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https://api.github.com/repos/huggingface/datasets/issues/6008	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6008/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6008/comments	https://api.github.com/repos/huggingface/datasets/issues/6008/events	https://github.com/huggingface/datasets/issues/6008	1,789,869,344	I_kwDODunzps5qrz0g	6,008	Dataset.from_generator consistently freezes at ~1000 rows	{ "login": "andreemic", "id": 27695722, "node_id": "MDQ6VXNlcjI3Njk1NzIy", "avatar_url": "https://avatars.githubusercontent.com/u/27695722?v=4", "gravatar_id": "", "url": "https://api.github.com/users/andreemic", "html_url": "https://github.com/andreemic", "followers_url": "https://api.github.com/users/andreemic/followers", "following_url": "https://api.github.com/users/andreemic/following{/other_user}", "gists_url": "https://api.github.com/users/andreemic/gists{/gist_id}", "starred_url": "https://api.github.com/users/andreemic/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andreemic/subscriptions", "organizations_url": "https://api.github.com/users/andreemic/orgs", "repos_url": "https://api.github.com/users/andreemic/repos", "events_url": "https://api.github.com/users/andreemic/events{/privacy}", "received_events_url": "https://api.github.com/users/andreemic/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n\r\nOur support for multi-dim arrays could be better, and we plan to improve it as part of https://github.com/huggingface/datasets/issues/5272.", "> By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n> \r\n> Our support for multi-dim arrays could be better, and we plan to improve it as part of #5272.\r\n\r\nThanks for the explanation! The Image array was just for demonstration, I use PIL Images in practice. Does that make a difference? What's the best approach for a dataset with PIL Images as rows?" ]	1,688,573,208,000	1,688,593,474,000	null	NONE	null	### Describe the bug Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset. I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset. Let me know if you have ideas how to resolve it! ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np def gen(): for row in range(10000): yield {"i": np.random.rand(512, 512, 3)} Dataset.from_generator(gen) # -> 90% of the time gets stuck around 1000 rows ``` ### Expected behavior Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 12.0.1 - Pandas version: 1.5.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6008/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6008/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6007	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6007/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6007/comments	https://api.github.com/repos/huggingface/datasets/issues/6007/events	https://github.com/huggingface/datasets/issues/6007	1,789,782,693	I_kwDODunzps5qreql	6,007	Get an error "OverflowError: Python int too large to convert to C long" when loading a large dataset	{ "login": "silverriver", "id": 2529049, "node_id": "MDQ6VXNlcjI1MjkwNDk=", "avatar_url": "https://avatars.githubusercontent.com/u/2529049?v=4", "gravatar_id": "", "url": "https://api.github.com/users/silverriver", "html_url": "https://github.com/silverriver", "followers_url": "https://api.github.com/users/silverriver/followers", "following_url": "https://api.github.com/users/silverriver/following{/other_user}", "gists_url": "https://api.github.com/users/silverriver/gists{/gist_id}", "starred_url": "https://api.github.com/users/silverriver/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/silverriver/subscriptions", "organizations_url": "https://api.github.com/users/silverriver/orgs", "repos_url": "https://api.github.com/users/silverriver/repos", "events_url": "https://api.github.com/users/silverriver/events{/privacy}", "received_events_url": "https://api.github.com/users/silverriver/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "This error means that one of the int32 (`Value(\"int32\")`) columns in the dataset has a value that is out of the valid (int32) range.\r\n\r\nI'll open a PR to print the name of a problematic column to make debugging such errors easier." ]	1,688,570,210,000	1,688,584,517,000	null	CONTRIBUTOR	null	### Describe the bug When load a large dataset with the following code ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ``` We encountered the error: "OverflowError: Python int too large to convert to C long" The error look something like: ``` OverflowError: Python int too large to convert to C long During handling of the above exception, another exception occurred: OverflowError Traceback (most recent call last) <ipython-input-7-0ed8700e662d> in <module> ----> 1 dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', cache_dir='/sfs/MNBVC/.cache/') /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, config_kwargs) 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, -> 1751 use_auth_token=use_auth_token, 1752 ) 1753 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, download_and_prepare_kwargs) 703 if not downloaded_from_gcs: 704 self._download_and_prepare( --> 705 dl_manager=dl_manager, verify_infos=verify_infos, download_and_prepare_kwargs 706 ) 707 # Sync info /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1225 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1228 1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, prepare_split_kwargs) 791 try: 792 # Prepare split will record examples associated to the split --> 793 self._prepare_split(split_generator, **prepare_split_kwargs) 794 except OSError as e: 795 raise OSError( /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) 1219 writer.write(example, key) 1220 finally: -> 1221 num_examples, num_bytes = writer.finalize() 1222 1223 split_generator.split_info.num_examples = num_examples /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in finalize(self, close_stream) 536 # Re-intializing to empty list for next batch 537 self.hkey_record = [] --> 538 self.write_examples_on_file() 539 if self.pa_writer is None: 540 if self.schema: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 407 # Since current_examples contains (example, key) tuples 408 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 409 self.write_batch(batch_examples=batch_examples) 410 self.current_examples = [] 411 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 506 col_try_type = try_features[col] if try_features is not None and col in try_features else None 507 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 508 arrays.append(pa.array(typed_sequence)) 509 inferred_features[col] = typed_sequence.get_inferred_type() 510 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 180 else: 181 trying_cast_to_python_objects = True --> 182 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) 183 # use smaller integer precisions if possible 184 if self.trying_int_optimization: /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() OverflowError: Python int too large to convert to C long ``` However, that dataset can be loaded in a streaming manner: ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', streaming=True) for i in dataset: pass # it work well ``` Another issue is reported in our dataset hub: https://huggingface.co/datasets/liwu/MNBVC/discussions/2 ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ### Expected behavior the dataset can be safely loaded ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-3.10.0-1160.an7.x86_64-x86_64-with-centos-7.9 - Python version: 3.6.8 - PyArrow version: 6.0.1 - Pandas version: 1.1.5	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6007/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6007/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6006	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6006/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6006/comments	https://api.github.com/repos/huggingface/datasets/issues/6006/events	https://github.com/huggingface/datasets/issues/6006	1,788,855,582	I_kwDODunzps5qn8Ue	6,006	NotADirectoryError when loading gigawords	{ "login": "xipq", "id": 115634163, "node_id": "U_kgDOBuRv8w", "avatar_url": "https://avatars.githubusercontent.com/u/115634163?v=4", "gravatar_id": "", "url": "https://api.github.com/users/xipq", "html_url": "https://github.com/xipq", "followers_url": "https://api.github.com/users/xipq/followers", "following_url": "https://api.github.com/users/xipq/following{/other_user}", "gists_url": "https://api.github.com/users/xipq/gists{/gist_id}", "starred_url": "https://api.github.com/users/xipq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xipq/subscriptions", "organizations_url": "https://api.github.com/users/xipq/orgs", "repos_url": "https://api.github.com/users/xipq/repos", "events_url": "https://api.github.com/users/xipq/events{/privacy}", "received_events_url": "https://api.github.com/users/xipq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "issue due to corrupted download files. resolved after cleaning download cache. sorry for any inconvinence." ]	1,688,538,221,000	1,688,538,662,000	1,688,538,661,000	NONE	null	### Describe the bug got `NotADirectoryError` whtn loading gigawords dataset ### Steps to reproduce the bug When running ``` import datasets datasets.load_dataset('gigaword') ``` Got the following exception: ```bash Traceback (most recent call last): [0/1862] File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1629, in _prepare_split_single for key, record in generator: File "/home/x/.cache/huggingface/modules/datasets_modules/datasets/gigaword/ea83a8b819190acac5f2dae011fad51dccf269a0604ec5dd24795b 64efb424b6/gigaword.py", line 115, in _generate_examples with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s: File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/streaming.py", line 71, in wrapper return function(args, use_auth_token=use_auth_token, kwargs) File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/download/streaming_download_manager.py", line 493, in xope n return open(main_hop, mode, args, kwargs) NotADirectoryError: [Errno 20] Not a directory: '/home/x/.cache/huggingface/datasets/downloads/6da52431bb5124d90cf51a0187d2dbee9046e 89780c4be7599794a4f559048ec/org_data/train.src.txt' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "gigaword.py", line 38, in <module> main() File "gigaword.py", line 35, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/home/x/MICL/preprocess/fewshot_gym_dataset.py", line 199, in generate_k_shot_data dataset = self.load_dataset() File "gigaword.py", line 29, in load_dataset return datasets.load_dataset('gigaword') File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/load.py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1670, in _download_and_prepare super()._download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, prepare_split_kwargs) File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1508, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1665, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Download and process the dataset successfully ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.0.0-1032-azure-x86_64-with-glibc2.10 - Python version: 3.8.0 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6006/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6006/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6005	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6005/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6005/comments	https://api.github.com/repos/huggingface/datasets/issues/6005/events	https://github.com/huggingface/datasets/pull/6005	1,788,103,576	PR_kwDODunzps5UoJ91	6,005	Drop Python 3.7 support	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "<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) \| 0.006152 / 0.011353 (-0.005200) \| 0.003916 / 0.011008 (-0.007092) \| 0.097355 / 0.038508 (0.058847) \| 0.037228 / 0.023109 (0.014119) \| 0.315753 / 0.275898 (0.039855) \| 0.387949 / 0.323480 (0.064470) \| 0.004804 / 0.007986 (-0.003181) \| 0.002975 / 0.004328 (-0.001353) \| 0.076932 / 0.004250 (0.072682) \| 0.053497 / 0.037052 (0.016445) \| 0.331143 / 0.258489 (0.072654) \| 0.388347 / 0.293841 (0.094506) \| 0.027535 / 0.128546 (-0.101011) \| 0.008509 / 0.075646 (-0.067137) \| 0.312639 / 0.419271 (-0.106632) \| 0.047212 / 0.043533 (0.003679) \| 0.316875 / 0.255139 (0.061736) \| 0.352191 / 0.283200 (0.068992) \| 0.021380 / 0.141683 (-0.120303) \| 1.541401 / 1.452155 (0.089247) \| 1.519420 / 1.492716 (0.026704) \|\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) \| 0.206332 / 0.018006 (0.188326) \| 0.412252 / 0.000490 (0.411762) \| 0.005119 / 0.000200 (0.004919) \| 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) \| 0.023856 / 0.037411 (-0.013556) \| 0.098216 / 0.014526 (0.083691) \| 0.106553 / 0.176557 (-0.070003) \| 0.168767 / 0.737135 (-0.568369) \| 0.109244 / 0.296338 (-0.187094) \|\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) \| 0.457580 / 0.215209 (0.242371) \| 4.583246 / 2.077655 (2.505591) \| 2.296356 / 1.504120 (0.792236) \| 2.096216 / 1.541195 (0.555021) \| 2.159086 / 1.468490 (0.690596) \| 0.557905 / 4.584777 (-4.026872) \| 3.345910 / 3.745712 (-0.399802) \| 1.767436 / 5.269862 (-3.502426) \| 1.021583 / 4.565676 (-3.544094) \| 0.067265 / 0.424275 (-0.357011) \| 0.011411 / 0.007607 (0.003804) \| 0.559841 / 0.226044 (0.333797) \| 5.586892 / 2.268929 (3.317963) \| 2.735520 / 55.444624 (-52.709104) \| 2.429393 / 6.876477 (-4.447084) \| 2.544901 / 2.142072 (0.402829) \| 0.667603 / 4.805227 (-4.137625) \| 0.136244 / 6.500664 (-6.364421) \| 0.066961 / 0.075469 (-0.008508) \|\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) \| 1.206529 / 1.841788 (-0.635259) \| 13.988306 / 8.074308 (5.913998) \| 13.481813 / 10.191392 (3.290421) \| 0.161901 / 0.680424 (-0.518523) \| 0.016850 / 0.534201 (-0.517351) \| 0.367657 / 0.579283 (-0.211626) \| 0.393343 / 0.434364 (-0.041021) \| 0.465288 / 0.540337 (-0.075050) \| 0.559888 / 1.386936 (-0.827048) \|\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) \| 0.005956 / 0.011353 (-0.005397) \| 0.003734 / 0.011008 (-0.007274) \| 0.077841 / 0.038508 (0.039333) \| 0.036532 / 0.023109 (0.013422) \| 0.438923 / 0.275898 (0.163025) \| 0.490133 / 0.323480 (0.166653) \| 0.004651 / 0.007986 (-0.003335) \| 0.002881 / 0.004328 (-0.001448) \| 0.077868 / 0.004250 (0.073618) \| 0.051700 / 0.037052 (0.014647) \| 0.448018 / 0.258489 (0.189529) \| 0.500304 / 0.293841 (0.206464) \| 0.029051 / 0.128546 (-0.099496) \| 0.008498 / 0.075646 (-0.067148) \| 0.082932 / 0.419271 (-0.336339) \| 0.043665 / 0.043533 (0.000132) \| 0.431613 / 0.255139 (0.176474) \| 0.458749 / 0.283200 (0.175549) \| 0.021951 / 0.141683 (-0.119731) \| 1.556043 / 1.452155 (0.103888) \| 1.588391 / 1.492716 (0.095675) \|\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) \| 0.220674 / 0.018006 (0.202667) \| 0.415408 / 0.000490 (0.414918) \| 0.002613 / 0.000200 (0.002413) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025548 / 0.037411 (-0.011863) \| 0.103633 / 0.014526 (0.089107) \| 0.115193 / 0.176557 (-0.061364) \| 0.163971 / 0.737135 (-0.573164) \| 0.114754 / 0.296338 (-0.181585) \|\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) \| 0.456823 / 0.215209 (0.241614) \| 4.569950 / 2.077655 (2.492296) \| 2.196339 / 1.504120 (0.692219) \| 1.985822 / 1.541195 (0.444628) \| 2.044083 / 1.468490 (0.575593) \| 0.567919 / 4.584777 (-4.016858) \| 3.397515 / 3.745712 (-0.348197) \| 1.741087 / 5.269862 (-3.528775) \| 1.041237 / 4.565676 (-3.524440) \| 0.068963 / 0.424275 (-0.355313) \| 0.011677 / 0.007607 (0.004070) \| 0.565010 / 0.226044 (0.338966) \| 5.625886 / 2.268929 (3.356957) \| 2.670658 / 55.444624 (-52.773967) \| 2.300279 / 6.876477 (-4.576198) \| 2.392178 / 2.142072 (0.250106) \| 0.680226 / 4.805227 (-4.125001) \| 0.139119 / 6.500664 (-6.361545) \| 0.067953 / 0.075469 (-0.007516) \|\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) \| 1.303280 / 1.841788 (-0.538507) \| 14.458686 / 8.074308 (6.384378) \| 14.409369 / 10.191392 (4.217977) \| 0.144581 / 0.680424 (-0.535843) \| 0.016634 / 0.534201 (-0.517567) \| 0.364607 / 0.579283 (-0.214676) \| 0.394521 / 0.434364 (-0.039843) \| 0.433417 / 0.540337 (-0.106921) \| 0.527127 / 1.386936 (-0.859809) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a36f9546484dceadb84a133c1a460281d018f8 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6005). 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) \| 0.006245 / 0.011353 (-0.005108) \| 0.003871 / 0.011008 (-0.007138) \| 0.098823 / 0.038508 (0.060315) \| 0.039853 / 0.023109 (0.016744) \| 0.314989 / 0.275898 (0.039091) \| 0.376733 / 0.323480 (0.053254) \| 0.004754 / 0.007986 (-0.003232) \| 0.002971 / 0.004328 (-0.001357) \| 0.078451 / 0.004250 (0.074201) \| 0.053160 / 0.037052 (0.016107) \| 0.324443 / 0.258489 (0.065954) \| 0.361488 / 0.293841 (0.067647) \| 0.027942 / 0.128546 (-0.100604) \| 0.008535 / 0.075646 (-0.067111) \| 0.315526 / 0.419271 (-0.103745) \| 0.045706 / 0.043533 (0.002174) \| 0.329614 / 0.255139 (0.074475) \| 0.336339 / 0.283200 (0.053139) \| 0.021278 / 0.141683 (-0.120405) \| 1.529710 / 1.452155 (0.077555) \| 1.566833 / 1.492716 (0.074116) \|\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) \| 0.215263 / 0.018006 (0.197257) \| 0.440320 / 0.000490 (0.439830) \| 0.002627 / 0.000200 (0.002427) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023971 / 0.037411 (-0.013441) \| 0.100549 / 0.014526 (0.086023) \| 0.106995 / 0.176557 (-0.069561) \| 0.169630 / 0.737135 (-0.567505) \| 0.111614 / 0.296338 (-0.184724) \|\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) \| 0.424911 / 0.215209 (0.209702) \| 4.246920 / 2.077655 (2.169266) \| 1.923321 / 1.504120 (0.419202) \| 1.714795 / 1.541195 (0.173600) \| 1.772906 / 1.468490 (0.304416) \| 0.554676 / 4.584777 (-4.030101) \| 3.478896 / 3.745712 (-0.266816) \| 2.800494 / 5.269862 (-2.469368) \| 1.382630 / 4.565676 (-3.183047) \| 0.067271 / 0.424275 (-0.357004) \| 0.010967 / 0.007607 (0.003360) \| 0.526769 / 0.226044 (0.300725) \| 5.288564 / 2.268929 (3.019636) \| 2.337459 / 55.444624 (-53.107165) \| 1.999975 / 6.876477 (-4.876502) \| 2.102680 / 2.142072 (-0.039392) \| 0.672181 / 4.805227 (-4.133046) \| 0.135097 / 6.500664 (-6.365567) \| 0.066950 / 0.075469 (-0.008519) \|\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) \| 1.264365 / 1.841788 (-0.577423) \| 14.282440 / 8.074308 (6.208132) \| 14.220200 / 10.191392 (4.028808) \| 0.139055 / 0.680424 (-0.541369) \| 0.016681 / 0.534201 (-0.517520) \| 0.367936 / 0.579283 (-0.211348) \| 0.393959 / 0.434364 (-0.040404) \| 0.424438 / 0.540337 (-0.115900) \| 0.508065 / 1.386936 (-0.878872) \|\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) \| 0.006514 / 0.011353 (-0.004839) \| 0.003890 / 0.011008 (-0.007118) \| 0.078871 / 0.038508 (0.040363) \| 0.038080 / 0.023109 (0.014971) \| 0.358282 / 0.275898 (0.082384) \| 0.430654 / 0.323480 (0.107174) \| 0.005712 / 0.007986 (-0.002273) \| 0.003030 / 0.004328 (-0.001299) \| 0.078636 / 0.004250 (0.074386) \| 0.057771 / 0.037052 (0.020719) \| 0.368814 / 0.258489 (0.110325) \| 0.437047 / 0.293841 (0.143206) \| 0.029470 / 0.128546 (-0.099076) \| 0.008523 / 0.075646 (-0.067124) \| 0.083334 / 0.419271 (-0.335938) \| 0.044505 / 0.043533 (0.000972) \| 0.357484 / 0.255139 (0.102345) \| 0.393839 / 0.283200 (0.110639) \| 0.023340 / 0.141683 (-0.118343) \| 1.561033 / 1.452155 (0.108878) \| 1.595560 / 1.492716 (0.102844) \|\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) \| 0.204149 / 0.018006 (0.186143) \| 0.442747 / 0.000490 (0.442257) \| 0.003105 / 0.000200 (0.002905) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027002 / 0.037411 (-0.010409) \| 0.105595 / 0.014526 (0.091070) \| 0.108695 / 0.176557 (-0.067861) \| 0.163182 / 0.737135 (-0.573953) \| 0.114999 / 0.296338 (-0.181339) \|\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) \| 0.483713 / 0.215209 (0.268504) \| 4.836063 / 2.077655 (2.758409) \| 2.488072 / 1.504120 (0.983952) \| 2.289556 / 1.541195 (0.748361) \| 2.342912 / 1.468490 (0.874422) \| 0.565937 / 4.584777 (-4.018840) \| 3.479085 / 3.745712 (-0.266627) \| 1.770922 / 5.269862 (-3.498940) \| 1.046084 / 4.565676 (-3.519592) \| 0.067857 / 0.424275 (-0.356418) \| 0.011283 / 0.007607 (0.003676) \| 0.592966 / 0.226044 (0.366921) \| 5.932842 / 2.268929 (3.663914) \| 2.956252 / 55.444624 (-52.488372) \| 2.602704 / 6.876477 (-4.273772) \| 2.715625 / 2.142072 (0.573552) \| 0.674299 / 4.805227 (-4.130929) \| 0.136039 / 6.500664 (-6.364625) \| 0.067629 / 0.075469 (-0.007840) \|\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) \| 1.333734 / 1.841788 (-0.508054) \| 14.561943 / 8.074308 (6.487634) \| 14.455385 / 10.191392 (4.263993) \| 0.132020 / 0.680424 (-0.548404) \| 0.016893 / 0.534201 (-0.517308) \| 0.367146 / 0.579283 (-0.212137) \| 0.399623 / 0.434364 (-0.034741) \| 0.432658 / 0.540337 (-0.107680) \| 0.530475 / 1.386936 (-0.856461) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18da5adb22b2b403b8d8ae673192746d2ed7e9f9 \"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) \| 0.006045 / 0.011353 (-0.005308) \| 0.003906 / 0.011008 (-0.007103) \| 0.097558 / 0.038508 (0.059050) \| 0.038827 / 0.023109 (0.015718) \| 0.393564 / 0.275898 (0.117666) \| 0.442459 / 0.323480 (0.118980) \| 0.004792 / 0.007986 (-0.003194) \| 0.002984 / 0.004328 (-0.001345) \| 0.076419 / 0.004250 (0.072169) \| 0.053606 / 0.037052 (0.016554) \| 0.409743 / 0.258489 (0.151254) \| 0.445753 / 0.293841 (0.151912) \| 0.027753 / 0.128546 (-0.100793) \| 0.008428 / 0.075646 (-0.067219) \| 0.310267 / 0.419271 (-0.109004) \| 0.057582 / 0.043533 (0.014049) \| 0.396624 / 0.255139 (0.141485) \| 0.416288 / 0.283200 (0.133089) \| 0.029048 / 0.141683 (-0.112635) \| 1.495362 / 1.452155 (0.043207) \| 1.546331 / 1.492716 (0.053615) \|\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) \| 0.203832 / 0.018006 (0.185826) \| 0.423649 / 0.000490 (0.423160) \| 0.004533 / 0.000200 (0.004333) \| 0.000076 / 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) \| 0.023084 / 0.037411 (-0.014328) \| 0.100503 / 0.014526 (0.085977) \| 0.105058 / 0.176557 (-0.071499) \| 0.168506 / 0.737135 (-0.568629) \| 0.112019 / 0.296338 (-0.184320) \|\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) \| 0.425877 / 0.215209 (0.210668) \| 4.251278 / 2.077655 (2.173624) \| 1.931339 / 1.504120 (0.427219) \| 1.730578 / 1.541195 (0.189383) \| 1.750637 / 1.468490 (0.282147) \| 0.559307 / 4.584777 (-4.025470) \| 3.461665 / 3.745712 (-0.284047) \| 2.826959 / 5.269862 (-2.442903) \| 1.418448 / 4.565676 (-3.147229) \| 0.067881 / 0.424275 (-0.356394) \| 0.011394 / 0.007607 (0.003787) \| 0.533226 / 0.226044 (0.307181) \| 5.341849 / 2.268929 (3.072921) \| 2.367832 / 55.444624 (-53.076792) \| 2.027240 / 6.876477 (-4.849236) \| 2.095852 / 2.142072 (-0.046220) \| 0.673790 / 4.805227 (-4.131437) \| 0.136044 / 6.500664 (-6.364620) \| 0.066350 / 0.075469 (-0.009119) \|\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) \| 1.203740 / 1.841788 (-0.638048) \| 13.720879 / 8.074308 (5.646571) \| 13.405939 / 10.191392 (3.214547) \| 0.146792 / 0.680424 (-0.533632) \| 0.016844 / 0.534201 (-0.517357) \| 0.373455 / 0.579283 (-0.205828) \| 0.394596 / 0.434364 (-0.039768) \| 0.464715 / 0.540337 (-0.075623) \| 0.558931 / 1.386936 (-0.828005) \|\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) \| 0.006118 / 0.011353 (-0.005235) \| 0.003817 / 0.011008 (-0.007191) \| 0.077494 / 0.038508 (0.038985) \| 0.037507 / 0.023109 (0.014398) \| 0.387030 / 0.275898 (0.111132) \| 0.437352 / 0.323480 (0.113872) \| 0.004810 / 0.007986 (-0.003176) \| 0.002935 / 0.004328 (-0.001394) \| 0.077143 / 0.004250 (0.072892) \| 0.053986 / 0.037052 (0.016933) \| 0.393164 / 0.258489 (0.134675) \| 0.449603 / 0.293841 (0.155762) \| 0.029303 / 0.128546 (-0.099244) \| 0.008481 / 0.075646 (-0.067165) \| 0.083363 / 0.419271 (-0.335908) \| 0.043877 / 0.043533 (0.000344) \| 0.378175 / 0.255139 (0.123036) \| 0.403996 / 0.283200 (0.120797) \| 0.021688 / 0.141683 (-0.119995) \| 1.541606 / 1.452155 (0.089452) \| 1.552996 / 1.492716 (0.060280) \|\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) \| 0.236759 / 0.018006 (0.218752) \| 0.416221 / 0.000490 (0.415732) \| 0.000862 / 0.000200 (0.000662) \| 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) \| 0.025543 / 0.037411 (-0.011868) \| 0.101731 / 0.014526 (0.087206) \| 0.108482 / 0.176557 (-0.068075) \| 0.160290 / 0.737135 (-0.576845) \| 0.111392 / 0.296338 (-0.184946) \|\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) \| 0.457767 / 0.215209 (0.242558) \| 4.565976 / 2.077655 (2.488321) \| 2.245413 / 1.504120 (0.741294) \| 2.031458 / 1.541195 (0.490264) \| 2.073193 / 1.468490 (0.604702) \| 0.560461 / 4.584777 (-4.024316) \| 3.422536 / 3.745712 (-0.323176) \| 2.977017 / 5.269862 (-2.292845) \| 1.377021 / 4.565676 (-3.188655) \| 0.068444 / 0.424275 (-0.355831) \| 0.011036 / 0.007607 (0.003429) \| 0.571501 / 0.226044 (0.345456) \| 5.702652 / 2.268929 (3.433723) \| 2.727132 / 55.444624 (-52.717492) \| 2.399269 / 6.876477 (-4.477208) \| 2.574281 / 2.142072 (0.432208) \| 0.682600 / 4.805227 (-4.122627) \| 0.136943 / 6.500664 (-6.363722) \| 0.067126 / 0.075469 (-0.008343) \|\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) \| 1.322196 / 1.841788 (-0.519592) \| 