 (8.707030) | 19.735028 / 10.191392 (9.543636) | 0.256872 / 0.680424 (-0.423551) | 0.029211 / 0.534201 (-0.504990) | 0.503292 / 0.579283 (-0.075991) | 0.584510 / 0.434364 (0.150146) | 0.580293 / 0.540337 (0.039955) | 0.678863 / 1.386936 (-0.708073) |\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) | 0.009972 / 0.011353 (-0.001381) | 0.006107 / 0.011008 (-0.004902) | 0.096188 / 0.038508 (0.057680) | 0.033320 / 0.023109 (0.010210) | 0.420789 / 0.275898 (0.144891) | 0.460488 / 0.323480 (0.137008) | 0.006492 / 0.007986 (-0.001493) | 0.005325 / 0.004328 (0.000997) | 0.094974 / 0.004250 (0.090723) | 0.047708 / 0.037052 (0.010655) | 0.426689 / 0.258489 (0.168200) | 0.476440 / 0.293841 (0.182599) | 0.052776 / 0.128546 (-0.075770) | 0.018779 / 0.075646 (-0.056868) | 0.119598 / 0.419271 (-0.299673) | 0.061800 / 0.043533 (0.018267) | 0.421305 / 0.255139 (0.166166) | 0.441125 / 0.283200 (0.157925) | 0.114221 / 0.141683 (-0.027462) | 1.712681 / 1.452155 (0.260526) | 1.852316 / 1.492716 (0.359600) |\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) | 0.272412 / 0.018006 (0.254405) | 0.583996 / 0.000490 (0.583506) | 0.000505 / 0.000200 (0.000305) | 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) | 0.029553 / 0.037411 (-0.007858) | 0.124921 / 0.014526 (0.110395) | 0.133338 / 0.176557 (-0.043218) | 0.193811 / 0.737135 (-0.543325) | 0.147973 / 0.296338 (-0.148365) |\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) | 0.595241 / 0.215209 (0.380032) | 6.012015 / 2.077655 (3.934360) | 2.611295 / 1.504120 (1.107175) | 2.290127 / 1.541195 (0.748932) | 2.300366 / 1.468490 (0.831876) | 1.197602 / 4.584777 (-3.387175) | 5.439064 / 3.745712 (1.693352) | 2.906088 / 5.269862 (-2.363773) | 1.919183 / 4.565676 (-2.646493) | 0.132166 / 0.424275 (-0.292109) | 0.014544 / 0.007607 (0.006937) | 0.726377 / 0.226044 (0.500333) | 7.361023 / 2.268929 (5.092094) | 3.289266 / 55.444624 (-52.155358) | 2.635570 / 6.876477 (-4.240907) | 2.595691 / 2.142072 (0.453619) | 1.329458 / 4.805227 (-3.475769) | 0.239419 / 6.500664 (-6.261245) | 0.076316 / 0.075469 (0.000847) |\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) | 1.547616 / 1.841788 (-0.294172) | 17.374315 / 8.074308 (9.300007) | 20.216275 / 10.191392 (10.024883) | 0.252102 / 0.680424 (-0.428322) | 0.027535 / 0.534201 (-0.506665) | 0.524618 / 0.579283 (-0.054666) | 0.596803 / 0.434364 (0.162439) | 0.652632 / 0.540337 (0.112294) | 0.762272 / 1.386936 (-0.624664) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c7d4b2f981f8cf639dcbd80f40a41aa5b1693c6 \"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) | 0.008236 / 0.011353 (-0.003117) | 0.006186 / 0.011008 (-0.004822) | 0.117852 / 0.038508 (0.079344) | 0.034711 / 0.023109 (0.011602) | 0.447564 / 0.275898 (0.171666) | 0.438727 / 0.323480 (0.115247) | 0.006576 / 0.007986 (-0.001410) | 0.005903 / 0.004328 (0.001574) | 0.094309 / 0.004250 (0.090059) | 0.042760 / 0.037052 (0.005708) | 0.393269 / 0.258489 (0.134780) | 0.438061 / 0.293841 (0.144220) | 0.059029 / 0.128546 (-0.069517) | 0.020296 / 0.075646 (-0.055350) | 0.412057 / 0.419271 (-0.007215) | 0.059808 / 0.043533 (0.016275) | 0.407243 / 0.255139 (0.152104) | 0.414290 / 0.283200 (0.131090) | 0.107701 / 0.141683 (-0.033981) | 1.671522 / 1.452155 (0.219367) | 1.775055 / 1.492716 (0.282338) |\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) | 0.275242 / 0.018006 (0.257236) | 0.599698 / 0.000490 (0.599208) | 0.001289 / 0.000200 (0.001089) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029579 / 0.037411 (-0.007832) | 0.127249 / 0.014526 (0.112723) | 0.137431 / 0.176557 (-0.039126) | 0.220330 / 0.737135 (-0.516805) | 0.133540 / 0.296338 (-0.162798) |\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) | 0.571989 / 0.215209 (0.356780) | 5.931503 / 2.077655 (3.853848) | 2.526646 / 1.504120 (1.022527) | 2.189476 / 1.541195 (0.648281) | 2.151935 / 1.468490 (0.683444) | 1.242440 / 4.584777 (-3.342337) | 5.599675 / 3.745712 (1.853963) | 3.242035 / 5.269862 (-2.027826) | 2.368361 / 4.565676 (-2.197315) | 0.145659 / 0.424275 (-0.278616) | 0.013813 / 0.007607 (0.006206) | 0.782495 / 0.226044 (0.556451) | 7.861619 / 2.268929 (5.592690) | 3.241001 / 55.444624 (-52.203623) | 2.611025 / 6.876477 (-4.265452) | 2.667263 / 2.142072 (0.525191) | 1.429992 / 4.805227 (-3.375235) | 0.243008 / 6.500664 (-6.257656) | 0.083686 / 0.075469 (0.008217) |\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) | 1.565526 / 1.841788 (-0.276262) | 18.260815 / 8.074308 (10.186507) | 22.586133 / 10.191392 (12.394741) | 0.231864 / 0.680424 (-0.448559) | 0.030877 / 0.534201 (-0.503324) | 0.569726 / 0.579283 (-0.009557) | 0.678638 / 0.434364 (0.244274) | 0.611810 / 0.540337 (0.071472) | 0.718771 / 1.386936 (-0.668165) |\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) | 0.009398 / 0.011353 (-0.001955) | 0.006452 / 0.011008 (-0.004556) | 0.103352 / 0.038508 (0.064844) | 0.034773 / 0.023109 (0.011664) | 0.523782 / 0.275898 (0.247884) | 0.523554 / 0.323480 (0.200074) | 0.006990 / 0.007986 (-0.000996) | 0.004994 / 0.004328 (0.000666) | 0.102199 / 0.004250 (0.097949) | 0.050087 / 0.037052 (0.013035) | 0.496662 / 0.258489 (0.238173) | 0.563130 / 0.293841 (0.269289) | 0.052851 / 0.128546 (-0.075695) | 0.019824 / 0.075646 (-0.055822) | 0.122657 / 0.419271 (-0.296614) | 0.057714 / 0.043533 (0.014181) | 0.470502 / 0.255139 (0.215363) | 0.518908 / 0.283200 (0.235708) | 0.114374 / 0.141683 (-0.027309) | 1.795918 / 1.452155 (0.343763) | 1.957461 / 1.492716 (0.464744) |\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) | 0.303921 / 0.018006 (0.285915) | 0.584406 / 0.000490 (0.583916) | 0.000444 / 0.000200 (0.000244) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032254 / 0.037411 (-0.005158) | 0.129966 / 0.014526 (0.115440) | 0.151000 / 0.176557 (-0.025557) | 0.234060 / 0.737135 (-0.503076) | 0.149444 / 0.296338 (-0.146895) |\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) | 0.666627 / 0.215209 (0.451418) | 7.054701 / 2.077655 (4.977046) | 2.836895 / 1.504120 (1.332775) | 2.561994 / 1.541195 (1.020799) | 2.672460 / 1.468490 (1.203970) | 1.411929 / 4.584777 (-3.172848) | 6.026918 / 3.745712 (2.281206) | 3.341745 / 5.269862 (-1.928116) | 2.280317 / 4.565676 (-2.285359) | 0.156635 / 0.424275 (-0.267641) | 0.014256 / 0.007607 (0.006649) | 0.804830 / 0.226044 (0.578786) | 8.106960 / 2.268929 (5.838031) | 3.597452 / 55.444624 (-51.847172) | 3.002847 / 6.876477 (-3.873630) | 2.931160 / 2.142072 (0.789088) | 1.484172 / 4.805227 (-3.321056) | 0.254166 / 6.500664 (-6.246498) | 0.080554 / 0.075469 (0.005085) |\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) | 1.809909 / 1.841788 (-0.031879) | 18.988994 / 8.074308 (10.914686) | 23.153442 / 10.191392 (12.962050) | 0.250554 / 0.680424 (-0.429870) | 0.048677 / 0.534201 (-0.485524) | 0.574109 / 0.579283 (-0.005174) | 0.640917 / 0.434364 (0.206553) | 0.725215 / 0.540337 (0.184878) | 0.878234 / 1.386936 (-0.508702) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e3667d6e17d68503469c8e88ec344b7cccfa2346 \"CML watermark\")\n" ]

"2023-03-29T18:24:13"

"2023-03-29T18:33:49"

"2023-03-29T18:24:22"

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[ "Hi! \r\n\r\nYou can fix the viewer by adding the `dataset_info` YAML field deleted in https://huggingface.co/datasets/Francesco/cell-towers/commit/b95b59ddd91ebe9c12920f0efe0ed415cd0d4298 back to the metadata section of the card. \r\n\r\nTo avoid this issue in the feature, you can use `huggingface_hub`'s [RepoCard](https://huggingface.co/docs/huggingface_hub/package_reference/cards) API to update the dataset card instead of `upload_file`:\r\n```python\r\nfrom huggingface_hub import DatasetCard\r\n# Load card\r\ncard = DatasetCard.load(\"<namespace>/<repo_id>\")\r\n# Modify card content\r\ncard.content = ...\r\n# Push card to the Hub\r\ncard.push_to_hub(\"<namespace>/<repo_id>\")\r\n```\r\n\r\nHowever, the best solution would be to use the features info stored in the header of the Parquet shards generated with `push_to_hub` on the viewer side to avoid unexpected issues such as this one. This shouldn't be too hard to address.", "Thanks for reporting @FrancescoSaverioZuppichini.\r\n\r\nFor future issues with your specific dataset, you can use its \"Community\" tab to start a conversation: https://huggingface.co/datasets/Francesco/cell-towers/discussions/new", "Thanks @albertvillanova , @mariosasko I was not aware of this requirement from the doc (must have skipped :sweat_smile: )\r\n\r\nConfirmed, adding back `dataset_info` fixed the issu" ]

"2023-03-29T15:25:30"

"2023-03-30T07:54:25"

"2023-03-30T07:54:25"