14.239509 / 8.074308 (6.165201) \| 14.235779 / 10.191392 (4.044387) \| 0.148262 / 0.680424 (-0.532162) \| 0.016566 / 0.534201 (-0.517635) \| 0.364034 / 0.579283 (-0.215249) \| 0.399157 / 0.434364 (-0.035207) \| 0.426348 / 0.540337 (-0.113990) \| 0.520804 / 1.386936 (-0.866132) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f57aae06bd325d76cb70cb774450f3a66f169cf \"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) \| 0.007808 / 0.011353 (-0.003545) \| 0.004706 / 0.011008 (-0.006303) \| 0.100530 / 0.038508 (0.062022) \| 0.052052 / 0.023109 (0.028943) \| 0.419300 / 0.275898 (0.143402) \| 0.488451 / 0.323480 (0.164971) \| 0.006350 / 0.007986 (-0.001636) \| 0.003875 / 0.004328 (-0.000453) \| 0.076489 / 0.004250 (0.072238) \| 0.077554 / 0.037052 (0.040502) \| 0.435863 / 0.258489 (0.177373) \| 0.483241 / 0.293841 (0.189400) \| 0.037518 / 0.128546 (-0.091028) \| 0.009857 / 0.075646 (-0.065789) \| 0.340933 / 0.419271 (-0.078339) \| 0.087046 / 0.043533 (0.043514) \| 0.410721 / 0.255139 (0.155582) \| 0.428995 / 0.283200 (0.145795) \| 0.041701 / 0.141683 (-0.099982) \| 1.821017 / 1.452155 (0.368862) \| 1.837021 / 1.492716 (0.344305) \|\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) \| 0.228444 / 0.018006 (0.210438) \| 0.480446 / 0.000490 (0.479956) \| 0.004963 / 0.000200 (0.004763) \| 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) \| 0.032485 / 0.037411 (-0.004926) \| 0.096500 / 0.014526 (0.081974) \| 0.111547 / 0.176557 (-0.065010) \| 0.178842 / 0.737135 (-0.558294) \| 0.111099 / 0.296338 (-0.185240) \|\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) \| 0.467159 / 0.215209 (0.251950) \| 4.701676 / 2.077655 (2.624021) \| 2.390560 / 1.504120 (0.886440) \| 2.197722 / 1.541195 (0.656528) \| 2.264705 / 1.468490 (0.796215) \| 0.568667 / 4.584777 (-4.016110) \| 4.200724 / 3.745712 (0.455012) \| 3.777625 / 5.269862 (-1.492236) \| 2.372451 / 4.565676 (-2.193225) \| 0.067562 / 0.424275 (-0.356714) \| 0.008947 / 0.007607 (0.001340) \| 0.556910 / 0.226044 (0.330865) \| 5.528927 / 2.268929 (3.259998) \| 2.902780 / 55.444624 (-52.541844) \| 2.507933 / 6.876477 (-4.368544) \| 2.734627 / 2.142072 (0.592554) \| 0.683305 / 4.805227 (-4.121922) \| 0.158288 / 6.500664 (-6.342376) \| 0.071252 / 0.075469 (-0.004217) \|\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) \| 1.487502 / 1.841788 (-0.354286) \| 22.193341 / 8.074308 (14.119033) \| 15.922607 / 10.191392 (5.731215) \| 0.172189 / 0.680424 (-0.508235) \| 0.021502 / 0.534201 (-0.512699) \| 0.471198 / 0.579283 (-0.108085) \| 0.475979 / 0.434364 (0.041615) \| 0.544675 / 0.540337 (0.004338) \| 0.756102 / 1.386936 (-0.630834) \|\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) \| 0.007635 / 0.011353 (-0.003717) \| 0.004614 / 0.011008 (-0.006394) \| 0.075852 / 0.038508 (0.037344) \| 0.049700 / 0.023109 (0.026591) \| 0.425957 / 0.275898 (0.150059) \| 0.512590 / 0.323480 (0.189110) \| 0.006921 / 0.007986 (-0.001065) \| 0.003714 / 0.004328 (-0.000615) \| 0.075536 / 0.004250 (0.071286) \| 0.070206 / 0.037052 (0.033153) \| 0.455706 / 0.258489 (0.197217) \| 0.512231 / 0.293841 (0.218390) \| 0.036685 / 0.128546 (-0.091861) \| 0.009793 / 0.075646 (-0.065853) \| 0.084208 / 0.419271 (-0.335064) \| 0.065262 / 0.043533 (0.021729) \| 0.423761 / 0.255139 (0.168622) \| 0.456791 / 0.283200 (0.173591) \| 0.044539 / 0.141683 (-0.097144) \| 1.797029 / 1.452155 (0.344874) \| 1.864124 / 1.492716 (0.371408) \|\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) \| 0.366840 / 0.018006 (0.348834) \| 0.479254 / 0.000490 (0.478765) \| 0.070383 / 0.000200 (0.070183) \| 0.000762 / 0.000054 (0.000707) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034233 / 0.037411 (-0.003178) \| 0.103140 / 0.014526 (0.088614) \| 0.117099 / 0.176557 (-0.059457) \| 0.178532 / 0.737135 (-0.558603) \| 0.120092 / 0.296338 (-0.176247) \|\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) \| 0.492993 / 0.215209 (0.277784) \| 4.878776 / 2.077655 (2.801121) \| 2.566666 / 1.504120 (1.062547) \| 2.356383 / 1.541195 (0.815188) \| 2.454723 / 1.468490 (0.986233) \| 0.571432 / 4.584777 (-4.013345) \| 4.240554 / 3.745712 (0.494842) \| 7.509259 / 5.269862 (2.239398) \| 4.040294 / 4.565676 (-0.525382) \| 0.067409 / 0.424275 (-0.356866) \| 0.008657 / 0.007607 (0.001050) \| 0.585751 / 0.226044 (0.359707) \| 5.967668 / 2.268929 (3.698739) \| 3.195573 / 55.444624 (-52.249052) \| 2.839772 / 6.876477 (-4.036704) \| 2.806319 / 2.142072 (0.664246) \| 0.681502 / 4.805227 (-4.123725) \| 0.158673 / 6.500664 (-6.341991) \| 0.073224 / 0.075469 (-0.002245) \|\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) \| 1.623335 / 1.841788 (-0.218453) \| 22.490806 / 8.074308 (14.416498) \| 16.762435 / 10.191392 (6.571043) \| 0.180961 / 0.680424 (-0.499463) \| 0.022716 / 0.534201 (-0.511485) \| 0.472910 / 0.579283 (-0.106373) \| 0.471616 / 0.434364 (0.037252) \| 0.548192 / 0.540337 (0.007854) \| 0.734357 / 1.386936 (-0.652579) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0498b47a00153d4730352b6595fc51ab054fb95 \"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) \| 0.005858 / 0.011353 (-0.005495) \| 0.003512 / 0.011008 (-0.007497) \| 0.079739 / 0.038508 (0.041231) \| 0.057736 / 0.023109 (0.034627) \| 0.317640 / 0.275898 (0.041742) \| 0.354157 / 0.323480 (0.030677) \| 0.004772 / 0.007986 (-0.003214) \| 0.002824 / 0.004328 (-0.001504) \| 0.063288 / 0.004250 (0.059037) \| 0.049542 / 0.037052 (0.012489) \| 0.323974 / 0.258489 (0.065485) \| 0.372149 / 0.293841 (0.078308) \| 0.026841 / 0.128546 (-0.101705) \| 0.007846 / 0.075646 (-0.067800) \| 0.262546 / 0.419271 (-0.156725) \| 0.051952 / 0.043533 (0.008420) \| 0.319439 / 0.255139 (0.064300) \| 0.343862 / 0.283200 (0.060663) \| 0.027021 / 0.141683 (-0.114662) \| 1.445211 / 1.452155 (-0.006944) \| 1.485006 / 1.492716 (-0.007711) \|\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) \| 0.183174 / 0.018006 (0.165167) \| 0.422794 / 0.000490 (0.422304) \| 0.004148 / 0.000200 (0.003948) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023037 / 0.037411 (-0.014374) \| 0.071300 / 0.014526 (0.056775) \| 0.083022 / 0.176557 (-0.093535) \| 0.146215 / 0.737135 (-0.590920) \| 0.082549 / 0.296338 (-0.213789) \|\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) \| 0.422846 / 0.215209 (0.207637) \| 4.215280 / 2.077655 (2.137626) \| 2.256802 / 1.504120 (0.752682) \| 2.056867 / 1.541195 (0.515673) \| 2.102478 / 1.468490 (0.633988) \| 0.497552 / 4.584777 (-4.087225) \| 3.049716 / 3.745712 (-0.695996) \| 4.209227 / 5.269862 (-1.060635) \| 2.599947 / 4.565676 (-1.965730) \| 0.059131 / 0.424275 (-0.365144) \| 0.006459 / 0.007607 (-0.001148) \| 0.495047 / 0.226044 (0.269003) \| 4.952332 / 2.268929 (2.683404) \| 2.675260 / 55.444624 (-52.769365) \| 2.333223 / 6.876477 (-4.543254) \| 2.449573 / 2.142072 (0.307500) \| 0.583420 / 4.805227 (-4.221807) \| 0.125140 / 6.500664 (-6.375524) \| 0.060209 / 0.075469 (-0.015260) \|\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) \| 1.215033 / 1.841788 (-0.626755) \| 18.101107 / 8.074308 (10.026799) \| 13.489222 / 10.191392 (3.297830) \| 0.147122 / 0.680424 (-0.533302) \| 0.016567 / 0.534201 (-0.517634) \| 0.329909 / 0.579283 (-0.249374) \| 0.340952 / 0.434364 (-0.093412) \| 0.379166 / 0.540337 (-0.161172) \| 0.510767 / 1.386936 (-0.876169) \|\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) \| 0.005942 / 0.011353 (-0.005411) \| 0.003628 / 0.011008 (-0.007380) \| 0.061975 / 0.038508 (0.023467) \| 0.058331 / 0.023109 (0.035221) \| 0.393277 / 0.275898 (0.117379) \| 0.410740 / 0.323480 (0.087261) \| 0.004546 / 0.007986 (-0.003440) \| 0.002826 / 0.004328 (-0.001503) \| 0.062216 / 0.004250 (0.057966) \| 0.049801 / 0.037052 (0.012748) \| 0.394070 / 0.258489 (0.135581) \| 0.414407 / 0.293841 (0.120566) \| 0.027161 / 0.128546 (-0.101385) \| 0.007901 / 0.075646 (-0.067746) \| 0.066778 / 0.419271 (-0.352493) \| 0.041354 / 0.043533 (-0.002179) \| 0.379432 / 0.255139 (0.124293) \| 0.402966 / 0.283200 (0.119766) \| 0.020279 / 0.141683 (-0.121404) \| 1.416986 / 1.452155 (-0.035169) \| 1.474335 / 1.492716 (-0.018382) \|\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) \| 0.226147 / 0.018006 (0.208140) \| 0.404361 / 0.000490 (0.403871) \| 0.000358 / 0.000200 (0.000158) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025105 / 0.037411 (-0.012306) \| 0.075849 / 0.014526 (0.061323) \| 0.084781 / 0.176557 (-0.091775) \| 0.137415 / 0.737135 (-0.599720) \| 0.086288 / 0.296338 (-0.210051) \|\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) \| 0.445925 / 0.215209 (0.230716) \| 4.453478 / 2.077655 (2.375823) \| 2.419048 / 1.504120 (0.914928) \| 2.246363 / 1.541195 (0.705168) \| 2.304022 / 1.468490 (0.835532) \| 0.499132 / 4.584777 (-4.085645) \| 3.001336 / 3.745712 (-0.744376) \| 2.902593 / 5.269862 (-2.367269) \| 1.819843 / 4.565676 (-2.745834) \| 0.057210 / 0.424275 (-0.367065) \| 0.006338 / 0.007607 (-0.001269) \| 0.523280 / 0.226044 (0.297236) \| 5.235969 / 2.268929 (2.967040) \| 2.897585 / 55.444624 (-52.547039) \| 2.541586 / 6.876477 (-4.334891) \| 2.564233 / 2.142072 (0.422160) \| 0.584714 / 4.805227 (-4.220513) \| 0.124611 / 6.500664 (-6.376053) \| 0.061774 / 0.075469 (-0.013695) \|\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) \| 1.349799 / 1.841788 (-0.491988) \| 18.225076 / 8.074308 (10.150768) \| 13.781518 / 10.191392 (3.590126) \| 0.130562 / 0.680424 (-0.549862) \| 0.016434 / 0.534201 (-0.517767) \| 0.331607 / 0.579283 (-0.247676) \| 0.343456 / 0.434364 (-0.090908) \| 0.380437 / 0.540337 (-0.159900) \| 0.522793 / 1.386936 (-0.864143) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f0a3dbbd2e7ace162346d95ec27db674e80c1e23 \"CML watermark\")\n" ]	1,688,482,957,000	1,688,573,751,000	null	CONTRIBUTOR	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6005/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6005/timeline	null	null	1	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6005", "html_url": "https://github.com/huggingface/datasets/pull/6005", "diff_url": "https://github.com/huggingface/datasets/pull/6005.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6005.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6004	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6004/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6004/comments	https://api.github.com/repos/huggingface/datasets/issues/6004/events	https://github.com/huggingface/datasets/pull/6004	1,786,636,368	PR_kwDODunzps5UjN2h	6,004	Misc improvements	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "<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) \| 0.006897 / 0.011353 (-0.004456) \| 0.004207 / 0.011008 (-0.006802) \| 0.104828 / 0.038508 (0.066320) \| 0.048054 / 0.023109 (0.024945) \| 0.373991 / 0.275898 (0.098093) \| 0.426740 / 0.323480 (0.103260) \| 0.005540 / 0.007986 (-0.002446) \| 0.003531 / 0.004328 (-0.000797) \| 0.079304 / 0.004250 (0.075053) \| 0.066996 / 0.037052 (0.029944) \| 0.370675 / 0.258489 (0.112186) \| 0.414154 / 0.293841 (0.120313) \| 0.031567 / 0.128546 (-0.096979) \| 0.008843 / 0.075646 (-0.066803) \| 0.357426 / 0.419271 (-0.061845) \| 0.067040 / 0.043533 (0.023508) \| 0.362384 / 0.255139 (0.107245) \| 0.376056 / 0.283200 (0.092856) \| 0.032985 / 0.141683 (-0.108697) \| 1.560603 / 1.452155 (0.108448) \| 1.619024 / 1.492716 (0.126308) \|\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) \| 0.229059 / 0.018006 (0.211053) \| 0.440513 / 0.000490 (0.440023) \| 0.004647 / 0.000200 (0.004447) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029517 / 0.037411 (-0.007894) \| 0.120974 / 0.014526 (0.106448) \| 0.125070 / 0.176557 (-0.051486) \| 0.184695 / 0.737135 (-0.552441) \| 0.130244 / 0.296338 (-0.166095) \|\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) \| 0.436930 / 0.215209 (0.221721) \| 4.356118 / 2.077655 (2.278463) \| 2.049169 / 1.504120 (0.545049) \| 1.842898 / 1.541195 (0.301703) \| 1.918948 / 1.468490 (0.450458) \| 0.553573 / 4.584777 (-4.031204) \| 3.883195 / 3.745712 (0.137483) \| 3.209780 / 5.269862 (-2.060081) \| 1.551707 / 4.565676 (-3.013970) \| 0.068181 / 0.424275 (-0.356094) \| 0.012370 / 0.007607 (0.004762) \| 0.539899 / 0.226044 (0.313854) \| 5.380008 / 2.268929 (3.111079) \| 2.518178 / 55.444624 (-52.926446) \| 2.174190 / 6.876477 (-4.702286) \| 2.317812 / 2.142072 (0.175740) \| 0.674154 / 4.805227 (-4.131073) \| 0.149313 / 6.500664 (-6.351351) \| 0.068297 / 0.075469 (-0.007172) \|\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) \| 1.261426 / 1.841788 (-0.580362) \| 15.316378 / 8.074308 (7.242070) \| 13.573512 / 10.191392 (3.382120) \| 0.190022 / 0.680424 (-0.490401) \| 0.018697 / 0.534201 (-0.515504) \| 0.448122 / 0.579283 (-0.131161) \| 0.435044 / 0.434364 (0.000681) \| 0.550065 / 0.540337 (0.009728) \| 0.653547 / 1.386936 (-0.733389) \|\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) \| 0.007116 / 0.011353 (-0.004237) \| 0.004375 / 0.011008 (-0.006633) \| 0.081793 / 0.038508 (0.043285) \| 0.047980 / 0.023109 (0.024871) \| 0.392185 / 0.275898 (0.116287) \| 0.462263 / 0.323480 (0.138783) \| 0.005574 / 0.007986 (-0.002412) \| 0.003552 / 0.004328 (-0.000776) \| 0.080413 / 0.004250 (0.076162) \| 0.065539 / 0.037052 (0.028487) \| 0.413137 / 0.258489 (0.154648) \| 0.467377 / 0.293841 (0.173536) \| 0.034386 / 0.128546 (-0.094160) \| 0.009183 / 0.075646 (-0.066464) \| 0.087542 / 0.419271 (-0.331730) \| 0.053954 / 0.043533 (0.010421) \| 0.385096 / 0.255139 (0.129957) \| 0.404900 / 0.283200 (0.121701) \| 0.025908 / 0.141683 (-0.115775) \| 1.550159 / 1.452155 (0.098005) \| 1.598794 / 1.492716 (0.106078) \|\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) \| 0.246222 / 0.018006 (0.228216) \| 0.441095 / 0.000490 (0.440605) \| 0.006863 / 0.000200 (0.006663) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032179 / 0.037411 (-0.005233) \| 0.120112 / 0.014526 (0.105586) \| 0.129326 / 0.176557 (-0.047230) \| 0.184542 / 0.737135 (-0.552593) \| 0.135038 / 0.296338 (-0.161300) \|\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) \| 0.459002 / 0.215209 (0.243793) \| 4.580258 / 2.077655 (2.502604) \| 2.296689 / 1.504120 (0.792569) \| 2.104338 / 1.541195 (0.563143) \| 2.182896 / 1.468490 (0.714406) \| 0.546447 / 4.584777 (-4.038330) \| 3.854047 / 3.745712 (0.108335) \| 1.873829 / 5.269862 (-3.396032) \| 1.116484 / 4.565676 (-3.449193) \| 0.067158 / 0.424275 (-0.357117) \| 0.012035 / 0.007607 (0.004428) \| 0.556642 / 0.226044 (0.330597) \| 5.574436 / 2.268929 (3.305508) \| 2.828223 / 55.444624 (-52.616402) \| 2.519851 / 6.876477 (-4.356626) \| 2.668594 / 2.142072 (0.526521) \| 0.675989 / 4.805227 (-4.129238) \| 0.146075 / 6.500664 (-6.354589) \| 0.067788 / 0.075469 (-0.007681) \|\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) \| 1.345958 / 1.841788 (-0.495830) \| 15.672748 / 8.074308 (7.598440) \| 14.937583 / 10.191392 (4.746191) \| 0.163479 / 0.680424 (-0.516945) \| 0.018364 / 0.534201 (-0.515837) \| 0.433296 / 0.579283 (-0.145987) \| 0.432463 / 0.434364 (-0.001901) \| 0.512000 / 0.540337 (-0.028338) \| 0.619397 / 1.386936 (-0.767539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0832d48a07ed00b406271f4b4439e6d54ae38ebf \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6004). All of your documentation changes will be reflected on that endpoint." ]	1,688,408,954,000	1,688,409,261,000	null	CONTRIBUTOR	null	Contains the following improvements: * fixes a "share dataset" link in README and modifies the "hosting" part in the disclaimer section * updates `Makefile` to also run the style checks on `utils` and `setup.py` * deletes a test for GH-hosted datasets (no longer supported) * deletes `convert_dataset.sh` (outdated) * aligns `utils/release.py` with `transformers` (the current version is outdated)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6004/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6004/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6004", "html_url": "https://github.com/huggingface/datasets/pull/6004", "diff_url": "https://github.com/huggingface/datasets/pull/6004.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6004.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6003	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6003/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6003/comments	https://api.github.com/repos/huggingface/datasets/issues/6003/events	https://github.com/huggingface/datasets/issues/6003	1,786,554,110	I_kwDODunzps5qfKb-	6,003	interleave_datasets & DataCollatorForLanguageModeling having a conflict ?	{ "login": "PonteIneptique", "id": 1929830, "node_id": "MDQ6VXNlcjE5Mjk4MzA=", "avatar_url": "https://avatars.githubusercontent.com/u/1929830?v=4", "gravatar_id": "", "url": "https://api.github.com/users/PonteIneptique", "html_url": "https://github.com/PonteIneptique", "followers_url": "https://api.github.com/users/PonteIneptique/followers", "following_url": "https://api.github.com/users/PonteIneptique/following{/other_user}", "gists_url": "https://api.github.com/users/PonteIneptique/gists{/gist_id}", "starred_url": "https://api.github.com/users/PonteIneptique/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/PonteIneptique/subscriptions", "organizations_url": "https://api.github.com/users/PonteIneptique/orgs", "repos_url": "https://api.github.com/users/PonteIneptique/repos", "events_url": "https://api.github.com/users/PonteIneptique/events{/privacy}", "received_events_url": "https://api.github.com/users/PonteIneptique/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[]	1,688,404,531,000	1,688,404,531,000	null	NONE	null	### Describe the bug Hi everyone :) I have two local & custom datasets (1 "sentence" per line) which I split along the 95/5 lines for pre-training a Bert model. I use a modified version of `run_mlm.py` in order to be able to make use of `interleave_dataset`: - `tokenize()` runs fine - `group_text()` runs fine Everytime, on step 19, I get ```pytb File "env/lib/python3.9/site-packages/transformers/data/data_collator.py", line 779, in torch_mask_tokens inputs[indices_random] = random_words[indices_random] RuntimeError: Index put requires the source and destination dtypes match, got Float for the destination and Long for the source. ``` I tried: - training without interleave on dataset 1, it runs - training without interleave on dataset 2, it runs - training without `.to_iterable_dataset()`, it hangs then crash - training without group_text() and padding to max_length seemed to fix the issue, but who knows if this was just because it was an issue that would come much later in terms of steps. I might have coded something wrong, but I don't get what ### Steps to reproduce the bug I have this function: ```py def build_dataset(path: str, percent: str): dataset = load_dataset( "text", data_files={"train": [path]}, split=f"train[{percent}]" ) dataset = dataset.map( lambda examples: tokenize(examples["text"]), batched=True, num_proc=num_proc, ) dataset = dataset.map( group_texts, batched=True, num_proc=num_proc, desc=f"Grouping texts in chunks of {tokenizer.max_seq_length}", remove_columns=["text"] ) print(len(dataset)) return dataset.to_iterable_dataset() ``` I hardcoded group_text: ```py def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, and if the total_length < max_seq_length we exclude this batch and return an empty dict. # We could add padding if the model supported it instead of this drop, you can customize this part to your needs. total_length = (total_length // 512) 512 # Split by chunks of max_len. result = { k: [t[i: i + 512] for i in range(0, total_length, 512)] for k, t in concatenated_examples.items() } # result = {k: [el for el in elements if el] for k, elements in result.items()} return result ``` And then I build datasets using the following code: ```py train1 = build_dataset("d1.txt", ":95%") train2 = build_dataset("d2.txt", ":95%") dev1 = build_dataset("d1.txt", "95%:") dev2 = build_dataset("d2.txt", "95%:") ``` and finally I run ```py train_dataset = interleave_datasets( [train1, train2], probabilities=[0.8, 0.2], seed=42 ) eval_dataset = interleave_datasets( [dev1, dev2], probabilities=[0.8, 0.2], seed=42 ) ``` Then I run the training part which remains mostly untouched: > CUDA_VISIBLE_DEVICES=1 python custom_dataset.py --model_type bert --per_device_train_batch_size 32 --do_train --output_dir /var/mlm/training-bert/model --max_seq_length 512 --save_steps 10000 --save_total_limit 3 --auto_find_batch_size --logging_dir ./logs-bert --learning_rate 0.0001 --do_train --num_train_epochs 25 --warmup_steps 10000 --max_step 45000 --fp16 ### Expected behavior The model should then train normally, but fails every time at the same step (19). printing the variables at `inputs[indices_random] = random_words[indices_random]` shows a magnificient empty tensor (, 32) [if I remember well] ### Environment info transformers[torch] 4.30.2 Ubuntu A100 0 CUDA 12 Driver Version: 525.116.04	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6003/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6003/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6002	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6002/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6002/comments	https://api.github.com/repos/huggingface/datasets/issues/6002/events	https://github.com/huggingface/datasets/pull/6002	1,786,053,060	PR_kwDODunzps5UhP-Z	6,002	Add KLUE-MRC metrics	{ "login": "ingyuseong", "id": 37537248, "node_id": "MDQ6VXNlcjM3NTM3MjQ4", "avatar_url": "https://avatars.githubusercontent.com/u/37537248?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ingyuseong", "html_url": "https://github.com/ingyuseong", "followers_url": "https://api.github.com/users/ingyuseong/followers", "following_url": "https://api.github.com/users/ingyuseong/following{/other_user}", "gists_url": "https://api.github.com/users/ingyuseong/gists{/gist_id}", "starred_url": "https://api.github.com/users/ingyuseong/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ingyuseong/subscriptions", "organizations_url": "https://api.github.com/users/ingyuseong/orgs", "repos_url": "https://api.github.com/users/ingyuseong/repos", "events_url": "https://api.github.com/users/ingyuseong/events{/privacy}", "received_events_url": "https://api.github.com/users/ingyuseong/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)." ]	1,688,386,270,000	1,688,398,457,000	null	NONE	null	## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension) Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue). KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC. Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC. - [x] All tests passed - [x] Added a metric card (referred the metric card of SQuAD 2.0) - [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed ### References - [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf) - [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue) - #2416	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6002/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6002/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6002", "html_url": "https://github.com/huggingface/datasets/pull/6002", "diff_url": "https://github.com/huggingface/datasets/pull/6002.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6002.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6001	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6001/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6001/comments	https://api.github.com/repos/huggingface/datasets/issues/6001/events	https://github.com/huggingface/datasets/pull/6001	1,782,516,627	PR_kwDODunzps5UVMMh	6,001	Align `column_names` type check with type hint in `sort`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006038 / 0.011353 (-0.005315) \| 0.003797 / 0.011008 (-0.007211) \| 0.097686 / 0.038508 (0.059178) \| 0.035235 / 0.023109 (0.012126) \| 0.317294 / 0.275898 (0.041396) \| 0.377682 / 0.323480 (0.054202) \| 0.003485 / 0.007986 (-0.004501) \| 0.003603 / 0.004328 (-0.000725) \| 0.077268 / 0.004250 (0.073017) \| 0.054649 / 0.037052 (0.017597) \| 0.322293 / 0.258489 (0.063804) \| 0.372277 / 0.293841 (0.078436) \| 0.027927 / 0.128546 (-0.100619) \| 0.008495 / 0.075646 (-0.067151) \| 0.313078 / 0.419271 (-0.106193) \| 0.046974 / 0.043533 (0.003441) \| 0.313848 / 0.255139 (0.058709) \| 0.338454 / 0.283200 (0.055255) \| 0.020462 / 0.141683 (-0.121221) \| 1.473027 / 1.452155 (0.020873) \| 1.539468 / 1.492716 (0.046752) \|\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) \| 0.221429 / 0.018006 (0.203423) \| 0.412044 / 0.000490 (0.411555) \| 0.005866 / 0.000200 (0.005666) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022870 / 0.037411 (-0.014541) \| 0.099129 / 0.014526 (0.084603) \| 0.103463 / 0.176557 (-0.073094) \| 0.164969 / 0.737135 (-0.572166) \| 0.110000 / 0.296338 (-0.186339) \|\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) \| 0.431311 / 0.215209 (0.216102) \| 4.293562 / 2.077655 (2.215907) \| 1.961209 / 1.504120 (0.457089) \| 1.733680 / 1.541195 (0.192485) \| 1.793171 / 1.468490 (0.324681) \| 0.568566 / 4.584777 (-4.016211) \| 3.401794 / 3.745712 (-0.343918) \| 1.827949 / 5.269862 (-3.441913) \| 1.055963 / 4.565676 (-3.509714) \| 0.068459 / 0.424275 (-0.355816) \| 0.011586 / 0.007607 (0.003979) \| 0.533936 / 0.226044 (0.307891) \| 5.347637 / 2.268929 (3.078708) \| 2.378056 / 55.444624 (-53.066569) \| 2.032159 / 6.876477 (-4.844318) \| 2.159064 / 2.142072 (0.016991) \| 0.674528 / 4.805227 (-4.130699) \| 0.136859 / 6.500664 (-6.363805) \| 0.066629 / 0.075469 (-0.008840) \|\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) \| 1.218084 / 1.841788 (-0.623704) \| 14.141710 / 8.074308 (6.067402) \| 13.588415 / 10.191392 (3.397023) \| 0.155104 / 0.680424 (-0.525320) \| 0.017160 / 0.534201 (-0.517041) \| 0.375558 / 0.579283 (-0.203725) \| 0.386293 / 0.434364 (-0.048071) \| 0.459476 / 0.540337 (-0.080862) \| 0.548561 / 1.386936 (-0.838375) \|\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) \| 0.005878 / 0.011353 (-0.005475) \| 0.003750 / 0.011008 (-0.007259) \| 0.077720 / 0.038508 (0.039212) \| 0.034955 / 0.023109 (0.011846) \| 0.357480 / 0.275898 (0.081582) \| 0.418210 / 0.323480 (0.094730) \| 0.004566 / 0.007986 (-0.003419) \| 0.002918 / 0.004328 (-0.001410) \| 0.076517 / 0.004250 (0.072266) \| 0.050202 / 0.037052 (0.013150) \| 0.368166 / 0.258489 (0.109677) \| 0.415681 / 0.293841 (0.121840) \| 0.029496 / 0.128546 (-0.099050) \| 0.008547 / 0.075646 (-0.067099) \| 0.083037 / 0.419271 (-0.336234) \| 0.045001 / 0.043533 (0.001468) \| 0.356503 / 0.255139 (0.101364) \| 0.383747 / 0.283200 (0.100547) \| 0.025071 / 0.141683 (-0.116612) \| 1.541985 / 1.452155 (0.089830) \| 1.594710 / 1.492716 (0.101994) \|\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) \| 0.204491 / 0.018006 (0.186484) \| 0.408686 / 0.000490 (0.408196) \| 0.002505 / 0.000200 (0.002305) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024446 / 0.037411 (-0.012965) \| 0.101432 / 0.014526 (0.086906) \| 0.108105 / 0.176557 (-0.068452) \| 0.161195 / 0.737135 (-0.575940) \| 0.112671 / 0.296338 (-0.183667) \|\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) \| 0.459697 / 0.215209 (0.244488) \| 4.570071 / 2.077655 (2.492416) \| 2.211547 / 1.504120 (0.707427) \| 1.996651 / 1.541195 (0.455457) \| 2.015621 / 1.468490 (0.547131) \| 0.567423 / 4.584777 (-4.017354) \| 3.408027 / 3.745712 (-0.337685) \| 2.913824 / 5.269862 (-2.356038) \| 1.423223 / 4.565676 (-3.142453) \| 0.068740 / 0.424275 (-0.355535) \| 0.010997 / 0.007607 (0.003390) \| 0.567340 / 0.226044 (0.341296) \| 5.666280 / 2.268929 (3.397351) \| 2.804934 / 55.444624 (-52.639690) \| 2.430761 / 6.876477 (-4.445716) \| 2.451820 / 2.142072 (0.309748) \| 0.681926 / 4.805227 (-4.123301) \| 0.137761 / 6.500664 (-6.362903) \| 0.067173 / 0.075469 (-0.008296) \|\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) \| 1.329853 / 1.841788 (-0.511934) \| 14.436232 / 8.074308 (6.361924) \| 14.398645 / 10.191392 (4.207253) \| 0.147421 / 0.680424 (-0.533002) \| 0.016743 / 0.534201 (-0.517458) \| 0.364964 / 0.579283 (-0.214319) \| 0.387072 / 0.434364 (-0.047292) \| 0.423892 / 0.540337 (-0.116445) \| 0.521304 / 1.386936 (-0.865632) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a62b6ce65f718e9ff4189da86d160ae4bb197fc2 \"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) \| 0.006463 / 0.011353 (-0.004889) \| 0.003923 / 0.011008 (-0.007086) \| 0.102096 / 0.038508 (0.063588) \| 0.040230 / 0.023109 (0.017121) \| 0.384688 / 0.275898 (0.108789) \| 0.445574 / 0.323480 (0.122094) \| 0.003590 / 0.007986 (-0.004395) \| 0.004023 / 0.004328 (-0.000306) \| 0.080125 / 0.004250 (0.075875) \| 0.057406 / 0.037052 (0.020354) \| 0.395049 / 0.258489 (0.136560) \| 0.438065 / 0.293841 (0.144224) \| 0.028963 / 0.128546 (-0.099583) \| 0.008693 / 0.075646 (-0.066954) \| 0.317158 / 0.419271 (-0.102114) \| 0.047930 / 0.043533 (0.004397) \| 0.382442 / 0.255139 (0.127303) \| 0.410665 / 0.283200 (0.127466) \| 0.020127 / 0.141683 (-0.121555) \| 1.558554 / 1.452155 (0.106400) \| 1.590959 / 1.492716 (0.098242) \|\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) \| 0.208826 / 0.018006 (0.190820) \| 0.432037 / 0.000490 (0.431547) \| 0.006509 / 0.000200 (0.006309) \| 0.000285 / 0.000054 (0.000230) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023460 / 0.037411 (-0.013951) \| 0.099070 / 0.014526 (0.084545) \| 0.105771 / 0.176557 (-0.070785) \| 0.166683 / 0.737135 (-0.570452) \| 0.108755 / 0.296338 (-0.187583) \|\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) \| 0.424324 / 0.215209 (0.209115) \| 4.225696 / 2.077655 (2.148042) \| 1.910955 / 1.504120 (0.406835) \| 1.704493 / 1.541195 (0.163298) \| 1.782784 / 1.468490 (0.314293) \| 0.562927 / 4.584777 (-4.021850) \| 3.380163 / 3.745712 (-0.365550) \| 1.779641 / 5.269862 (-3.490221) \| 1.029134 / 4.565676 (-3.536543) \| 0.068325 / 0.424275 (-0.355950) \| 0.011528 / 0.007607 (0.003921) \| 0.530141 / 0.226044 (0.304097) \| 5.323443 / 2.268929 (3.054514) \| 2.346956 / 55.444624 (-53.097668) \| 2.013335 / 6.876477 (-4.863142) \| 2.118531 / 2.142072 (-0.023541) \| 0.675206 / 4.805227 (-4.130021) \| 0.135473 / 6.500664 (-6.365191) \| 0.064804 / 0.075469 (-0.010665) \|\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) \| 1.240179 / 1.841788 (-0.601608) \| 14.692449 / 8.074308 (6.618141) \| 13.672223 / 10.191392 (3.480831) \| 0.147748 / 0.680424 (-0.532676) \| 0.017119 / 0.534201 (-0.517082) \| 0.369481 / 0.579283 (-0.209802) \| 0.390133 / 0.434364 (-0.044231) \| 0.458768 / 0.540337 (-0.081569) \| 0.548989 / 1.386936 (-0.837947) \|\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) \| 0.006319 / 0.011353 (-0.005034) \| 0.003975 / 0.011008 (-0.007033) \| 0.077886 / 0.038508 (0.039378) \| 0.038322 / 0.023109 (0.015213) \| 0.379851 / 0.275898 (0.103953) \| 0.456749 / 0.323480 (0.133269) \| 0.005320 / 0.007986 (-0.002665) \| 0.003135 / 0.004328 (-0.001194) \| 0.078272 / 0.004250 (0.074022) \| 0.059919 / 0.037052 (0.022866) \| 0.430062 / 0.258489 (0.171573) \| 0.477432 / 0.293841 (0.183591) \| 0.029713 / 0.128546 (-0.098833) \| 0.008704 / 0.075646 (-0.066942) \| 0.082488 / 0.419271 (-0.336784) \| 0.044667 / 0.043533 (0.001134) \| 0.354910 / 0.255139 (0.099771) \| 0.434637 / 0.283200 (0.151438) \| 0.026402 / 0.141683 (-0.115281) \| 1.528825 / 1.452155 (0.076671) \| 1.548209 / 1.492716 (0.055493) \|\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) \| 0.237988 / 0.018006 (0.219982) \| 0.420402 / 0.000490 (0.419913) \| 0.003098 / 0.000200 (0.002898) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026253 / 0.037411 (-0.011159) \| 0.106137 / 0.014526 (0.091611) \| 0.110273 / 0.176557 (-0.066284) \| 0.165316 / 0.737135 (-0.571819) \| 0.115720 / 0.296338 (-0.180619) \|\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) \| 0.454244 / 0.215209 (0.239035) \| 4.526018 / 2.077655 (2.448364) \| 2.395985 / 1.504120 (0.891865) \| 2.234822 / 1.541195 (0.693627) \| 2.370235 / 1.468490 (0.901745) \| 0.567607 / 4.584777 (-4.017169) \| 3.650156 / 3.745712 (-0.095556) \| 3.360094 / 5.269862 (-1.909768) \| 1.415252 / 4.565676 (-3.150424) \| 0.068012 / 0.424275 (-0.356263) \| 0.011135 / 0.007607 (0.003528) \| 0.561967 / 0.226044 (0.335923) \| 5.621819 / 2.268929 (3.352890) \| 2.676912 / 55.444624 (-52.767712) \| 2.338306 / 6.876477 (-4.538171) \| 2.430888 / 2.142072 (0.288815) \| 0.684576 / 4.805227 (-4.120651) \| 0.138923 / 6.500664 (-6.361741) \| 0.069933 / 0.075469 (-0.005536) \|\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) \| 1.313383 / 1.841788 (-0.528405) \| 15.125088 / 8.074308 (7.050780) \| 14.801501 / 10.191392 (4.610109) \| 0.134235 / 0.680424 (-0.546189) \| 0.017058 / 0.534201 (-0.517143) \| 0.365166 / 0.579283 (-0.214117) \| 0.395415 / 0.434364 (-0.038949) \| 0.419355 / 0.540337 (-0.120983) \| 0.513411 / 1.386936 (-0.873525) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b9649b3cfb49342e44873ce7e29e0c75eaf3efa \"CML watermark\")\n" ]	1,688,130,950,000	1,688,134,712,000	1,688,134,284,000	CONTRIBUTOR	null	Fix #5998	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6001/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6001/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6001", "html_url": "https://github.com/huggingface/datasets/pull/6001", "diff_url": "https://github.com/huggingface/datasets/pull/6001.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6001.patch", "merged_at": "2023-06-30T14:11:24" }	true
https://api.github.com/repos/huggingface/datasets/issues/6000	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6000/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6000/comments	https://api.github.com/repos/huggingface/datasets/issues/6000/events	https://github.com/huggingface/datasets/pull/6000	1,782,456,878	PR_kwDODunzps5UU_FB	6,000	Pin `joblib` to avoid `joblibspark` test failures	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "<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) \| 0.006722 / 0.011353 (-0.004631) \| 0.004425 / 0.011008 (-0.006583) \| 0.100850 / 0.038508 (0.062341) \| 0.040816 / 0.023109 (0.017707) \| 0.348823 / 0.275898 (0.072925) \| 0.446285 / 0.323480 (0.122805) \| 0.005738 / 0.007986 (-0.002247) \| 0.003517 / 0.004328 (-0.000811) \| 0.078824 / 0.004250 (0.074574) \| 0.064695 / 0.037052 (0.027643) \| 0.389894 / 0.258489 (0.131405) \| 0.416107 / 0.293841 (0.122266) \| 0.028850 / 0.128546 (-0.099696) \| 0.009011 / 0.075646 (-0.066635) \| 0.323117 / 0.419271 (-0.096154) \| 0.049162 / 0.043533 (0.005629) \| 0.340144 / 0.255139 (0.085005) \| 0.382072 / 0.283200 (0.098872) \| 0.023160 / 0.141683 (-0.118523) \| 1.549218 / 1.452155 (0.097063) \| 1.581266 / 1.492716 (0.088550) \|\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) \| 0.293360 / 0.018006 (0.275353) \| 0.602189 / 0.000490 (0.601700) \| 0.004608 / 0.000200 (0.004408) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028144 / 0.037411 (-0.009267) \| 0.107088 / 0.014526 (0.092562) \| 0.112188 / 0.176557 (-0.064369) \| 0.174669 / 0.737135 (-0.562466) \| 0.116359 / 0.296338 (-0.179980) \|\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) \| 0.422911 / 0.215209 (0.207702) \| 4.231524 / 2.077655 (2.153869) \| 1.906711 / 1.504120 (0.402591) \| 1.706841 / 1.541195 (0.165646) \| 1.792066 / 1.468490 (0.323576) \| 0.559221 / 4.584777 (-4.025556) \| 3.434280 / 3.745712 (-0.311433) \| 1.918714 / 5.269862 (-3.351148) \| 1.073070 / 4.565676 (-3.492606) \| 0.067891 / 0.424275 (-0.356384) \| 0.011927 / 0.007607 (0.004320) \| 0.530843 / 0.226044 (0.304799) \| 5.309213 / 2.268929 (3.040285) \| 2.439246 / 55.444624 (-53.005378) \| 2.101245 / 6.876477 (-4.775231) \| 2.177436 / 2.142072 (0.035363) \| 0.672150 / 4.805227 (-4.133077) \| 0.137571 / 6.500664 (-6.363093) \| 0.068343 / 0.075469 (-0.007126) \|\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) \| 1.265262 / 1.841788 (-0.576525) \| 14.988021 / 8.074308 (6.913713) \| 13.611677 / 10.191392 (3.420285) \| 0.171389 / 0.680424 (-0.509035) \| 0.017681 / 0.534201 (-0.516520) \| 0.377542 / 0.579283 (-0.201741) \| 0.399475 / 0.434364 (-0.034889) \| 0.469553 / 0.540337 (-0.070785) \| 0.561888 / 1.386936 (-0.825048) \|\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) \| 0.006782 / 0.011353 (-0.004571) \| 0.004412 / 0.011008 (-0.006597) \| 0.078594 / 0.038508 (0.040086) \| 0.039930 / 0.023109 (0.016820) \| 0.371879 / 0.275898 (0.095981) \| 0.444910 / 0.323480 (0.121430) \| 0.005707 / 0.007986 (-0.002279) \| 0.003901 / 0.004328 (-0.000427) \| 0.080125 / 0.004250 (0.075875) \| 0.063977 / 0.037052 (0.026925) \| 0.382781 / 0.258489 (0.124292) \| 0.441791 / 0.293841 (0.147950) \| 0.030428 / 0.128546 (-0.098118) \| 0.009008 / 0.075646 (-0.066638) \| 0.084447 / 0.419271 (-0.334824) \| 0.044432 / 0.043533 (0.000899) \| 0.365686 / 0.255139 (0.110547) \| 0.394312 / 0.283200 (0.111113) \| 0.024508 / 0.141683 (-0.117175) \| 1.577020 / 1.452155 (0.124865) \| 1.630259 / 1.492716 (0.137543) \|\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) \| 0.307960 / 0.018006 (0.289953) \| 0.591473 / 0.000490 (0.590983) \| 0.008098 / 0.000200 (0.007898) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029567 / 0.037411 (-0.007845) \| 0.112773 / 0.014526 (0.098247) \| 0.117362 / 0.176557 (-0.059194) \| 0.174293 / 0.737135 (-0.562843) \| 0.123156 / 0.296338 (-0.173182) \|\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) \| 0.457475 / 0.215209 (0.242266) \| 4.599067 / 2.077655 (2.521412) \| 2.262638 / 1.504120 (0.758518) \| 2.124943 / 1.541195 (0.583748) \| 2.339912 / 1.468490 (0.871422) \| 0.566264 / 4.584777 (-4.018513) \| 3.489261 / 3.745712 (-0.256451) \| 1.925151 / 5.269862 (-3.344711) \| 1.099389 / 4.565676 (-3.466287) \| 0.068232 / 0.424275 (-0.356043) \| 0.011660 / 0.007607 (0.004052) \| 0.571227 / 0.226044 (0.345183) \| 5.702059 / 2.268929 (3.433130) \| 2.837701 / 55.444624 (-52.606924) \| 2.605468 / 6.876477 (-4.271008) \| 2.818396 / 2.142072 (0.676323) \| 0.681856 / 4.805227 (-4.123371) \| 0.141401 / 6.500664 (-6.359263) \| 0.069728 / 0.075469 (-0.005741) \|\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) \| 1.354935 / 1.841788 (-0.486853) \| 15.437404 / 8.074308 (7.363095) \| 15.415193 / 10.191392 (5.223801) \| 0.153459 / 0.680424 (-0.526964) \| 0.017190 / 0.534201 (-0.517011) \| 0.367256 / 0.579283 (-0.212027) \| 0.392709 / 0.434364 (-0.041655) \| 0.426125 / 0.540337 (-0.114213) \| 0.522612 / 1.386936 (-0.864324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#25ac13d8ab23e7d99252ce083a45e8333b6bbcdc \"CML watermark\")\n", "_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) \| 0.009183 / 0.011353 (-0.002170) \| 0.005232 / 0.011008 (-0.005776) \| 0.120349 / 0.038508 (0.081841) \| 0.044715 / 0.023109 (0.021606) \| 0.361519 / 0.275898 (0.085621) \| 0.463702 / 0.323480 (0.140223) \| 0.005842 / 0.007986 (-0.002144) \| 0.004041 / 0.004328 (-0.000288) \| 0.096953 / 0.004250 (0.092703) \| 0.070593 / 0.037052 (0.033540) \| 0.409790 / 0.258489 (0.151301) \| 0.477452 / 0.293841 (0.183611) \| 0.045827 / 0.128546 (-0.082719) \| 0.014038 / 0.075646 (-0.061608) \| 0.421317 / 0.419271 (0.002045) \| 0.065276 / 0.043533 (0.021743) \| 0.360074 / 0.255139 (0.104935) \| 0.409147 / 0.283200 (0.125947) \| 0.032444 / 0.141683 (-0.109238) \| 1.739257 / 1.452155 (0.287102) \| 1.831408 / 1.492716 (0.338692) \|\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) \| 0.274852 / 0.018006 (0.256846) \| 0.596320 / 0.000490 (0.595830) \| 0.006399 / 0.000200 (0.006199) \| 0.000133 / 0.000054 (0.000079) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031400 / 0.037411 (-0.006012) \| 0.127052 / 0.014526 (0.112526) \| 0.134269 / 0.176557 (-0.042288) \| 0.225998 / 0.737135 (-0.511137) \| 0.150019 / 0.296338 (-0.146319) \|\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) \| 0.654202 / 0.215209 (0.438993) \| 6.216735 / 2.077655 (4.139081) \| 2.440214 / 1.504120 (0.936094) \| 2.150575 / 1.541195 (0.609380) \| 2.124790 / 1.468490 (0.656300) \| 0.923514 / 4.584777 (-3.661263) \| 5.556924 / 3.745712 (1.811212) \| 2.843886 / 5.269862 (-2.425975) \| 1.834232 / 4.565676 (-2.731444) \| 0.111735 / 0.424275 (-0.312540) \| 0.014823 / 0.007607 (0.007216) \| 0.820503 / 0.226044 (0.594459) \| 7.887737 / 2.268929 (5.618809) \| 3.120307 / 55.444624 (-52.324317) \| 2.405856 / 6.876477 (-4.470621) \| 2.411239 / 2.142072 (0.269167) \| 1.071283 / 4.805227 (-3.733944) \| 0.227738 / 6.500664 (-6.272926) \| 0.073516 / 0.075469 (-0.001953) \|\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) \| 1.531806 / 1.841788 (-0.309982) \| 18.547661 / 8.074308 (10.473353) \| 21.083922 / 10.191392 (10.892530) \| 0.241706 / 0.680424 (-0.438718) \| 0.034169 / 0.534201 (-0.500032) \| 0.497514 / 0.579283 (-0.081769) \| 0.599801 / 0.434364 (0.165437) \| 0.576465 / 0.540337 (0.036127) \| 0.673509 / 1.386936 (-0.713427) \|\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) \| 0.007558 / 0.011353 (-0.003795) \| 0.005001 / 0.011008 (-0.006008) \| 0.093809 / 0.038508 (0.055301) \| 0.039792 / 0.023109 (0.016683) \| 0.456869 / 0.275898 (0.180971) \| 0.493370 / 0.323480 (0.169891) \| 0.005561 / 0.007986 (-0.002424) \| 0.003982 / 0.004328 (-0.000346) \| 0.085421 / 0.004250 (0.081170) \| 0.059817 / 0.037052 (0.022765) \| 0.468040 / 0.258489 (0.209550) \| 0.514853 / 0.293841 (0.221012) \| 0.044267 / 0.128546 (-0.084279) \| 0.012674 / 0.075646 (-0.062972) \| 0.098324 / 0.419271 (-0.320948) \| 0.056604 / 0.043533 (0.013071) \| 0.432200 / 0.255139 (0.177061) \| 0.459812 / 0.283200 (0.176612) \| 0.033872 / 0.141683 (-0.107811) \| 1.618576 / 1.452155 (0.166421) \| 1.676562 / 1.492716 (0.183846) \|\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) \| 0.230625 / 0.018006 (0.212619) \| 0.600558 / 0.000490 (0.600068) \| 0.003419 / 0.000200 (0.003219) \| 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) \| 0.026916 / 0.037411 (-0.010496) \| 0.103003 / 0.014526 (0.088478) \| 0.117078 / 0.176557 (-0.059478) \| 0.169359 / 0.737135 (-0.567776) \| 0.120305 / 0.296338 (-0.176034) \|\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) \| 0.616877 / 0.215209 (0.401668) \| 6.157232 / 2.077655 (4.079577) \| 2.869219 / 1.504120 (1.365099) \| 2.381410 / 1.541195 (0.840216) \| 2.417357 / 1.468490 (0.948867) \| 0.914947 / 4.584777 (-3.669830) \| 5.718526 / 3.745712 (1.972814) \| 2.757253 / 5.269862 (-2.512609) \| 1.794122 / 4.565676 (-2.771554) \| 0.108423 / 0.424275 (-0.315852) \| 0.013378 / 0.007607 (0.005771) \| 0.831067 / 0.226044 (0.605023) \| 8.478946 / 2.268929 (6.210018) \| 3.685937 / 55.444624 (-51.758687) \| 2.867472 / 6.876477 (-4.009005) \| 2.895975 / 2.142072 (0.753903) \| 1.137547 / 4.805227 (-3.667681) \| 0.213891 / 6.500664 (-6.286773) \| 0.075825 / 0.075469 (0.000356) \|\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) \| 1.621193 / 1.841788 (-0.220594) \| 17.322110 / 8.074308 (9.247802) \| 21.804016 / 10.191392 (11.612624) \| 0.243692 / 0.680424 (-0.436732) \| 0.030331 / 0.534201 (-0.503870) \| 0.492186 / 0.579283 (-0.087097) \| 0.632583 / 0.434364 (0.198219) \| 0.576265 / 0.540337 (0.035927) \| 0.713165 / 1.386936 (-0.673771) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a293ceb5aa41c4ae265c0e2aa9ada2d544466121 \"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) \| 0.008916 / 0.011353 (-0.002437) \| 0.004737 / 0.011008 (-0.006271) \| 0.134271 / 0.038508 (0.095763) \| 0.054472 / 0.023109 (0.031363) \| 0.380942 / 0.275898 (0.105044) \| 0.474138 / 0.323480 (0.150658) \| 0.007917 / 0.007986 (-0.000068) \| 0.003748 / 0.004328 (-0.000580) \| 0.092765 / 0.004250 (0.088515) \| 0.077873 / 0.037052 (0.040821) \| 0.397533 / 0.258489 (0.139043) \| 0.454737 / 0.293841 (0.160896) \| 0.039901 / 0.128546 (-0.088645) \| 0.010188 / 0.075646 (-0.065458) \| 0.447312 / 0.419271 (0.028040) \| 0.068684 / 0.043533 (0.025151) \| 0.371554 / 0.255139 (0.116415) \| 0.459655 / 0.283200 (0.176455) \| 0.027157 / 0.141683 (-0.114526) \| 1.874643 / 1.452155 (0.422488) \| 2.014800 / 1.492716 (0.522083) \|\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) \| 0.227079 / 0.018006 (0.209073) \| 0.483241 / 0.000490 (0.482751) \| 0.012404 / 0.000200 (0.012204) \| 0.000409 / 0.000054 (0.000354) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033135 / 0.037411 (-0.004277) \| 0.137782 / 0.014526 (0.123257) \| 0.142951 / 0.176557 (-0.033605) \| 0.209825 / 0.737135 (-0.527311) \| 0.152438 / 0.296338 (-0.143900) \|\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) \| 0.513066 / 0.215209 (0.297857) \| 5.122776 / 2.077655 (3.045121) \| 2.399270 / 1.504120 (0.895150) \| 2.180143 / 1.541195 (0.638949) \| 2.286395 / 1.468490 (0.817905) \| 0.641866 / 4.584777 (-3.942911) \| 4.694922 / 3.745712 (0.949210) \| 2.543390 / 5.269862 (-2.726472) \| 1.398592 / 4.565676 (-3.167084) \| 0.088662 / 0.424275 (-0.335613) \| 0.015854 / 0.007607 (0.008247) \| 0.688891 / 0.226044 (0.462847) \| 6.370148 / 2.268929 (4.101220) \| 2.949974 / 55.444624 (-52.494650) \| 2.538049 / 6.876477 (-4.338428) \| 2.699380 / 2.142072 (0.557308) \| 0.792670 / 4.805227 (-4.012557) \| 0.169126 / 6.500664 (-6.331538) \| 0.078511 / 0.075469 (0.003042) \|\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) \| 1.609119 / 1.841788 (-0.232669) \| 18.785069 / 8.074308 (10.710761) \| 16.670783 / 10.191392 (6.479391) \| 0.213081 / 0.680424 (-0.467343) \| 0.023904 / 0.534201 (-0.510296) \| 0.567720 / 0.579283 (-0.011564) \| 0.505806 / 0.434364 (0.071442) \| 0.649466 / 0.540337 (0.109129) \| 0.773174 / 1.386936 (-0.613762) \|\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) \| 0.008036 / 0.011353 (-0.003317) \| 0.004808 / 0.011008 (-0.006201) \| 0.094316 / 0.038508 (0.055808) \| 0.056174 / 0.023109 (0.033065) \| 0.481618 / 0.275898 (0.205720) \| 0.565300 / 0.323480 (0.241820) \| 0.006339 / 0.007986 (-0.001646) \| 0.003950 / 0.004328 (-0.000379) \| 0.093389 / 0.004250 (0.089139) \| 0.076163 / 0.037052 (0.039111) \| 0.489013 / 0.258489 (0.230524) \| 0.565451 / 0.293841 (0.271611) \| 0.039392 / 0.128546 (-0.089155) \| 0.010553 / 0.075646 (-0.065093) \| 0.101406 / 0.419271 (-0.317865) \| 0.062355 / 0.043533 (0.018822) \| 0.470461 / 0.255139 (0.215322) \| 0.502574 / 0.283200 (0.219375) \| 0.030196 / 0.141683 (-0.111486) \| 1.893926 / 1.452155 (0.441771) \| 1.958902 / 1.492716 (0.466185) \|\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) \| 0.198074 / 0.018006 (0.180068) \| 0.476828 / 0.000490 (0.476338) \| 0.003457 / 0.000200 (0.003257) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037576 / 0.037411 (0.000165) \| 0.146663 / 0.014526 (0.132138) \| 0.152969 / 0.176557 (-0.023588) \| 0.218683 / 0.737135 (-0.518452) \| 0.161552 / 0.296338 (-0.134786) \|\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) \| 0.525988 / 0.215209 (0.310779) \| 5.234673 / 2.077655 (3.157018) \| 2.571668 / 1.504120 (1.067548) \| 2.339760 / 1.541195 (0.798565) \| 2.422886 / 1.468490 (0.954395) \| 0.651537 / 4.584777 (-3.933240) \| 4.811148 / 3.745712 (1.065436) \| 4.451165 / 5.269862 (-0.818697) \| 2.016283 / 4.565676 (-2.549394) \| 0.096393 / 0.424275 (-0.327882) \| 0.015222 / 0.007607 (0.007615) \| 0.739132 / 0.226044 (0.513087) \| 6.813327 / 2.268929 (4.544399) \| 3.169018 / 55.444624 (-52.275606) \| 2.783120 / 6.876477 (-4.093356) \| 2.918979 / 2.142072 (0.776907) \| 0.797476 / 4.805227 (-4.007751) \| 0.171038 / 6.500664 (-6.329626) \| 0.079878 / 0.075469 (0.004409) \|\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) \| 1.595082 / 1.841788 (-0.246705) \| 19.685844 / 8.074308 (11.611536) \| 17.518989 / 10.191392 (7.327597) \| 0.220015 / 0.680424 (-0.460409) \| 0.026351 / 0.534201 (-0.507850) \| 0.578977 / 0.579283 (-0.000306) \| 0.549564 / 0.434364 (0.115200) \| 0.667564 / 0.540337 (0.127227) \| 0.802121 / 1.386936 (-0.584815) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e9aee64766aaddfda60a735cfc93345aed64bdcf \"CML watermark\")\n" ]	1,688,128,614,000	1,688,131,025,000	1,688,130,507,000	CONTRIBUTOR	null	`joblibspark` doesn't support the latest `joblib` release. See https://github.com/huggingface/datasets/actions/runs/5401870932/jobs/9812337078 for the errors	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6000/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6000/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6000", "html_url": "https://github.com/huggingface/datasets/pull/6000", "diff_url": "https://github.com/huggingface/datasets/pull/6000.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6000.patch", "merged_at": "2023-06-30T13:08:27" }	true