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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) | 0.007017 / 0.011353 (-0.004335) | 0.004917 / 0.011008 (-0.006091) | 0.098391 / 0.038508 (0.059883) | 0.032677 / 0.023109 (0.009568) | 0.312126 / 0.275898 (0.036227) | 0.352477 / 0.323480 (0.028998) | 0.005960 / 0.007986 (-0.002025) | 0.003801 / 0.004328 (-0.000528) | 0.073916 / 0.004250 (0.069666) | 0.045610 / 0.037052 (0.008557) | 0.319626 / 0.258489 (0.061137) | 0.370575 / 0.293841 (0.076734) | 0.035888 / 0.128546 (-0.092658) | 0.012012 / 0.075646 (-0.063635) | 0.338290 / 0.419271 (-0.080982) | 0.049452 / 0.043533 (0.005919) | 0.301226 / 0.255139 (0.046087) | 0.336744 / 0.283200 (0.053545) | 0.100835 / 0.141683 (-0.040847) | 1.500008 / 1.452155 (0.047853) | 1.566757 / 1.492716 (0.074041) |\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) | 0.220668 / 0.018006 (0.202662) | 0.449273 / 0.000490 (0.448784) | 0.003861 / 0.000200 (0.003661) | 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) | 0.026847 / 0.037411 (-0.010565) | 0.105916 / 0.014526 (0.091390) | 0.116245 / 0.176557 (-0.060312) | 0.172617 / 0.737135 (-0.564519) | 0.122846 / 0.296338 (-0.173492) |\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) | 0.417906 / 0.215209 (0.202697) | 4.169092 / 2.077655 (2.091437) | 1.934439 / 1.504120 (0.430319) | 1.735718 / 1.541195 (0.194523) | 1.828205 / 1.468490 (0.359715) | 0.697446 / 4.584777 (-3.887331) | 3.802830 / 3.745712 (0.057118) | 3.686464 / 5.269862 (-1.583398) | 1.863924 / 4.565676 (-2.701752) | 0.086520 / 0.424275 (-0.337755) | 0.012101 / 0.007607 (0.004493) | 0.521252 / 0.226044 (0.295208) | 5.200937 / 2.268929 (2.932009) | 2.414290 / 55.444624 (-53.030334) | 2.070890 / 6.876477 (-4.805587) | 2.237693 / 2.142072 (0.095621) | 0.843417 / 4.805227 (-3.961811) | 0.167856 / 6.500664 (-6.332809) | 0.064997 / 0.075469 (-0.010472) |\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) | 1.212334 / 1.841788 (-0.629454) | 14.710632 / 8.074308 (6.636324) | 14.877489 / 10.191392 (4.686097) | 0.151268 / 0.680424 (-0.529156) | 0.018663 / 0.534201 (-0.515538) | 0.429678 / 0.579283 (-0.149605) | 0.425054 / 0.434364 (-0.009310) | 0.502804 / 0.540337 (-0.037533) | 0.587932 / 1.386936 (-0.799004) |\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) | 0.007462 / 0.011353 (-0.003891) | 0.005307 / 0.011008 (-0.005701) | 0.074309 / 0.038508 (0.035801) | 0.033437 / 0.023109 (0.010328) | 0.355087 / 0.275898 (0.079189) | 0.391417 / 0.323480 (0.067937) | 0.005904 / 0.007986 (-0.002082) | 0.004062 / 0.004328 (-0.000266) | 0.073801 / 0.004250 (0.069550) | 0.048503 / 0.037052 (0.011451) | 0.359547 / 0.258489 (0.101058) | 0.405325 / 0.293841 (0.111484) | 0.036615 / 0.128546 (-0.091931) | 0.012185 / 0.075646 (-0.063461) | 0.086829 / 0.419271 (-0.332443) | 0.049101 / 0.043533 (0.005569) | 0.334259 / 0.255139 (0.079120) | 0.376317 / 0.283200 (0.093117) | 0.099935 / 0.141683 (-0.041748) | 1.483166 / 1.452155 (0.031011) | 1.569092 / 1.492716 (0.076375) |\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) | 0.207528 / 0.018006 (0.189521) | 0.437473 / 0.000490 (0.436983) | 0.004915 / 0.000200 (0.004715) | 0.000097 / 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) | 0.028632 / 0.037411 (-0.008780) | 0.111782 / 0.014526 (0.097256) | 0.122545 / 0.176557 (-0.054011) | 0.171191 / 0.737135 (-0.565945) | 0.128999 / 0.296338 (-0.167339) |\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) | 0.424422 / 0.215209 (0.209213) | 4.239488 / 2.077655 (2.161833) | 2.027969 / 1.504120 (0.523849) | 1.800667 / 1.541195 (0.259473) | 1.898701 / 1.468490 (0.430211) | 0.711453 / 4.584777 (-3.873324) | 3.766696 / 3.745712 (0.020984) | 2.107530 / 5.269862 (-3.162331) | 1.347137 / 4.565676 (-3.218540) | 0.086823 / 0.424275 (-0.337452) | 0.012137 / 0.007607 (0.004530) | 0.523143 / 0.226044 (0.297099) | 5.273434 / 2.268929 (3.004505) | 2.545463 / 55.444624 (-52.899161) | 2.246683 / 6.876477 (-4.629793) | 2.296862 / 2.142072 (0.154789) | 0.855690 / 4.805227 (-3.949538) | 0.168526 / 6.500664 (-6.332138) | 0.063392 / 0.075469 (-0.012078) |\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) | 1.248926 / 1.841788 (-0.592862) | 14.676308 / 8.074308 (6.602000) | 14.524364 / 10.191392 (4.332972) | 0.184138 / 0.680424 (-0.496286) | 0.017259 / 0.534201 (-0.516942) | 0.433875 / 0.579283 (-0.145408) | 0.416787 / 0.434364 (-0.017577) | 0.532391 / 0.540337 (-0.007947) | 0.628572 / 1.386936 (-0.758364) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3929cc227a474ce0c716146c8d14ae94f8a7625b \"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) | 0.006469 / 0.011353 (-0.004884) | 0.004499 / 0.011008 (-0.006510) | 0.098856 / 0.038508 (0.060348) | 0.027753 / 0.023109 (0.004644) | 0.321348 / 0.275898 (0.045450) | 0.351480 / 0.323480 (0.028000) | 0.004949 / 0.007986 (-0.003036) | 0.004655 / 0.004328 (0.000327) | 0.076732 / 0.004250 (0.072482) | 0.036175 / 0.037052 (-0.000878) | 0.310111 / 0.258489 (0.051622) | 0.372427 / 0.293841 (0.078586) | 0.031947 / 0.128546 (-0.096599) | 0.011669 / 0.075646 (-0.063977) | 0.323086 / 0.419271 (-0.096186) | 0.043578 / 0.043533 (0.000045) | 0.325549 / 0.255139 (0.070410) | 0.363827 / 0.283200 (0.080627) | 0.087819 / 0.141683 (-0.053864) | 1.479429 / 1.452155 (0.027274) | 1.549797 / 1.492716 (0.057080) |\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) | 0.178502 / 0.018006 (0.160496) | 0.415954 / 0.000490 (0.415465) | 0.008767 / 0.000200 (0.008567) | 0.000429 / 0.000054 (0.000375) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023639 / 0.037411 (-0.013772) | 0.096266 / 0.014526 (0.081740) | 0.106406 / 0.176557 (-0.070151) | 0.168819 / 0.737135 (-0.568317) | 0.109158 / 0.296338 (-0.187181) |\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) | 0.420729 / 0.215209 (0.205520) | 4.219469 / 2.077655 (2.141814) | 1.885673 / 1.504120 (0.381553) | 1.681868 / 1.541195 (0.140674) | 1.709240 / 1.468490 (0.240749) | 0.694763 / 4.584777 (-3.890014) | 3.395377 / 3.745712 (-0.350335) | 1.846811 / 5.269862 (-3.423051) | 1.158381 / 4.565676 (-3.407296) | 0.082717 / 0.424275 (-0.341558) | 0.012302 / 0.007607 (0.004695) | 0.518148 / 0.226044 (0.292103) | 5.189590 / 2.268929 (2.920661) | 2.294127 / 55.444624 (-53.150498) | 1.960080 / 6.876477 (-4.916397) | 2.045359 / 2.142072 (-0.096713) | 0.803739 / 4.805227 (-4.001488) | 0.152322 / 6.500664 (-6.348342) | 0.067051 / 0.075469 (-0.008418) |\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) | 1.206582 / 1.841788 (-0.635206) | 13.590515 / 8.074308 (5.516207) | 14.083739 / 10.191392 (3.892347) | 0.128738 / 0.680424 (-0.551686) | 0.016577 / 0.534201 (-0.517624) | 0.375499 / 0.579283 (-0.203784) | 0.383256 / 0.434364 (-0.051108) | 0.439441 / 0.540337 (-0.100896) | 0.518102 / 1.386936 (-0.868834) |\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) | 0.006708 / 0.011353 (-0.004645) | 0.004591 / 0.011008 (-0.006417) | 0.076512 / 0.038508 (0.038004) | 0.027977 / 0.023109 (0.004868) | 0.341915 / 0.275898 (0.066017) | 0.374381 / 0.323480 (0.050901) | 0.004985 / 0.007986 (-0.003001) | 0.003374 / 0.004328 (-0.000954) | 0.075334 / 0.004250 (0.071083) | 0.037522 / 0.037052 (0.000470) | 0.341702 / 0.258489 (0.083213) | 0.384342 / 0.293841 (0.090501) | 0.032231 / 0.128546 (-0.096315) | 0.011494 / 0.075646 (-0.064153) | 0.084897 / 0.419271 (-0.334375) | 0.041914 / 0.043533 (-0.001619) | 0.342030 / 0.255139 (0.086891) | 0.371024 / 0.283200 (0.087825) | 0.089936 / 0.141683 (-0.051746) | 1.497242 / 1.452155 (0.045087) | 1.585203 / 1.492716 (0.092486) |\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) | 0.227681 / 0.018006 (0.209674) | 0.398995 / 0.000490 (0.398505) | 0.003232 / 0.000200 (0.003032) | 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) | 0.024705 / 0.037411 (-0.012706) | 0.099906 / 0.014526 (0.085380) | 0.106806 / 0.176557 (-0.069750) | 0.157521 / 0.737135 (-0.579614) | 0.110803 / 0.296338 (-0.185535) |\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) | 0.457442 / 0.215209 (0.242233) | 4.580101 / 2.077655 (2.502446) | 2.094687 / 1.504120 (0.590567) | 1.880722 / 1.541195 (0.339528) | 1.938746 / 1.468490 (0.470256) | 0.700933 / 4.584777 (-3.883844) | 3.416278 / 3.745712 (-0.329434) | 2.852183 / 5.269862 (-2.417679) | 1.602659 / 4.565676 (-2.963017) | 0.083949 / 0.424275 (-0.340326) | 0.012255 / 0.007607 (0.004648) | 0.551631 / 0.226044 (0.325586) | 5.539225 / 2.268929 (3.270296) | 2.707298 / 55.444624 (-52.737326) | 2.354720 / 6.876477 (-4.521757) | 2.320790 / 2.142072 (0.178717) | 0.807152 / 4.805227 (-3.998075) | 0.152048 / 6.500664 (-6.348616) | 0.067723 / 0.075469 (-0.007746) |\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) | 1.295690 / 1.841788 (-0.546097) | 13.738082 / 8.074308 (5.663774) | 14.129549 / 10.191392 (3.938157) | 0.161568 / 0.680424 (-0.518855) | 0.016678 / 0.534201 (-0.517522) | 0.386609 / 0.579283 (-0.192674) | 0.383538 / 0.434364 (-0.050826) | 0.477872 / 0.540337 (-0.062465) | 0.564547 / 1.386936 (-0.822389) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2ab4c98618bce7c1f60ce96d4a853a940ae4b250 \"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) | 0.007247 / 0.011353 (-0.004106) | 0.005044 / 0.011008 (-0.005964) | 0.095135 / 0.038508 (0.056627) | 0.033622 / 0.023109 (0.010513) | 0.309969 / 0.275898 (0.034071) | 0.340354 / 0.323480 (0.016875) | 0.005635 / 0.007986 (-0.002351) | 0.003938 / 0.004328 (-0.000391) | 0.072089 / 0.004250 (0.067838) | 0.045592 / 0.037052 (0.008539) | 0.316620 / 0.258489 (0.058131) | 0.358174 / 0.293841 (0.064333) | 0.036446 / 0.128546 (-0.092100) | 0.011961 / 0.075646 (-0.063685) | 0.332299 / 0.419271 (-0.086973) | 0.049955 / 0.043533 (0.006422) | 0.307638 / 0.255139 (0.052499) | 0.331719 / 0.283200 (0.048519) | 0.095115 / 0.141683 (-0.046568) | 1.457960 / 1.452155 (0.005806) | 1.502812 / 1.492716 (0.010096) |\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) | 0.223747 / 0.018006 (0.205740) | 0.444837 / 0.000490 (0.444347) | 0.002583 / 0.000200 (0.002383) | 0.000084 / 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) | 0.026461 / 0.037411 (-0.010951) | 0.103946 / 0.014526 (0.089420) | 0.114355 / 0.176557 (-0.062201) | 0.170076 / 0.737135 (-0.567059) | 0.121087 / 0.296338 (-0.175252) |\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) | 0.403252 / 0.215209 (0.188043) | 4.016911 / 2.077655 (1.939257) | 1.787168 / 1.504120 (0.283048) | 1.605206 / 1.541195 (0.064012) | 1.657012 / 1.468490 (0.188522) | 0.701425 / 4.584777 (-3.883352) | 3.818308 / 3.745712 (0.072596) | 3.493757 / 5.269862 (-1.776105) | 1.860534 / 4.565676 (-2.705142) | 0.084994 / 0.424275 (-0.339281) | 0.011904 / 0.007607 (0.004297) | 0.534199 / 0.226044 (0.308155) | 4.992703 / 2.268929 (2.723774) | 2.286231 / 55.444624 (-53.158393) | 1.918163 / 6.876477 (-4.958314) | 2.029811 / 2.142072 (-0.112262) | 0.837532 / 4.805227 (-3.967695) | 0.168545 / 6.500664 (-6.332119) | 0.062866 / 0.075469 (-0.012604) |\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) | 1.172862 / 1.841788 (-0.668926) | 14.966793 / 8.074308 (6.892485) | 14.202079 / 10.191392 (4.010687) | 0.144688 / 0.680424 (-0.535736) | 0.017499 / 0.534201 (-0.516702) | 0.443081 / 0.579283 (-0.136202) | 0.427496 / 0.434364 (-0.006868) | 0.525182 / 0.540337 (-0.015155) | 0.611849 / 1.386936 (-0.775087) |\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) | 0.007264 / 0.011353 (-0.004089) | 0.005106 / 0.011008 (-0.005902) | 0.074101 / 0.038508 (0.035593) | 0.033388 / 0.023109 (0.010279) | 0.337108 / 0.275898 (0.061210) | 0.369820 / 0.323480 (0.046340) | 0.005701 / 0.007986 (-0.002284) | 0.003976 / 0.004328 (-0.000353) | 0.073517 / 0.004250 (0.069267) | 0.048741 / 0.037052 (0.011688) | 0.339118 / 0.258489 (0.080629) | 0.398687 / 0.293841 (0.104846) | 0.036661 / 0.128546 (-0.091886) | 0.012082 / 0.075646 (-0.063564) | 0.086743 / 0.419271 (-0.332529) | 0.050150 / 0.043533 (0.006617) | 0.335572 / 0.255139 (0.080433) | 0.354306 / 0.283200 (0.071107) | 0.102074 / 0.141683 (-0.039609) | 1.442911 / 1.452155 (-0.009244) | 1.531564 / 1.492716 (0.038848) |\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) | 0.183163 / 0.018006 (0.165157) | 0.439273 / 0.000490 (0.438783) | 0.002765 / 0.000200 (0.002565) | 0.000225 / 0.000054 (0.000171) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028185 / 0.037411 (-0.009227) | 0.107337 / 0.014526 (0.092811) | 0.119925 / 0.176557 (-0.056631) | 0.172120 / 0.737135 (-0.565015) | 0.124332 / 0.296338 (-0.172007) |\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) | 0.428750 / 0.215209 (0.213541) | 4.268933 / 2.077655 (2.191279) | 2.050135 / 1.504120 (0.546015) | 1.837567 / 1.541195 (0.296372) | 1.907040 / 1.468490 (0.438549) | 0.694162 / 4.584777 (-3.890615) | 3.831542 / 3.745712 (0.085830) | 3.476580 / 5.269862 (-1.793281) | 1.855097 / 4.565676 (-2.710580) | 0.085816 / 0.424275 (-0.338459) | 0.012195 / 0.007607 (0.004588) | 0.544920 / 0.226044 (0.318876) | 5.332977 / 2.268929 (3.064049) | 2.592097 / 55.444624 (-52.852527) | 2.295411 / 6.876477 (-4.581065) | 2.330803 / 2.142072 (0.188730) | 0.833268 / 4.805227 (-3.971959) | 0.177698 / 6.500664 (-6.322966) | 0.063780 / 0.075469 (-0.011689) |\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) | 1.273361 / 1.841788 (-0.568427) | 14.981380 / 8.074308 (6.907072) | 14.395166 / 10.191392 (4.203774) | 0.186590 / 0.680424 (-0.493834) | 0.017676 / 0.534201 (-0.516525) | 0.432100 / 0.579283 (-0.147183) | 0.422490 / 0.434364 (-0.011874) | 0.531421 / 0.540337 (-0.008916) | 0.628548 / 1.386936 (-0.758388) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b16e08dd599f4646a77a5ca88b6445467e1e7e9 \"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) | 0.009005 / 0.011353 (-0.002348) | 0.005803 / 0.011008 (-0.005205) | 0.103491 / 0.038508 (0.064983) | 0.048099 / 0.023109 (0.024990) | 0.304026 / 0.275898 (0.028128) | 0.340840 / 0.323480 (0.017360) | 0.006782 / 0.007986 (-0.001204) | 0.004625 / 0.004328 (0.000296) | 0.076695 / 0.004250 (0.072445) | 0.057541 / 0.037052 (0.020489) | 0.304015 / 0.258489 (0.045526) | 0.347822 / 0.293841 (0.053981) | 0.037904 / 0.128546 (-0.090642) | 0.012686 / 0.075646 (-0.062960) | 0.368093 / 0.419271 (-0.051179) | 0.051795 / 0.043533 (0.008262) | 0.302553 / 0.255139 (0.047415) | 0.328581 / 0.283200 (0.045381) | 0.108947 / 0.141683 (-0.032736) | 1.449770 / 1.452155 (-0.002385) | 1.541944 / 1.492716 (0.049227) |\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) | 0.207529 / 0.018006 (0.189523) | 0.455313 / 0.000490 (0.454823) | 0.008276 / 0.000200 (0.008076) | 0.000322 / 0.000054 (0.000268) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030564 / 0.037411 (-0.006848) | 0.122790 / 0.014526 (0.108264) | 0.126981 / 0.176557 (-0.049576) | 0.187203 / 0.737135 (-0.549932) | 0.129931 / 0.296338 (-0.166408) |\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) | 0.402680 / 0.215209 (0.187471) | 4.017505 / 2.077655 (1.939850) | 1.801480 / 1.504120 (0.297360) | 1.647984 / 1.541195 (0.106790) | 1.702596 / 1.468490 (0.234106) | 0.717469 / 4.584777 (-3.867308) | 3.793813 / 3.745712 (0.048101) | 2.288014 / 5.269862 (-2.981848) | 1.497545 / 4.565676 (-3.068132) | 0.091241 / 0.424275 (-0.333034) | 0.013115 / 0.007607 (0.005508) | 0.498567 / 0.226044 (0.272522) | 4.990203 / 2.268929 (2.721275) | 2.334983 / 55.444624 (-53.109642) | 2.047888 / 6.876477 (-4.828589) | 2.167825 / 2.142072 (0.025753) | 0.863769 / 4.805227 (-3.941459) | 0.172699 / 6.500664 (-6.327965) | 0.069285 / 0.075469 (-0.006184) |\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) | 1.397331 / 1.841788 (-0.444457) | 16.678240 / 8.074308 (8.603932) | 16.665143 / 10.191392 (6.473751) | 0.151011 / 0.680424 (-0.529412) | 0.018303 / 0.534201 (-0.515898) | 0.445389 / 0.579283 (-0.133894) | 0.444644 / 0.434364 (0.010280) | 0.524647 / 0.540337 (-0.015690) | 0.629747 / 1.386936 (-0.757189) |\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) | 0.008853 / 0.011353 (-0.002499) | 0.006196 / 0.011008 (-0.004813) | 0.078595 / 0.038508 (0.040087) | 0.048348 / 0.023109 (0.025239) | 0.347038 / 0.275898 (0.071140) | 0.385807 / 0.323480 (0.062327) | 0.007047 / 0.007986 (-0.000938) | 0.004772 / 0.004328 (0.000443) | 0.076116 / 0.004250 (0.071866) | 0.058805 / 0.037052 (0.021752) | 0.345731 / 0.258489 (0.087242) | 0.401589 / 0.293841 (0.107748) | 0.039349 / 0.128546 (-0.089197) | 0.012949 / 0.075646 (-0.062697) | 0.089761 / 0.419271 (-0.329511) | 0.060001 / 0.043533 (0.016468) | 0.351587 / 0.255139 (0.096448) | 0.377708 / 0.283200 (0.094509) | 0.117391 / 0.141683 (-0.024292) | 1.471622 / 1.452155 (0.019467) | 1.568759 / 1.492716 (0.076042) |\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) | 0.191390 / 0.018006 (0.173384) | 0.469033 / 0.000490 (0.468544) | 0.003615 / 0.000200 (0.003415) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032706 / 0.037411 (-0.004706) | 0.127095 / 0.014526 (0.112569) | 0.128755 / 0.176557 (-0.047801) | 0.182590 / 0.737135 (-0.554545) | 0.136939 / 0.296338 (-0.159400) |\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) | 0.427392 / 0.215209 (0.212183) | 4.246708 / 2.077655 (2.169053) | 2.115557 / 1.504120 (0.611437) | 2.021221 / 1.541195 (0.480026) | 2.177559 / 1.468490 (0.709069) | 0.713930 / 4.584777 (-3.870847) | 4.192467 / 3.745712 (0.446755) | 3.645437 / 5.269862 (-1.624424) | 1.964986 / 4.565676 (-2.600690) | 0.089436 / 0.424275 (-0.334839) | 0.012917 / 0.007607 (0.005310) | 0.530468 / 0.226044 (0.304423) | 5.310759 / 2.268929 (3.041831) | 2.613566 / 55.444624 (-52.831058) | 2.350443 / 6.876477 (-4.526034) | 2.385278 / 2.142072 (0.243205) | 0.862838 / 4.805227 (-3.942389) | 0.172246 / 6.500664 (-6.328418) | 0.069570 / 0.075469 (-0.005899) |\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) | 1.310008 / 1.841788 (-0.531780) | 16.557079 / 8.074308 (8.482771) | 15.818145 / 10.191392 (5.626752) | 0.180337 / 0.680424 (-0.500087) | 0.018117 / 0.534201 (-0.516083) | 0.433189 / 0.579283 (-0.146095) | 0.429276 / 0.434364 (-0.005088) | 0.539757 / 0.540337 (-0.000580) | 0.640905 / 1.386936 (-0.746031) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b16e08dd599f4646a77a5ca88b6445467e1e7e9 \"CML watermark\")\n" ]

"2023-03-29T15:06:07"

"2023-03-29T18:30:34"

"2023-03-29T18:15:54"