https://api.github.com/repos/huggingface/datasets/issues/5999	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5999/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5999/comments	https://api.github.com/repos/huggingface/datasets/issues/5999/events	https://github.com/huggingface/datasets/issues/5999	1,781,851,513	I_kwDODunzps5qNOV5	5,999	Getting a 409 error while loading xglue dataset	{ "login": "Praful932", "id": 45713796, "node_id": "MDQ6VXNlcjQ1NzEzNzk2", "avatar_url": "https://avatars.githubusercontent.com/u/45713796?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Praful932", "html_url": "https://github.com/Praful932", "followers_url": "https://api.github.com/users/Praful932/followers", "following_url": "https://api.github.com/users/Praful932/following{/other_user}", "gists_url": "https://api.github.com/users/Praful932/gists{/gist_id}", "starred_url": "https://api.github.com/users/Praful932/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Praful932/subscriptions", "organizations_url": "https://api.github.com/users/Praful932/orgs", "repos_url": "https://api.github.com/users/Praful932/repos", "events_url": "https://api.github.com/users/Praful932/events{/privacy}", "received_events_url": "https://api.github.com/users/Praful932/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	[ "Thanks for reporting, @Praful932.\r\n\r\nLet's continue the conversation on the Hub: https://huggingface.co/datasets/xglue/discussions/5" ]	1,688,098,434,000	1,688,104,643,000	1,688,104,642,000	NONE	null	### Describe the bug Unable to load xglue dataset ### Steps to reproduce the bug ```python import datasets dataset = datasets.load_dataset("xglue", "ntg") ``` > ConnectionError: Couldn't reach https://xglue.blob.core.windows.net/xglue/xglue_full_dataset.tar.gz (error 409) ### Expected behavior Expected the dataset to load ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.107+-x86_64-with-glibc2.31 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5999/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5999/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5998	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5998/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5998/comments	https://api.github.com/repos/huggingface/datasets/issues/5998/events	https://github.com/huggingface/datasets/issues/5998	1,781,805,018	I_kwDODunzps5qNC_a	5,998	The current implementation has a potential bug in the sort method	{ "login": "wangyuxinwhy", "id": 22192665, "node_id": "MDQ6VXNlcjIyMTkyNjY1", "avatar_url": "https://avatars.githubusercontent.com/u/22192665?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wangyuxinwhy", "html_url": "https://github.com/wangyuxinwhy", "followers_url": "https://api.github.com/users/wangyuxinwhy/followers", "following_url": "https://api.github.com/users/wangyuxinwhy/following{/other_user}", "gists_url": "https://api.github.com/users/wangyuxinwhy/gists{/gist_id}", "starred_url": "https://api.github.com/users/wangyuxinwhy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/wangyuxinwhy/subscriptions", "organizations_url": "https://api.github.com/users/wangyuxinwhy/orgs", "repos_url": "https://api.github.com/users/wangyuxinwhy/repos", "events_url": "https://api.github.com/users/wangyuxinwhy/events{/privacy}", "received_events_url": "https://api.github.com/users/wangyuxinwhy/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Thanks for reporting, @wangyuxinwhy. " ]	1,688,095,017,000	1,688,134,863,000	1,688,134,285,000	NONE	null	### Describe the bug In the sort method，here's a piece of code ```python # column_names: Union[str, Sequence_[str]] # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): column_names = [column_names] ``` I get an error when I pass in a tuple based on the column_names type annotation, it will raise an errror.As in the example below, while the type annotation implies that a tuple can be passed. ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` Of course, after I modified the tuple into a list, everything worked fine Change the code to the following so there will be no problem ```python # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): if isinstance(column_names, str): column_names = [column_names] else: column_names = list(column_names) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` ### Expected behavior Passing tuple into column_names should be equivalent to passing list ### Environment info - `datasets` version: 2.13.0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5998/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5998/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5997	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5997/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5997/comments	https://api.github.com/repos/huggingface/datasets/issues/5997/events	https://github.com/huggingface/datasets/issues/5997	1,781,582,818	I_kwDODunzps5qMMvi	5,997	extend the map function so it can wrap around long text that does not fit in the context window	{ "login": "siddhsql", "id": 127623723, "node_id": "U_kgDOB5tiKw", "avatar_url": "https://avatars.githubusercontent.com/u/127623723?v=4", "gravatar_id": "", "url": "https://api.github.com/users/siddhsql", "html_url": "https://github.com/siddhsql", "followers_url": "https://api.github.com/users/siddhsql/followers", "following_url": "https://api.github.com/users/siddhsql/following{/other_user}", "gists_url": "https://api.github.com/users/siddhsql/gists{/gist_id}", "starred_url": "https://api.github.com/users/siddhsql/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/siddhsql/subscriptions", "organizations_url": "https://api.github.com/users/siddhsql/orgs", "repos_url": "https://api.github.com/users/siddhsql/repos", "events_url": "https://api.github.com/users/siddhsql/events{/privacy}", "received_events_url": "https://api.github.com/users/siddhsql/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	[ "I just noticed the [docs](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2881C11-L2881C200) say:\r\n\r\n>If batched is `True` and `batch_size` is `n > 1`, then the function takes a batch of `n` examples as input and can return a batch with `n` examples, or with an arbitrary number of examples.\r\n\r\nso maybe this is a bug then.", "All the values in a batch must be of the same length. So one solution is dropping all the input columns:\r\n```python\r\ndata = data.map(lambda samples: tokenizer(samples[\"text\"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True, remove_columns=data.column_names)\r\n```\r\n\r\nAnother is padding/transforming the input columns to the tokenizer output's length (447). " ]	1,688,076,921,000	1,688,407,132,000	null	NONE	null	### Feature request I understand `dataset` provides a [`map`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2849) function. This function in turn takes in a callable that is used to tokenize the text on which a model is trained. Frequently this text will not fit within a models's context window. In this case it would be useful to wrap around the text into multiple rows with each row fitting the model's context window. I tried to do it using this code as example which in turn I have borrowed from [here](https://stackoverflow.com/a/76343993/147530): ``` data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True) ``` but running the code gives me this error: ``` File "/llm/fine-tune.py", line 117, in <module> data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(dataset_kwargs): File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3480, in _map_single writer.write_batch(batch) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_writer.py", line 556, in write_batch pa_table = pa.Table.from_arrays(arrays, schema=schema) File "pyarrow/table.pxi", line 3798, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 2962, in pyarrow.lib.Table.validate File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 394 but got length 447 ``` The lambda function I have provided is correctly chopping up long text so it wraps around (and because of this 394 samples become 447 after wrap around) but the dataset `map` function does not like it. ### Motivation please see above ### Your contribution I'm afraid I don't have much knowledge to help	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5997/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5997/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5996	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5996/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5996/comments	https://api.github.com/repos/huggingface/datasets/issues/5996/events	https://github.com/huggingface/datasets/pull/5996	1,779,294,374	PR_kwDODunzps5UKP0i	5,996	Deprecate `use_auth_token` in favor of `token`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006134 / 0.011353 (-0.005219) \| 0.003816 / 0.011008 (-0.007193) \| 0.098226 / 0.038508 (0.059718) \| 0.036830 / 0.023109 (0.013721) \| 0.314551 / 0.275898 (0.038653) \| 0.372251 / 0.323480 (0.048771) \| 0.004762 / 0.007986 (-0.003224) \| 0.003041 / 0.004328 (-0.001287) \| 0.077651 / 0.004250 (0.073401) \| 0.052445 / 0.037052 (0.015393) \| 0.324632 / 0.258489 (0.066143) \| 0.365724 / 0.293841 (0.071883) \| 0.028069 / 0.128546 (-0.100477) \| 0.008444 / 0.075646 (-0.067203) \| 0.312767 / 0.419271 (-0.106505) \| 0.047773 / 0.043533 (0.004240) \| 0.305317 / 0.255139 (0.050178) \| 0.332007 / 0.283200 (0.048807) \| 0.018985 / 0.141683 (-0.122698) \| 1.538022 / 1.452155 (0.085868) \| 1.575898 / 1.492716 (0.083182) \|\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) \| 0.204780 / 0.018006 (0.186774) \| 0.428125 / 0.000490 (0.427635) \| 0.003454 / 0.000200 (0.003254) \| 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) \| 0.025064 / 0.037411 (-0.012348) \| 0.099419 / 0.014526 (0.084893) \| 0.111068 / 0.176557 (-0.065489) \| 0.169775 / 0.737135 (-0.567361) \| 0.112067 / 0.296338 (-0.184271) \|\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) \| 0.429642 / 0.215209 (0.214433) \| 4.275556 / 2.077655 (2.197901) \| 1.914658 / 1.504120 (0.410539) \| 1.706556 / 1.541195 (0.165361) \| 1.754228 / 1.468490 (0.285738) \| 0.563669 / 4.584777 (-4.021108) \| 3.391501 / 3.745712 (-0.354211) \| 1.791517 / 5.269862 (-3.478345) \| 1.030704 / 4.565676 (-3.534973) \| 0.070882 / 0.424275 (-0.353393) \| 0.011351 / 0.007607 (0.003744) \| 0.529438 / 0.226044 (0.303394) \| 5.294316 / 2.268929 (3.025387) \| 2.344653 / 55.444624 (-53.099972) \| 1.997468 / 6.876477 (-4.879009) \| 2.108932 / 2.142072 (-0.033140) \| 0.676794 / 4.805227 (-4.128433) \| 0.135058 / 6.500664 (-6.365607) \| 0.065857 / 0.075469 (-0.009612) \|\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) \| 1.231864 / 1.841788 (-0.609924) \| 13.986694 / 8.074308 (5.912386) \| 13.306600 / 10.191392 (3.115208) \| 0.145520 / 0.680424 (-0.534904) \| 0.016717 / 0.534201 (-0.517484) \| 0.366303 / 0.579283 (-0.212980) \| 0.391637 / 0.434364 (-0.042727) \| 0.425445 / 0.540337 (-0.114892) \| 0.507719 / 1.386936 (-0.879217) \|\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) \| 0.006236 / 0.011353 (-0.005116) \| 0.003766 / 0.011008 (-0.007242) \| 0.076794 / 0.038508 (0.038286) \| 0.037210 / 0.023109 (0.014101) \| 0.378387 / 0.275898 (0.102489) \| 0.425456 / 0.323480 (0.101977) \| 0.004694 / 0.007986 (-0.003291) \| 0.002921 / 0.004328 (-0.001407) \| 0.076985 / 0.004250 (0.072735) \| 0.052188 / 0.037052 (0.015136) \| 0.394385 / 0.258489 (0.135896) \| 0.432527 / 0.293841 (0.138686) \| 0.029091 / 0.128546 (-0.099455) \| 0.008364 / 0.075646 (-0.067282) \| 0.082583 / 0.419271 (-0.336689) \| 0.042928 / 0.043533 (-0.000605) \| 0.375321 / 0.255139 (0.120182) \| 0.391719 / 0.283200 (0.108519) \| 0.019388 / 0.141683 (-0.122295) \| 1.550644 / 1.452155 (0.098489) \| 1.604882 / 1.492716 (0.112166) \|\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) \| 0.236859 / 0.018006 (0.218853) \| 0.418528 / 0.000490 (0.418039) \| 0.000388 / 0.000200 (0.000188) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025548 / 0.037411 (-0.011863) \| 0.100644 / 0.014526 (0.086118) \| 0.109102 / 0.176557 (-0.067455) \| 0.161694 / 0.737135 (-0.575441) \| 0.112088 / 0.296338 (-0.184250) \|\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) \| 0.484128 / 0.215209 (0.268919) \| 4.849952 / 2.077655 (2.772297) \| 2.512769 / 1.504120 (1.008649) \| 2.303295 / 1.541195 (0.762100) \| 2.356699 / 1.468490 (0.888209) \| 0.564181 / 4.584777 (-4.020596) \| 3.421393 / 3.745712 (-0.324319) \| 2.570875 / 5.269862 (-2.698987) \| 1.474307 / 4.565676 (-3.091370) \| 0.068035 / 0.424275 (-0.356240) \| 0.011300 / 0.007607 (0.003693) \| 0.587867 / 0.226044 (0.361823) \| 5.862447 / 2.268929 (3.593519) \| 3.004017 / 55.444624 (-52.440607) \| 2.664989 / 6.876477 (-4.211488) \| 2.740020 / 2.142072 (0.597948) \| 0.680840 / 4.805227 (-4.124387) \| 0.137001 / 6.500664 (-6.363663) \| 0.068098 / 0.075469 (-0.007371) \|\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) \| 1.297362 / 1.841788 (-0.544426) \| 14.207891 / 8.074308 (6.133583) \| 14.087562 / 10.191392 (3.896170) \| 0.149514 / 0.680424 (-0.530910) \| 0.016566 / 0.534201 (-0.517635) \| 0.367602 / 0.579283 (-0.211681) \| 0.400692 / 0.434364 (-0.033671) \| 0.432907 / 0.540337 (-0.107431) \| 0.525924 / 1.386936 (-0.861012) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ec069feaaf6c28d4e4df76d344693b591a74c3f \"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) \| 0.006223 / 0.011353 (-0.005130) \| 0.003672 / 0.011008 (-0.007336) \| 0.097451 / 0.038508 (0.058943) \| 0.036243 / 0.023109 (0.013133) \| 0.375650 / 0.275898 (0.099752) \| 0.431652 / 0.323480 (0.108172) \| 0.004758 / 0.007986 (-0.003227) \| 0.002941 / 0.004328 (-0.001387) \| 0.077383 / 0.004250 (0.073132) \| 0.055342 / 0.037052 (0.018289) \| 0.390335 / 0.258489 (0.131846) \| 0.427867 / 0.293841 (0.134026) \| 0.027619 / 0.128546 (-0.100927) \| 0.008244 / 0.075646 (-0.067402) \| 0.313499 / 0.419271 (-0.105773) \| 0.054987 / 0.043533 (0.011454) \| 0.394044 / 0.255139 (0.138905) \| 0.398784 / 0.283200 (0.115584) \| 0.026499 / 0.141683 (-0.115184) \| 1.496907 / 1.452155 (0.044753) \| 1.554465 / 1.492716 (0.061749) \|\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) \| 0.241197 / 0.018006 (0.223190) \| 0.427856 / 0.000490 (0.427366) \| 0.006264 / 0.000200 (0.006065) \| 0.000218 / 0.000054 (0.000164) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025550 / 0.037411 (-0.011862) \| 0.104426 / 0.014526 (0.089901) \| 0.110310 / 0.176557 (-0.066246) \| 0.173813 / 0.737135 (-0.563322) \| 0.112129 / 0.296338 (-0.184209) \|\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) \| 0.458806 / 0.215209 (0.243597) \| 4.576351 / 2.077655 (2.498697) \| 2.265670 / 1.504120 (0.761550) \| 2.073230 / 1.541195 (0.532035) \| 2.135283 / 1.468490 (0.666793) \| 0.562506 / 4.584777 (-4.022271) \| 3.375101 / 3.745712 (-0.370611) \| 1.734393 / 5.269862 (-3.535469) \| 1.026622 / 4.565676 (-3.539054) \| 0.068144 / 0.424275 (-0.356131) \| 0.011092 / 0.007607 (0.003485) \| 0.562779 / 0.226044 (0.336734) \| 5.608256 / 2.268929 (3.339328) \| 2.706468 / 55.444624 (-52.738157) \| 2.381607 / 6.876477 (-4.494869) \| 2.451027 / 2.142072 (0.308954) \| 0.671590 / 4.805227 (-4.133637) \| 0.135749 / 6.500664 (-6.364915) \| 0.065389 / 0.075469 (-0.010080) \|\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) \| 1.244806 / 1.841788 (-0.596981) \| 14.042150 / 8.074308 (5.967841) \| 14.246612 / 10.191392 (4.055220) \| 0.134309 / 0.680424 (-0.546114) \| 0.017082 / 0.534201 (-0.517119) \| 0.366043 / 0.579283 (-0.213240) \| 0.400748 / 0.434364 (-0.033616) \| 0.425695 / 0.540337 (-0.114643) \| 0.509355 / 1.386936 (-0.877581) \|\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) \| 0.006134 / 0.011353 (-0.005219) \| 0.003980 / 0.011008 (-0.007028) \| 0.078353 / 0.038508 (0.039845) \| 0.038011 / 0.023109 (0.014902) \| 0.375784 / 0.275898 (0.099886) \| 0.433619 / 0.323480 (0.110139) \| 0.004897 / 0.007986 (-0.003088) \| 0.002981 / 0.004328 (-0.001347) \| 0.077362 / 0.004250 (0.073112) \| 0.056108 / 0.037052 (0.019056) \| 0.395984 / 0.258489 (0.137495) \| 0.427397 / 0.293841 (0.133556) \| 0.029325 / 0.128546 (-0.099221) \| 0.008498 / 0.075646 (-0.067148) \| 0.082478 / 0.419271 (-0.336794) \| 0.044085 / 0.043533 (0.000552) \| 0.389923 / 0.255139 (0.134784) \| 0.391180 / 0.283200 (0.107980) \| 0.022452 / 0.141683 (-0.119231) \| 1.507758 / 1.452155 (0.055603) \| 1.530459 / 1.492716 (0.037743) \|\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) \| 0.230928 / 0.018006 (0.212922) \| 0.408484 / 0.000490 (0.407995) \| 0.000806 / 0.000200 (0.000606) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025183 / 0.037411 (-0.012228) \| 0.102292 / 0.014526 (0.087766) \| 0.108142 / 0.176557 (-0.068415) \| 0.161172 / 0.737135 (-0.575963) \| 0.114476 / 0.296338 (-0.181862) \|\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) \| 0.482978 / 0.215209 (0.267769) \| 4.816103 / 2.077655 (2.738448) \| 2.505567 / 1.504120 (1.001447) \| 2.302598 / 1.541195 (0.761404) \| 2.371238 / 1.468490 (0.902748) \| 0.567467 / 4.584777 (-4.017310) \| 3.363407 / 3.745712 (-0.382306) \| 1.746213 / 5.269862 (-3.523649) \| 1.035468 / 4.565676 (-3.530208) \| 0.068431 / 0.424275 (-0.355844) \| 0.011069 / 0.007607 (0.003462) \| 0.598241 / 0.226044 (0.372196) \| 5.953927 / 2.268929 (3.684999) \| 3.007493 / 55.444624 (-52.437132) \| 2.629399 / 6.876477 (-4.247078) \| 2.737201 / 2.142072 (0.595129) \| 0.682456 / 4.805227 (-4.122771) \| 0.137613 / 6.500664 (-6.363051) \| 0.067941 / 0.075469 (-0.007528) \|\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) \| 1.306015 / 1.841788 (-0.535772) \| 14.359240 / 8.074308 (6.284932) \| 14.187601 / 10.191392 (3.996209) \| 0.138612 / 0.680424 (-0.541812) \| 0.016708 / 0.534201 (-0.517493) \| 0.366365 / 0.579283 (-0.212918) \| 0.396982 / 0.434364 (-0.037382) \| 0.426939 / 0.540337 (-0.113398) \| 0.520064 / 1.386936 (-0.866872) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#21d0fd041a5eca02d3ee787396216ac613c662ac \"CML watermark\")\n", "They use `token` and emit a deprecation warning if `use_auth_token` is passed instead (see https://github.com/huggingface/transformers/blob/78a2b19fc84ed55c65f4bf20a901edb7ceb73c5f/src/transformers/modeling_utils.py#L1933). \r\n\r\nI think we can update the `examples` scripts after merging this PR.", "> I think we can update the examples scripts after merging this PR.\r\n\r\nWe should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the `token` arg", "> We should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the token arg\r\n\r\nThis would avoid the warning only for the latest `datasets` release. TBH, I don't think this is worth the hassle, considering how simple it is to remove it.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007644 / 0.011353 (-0.003709) \| 0.004667 / 0.011008 (-0.006341) \| 0.117347 / 0.038508 (0.078839) \| 0.050620 / 0.023109 (0.027510) \| 0.415402 / 0.275898 (0.139504) \| 0.485898 / 0.323480 (0.162418) \| 0.005848 / 0.007986 (-0.002138) \| 0.003736 / 0.004328 (-0.000592) \| 0.089798 / 0.004250 (0.085547) \| 0.069344 / 0.037052 (0.032292) \| 0.441684 / 0.258489 (0.183195) \| 0.468972 / 0.293841 (0.175131) \| 0.036637 / 0.128546 (-0.091909) \| 0.010219 / 0.075646 (-0.065427) \| 0.394293 / 0.419271 (-0.024978) \| 0.061462 / 0.043533 (0.017929) \| 0.409448 / 0.255139 (0.154309) \| 0.431557 / 0.283200 (0.148358) \| 0.027795 / 0.141683 (-0.113888) \| 1.837844 / 1.452155 (0.385690) \| 1.862683 / 1.492716 (0.369967) \|\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) \| 0.230500 / 0.018006 (0.212494) \| 0.483139 / 0.000490 (0.482649) \| 0.006517 / 0.000200 (0.006317) \| 0.000143 / 0.000054 (0.000088) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033152 / 0.037411 (-0.004259) \| 0.133673 / 0.014526 (0.119147) \| 0.143853 / 0.176557 (-0.032704) \| 0.215254 / 0.737135 (-0.521882) \| 0.150676 / 0.296338 (-0.145662) \|\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) \| 0.503796 / 0.215209 (0.288587) \| 5.049981 / 2.077655 (2.972326) \| 2.399427 / 1.504120 (0.895307) \| 2.167635 / 1.541195 (0.626441) \| 2.257448 / 1.468490 (0.788958) \| 0.641298 / 4.584777 (-3.943479) \| 4.828676 / 3.745712 (1.082964) \| 4.346069 / 5.269862 (-0.923793) \| 2.103890 / 4.565676 (-2.461786) \| 0.079115 / 0.424275 (-0.345160) \| 0.013377 / 0.007607 (0.005770) \| 0.621207 / 0.226044 (0.395162) \| 6.190939 / 2.268929 (3.922011) \| 2.920129 / 55.444624 (-52.524495) \| 2.549225 / 6.876477 (-4.327252) \| 2.719221 / 2.142072 (0.577149) \| 0.790949 / 4.805227 (-4.014278) \| 0.172032 / 6.500664 (-6.328632) \| 0.077779 / 0.075469 (0.002310) \|\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) \| 1.432572 / 1.841788 (-0.409216) \| 21.000031 / 8.074308 (12.925723) \| 17.555093 / 10.191392 (7.363701) \| 0.166646 / 0.680424 (-0.513778) \| 0.020451 / 0.534201 (-0.513750) \| 0.488767 / 0.579283 (-0.090516) \| 0.737036 / 0.434364 (0.302672) \| 0.621694 / 0.540337 (0.081356) \| 0.732074 / 1.386936 (-0.654862) \|\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) \| 0.008198 / 0.011353 (-0.003155) \| 0.004987 / 0.011008 (-0.006021) \| 0.090714 / 0.038508 (0.052206) \| 0.053379 / 0.023109 (0.030270) \| 0.425199 / 0.275898 (0.149301) \| 0.514036 / 0.323480 (0.190556) \| 0.006043 / 0.007986 (-0.001943) \| 0.003888 / 0.004328 (-0.000441) \| 0.088294 / 0.004250 (0.084043) \| 0.073024 / 0.037052 (0.035971) \| 0.435983 / 0.258489 (0.177494) \| 0.514293 / 0.293841 (0.220452) \| 0.039451 / 0.128546 (-0.089095) \| 0.010439 / 0.075646 (-0.065207) \| 0.096885 / 0.419271 (-0.322387) \| 0.060165 / 0.043533 (0.016632) \| 0.421053 / 0.255139 (0.165914) \| 0.455545 / 0.283200 (0.172345) \| 0.027234 / 0.141683 (-0.114449) \| 1.768975 / 1.452155 (0.316820) \| 1.842853 / 1.492716 (0.350137) \|\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) \| 0.278940 / 0.018006 (0.260933) \| 0.480709 / 0.000490 (0.480219) \| 0.000436 / 0.000200 (0.000236) \| 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) \| 0.034900 / 0.037411 (-0.002511) \| 0.144893 / 0.014526 (0.130368) \| 0.149567 / 0.176557 (-0.026989) \| 0.213200 / 0.737135 (-0.523935) \| 0.156735 / 0.296338 (-0.139604) \|\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) \| 0.535897 / 0.215209 (0.320687) \| 5.336998 / 2.077655 (3.259343) \| 2.685854 / 1.504120 (1.181734) \| 2.470177 / 1.541195 (0.928983) \| 2.547495 / 1.468490 (1.079004) \| 0.642830 / 4.584777 (-3.941947) \| 4.595866 / 3.745712 (0.850154) \| 2.186696 / 5.269862 (-3.083165) \| 1.317969 / 4.565676 (-3.247708) \| 0.079268 / 0.424275 (-0.345007) \| 0.013792 / 0.007607 (0.006185) \| 0.662236 / 0.226044 (0.436192) \| 6.604775 / 2.268929 (4.335847) \| 3.355888 / 55.444624 (-52.088736) \| 2.968911 / 6.876477 (-3.907565) \| 3.121862 / 2.142072 (0.979790) \| 0.794752 / 4.805227 (-4.010475) \| 0.170800 / 6.500664 (-6.329864) \| 0.078393 / 0.075469 (0.002924) \|\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) \| 1.601605 / 1.841788 (-0.240183) \| 20.743553 / 8.074308 (12.669245) \| 17.543968 / 10.191392 (7.352576) \| 0.221884 / 0.680424 (-0.458540) \| 0.020779 / 0.534201 (-0.513422) \| 0.479677 / 0.579283 (-0.099606) \| 0.516207 / 0.434364 (0.081843) \| 0.564046 / 0.540337 (0.023709) \| 0.711336 / 1.386936 (-0.675600) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#819bb4346434912eb405ce3f3e9f21dc25a2fe85 \"CML watermark\")\n", "Yes, sounds great! Thanks", "yup" ]	1,687,969,598,000	1,688,570,540,000	1,688,400,213,000	CONTRIBUTOR	null	... to be consistent with `transformers` and `huggingface_hub`.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5996/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5996/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5996", "html_url": "https://github.com/huggingface/datasets/pull/5996", "diff_url": "https://github.com/huggingface/datasets/pull/5996.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5996.patch", "merged_at": "2023-07-03T16:03:33" }	true
https://api.github.com/repos/huggingface/datasets/issues/5995	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5995/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5995/comments	https://api.github.com/repos/huggingface/datasets/issues/5995/events	https://github.com/huggingface/datasets/pull/5995	1,777,088,925	PR_kwDODunzps5UCvYJ	5,995	Support returning dataframe in map transform	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.009725 / 0.011353 (-0.001628) \| 0.006014 / 0.011008 (-0.004994) \| 0.136039 / 0.038508 (0.097531) \| 0.049685 / 0.023109 (0.026576) \| 0.492967 / 0.275898 (0.217068) \| 0.553775 / 0.323480 (0.230295) \| 0.007421 / 0.007986 (-0.000564) \| 0.004686 / 0.004328 (0.000357) \| 0.106639 / 0.004250 (0.102389) \| 0.073483 / 0.037052 (0.036431) \| 0.507194 / 0.258489 (0.248705) \| 0.535760 / 0.293841 (0.241919) \| 0.049666 / 0.128546 (-0.078880) \| 0.014139 / 0.075646 (-0.061507) \| 0.435459 / 0.419271 (0.016188) \| 0.076026 / 0.043533 (0.032493) \| 0.454542 / 0.255139 (0.199403) \| 0.512724 / 0.283200 (0.229524) \| 0.034969 / 0.141683 (-0.106713) \| 1.881048 / 1.452155 (0.428893) \| 1.959915 / 1.492716 (0.467199) \|\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) \| 0.265322 / 0.018006 (0.247316) \| 0.573963 / 0.000490 (0.573474) \| 0.017493 / 0.000200 (0.017293) \| 0.000637 / 0.000054 (0.000582) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028712 / 0.037411 (-0.008699) \| 0.149554 / 0.014526 (0.135029) \| 0.130013 / 0.176557 (-0.046544) \| 0.203408 / 0.737135 (-0.533727) \| 0.144778 / 0.296338 (-0.151561) \|\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) \| 0.664198 / 0.215209 (0.448989) \| 6.418054 / 2.077655 (4.340399) \| 2.602338 / 1.504120 (1.098219) \| 2.212992 / 1.541195 (0.671797) \| 2.214309 / 1.468490 (0.745819) \| 0.914772 / 4.584777 (-3.670005) \| 5.824831 / 3.745712 (2.079119) \| 2.865381 / 5.269862 (-2.404481) \| 1.906020 / 4.565676 (-2.659657) \| 0.106947 / 0.424275 (-0.317328) \| 0.013467 / 0.007607 (0.005860) \| 0.834556 / 0.226044 (0.608512) \| 8.237078 / 2.268929 (5.968150) \| 3.380919 / 55.444624 (-52.063705) \| 2.656713 / 6.876477 (-4.219764) \| 2.834941 / 2.142072 (0.692869) \| 1.151241 / 4.805227 (-3.653986) \| 0.220860 / 6.500664 (-6.279804) \| 0.080781 / 0.075469 (0.005312) \|\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) \| 1.655128 / 1.841788 (-0.186660) \| 18.696108 / 8.074308 (10.621800) \| 22.882108 / 10.191392 (12.690716) \| 0.236041 / 0.680424 (-0.444383) \| 0.031073 / 0.534201 (-0.503128) \| 0.525263 / 0.579283 (-0.054021) \| 0.632933 / 0.434364 (0.198569) \| 0.707228 / 0.540337 (0.166890) \| 0.753508 / 1.386936 (-0.633428) \|\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) \| 0.009875 / 0.011353 (-0.001478) \| 0.005135 / 0.011008 (-0.005873) \| 0.101307 / 0.038508 (0.062799) \| 0.044895 / 0.023109 (0.021786) \| 0.497824 / 0.275898 (0.221926) \| 0.573098 / 0.323480 (0.249618) \| 0.006669 / 0.007986 (-0.001317) \| 0.004289 / 0.004328 (-0.000039) \| 0.105824 / 0.004250 (0.101573) \| 0.061002 / 0.037052 (0.023950) \| 0.510127 / 0.258489 (0.251638) \| 0.581387 / 0.293841 (0.287546) \| 0.052843 / 0.128546 (-0.075703) \| 0.015506 / 0.075646 (-0.060140) \| 0.116057 / 0.419271 (-0.303215) \| 0.063444 / 0.043533 (0.019912) \| 0.479366 / 0.255139 (0.224227) \| 0.518419 / 0.283200 (0.235220) \| 0.034876 / 0.141683 (-0.106806) \| 2.018446 / 1.452155 (0.566292) \| 1.960755 / 1.492716 (0.468039) \|\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) \| 0.269077 / 0.018006 (0.251070) \| 0.606059 / 0.000490 (0.605569) \| 0.000488 / 0.000200 (0.000288) \| 0.000093 / 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) \| 0.032465 / 0.037411 (-0.004946) \| 0.136517 / 0.014526 (0.121991) \| 0.147740 / 0.176557 (-0.028816) \| 0.193802 / 0.737135 (-0.543334) \| 0.151876 / 0.296338 (-0.144462) \|\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) \| 0.709866 / 0.215209 (0.494657) \| 6.848193 / 2.077655 (4.770538) \| 3.310853 / 1.504120 (1.806733) \| 2.940813 / 1.541195 (1.399619) \| 2.934934 / 1.468490 (1.466444) \| 0.927104 / 4.584777 (-3.657673) \| 5.921607 / 3.745712 (2.175895) \| 4.926558 / 5.269862 (-0.343303) \| 2.853269 / 4.565676 (-1.712407) \| 0.120278 / 0.424275 (-0.303998) \| 0.015468 / 0.007607 (0.007861) \| 0.820509 / 0.226044 (0.594464) \| 8.263136 / 2.268929 (5.994208) \| 3.780214 / 55.444624 (-51.664410) \| 3.108482 / 6.876477 (-3.767995) \| 3.101544 / 2.142072 (0.959471) \| 1.165539 / 4.805227 (-3.639688) \| 0.229215 / 6.500664 (-6.271449) \| 0.079862 / 0.075469 (0.004393) \|\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) \| 1.775071 / 1.841788 (-0.066717) \| 19.327621 / 8.074308 (11.253313) \| 23.057537 / 10.191392 (12.866145) \| 0.250649 / 0.680424 (-0.429775) \| 0.029767 / 0.534201 (-0.504434) \| 0.554774 / 0.579283 (-0.024509) \| 0.651919 / 0.434364 (0.217555) \| 0.651641 / 0.540337 (0.111304) \| 0.762386 / 1.386936 (-0.624550) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fdc3ce7060366f480621e8640903c9ab476164e7 \"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) \| 0.005997 / 0.011353 (-0.005356) \| 0.003892 / 0.011008 (-0.007116) \| 0.098020 / 0.038508 (0.059512) \| 0.042584 / 0.023109 (0.019475) \| 0.317909 / 0.275898 (0.042011) \| 0.395042 / 0.323480 (0.071563) \| 0.005358 / 0.007986 (-0.002628) \| 0.003266 / 0.004328 (-0.001062) \| 0.076698 / 0.004250 (0.072447) \| 0.062331 / 0.037052 (0.025279) \| 0.334900 / 0.258489 (0.076411) \| 0.379355 / 0.293841 (0.085514) \| 0.030815 / 0.128546 (-0.097731) \| 0.008596 / 0.075646 (-0.067050) \| 0.327739 / 0.419271 (-0.091533) \| 0.054061 / 0.043533 (0.010528) \| 0.311044 / 0.255139 (0.055905) \| 0.336705 / 0.283200 (0.053506) \| 0.022785 / 0.141683 (-0.118898) \| 1.516793 / 1.452155 (0.064639) \| 1.590435 / 1.492716 (0.097719) \|\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) \| 0.289157 / 0.018006 (0.271151) \| 0.531074 / 0.000490 (0.530585) \| 0.004672 / 0.000200 (0.004472) \| 0.000095 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026173 / 0.037411 (-0.011238) \| 0.105723 / 0.014526 (0.091197) \| 0.118010 / 0.176557 (-0.058547) \| 0.178062 / 0.737135 (-0.559073) \| 0.120059 / 0.296338 (-0.176279) \|\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) \| 0.410870 / 0.215209 (0.195661) \| 4.042183 / 2.077655 (1.964528) \| 1.830059 / 1.504120 (0.325939) \| 1.638996 / 1.541195 (0.097802) \| 1.701368 / 1.468490 (0.232878) \| 0.529915 / 4.584777 (-4.054861) \| 3.693308 / 3.745712 (-0.052404) \| 1.827875 / 5.269862 (-3.441986) \| 1.063237 / 4.565676 (-3.502440) \| 0.065368 / 0.424275 (-0.358907) \| 0.010986 / 0.007607 (0.003379) \| 0.509399 / 0.226044 (0.283354) \| 5.092739 / 2.268929 (2.823810) \| 2.293490 / 55.444624 (-53.151135) \| 1.958742 / 6.876477 (-4.917735) \| 2.024985 / 2.142072 (-0.117088) \| 0.646978 / 4.805227 (-4.158249) \| 0.138616 / 6.500664 (-6.362048) \| 0.062101 / 0.075469 (-0.013368) \|\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) \| 1.202016 / 1.841788 (-0.639772) \| 14.493204 / 8.074308 (6.418896) \| 12.992160 / 10.191392 (2.800768) \| 0.188922 / 0.680424 (-0.491502) \| 0.017594 / 0.534201 (-0.516606) \| 0.399917 / 0.579283 (-0.179367) \| 0.429760 / 0.434364 (-0.004604) \| 0.497906 / 0.540337 (-0.042431) \| 0.608745 / 1.386936 (-0.778191) \|\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) \| 0.006164 / 0.011353 (-0.005189) \| 0.003980 / 0.011008 (-0.007028) \| 0.074676 / 0.038508 (0.036168) \| 0.041337 / 0.023109 (0.018228) \| 0.400981 / 0.275898 (0.125083) \| 0.448791 / 0.323480 (0.125312) \| 0.004063 / 0.007986 (-0.003923) \| 0.004443 / 0.004328 (0.000114) \| 0.075011 / 0.004250 (0.070760) \| 0.056494 / 0.037052 (0.019441) \| 0.402054 / 0.258489 (0.143565) \| 0.446122 / 0.293841 (0.152281) \| 0.031752 / 0.128546 (-0.096794) \| 0.008835 / 0.075646 (-0.066811) \| 0.081226 / 0.419271 (-0.338046) \| 0.051501 / 0.043533 (0.007969) \| 0.383674 / 0.255139 (0.128535) \| 0.405524 / 0.283200 (0.122325) \| 0.025929 / 0.141683 (-0.115754) \| 1.492985 / 1.452155 (0.040830) \| 1.541601 / 1.492716 (0.048885) \|\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) \| 0.305149 / 0.018006 (0.287142) \| 0.497259 / 0.000490 (0.496770) \| 0.000420 / 0.000200 (0.000220) \| 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) \| 0.027933 / 0.037411 (-0.009479) \| 0.111900 / 0.014526 (0.097374) \| 0.124879 / 0.176557 (-0.051678) \| 0.178952 / 0.737135 (-0.558184) \| 0.127698 / 0.296338 (-0.168640) \|\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) \| 0.448525 / 0.215209 (0.233316) \| 4.486791 / 2.077655 (2.409137) \| 2.256687 / 1.504120 (0.752567) \| 2.061078 / 1.541195 (0.519884) \| 2.078924 / 1.468490 (0.610434) \| 0.534412 / 4.584777 (-4.050365) \| 3.721098 / 3.745712 (-0.024614) \| 1.818735 / 5.269862 (-3.451127) \| 1.104198 / 4.565676 (-3.461479) \| 0.066277 / 0.424275 (-0.357998) \| 0.011441 / 0.007607 (0.003834) \| 0.550140 / 0.226044 (0.324095) \| 5.498079 / 2.268929 (3.229150) \| 2.717398 / 55.444624 (-52.727227) \| 2.410194 / 6.876477 (-4.466283) \| 2.405304 / 2.142072 (0.263231) \| 0.665432 / 4.805227 (-4.139796) \| 0.141488 / 6.500664 (-6.359177) \| 0.064051 / 0.075469 (-0.011419) \|\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) \| 1.272334 / 1.841788 (-0.569454) \| 14.901608 / 8.074308 (6.827300) \| 14.287857 / 10.191392 (4.096465) \| 0.165337 / 0.680424 (-0.515086) \| 0.017402 / 0.534201 (-0.516799) \| 0.398120 / 0.579283 (-0.181163) \| 0.416539 / 0.434364 (-0.017825) \| 0.463890 / 0.540337 (-0.076447) \| 0.567909 / 1.386936 (-0.819027) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#504ec0f2e00ee38e0993ed1e4f1e10f1eefaea0d \"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) \| 0.009434 / 0.011353 (-0.001919) \| 0.005567 / 0.011008 (-0.005441) \| 0.122652 / 0.038508 (0.084144) \| 0.050177 / 0.023109 (0.027067) \| 0.384292 / 0.275898 (0.108394) \| 0.446608 / 0.323480 (0.123128) \| 0.006502 / 0.007986 (-0.001484) \| 0.004523 / 0.004328 (0.000194) \| 0.100581 / 0.004250 (0.096331) \| 0.073615 / 0.037052 (0.036563) \| 0.420179 / 0.258489 (0.161690) \| 0.474631 / 0.293841 (0.180790) \| 0.047942 / 0.128546 (-0.080604) \| 0.013864 / 0.075646 (-0.061783) \| 0.419384 / 0.419271 (0.000112) \| 0.088317 / 0.043533 (0.044784) \| 0.379620 / 0.255139 (0.124481) \| 0.412639 / 0.283200 (0.129440) \| 0.048947 / 0.141683 (-0.092736) \| 1.823498 / 1.452155 (0.371343) \| 1.966629 / 1.492716 (0.473913) \|\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) \| 0.300669 / 0.018006 (0.282663) \| 0.593499 / 0.000490 (0.593009) \| 0.007247 / 0.000200 (0.007047) \| 0.000114 / 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) \| 0.030556 / 0.037411 (-0.006856) \| 0.119252 / 0.014526 (0.104726) \| 0.131403 / 0.176557 (-0.045153) \| 0.201845 / 0.737135 (-0.535291) \| 0.139350 / 0.296338 (-0.156989) \|\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) \| 0.652400 / 0.215209 (0.437191) \| 6.536540 / 2.077655 (4.458886) \| 2.644565 / 1.504120 (1.140445) \| 2.245181 / 1.541195 (0.703986) \| 2.316030 / 1.468490 (0.847540) \| 0.922535 / 4.584777 (-3.662242) \| 5.469065 / 3.745712 (1.723353) \| 2.800489 / 5.269862 (-2.469373) \| 1.749042 / 4.565676 (-2.816635) \| 0.108444 / 0.424275 (-0.315831) \| 0.015651 / 0.007607 (0.008044) \| 0.846085 / 0.226044 (0.620041) \| 8.018460 / 2.268929 (5.749531) \| 3.338710 / 55.444624 (-52.105914) \| 2.675998 / 6.876477 (-4.200479) \| 2.918550 / 2.142072 (0.776478) \| 1.135145 / 4.805227 (-3.670082) \| 0.215165 / 6.500664 (-6.285499) \| 0.082066 / 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) \| 1.561661 / 1.841788 (-0.280127) \| 18.519035 / 8.074308 (10.444727) \| 19.046300 / 10.191392 (8.854908) \| 0.236890 / 0.680424 (-0.443534) \| 0.027681 / 0.534201 (-0.506520) \| 0.511998 / 0.579283 (-0.067285) \| 0.591627 / 0.434364 (0.157264) \| 0.562021 / 0.540337 (0.021683) \| 0.679354 / 1.386936 (-0.707582) \|\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) \| 0.009643 / 0.011353 (-0.001710) \| 0.005768 / 0.011008 (-0.005241) \| 0.104430 / 0.038508 (0.065922) \| 0.050044 / 0.023109 (0.026935) \| 0.464117 / 0.275898 (0.188219) \| 0.518439 / 0.323480 (0.194959) \| 0.006935 / 0.007986 (-0.001051) \| 0.004316 / 0.004328 (-0.000013) \| 0.094330 / 0.004250 (0.090080) \| 0.071451 / 0.037052 (0.034399) \| 0.492248 / 0.258489 (0.233759) \| 0.555740 / 0.293841 (0.261899) \| 0.047836 / 0.128546 (-0.080711) \| 0.014788 / 0.075646 (-0.060859) \| 0.107590 / 0.419271 (-0.311682) \| 0.064396 / 0.043533 (0.020863) \| 0.451529 / 0.255139 (0.196390) \| 0.475025 / 0.283200 (0.191826) \| 0.040006 / 0.141683 (-0.101677) \| 1.797107 / 1.452155 (0.344953) \| 1.879261 / 1.492716 (0.386545) \|\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) \| 0.298458 / 0.018006 (0.280451) \| 0.613022 / 0.000490 (0.612532) \| 0.003582 / 0.000200 (0.003382) \| 0.000106 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030179 / 0.037411 (-0.007232) \| 0.123286 / 0.014526 (0.108760) \| 0.132070 / 0.176557 (-0.044486) \| 0.190883 / 0.737135 (-0.546252) \| 0.138526 / 0.296338 (-0.157812) \|\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) \| 0.666908 / 0.215209 (0.451699) \| 6.489035 / 2.077655 (4.411381) \| 2.897027 / 1.504120 (1.392907) \| 2.565150 / 1.541195 (1.023956) \| 2.504827 / 1.468490 (1.036336) \| 0.916112 / 4.584777 (-3.668665) \| 5.651751 / 3.745712 (1.906039) \| 2.743382 / 5.269862 (-2.526479) \| 1.773338 / 4.565676 (-2.792338) \| 0.128764 / 0.424275 (-0.295511) \| 0.013140 / 0.007607 (0.005533) \| 0.803281 / 0.226044 (0.577236) \| 8.258874 / 2.268929 (5.989945) \| 3.633260 / 55.444624 (-51.811364) \| 2.878827 / 6.876477 (-3.997649) \| 2.977178 / 2.142072 (0.835106) \| 1.130467 / 4.805227 (-3.674760) \| 0.226381 / 6.500664 (-6.274283) \| 0.081550 / 0.075469 (0.006081) \|\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) \| 1.842927 / 1.841788 (0.001139) \| 18.411520 / 8.074308 (10.337212) \| 21.118228 / 10.191392 (10.926836) \| 0.231526 / 0.680424 (-0.448898) \| 0.029300 / 0.534201 (-0.504901) \| 0.527450 / 0.579283 (-0.051834) \| 0.618873 / 0.434364 (0.184509) \| 0.593314 / 0.540337 (0.052976) \| 0.734430 / 1.386936 (-0.652506) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d2b8854c265b4dc202e480427890f472b34ea15 \"CML watermark\")\n" ]	1,687,875,308,000	1,687,960,562,000	1,687,959,993,000	CONTRIBUTOR	null	Allow returning Pandas DataFrames in `map` transforms. (Plus, raise an error in the non-batched mode if a returned PyArrow table/Pandas DataFrame has more than one row)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5995/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5995/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5995", "html_url": "https://github.com/huggingface/datasets/pull/5995", "diff_url": "https://github.com/huggingface/datasets/pull/5995.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5995.patch", "merged_at": "2023-06-28T13:46:33" }	true
https://api.github.com/repos/huggingface/datasets/issues/5994	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5994/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5994/comments	https://api.github.com/repos/huggingface/datasets/issues/5994/events	https://github.com/huggingface/datasets/pull/5994	1,776,829,004	PR_kwDODunzps5UB1cA	5,994	Fix select_columns columns order	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "<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) \| 0.005969 / 0.011353 (-0.005384) \| 0.003687 / 0.011008 (-0.007321) \| 0.100843 / 0.038508 (0.062335) \| 0.036912 / 0.023109 (0.013803) \| 0.312389 / 0.275898 (0.036491) \| 0.370335 / 0.323480 (0.046855) \| 0.003434 / 0.007986 (-0.004552) \| 0.003710 / 0.004328 (-0.000619) \| 0.076899 / 0.004250 (0.072648) \| 0.053647 / 0.037052 (0.016594) \| 0.324825 / 0.258489 (0.066336) \| 0.367711 / 0.293841 (0.073870) \| 0.028079 / 0.128546 (-0.100467) \| 0.008326 / 0.075646 (-0.067320) \| 0.312342 / 0.419271 (-0.106930) \| 0.047423 / 0.043533 (0.003890) \| 0.321063 / 0.255139 (0.065924) \| 0.336508 / 0.283200 (0.053308) \| 0.019973 / 0.141683 (-0.121710) \| 1.529334 / 1.452155 (0.077179) \| 1.573746 / 1.492716 (0.081030) \|\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) \| 0.210849 / 0.018006 (0.192843) \| 0.418798 / 0.000490 (0.418309) \| 0.007347 / 0.000200 (0.007147) \| 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) \| 0.022718 / 0.037411 (-0.014694) \| 0.098400 / 0.014526 (0.083874) \| 0.106590 / 0.176557 (-0.069967) \| 0.168460 / 0.737135 (-0.568675) \| 0.108401 / 0.296338 (-0.187938) \|\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) \| 0.443066 / 0.215209 (0.227857) \| 4.416658 / 2.077655 (2.339003) \| 2.088844 / 1.504120 (0.584724) \| 1.879564 / 1.541195 (0.338369) \| 1.933815 / 1.468490 (0.465325) \| 0.565085 / 4.584777 (-4.019692) \| 3.412440 / 3.745712 (-0.333273) \| 1.754686 / 5.269862 (-3.515175) \| 1.024576 / 4.565676 (-3.541100) \| 0.067909 / 0.424275 (-0.356366) \| 0.011054 / 0.007607 (0.003447) \| 0.534748 / 0.226044 (0.308703) \| 5.351457 / 2.268929 (3.082529) \| 2.517368 / 55.444624 (-52.927256) \| 2.182762 / 6.876477 (-4.693715) \| 2.238205 / 2.142072 (0.096133) \| 0.672962 / 4.805227 (-4.132265) \| 0.136098 / 6.500664 (-6.364566) \| 0.066534 / 0.075469 (-0.008935) \|\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) \| 1.281241 / 1.841788 (-0.560547) \| 13.872881 / 8.074308 (5.798573) \| 13.161023 / 10.191392 (2.969631) \| 0.130011 / 0.680424 (-0.550412) \| 0.016759 / 0.534201 (-0.517442) \| 0.359802 / 0.579283 (-0.219481) \| 0.392577 / 0.434364 (-0.041787) \| 0.427742 / 0.540337 (-0.112595) \| 0.522241 / 1.386936 (-0.864695) \|\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) \| 0.005985 / 0.011353 (-0.005368) \| 0.003705 / 0.011008 (-0.007304) \| 0.077699 / 0.038508 (0.039191) \| 0.035686 / 0.023109 (0.012577) \| 0.420356 / 0.275898 (0.144458) \| 0.476753 / 0.323480 (0.153273) \| 0.003510 / 0.007986 (-0.004475) \| 0.002807 / 0.004328 (-0.001521) \| 0.077151 / 0.004250 (0.072901) \| 0.046420 / 0.037052 (0.009368) \| 0.391781 / 0.258489 (0.133292) \| 0.461128 / 0.293841 (0.167287) \| 0.027847 / 0.128546 (-0.100699) \| 0.008322 / 0.075646 (-0.067324) \| 0.082768 / 0.419271 (-0.336503) \| 0.042629 / 0.043533 (-0.000904) \| 0.405745 / 0.255139 (0.150606) \| 0.430797 / 0.283200 (0.147598) \| 0.019832 / 0.141683 (-0.121851) \| 1.556208 / 1.452155 (0.104054) \| 1.612166 / 1.492716 (0.119450) \|\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) \| 0.230633 / 0.018006 (0.212626) \| 0.401667 / 0.000490 (0.401178) \| 0.000776 / 0.000200 (0.000576) \| 0.000069 / 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) \| 0.024959 / 0.037411 (-0.012452) \| 0.100560 / 0.014526 (0.086034) \| 0.109175 / 0.176557 (-0.067382) \| 0.159919 / 0.737135 (-0.577217) \| 0.112810 / 0.296338 (-0.183528) \|\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) \| 0.460601 / 0.215209 (0.245392) \| 4.620039 / 2.077655 (2.542385) \| 2.257900 / 1.504120 (0.753780) \| 2.039192 / 1.541195 (0.497997) \| 2.064451 / 1.468490 (0.595961) \| 0.557887 / 4.584777 (-4.026890) \| 3.356100 / 3.745712 (-0.389612) \| 1.703578 / 5.269862 (-3.566284) \| 1.024984 / 4.565676 (-3.540693) \| 0.067602 / 0.424275 (-0.356673) \| 0.011450 / 0.007607 (0.003842) \| 0.563230 / 0.226044 (0.337186) \| 5.632150 / 2.268929 (3.363221) \| 2.698701 / 55.444624 (-52.745924) \| 2.363218 / 6.876477 (-4.513259) \| 2.363997 / 2.142072 (0.221925) \| 0.671260 / 4.805227 (-4.133967) \| 0.136166 / 6.500664 (-6.364499) \| 0.067094 / 0.075469 (-0.008375) \|\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) \| 1.303030 / 1.841788 (-0.538757) \| 14.137277 / 8.074308 (6.062969) \| 13.937631 / 10.191392 (3.746239) \| 0.162626 / 0.680424 (-0.517798) \| 0.016687 / 0.534201 (-0.517514) \| 0.363657 / 0.579283 (-0.215626) \| 0.392021 / 0.434364 (-0.042343) \| 0.427275 / 0.540337 (-0.113062) \| 0.512192 / 1.386936 (-0.874744) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#42603528d9bd8c3ab287ed0eadc7fa3d1ef4cfd8 \"CML watermark\")\n", "_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) \| 0.005974 / 0.011353 (-0.005378) \| 0.003947 / 0.011008 (-0.007061) \| 0.098604 / 0.038508 (0.060096) \| 0.036947 / 0.023109 (0.013838) \| 0.311844 / 0.275898 (0.035946) \| 0.375243 / 0.323480 (0.051763) \| 0.003453 / 0.007986 (-0.004533) \| 0.003834 / 0.004328 (-0.000495) \| 0.077943 / 0.004250 (0.073692) \| 0.052956 / 0.037052 (0.015904) \| 0.320812 / 0.258489 (0.062323) \| 0.373963 / 0.293841 (0.080122) \| 0.028382 / 0.128546 (-0.100164) \| 0.008525 / 0.075646 (-0.067121) \| 0.311306 / 0.419271 (-0.107965) \| 0.047029 / 0.043533 (0.003496) \| 0.309933 / 0.255139 (0.054794) \| 0.335114 / 0.283200 (0.051915) \| 0.019629 / 0.141683 (-0.122054) \| 1.569771 / 1.452155 (0.117617) \| 1.585899 / 1.492716 (0.093182) \|\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) \| 0.216565 / 0.018006 (0.198559) \| 0.426717 / 0.000490 (0.426228) \| 0.003609 / 0.000200 (0.003409) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023079 / 0.037411 (-0.014332) \| 0.096954 / 0.014526 (0.082428) \| 0.105398 / 0.176557 (-0.071158) \| 0.165433 / 0.737135 (-0.571703) \| 0.109703 / 0.296338 (-0.186636) \|\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) \| 0.456227 / 0.215209 (0.241018) \| 4.529857 / 2.077655 (2.452202) \| 2.214054 / 1.504120 (0.709934) \| 2.029716 / 1.541195 (0.488521) \| 2.081175 / 1.468490 (0.612685) \| 0.563642 / 4.584777 (-4.021135) \| 3.355393 / 3.745712 (-0.390320) \| 1.765938 / 5.269862 (-3.503924) \| 1.039062 / 4.565676 (-3.526615) \| 0.067952 / 0.424275 (-0.356323) \| 0.011044 / 0.007607 (0.003437) \| 0.556935 / 0.226044 (0.330890) \| 5.588167 / 2.268929 (3.319239) \| 2.667217 / 55.444624 (-52.777407) \| 2.337383 / 6.876477 (-4.539094) \| 2.429590 / 2.142072 (0.287517) \| 0.676972 / 4.805227 (-4.128256) \| 0.135782 / 6.500664 (-6.364882) \| 0.066323 / 0.075469 (-0.009146) \|\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) \| 1.237358 / 1.841788 (-0.604429) \| 13.910492 / 8.074308 (5.836184) \| 13.227275 / 10.191392 (3.035883) \| 0.146857 / 0.680424 (-0.533567) \| 0.016991 / 0.534201 (-0.517210) \| 0.363637 / 0.579283 (-0.215646) \| 0.392462 / 0.434364 (-0.041902) \| 0.450009 / 0.540337 (-0.090329) \| 0.536077 / 1.386936 (-0.850859) \|\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) \| 0.006067 / 0.011353 (-0.005286) \| 0.003851 / 0.011008 (-0.007158) \| 0.078462 / 0.038508 (0.039954) \| 0.036221 / 0.023109 (0.013112) \| 0.389195 / 0.275898 (0.113297) \| 0.428710 / 0.323480 (0.105230) \| 0.004645 / 0.007986 (-0.003341) \| 0.002973 / 0.004328 (-0.001355) \| 0.078299 / 0.004250 (0.074048) \| 0.047076 / 0.037052 (0.010024) \| 0.375673 / 0.258489 (0.117184) \| 0.432352 / 0.293841 (0.138511) \| 0.028212 / 0.128546 (-0.100334) \| 0.008475 / 0.075646 (-0.067172) \| 0.083902 / 0.419271 (-0.335369) \| 0.046699 / 0.043533 (0.003166) \| 0.364502 / 0.255139 (0.109363) \| 0.389792 / 0.283200 (0.106592) \| 0.025266 / 0.141683 (-0.116417) \| 1.517458 / 1.452155 (0.065303) \| 1.543634 / 1.492716 (0.050918) \|\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) \| 0.236479 / 0.018006 (0.218472) \| 0.411528 / 0.000490 (0.411038) \| 0.005213 / 0.000200 (0.005013) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025764 / 0.037411 (-0.011647) \| 0.103174 / 0.014526 (0.088648) \| 0.110609 / 0.176557 (-0.065948) \| 0.164630 / 0.737135 (-0.572506) \| 0.114863 / 0.296338 (-0.181475) \|\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) \| 0.457155 / 0.215209 (0.241946) \| 4.550675 / 2.077655 (2.473021) \| 2.350473 / 1.504120 (0.846353) \| 2.204919 / 1.541195 (0.663724) \| 2.076724 / 1.468490 (0.608234) \| 0.563107 / 4.584777 (-4.021670) \| 3.390669 / 3.745712 (-0.355043) \| 1.741111 / 5.269862 (-3.528751) \| 1.033268 / 4.565676 (-3.532408) \| 0.068400 / 0.424275 (-0.355875) \| 0.011607 / 0.007607 (0.004000) \| 0.561944 / 0.226044 (0.335900) \| 5.620224 / 2.268929 (3.351296) \| 2.705241 / 55.444624 (-52.739384) \| 2.344520 / 6.876477 (-4.531957) \| 2.386119 / 2.142072 (0.244046) \| 0.681583 / 4.805227 (-4.123644) \| 0.137272 / 6.500664 (-6.363392) \| 0.069217 / 0.075469 (-0.006252) \|\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) \| 1.322690 / 1.841788 (-0.519098) \| 14.464953 / 8.074308 (6.390645) \| 14.269350 / 10.191392 (4.077958) \| 0.158879 / 0.680424 (-0.521545) \| 0.016722 / 0.534201 (-0.517479) \| 0.360299 / 0.579283 (-0.218984) \| 0.391609 / 0.434364 (-0.042755) \| 0.420507 / 0.540337 (-0.119831) \| 0.512822 / 1.386936 (-0.874114) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca68191900d97b29abb3c2c4ba0502fe30d137d1 \"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) \| 0.007106 / 0.011353 (-0.004247) \| 0.005224 / 0.011008 (-0.005784) \| 0.127563 / 0.038508 (0.089055) \| 0.055067 / 0.023109 (0.031958) \| 0.418660 / 0.275898 (0.142761) \| 0.487891 / 0.323480 (0.164411) \| 0.005712 / 0.007986 (-0.002274) \| 0.004585 / 0.004328 (0.000256) \| 0.090994 / 0.004250 (0.086743) \| 0.071837 / 0.037052 (0.034784) \| 0.446957 / 0.258489 (0.188468) \| 0.475966 / 0.293841 (0.182125) \| 0.038062 / 0.128546 (-0.090484) \| 0.010056 / 0.075646 (-0.065590) \| 0.406796 / 0.419271 (-0.012475) \| 0.066542 / 0.043533 (0.023009) \| 0.413676 / 0.255139 (0.158537) \| 0.448624 / 0.283200 (0.165424) \| 0.030332 / 0.141683 (-0.111351) \| 1.895307 / 1.452155 (0.443152) \| 1.904411 / 1.492716 (0.411694) \|\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) \| 0.221246 / 0.018006 (0.203240) \| 0.461288 / 0.000490 (0.460799) \| 0.005957 / 0.000200 (0.005757) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029255 / 0.037411 (-0.008156) \| 0.131299 / 0.014526 (0.116773) \| 0.135814 / 0.176557 (-0.040742) \| 0.201342 / 0.737135 (-0.535793) \| 0.141748 / 0.296338 (-0.154591) \|\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) \| 0.463936 / 0.215209 (0.248727) \| 4.709621 / 2.077655 (2.631966) \| 2.093844 / 1.504120 (0.589724) \| 1.897963 / 1.541195 (0.356768) \| 1.927865 / 1.468490 (0.459375) \| 0.610879 / 4.584777 (-3.973898) \| 4.481370 / 3.745712 (0.735658) \| 2.112235 / 5.269862 (-3.157627) \| 1.203349 / 4.565676 (-3.362327) \| 0.074828 / 0.424275 (-0.349447) \| 0.013121 / 0.007607 (0.005514) \| 0.580894 / 0.226044 (0.354849) \| 5.801872 / 2.268929 (3.532943) \| 2.579950 / 55.444624 (-52.864674) \| 2.251569 / 6.876477 (-4.624908) \| 2.421305 / 2.142072 (0.279232) \| 0.760938 / 4.805227 (-4.044289) \| 0.169554 / 6.500664 (-6.331110) \| 0.077499 / 0.075469 (0.002030) \|\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) \| 1.410419 / 1.841788 (-0.431368) \| 17.442331 / 8.074308 (9.368023) \| 15.782183 / 10.191392 (5.590791) \| 0.180649 / 0.680424 (-0.499775) \| 0.021790 / 0.534201 (-0.512411) \| 0.511040 / 0.579283 (-0.068243) \| 0.510472 / 0.434364 (0.076108) \| 0.607141 / 0.540337 (0.066804) \| 0.724794 / 1.386936 (-0.662142) \|\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) \| 0.007280 / 0.011353 (-0.004073) \| 0.004712 / 0.011008 (-0.006296) \| 0.089225 / 0.038508 (0.050717) \| 0.053157 / 0.023109 (0.030048) \| 0.431949 / 0.275898 (0.156051) \| 0.478128 / 0.323480 (0.154648) \| 0.006181 / 0.007986 (-0.001804) \| 0.003387 / 0.004328 (-0.000941) \| 0.083741 / 0.004250 (0.079490) \| 0.071610 / 0.037052 (0.034557) \| 0.414698 / 0.258489 (0.156209) \| 0.484422 / 0.293841 (0.190581) \| 0.034988 / 0.128546 (-0.093558) \| 0.009831 / 0.075646 (-0.065816) \| 0.089644 / 0.419271 (-0.329628) \| 0.057053 / 0.043533 (0.013520) \| 0.413144 / 0.255139 (0.158005) \| 0.445464 / 0.283200 (0.162264) \| 0.026109 / 0.141683 (-0.115574) \| 1.842899 / 1.452155 (0.390745) \| 1.923774 / 1.492716 (0.431057) \|\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) \| 0.245051 / 0.018006 (0.227045) \| 0.460444 / 0.000490 (0.459954) \| 0.000444 / 0.000200 (0.000244) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034835 / 0.037411 (-0.002577) \| 0.130078 / 0.014526 (0.115553) \| 0.147012 / 0.176557 (-0.029544) \| 0.203097 / 0.737135 (-0.534038) \| 0.149636 / 0.296338 (-0.146702) \|\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) \| 0.521664 / 0.215209 (0.306455) \| 5.283865 / 2.077655 (3.206210) \| 2.456701 / 1.504120 (0.952581) \| 2.266059 / 1.541195 (0.724864) \| 2.295387 / 1.468490 (0.826897) \| 0.613200 / 4.584777 (-3.971577) \| 4.526107 / 3.745712 (0.780394) \| 2.047327 / 5.269862 (-3.222535) \| 1.261063 / 4.565676 (-3.304614) \| 0.070402 / 0.424275 (-0.353873) \| 0.014128 / 0.007607 (0.006521) \| 0.620929 / 0.226044 (0.394884) \| 6.109127 / 2.268929 (3.840198) \| 3.081406 / 55.444624 (-52.363218) \| 2.658224 / 6.876477 (-4.218253) \| 2.671974 / 2.142072 (0.529902) \| 0.744081 / 4.805227 (-4.061146) \| 0.161498 / 6.500664 (-6.339166) \| 0.075148 / 0.075469 (-0.000321) \|\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) \| 1.585640 / 1.841788 (-0.256148) \| 17.884321 / 8.074308 (9.810013) \| 15.938937 / 10.191392 (5.747545) \| 0.220818 / 0.680424 (-0.459605) \| 0.021452 / 0.534201 (-0.512749) \| 0.499747 / 0.579283 (-0.079536) \| 0.512318 / 0.434364 (0.077954) \| 0.562853 / 0.540337 (0.022515) \| 0.678512 / 1.386936 (-0.708424) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa50937d82256827aee3dbd749c7a23555e05e38 \"CML watermark\")\n" ]	1,687,869,166,000	1,687,880,447,000	1,687,879,963,000	MEMBER	null	Fix the order of the columns in dataset.features when the order changes with `dataset.select_columns()`. I also fixed the same issue for `dataset.flatten()` Close https://github.com/huggingface/datasets/issues/5993	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5994/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5994/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5994", "html_url": "https://github.com/huggingface/datasets/pull/5994", "diff_url": "https://github.com/huggingface/datasets/pull/5994.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5994.patch", "merged_at": "2023-06-27T15:32:43" }	true