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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) | 0.006935 / 0.011353 (-0.004418) | 0.004711 / 0.011008 (-0.006297) | 0.098461 / 0.038508 (0.059953) | 0.028889 / 0.023109 (0.005780) | 0.332167 / 0.275898 (0.056269) | 0.363309 / 0.323480 (0.039829) | 0.005179 / 0.007986 (-0.002807) | 0.004783 / 0.004328 (0.000455) | 0.074293 / 0.004250 (0.070043) | 0.038778 / 0.037052 (0.001726) | 0.318871 / 0.258489 (0.060382) | 0.362975 / 0.293841 (0.069134) | 0.032897 / 0.128546 (-0.095649) | 0.011685 / 0.075646 (-0.063961) | 0.322824 / 0.419271 (-0.096447) | 0.043842 / 0.043533 (0.000309) | 0.334789 / 0.255139 (0.079650) | 0.352922 / 0.283200 (0.069723) | 0.089692 / 0.141683 (-0.051991) | 1.490110 / 1.452155 (0.037955) | 1.601530 / 1.492716 (0.108813) |\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) | 0.201882 / 0.018006 (0.183875) | 0.410875 / 0.000490 (0.410385) | 0.002472 / 0.000200 (0.002272) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023636 / 0.037411 (-0.013775) | 0.102168 / 0.014526 (0.087642) | 0.107247 / 0.176557 (-0.069310) | 0.171858 / 0.737135 (-0.565278) | 0.110619 / 0.296338 (-0.185720) |\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) | 0.433740 / 0.215209 (0.218531) | 4.332121 / 2.077655 (2.254466) | 2.075398 / 1.504120 (0.571278) | 1.941074 / 1.541195 (0.399879) | 2.033331 / 1.468490 (0.564841) | 0.697134 / 4.584777 (-3.887643) | 3.463855 / 3.745712 (-0.281857) | 3.080446 / 5.269862 (-2.189416) | 1.575020 / 4.565676 (-2.990656) | 0.083054 / 0.424275 (-0.341221) | 0.012454 / 0.007607 (0.004847) | 0.537996 / 0.226044 (0.311951) | 5.366765 / 2.268929 (3.097836) | 2.464398 / 55.444624 (-52.980227) | 2.143912 / 6.876477 (-4.732564) | 2.245706 / 2.142072 (0.103634) | 0.801397 / 4.805227 (-4.003831) | 0.150954 / 6.500664 (-6.349710) | 0.066758 / 0.075469 (-0.008711) |\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) | 1.216412 / 1.841788 (-0.625376) | 13.679322 / 8.074308 (5.605014) | 14.055286 / 10.191392 (3.863894) | 0.130264 / 0.680424 (-0.550160) | 0.016566 / 0.534201 (-0.517635) | 0.379126 / 0.579283 (-0.200157) | 0.390815 / 0.434364 (-0.043549) | 0.437586 / 0.540337 (-0.102751) | 0.526822 / 1.386936 (-0.860114) |\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) | 0.006898 / 0.011353 (-0.004455) | 0.004705 / 0.011008 (-0.006304) | 0.078592 / 0.038508 (0.040084) | 0.028635 / 0.023109 (0.005525) | 0.340143 / 0.275898 (0.064245) | 0.377526 / 0.323480 (0.054047) | 0.005645 / 0.007986 (-0.002340) | 0.003533 / 0.004328 (-0.000796) | 0.078441 / 0.004250 (0.074191) | 0.039408 / 0.037052 (0.002356) | 0.342303 / 0.258489 (0.083814) | 0.386837 / 0.293841 (0.092996) | 0.032427 / 0.128546 (-0.096119) | 0.011763 / 0.075646 (-0.063883) | 0.087984 / 0.419271 (-0.331287) | 0.042126 / 0.043533 (-0.001406) | 0.339951 / 0.255139 (0.084812) | 0.366165 / 0.283200 (0.082966) | 0.091414 / 0.141683 (-0.050269) | 1.502034 / 1.452155 (0.049880) | 1.597901 / 1.492716 (0.105184) |\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) | 0.232122 / 0.018006 (0.214115) | 0.410205 / 0.000490 (0.409715) | 0.000418 / 0.000200 (0.000218) | 0.000063 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026013 / 0.037411 (-0.011399) | 0.105520 / 0.014526 (0.090995) | 0.108649 / 0.176557 (-0.067908) | 0.159324 / 0.737135 (-0.577811) | 0.114033 / 0.296338 (-0.182306) |\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) | 0.455634 / 0.215209 (0.240425) | 4.508544 / 2.077655 (2.430889) | 2.087065 / 1.504120 (0.582945) | 1.872622 / 1.541195 (0.331427) | 1.935617 / 1.468490 (0.467127) | 0.696909 / 4.584777 (-3.887868) | 3.449365 / 3.745712 (-0.296348) | 3.008399 / 5.269862 (-2.261462) | 1.459245 / 4.565676 (-3.106431) | 0.083637 / 0.424275 (-0.340638) | 0.012358 / 0.007607 (0.004750) | 0.547232 / 0.226044 (0.321187) | 5.522395 / 2.268929 (3.253466) | 2.691019 / 55.444624 (-52.753605) | 2.408083 / 6.876477 (-4.468394) | 2.369239 / 2.142072 (0.227166) | 0.807148 / 4.805227 (-3.998080) | 0.152030 / 6.500664 (-6.348634) | 0.067883 / 0.075469 (-0.007586) |\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) | 1.336956 / 1.841788 (-0.504832) | 14.403730 / 8.074308 (6.329422) | 14.854084 / 10.191392 (4.662692) | 0.146530 / 0.680424 (-0.533894) | 0.016611 / 0.534201 (-0.517590) | 0.398557 / 0.579283 (-0.180726) | 0.393194 / 0.434364 (-0.041170) | 0.486824 / 0.540337 (-0.053513) | 0.572844 / 1.386936 (-0.814092) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#411f9cc281e50954ea0c903e7a0a6618b3d31b9e \"CML watermark\")\n" ]

"2023-03-29T15:00:50"

"2023-03-29T17:36:06"

"2023-03-29T17:28:57"

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"2023-03-29T15:00:30"

"2023-03-29T17:28:58"

"2023-03-29T17:28:58"

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[ "Good idea, I think I've seen this a couple of times before too on the forums. I can work on this :)", "Closed in #5693 " ]

"2023-03-29T11:44:49"

"2023-04-03T18:31:11"

"2023-04-03T18:31:11"

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[]

[]

[ "_The documentation is not available anymore as the PR was closed or merged._", "_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) | 0.006772 / 0.011353 (-0.004581) | 0.004674 / 0.011008 (-0.006335) | 0.098702 / 0.038508 (0.060194) | 0.028257 / 0.023109 (0.005148) | 0.368008 / 0.275898 (0.092110) | 0.402825 / 0.323480 (0.079345) | 0.005158 / 0.007986 (-0.002828) | 0.003470 / 0.004328 (-0.000858) | 0.075541 / 0.004250 (0.071291) | 0.039755 / 0.037052 (0.002702) | 0.373431 / 0.258489 (0.114942) | 0.410159 / 0.293841 (0.116318) | 0.031355 / 0.128546 (-0.097192) | 0.011632 / 0.075646 (-0.064014) | 0.325475 / 0.419271 (-0.093797) | 0.042574 / 0.043533 (-0.000958) | 0.373629 / 0.255139 (0.118490) | 0.393921 / 0.283200 (0.110721) | 0.084669 / 0.141683 (-0.057013) | 1.459947 / 1.452155 (0.007792) | 1.529593 / 1.492716 (0.036877) |\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) | 0.189994 / 0.018006 (0.171988) | 0.409091 / 0.000490 (0.408602) | 0.003693 / 0.000200 (0.003493) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024649 / 0.037411 (-0.012762) | 0.097702 / 0.014526 (0.083177) | 0.103650 / 0.176557 (-0.072906) | 0.167141 / 0.737135 (-0.569994) | 0.108460 / 0.296338 (-0.187879) |\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) | 0.429544 / 0.215209 (0.214335) | 4.277106 / 2.077655 (2.199451) | 2.018745 / 1.504120 (0.514625) | 1.814782 / 1.541195 (0.273587) | 1.897030 / 1.468490 (0.428540) | 0.700332 / 4.584777 (-3.884445) | 3.421761 / 3.745712 (-0.323951) | 3.008281 / 5.269862 (-2.261581) | 1.554230 / 4.565676 (-3.011446) | 0.082922 / 0.424275 (-0.341353) | 0.012312 / 0.007607 (0.004705) | 0.527757 / 0.226044 (0.301713) | 5.287450 / 2.268929 (3.018522) | 2.329083 / 55.444624 (-53.115542) | 2.016651 / 6.876477 (-4.859826) | 2.214510 / 2.142072 (0.072437) | 0.807676 / 4.805227 (-3.997551) | 0.151752 / 6.500664 (-6.348912) | 0.066819 / 0.075469 (-0.008651) |\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) | 1.239522 / 1.841788 (-0.602266) | 13.923672 / 8.074308 (5.849364) | 14.317394 / 10.191392 (4.126002) | 0.159379 / 0.680424 (-0.521045) | 0.016537 / 0.534201 (-0.517664) | 0.376808 / 0.579283 (-0.202475) | 0.376351 / 0.434364 (-0.058012) | 0.437124 / 0.540337 (-0.103213) | 0.520589 / 1.386936 (-0.866347) |\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) | 0.006892 / 0.011353 (-0.004461) | 0.004671 / 0.011008 (-0.006337) | 0.075841 / 0.038508 (0.037333) | 0.028713 / 0.023109 (0.005604) | 0.345105 / 0.275898 (0.069207) | 0.380694 / 0.323480 (0.057214) | 0.005155 / 0.007986 (-0.002830) | 0.003379 / 0.004328 (-0.000949) | 0.075134 / 0.004250 (0.070883) | 0.039990 / 0.037052 (0.002938) | 0.345540 / 0.258489 (0.087051) | 0.389913 / 0.293841 (0.096072) | 0.032089 / 0.128546 (-0.096458) | 0.011583 / 0.075646 (-0.064063) | 0.085169 / 0.419271 (-0.334102) | 0.041847 / 0.043533 (-0.001686) | 0.341504 / 0.255139 (0.086365) | 0.367582 / 0.283200 (0.084382) | 0.092684 / 0.141683 (-0.048999) | 1.498647 / 1.452155 (0.046492) | 1.549056 / 1.492716 (0.056339) |\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) | 0.228643 / 0.018006 (0.210637) | 0.410680 / 0.000490 (0.410191) | 0.000398 / 0.000200 (0.000198) | 0.000059 / 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) | 0.025354 / 0.037411 (-0.012057) | 0.101567 / 0.014526 (0.087041) | 0.108340 / 0.176557 (-0.068217) | 0.157804 / 0.737135 (-0.579332) | 0.113985 / 0.296338 (-0.182354) |\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) | 0.436427 / 0.215209 (0.221218) | 4.359331 / 2.077655 (2.281676) | 2.047877 / 1.504120 (0.543757) | 1.844242 / 1.541195 (0.303047) | 1.924553 / 1.468490 (0.456063) | 0.695986 / 4.584777 (-3.888791) | 3.435571 / 3.745712 (-0.310141) | 1.905189 / 5.269862 (-3.364673) | 1.198542 / 4.565676 (-3.367134) | 0.083386 / 0.424275 (-0.340889) | 0.012442 / 0.007607 (0.004835) | 0.542562 / 0.226044 (0.316517) | 5.416554 / 2.268929 (3.147625) | 2.499496 / 55.444624 (-52.945128) | 2.160658 / 6.876477 (-4.715819) | 2.210535 / 2.142072 (0.068462) | 0.803324 / 4.805227 (-4.001903) | 0.151735 / 6.500664 (-6.348929) | 0.068392 / 0.075469 (-0.007078) |\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) | 1.319915 / 1.841788 (-0.521873) | 14.176755 / 8.074308 (6.102446) | 14.376366 / 10.191392 (4.184974) | 0.141219 / 0.680424 (-0.539204) | 0.017181 / 0.534201 (-0.517020) | 0.383589 / 0.579283 (-0.195694) | 0.389352 / 0.434364 (-0.045012) | 0.474465 / 0.540337 (-0.065873) | 0.563047 / 1.386936 (-0.823889) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c33e8ce68b5000988bf6b2e4bca27ffaa469acea \"CML watermark\")\n" ]

"2023-03-29T09:50:23"

"2023-03-30T13:14:19"

"2023-03-30T13:07:18"

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[ "Hi ! AFAIK a dataset must be present on a local disk to be able to efficiently memory map the datasets Arrow files. What makes you think that it is possible to load from a cloud storage and have good performance ?\r\n\r\nAnyway it's already possible to download_and_prepare a dataset as Arrow files in a cloud storage with:\r\n```python\r\nbuilder = load_dataset_builder(..., cache_dir=\"/temp/dir\")\r\nbuilder.download_and_prepare(\"/cloud_dir\")\r\n```\r\n\r\nbut then \r\n```python\r\nds = builder.as_dataset()\r\n```\r\nwould fail if \"/cloud_dir\" is not a local directory.", "In my use case, I am trying to mount the S3 bucket as local system with S3FS-FUSE / [goofys](https://github.com/kahing/goofys). I want to use S3 to save the download data and save checkpoint for training for persistent. Setting the s3 location as cache directory is not fast enough. That is why I want to set a work directory for temp data for memory map and only save the final result to s3 cache. ", "You can try setting `HF_DATASETS_DOWNLOADED_DATASETS_PATH` and `HF_DATASETS_EXTRACTED_DATASETS_PATH` to S3, and `HF_DATASETS_CACHE` to your local disk.\r\n\r\nThis way all your downloaded and extracted data are on your mounted S3, but the datasets Arrow files are on your local disk" ]

"2023-03-29T07:21:09"

"2023-04-04T09:38:24"

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"2023-03-29T04:36:28"

"2023-03-29T04:36:28"

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"2023-03-29T00:01:31"

"2023-03-29T00:01:31"

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[ "The dataset still can be found, if instead of using the search form you just enter the language code in the url, like https://huggingface.co/datasets?language=language:myv. \r\n\r\nBut of course having a more complete list of languages in the search form (or just a fallback to the language codes, if they are missing from the code=>language mapping) would be much more convenient!", "Hi! I've opened a PR to make these languages searchable on the Hub.", "Thanks @mariosasko!\r\nDo you think it is possible to turn this into a more scalable pipeline? Such as:\r\n1. Looping through all the datasets on the hub and collecting the set of all their language codes;\r\n2. Selecting the codes not covered yet in `Language.ts`\r\n3. Looking up their codes at https://iso639-3.sil.org/code_tables/639/data\r\n4. Adding all the newly found language codes to `Language.ts`", "@avidale This has been discussed in https://github.com/huggingface/datasets/issues/4881, so also feel free to share your opinion there." ]

"2023-03-27T09:42:28"

"2023-03-30T08:08:15"

"2023-03-30T08:08:15"

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[ "Hi! You can contact `security@huggingface.co` to report this vulnerability." ]

"2023-03-26T20:55:58"

"2023-03-27T21:01:55"

"2023-03-27T21:01:55"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> download_and_prepare is not called when streaming a dataset, so we may need to have storage_options in the DatasetBuilder.__init__ ? This way it could also be passed later to as_streaming_dataset and the StreamingDownloadManager\r\n\r\n> Currently the storage_options parameter in download_and_prepare are for the target filesystem where the dataset must be downloaded and prepared as arrow files\r\n\r\nAh, I noted this when looking for ways to plumb down `storage_options` although I think I was looking at adding to `BuilderConfig`. The `DatasetBuilder` constructor looks more appropriate for this, will get that added in a future commit", "Noting as experimental SGTM. The only tests I can think of to add at the moment would be mocks that assert the storage options get passed all the way down using `mock.assert_called_with` but if Hugging Face has some S3/GCS buckets for testing, maybe those would be better in a future PR. Let me know what you think", "I think adding tests with the mockfs fixture will do the job. Tests and docs can be added when request_etag and is_remote_url support fsspec (right now they would fail with mockfs).\r\n\r\nLet's see in a subsequent PR, this is exciting ! :)", "<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) | 0.009217 / 0.011353 (-0.002136) | 0.006275 / 0.011008 (-0.004733) | 0.124361 / 0.038508 (0.085853) | 0.035680 / 0.023109 (0.012570) | 0.395255 / 0.275898 (0.119357) | 0.426104 / 0.323480 (0.102624) | 0.006822 / 0.007986 (-0.001163) | 0.004467 / 0.004328 (0.000138) | 0.099404 / 0.004250 (0.095153) | 0.051919 / 0.037052 (0.014867) | 0.388286 / 0.258489 (0.129797) | 0.426361 / 0.293841 (0.132520) | 0.053100 / 0.128546 (-0.075446) | 0.019453 / 0.075646 (-0.056194) | 0.433139 / 0.419271 (0.013867) | 0.063240 / 0.043533 (0.019707) | 0.381175 / 0.255139 (0.126036) | 0.411686 / 0.283200 (0.128487) | 0.104843 / 0.141683 (-0.036840) | 1.853582 / 1.452155 (0.401427) | 1.935644 / 1.492716 (0.442928) |\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) | 0.218969 / 0.018006 (0.200963) | 0.515011 / 0.000490 (0.514522) | 0.004017 / 0.000200 (0.003818) | 0.000097 / 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) | 0.028975 / 0.037411 (-0.008437) | 0.125239 / 0.014526 (0.110713) | 0.131371 / 0.176557 (-0.045185) | 0.203864 / 0.737135 (-0.533271) | 0.140784 / 0.296338 (-0.155554) |\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) | 0.620701 / 0.215209 (0.405492) | 6.263557 / 2.077655 (4.185903) | 2.510058 / 1.504120 (1.005938) | 2.085892 / 1.541195 (0.544697) | 2.170362 / 1.468490 (0.701872) | 1.325600 / 4.584777 (-3.259177) | 5.583355 / 3.745712 (1.837642) | 5.092791 / 5.269862 (-0.177071) | 2.814766 / 4.565676 (-1.750911) | 0.153568 / 0.424275 (-0.270707) | 0.014850 / 0.007607 (0.007243) | 0.787011 / 0.226044 (0.560967) | 7.948813 / 2.268929 (5.679885) | 3.320831 / 55.444624 (-52.123793) | 2.526327 / 6.876477 (-4.350150) | 2.691651 / 2.142072 (0.549579) | 1.521199 / 4.805227 (-3.284028) | 0.269738 / 6.500664 (-6.230926) | 0.082959 / 0.075469 (0.007490) |\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) | 1.740056 / 1.841788 (-0.101732) | 17.699732 / 8.074308 (9.625424) | 22.450689 / 10.191392 (12.259297) | 0.229350 / 0.680424 (-0.451073) | 0.027486 / 0.534201 (-0.506715) | 0.536153 / 0.579283 (-0.043130) | 0.608166 / 0.434364 (0.173802) | 0.629144 / 0.540337 (0.088807) | 0.732671 / 1.386936 (-0.654265) |\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) | 0.010147 / 0.011353 (-0.001206) | 0.006484 / 0.011008 (-0.004524) | 0.098664 / 0.038508 (0.060156) | 0.036400 / 0.023109 (0.013291) | 0.432895 / 0.275898 (0.156997) | 0.466433 / 0.323480 (0.142954) | 0.008102 / 0.007986 (0.000117) | 0.004554 / 0.004328 (0.000225) | 0.100466 / 0.004250 (0.096216) | 0.054066 / 0.037052 (0.017013) | 0.439177 / 0.258489 (0.180688) | 0.502907 / 0.293841 (0.209066) | 0.059210 / 0.128546 (-0.069336) | 0.020220 / 0.075646 (-0.055426) | 0.124671 / 0.419271 (-0.294600) | 0.064278 / 0.043533 (0.020746) | 0.435659 / 0.255139 (0.180520) | 0.459670 / 0.283200 (0.176471) | 0.115574 / 0.141683 (-0.026109) | 1.826360 / 1.452155 (0.374205) | 1.943199 / 1.492716 (0.450483) |\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) | 0.238463 / 0.018006 (0.220457) | 0.534889 / 0.000490 (0.534400) | 0.000404 / 0.000200 (0.000204) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033210 / 0.037411 (-0.004201) | 0.133529 / 0.014526 (0.119003) | 0.143813 / 0.176557 (-0.032743) | 0.213079 / 0.737135 (-0.524056) | 0.148427 / 0.296338 (-0.147912) |\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) | 0.656819 / 0.215209 (0.441610) | 6.414860 / 2.077655 (4.337205) | 2.756182 / 1.504120 (1.252062) | 2.405268 / 1.541195 (0.864073) | 2.436418 / 1.468490 (0.967928) | 1.289828 / 4.584777 (-3.294949) | 5.572731 / 3.745712 (1.827018) | 3.185432 / 5.269862 (-2.084429) | 2.093220 / 4.565676 (-2.472457) | 0.144817 / 0.424275 (-0.279458) | 0.015674 / 0.007607 (0.008067) | 0.801238 / 0.226044 (0.575194) | 7.955925 / 2.268929 (5.686996) | 3.605670 / 55.444624 (-51.838955) | 2.837568 / 6.876477 (-4.038908) | 2.873848 / 2.142072 (0.731775) | 1.493512 / 4.805227 (-3.311715) | 0.266251 / 6.500664 (-6.234413) | 0.082417 / 0.075469 (0.006948) |\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) | 1.608685 / 1.841788 (-0.233103) | 18.587875 / 8.074308 (10.513567) | 21.786119 / 10.191392 (11.594727) | 0.261748 / 0.680424 (-0.418675) | 0.026228 / 0.534201 (-0.507973) | 0.553538 / 0.579283 (-0.025745) | 0.599780 / 0.434364 (0.165416) | 0.665663 / 0.540337 (0.125325) | 0.792785 / 1.386936 (-0.594151) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1520e017a9bb6f80e82a38b578213e418ad7e845 \"CML watermark\")\n" ]