https://api.github.com/repos/huggingface/datasets/issues/5993	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5993/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5993/comments	https://api.github.com/repos/huggingface/datasets/issues/5993/events	https://github.com/huggingface/datasets/issues/5993	1,776,643,555	I_kwDODunzps5p5W3j	5,993	ValueError: Table schema does not match schema used to create file	{ "login": "exs-avianello", "id": 128361578, "node_id": "U_kgDOB6akag", "avatar_url": "https://avatars.githubusercontent.com/u/128361578?v=4", "gravatar_id": "", "url": "https://api.github.com/users/exs-avianello", "html_url": "https://github.com/exs-avianello", "followers_url": "https://api.github.com/users/exs-avianello/followers", "following_url": "https://api.github.com/users/exs-avianello/following{/other_user}", "gists_url": "https://api.github.com/users/exs-avianello/gists{/gist_id}", "starred_url": "https://api.github.com/users/exs-avianello/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/exs-avianello/subscriptions", "organizations_url": "https://api.github.com/users/exs-avianello/orgs", "repos_url": "https://api.github.com/users/exs-avianello/repos", "events_url": "https://api.github.com/users/exs-avianello/events{/privacy}", "received_events_url": "https://api.github.com/users/exs-avianello/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]	[ "We'll do a new release of `datasets` soon to make the fix available :)\r\n\r\nIn the meantime you can use `datasets` from source (main)", "Thank you very much @lhoestq ! 🚀 " ]	1,687,863,247,000	1,687,880,202,000	1,687,879,964,000	NONE	null	### Describe the bug Saving a dataset as parquet fails with a `ValueError: Table schema does not match schema used to create file` if the dataset was obtained out of a `.select_columns()` call with columns selected out of order. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_parquet("demo.parquet") ``` ```shell >>> ValueError: Table schema does not match schema used to create file: table: x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 vs. file: x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ``` --- I think this is because after the `.select_columns()` call with out of order columns, the output dataset features' schema ends up being out of sync with the schema of the arrow table backing it. ```python ds.features.arrow_schema >>> x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ds.data.schema >>> x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 ``` So when we call `.to_parquet()`, the call behind the scenes to `datasets.io.parquet.ParquetDatasetWriter(...).write()` which initialises the backend `pyarrow.parquet.ParquetWriter` with `schema = self.dataset.features.arrow_schema` triggers `pyarrow` on write when [it checks](https://github.com/apache/arrow/blob/11b140a734a516e436adaddaeb35d23f30dcce44/python/pyarrow/parquet/core.py#L1086-L1090) that the `ParquetWriter` schema matches the schema of the table being written 🙌 https://github.com/huggingface/datasets/blob/6ed837325cb539a5deb99129e5ad181d0269e050/src/datasets/io/parquet.py#L139-L141 ### Expected behavior The dataset gets successfully saved as parquet. *In the same way as it does if saving it as csv: ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_csv("demo.csv") ``` ### Environment info `python==3.11` `datasets==2.13.1`	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5993/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5993/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5992	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5992/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5992/comments	https://api.github.com/repos/huggingface/datasets/issues/5992/events	https://github.com/huggingface/datasets/pull/5992	1,776,460,964	PR_kwDODunzps5UAk3C	5,992	speedup	{ "login": "qgallouedec", "id": 45557362, "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "gravatar_id": "", "url": "https://api.github.com/users/qgallouedec", "html_url": "https://github.com/qgallouedec", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "repos_url": "https://api.github.com/users/qgallouedec/repos", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5992). All of your documentation changes will be reflected on that endpoint." ]	1,687,857,478,000	1,687,857,787,000	1,687,857,484,000	CONTRIBUTOR	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5992/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5992/timeline	null	null	1	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5992", "html_url": "https://github.com/huggingface/datasets/pull/5992", "diff_url": "https://github.com/huggingface/datasets/pull/5992.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5992.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/5991	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5991/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5991/comments	https://api.github.com/repos/huggingface/datasets/issues/5991/events	https://github.com/huggingface/datasets/issues/5991	1,774,456,518	I_kwDODunzps5pxA7G	5,991	`map` with any joblib backend	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	[]	1,687,775,622,000	1,687,775,622,000	null	MEMBER	null	We recently enabled the (experimental) parallel backend switch for data download and extraction but not for `map` yet. Right now we're using our `iflatmap_unordered` implementation for multiprocessing that uses a shared Queue to gather progress updates from the subprocesses and show a progress bar in the main process. If a Queue implementation that would work on any joblib backend by leveraging the filesystem that is shared among workers, we can have `iflatmap_unordered` for joblib and therefore a `map` with any joblib backend with a progress bar ! Note that the Queue doesn't need to be that optimized though since we can choose a small frequency for progress updates (like 1 update per second).	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5991/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5991/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5989	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5989/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5989/comments	https://api.github.com/repos/huggingface/datasets/issues/5989/events	https://github.com/huggingface/datasets/issues/5989	1,774,134,091	I_kwDODunzps5pvyNL	5,989	Set a rule on the config and split names	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "in this case we need to decide what to do with the existing datasets with white space characters (there shouldn't be a lot of them I think)", "I imagine that we should stop supporting them, and help the user fix them?" ]	1,687,764,854,000	1,687,785,178,000	null	CONTRIBUTOR	null	> should we actually allow characters like spaces? maybe it's better to add validation for whitespace symbols and directly in datasets and raise https://github.com/huggingface/datasets-server/issues/853	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5989/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5989/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5988	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5988/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5988/comments	https://api.github.com/repos/huggingface/datasets/issues/5988/events	https://github.com/huggingface/datasets/issues/5988	1,773,257,828	I_kwDODunzps5pscRk	5,988	ConnectionError: Couldn't reach dataset_infos.json	{ "login": "yulingao", "id": 20674868, "node_id": "MDQ6VXNlcjIwNjc0ODY4", "avatar_url": "https://avatars.githubusercontent.com/u/20674868?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yulingao", "html_url": "https://github.com/yulingao", "followers_url": "https://api.github.com/users/yulingao/followers", "following_url": "https://api.github.com/users/yulingao/following{/other_user}", "gists_url": "https://api.github.com/users/yulingao/gists{/gist_id}", "starred_url": "https://api.github.com/users/yulingao/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yulingao/subscriptions", "organizations_url": "https://api.github.com/users/yulingao/orgs", "repos_url": "https://api.github.com/users/yulingao/repos", "events_url": "https://api.github.com/users/yulingao/events{/privacy}", "received_events_url": "https://api.github.com/users/yulingao/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Unfortunately, I can't reproduce the error. What does the following code return for you?\r\n```python\r\nimport requests\r\nfrom huggingface_hub import hf_hub_url\r\nr = requests.get(hf_hub_url(\"codeparrot/codeparrot-clean-train\", \"dataset_infos.json\", repo_type=\"dataset\"))\r\n```\r\n\r\nAlso, can you provide more info about your network (region, proxies, etc.)?" ]	1,687,696,771,000	1,687,869,514,000	null	NONE	null	### Describe the bug I'm trying to load codeparrot/codeparrot-clean-train, but get the following error: ConnectionError: Couldn't reach https://huggingface.co/datasets/codeparrot/codeparrot-clean-train/resolve/main/dataset_infos.json (ConnectionError(ProtocolError('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer')))) ### Steps to reproduce the bug train_data = load_dataset('codeparrot/codeparrot-clean-train', split='train') ### Expected behavior download the dataset ### Environment info centos7	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5988/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5988/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5987	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5987/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5987/comments	https://api.github.com/repos/huggingface/datasets/issues/5987/events	https://github.com/huggingface/datasets/issues/5987	1,773,047,909	I_kwDODunzps5prpBl	5,987	Why max_shard_size is not supported in load_dataset and passed to download_and_prepare	{ "login": "npuichigo", "id": 11533479, "node_id": "MDQ6VXNlcjExNTMzNDc5", "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "gravatar_id": "", "url": "https://api.github.com/users/npuichigo", "html_url": "https://github.com/npuichigo", "followers_url": "https://api.github.com/users/npuichigo/followers", "following_url": "https://api.github.com/users/npuichigo/following{/other_user}", "gists_url": "https://api.github.com/users/npuichigo/gists{/gist_id}", "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "repos_url": "https://api.github.com/users/npuichigo/repos", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Can you explain your use case for `max_shard_size`? \r\n\r\nOn some systems, there is a limit to the size of a memory-mapped file, so we could consider exposing this parameter in `load_dataset`.", "In my use case, users may choose a proper size to balance the cost and benefit of using large shard size. (On azure blob or hdfs which may automatically download the shard from background)", "But `load_dataset` doesn't support caching (and reading) Arrow datasets from remote storage. \r\n\r\n`load_datset_builder` + `download_and_prepare` is not equal to `load_dataset`. The latter has one more step, `builder.as_dataset`, that memory-maps Arrow files, which only works for local files.", "Thanks. So if I want to use `IterableDataset` and control the size of single arrow file, how should I organize the data loader? Maybe `load_dataset_build` + `download_and_prepare` + `builder.as_dataset` + `dataset.to_iterable_dataset`?", "Yes, this should work.\r\n\r\nI think we can expose `max_shard_size` in `load_dataset`, so feel free to open a PR." ]	1,687,666,753,000	1,688,054,768,000	1,688,054,768,000	NONE	null	### Describe the bug https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809 What I can to is break the `load_dataset` and use `load_datset_builder` + `download_and_prepare` instead. ### Steps to reproduce the bug https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809 ### Expected behavior Users can define the max shard size. ### Environment info datasets==2.13.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5987/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5987/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5986	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5986/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5986/comments	https://api.github.com/repos/huggingface/datasets/issues/5986/events	https://github.com/huggingface/datasets/pull/5986	1,772,233,111	PR_kwDODunzps5TygOZ	5,986	Make IterableDataset.from_spark more efficient	{ "login": "mathewjacob1002", "id": 134338709, "node_id": "U_kgDOCAHYlQ", "avatar_url": "https://avatars.githubusercontent.com/u/134338709?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mathewjacob1002", "html_url": "https://github.com/mathewjacob1002", "followers_url": "https://api.github.com/users/mathewjacob1002/followers", "following_url": "https://api.github.com/users/mathewjacob1002/following{/other_user}", "gists_url": "https://api.github.com/users/mathewjacob1002/gists{/gist_id}", "starred_url": "https://api.github.com/users/mathewjacob1002/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mathewjacob1002/subscriptions", "organizations_url": "https://api.github.com/users/mathewjacob1002/orgs", "repos_url": "https://api.github.com/users/mathewjacob1002/repos", "events_url": "https://api.github.com/users/mathewjacob1002/events{/privacy}", "received_events_url": "https://api.github.com/users/mathewjacob1002/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "@lhoestq would you be able to review this please and also approve the workflow?", "Sounds good to me :) feel free to run `make style` to apply code formatting", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5986). All of your documentation changes will be reflected on that endpoint." ]	1,687,558,700,000	1,688,577,663,000	null	NONE	null	Moved the code from using collect() to using toLocalIterator, which allows for prefetching partitions that will be selected next, thus allowing for better performance when iterating.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5986/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5986/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5986", "html_url": "https://github.com/huggingface/datasets/pull/5986", "diff_url": "https://github.com/huggingface/datasets/pull/5986.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5986.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/5985	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5985/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5985/comments	https://api.github.com/repos/huggingface/datasets/issues/5985/events	https://github.com/huggingface/datasets/issues/5985	1,771,588,158	I_kwDODunzps5pmEo-	5,985	Cannot reuse tokenizer object for dataset map	{ "login": "vikigenius", "id": 12724810, "node_id": "MDQ6VXNlcjEyNzI0ODEw", "avatar_url": "https://avatars.githubusercontent.com/u/12724810?v=4", "gravatar_id": "", "url": "https://api.github.com/users/vikigenius", "html_url": "https://github.com/vikigenius", "followers_url": "https://api.github.com/users/vikigenius/followers", "following_url": "https://api.github.com/users/vikigenius/following{/other_user}", "gists_url": "https://api.github.com/users/vikigenius/gists{/gist_id}", "starred_url": "https://api.github.com/users/vikigenius/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vikigenius/subscriptions", "organizations_url": "https://api.github.com/users/vikigenius/orgs", "repos_url": "https://api.github.com/users/vikigenius/repos", "events_url": "https://api.github.com/users/vikigenius/events{/privacy}", "received_events_url": "https://api.github.com/users/vikigenius/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892865, "node_id": "MDU6TGFiZWwxOTM1ODkyODY1", "url": "https://api.github.com/repos/huggingface/datasets/labels/duplicate", "name": "duplicate", "color": "cfd3d7", "default": true, "description": "This issue or pull request already exists" } ]	open	false	null	[]	[ "This is a known issue: https://github.com/huggingface/datasets/issues/3847.\r\n\r\nFixing this requires significant work - rewriting the `tokenizers` lib to make them immutable.\r\n\r\nThe current solution is to pass `cache_file_name` to `map` to use that file for caching or calling a tokenizer before `map` (with the same set of parameters as the ones in the map transform)" ]	1,687,531,531,000	1,687,782,890,000	null	NONE	null	### Describe the bug Related to https://github.com/huggingface/transformers/issues/24441. Not sure if this is a tokenizer issue or caching issue, so filing in both. Passing the tokenizer to the dataset map function causes the tokenizer to be fingerprinted weirdly. After calling the tokenizer with arguments like padding and truncation the tokenizer object changes interanally, even though the hash remains the same. But dumps is able to detect that internal change which causes the tokenizer object's fingerprint to change. ### Steps to reproduce the bug ```python from transformers import AutoTokenizer from datasets.utils.py_utils import dumps # Huggingface datasets t = AutoTokenizer.from_pretrained('bert-base-uncased') t.save_pretrained("tok1") th1 = hash(dumps(t)) text = "This is an example text" ttext = t(text, max_length=512, padding="max_length", truncation=True) t.save_pretrained("tok2") th2 = hash(dumps(t)) assert th1 == th2 # Assertion Error ``` But if you use just the hash of the object without dumps, the hashes don't change ```python from transformers import AutoTokenizer from datasets.utils.py_utils import dumps # Huggingface datasets t = AutoTokenizer.from_pretrained('bert-base-uncased') th1 = hash(t) # Just hash no dumps text = "This is an example text" ttext = t(text, max_length=512, padding="max_length", truncation=True) th2 = hash(t) # Just hash no dumps assert th1 == th2 # This is OK ``` This causes situations such as the following 1. Create a text file like this `yes "This is an example text" \| head -n 10000 > lines.txt` ```python from transformers import AutoTokenizer import datasets class TokenizeMapper(object): """Mapper for tokenizer. This is needed because the caching mechanism of HuggingFace does not work on lambdas. Each time a new lambda will be created by a new process which will lead to a different hash. This way we can have a universal mapper object in init and reuse it with the same hash for each process. """ def __init__(self, tokenizer): """Initialize the tokenizer.""" self.tokenizer = tokenizer def __call__(self, examples, **kwargs): """Run the mapper.""" texts = examples["text"] tt = self.tokenizer(texts, max_length=256, padding="max_length", truncation=True) batch_outputs = { "input_ids": tt.input_ids, "attention_mask": tt.attention_mask, } return batch_outputs t = AutoTokenizer.from_pretrained('bert-base-uncased') mapper = TokenizeMapper(t) ds = datasets.load_dataset("text", data_files="lines.txt") mds1 = ds.map( mapper, batched=False, remove_columns=["text"], ).with_format("torch") mds2 = ds.map( mapper, batched=False, remove_columns=["text"], ).with_format("torch") ``` The second call to map should reuse the cached processed dataset from mds1, but it instead it redoes the tokenization because of the behavior of dumps. ### Expected behavior We should be able to initialize a tokenizer. And reusing it should let us reuse the same map computation for the same dataset. The second call to map should reuse the cached processed dataset from mds1, but it instead it redoes the tokenization because of the behavior of dumps. ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-6.1.31_1-x86_64-with-glibc2.36 - Python version: 3.9.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5985/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5985/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5984	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5984/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5984/comments	https://api.github.com/repos/huggingface/datasets/issues/5984/events	https://github.com/huggingface/datasets/issues/5984	1,771,571,458	I_kwDODunzps5pmAkC	5,984	AutoSharding IterableDataset's when num_workers > 1	{ "login": "mathephysicist", "id": 25594384, "node_id": "MDQ6VXNlcjI1NTk0Mzg0", "avatar_url": "https://avatars.githubusercontent.com/u/25594384?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mathephysicist", "html_url": "https://github.com/mathephysicist", "followers_url": "https://api.github.com/users/mathephysicist/followers", "following_url": "https://api.github.com/users/mathephysicist/following{/other_user}", "gists_url": "https://api.github.com/users/mathephysicist/gists{/gist_id}", "starred_url": "https://api.github.com/users/mathephysicist/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mathephysicist/subscriptions", "organizations_url": "https://api.github.com/users/mathephysicist/orgs", "repos_url": "https://api.github.com/users/mathephysicist/repos", "events_url": "https://api.github.com/users/mathephysicist/events{/privacy}", "received_events_url": "https://api.github.com/users/mathephysicist/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	[ "For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n\r\n@lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n\r\nPS: I don't expect significant speed-up for local, uncompressed Arrow files.", "Alternatively we could support multiprocessing map for iterable datasets and let the user do the CPU intensive task there ?\r\n\r\nThis way it would work on arrow data but also on any iterable dataset", "> For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n> \r\n> @lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n> \r\n> PS: I don't expect significant speed-up for local, uncompressed Arrow files.\r\n\r\nCould you explain why you'd need to change the arrow format?\r\n\r\nWhen we use streaming datasets we simply determine the number of worker shards and then add some modulo logic at the appropriate place. Worst case scenario, you'd skip streaming entries according to the number of shards.\r\n\r\nFor PyTorch, I'd be happy to provide an implementation or a sketch thereof, if you point me toward what the testing requirements would be for such a PR.", "> Could you explain why you'd need to change the arrow format?\r\n\r\nThis way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.", "> > Could you explain why you'd need to change the arrow format?\r\n> \r\n> This way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.\r\n\r\nI guess I don't understand why you'd need to subset the dataset in the first place. \r\nIt seems sufficient to figure out how to offset or skip rows.\r\n\r\nFor instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\nThat's one way to do it, where of course you'd need to account for gpu sharding as well.\r\n\r\n\r\nOtherwise, how did you implement worker/node/GPU sharding for iterable/streaming data where you do not have index information or prior splits (e.g. files)?", "> For instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\n\r\nThat works indeed ! And what we meant is that you can make it even faster to instantiate. Indeed using RecordBatchStreamReader you need to get the list of all the record batches in each worker, whereas you could just get the list of record batches per worker if you use the record batches locations in the Arrow IPC file footer. This would be especially appreciated to have a fast instantiation in case you have tens of thousands of Arrow files for example." ]	1,687,530,860,000	1,688,490,236,000	null	NONE	null	### Feature request Minimal Example ``` import torch from datasets import IterableDataset d = IterableDataset.from_file(<file_name>) dl = torch.utils.data.dataloader.DataLoader(d,num_workers=3) for sample in dl: print(sample) ``` Warning: Too many dataloader workers: 2 (max is dataset.n_shards=1). Stopping 1 dataloader workers. To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=1. To enable more parallelism, please split the dataset in more files than 1. Expected Behavior: Dataset is sharded each cpu uses subset (contiguously - so you can do checkpoint loading/saving) ### Motivation I have a lot of unused cpu's and would like to be able to shard iterable datasets with pytorch's dataloader when num_workers > 1. This is for a very large single file. I am aware that we can use the `split_dataset_by_node` to ensure that each node (for distributed) gets different shards, but we should extend it so that this also continues for multiple workers. ### Your contribution If someone points me to what needs to change, I can create a PR.