"2023-03-26T20:09:37"

"2023-03-28T15:03:38"

"2023-03-28T14:54:17"

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[ "Hi ! It's been fixed by https://github.com/huggingface/datasets/pull/5598. We're doing a new release tomorrow with the fix and you'll be able to push your 100k images ;)\r\n\r\nBasically `push_to_hub` used to fail if the remote repository already exists and has a README.md without dataset_info in the YAML tags.\r\n\r\nIn the meantime you can install datasets from source", "Hi @lhoestq ,\r\n\r\nWhat version of datasets library fix this case? I am using the last `v2.10.1` and I get the same error.", "We just released 2.11 which includes a fix :)" ]

"2023-03-26T17:42:13"

"2023-03-30T08:11:05"

"2023-03-30T08:11:05"

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[ "Sounds like an issue with incompatible `transformers` dependencies versions.\r\n\r\nCan you try to update `transformers` ?\r\n\r\nEDIT: I checked the `transformers` dependencies and it seems like you need `tokenizers>=0.10.1,<0.11` with `transformers==4.5.1`\r\n\r\nEDIT2: this old version of `datasets` seems to import `transformers` but it's no longer the case, so you could also simply update `datasets` and `transformers` won't be imported", "Thank you for advising me to update these libraries versions.\r\n\r\nI can implement codes using `datasets==2.10.1` and `transformers==4.27.3`" ]

"2023-03-26T09:40:34"

"2023-03-28T07:43:44"

"2023-03-28T07:43:43"

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[ "Hi ! This sounds related to https://github.com/huggingface/datasets/issues/5406\r\n\r\nUpdating `datasets` fixes the issue ;)", "Thanks @lhoestq!\r\n\r\nI'll close this issue now." ]

"2023-03-25T18:06:15"

"2023-03-27T22:54:56"

"2023-03-27T22:54:56"

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[ "Do I miss something here?", "Hi! \r\n\r\nThe first dataset stores images as bytes (the \"image\" column type is `datasets.Image()`) and decodes them as `PIL.Image` objects and the second dataset stores them as variable-length lists (the \"image\" column type is `datasets.Sequence(...)`)), so I guess going from `arrow bytes -> NumPy -> decoding as PIL.Image -> PyTorch` is faster than going from `arrow list -> NumPy -> PyTorch`. \r\n\r\nTo store image bytes in the second example, you can do the following:\r\n\r\n```python\r\ndef transform(example):\r\n example[\"image2\"] = cv2.imread(example[\"image_file_path\"])\r\n return example\r\n\r\nfeatures = dataset.features.copy()\r\ndel features[\"image\"]\r\nfeatures[\"image2\"] = datasets.Image()\r\ndataset2 = dataset.map(transform, remove_columns=[\"image\"], features=features)\r\n\r\nfor x in DataLoader(dataset2.with_format(\"torch\"), batch_size=16, shuffle=True, num_workers=8):\r\n pass\r\n```", "Thanks, @mariosasko. I could not understand why a (decoded) sequence should be MUCH slower than an encoded image (that must be decoded every time). At any rate, I tried you suggestion. It made the `map` step to run extremely slow (consumes all the 16GB of memory and starts swapping)\r\n\r\nI tried also the easiest (as I see it) scenario, where images are kept as bytes, but it made things even worse: not only it was extremely slow, but also crashes\r\n\r\n```python\r\n\r\ndef transform(example):\r\n example[\"image2\"] = cv2.imread(example[\"image_file_path\"]).tobytes()\r\n return example\r\n\r\ndataset2 = dataset.map(transform, remove_columns=[\"image\"])\r\n\r\nfor x in DataLoader(dataset2.with_format(\"torch\"), batch_size=16, shuffle=True, num_workers=8):\r\n pass\r\n\r\n\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nOutput exceeds the size limit. Open the full output data in a text editor\r\n---------------------------------------------------------------------------\r\nRuntimeError Traceback (most recent call last)\r\nFile ~/virtenvs/py310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1133, in _MultiProcessingDataLoaderIter._try_get_data(self, timeout)\r\n 1132 try:\r\n-> 1133 data = self._data_queue.get(timeout=timeout)\r\n 1134 return (True, data)\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/queues.py:113, in Queue.get(self, block, timeout)\r\n 112 timeout = deadline - time.monotonic()\r\n--> 113 if not self._poll(timeout):\r\n 114 raise Empty\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/connection.py:257, in _ConnectionBase.poll(self, timeout)\r\n 256 self._check_readable()\r\n--> 257 return self._poll(timeout)\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/connection.py:424, in Connection._poll(self, timeout)\r\n 423 def _poll(self, timeout):\r\n--> 424 r = wait([self], timeout)\r\n 425 return bool(r)\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/connection.py:931, in wait(object_list, timeout)\r\n 930 while True:\r\n--> 931 ready = selector.select(timeout)\r\n 932 if ready:\r\n...\r\n-> 1146 raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e\r\n 1147 if isinstance(e, queue.Empty):\r\n 1148 return (False, None)\r\n\r\nRuntimeError: DataLoader worker (pid(s) 195393) exited unexpectedly\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\n```\r\n", "Correction: the `beans` dataset stores the image file paths, not the bytes.\r\n\r\nFor your use case, I think it makes more sense to use `with_tranform` than `map` and lazily decode images with `cv2.imread` when indexing an example/batch:\r\n```python\r\nimport cv2\r\n\r\ndef transform(batch):\r\n batch[\"image2\"] = np.stack([cv2.imread(image_file_path) for image_file_path in batch[\"image_file_path\"]])\r\n return batch\r\n\r\ndataset = dataset.with_transform(transform)\r\n```\r\n", "This is incorrect.\n\nDid you try to run it? dataset[0] returns a tensor of numbers. dataset2[0]\nreturns the same tensor, but after a few long seconds. Looping over a\nthousand of images cannot take 15 minutes.\n\nOn Fri, 24 Mar 2023 at 19:28 Mario Šaško ***@***.***> wrote:\n\n> Correction: the beans dataset stores the image file paths, not the bytes.\n>\n> For your use case, I think it makes more sense to use with_tranform than\n> map and lazily decode images with cv2.imread when accessing an\n> example/batch:\n>\n> import cv2\n> def transform(batch):\n> batch[\"image2\"] = np.stack([cv2.imread(image_file_path) for image_file_path in batch[\"image_file_path\"]])\n> return batch\n> dataset = dataset.with_transform(transform)\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5669#issuecomment-1483084347>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AASS73SHRWXIQX6SCYCJ7ITW5XDUDANCNFSM6AAAAAAWFSHWEM>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n", "I updated the transform with the NumPy -> PyTorch conversion.\r\n\r\nI'm sharing the entire code:\r\n```python\r\nimport cv2\r\nimport numpy as np\r\nimport datasets\r\nimport torch\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\ndataset = load_dataset(\"beans\", split=\"train\")\r\n\r\ndef transform(batch):\r\n # # Pillow decodes as RGB\r\n # batch[\"image\"] = torch.stack([torch.from_numpy(cv2.cvtColor(cv2.imread(image_file_path), cv2.COLOR_BGR2RGB)) for image_file_path in batch[\"image_file_path\"]])\r\n batch[\"image\"] = torch.stack([torch.from_numpy(cv2.imread(image_file_path)) for image_file_path in batch[\"image_file_path\"]])\r\n batch[\"labels\"] = torch.tensor(batch[\"labels\"])\r\n return batch\r\n\r\ndataset2 = dataset.cast_column(\"image\", datasets.Image(decode=False)).with_transform(transform)\r\n\r\nfor x in DataLoader(dataset2, batch_size=16, shuffle=True, num_workers=8):\r\n pass\r\n```\r\n\r\nThis code is ≈ 10% faster on my machine than the default decoding with Pillow and `.with_format(\"torch\")`.", "Thanks, @mariosasko \r\nMy question remain unanswered though. Why is the `map`ed dataset so slow? My understanding is that a dataset of numpy arrays should be must faster than a dataset that has to decode images into numpy arrays every time one accesses an item. " ]

"2023-03-23T18:20:20"

"2023-04-09T18:56:23"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5668). All of your documentation changes will be reflected on that endpoint.", "My previous comment didn't create the retro-link in the PR. I write it here again.\r\n\r\nYou can check the context and the discussions we had about this feature enhancement in this PR:\r\n- #2249" ]

"2023-03-23T17:53:39"

"2023-03-24T06:43:14"

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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) | 0.008592 / 0.011353 (-0.002761) | 0.005182 / 0.011008 (-0.005826) | 0.097916 / 0.038508 (0.059408) | 0.034612 / 0.023109 (0.011503) | 0.313760 / 0.275898 (0.037862) | 0.353422 / 0.323480 (0.029942) | 0.005880 / 0.007986 (-0.002106) | 0.004123 / 0.004328 (-0.000205) | 0.073634 / 0.004250 (0.069384) | 0.049349 / 0.037052 (0.012297) | 0.317381 / 0.258489 (0.058892) | 0.365821 / 0.293841 (0.071980) | 0.036482 / 0.128546 (-0.092065) | 0.012126 / 0.075646 (-0.063521) | 0.334640 / 0.419271 (-0.084631) | 0.050551 / 0.043533 (0.007018) | 0.310472 / 0.255139 (0.055333) | 0.349049 / 0.283200 (0.065850) | 0.101343 / 0.141683 (-0.040340) | 1.447903 / 1.452155 (-0.004252) | 1.518793 / 1.492716 (0.026077) |\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) | 0.210971 / 0.018006 (0.192965) | 0.449471 / 0.000490 (0.448982) | 0.003596 / 0.000200 (0.003396) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027386 / 0.037411 (-0.010025) | 0.112683 / 0.014526 (0.098157) | 0.117603 / 0.176557 (-0.058954) | 0.174186 / 0.737135 (-0.562949) | 0.123510 / 0.296338 (-0.172829) |\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) | 0.422595 / 0.215209 (0.207386) | 4.224713 / 2.077655 (2.147058) | 2.006359 / 1.504120 (0.502240) | 1.823767 / 1.541195 (0.282572) | 1.898340 / 1.468490 (0.429849) | 0.721656 / 4.584777 (-3.863121) | 3.823498 / 3.745712 (0.077785) | 2.172380 / 5.269862 (-3.097481) | 1.469773 / 4.565676 (-3.095904) | 0.086978 / 0.424275 (-0.337297) | 0.012642 / 0.007607 (0.005035) | 0.517830 / 0.226044 (0.291785) | 5.171150 / 2.268929 (2.902221) | 2.495238 / 55.444624 (-52.949386) | 2.114380 / 6.876477 (-4.762097) | 2.274329 / 2.142072 (0.132257) | 0.863855 / 4.805227 (-3.941372) | 0.174127 / 6.500664 (-6.326537) | 0.065939 / 0.075469 (-0.009530) |\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) | 1.208831 / 1.841788 (-0.632957) | 15.016704 / 8.074308 (6.942396) | 14.721231 / 10.191392 (4.529839) | 0.144140 / 0.680424 (-0.536284) | 0.017781 / 0.534201 (-0.516420) | 0.425679 / 0.579283 (-0.153604) | 0.416747 / 0.434364 (-0.017617) | 0.490160 / 0.540337 (-0.050177) | 0.583639 / 1.386936 (-0.803297) |\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) | 0.007670 / 0.011353 (-0.003683) | 0.005383 / 0.011008 (-0.005626) | 0.075756 / 0.038508 (0.037248) | 0.033373 / 0.023109 (0.010263) | 0.341017 / 0.275898 (0.065119) | 0.378890 / 0.323480 (0.055410) | 0.005945 / 0.007986 (-0.002040) | 0.004179 / 0.004328 (-0.000150) | 0.074588 / 0.004250 (0.070337) | 0.048564 / 0.037052 (0.011511) | 0.338774 / 0.258489 (0.080285) | 0.391081 / 0.293841 (0.097240) | 0.036659 / 0.128546 (-0.091887) | 0.012241 / 0.075646 (-0.063406) | 0.086910 / 0.419271 (-0.332361) | 0.049745 / 0.043533 (0.006212) | 0.332810 / 0.255139 (0.077671) | 0.360317 / 0.283200 (0.077117) | 0.103399 / 0.141683 (-0.038283) | 1.456754 / 1.452155 (0.004599) | 1.542644 / 1.492716 (0.049928) |\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) | 0.207182 / 0.018006 (0.189176) | 0.455659 / 0.000490 (0.455169) | 0.003609 / 0.000200 (0.003409) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029556 / 0.037411 (-0.007856) | 0.114215 / 0.014526 (0.099690) | 0.127721 / 0.176557 (-0.048836) | 0.177070 / 0.737135 (-0.560065) | 0.128840 / 0.296338 (-0.167499) |\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) | 0.428176 / 0.215209 (0.212967) | 4.274324 / 2.077655 (2.196669) | 2.020058 / 1.504120 (0.515938) | 1.823343 / 1.541195 (0.282148) | 1.924688 / 1.468490 (0.456198) | 0.719195 / 4.584777 (-3.865582) | 3.760445 / 3.745712 (0.014733) | 2.133813 / 5.269862 (-3.136049) | 1.364876 / 4.565676 (-3.200801) | 0.087523 / 0.424275 (-0.336752) | 0.013712 / 0.007607 (0.006105) | 0.528403 / 0.226044 (0.302359) | 5.307780 / 2.268929 (3.038851) | 2.496747 / 55.444624 (-52.947877) | 2.169136 / 6.876477 (-4.707341) | 2.235719 / 2.142072 (0.093646) | 0.875281 / 4.805227 (-3.929946) | 0.172369 / 6.500664 (-6.328295) | 0.064667 / 0.075469 (-0.010802) |\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) | 1.262594 / 1.841788 (-0.579193) | 15.182681 / 8.074308 (7.108373) | 14.725663 / 10.191392 (4.534271) | 0.180961 / 0.680424 (-0.499462) | 0.017632 / 0.534201 (-0.516569) | 0.427531 / 0.579283 (-0.151752) | 0.431741 / 0.434364 (-0.002622) | 0.503251 / 0.540337 (-0.037087) | 0.597423 / 1.386936 (-0.789513) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f4cf224dcb1043a272971ed331a214cf65c504be \"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) | 0.009761 / 0.011353 (-0.001592) | 0.006779 / 0.011008 (-0.004229) | 0.132786 / 0.038508 (0.094277) | 0.037721 / 0.023109 (0.014611) | 0.435685 / 0.275898 (0.159787) | 0.447488 / 0.323480 (0.124009) | 0.006848 / 0.007986 (-0.001137) | 0.005099 / 0.004328 (0.000771) | 0.097384 / 0.004250 (0.093133) | 0.056663 / 0.037052 (0.019610) | 0.463407 / 0.258489 (0.204918) | 0.502544 / 0.293841 (0.208703) | 0.053817 / 0.128546 (-0.074729) | 0.020253 / 0.075646 (-0.055393) | 0.446653 / 0.419271 (0.027382) | 0.064465 / 0.043533 (0.020932) | 0.455375 / 0.255139 (0.200236) | 0.458378 / 0.283200 (0.175178) | 0.109124 / 0.141683 (-0.032559) | 1.957338 / 1.452155 (0.505184) | 1.960391 / 1.492716 (0.467674) |\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) | 0.219566 / 0.018006 (0.201560) | 0.558181 / 0.000490 (0.557691) | 0.004678 / 0.000200 (0.004478) | 0.000125 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032643 / 0.037411 (-0.004768) | 0.147375 / 0.014526 (0.132849) | 0.130821 / 0.176557 (-0.045736) | 0.203202 / 0.737135 (-0.533933) | 0.145186 / 0.296338 (-0.151153) |\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) | 0.665773 / 0.215209 (0.450564) | 6.674021 / 2.077655 (4.596366) | 2.662372 / 1.504120 (1.158253) | 2.333327 / 1.541195 (0.792132) | 2.221413 / 1.468490 (0.752923) | 1.287001 / 4.584777 (-3.297776) | 5.534326 / 3.745712 (1.788614) | 3.188809 / 5.269862 (-2.081052) | 2.261717 / 4.565676 (-2.303960) | 0.151910 / 0.424275 (-0.272366) | 0.020509 / 0.007607 (0.012902) | 0.863608 / 0.226044 (0.637564) | 8.442155 / 2.268929 (6.173227) | 3.438260 / 55.444624 (-52.006364) | 2.692503 / 6.876477 (-4.183974) | 2.810997 / 2.142072 (0.668925) | 1.477345 / 4.805227 (-3.327882) | 0.261942 / 6.500664 (-6.238722) | 0.086347 / 0.075469 (0.010878) |\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) | 1.529072 / 1.841788 (-0.312716) | 17.213019 / 8.074308 (9.138711) | 21.887309 / 10.191392 (11.695917) | 0.259660 / 0.680424 (-0.420763) | 0.027916 / 0.534201 (-0.506285) | 0.554103 / 0.579283 (-0.025180) | 0.614566 / 0.434364 (0.180202) | 0.700456 / 0.540337 (0.160119) | 0.756860 / 1.386936 (-0.630077) |\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) | 0.009267 / 0.011353 (-0.002086) | 0.006414 / 0.011008 (-0.004594) | 0.102404 / 0.038508 (0.063896) | 0.034885 / 0.023109 (0.011776) | 0.413191 / 0.275898 (0.137293) | 0.483901 / 0.323480 (0.160422) | 0.006614 / 0.007986 (-0.001372) | 0.004608 / 0.004328 (0.000280) | 0.096717 / 0.004250 (0.092467) | 0.055123 / 0.037052 (0.018071) | 0.417786 / 0.258489 (0.159297) | 0.490886 / 0.293841 (0.197045) | 0.056951 / 0.128546 (-0.071595) | 0.021073 / 0.075646 (-0.054574) | 0.116576 / 0.419271 (-0.302695) | 0.063968 / 0.043533 (0.020435) | 0.420495 / 0.255139 (0.165356) | 0.449667 / 0.283200 (0.166467) | 0.115318 / 0.141683 (-0.026365) | 1.899398 / 1.452155 (0.447243) | 1.992175 / 1.492716 (0.499459) |\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) | 0.233076 / 0.018006 (0.215070) | 0.518377 / 0.000490 (0.517887) | 0.000809 / 0.000200 (0.000609) | 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) | 0.030951 / 0.037411 (-0.006460) | 0.134940 / 0.014526 (0.120414) | 0.147789 / 0.176557 (-0.028767) | 0.205854 / 0.737135 (-0.531281) | 0.146726 / 0.296338 (-0.149613) |\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) | 0.648006 / 0.215209 (0.432797) | 6.416688 / 2.077655 (4.339033) | 2.696462 / 1.504120 (1.192342) | 2.293071 / 1.541195 (0.751877) | 2.319426 / 1.468490 (0.850935) | 1.332398 / 4.584777 (-3.252379) | 5.706956 / 3.745712 (1.961244) | 4.464473 / 5.269862 (-0.805388) | 2.817364 / 4.565676 (-1.748312) | 0.157595 / 0.424275 (-0.266680) | 0.015721 / 0.007607 (0.008114) | 0.806055 / 0.226044 (0.580010) | 7.927795 / 2.268929 (5.658866) | 3.461251 / 55.444624 (-51.983373) | 2.664466 / 6.876477 (-4.212010) | 2.660041 / 2.142072 (0.517968) | 1.531135 / 4.805227 (-3.274092) | 0.260293 / 6.500664 (-6.240371) | 0.077440 / 0.075469 (0.001971) |\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) | 1.687325 / 1.841788 (-0.154463) | 17.905080 / 8.074308 (9.830772) | 21.046794 / 10.191392 (10.855402) | 0.245335 / 0.680424 (-0.435089) | 0.026830 / 0.534201 (-0.507371) | 0.510798 / 0.579283 (-0.068485) | 0.590041 / 0.434364 (0.155677) | 0.607440 / 0.540337 (0.067102) | 0.725030 / 1.386936 (-0.661906) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#91dcb3636e410a249177f5e0508ed101ad7ee25b \"CML watermark\")\n", "I self-assigned #5666 and I was working on it... without success: https://github.com/huggingface/datasets/tree/fix-5666\r\n\r\nI think your approach is the right one because installation of jax is not trivial...\r\n\r\nNext time it would be better that you self-assign an issue before working on it, so that we avoid duplicate work... :sweat_smile: ", "Oh sorry I forgot to self assign this time", "<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) | 0.008436 / 0.011353 (-0.002917) | 0.005702 / 0.011008 (-0.005306) | 0.113518 / 0.038508 (0.075010) | 0.039639 / 0.023109 (0.016530) | 0.353200 / 0.275898 (0.077302) | 0.382428 / 0.323480 (0.058948) | 0.007419 / 0.007986 (-0.000566) | 0.005640 / 0.004328 (0.001311) | 0.083905 / 0.004250 (0.079655) | 0.053258 / 0.037052 (0.016205) | 0.371069 / 0.258489 (0.112580) | 0.390439 / 0.293841 (0.096598) | 0.042679 / 0.128546 (-0.085867) | 0.013438 / 0.075646 (-0.062208) | 0.390116 / 0.419271 (-0.029155) | 0.068782 / 0.043533 (0.025249) | 0.352620 / 0.255139 (0.097481) | 0.371939 / 0.283200 (0.088739) | 0.126157 / 0.141683 (-0.015525) | 1.694638 / 1.452155 (0.242484) | 1.799211 / 1.492716 (0.306495) |\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) | 0.260099 / 0.018006 (0.242092) | 0.489852 / 0.000490 (0.489362) | 0.012549 / 0.000200 (0.012349) | 0.000275 / 0.000054 (0.000221) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032235 / 0.037411 (-0.005177) | 0.125325 / 0.014526 (0.110799) | 0.137242 / 0.176557 (-0.039315) | 0.206566 / 0.737135 (-0.530570) | 0.143260 / 0.296338 (-0.153078) |\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) | 0.478510 / 0.215209 (0.263301) | 4.746439 / 2.077655 (2.668784) | 2.195072 / 1.504120 (0.690952) | 1.958163 / 1.541195 (0.416969) | 2.028566 / 1.468490 (0.560075) | 0.821289 / 4.584777 (-3.763488) | 4.765529 / 3.745712 (1.019817) | 2.378753 / 5.269862 (-2.891108) | 1.514776 / 4.565676 (-3.050900) | 0.100673 / 0.424275 (-0.323602) | 0.014720 / 0.007607 (0.007113) | 0.606388 / 0.226044 (0.380343) | 5.975285 / 2.268929 (3.706357) | 2.866762 / 55.444624 (-52.577862) | 2.392132 / 6.876477 (-4.484345) | 2.546487 / 2.142072 (0.404415) | 0.982394 / 4.805227 (-3.822833) | 0.201195 / 6.500664 (-6.299469) | 0.077781 / 0.075469 (0.002312) |\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) | 1.420613 / 1.841788 (-0.421174) | 17.743030 / 8.074308 (9.668722) | 16.752344 / 10.191392 (6.560951) | 0.167464 / 0.680424 (-0.512960) | 0.020908 / 0.534201 (-0.513293) | 0.502919 / 0.579283 (-0.076364) | 0.506375 / 0.434364 (0.072011) | 0.602695 / 0.540337 (0.062358) | 0.689398 / 1.386936 (-0.697538) |\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) | 0.008713 / 0.011353 (-0.002640) | 0.006152 / 0.011008 (-0.004856) | 0.091264 / 0.038508 (0.052756) | 0.040284 / 0.023109 (0.017174) | 0.417598 / 0.275898 (0.141700) | 0.460141 / 0.323480 (0.136661) | 0.006589 / 0.007986 (-0.001397) | 0.004671 / 0.004328 (0.000343) | 0.089360 / 0.004250 (0.085110) | 0.055113 / 0.037052 (0.018061) | 0.415241 / 0.258489 (0.156752) | 0.470566 / 0.293841 (0.176725) | 0.042963 / 0.128546 (-0.085584) | 0.014421 / 0.075646 (-0.061225) | 0.106333 / 0.419271 (-0.312939) | 0.057810 / 0.043533 (0.014277) | 0.417889 / 0.255139 (0.162750) | 0.444236 / 0.283200 (0.161036) | 0.119508 / 0.141683 (-0.022175) | 1.736209 / 1.452155 (0.284055) | 1.790319 / 1.492716 (0.297602) |\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) | 0.219184 / 0.018006 (0.201178) | 0.493931 / 0.000490 (0.493441) | 0.006727 / 0.000200 (0.006527) | 0.000103 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034415 / 0.037411 (-0.002996) | 0.132165 / 0.014526 (0.117639) | 0.143138 / 0.176557 (-0.033418) | 0.200052 / 0.737135 (-0.537083) | 0.148906 / 0.296338 (-0.147433) |\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) | 0.483686 / 0.215209 (0.268476) | 4.849874 / 2.077655 (2.772220) | 2.374276 / 1.504120 (0.870156) | 2.168334 / 1.541195 (0.627139) | 2.285983 / 1.468490 (0.817493) | 0.833041 / 4.584777 (-3.751735) | 4.665915 / 3.745712 (0.920203) | 4.543559 / 5.269862 (-0.726302) | 2.246926 / 4.565676 (-2.318750) | 0.098490 / 0.424275 (-0.325785) | 0.014934 / 0.007607 (0.007327) | 0.591878 / 0.226044 (0.365834) | 6.039852 / 2.268929 (3.770923) | 2.881244 / 55.444624 (-52.563381) | 2.486297 / 6.876477 (-4.390179) | 2.564642 / 2.142072 (0.422569) | 0.985684 / 4.805227 (-3.819543) | 0.199101 / 6.500664 (-6.301563) | 0.078138 / 0.075469 (0.002669) |\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) | 1.647744 / 1.841788 (-0.194043) | 18.986464 / 8.074308 (10.912156) | 17.246575 / 10.191392 (7.055183) | 0.219151 / 0.680424 (-0.461273) | 0.022219 / 0.534201 (-0.511982) | 0.547207 / 0.579283 (-0.032076) | 0.525943 / 0.434364 (0.091579) | 0.616909 / 0.540337 (0.076572) | 0.757423 / 1.386936 (-0.629513) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f423b69cd4371bd03bb819c60450534f8850ad61 \"CML watermark\")\n" ]

"2023-03-23T15:41:09"

"2023-03-23T16:23:11"

"2023-03-23T16:14:52"

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"2023-03-23T14:37:51"

"2023-03-23T16:14:54"

"2023-03-23T16:14:54"

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"2023-03-23T09:53:04"

"2023-03-23T09:53:16"

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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) | 0.007500 / 0.011353 (-0.003853) | 0.005279 / 0.011008 (-0.005729) | 0.098848 / 0.038508 (0.060340) | 0.035290 / 0.023109 (0.012181) | 0.342676 / 0.275898 (0.066778) | 0.375310 / 0.323480 (0.051830) | 0.006037 / 0.007986 (-0.001948) | 0.004143 / 0.004328 (-0.000185) | 0.075757 / 0.004250 (0.071506) | 0.049436 / 0.037052 (0.012383) | 0.344734 / 0.258489 (0.086245) | 0.388111 / 0.293841 (0.094270) | 0.037079 / 0.128546 (-0.091467) | 0.011986 / 0.075646 (-0.063660) | 0.333911 / 0.419271 (-0.085361) | 0.050415 / 0.043533 (0.006882) | 0.341723 / 0.255139 (0.086584) | 0.364136 / 0.283200 (0.080936) | 0.099371 / 0.141683 (-0.042312) | 1.467030 / 1.452155 (0.014876) | 1.565472 / 1.492716 (0.072755) |\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) | 0.212534 / 0.018006 (0.194528) | 0.435854 / 0.000490 (0.435364) | 0.000419 / 0.000200 (0.000219) | 0.000060 / 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) | 0.027957 / 0.037411 (-0.009454) | 0.106835 / 0.014526 (0.092309) | 0.115733 / 0.176557 (-0.060824) | 0.172374 / 0.737135 (-0.564761) | 0.121907 / 0.296338 (-0.174431) |\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) | 0.413195 / 0.215209 (0.197986) | 4.144775 / 2.077655 (2.067120) | 1.885647 / 1.504120 (0.381527) | 1.645525 / 1.541195 (0.104331) | 1.690117 / 1.468490 (0.221627) | 0.705787 / 4.584777 (-3.878989) | 3.763338 / 3.745712 (0.017626) | 2.163044 / 5.269862 (-3.106818) | 1.478619 / 4.565676 (-3.087057) | 0.086458 / 0.424275 (-0.337817) | 0.012711 / 0.007607 (0.005103) | 0.503592 / 0.226044 (0.277547) | 5.031176 / 2.268929 (2.762248) | 2.345348 / 55.444624 (-53.099276) | 2.064573 / 6.876477 (-4.811903) | 2.203937 / 2.142072 (0.061865) | 0.838761 / 4.805227 (-3.966466) | 0.170116 / 6.500664 (-6.330548) | 0.064012 / 0.075469 (-0.011457) |\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) | 1.190887 / 1.841788 (-0.650901) | 15.091466 / 8.074308 (7.017158) | 14.549112 / 10.191392 (4.357720) | 0.180603 / 0.680424 (-0.499820) | 0.017387 / 0.534201 (-0.516814) | 0.421372 / 0.579283 (-0.157911) | 0.434644 / 0.434364 (0.000281) | 0.496958 / 0.540337 (-0.043380) | 0.593995 / 1.386936 (-0.792941) |\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) | 0.007790 / 0.011353 (-0.003563) | 0.005307 / 0.011008 (-0.005701) | 0.074779 / 0.038508 (0.036271) | 0.034442 / 0.023109 (0.011332) | 0.337973 / 0.275898 (0.062075) | 0.371944 / 0.323480 (0.048464) | 0.006088 / 0.007986 (-0.001897) | 0.005619 / 0.004328 (0.001291) | 0.073757 / 0.004250 (0.069507) | 0.049385 / 0.037052 (0.012333) | 0.338326 / 0.258489 (0.079837) | 0.387916 / 0.293841 (0.094075) | 0.037197 / 0.128546 (-0.091350) | 0.012371 / 0.075646 (-0.063275) | 0.086938 / 0.419271 (-0.332334) | 0.051379 / 0.043533 (0.007846) | 0.331580 / 0.255139 (0.076441) | 0.355765 / 0.283200 (0.072565) | 0.103368 / 0.141683 (-0.038315) | 1.475963 / 1.452155 (0.023808) | 1.530579 / 1.492716 (0.037863) |\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) | 0.223037 / 0.018006 (0.205031) | 0.441795 / 0.000490 (0.441305) | 0.003937 / 0.000200 (0.003737) | 0.000090 / 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) | 0.030081 / 0.037411 (-0.007330) | 0.110366 / 0.014526 (0.095841) | 0.124097 / 0.176557 (-0.052459) | 0.176237 / 0.737135 (-0.560898) | 0.127045 / 0.296338 (-0.169293) |\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) | 0.420191 / 0.215209 (0.204982) | 4.186721 / 2.077655 (2.109066) | 1.992336 / 1.504120 (0.488216) | 1.800567 / 1.541195 (0.259373) | 1.917982 / 1.468490 (0.449491) | 0.700932 / 4.584777 (-3.883845) | 3.888631 / 3.745712 (0.142918) | 2.138168 / 5.269862 (-3.131693) | 1.364636 / 4.565676 (-3.201041) | 0.085404 / 0.424275 (-0.338871) | 0.012550 / 0.007607 (0.004943) | 0.526110 / 0.226044 (0.300066) | 5.258717 / 2.268929 (2.989789) | 2.454287 / 55.444624 (-52.990338) | 2.130539 / 6.876477 (-4.745937) | 2.207982 / 2.142072 (0.065909) | 0.839242 / 4.805227 (-3.965985) | 0.167611 / 6.500664 (-6.333053) | 0.065706 / 0.075469 (-0.009763) |\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) | 1.266125 / 1.841788 (-0.575662) | 15.480513 / 8.074308 (7.406205) | 14.959376 / 10.191392 (4.767983) | 0.149195 / 0.680424 (-0.531229) | 0.017881 / 0.534201 (-0.516320) | 0.430863 / 0.579283 (-0.148420) | 0.432878 / 0.434364 (-0.001485) | 0.499605 / 0.540337 (-0.040733) | 0.605592 / 1.386936 (-0.781344) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c20230f8d8762fb67523677093e95e773ce88786 \"CML watermark\")\n" ]

"2023-03-23T09:52:26"

"2023-03-23T10:17:11"

"2023-03-23T10:09:54"

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"2023-03-23T09:39:43"

"2023-03-23T10:09:55"

"2023-03-23T10:09:55"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I am merging because the CI error is 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) | 0.009448 / 0.011353 (-0.001905) | 0.006156 / 0.011008 (-0.004852) | 0.123656 / 0.038508 (0.085147) | 0.034998 / 0.023109 (0.011889) | 0.374722 / 0.275898 (0.098824) | 0.418912 / 0.323480 (0.095432) | 0.007348 / 0.007986 (-0.000637) | 0.004779 / 0.004328 (0.000450) | 0.097541 / 0.004250 (0.093291) | 0.052523 / 0.037052 (0.015471) | 0.380118 / 0.258489 (0.121628) | 0.429448 / 0.293841 (0.135607) | 0.055156 / 0.128546 (-0.073390) | 0.019884 / 0.075646 (-0.055763) | 0.429613 / 0.419271 (0.010341) | 0.067554 / 0.043533 (0.024021) | 0.373940 / 0.255139 (0.118801) | 0.408115 / 0.283200 (0.124916) | 0.111353 / 0.141683 (-0.030329) | 1.821013 / 1.452155 (0.368858) | 1.972882 / 1.492716 (0.480165) |\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) | 0.236686 / 0.018006 (0.218679) | 0.516519 / 0.000490 (0.516029) | 0.009582 / 0.000200 (0.009383) | 0.000404 / 0.000054 (0.000349) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029425 / 0.037411 (-0.007986) | 0.123972 / 0.014526 (0.109446) | 0.133768 / 0.176557 (-0.042789) | 0.207562 / 0.737135 (-0.529573) | 0.142841 / 0.296338 (-0.153497) |\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) | 0.618531 / 0.215209 (0.403322) | 6.216854 / 2.077655 (4.139199) | 2.480138 / 1.504120 (0.976018) | 2.139884 / 1.541195 (0.598689) | 2.122992 / 1.468490 (0.654502) | 1.233824 / 4.584777 (-3.350953) | 5.426142 / 3.745712 (1.680430) | 4.891039 / 5.269862 (-0.378822) | 2.767033 / 4.565676 (-1.798643) | 0.142224 / 0.424275 (-0.282051) | 0.015754 / 0.007607 (0.008147) | 0.772210 / 0.226044 (0.546166) | 7.620484 / 2.268929 (5.351556) | 3.141617 / 55.444624 (-52.303007) | 2.471406 / 6.876477 (-4.405070) | 2.648008 / 2.142072 (0.505935) | 1.429281 / 4.805227 (-3.375946) | 0.255981 / 6.500664 (-6.244683) | 0.077710 / 0.075469 (0.002241) |\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) | 1.547714 / 1.841788 (-0.294073) | 17.859985 / 8.074308 (9.785677) | 21.791878 / 10.191392 (11.600486) | 0.238569 / 0.680424 (-0.441854) | 0.027520 / 0.534201 (-0.506681) | 0.553960 / 0.579283 (-0.025324) | 0.616165 / 0.434364 (0.181801) | 0.622492 / 0.540337 (0.082154) | 0.716345 / 1.386936 (-0.670591) |\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) | 0.009624 / 0.011353 (-0.001729) | 0.006091 / 0.011008 (-0.004917) | 0.096623 / 0.038508 (0.058115) | 0.034903 / 0.023109 (0.011793) | 0.421009 / 0.275898 (0.145111) | 0.459236 / 0.323480 (0.135756) | 0.007778 / 0.007986 (-0.000207) | 0.004726 / 0.004328 (0.000398) | 0.099603 / 0.004250 (0.095353) | 0.051426 / 0.037052 (0.014373) | 0.420461 / 0.258489 (0.161972) | 0.469747 / 0.293841 (0.175906) | 0.053769 / 0.128546 (-0.074777) | 0.020636 / 0.075646 (-0.055011) | 0.115785 / 0.419271 (-0.303486) | 0.062692 / 0.043533 (0.019160) | 0.419388 / 0.255139 (0.164249) | 0.448675 / 0.283200 (0.165475) | 0.112099 / 0.141683 (-0.029584) | 1.787982 / 1.452155 (0.335827) | 1.884581 / 1.492716 (0.391864) |\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) | 0.208837 / 0.018006 (0.190831) | 0.515593 / 0.000490 (0.515103) | 0.000447 / 0.000200 (0.000247) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031025 / 0.037411 (-0.006386) | 0.125179 / 0.014526 (0.110653) | 0.137050 / 0.176557 (-0.039506) | 0.203582 / 0.737135 (-0.533553) | 0.139209 / 0.296338 (-0.157130) |\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) | 0.601507 / 0.215209 (0.386298) | 6.034778 / 2.077655 (3.957123) | 2.550277 / 1.504120 (1.046157) | 2.242277 / 1.541195 (0.701082) | 2.306378 / 1.468490 (0.837888) | 1.251219 / 4.584777 (-3.333558) | 5.448698 / 3.745712 (1.702986) | 3.044666 / 5.269862 (-2.225196) | 2.000684 / 4.565676 (-2.564992) | 0.148385 / 0.424275 (-0.275890) | 0.015175 / 0.007607 (0.007567) | 0.800839 / 0.226044 (0.574795) | 8.062099 / 2.268929 (5.793171) | 3.400980 / 55.444624 (-52.043644) | 2.639583 / 6.876477 (-4.236894) | 2.660691 / 2.142072 (0.518618) | 1.467715 / 4.805227 (-3.337512) | 0.266429 / 6.500664 (-6.234235) | 0.076981 / 0.075469 (0.001512) |\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) | 1.621128 / 1.841788 (-0.220659) | 17.949989 / 8.074308 (9.875680) | 20.946426 / 10.191392 (10.755034) | 0.259357 / 0.680424 (-0.421067) | 0.026094 / 0.534201 (-0.508107) | 0.527840 / 0.579283 (-0.051443) | 0.629027 / 0.434364 (0.194663) | 0.603931 / 0.540337 (0.063594) | 0.711370 / 1.386936 (-0.675566) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2ccf01db81bb7b70f3ea97b185e345c2b1df0274 \"CML watermark\")\n" ]