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5984/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5984/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5983	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5983/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5983/comments	https://api.github.com/repos/huggingface/datasets/issues/5983/events	https://github.com/huggingface/datasets/pull/5983	1,770,578,804	PR_kwDODunzps5TtDdy	5,983	replaced PathLike as a variable for save_to_disk for dataset_path wit…	{ "login": "benjaminbrown038", "id": 35114142, "node_id": "MDQ6VXNlcjM1MTE0MTQy", "avatar_url": "https://avatars.githubusercontent.com/u/35114142?v=4", "gravatar_id": "", "url": "https://api.github.com/users/benjaminbrown038", "html_url": "https://github.com/benjaminbrown038", "followers_url": "https://api.github.com/users/benjaminbrown038/followers", "following_url": "https://api.github.com/users/benjaminbrown038/following{/other_user}", "gists_url": "https://api.github.com/users/benjaminbrown038/gists{/gist_id}", "starred_url": "https://api.github.com/users/benjaminbrown038/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/benjaminbrown038/subscriptions", "organizations_url": "https://api.github.com/users/benjaminbrown038/orgs", "repos_url": "https://api.github.com/users/benjaminbrown038/repos", "events_url": "https://api.github.com/users/benjaminbrown038/events{/privacy}", "received_events_url": "https://api.github.com/users/benjaminbrown038/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[]	1,687,481,825,000	1,687,481,825,000	null	NONE	null	…h str like that of load_from_disk	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5983/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5983/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5983", "html_url": "https://github.com/huggingface/datasets/pull/5983", "diff_url": "https://github.com/huggingface/datasets/pull/5983.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5983.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/5982	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5982/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5982/comments	https://api.github.com/repos/huggingface/datasets/issues/5982/events	https://github.com/huggingface/datasets/issues/5982	1,770,333,296	I_kwDODunzps5phSRw	5,982	404 on Datasets Documentation Page	{ "login": "kmulka-bloomberg", "id": 118509387, "node_id": "U_kgDOBxBPSw", "avatar_url": "https://avatars.githubusercontent.com/u/118509387?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kmulka-bloomberg", "html_url": "https://github.com/kmulka-bloomberg", "followers_url": "https://api.github.com/users/kmulka-bloomberg/followers", "following_url": "https://api.github.com/users/kmulka-bloomberg/following{/other_user}", "gists_url": "https://api.github.com/users/kmulka-bloomberg/gists{/gist_id}", "starred_url": "https://api.github.com/users/kmulka-bloomberg/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kmulka-bloomberg/subscriptions", "organizations_url": "https://api.github.com/users/kmulka-bloomberg/orgs", "repos_url": "https://api.github.com/users/kmulka-bloomberg/repos", "events_url": "https://api.github.com/users/kmulka-bloomberg/events{/privacy}", "received_events_url": "https://api.github.com/users/kmulka-bloomberg/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "This wasn’t working for me a bit earlier, but it looks to be back up now", "We had a minor issue updating the docs after the latest release. It should work now :)." ]	1,687,464,897,000	1,687,794,303,000	1,687,794,303,000	NONE	null	### Describe the bug Getting a 404 from the Hugging Face Datasets docs page: https://huggingface.co/docs/datasets/index ### Steps to reproduce the bug 1. Go to URL https://huggingface.co/docs/datasets/index 2. Notice 404 not found ### Expected behavior URL should either show docs or redirect to new location ### Environment info hugginface.co	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5982/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5982/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5981	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5981/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5981/comments	https://api.github.com/repos/huggingface/datasets/issues/5981/events	https://github.com/huggingface/datasets/issues/5981	1,770,310,087	I_kwDODunzps5phMnH	5,981	Only two cores are getting used in sagemaker with pytorch 3.10 kernel	{ "login": "mmr-crexi", "id": 107141022, "node_id": "U_kgDOBmLXng", "avatar_url": "https://avatars.githubusercontent.com/u/107141022?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mmr-crexi", "html_url": "https://github.com/mmr-crexi", "followers_url": "https://api.github.com/users/mmr-crexi/followers", "following_url": "https://api.github.com/users/mmr-crexi/following{/other_user}", "gists_url": "https://api.github.com/users/mmr-crexi/gists{/gist_id}", "starred_url": "https://api.github.com/users/mmr-crexi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mmr-crexi/subscriptions", "organizations_url": "https://api.github.com/users/mmr-crexi/orgs", "repos_url": "https://api.github.com/users/mmr-crexi/repos", "events_url": "https://api.github.com/users/mmr-crexi/events{/privacy}", "received_events_url": "https://api.github.com/users/mmr-crexi/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "I think it's more likely that this issue is related to PyTorch than Datasets, as PyTorch (on import) registers functions to execute when forking a process. Maybe this is the culprit: https://github.com/pytorch/pytorch/issues/99625", "From reading that ticket, it may be down in mkl? Is it worth hotfixing in the meantime, with the express intention of turning it off? I know that's a horribly crufty solution, but it's also deeply frustrating to be limited to 2 cores for operations as simple as filtration." ]	1,687,463,851,000	1,687,809,227,000	null	NONE	null	### Describe the bug When using the newer pytorch 3.10 kernel, only 2 cores are being used by huggingface filter and map functions. The Pytorch 3.9 kernel would use as many cores as specified in the num_proc field. We have solved this in our own code by placing the following snippet in the code that is called inside subprocesses: ```os.sched_setaffinity(0, {i for i in range(1000)})``` The problem, as near as we can tell, us that once upon a time, cpu affinity was set using a bitmask ("0xfffff" and the like), and affinity recently changed to a list of processors rather than to using the mask. As such, only processors 1 and 17 are shown to be working in htop. ![Selection_072](https://github.com/huggingface/datasets/assets/107141022/04c5a824-5321-4531-afca-7bc84dff36b4) When running functions via `map`, the above resetting of affinity works to spread across the cores. When using `filter`, however, only two cores are active. ### Steps to reproduce the bug Repro steps: 1. Create an aws sagemaker instance 2. use the pytorch 3_10 kernel 3. Load a dataset 4. run a filter operation 5. watch as only 2 cores are used when num_proc > 2 6. run a map operation 7. watch as only 2 cores are used when num_proc > 2 8. run a map operation with processor affinity reset inside the function called via map 9. Watch as all cores run ### Expected behavior All specified cores are used via the num_proc argument. ### Environment info AWS sagemaker with the following init script run in the terminal after instance creation: conda init bash bash conda activate pytorch_p310 pip install Wand PyPDF pytesseract datasets seqeval pdfplumber transformers pymupdf sentencepiece timm donut-python accelerate optimum xgboost python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' sudo yum -y install htop sudo yum -y update sudo yum -y install wget libstdc++ autoconf automake libtool autoconf-archive pkg-config gcc gcc-c++ make libjpeg-devel libpng-devel libtiff-devel zlib-devel	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5981/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5981/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5980	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5980/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5980/comments	https://api.github.com/repos/huggingface/datasets/issues/5980/events	https://github.com/huggingface/datasets/issues/5980	1,770,255,973	I_kwDODunzps5pg_Zl	5,980	Viewing dataset card returns “502 Bad Gateway”	{ "login": "tbenthompson", "id": 4241811, "node_id": "MDQ6VXNlcjQyNDE4MTE=", "avatar_url": "https://avatars.githubusercontent.com/u/4241811?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tbenthompson", "html_url": "https://github.com/tbenthompson", "followers_url": "https://api.github.com/users/tbenthompson/followers", "following_url": "https://api.github.com/users/tbenthompson/following{/other_user}", "gists_url": "https://api.github.com/users/tbenthompson/gists{/gist_id}", "starred_url": "https://api.github.com/users/tbenthompson/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/tbenthompson/subscriptions", "organizations_url": "https://api.github.com/users/tbenthompson/orgs", "repos_url": "https://api.github.com/users/tbenthompson/repos", "events_url": "https://api.github.com/users/tbenthompson/events{/privacy}", "received_events_url": "https://api.github.com/users/tbenthompson/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Can you try again? Maybe there was a minor outage.", "Yes, it seems to be working now. In case it's helpful, the outage lasted several days. It was failing as late as yesterday morning. ", "we fixed something on the server side, glad it's fixed now" ]	1,687,461,288,000	1,687,855,099,000	1,687,790,565,000	NONE	null	The url is: https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams I am able to successfully view the “Files and versions” tab: [Confirm-Labs/pile_ngrams_trigrams at main](https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams/tree/main) Any help would be appreciated! Thanks! I hope this is the right place to report an issue like this.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5980/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5980/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5979	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5979/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5979/comments	https://api.github.com/repos/huggingface/datasets/issues/5979/events	https://github.com/huggingface/datasets/pull/5979	1,770,198,250	PR_kwDODunzps5TrxS_	5,979	set dev version	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5979). 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) \| 0.008087 / 0.011353 (-0.003266) \| 0.004691 / 0.011008 (-0.006317) \| 0.121545 / 0.038508 (0.083037) \| 0.057436 / 0.023109 (0.034326) \| 0.368864 / 0.275898 (0.092966) \| 0.457199 / 0.323480 (0.133719) \| 0.006745 / 0.007986 (-0.001241) \| 0.003689 / 0.004328 (-0.000640) \| 0.090480 / 0.004250 (0.086229) \| 0.071368 / 0.037052 (0.034316) \| 0.372788 / 0.258489 (0.114299) \| 0.429894 / 0.293841 (0.136053) \| 0.037544 / 0.128546 (-0.091002) \| 0.010142 / 0.075646 (-0.065505) \| 0.420467 / 0.419271 (0.001196) \| 0.064359 / 0.043533 (0.020826) \| 0.370345 / 0.255139 (0.115206) \| 0.405220 / 0.283200 (0.122020) \| 0.028410 / 0.141683 (-0.113273) \| 1.824845 / 1.452155 (0.372690) \| 1.888109 / 1.492716 (0.395392) \|\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) \| 0.234585 / 0.018006 (0.216578) \| 0.499965 / 0.000490 (0.499476) \| 0.000461 / 0.000200 (0.000261) \| 0.000064 / 0.000054 (0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032294 / 0.037411 (-0.005117) \| 0.131769 / 0.014526 (0.117243) \| 0.146472 / 0.176557 (-0.030085) \| 0.210035 / 0.737135 (-0.527100) \| 0.145600 / 0.296338 (-0.150739) \|\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) \| 0.507455 / 0.215209 (0.292246) \| 5.080090 / 2.077655 (3.002435) \| 2.506104 / 1.504120 (1.001984) \| 2.297655 / 1.541195 (0.756460) \| 2.324920 / 1.468490 (0.856430) \| 0.645003 / 4.584777 (-3.939774) \| 4.677856 / 3.745712 (0.932144) \| 2.254179 / 5.269862 (-3.015683) \| 1.280663 / 4.565676 (-3.285013) \| 0.078809 / 0.424275 (-0.345466) \| 0.014059 / 0.007607 (0.006452) \| 0.628053 / 0.226044 (0.402009) \| 6.327289 / 2.268929 (4.058360) \| 2.957918 / 55.444624 (-52.486706) \| 2.571568 / 6.876477 (-4.304909) \| 2.708766 / 2.142072 (0.566694) \| 0.772868 / 4.805227 (-4.032360) \| 0.164835 / 6.500664 (-6.335829) \| 0.075334 / 0.075469 (-0.000135) \|\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) \| 1.471930 / 1.841788 (-0.369858) \| 17.917340 / 8.074308 (9.843032) \| 15.719327 / 10.191392 (5.527935) \| 0.191999 / 0.680424 (-0.488424) \| 0.022464 / 0.534201 (-0.511737) \| 0.511038 / 0.579283 (-0.068245) \| 0.512050 / 0.434364 (0.077686) \| 0.608711 / 0.540337 (0.068373) \| 0.749660 / 1.386936 (-0.637276) \|\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) \| 0.008028 / 0.011353 (-0.003325) \| 0.004908 / 0.011008 (-0.006100) \| 0.092294 / 0.038508 (0.053786) \| 0.053051 / 0.023109 (0.029942) \| 0.453862 / 0.275898 (0.177964) \| 0.512548 / 0.323480 (0.189068) \| 0.004817 / 0.007986 (-0.003168) \| 0.005330 / 0.004328 (0.001002) \| 0.095600 / 0.004250 (0.091350) \| 0.068763 / 0.037052 (0.031710) \| 0.453654 / 0.258489 (0.195165) \| 0.504995 / 0.293841 (0.211154) \| 0.038123 / 0.128546 (-0.090423) \| 0.010650 / 0.075646 (-0.064996) \| 0.102854 / 0.419271 (-0.316417) \| 0.062973 / 0.043533 (0.019440) \| 0.430420 / 0.255139 (0.175281) \| 0.465448 / 0.283200 (0.182248) \| 0.029736 / 0.141683 (-0.111947) \| 1.844225 / 1.452155 (0.392070) \| 1.934685 / 1.492716 (0.441968) \|\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) \| 0.227797 / 0.018006 (0.209791) \| 0.467868 / 0.000490 (0.467378) \| 0.004531 / 0.000200 (0.004331) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035632 / 0.037411 (-0.001780) \| 0.145943 / 0.014526 (0.131417) \| 0.151944 / 0.176557 (-0.024613) \| 0.220519 / 0.737135 (-0.516616) \| 0.159732 / 0.296338 (-0.136606) \|\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) \| 0.520641 / 0.215209 (0.305432) \| 5.184740 / 2.077655 (3.107086) \| 2.538751 / 1.504120 (1.034631) \| 2.316571 / 1.541195 (0.775377) \| 2.387898 / 1.468490 (0.919408) \| 0.614515 / 4.584777 (-3.970262) \| 4.573142 / 3.745712 (0.827430) \| 4.657052 / 5.269862 (-0.612809) \| 2.159664 / 4.565676 (-2.406013) \| 0.079713 / 0.424275 (-0.344562) \| 0.014462 / 0.007607 (0.006855) \| 0.656611 / 0.226044 (0.430566) \| 6.481630 / 2.268929 (4.212702) \| 3.135047 / 55.444624 (-52.309577) \| 2.757502 / 6.876477 (-4.118975) \| 2.851488 / 2.142072 (0.709415) \| 0.790795 / 4.805227 (-4.014432) \| 0.172358 / 6.500664 (-6.328306) \| 0.080255 / 0.075469 (0.004786) \|\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) \| 1.571391 / 1.841788 (-0.270396) \| 19.025224 / 8.074308 (10.950916) \| 17.079230 / 10.191392 (6.887838) \| 0.172823 / 0.680424 (-0.507601) \| 0.021845 / 0.534201 (-0.512356) \| 0.522286 / 0.579283 (-0.056998) \| 0.510406 / 0.434364 (0.076042) \| 0.604830 / 0.540337 (0.064493) \| 0.735466 / 1.386936 (-0.651471) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4084609bdc40d173d1daa74ad2fe98f3ead72f8e \"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) \| 0.010025 / 0.011353 (-0.001328) \| 0.005699 / 0.011008 (-0.005310) \| 0.134194 / 0.038508 (0.095686) \| 0.056154 / 0.023109 (0.033045) \| 0.470091 / 0.275898 (0.194193) \| 0.539225 / 0.323480 (0.215745) \| 0.006659 / 0.007986 (-0.001326) \| 0.004468 / 0.004328 (0.000140) \| 0.110040 / 0.004250 (0.105790) \| 0.074172 / 0.037052 (0.037119) \| 0.497450 / 0.258489 (0.238961) \| 0.535048 / 0.293841 (0.241207) \| 0.051195 / 0.128546 (-0.077352) \| 0.014926 / 0.075646 (-0.060721) \| 0.461334 / 0.419271 (0.042062) \| 0.073773 / 0.043533 (0.030240) \| 0.450741 / 0.255139 (0.195602) \| 0.474853 / 0.283200 (0.191653) \| 0.036372 / 0.141683 (-0.105311) \| 1.982873 / 1.452155 (0.530719) \| 1.989912 / 1.492716 (0.497196) \|\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) \| 0.287817 / 0.018006 (0.269811) \| 0.613415 / 0.000490 (0.612926) \| 0.007082 / 0.000200 (0.006882) \| 0.000100 / 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) \| 0.031119 / 0.037411 (-0.006292) \| 0.129886 / 0.014526 (0.115361) \| 0.143492 / 0.176557 (-0.033065) \| 0.208536 / 0.737135 (-0.528600) \| 0.147081 / 0.296338 (-0.149257) \|\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) \| 0.668312 / 0.215209 (0.453103) \| 6.568609 / 2.077655 (4.490955) \| 2.708788 / 1.504120 (1.204668) \| 2.366737 / 1.541195 (0.825542) \| 2.392598 / 1.468490 (0.924108) \| 0.967582 / 4.584777 (-3.617195) \| 5.582743 / 3.745712 (1.837031) \| 3.021607 / 5.269862 (-2.248255) \| 1.866402 / 4.565676 (-2.699275) \| 0.115998 / 0.424275 (-0.308277) \| 0.015571 / 0.007607 (0.007964) \| 0.820069 / 0.226044 (0.594025) \| 8.229725 / 2.268929 (5.960797) \| 3.437068 / 55.444624 (-52.007557) \| 2.902312 / 6.876477 (-3.974164) \| 3.025874 / 2.142072 (0.883802) \| 1.230359 / 4.805227 (-3.574868) \| 0.237341 / 6.500664 (-6.263323) \| 0.089923 / 0.075469 (0.014453) \|\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) \| 1.670970 / 1.841788 (-0.170818) \| 19.667167 / 8.074308 (11.592859) \| 21.624423 / 10.191392 (11.433031) \| 0.231683 / 0.680424 (-0.448741) \| 0.029145 / 0.534201 (-0.505056) \| 0.543441 / 0.579283 (-0.035842) \| 0.617510 / 0.434364 (0.183146) \| 0.612662 / 0.540337 (0.072324) \| 0.790589 / 1.386936 (-0.596347) \|\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) \| 0.010324 / 0.011353 (-0.001029) \| 0.005339 / 0.011008 (-0.005669) \| 0.104762 / 0.038508 (0.066254) \| 0.052631 / 0.023109 (0.029522) \| 0.485864 / 0.275898 (0.209966) \| 0.595768 / 0.323480 (0.272288) \| 0.007417 / 0.007986 (-0.000569) \| 0.005229 / 0.004328 (0.000900) \| 0.100775 / 0.004250 (0.096524) \| 0.067144 / 0.037052 (0.030092) \| 0.522269 / 0.258489 (0.263780) \| 0.592597 / 0.293841 (0.298756) \| 0.051101 / 0.128546 (-0.077446) \| 0.015277 / 0.075646 (-0.060369) \| 0.115530 / 0.419271 (-0.303741) \| 0.071922 / 0.043533 (0.028390) \| 0.490208 / 0.255139 (0.235069) \| 0.578936 / 0.283200 (0.295736) \| 0.040382 / 0.141683 (-0.101301) \| 1.986059 / 1.452155 (0.533904) \| 2.040600 / 1.492716 (0.547883) \|\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) \| 0.300399 / 0.018006 (0.282393) \| 0.624702 / 0.000490 (0.624212) \| 0.004908 / 0.000200 (0.004708) \| 0.000155 / 0.000054 (0.000100) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038031 / 0.037411 (0.000619) \| 0.140353 / 0.014526 (0.125828) \| 0.152600 / 0.176557 (-0.023956) \| 0.219165 / 0.737135 (-0.517970) \| 0.154232 / 0.296338 (-0.142106) \|\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) \| 0.698855 / 0.215209 (0.483646) \| 7.125543 / 2.077655 (5.047889) \| 3.251222 / 1.504120 (1.747102) \| 2.953404 / 1.541195 (1.412209) \| 3.051108 / 1.468490 (1.582618) \| 0.962068 / 4.584777 (-3.622709) \| 5.789579 / 3.745712 (2.043867) \| 5.193271 / 5.269862 (-0.076591) \| 2.757886 / 4.565676 (-1.807790) \| 0.111865 / 0.424275 (-0.312410) \| 0.014684 / 0.007607 (0.007077) \| 0.875967 / 0.226044 (0.649923) \| 8.818359 / 2.268929 (6.549430) \| 4.165216 / 55.444624 (-51.279408) \| 3.372059 / 6.876477 (-3.504418) \| 3.486886 / 2.142072 (1.344813) \| 1.232276 / 4.805227 (-3.572951) \| 0.238967 / 6.500664 (-6.261697) \| 0.091584 / 0.075469 (0.016115) \|\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) \| 1.850755 / 1.841788 (0.008968) \| 20.058756 / 8.074308 (11.984448) \| 23.761271 / 10.191392 (13.569879) \| 0.231826 / 0.680424 (-0.448598) \| 0.030119 / 0.534201 (-0.504082) \| 0.532614 / 0.579283 (-0.046669) \| 0.628968 / 0.434364 (0.194604) \| 0.628403 / 0.540337 (0.088066) \| 0.745648 / 1.386936 (-0.641288) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8a797cc92e860c8d0df71e0aa826f4d2690713e \"CML watermark\")\n" ]	1,687,458,734,000	1,687,459,342,000	1,687,458,742,000	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5979/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5979/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5979", "html_url": "https://github.com/huggingface/datasets/pull/5979", "diff_url": "https://github.com/huggingface/datasets/pull/5979.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5979.patch", "merged_at": "2023-06-22T18:32:22" }	true
https://api.github.com/repos/huggingface/datasets/issues/5978	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5978/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5978/comments	https://api.github.com/repos/huggingface/datasets/issues/5978/events	https://github.com/huggingface/datasets/pull/5978	1,770,187,053	PR_kwDODunzps5Tru2_	5,978	Release: 2.13.1	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006173 / 0.011353 (-0.005180) \| 0.003773 / 0.011008 (-0.007235) \| 0.099499 / 0.038508 (0.060991) \| 0.037918 / 0.023109 (0.014809) \| 0.321329 / 0.275898 (0.045431) \| 0.379739 / 0.323480 (0.056259) \| 0.004664 / 0.007986 (-0.003322) \| 0.002943 / 0.004328 (-0.001385) \| 0.077759 / 0.004250 (0.073509) \| 0.055271 / 0.037052 (0.018219) \| 0.329428 / 0.258489 (0.070939) \| 0.378731 / 0.293841 (0.084890) \| 0.027737 / 0.128546 (-0.100810) \| 0.008566 / 0.075646 (-0.067081) \| 0.313220 / 0.419271 (-0.106052) \| 0.047101 / 0.043533 (0.003568) \| 0.316211 / 0.255139 (0.061072) \| 0.341826 / 0.283200 (0.058626) \| 0.020838 / 0.141683 (-0.120845) \| 1.550064 / 1.452155 (0.097909) \| 1.706518 / 1.492716 (0.213801) \|\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) \| 0.203093 / 0.018006 (0.185087) \| 0.425345 / 0.000490 (0.424856) \| 0.004800 / 0.000200 (0.004600) \| 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) \| 0.024590 / 0.037411 (-0.012821) \| 0.098115 / 0.014526 (0.083589) \| 0.108274 / 0.176557 (-0.068282) \| 0.170804 / 0.737135 (-0.566332) \| 0.110560 / 0.296338 (-0.185778) \|\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) \| 0.425251 / 0.215209 (0.210042) \| 4.239075 / 2.077655 (2.161421) \| 1.955601 / 1.504120 (0.451481) \| 1.774796 / 1.541195 (0.233602) \| 1.826641 / 1.468490 (0.358150) \| 0.558777 / 4.584777 (-4.026000) \| 3.361697 / 3.745712 (-0.384015) \| 1.764468 / 5.269862 (-3.505394) \| 1.032280 / 4.565676 (-3.533396) \| 0.067872 / 0.424275 (-0.356403) \| 0.010998 / 0.007607 (0.003391) \| 0.525682 / 0.226044 (0.299637) \| 5.254356 / 2.268929 (2.985427) \| 2.384332 / 55.444624 (-53.060292) \| 2.045578 / 6.876477 (-4.830898) \| 2.170914 / 2.142072 (0.028841) \| 0.674782 / 4.805227 (-4.130445) \| 0.135351 / 6.500664 (-6.365314) \| 0.066591 / 0.075469 (-0.008878) \|\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) \| 1.209181 / 1.841788 (-0.632606) \| 14.044518 / 8.074308 (5.970210) \| 13.184705 / 10.191392 (2.993313) \| 0.130836 / 0.680424 (-0.549588) \| 0.016582 / 0.534201 (-0.517619) \| 0.360005 / 0.579283 (-0.219279) \| 0.379519 / 0.434364 (-0.054845) \| 0.422174 / 0.540337 (-0.118164) \| 0.515546 / 1.386936 (-0.871390) \|\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) \| 0.006293 / 0.011353 (-0.005060) \| 0.003784 / 0.011008 (-0.007224) \| 0.079248 / 0.038508 (0.040739) \| 0.038452 / 0.023109 (0.015343) \| 0.444727 / 0.275898 (0.168829) \| 0.500535 / 0.323480 (0.177055) \| 0.003455 / 0.007986 (-0.004531) \| 0.002873 / 0.004328 (-0.001455) \| 0.077439 / 0.004250 (0.073189) \| 0.047855 / 0.037052 (0.010803) \| 0.448049 / 0.258489 (0.189560) \| 0.509517 / 0.293841 (0.215676) \| 0.028359 / 0.128546 (-0.100188) \| 0.008503 / 0.075646 (-0.067143) \| 0.084961 / 0.419271 (-0.334310) \| 0.042880 / 0.043533 (-0.000653) \| 0.436628 / 0.255139 (0.181489) \| 0.456574 / 0.283200 (0.173375) \| 0.019539 / 0.141683 (-0.122144) \| 1.561273 / 1.452155 (0.109118) \| 1.572018 / 1.492716 (0.079301) \|\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) \| 0.230250 / 0.018006 (0.212244) \| 0.415189 / 0.000490 (0.414700) \| 0.003213 / 0.000200 (0.003013) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025541 / 0.037411 (-0.011871) \| 0.102326 / 0.014526 (0.087800) \| 0.110258 / 0.176557 (-0.066298) \| 0.162488 / 0.737135 (-0.574647) \| 0.112782 / 0.296338 (-0.183556) \|\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) \| 0.457936 / 0.215209 (0.242727) \| 4.581503 / 2.077655 (2.503848) \| 2.237659 / 1.504120 (0.733540) \| 2.029960 / 1.541195 (0.488765) \| 2.082911 / 1.468490 (0.614421) \| 0.556485 / 4.584777 (-4.028292) \| 3.384418 / 3.745712 (-0.361295) \| 1.748809 / 5.269862 (-3.521053) \| 1.034759 / 4.565676 (-3.530917) \| 0.067500 / 0.424275 (-0.356776) \| 0.011425 / 0.007607 (0.003818) \| 0.561340 / 0.226044 (0.335295) \| 5.623629 / 2.268929 (3.354701) \| 2.733587 / 55.444624 (-52.711038) \| 2.401578 / 6.876477 (-4.474899) \| 2.524569 / 2.142072 (0.382496) \| 0.673170 / 4.805227 (-4.132057) \| 0.136681 / 6.500664 (-6.363983) \| 0.068060 / 0.075469 (-0.007409) \|\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) \| 1.318651 / 1.841788 (-0.523137) \| 14.362123 / 8.074308 (6.287815) \| 14.385964 / 10.191392 (4.194572) \| 