"2023-03-23T09:18:58"

"2023-03-23T09:46:59"

"2023-03-23T09:37:49"

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"2023-03-23T09:13:01"

"2023-03-23T09:37:51"

"2023-03-23T09:37:51"

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[]

[ "You can convert any dataset to pandas to be used with imbalanced-learn using `.to_pandas()`\r\n\r\nOtherwise if you want to keep a `Dataset` object and still use e.g. [make_imbalance](https://imbalanced-learn.org/stable/references/generated/imblearn.datasets.make_imbalance.html#imblearn.datasets.make_imbalance), you just need to pass the list of rows ids and labels:\r\n\r\n```python\r\nrow_indices = list(range(len(dataset)))\r\nresampled_row_indices, _ = make_imbalance(\r\n row_indices,\r\n dataset[\"label\"],\r\n sampling_strategy={0: 25, 1: 50, 2: 50},\r\n random_state=RANDOM_STATE,\r\n)\r\n\r\nresampled_dataset = dataset.select(resampled_row_indices)\r\n```" ]

"2023-03-22T11:05:17"

"2023-03-23T17:00:39"

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[ "cc @polinaeterna @lhoestq ", "@sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). \r\nRequired `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume it **might not** be the case for some non standard Linux distributions. \r\nThe only solution for using `soundfile` here is to build [`libsndfile`](https://github.com/libsndfile/libsndfile) from source:\r\n\r\n```bash\r\ngit clone https://github.com/libsndfile/libsndfile.git\r\ncd libsndfile/\r\nautoreconf -vif\r\n./configure --enable-werror \r\nmake\r\nmake install\r\n```\r\nfor this, some building libraries should be installed, for Debian/Ubuntu it's like:\r\n```bash\r\napt install autoconf autogen automake build-essential libasound2-dev \\\r\n libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n libmpg123-dev pkg-config python\r\n```\r\nbut for other Linux distributions it might be different.\r\n\r\nWhen the binary is compiled, it should be put into location where `soundfile` would search for it (the directory is named `_soundfile_data`), it depends on where`libsdfile` (from the previous step) and `soundfile` were installed, might be something like this:\r\n\r\n```bash\r\ncp /usr/local/lib/libsndfile.so /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\ncp /usr/local/lib/libsndfile.la /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\n```\r\n\r\nAnother solution is to not use `soundfile` and apply custom processing function with `torchaudio` while setting `decode=False` in `Audio` feature and passing custom function to `.map`. ", "Not sure if it may help, but you could also try updating `pip` before installing soundfile", "@lhoestq @sanchit-gandhi. I encountered the same error (also on the TPU v4) when trying to run `datasets` from source.\r\n\r\nDowngrading soundfile with `pip install soundfile==0.12.0` seems to fix the issue for me.", "Maybe let's open an issue at https://github.com/bastibe/python-soundfile/issues in case they might know why you get `OSError: cannot load library 'libsndfile.so'` ?", "> @sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). Required `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume it **might not** be the case for some non standard Linux distributions. The only solution for using `soundfile` here is to build [`libsndfile`](https://github.com/libsndfile/libsndfile) from source:\r\n> \r\n> ```shell\r\n> git clone https://github.com/libsndfile/libsndfile.git\r\n> cd libsndfile/\r\n> autoreconf -vif\r\n> ./configure --enable-werror \r\n> make\r\n> make install\r\n> ```\r\n\r\nThis fixed the issue for me. After installing libsndfile as described above, I had to uninstall soundfile and re-install it with this command. `pip install \"soundfile>=0.12.1\"`", "Thank you so much for the comprehensive instructions @polinaeterna! Also confirming that they worked for me 🤗 In my case, I had to run several of these commands under \"sudo\" for privileges, but otherwise this workaround gave a successful `libsndfile` install:\r\n\r\n1. Grab source code:\r\n```\r\ngit clone https://github.com/libsndfile/libsndfile.git\r\n```\r\n\r\n2. Set up a build environment:\r\n```\r\nsudo apt install autoconf autogen automake build-essential libasound2-dev \\\r\n libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n libmpg123-dev pkg-config python\r\n```\r\n\r\n3. Build and test `libsndfile`:\r\n\r\n```\r\nautoreconf -vif\r\n./configure --enable-werror\r\nsudo make\r\nsudo make check\r\n```\r\n\r\n4. Create `_soundfile_data` submodule (if it does not exist already):\r\n```\r\nsudo mkdir /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n```\r\n\r\n5. Copy `libsndfile` files into submodule:\r\n```\r\nsudo cp /usr/local/lib/libsndfile.* /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n```", "On a different machine, I also tried separately by first upgrading pip, then installing soundfile. This worked too! Thanks @lhoestq 🙌" ]

"2023-03-22T10:07:33"

"2023-04-07T08:51:28"

"2023-04-07T08:51:28"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007351 / 0.011353 (-0.004002) | 0.005025 / 0.011008 (-0.005983) | 0.095978 / 0.038508 (0.057470) | 0.033486 / 0.023109 (0.010377) | 0.294427 / 0.275898 (0.018529) | 0.325157 / 0.323480 (0.001677) | 0.005671 / 0.007986 (-0.002315) | 0.005284 / 0.004328 (0.000955) | 0.073159 / 0.004250 (0.068909) | 0.045162 / 0.037052 (0.008110) | 0.294004 / 0.258489 (0.035515) | 0.343545 / 0.293841 (0.049704) | 0.036857 / 0.128546 (-0.091689) | 0.012245 / 0.075646 (-0.063401) | 0.332258 / 0.419271 (-0.087014) | 0.051909 / 0.043533 (0.008377) | 0.295701 / 0.255139 (0.040562) | 0.315247 / 0.283200 (0.032048) | 0.102363 / 0.141683 (-0.039320) | 1.441944 / 1.452155 (-0.010211) | 1.527161 / 1.492716 (0.034445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211769 / 0.018006 (0.193763) | 0.452015 / 0.000490 (0.451525) | 0.004041 / 0.000200 (0.003841) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027396 / 0.037411 (-0.010015) | 0.108318 / 0.014526 (0.093793) | 0.116851 / 0.176557 (-0.059706) | 0.172658 / 0.737135 (-0.564478) | 0.122876 / 0.296338 (-0.173462) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406484 / 0.215209 (0.191275) | 4.053849 / 2.077655 (1.976194) | 1.842947 / 1.504120 (0.338827) | 1.649473 / 1.541195 (0.108278) | 1.728629 / 1.468490 (0.260139) | 0.699519 / 4.584777 (-3.885258) | 3.730823 / 3.745712 (-0.014889) | 2.139624 / 5.269862 (-3.130237) | 1.487839 / 4.565676 (-3.077837) | 0.086699 / 0.424275 (-0.337576) | 0.012815 / 0.007607 (0.005208) | 0.514014 / 0.226044 (0.287969) | 5.153315 / 2.268929 (2.884387) | 2.324431 / 55.444624 (-53.120193) | 1.971533 / 6.876477 (-4.904944) | 2.074480 / 2.142072 (-0.067592) | 0.842419 / 4.805227 (-3.962808) | 0.169140 / 6.500664 (-6.331524) | 0.065206 / 0.075469 (-0.010263) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180887 / 1.841788 (-0.660901) | 14.627401 / 8.074308 (6.553093) | 14.382699 / 10.191392 (4.191307) | 0.143986 / 0.680424 (-0.536438) | 0.017460 / 0.534201 (-0.516741) | 0.422100 / 0.579283 (-0.157183) | 0.417474 / 0.434364 (-0.016890) | 0.493712 / 0.540337 (-0.046625) | 0.589744 / 1.386936 (-0.797193) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007538 / 0.011353 (-0.003815) | 0.005122 / 0.011008 (-0.005887) | 0.073858 / 0.038508 (0.035350) | 0.034561 / 0.023109 (0.011451) | 0.341250 / 0.275898 (0.065352) | 0.373063 / 0.323480 (0.049583) | 0.005785 / 0.007986 (-0.002200) | 0.005393 / 0.004328 (0.001065) | 0.072354 / 0.004250 (0.068104) | 0.047005 / 0.037052 (0.009953) | 0.341179 / 0.258489 (0.082690) | 0.386299 / 0.293841 (0.092458) | 0.038315 / 0.128546 (-0.090231) | 0.012200 / 0.075646 (-0.063446) | 0.086132 / 0.419271 (-0.333140) | 0.049873 / 0.043533 (0.006340) | 0.337985 / 0.255139 (0.082846) | 0.354806 / 0.283200 (0.071607) | 0.103557 / 0.141683 (-0.038126) | 1.445682 / 1.452155 (-0.006473) | 1.551008 / 1.492716 (0.058291) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235873 / 0.018006 (0.217867) | 0.448445 / 0.000490 (0.447955) | 0.001307 / 0.000200 (0.001108) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029809 / 0.037411 (-0.007603) | 0.108833 / 0.014526 (0.094307) | 0.123289 / 0.176557 (-0.053268) | 0.176516 / 0.737135 (-0.560620) | 0.127186 / 0.296338 (-0.169153) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422037 / 0.215209 (0.206828) | 4.188073 / 2.077655 (2.110418) | 1.999295 / 1.504120 (0.495175) | 1.809229 / 1.541195 (0.268034) | 1.930798 / 1.468490 (0.462308) | 0.694371 / 4.584777 (-3.890406) | 3.833432 / 3.745712 (0.087719) | 3.235600 / 5.269862 (-2.034262) | 1.867822 / 4.565676 (-2.697854) | 0.085734 / 0.424275 (-0.338541) | 0.012727 / 0.007607 (0.005120) | 0.542261 / 0.226044 (0.316217) | 5.289366 / 2.268929 (3.020437) | 2.469636 / 55.444624 (-52.974988) | 2.139392 / 6.876477 (-4.737084) | 2.193305 / 2.142072 (0.051233) | 0.846747 / 4.805227 (-3.958481) | 0.168965 / 6.500664 (-6.331699) | 0.064463 / 0.075469 (-0.011006) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263818 / 1.841788 (-0.577970) | 15.254642 / 8.074308 (7.180334) | 14.428111 / 10.191392 (4.236719) | 0.164770 / 0.680424 (-0.515654) | 0.017476 / 0.534201 (-0.516725) | 0.420198 / 0.579283 (-0.159085) | 0.443250 / 0.434364 (0.008886) | 0.496904 / 0.540337 (-0.043434) | 0.596541 / 1.386936 (-0.790395) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4db8e33eb9cf6cd4453cdfa246c065e0eedf170c \"CML watermark\")\n" ]

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

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

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

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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) | 0.007980 / 0.011353 (-0.003373) | 0.005351 / 0.011008 (-0.005657) | 0.096325 / 0.038508 (0.057817) | 0.034204 / 0.023109 (0.011095) | 0.328080 / 0.275898 (0.052182) | 0.361519 / 0.323480 (0.038039) | 0.005954 / 0.007986 (-0.002032) | 0.004106 / 0.004328 (-0.000222) | 0.072827 / 0.004250 (0.068576) | 0.050522 / 0.037052 (0.013470) | 0.326975 / 0.258489 (0.068486) | 0.373180 / 0.293841 (0.079339) | 0.037024 / 0.128546 (-0.091522) | 0.012347 / 0.075646 (-0.063299) | 0.332341 / 0.419271 (-0.086931) | 0.050695 / 0.043533 (0.007162) | 0.328298 / 0.255139 (0.073159) | 0.352808 / 0.283200 (0.069608) | 0.101637 / 0.141683 (-0.040046) | 1.435172 / 1.452155 (-0.016982) | 1.529797 / 1.492716 (0.037080) |\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) | 0.305727 / 0.018006 (0.287721) | 0.583951 / 0.000490 (0.583462) | 0.011699 / 0.000200 (0.011499) | 0.000345 / 0.000054 (0.000290) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027917 / 0.037411 (-0.009495) | 0.107698 / 0.014526 (0.093173) | 0.120572 / 0.176557 (-0.055985) | 0.176066 / 0.737135 (-0.561069) | 0.125348 / 0.296338 (-0.170991) |\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) | 0.411980 / 0.215209 (0.196771) | 4.113135 / 2.077655 (2.035480) | 1.868725 / 1.504120 (0.364605) | 1.677422 / 1.541195 (0.136227) | 1.796759 / 1.468490 (0.328269) | 0.701957 / 4.584777 (-3.882820) | 3.830742 / 3.745712 (0.085030) | 2.170444 / 5.269862 (-3.099418) | 1.345097 / 4.565676 (-3.220580) | 0.086661 / 0.424275 (-0.337614) | 0.013073 / 0.007607 (0.005466) | 0.519150 / 0.226044 (0.293106) | 5.193447 / 2.268929 (2.924518) | 2.391155 / 55.444624 (-53.053470) | 2.076610 / 6.876477 (-4.799867) | 2.245557 / 2.142072 (0.103484) | 0.846496 / 4.805227 (-3.958731) | 0.169246 / 6.500664 (-6.331418) | 0.066360 / 0.075469 (-0.009109) |\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) | 1.196344 / 1.841788 (-0.645444) | 15.640363 / 8.074308 (7.566055) | 14.936144 / 10.191392 (4.744752) | 0.163613 / 0.680424 (-0.516811) | 0.017900 / 0.534201 (-0.516301) | 0.425377 / 0.579283 (-0.153906) | 0.431119 / 0.434364 (-0.003245) | 0.513669 / 0.540337 (-0.026669) | 0.592970 / 1.386936 (-0.793966) |\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) | 0.007958 / 0.011353 (-0.003395) | 0.005707 / 0.011008 (-0.005301) | 0.075377 / 0.038508 (0.036869) | 0.037126 / 0.023109 (0.014016) | 0.344589 / 0.275898 (0.068691) | 0.381060 / 0.323480 (0.057580) | 0.006592 / 0.007986 (-0.001393) | 0.004479 / 0.004328 (0.000151) | 0.074456 / 0.004250 (0.070206) | 0.054087 / 0.037052 (0.017035) | 0.344942 / 0.258489 (0.086453) | 0.393174 / 0.293841 (0.099333) | 0.037926 / 0.128546 (-0.090620) | 0.012638 / 0.075646 (-0.063009) | 0.087743 / 0.419271 (-0.331529) | 0.050081 / 0.043533 (0.006548) | 0.340406 / 0.255139 (0.085267) | 0.361487 / 0.283200 (0.078287) | 0.108546 / 0.141683 (-0.033137) | 1.424626 / 1.452155 (-0.027529) | 1.553958 / 1.492716 (0.061242) |\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) | 0.329922 / 0.018006 (0.311916) | 0.523239 / 0.000490 (0.522749) | 0.012164 / 0.000200 (0.011964) | 0.000137 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031935 / 0.037411 (-0.005477) | 0.115680 / 0.014526 (0.101154) | 0.130062 / 0.176557 (-0.046494) | 0.180679 / 0.737135 (-0.556457) | 0.135548 / 0.296338 (-0.160790) |\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) | 0.429648 / 0.215209 (0.214439) | 4.303342 / 2.077655 (2.225687) | 1.999395 / 1.504120 (0.495275) | 1.810354 / 1.541195 (0.269160) | 1.963132 / 1.468490 (0.494642) | 0.701654 / 4.584777 (-3.883122) | 3.844687 / 3.745712 (0.098975) | 2.153425 / 5.269862 (-3.116436) | 1.351541 / 4.565676 (-3.214135) | 0.086292 / 0.424275 (-0.337983) | 0.012491 / 0.007607 (0.004883) | 0.523144 / 0.226044 (0.297099) | 5.243283 / 2.268929 (2.974355) | 2.465849 / 55.444624 (-52.978775) | 2.154505 / 6.876477 (-4.721972) | 2.245500 / 2.142072 (0.103428) | 0.838902 / 4.805227 (-3.966326) | 0.169441 / 6.500664 (-6.331223) | 0.065631 / 0.075469 (-0.009838) |\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) | 1.262175 / 1.841788 (-0.579612) | 15.424650 / 8.074308 (7.350342) | 15.000718 / 10.191392 (4.809326) | 0.186328 / 0.680424 (-0.494096) | 0.018076 / 0.534201 (-0.516125) | 0.433458 / 0.579283 (-0.145825) | 0.424213 / 0.434364 (-0.010151) | 0.546568 / 0.540337 (0.006231) | 0.643529 / 1.386936 (-0.743407) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ea7298bf121d7ae8079f0a59deb67c2fa1d4df6a \"CML watermark\")\n" ]

"2023-03-21T15:23:29"

"2023-03-23T14:14:50"

"2023-03-23T13:15:46"

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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) | 0.009691 / 0.011353 (-0.001662) | 0.006160 / 0.011008 (-0.004848) | 0.127528 / 0.038508 (0.089020) | 0.034445 / 0.023109 (0.011335) | 0.391483 / 0.275898 (0.115585) | 0.425922 / 0.323480 (0.102442) | 0.006621 / 0.007986 (-0.001365) | 0.004550 / 0.004328 (0.000221) | 0.099134 / 0.004250 (0.094884) | 0.051089 / 0.037052 (0.014037) | 0.398675 / 0.258489 (0.140186) | 0.456740 / 0.293841 (0.162899) | 0.052279 / 0.128546 (-0.076267) | 0.020878 / 0.075646 (-0.054768) | 0.414954 / 0.419271 (-0.004317) | 0.061903 / 0.043533 (0.018370) | 0.393088 / 0.255139 (0.137949) | 0.410289 / 0.283200 (0.127089) | 0.101684 / 0.141683 (-0.039998) | 1.747102 / 1.452155 (0.294947) | 1.896976 / 1.492716 (0.404260) |\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) | 0.203193 / 0.018006 (0.185187) | 0.495011 / 0.000490 (0.494521) | 0.006290 / 0.000200 (0.006090) | 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) | 0.034840 / 0.037411 (-0.002571) | 0.122529 / 0.014526 (0.108003) | 0.133870 / 0.176557 (-0.042686) | 0.207771 / 0.737135 (-0.529364) | 0.141441 / 0.296338 (-0.154897) |\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) | 0.604190 / 0.215209 (0.388981) | 6.040295 / 2.077655 (3.962641) | 2.405703 / 1.504120 (0.901583) | 2.062767 / 1.541195 (0.521572) | 2.079313 / 1.468490 (0.610823) | 1.240107 / 4.584777 (-3.344670) | 5.316583 / 3.745712 (1.570871) | 3.104758 / 5.269862 (-2.165103) | 2.056489 / 4.565676 (-2.509187) | 0.149060 / 0.424275 (-0.275215) | 0.014467 / 0.007607 (0.006860) | 0.736882 / 0.226044 (0.510838) | 7.324142 / 2.268929 (5.055213) | 3.048752 / 55.444624 (-52.395872) | 2.385013 / 6.876477 (-4.491463) | 2.457478 / 2.142072 (0.315405) | 1.459276 / 4.805227 (-3.345951) | 0.253882 / 6.500664 (-6.246782) | 0.076756 / 0.075469 (0.001287) |\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) | 1.499166 / 1.841788 (-0.342622) | 17.294165 / 8.074308 (9.219857) | 20.385668 / 10.191392 (10.194276) | 0.254633 / 0.680424 (-0.425791) | 0.026253 / 0.534201 (-0.507948) | 0.532928 / 0.579283 (-0.046355) | 0.606095 / 0.434364 (0.171731) | 0.615025 / 0.540337 (0.074687) | 0.728651 / 1.386936 (-0.658285) |\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) | 0.009376 / 0.011353 (-0.001977) | 0.005981 / 0.011008 (-0.005027) | 0.109898 / 0.038508 (0.071390) | 0.033746 / 0.023109 (0.010637) | 0.410226 / 0.275898 (0.134328) | 0.470606 / 0.323480 (0.147126) | 0.006706 / 0.007986 (-0.001279) | 0.004482 / 0.004328 (0.000153) | 0.092280 / 0.004250 (0.088030) | 0.047988 / 0.037052 (0.010935) | 0.430628 / 0.258489 (0.172139) | 0.480668 / 0.293841 (0.186827) | 0.052099 / 0.128546 (-0.076447) | 0.018743 / 0.075646 (-0.056903) | 0.112204 / 0.419271 (-0.307068) | 0.059838 / 0.043533 (0.016305) | 0.418230 / 0.255139 (0.163091) | 0.451568 / 0.283200 (0.168368) | 0.107026 / 0.141683 (-0.034657) | 1.708111 / 1.452155 (0.255956) | 1.839268 / 1.492716 (0.346552) |\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) | 0.229558 / 0.018006 (0.211552) | 0.488099 / 0.000490 (0.487609) | 0.004643 / 0.000200 (0.004443) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030461 / 0.037411 (-0.006951) | 0.120993 / 0.014526 (0.106467) | 0.130874 / 0.176557 (-0.045682) | 0.193550 / 0.737135 (-0.543585) | 0.138164 / 0.296338 (-0.158174) |\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) | 0.635709 / 0.215209 (0.420500) | 6.225112 / 2.077655 (4.147457) | 2.639584 / 1.504120 (1.135465) | 2.254487 / 1.541195 (0.713293) | 2.280478 / 1.468490 (0.811988) | 1.205712 / 4.584777 (-3.379065) | 5.367845 / 3.745712 (1.622133) | 3.020207 / 5.269862 (-2.249655) | 2.001897 / 4.565676 (-2.563779) | 0.149582 / 0.424275 (-0.274693) | 0.014867 / 0.007607 (0.007260) | 0.759050 / 0.226044 (0.533006) | 7.692969 / 2.268929 (5.424041) | 3.274009 / 55.444624 (-52.170615) | 2.635529 / 6.876477 (-4.240948) | 2.672960 / 2.142072 (0.530888) | 1.426487 / 4.805227 (-3.378740) | 0.253368 / 6.500664 (-6.247296) | 0.078650 / 0.075469 (0.003181) |\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) | 1.620265 / 1.841788 (-0.221523) | 17.674168 / 8.074308 (9.599860) | 21.120528 / 10.191392 (10.929136) | 0.244205 / 0.680424 (-0.436218) | 0.029646 / 0.534201 (-0.504555) | 0.510948 / 0.579283 (-0.068335) | 0.586255 / 0.434364 (0.151891) | 0.589286 / 0.540337 (0.048949) | 0.736561 / 1.386936 (-0.650375) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de5fe9ae5df84c489e08dcbdc3d2d20272b312c3 \"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) | 0.007778 / 0.011353 (-0.003575) | 0.005432 / 0.011008 (-0.005577) | 0.098776 / 0.038508 (0.060268) | 0.035196 / 0.023109 (0.012087) | 0.305646 / 0.275898 (0.029748) | 0.342661 / 0.323480 (0.019181) | 0.006513 / 0.007986 (-0.001472) | 0.005897 / 0.004328 (0.001568) | 0.075797 / 0.004250 (0.071547) | 0.056060 / 0.037052 (0.019007) | 0.306645 / 0.258489 (0.048156) | 0.352447 / 0.293841 (0.058606) | 0.037304 / 0.128546 (-0.091242) | 0.012514 / 0.075646 (-0.063132) | 0.334949 / 0.419271 (-0.084323) | 0.051600 / 0.043533 (0.008067) | 0.302302 / 0.255139 (0.047163) | 0.322238 / 0.283200 (0.039038) | 0.106896 / 0.141683 (-0.034787) | 1.483163 / 1.452155 (0.031008) | 1.587483 / 1.492716 (0.094767) |\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) | 0.292318 / 0.018006 (0.274312) | 0.541541 / 0.000490 (0.541051) | 0.008342 / 0.000200 (0.008142) | 0.000339 / 0.000054 (0.000285) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028287 / 0.037411 (-0.009124) | 0.107775 / 0.014526 (0.093250) | 0.119112 / 0.176557 (-0.057445) | 0.174002 / 0.737135 (-0.563134) | 0.126531 / 0.296338 (-0.169808) |\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) | 0.401684 / 0.215209 (0.186475) | 4.024708 / 2.077655 (1.947053) | 1.812763 / 1.504120 (0.308643) | 1.629540 / 1.541195 (0.088345) | 1.731733 / 1.468490 (0.263243) | 0.711066 / 4.584777 (-3.873711) | 3.867499 / 3.745712 (0.121786) | 3.615968 / 5.269862 (-1.653893) | 1.876077 / 4.565676 (-2.689600) | 0.087003 / 0.424275 (-0.337272) | 0.012445 / 0.007607 (0.004838) | 0.499106 / 0.226044 (0.273061) | 4.975920 / 2.268929 (2.706992) | 2.279074 / 55.444624 (-53.165550) | 1.952311 / 6.876477 (-4.924166) | 2.167480 / 2.142072 (0.025408) | 0.855882 / 4.805227 (-3.949346) | 0.171378 / 6.500664 (-6.329287) | 0.066731 / 0.075469 (-0.008738) |\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) | 1.184226 / 1.841788 (-0.657561) | 15.383396 / 8.074308 (7.309088) | 15.069783 / 10.191392 (4.878391) | 0.161489 / 0.680424 (-0.518935) | 0.017763 / 0.534201 (-0.516438) | 0.427103 / 0.579283 (-0.152180) | 0.434295 / 0.434364 (-0.000069) | 0.496848 / 0.540337 (-0.043489) | 0.592572 / 1.386936 (-0.794364) |\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) | 0.008014 / 0.011353 (-0.003339) | 0.005607 / 0.011008 (-0.005401) | 0.076826 / 0.038508 (0.038318) | 0.035283 / 0.023109 (0.012174) | 0.347809 / 0.275898 (0.071911) | 0.382482 / 0.323480 (0.059003) | 0.006276 / 0.007986 (-0.001709) | 0.005978 / 0.004328 (0.001650) | 0.074938 / 0.004250 (0.070687) | 0.054323 / 0.037052 (0.017271) | 0.344027 / 0.258489 (0.085538) | 0.397623 / 0.293841 (0.103783) | 0.037851 / 0.128546 (-0.090695) | 0.012649 / 0.075646 (-0.062997) | 0.086169 / 0.419271 (-0.333103) | 0.051510 / 0.043533 (0.007977) | 0.341112 / 0.255139 (0.085973) | 0.357957 / 0.283200 (0.074757) | 0.110949 / 0.141683 (-0.030734) | 1.479573 / 1.452155 (0.027419) | 1.578572 / 1.492716 (0.085855) |\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) | 0.310678 / 0.018006 (0.292672) | 0.525504 / 0.000490 (0.525015) | 0.000447 / 0.000200 (0.000247) | 0.000060 / 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) | 0.031262 / 0.037411 (-0.006149) | 0.113801 / 0.014526 (0.099275) | 0.124967 / 0.176557 (-0.051590) | 0.175226 / 0.737135 (-0.561909) | 0.129377 / 0.296338 (-0.166962) |\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) | 0.420672 / 0.215209 (0.205463) | 4.181337 / 2.077655 (2.103682) | 1.985524 / 1.504120 (0.481404) | 1.803468 / 1.541195 (0.262273) | 1.952915 / 1.468490 (0.484425) | 0.710928 / 4.584777 (-3.873849) | 3.886245 / 3.745712 (0.140533) | 3.737837 / 5.269862 (-1.532024) | 1.806859 / 4.565676 (-2.758818) | 0.088461 / 0.424275 (-0.335814) | 0.013125 / 0.007607 (0.005518) | 0.522410 / 0.226044 (0.296365) | 5.232591 / 2.268929 (2.963663) | 2.451188 / 55.444624 (-52.993437) | 2.127725 / 6.876477 (-4.748751) | 2.232859 / 2.142072 (0.090786) | 0.854257 / 4.805227 (-3.950970) | 0.171004 / 6.500664 (-6.329661) | 0.066724 / 0.075469 (-0.008746) |\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) | 1.257700 / 1.841788 (-0.584088) | 15.738605 / 8.074308 (7.664297) | 15.021698 / 10.191392 (4.830306) | 0.147422 / 0.680424 (-0.533002) | 0.017928 / 0.534201 (-0.516273) | 0.428121 / 0.579283 (-0.151162) | 0.432056 / 0.434364 (-0.002308) | 0.498318 / 0.540337 (-0.042020) | 0.591040 / 1.386936 (-0.795896) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ac74267032ef3608779a8c8c4361b95a83ecbcb \"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) | 0.007014 / 0.011353 (-0.004339) | 0.004792 / 0.011008 (-0.006216) | 0.099822 / 0.038508 (0.061314) | 0.029333 / 0.023109 (0.006224) | 0.306453 / 0.275898 (0.030555) | 0.344598 / 0.323480 (0.021118) | 0.005121 / 0.007986 (-0.002865) | 0.004850 / 0.004328 (0.000522) | 0.076668 / 0.004250 (0.072417) | 0.039980 / 0.037052 (0.002927) | 0.312276 / 0.258489 (0.053787) | 0.354722 / 0.293841 (0.060881) | 0.031653 / 0.128546 (-0.096893) | 0.011743 / 0.075646 (-0.063903) | 0.322998 / 0.419271 (-0.096274) | 0.042813 / 0.043533 (-0.000720) | 0.308855 / 0.255139 (0.053716) | 0.332650 / 0.283200 (0.049451) | 0.087155 / 0.141683 (-0.054528) | 1.454946 / 1.452155 (0.002791) | 1.550589 / 1.492716 (0.057873) |\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) | 0.192921 / 0.018006 (0.174914) | 0.411155 / 0.000490 (0.410666) | 0.004779 / 0.000200 (0.004579) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024462 / 0.037411 (-0.012950) | 0.100320 / 0.014526 (0.085794) | 0.105509 / 0.176557 (-0.071048) | 0.168533 / 0.737135 (-0.568602) | 0.110018 / 0.296338 (-0.186321) |\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) | 0.415025 / 0.215209 (0.199816) | 4.144583 / 2.077655 (2.066928) | 1.871627 / 1.504120 (0.367507) | 1.671638 / 1.541195 (0.130443) | 1.734458 / 1.468490 (0.265968) | 0.693435 / 4.584777 (-3.891342) | 3.487999 / 3.745712 (-0.257713) | 3.196553 / 5.269862 (-2.073308) | 1.628499 / 4.565676 (-2.937178) | 0.082999 / 0.424275 (-0.341276) | 0.012822 / 0.007607 (0.005215) | 0.514904 / 0.226044 (0.288860) | 5.157525 / 2.268929 (2.888596) | 2.313093 / 55.444624 (-53.131531) | 1.968335 / 6.876477 (-4.908142) | 2.083462 / 2.142072 (-0.058610) | 0.804485 / 4.805227 (-4.000742) | 0.152290 / 6.500664 (-6.348374) | 0.066813 / 0.075469 (-0.008656) |\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) | 1.210370 / 1.841788 (-0.631418) | 14.261779 / 8.074308 (6.187471) | 14.268121 / 10.191392 (4.076729) | 0.149216 / 0.680424 (-0.531207) | 0.016529 / 0.534201 (-0.517672) | 0.378814 / 0.579283 (-0.200469) | 0.386304 / 0.434364 (-0.048060) | 0.439653 / 0.540337 (-0.100684) | 0.523658 / 1.386936 (-0.863278) |\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) | 0.006979 / 0.011353 (-0.004374) | 0.004718 / 0.011008 (-0.006290) | 0.077023 / 0.038508 (0.038514) | 0.029080 / 0.023109 (0.005971) | 0.343145 / 0.275898 (0.067247) | 0.380633 / 0.323480 (0.057153) | 0.006057 / 0.007986 (-0.001928) | 0.003541 / 0.004328 (-0.000788) | 0.075773 / 0.004250 (0.071523) | 0.039112 / 0.037052 (0.002060) | 0.342355 / 0.258489 (0.083866) | 0.386002 / 0.293841 (0.092161) | 0.033238 / 0.128546 (-0.095308) | 0.011696 / 0.075646 (-0.063950) | 0.086178 / 0.419271 (-0.333093) | 0.045219 / 0.043533 (0.001686) | 0.360710 / 0.255139 (0.105571) | 0.367490 / 0.283200 (0.084290) | 0.093041 / 0.141683 (-0.048642) | 1.523670 / 1.452155 (0.071516) | 1.595280 / 1.492716 (0.102564) |\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) | 0.235888 / 0.018006 (0.217882) | 0.410205 / 0.000490 (0.409715) | 0.000405 / 0.000200 (0.000205) | 0.000059 / 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) | 0.025752 / 0.037411 (-0.011659) | 0.103343 / 0.014526 (0.088818) | 0.108722 / 0.176557 (-0.067834) | 0.159241 / 0.737135 (-0.577894) | 0.113684 / 0.296338 (-0.182654) |\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) | 0.441809 / 0.215209 (0.226600) | 4.410893 / 2.077655 (2.333238) | 2.104061 / 1.504120 (0.599941) | 1.854016 / 1.541195 (0.312821) | 1.947100 / 1.468490 (0.478610) | 0.697682 / 4.584777 (-3.887095) | 3.467513 / 3.745712 (-0.278199) | 1.911603 / 5.269862 (-3.358258) | 1.187479 / 4.565676 (-3.378197) | 0.083153 / 0.424275 (-0.341122) | 0.012651 / 0.007607 (0.005044) | 0.542081 / 0.226044 (0.316036) | 5.444622 / 2.268929 (3.175693) | 2.524236 / 55.444624 (-52.920388) | 2.190463 / 6.876477 (-4.686014) | 2.265764 / 2.142072 (0.123691) | 0.810778 / 4.805227 (-3.994450) | 0.152459 / 6.500664 (-6.348205) | 0.067815 / 0.075469 (-0.007654) |\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) | 1.334388 / 1.841788 (-0.507400) | 14.640459 / 8.074308 (6.566151) | 14.714874 / 10.191392 (4.523482) | 0.153479 / 0.680424 (-0.526945) | 0.016709 / 0.534201 (-0.517492) | 0.379427 / 0.579283 (-0.199856) | 0.391602 / 0.434364 (-0.042762) | 0.438297 / 0.540337 (-0.102041) | 0.524170 / 1.386936 (-0.862766) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b277cef5cb56c0c506eda082fb69fddb839156a1 \"CML watermark\")\n" ]

"2023-03-21T14:18:09"

"2023-03-23T13:19:27"

"2023-03-23T13:12:25"

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[ "Upd. the above code works if we replace `25` with `1`, but the result value at key \"hr\" is not a tensor but a list of lists of lists of uint8.\r\n\r\nAdding `train_data.set_format(\"torch\")` after map fixes this, but the original issue remains\r\n\r\n", "As a workaround, one can replace\r\n`return {\"hr\": torch.stack([crop_transf(tensor) for _ in range(25)])}`\r\nwith\r\n`return {f\"hr_crop_{i}\": crop_transf(tensor) for i in range(25)}`\r\nand then choose appropriate crop randomly in further processing, but I still don't understand why the original approach doesn't work(\r\n" ]

"2023-03-21T09:33:27"

"2023-03-21T10:32:07"

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[ "I agree this should be documented" ]

"2023-03-21T05:25:35"

"2023-03-24T16:36:23"

"2023-03-24T16:36:23"

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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) | 0.007455 / 0.011353 (-0.003898) | 0.005278 / 0.011008 (-0.005731) | 0.098981 / 0.038508 (0.060473) | 0.029208 / 0.023109 (0.006099) | 0.304132 / 0.275898 (0.028234) | 0.340010 / 0.323480 (0.016530) | 0.005514 / 0.007986 (-0.002472) | 0.003636 / 0.004328 (-0.000692) | 0.076737 / 0.004250 (0.072486) | 0.041985 / 0.037052 (0.004933) | 0.314941 / 0.258489 (0.056452) | 0.346686 / 0.293841 (0.052845) | 0.032528 / 0.128546 (-0.096018) | 0.011795 / 0.075646 (-0.063851) | 0.322122 / 0.419271 (-0.097150) | 0.051548 / 0.043533 (0.008015) | 0.310561 / 0.255139 (0.055422) | 0.329443 / 0.283200 (0.046243) | 0.092820 / 0.141683 (-0.048863) | 1.495764 / 1.452155 (0.043609) | 1.586734 / 1.492716 (0.094018) |\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) | 0.195830 / 0.018006 (0.177824) | 0.422075 / 0.000490 (0.421586) | 0.005483 / 0.000200 (0.005283) | 0.000133 / 0.000054 (0.000078) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023468 / 0.037411 (-0.013943) | 0.097713 / 0.014526 (0.083187) | 0.105331 / 0.176557 (-0.071225) | 0.166237 / 0.737135 (-0.570898) | 0.108924 / 0.296338 (-0.187415) |\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) | 0.671901 / 0.215209 (0.456692) | 6.745691 / 2.077655 (4.668036) | 2.132508 / 1.504120 (0.628388) | 1.693808 / 1.541195 (0.152614) | 1.715282 / 1.468490 (0.246792) | 0.955354 / 4.584777 (-3.629422) | 3.810296 / 3.745712 (0.064584) | 2.214891 / 5.269862 (-3.054970) | 1.461513 / 4.565676 (-3.104164) | 0.109846 / 0.424275 (-0.314430) | 0.013546 / 0.007607 (0.005939) | 0.780046 / 0.226044 (0.554001) | 7.789020 / 2.268929 (5.520091) | 2.602411 / 55.444624 (-52.842213) | 1.995096 / 6.876477 (-4.881380) | 2.009022 / 2.142072 (-0.133051) | 1.069215 / 4.805227 (-3.736012) | 0.179812 / 6.500664 (-6.320852) | 0.068125 / 0.075469 (-0.007344) |\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) | 1.201866 / 1.841788 (-0.639921) | 13.878814 / 8.074308 (5.804506) | 14.179264 / 10.191392 (3.987872) | 0.128908 / 0.680424 (-0.551515) | 0.017257 / 0.534201 (-0.516944) | 0.379500 / 0.579283 (-0.199783) | 0.393308 / 0.434364 (-0.041056) | 0.444700 / 0.540337 (-0.095638) | 0.531043 / 1.386936 (-0.855893) |\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) | 0.007413 / 0.011353 (-0.003940) | 0.005431 / 0.011008 (-0.005577) | 0.078158 / 0.038508 (0.039650) | 0.028837 / 0.023109 (0.005728) | 0.343635 / 0.275898 (0.067737) | 0.383041 / 0.323480 (0.059561) | 0.005283 / 0.007986 (-0.002703) | 0.003673 / 0.004328 (-0.000655) | 0.076461 / 0.004250 (0.072211) | 0.038625 / 0.037052 (0.001573) | 0.341109 / 0.258489 (0.082620) | 0.387027 / 0.293841 (0.093186) | 0.032512 / 0.128546 (-0.096034) | 0.011903 / 0.075646 (-0.063744) | 0.086340 / 0.419271 (-0.332931) | 0.043211 / 0.043533 (-0.000321) | 0.339994 / 0.255139 (0.084855) | 0.370868 / 0.283200 (0.087668) | 0.091679 / 0.141683 (-0.050004) | 1.547188 / 1.452155 (0.095033) | 1.578545 / 1.492716 (0.085829) |\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) | 0.216981 / 0.018006 (0.198975) | 0.412206 / 0.000490 (0.411716) | 0.004243 / 0.000200 (0.004043) | 0.000130 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025392 / 0.037411 (-0.012020) | 0.102577 / 0.014526 (0.088051) | 0.107672 / 0.176557 (-0.068884) | 0.160657 / 0.737135 (-0.576478) | 0.111646 / 0.296338 (-0.184692) |\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) | 0.698815 / 0.215209 (0.483606) | 6.958931 / 2.077655 (4.881276) | 2.344216 / 1.504120 (0.840096) | 1.907752 / 1.541195 (0.366557) | 1.964251 / 1.468490 (0.495761) | 0.950754 / 4.584777 (-3.634023) | 3.829700 / 3.745712 (0.083988) | 3.055565 / 5.269862 (-2.214297) | 1.575851 / 4.565676 (-2.989825) | 0.109227 / 0.424275 (-0.315048) | 0.013163 / 0.007607 (0.005556) | 0.804613 / 0.226044 (0.578569) | 8.015035 / 2.268929 (5.746107) | 2.796358 / 55.444624 (-52.648266) | 2.212561 / 6.876477 (-4.663916) | 2.229918 / 2.142072 (0.087845) | 1.062041 / 4.805227 (-3.743186) | 0.181384 / 6.500664 (-6.319280) | 0.068564 / 0.075469 (-0.006905) |\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) | 1.287904 / 1.841788 (-0.553884) | 14.539222 / 8.074308 (6.464914) | 14.232097 / 10.191392 (4.040705) | 0.130870 / 0.680424 (-0.549554) | 0.016710 / 0.534201 (-0.517491) | 0.384454 / 0.579283 (-0.194829) | 0.391750 / 0.434364 (-0.042614) | 0.443995 / 0.540337 (-0.096343) | 0.526255 / 1.386936 (-0.860681) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bd46874a580b888bdc82b53daace79884f04bc62 \"CML watermark\")\n", "Arf I need to fix some tests first - sorry", "<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) | 0.008393 / 0.011353 (-0.002959) | 0.005635 / 0.011008 (-0.005373) | 0.114840 / 0.038508 (0.076332) | 0.039272 / 0.023109 (0.016163) | 0.352116 / 0.275898 (0.076218) | 0.386614 / 0.323480 (0.063134) | 0.006348 / 0.007986 (-0.001638) | 0.005872 / 0.004328 (0.001544) | 0.086437 / 0.004250 (0.082187) | 0.054003 / 0.037052 (0.016951) | 0.350302 / 0.258489 (0.091813) | 0.400148 / 0.293841 (0.106308) | 0.042436 / 0.128546 (-0.086111) | 0.013987 / 0.075646 (-0.061660) | 0.399434 / 0.419271 (-0.019837) | 0.059223 / 0.043533 (0.015690) | 0.354511 / 0.255139 (0.099372) | 0.377764 / 0.283200 (0.094564) | 0.112297 / 0.141683 (-0.029386) | 1.677483 / 1.452155 (0.225328) | 1.784942 / 1.492716 (0.292226) |\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) | 0.233334 / 0.018006 (0.215328) | 0.450575 / 0.000490 (0.450085) | 0.000376 / 0.000200 (0.000176) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031995 / 0.037411 (-0.005416) | 0.126798 / 0.014526 (0.112272) | 0.138453 / 0.176557 (-0.038104) | 0.207360 / 0.737135 (-0.529775) | 0.147744 / 0.296338 (-0.148594) |\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) | 0.481496 / 0.215209 (0.266287) | 4.810495 / 2.077655 (2.732840) | 2.457917 / 1.504120 (0.953797) | 2.300073 / 1.541195 (0.758879) | 2.065595 / 1.468490 (0.597105) | 0.814589 / 4.584777 (-3.770188) | 4.566496 / 3.745712 (0.820784) | 2.386947 / 5.269862 (-2.882914) | 1.531639 / 4.565676 (-3.034037) | 0.099569 / 0.424275 (-0.324706) | 0.014971 / 0.007607 (0.007364) | 0.590359 / 0.226044 (0.364314) | 5.885250 / 2.268929 (3.616322) | 2.706799 / 55.444624 (-52.737826) | 2.324485 / 6.876477 (-4.551992) | 2.452751 / 2.142072 (0.310678) | 0.966955 / 4.805227 (-3.838272) | 0.198165 / 6.500664 (-6.302499) | 0.076877 / 0.075469 (0.001408) |\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) | 1.499085 / 1.841788 (-0.342702) | 17.705516 / 8.074308 (9.631208) | 16.481174 / 10.191392 (6.289782) | 0.191832 / 0.680424 (-0.488592) | 0.021417 / 0.534201 (-0.512784) | 0.519647 / 0.579283 (-0.059636) | 0.498432 / 0.434364 (0.064068) | 0.598206 / 0.540337 (0.057868) | 0.700990 / 1.386936 (-0.685946) |\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) | 0.008746 / 0.011353 (-0.002607) | 0.006052 / 0.011008 (-0.004956) | 0.092938 / 0.038508 (0.054430) | 0.038932 / 0.023109 (0.015823) | 0.406919 / 0.275898 (0.131021) | 0.444325 / 0.323480 (0.120845) | 0.006735 / 0.007986 (-0.001251) | 0.005972 / 0.004328 (0.001643) | 0.088152 / 0.004250 (0.083902) | 0.051009 / 0.037052 (0.013957) | 0.407415 / 0.258489 (0.148926) | 0.481048 / 0.293841 (0.187207) | 0.043268 / 0.128546 (-0.085278) | 0.014574 / 0.075646 (-0.061072) | 0.103555 / 0.419271 (-0.315716) | 0.058251 / 0.043533 (0.014719) | 0.406294 / 0.255139 (0.151155) | 0.429229 / 0.283200 (0.146029) | 0.116977 / 0.141683 (-0.024705) | 1.765885 / 1.452155 (0.313730) | 1.885557 / 1.492716 (0.392841) |\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) | 0.284014 / 0.018006 (0.266008) | 0.458066 / 0.000490 (0.457576) | 0.022286 / 0.000200 (0.022086) | 0.000158 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033971 / 0.037411 (-0.003440) | 0.132030 / 0.014526 (0.117504) | 0.141725 / 0.176557 (-0.034831) | 0.199818 / 0.737135 (-0.537318) | 0.149176 / 0.296338 (-0.147162) |\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) | 0.511463 / 0.215209 (0.296254) | 4.917921 / 2.077655 (2.840267) | 2.382377 / 1.504120 (0.878257) | 2.154599 / 1.541195 (0.613404) | 2.247858 / 1.468490 (0.779368) | 0.834524 / 4.584777 (-3.750253) | 4.560010 / 3.745712 (0.814297) | 2.403055 / 5.269862 (-2.866806) | 1.780784 / 4.565676 (-2.784893) | 0.101409 / 0.424275 (-0.322866) | 0.014657 / 0.007607 (0.007050) | 0.610137 / 0.226044 (0.384093) | 6.051011 / 2.268929 (3.782083) | 2.887357 / 55.444624 (-52.557267) | 2.518225 / 6.876477 (-4.358252) | 2.559654 / 2.142072 (0.417582) | 0.981226 / 4.805227 (-3.824001) | 0.197323 / 6.500664 (-6.303341) | 0.076851 / 0.075469 (0.001382) |\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) | 1.554662 / 1.841788 (-0.287126) | 18.038993 / 8.074308 (9.964685) | 16.719948 / 10.191392 (6.528556) | 0.195641 / 0.680424 (-0.484783) | 0.020699 / 0.534201 (-0.513502) | 0.498949 / 0.579283 (-0.080334) | 0.487775 / 0.434364 (0.053411) | 0.591413 / 0.540337 (0.051075) | 0.708520 / 1.386936 (-0.678416) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#39de0d78224c070be33d0820ec9203018fb721d1 \"CML watermark\")\n", "Ready for review @mariosasko :)", "Yea it does behave as expected, but modifying a dataset's features dict is not recommended and can lead to unpredictable behaviors. By copying the features, we make sure users don't modify the dataset's features dict.\r\n\r\nSince the attribute is public, users expect to be able to do whatever they want with it, without checking if they have to copy it or not", "<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) | 0.008069 / 0.011353 (-0.003284) | 0.005051 / 0.011008 (-0.005958) | 0.096587 / 0.038508 (0.058079) | 0.032954 / 0.023109 (0.009844) | 0.317877 / 0.275898 (0.041979) | 0.328677 / 0.323480 (0.005197) | 0.005524 / 0.007986 (-0.002462) | 0.003958 / 0.004328 (-0.000370) | 0.072692 / 0.004250 (0.068441) | 0.044554 / 0.037052 (0.007502) | 0.311121 / 0.258489 (0.052632) | 0.355912 / 0.293841 (0.062071) | 0.035934 / 0.128546 (-0.092612) | 0.012056 / 0.075646 (-0.063590) | 0.332575 / 0.419271 (-0.086696) | 0.049788 / 0.043533 (0.006255) | 0.307918 / 0.255139 (0.052779) | 0.326757 / 0.283200 (0.043557) | 0.098671 / 0.141683 (-0.043012) | 1.424625 / 1.452155 (-0.027530) | 1.507944 / 1.492716 (0.015228) |\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) | 0.207976 / 0.018006 (0.189970) | 0.439604 / 0.000490 (0.439114) | 0.000435 / 0.000200 (0.000235) | 0.000057 / 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) | 0.026961 / 0.037411 (-0.010451) | 0.106627 / 0.014526 (0.092101) | 0.115292 / 0.176557 (-0.061264) | 0.171901 / 0.737135 (-0.565234) | 0.123276 / 0.296338 (-0.173062) |\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) | 0.407679 / 0.215209 (0.192469) | 4.071958 / 2.077655 (1.994303) | 1.854270 / 1.504120 (0.350151) | 1.678406 / 1.541195 (0.137211) | 1.715890 / 1.468490 (0.247400) | 0.705536 / 4.584777 (-3.879241) | 3.774198 / 3.745712 (0.028486) | 2.096429 / 5.269862 (-3.173432) | 1.431810 / 4.565676 (-3.133866) | 0.085557 / 0.424275 (-0.338718) | 0.012191 / 0.007607 (0.004584) | 0.502937 / 0.226044 (0.276893) | 5.034391 / 2.268929 (2.765463) | 2.393826 / 55.444624 (-53.050799) | 2.037383 / 6.876477 (-4.839094) | 2.192037 / 2.142072 (0.049964) | 0.829298 / 4.805227 (-3.975929) | 0.167781 / 6.500664 (-6.332883) | 0.063405 / 0.075469 (-0.012064) |\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) | 1.179189 / 1.841788 (-0.662599) | 14.464132 / 8.074308 (6.389824) | 14.869024 / 10.191392 (4.677632) | 0.172864 / 0.680424 (-0.507560) | 0.017817 / 0.534201 (-0.516384) | 0.427849 / 0.579283 (-0.151434) | 0.434447 / 0.434364 (0.000083) | 0.502077 / 0.540337 (-0.038260) | 0.599587 / 1.386936 (-0.787349) |\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) | 0.007366 / 0.011353 (-0.003987) | 0.004939 / 0.011008 (-0.006069) | 0.074982 / 0.038508 (0.036474) | 0.032611 / 0.023109 (0.009501) | 0.340670 / 0.275898 (0.064772) | 0.372471 / 0.323480 (0.048991) | 0.005567 / 0.007986 (-0.002418) | 0.003956 / 0.004328 (-0.000372) | 0.074550 / 0.004250 (0.070300) | 0.047097 / 0.037052 (0.010045) | 0.337049 / 0.258489 (0.078560) | 0.391512 / 0.293841 (0.097671) | 0.035712 / 0.128546 (-0.092835) | 0.012040 / 0.075646 (-0.063606) | 0.087126 / 0.419271 (-0.332146) | 0.048290 / 0.043533 (0.004757) | 0.335069 / 0.255139 (0.079930) | 0.362080 / 0.283200 (0.078881) | 0.098606 / 0.141683 (-0.043077) | 1.456802 / 1.452155 (0.004647) | 1.554652 / 1.492716 (0.061936) |\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) | 0.200015 / 0.018006 (0.182009) | 0.442772 / 0.000490 (0.442283) | 0.004594 / 0.000200 (0.004394) | 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) | 0.028510 / 0.037411 (-0.008901) | 0.109654 / 0.014526 (0.095128) | 0.119921 / 0.176557 (-0.056636) | 0.170289 / 0.737135 (-0.566846) | 0.125288 / 0.296338 (-0.171051) |\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) | 0.430919 / 0.215209 (0.215710) | 4.274132 / 2.077655 (2.196478) | 2.014385 / 1.504120 (0.510265) | 1.822094 / 1.541195 (0.280899) | 1.938188 / 1.468490 (0.469698) | 0.707812 / 4.584777 (-3.876965) | 3.925730 / 3.745712 (0.180018) | 2.117481 / 5.269862 (-3.152381) | 1.369521 / 4.565676 (-3.196155) | 0.088414 / 0.424275 (-0.335861) | 0.013101 / 0.007607 (0.005494) | 0.538468 / 0.226044 (0.312424) | 5.384614 / 2.268929 (3.115685) | 2.487709 / 55.444624 (-52.956915) | 2.152060 / 6.876477 (-4.724417) | 2.225777 / 2.142072 (0.083705) | 0.856749 / 4.805227 (-3.948479) | 0.173299 / 6.500664 (-6.327366) | 0.068872 / 0.075469 (-0.006597) |\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) | 1.268009 / 1.841788 (-0.573778) | 15.102648 / 8.074308 (7.028340) | 14.216810 / 10.191392 (4.025418) | 0.163661 / 0.680424 (-0.516763) | 0.017394 / 0.534201 (-0.516807) | 0.418030 / 0.579283 (-0.161253) | 0.413717 / 0.434364 (-0.020647) | 0.487526 / 0.540337 (-0.052811) | 0.581499 / 1.386936 (-0.805437) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#46bb11e96d053c497035a2436702860de9960a65 \"CML watermark\")\n" ]

"2023-03-20T17:17:23"

"2023-03-23T13:19:19"

"2023-03-23T13:12:08"

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[ "`save_to_disk` should indeed expand `~`. Marking it as a \"good first issue\".", "#self-assign" ]

"2023-03-20T12:02:18"

"2023-03-25T20:28:03"

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