0.149914 / 0.680424 (-0.530510) \| 0.016877 / 0.534201 (-0.517324) \| 0.358406 / 0.579283 (-0.220877) \| 0.394349 / 0.434364 (-0.040015) \| 0.422471 / 0.540337 (-0.117866) \| 0.513807 / 1.386936 (-0.873129) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b9ce11d1b94e6178df663ff5fcad029849d10fb \"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) \| 0.006272 / 0.011353 (-0.005080) \| 0.003903 / 0.011008 (-0.007105) \| 0.100180 / 0.038508 (0.061672) \| 0.037799 / 0.023109 (0.014690) \| 0.385627 / 0.275898 (0.109729) \| 0.446518 / 0.323480 (0.123038) \| 0.004811 / 0.007986 (-0.003175) \| 0.003032 / 0.004328 (-0.001296) \| 0.077063 / 0.004250 (0.072812) \| 0.055564 / 0.037052 (0.018512) \| 0.397346 / 0.258489 (0.138857) \| 0.443242 / 0.293841 (0.149401) \| 0.027904 / 0.128546 (-0.100642) \| 0.008386 / 0.075646 (-0.067260) \| 0.315013 / 0.419271 (-0.104259) \| 0.047943 / 0.043533 (0.004410) \| 0.378443 / 0.255139 (0.123304) \| 0.411472 / 0.283200 (0.128272) \| 0.020465 / 0.141683 (-0.121218) \| 1.526594 / 1.452155 (0.074439) \| 1.547018 / 1.492716 (0.054301) \|\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) \| 0.219377 / 0.018006 (0.201370) \| 0.430254 / 0.000490 (0.429764) \| 0.003218 / 0.000200 (0.003018) \| 0.000072 / 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) \| 0.023667 / 0.037411 (-0.013744) \| 0.099143 / 0.014526 (0.084617) \| 0.106044 / 0.176557 (-0.070513) \| 0.166186 / 0.737135 (-0.570949) \| 0.108736 / 0.296338 (-0.187603) \|\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) \| 0.437971 / 0.215209 (0.222762) \| 4.363675 / 2.077655 (2.286021) \| 2.011993 / 1.504120 (0.507873) \| 1.845189 / 1.541195 (0.303994) \| 1.831848 / 1.468490 (0.363358) \| 0.562402 / 4.584777 (-4.022375) \| 3.365259 / 3.745712 (-0.380453) \| 1.781491 / 5.269862 (-3.488371) \| 1.023454 / 4.565676 (-3.542223) \| 0.067857 / 0.424275 (-0.356418) \| 0.011076 / 0.007607 (0.003469) \| 0.532267 / 0.226044 (0.306223) \| 5.340344 / 2.268929 (3.071415) \| 2.388649 / 55.444624 (-53.055976) \| 2.055373 / 6.876477 (-4.821104) \| 2.205047 / 2.142072 (0.062975) \| 0.672909 / 4.805227 (-4.132318) \| 0.135244 / 6.500664 (-6.365420) \| 0.066184 / 0.075469 (-0.009285) \|\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) \| 1.206838 / 1.841788 (-0.634950) \| 13.967075 / 8.074308 (5.892767) \| 13.143971 / 10.191392 (2.952579) \| 0.143991 / 0.680424 (-0.536433) \| 0.016673 / 0.534201 (-0.517527) \| 0.376180 / 0.579283 (-0.203103) \| 0.386550 / 0.434364 (-0.047814) \| 0.440590 / 0.540337 (-0.099747) \| 0.529974 / 1.386936 (-0.856962) \|\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) \| 0.006299 / 0.011353 (-0.005054) \| 0.003784 / 0.011008 (-0.007224) \| 0.077875 / 0.038508 (0.039367) \| 0.038689 / 0.023109 (0.015580) \| 0.421684 / 0.275898 (0.145786) \| 0.472649 / 0.323480 (0.149169) \| 0.003570 / 0.007986 (-0.004415) \| 0.004448 / 0.004328 (0.000120) \| 0.077867 / 0.004250 (0.073616) \| 0.049514 / 0.037052 (0.012462) \| 0.375983 / 0.258489 (0.117494) \| 0.470632 / 0.293841 (0.176791) \| 0.028238 / 0.128546 (-0.100308) \| 0.008462 / 0.075646 (-0.067185) \| 0.082452 / 0.419271 (-0.336819) \| 0.043617 / 0.043533 (0.000084) \| 0.400874 / 0.255139 (0.145735) \| 0.426191 / 0.283200 (0.142992) \| 0.020602 / 0.141683 (-0.121081) \| 1.567658 / 1.452155 (0.115504) \| 1.572610 / 1.492716 (0.079893) \|\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) \| 0.246144 / 0.018006 (0.228138) \| 0.419402 / 0.000490 (0.418913) \| 0.001691 / 0.000200 (0.001491) \| 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) \| 0.026105 / 0.037411 (-0.011306) \| 0.104734 / 0.014526 (0.090208) \| 0.110257 / 0.176557 (-0.066300) \| 0.161429 / 0.737135 (-0.575706) \| 0.114367 / 0.296338 (-0.181972) \|\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) \| 0.453352 / 0.215209 (0.238143) \| 4.537924 / 2.077655 (2.460269) \| 2.196193 / 1.504120 (0.692073) \| 2.002087 / 1.541195 (0.460892) \| 2.041722 / 1.468490 (0.573231) \| 0.561643 / 4.584777 (-4.023134) \| 3.449108 / 3.745712 (-0.296605) \| 2.862800 / 5.269862 (-2.407062) \| 1.387895 / 4.565676 (-3.177782) \| 0.068076 / 0.424275 (-0.356199) \| 0.011568 / 0.007607 (0.003961) \| 0.559279 / 0.226044 (0.333235) \| 5.598738 / 2.268929 (3.329809) \| 2.676649 / 55.444624 (-52.767975) \| 2.334588 / 6.876477 (-4.541889) \| 2.376215 / 2.142072 (0.234142) \| 0.673109 / 4.805227 (-4.132118) \| 0.137587 / 6.500664 (-6.363077) \| 0.069131 / 0.075469 (-0.006338) \|\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) \| 1.307332 / 1.841788 (-0.534456) \| 14.536036 / 8.074308 (6.461728) \| 14.173734 / 10.191392 (3.982342) \| 0.145143 / 0.680424 (-0.535281) \| 0.016662 / 0.534201 (-0.517539) \| 0.366901 / 0.579283 (-0.212383) \| 0.394498 / 0.434364 (-0.039866) \| 0.430546 / 0.540337 (-0.109792) \| 0.518950 / 1.386936 (-0.867986) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"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) \| 0.008122 / 0.011353 (-0.003231) \| 0.005585 / 0.011008 (-0.005424) \| 0.121219 / 0.038508 (0.082711) \| 0.047616 / 0.023109 (0.024507) \| 0.440576 / 0.275898 (0.164678) \| 0.491053 / 0.323480 (0.167573) \| 0.004774 / 0.007986 (-0.003211) \| 0.006758 / 0.004328 (0.002430) \| 0.103852 / 0.004250 (0.099602) \| 0.071560 / 0.037052 (0.034508) \| 0.463107 / 0.258489 (0.204618) \| 0.516904 / 0.293841 (0.223063) \| 0.048052 / 0.128546 (-0.080494) \| 0.013679 / 0.075646 (-0.061968) \| 0.428383 / 0.419271 (0.009112) \| 0.069468 / 0.043533 (0.025936) \| 0.432593 / 0.255139 (0.177454) \| 0.471810 / 0.283200 (0.188611) \| 0.037541 / 0.141683 (-0.104142) \| 1.823490 / 1.452155 (0.371335) \| 1.922558 / 1.492716 (0.429842) \|\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) \| 0.252315 / 0.018006 (0.234309) \| 0.541757 / 0.000490 (0.541267) \| 0.000373 / 0.000200 (0.000173) \| 0.000083 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030361 / 0.037411 (-0.007050) \| 0.125928 / 0.014526 (0.111402) \| 0.145102 / 0.176557 (-0.031455) \| 0.209798 / 0.737135 (-0.527337) \| 0.147349 / 0.296338 (-0.148990) \|\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) \| 0.627554 / 0.215209 (0.412345) \| 5.917422 / 2.077655 (3.839767) \| 2.491083 / 1.504120 (0.986963) \| 2.147078 / 1.541195 (0.605883) \| 2.167511 / 1.468490 (0.699021) \| 0.903061 / 4.584777 (-3.681716) \| 5.518537 / 3.745712 (1.772825) \| 2.654348 / 5.269862 (-2.615514) \| 1.645121 / 4.565676 (-2.920556) \| 0.103782 / 0.424275 (-0.320493) \| 0.013048 / 0.007607 (0.005441) \| 0.756732 / 0.226044 (0.530687) \| 7.622873 / 2.268929 (5.353945) \| 3.122689 / 55.444624 (-52.321936) \| 2.537735 / 6.876477 (-4.338742) \| 2.640090 / 2.142072 (0.498018) \| 1.128635 / 4.805227 (-3.676593) \| 0.228089 / 6.500664 (-6.272575) \| 0.086207 / 0.075469 (0.010738) \|\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) \| 1.561591 / 1.841788 (-0.280197) \| 18.110299 / 8.074308 (10.035991) \| 20.718017 / 10.191392 (10.526625) \| 0.225741 / 0.680424 (-0.454682) \| 0.031738 / 0.534201 (-0.502463) \| 0.530789 / 0.579283 (-0.048495) \| 0.607364 / 0.434364 (0.173000) \| 0.581593 / 0.540337 (0.041256) \| 0.726033 / 1.386936 (-0.660903) \|\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) \| 0.009323 / 0.011353 (-0.002030) \| 0.005360 / 0.011008 (-0.005649) \| 0.103608 / 0.038508 (0.065100) \| 0.050158 / 0.023109 (0.027049) \| 0.499906 / 0.275898 (0.224008) \| 0.561005 / 0.323480 (0.237525) \| 0.005093 / 0.007986 (-0.002892) \| 0.008285 / 0.004328 (0.003956) \| 0.103446 / 0.004250 (0.099196) \| 0.061478 / 0.037052 (0.024426) \| 0.494016 / 0.258489 (0.235527) \| 0.537550 / 0.293841 (0.243709) \| 0.048829 / 0.128546 (-0.079717) \| 0.017032 / 0.075646 (-0.058614) \| 0.107748 / 0.419271 (-0.311524) \| 0.065607 / 0.043533 (0.022074) \| 0.488709 / 0.255139 (0.233570) \| 0.512023 / 0.283200 (0.228823) \| 0.032067 / 0.141683 (-0.109616) \| 1.907585 / 1.452155 (0.455431) \| 1.960994 / 1.492716 (0.468278) \|\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) \| 0.278378 / 0.018006 (0.260371) \| 0.551474 / 0.000490 (0.550985) \| 0.006886 / 0.000200 (0.006686) \| 0.000106 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030674 / 0.037411 (-0.006737) \| 0.135179 / 0.014526 (0.120654) \| 0.133703 / 0.176557 (-0.042853) \| 0.198923 / 0.737135 (-0.538212) \| 0.155108 / 0.296338 (-0.141231) \|\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) \| 0.690566 / 0.215209 (0.475357) \| 6.789594 / 2.077655 (4.711940) \| 2.940668 / 1.504120 (1.436549) \| 2.562431 / 1.541195 (1.021236) \| 2.554232 / 1.468490 (1.085742) \| 0.888470 / 4.584777 (-3.696307) \| 5.672318 / 3.745712 (1.926606) \| 2.741626 / 5.269862 (-2.528236) \| 1.818336 / 4.565676 (-2.747340) \| 0.110434 / 0.424275 (-0.313841) \| 0.014114 / 0.007607 (0.006507) \| 0.830632 / 0.226044 (0.604588) \| 8.270787 / 2.268929 (6.001859) \| 3.723486 / 55.444624 (-51.721139) \| 2.993671 / 6.876477 (-3.882806) \| 2.918273 / 2.142072 (0.776201) \| 1.105337 / 4.805227 (-3.699891) \| 0.222976 / 6.500664 (-6.277688) \| 0.085290 / 0.075469 (0.009820) \|\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) \| 1.816027 / 1.841788 (-0.025760) \| 18.496850 / 8.074308 (10.422541) \| 20.457032 / 10.191392 (10.265640) \| 0.243533 / 0.680424 (-0.436891) \| 0.027044 / 0.534201 (-0.507157) \| 0.500752 / 0.579283 (-0.078531) \| 0.620963 / 0.434364 (0.186599) \| 0.607995 / 0.540337 (0.067658) \| 0.722915 / 1.386936 (-0.664021) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n" ]	1,687,458,191,000	1,687,459,224,000	1,687,458,616,000	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5978/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5978/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5978", "html_url": "https://github.com/huggingface/datasets/pull/5978", "diff_url": "https://github.com/huggingface/datasets/pull/5978.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5978.patch", "merged_at": "2023-06-22T18:30:16" }	true
https://api.github.com/repos/huggingface/datasets/issues/5976	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5976/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5976/comments	https://api.github.com/repos/huggingface/datasets/issues/5976/events	https://github.com/huggingface/datasets/pull/5976	1,768,503,913	PR_kwDODunzps5TmAFp	5,976	Avoid stuck map operation when subprocesses crashes	{ "login": "pappacena", "id": 1213561, "node_id": "MDQ6VXNlcjEyMTM1NjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1213561?v=4", "gravatar_id": "", "url": "https://api.github.com/users/pappacena", "html_url": "https://github.com/pappacena", "followers_url": "https://api.github.com/users/pappacena/followers", "following_url": "https://api.github.com/users/pappacena/following{/other_user}", "gists_url": "https://api.github.com/users/pappacena/gists{/gist_id}", "starred_url": "https://api.github.com/users/pappacena/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/pappacena/subscriptions", "organizations_url": "https://api.github.com/users/pappacena/orgs", "repos_url": "https://api.github.com/users/pappacena/repos", "events_url": "https://api.github.com/users/pappacena/events{/privacy}", "received_events_url": "https://api.github.com/users/pappacena/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Hi ! Do you think this can be fixed at the Pool level ? Ideally it should be the Pool responsibility to handle this, not the `map` code. We could even subclass Pool if needed (at least the one from `multiprocess`)", "@lhoestq it makes sense to me. Just pushed a refactoring creating a `class ProcessPool(multiprocess.pool.Pool)` to keep track of the PID changes.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5976). All of your documentation changes will be reflected on that endpoint.", "I managed to raise an error without subclassing Pool with two additions to `iflatmap_unordered`:\r\n\r\n1. at the beggining\r\n```python\r\noriginal_pool = list(pool._pool)\r\n```\r\n\r\n2. in the loop\r\n```python\r\nif any(async_result._pool != original_pool for async_result in async_results) and queue.empty():\r\n raise RuntimeError(\r\n \"One of the subprocesses has abruptly died during map operation.\"\r\n \"To debug the error, disable multiprocessing.\"\r\n )\r\n```\r\n\r\nIt's still a fix that only works for `iflatmap_unordered` (so not for map, imap etc) but is maybe simpler that subclassing. It also works for both multiprocessing.Pool and multiprocess.Pool", "@lhoestq sorry for the delay. Busy weeks here. \r\n\r\nI just pushed the change you requested. It looks closer to the original proposal, actually.\r\n\r\nIt seems that `map` actually uses `iflatmap_unordered` ([here](https://github.com/huggingface/datasets/blob/819bb4346434912eb405ce3f3e9f21dc25a2fe85/src/datasets/arrow_dataset.py#L1509)). I think this solution works fine for the `map` method (which is the one being tested by the new `tests/test_arrow_dataset.py::BaseDatasetTest::test_map_crash_subprocess`, right?).", "Yes fixing iflatmap_unordered does fix Dataset.map, but it won't fix any Pool.map that we may use elsewhere so we'll have to keep this in mind.", "It looks all good to me, feel free to fix code formatting by running `make style` and we can merge :)" ]	1,687,382,311,000	1,688,595,697,000	null	NONE	null	I've been using Dataset.map() with `num_proc=os.cpu_count()` to leverage multicore processing for my datasets, but from time to time I get stuck processes waiting forever. Apparently, when one of the subprocesses is abruptly killed (OOM killer, segfault, SIGKILL, etc), the main process keeps waiting for the async task sent to that child process to finish. It seems to be easy to reproduce the issue with the following script: ``` import os from datasets import Dataset, Features, Value def do_stuck(item): os.kill(os.getpid(), 9) data = { "col1": list(range(5)), "col2": list(range(5)), } ds = Dataset.from_dict( data, features=Features({ "col1": Value("int64"), "col2": Value("int64"), }), ) print(ds.map(do_stuck, num_proc=4)) ``` This is an old behavior in Python, which apparently was fixed a few years ago in `concurrent.futures.ProcessPoolExecutor` ([ref](https://bugs.python.org/issue9205)), but not in `multiprocessing.pool.Pool` / `multiprocess.pool.Pool`, which is used by `Dataset.map` ([ref](https://bugs.python.org/issue22393)). This PR is an idea to try to detect when a child process gets killed, and raises a `RuntimeError` warning the dataset.map() caller. EDIT: Related proposal for future improvement: https://github.com/huggingface/datasets/discussions/5977	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5976/reactions", "total_count": 2, "+1": 2, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5976/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5976", "html_url": "https://github.com/huggingface/datasets/pull/5976", "diff_url": "https://github.com/huggingface/datasets/pull/5976.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5976.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/5975	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5975/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5975/comments	https://api.github.com/repos/huggingface/datasets/issues/5975/events	https://github.com/huggingface/datasets/issues/5975	1,768,271,343	I_kwDODunzps5pZa3v	5,975	Streaming Dataset behind Proxy - FileNotFoundError	{ "login": "Veluchs", "id": 135350576, "node_id": "U_kgDOCBFJMA", "avatar_url": "https://avatars.githubusercontent.com/u/135350576?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Veluchs", "html_url": "https://github.com/Veluchs", "followers_url": "https://api.github.com/users/Veluchs/followers", "following_url": "https://api.github.com/users/Veluchs/following{/other_user}", "gists_url": "https://api.github.com/users/Veluchs/gists{/gist_id}", "starred_url": "https://api.github.com/users/Veluchs/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Veluchs/subscriptions", "organizations_url": "https://api.github.com/users/Veluchs/orgs", "repos_url": "https://api.github.com/users/Veluchs/repos", "events_url": "https://api.github.com/users/Veluchs/events{/privacy}", "received_events_url": "https://api.github.com/users/Veluchs/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Duplicate of #", "Hi ! can you try to set the upper case environment variables `HTTP_PROXY` and `HTTPS_PROXY` ?\r\n\r\nWe use `aiohttp` for streaming and it uses case sensitive environment variables", "Hi, thanks for the quick reply.\r\n\r\nI set the uppercase env variables with\r\n\r\n`\r\nos.environ['HTTP_PROXY'] = \"http://example.com:xxxx\" \r\nos.environ['HTTPS_PROXY'] = \"http://example.com:xxxx\" \r\n`\r\n\r\nHowever, I still get the same error.\r\n\r\nOne thing that could be helpfull: When downloading a dataset without streaming i get the following message:\r\n_HF google storage unreachable. Downloading and preparing it from source_.\r\nThe download does however work as expected.\r\n", "Are you able to use `aiohttp` to get the file at `https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json` using your proxy ?", "It only works when passing trust_env=True when creating the ClientSession, as well as setting ssl=False.\r\n\r\nWorking Example:\r\n\r\n```\r\nimport os\r\n\r\nos.environ['HTTP_PROXY'] = \"xyz\"\r\nos.environ['HTTPS_PROXY'] = \"xyz\"\r\n\r\nimport asyncio\r\nimport aiohttp\r\n\r\nasync def download_pep(url):\r\n async with aiohttp.ClientSession(trust_env=True) as session:\r\n print(\"1\")\r\n async with session.get(url, ssl=False) as resp:\r\n print(\"2\")\r\n content = await resp.text()\r\n print(content)\r\n return content\r\n\r\nasyncio.run(download_pep(\"https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\"))\r\n```\r\n\r\n\r\n\r\nSSL Verification has been a problem with other packages as well. Usually I circumvent the problem by setting\r\n```\r\nimport ssl\r\nssl._create_default_https_context = ssl._create_unverified_context\r\n```\r\n(probably not the best idea for security), although here aiohttp does not seem to use this default context.", "We do pass `trust_env` as well. Could you share the full stack trace you get when streaming using `datasets` ? That could help locate where we might have forgotten to pass `trust_env`", "Is there a way to disable ssl verification when streaming a dataset. I suspect this might be the isssue with my proxy.\r\n\r\n\r\nHere you go:\r\n\r\n```\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[8], line 3\r\n 1 from datasets import load_dataset\r\n----> 3 ds = load_dataset(\"facebook/voxpopuli\", name=\"de\", streaming=True)\r\n 5 sample = next(iter(ds))\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, *config_kwargs)\r\n 1788 # Return iterable dataset in case of streaming\r\n 1789 if streaming:\r\n-> 1790 return builder_instance.as_streaming_dataset(split=split)\r\n 1792 # Some datasets are already processed on the HF google storage\r\n 1793 # Don't try downloading from Google storage for the packaged datasets as text, json, csv or pandas\r\n 1794 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281), in DatasetBuilder.as_streaming_dataset(self, split, base_path)\r\n 1274 dl_manager = StreamingDownloadManager(\r\n 1275 base_path=base_path or self.base_path,\r\n 1276 download_config=DownloadConfig(use_auth_token=self.use_auth_token, storage_options=self.storage_options),\r\n 1277 dataset_name=self.name,\r\n 1278 data_dir=self.config.data_dir,\r\n 1279 )\r\n 1280 self._check_manual_download(dl_manager)\r\n-> 1281 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n 1282 # By default, return all splits\r\n 1283 if split is None:\r\n\r\nFile [~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120), in Voxpopuli._split_generators(self, dl_manager)\r\n 118 def _split_generators(self, dl_manager):\r\n 119 n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE)\r\n--> 120 with open(n_shards_path) as f:\r\n 121 n_shards = json.load(f)\r\n 123 if self.config.name == \"en_accented\":\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71), in extend_module_for_streaming..wrap_auth..wrapper(args, *kwargs)\r\n 69 @wraps(function)\r\n 70 def wrapper(args, *kwargs):\r\n---> 71 return function(args, use_auth_token=use_auth_token, *kwargs)\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517), in xopen(file, mode, use_auth_token, args, **kwargs)\r\n 515 except FileNotFoundError:\r\n 516 if file.startswith(config.HF_ENDPOINT):\r\n--> 517 raise FileNotFoundError(\r\n 518 file + \"\\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\"\r\n 519 ) from None\r\n 520 else:\r\n 521 raise\r\n\r\nFileNotFoundError: https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\r\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```", "> Is there a way to disable ssl verification when streaming a dataset.\r\n\r\nI don't think so.\r\n\r\nWe use `fsspec` HTTPFileSystem implementation that is based on `aiohttp`. If you register a subclass of HTTPFileSystem that has SSL disabled by default it could work, but I wouldn't recommended it because it can raise security issues.", "Okay thanks for your help! I guess I have to figure out how to improve the proxy environment / see if I can make it work with ssl connections." ]	1,687,374,602,000	1,688,104,539,000	1,688,104,538,000	NONE	null	### Describe the bug When trying to stream a dataset i get the following error after a few minutes of waiting. ``` FileNotFoundError: https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json If the repo is private or gated, make sure to log in with `huggingface-cli login`. ``` I have already set the proxy environment variables. Downloading a Dataset without streaming works as expected. Still i suspect that this is connected to being behind a proxy. Is there a way to set the proxy for streaming datasets? Possibly a keyword argument that gets passed to ffspec? ### Steps to reproduce the bug This is the code i use. ``` import os os.environ['http_proxy'] = "http://example.com:xxxx" os.environ['https_proxy'] = "http://example.com:xxxx" from datasets import load_dataset ds = load_dataset("facebook/voxpopuli", name="de", streaming=True) ``` ### Expected behavior I would expect the streaming functionality to use the set proxy settings. ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-73-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5975/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5975/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5974	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5974/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5974/comments	https://api.github.com/repos/huggingface/datasets/issues/5974/events	https://github.com/huggingface/datasets/pull/5974	1,767,981,231	PR_kwDODunzps5TkXCb	5,974	Deprecate `errors` param in favor of `encoding_errors` in text builder	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006518 / 0.011353 (-0.004835) \| 0.004121 / 0.011008 (-0.006887) \| 0.103350 / 0.038508 (0.064842) \| 0.045030 / 0.023109 (0.021920) \| 0.351670 / 0.275898 (0.075772) \| 0.408110 / 0.323480 (0.084630) \| 0.003883 / 0.007986 (-0.004102) \| 0.003352 / 0.004328 (-0.000977) \| 0.078786 / 0.004250 (0.074535) \| 0.063977 / 0.037052 (0.026925) \| 0.369759 / 0.258489 (0.111270) \| 0.415103 / 0.293841 (0.121262) \| 0.033069 / 0.128546 (-0.095477) \| 0.008863 / 0.075646 (-0.066783) \| 0.353660 / 0.419271 (-0.065611) \| 0.055714 / 0.043533 (0.012181) \| 0.350458 / 0.255139 (0.095319) \| 0.369505 / 0.283200 (0.086305) \| 0.022822 / 0.141683 (-0.118861) \| 1.537588 / 1.452155 (0.085433) \| 1.590569 / 1.492716 (0.097853) \|\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) \| 0.206826 / 0.018006 (0.188819) \| 0.471625 / 0.000490 (0.471135) \| 0.005188 / 0.000200 (0.004988) \| 0.000316 / 0.000054 (0.000261) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028148 / 0.037411 (-0.009263) \| 0.111941 / 0.014526 (0.097415) \| 0.122106 / 0.176557 (-0.054451) \| 0.181127 / 0.737135 (-0.556009) \| 0.127534 / 0.296338 (-0.168805) \|\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) \| 0.409520 / 0.215209 (0.194311) \| 4.098455 / 2.077655 (2.020800) \| 1.852447 / 1.504120 (0.348327) \| 1.657036 / 1.541195 (0.115842) \| 1.709624 / 1.468490 (0.241134) \| 0.542806 / 4.584777 (-4.041970) \| 3.809352 / 3.745712 (0.063640) \| 1.855412 / 5.269862 (-3.414449) \| 1.109180 / 4.565676 (-3.456497) \| 0.066801 / 0.424275 (-0.357474) \| 0.011832 / 0.007607 (0.004225) \| 0.518338 / 0.226044 (0.292293) \| 5.190108 / 2.268929 (2.921179) \| 2.320602 / 55.444624 (-53.124023) \| 1.991416 / 6.876477 (-4.885060) \| 2.106989 / 2.142072 (-0.035084) \| 0.668914 / 4.805227 (-4.136313) \| 0.145325 / 6.500664 (-6.355340) \| 0.065145 / 0.075469 (-0.010324) \|\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) \| 1.254706 / 1.841788 (-0.587082) \| 14.707264 / 8.074308 (6.632956) \| 14.615423 / 10.191392 (4.424031) \| 0.170764 / 0.680424 (-0.509659) \| 0.017905 / 0.534201 (-0.516296) \| 0.435606 / 0.579283 (-0.143677) \| 0.434648 / 0.434364 (0.000284) \| 0.520813 / 0.540337 (-0.019524) \| 0.633902 / 1.386936 (-0.753034) \|\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) \| 0.007212 / 0.011353 (-0.004141) \| 0.004301 / 0.011008 (-0.006707) \| 0.080767 / 0.038508 (0.042258) \| 0.051949 / 0.023109 (0.028840) \| 0.398473 / 0.275898 (0.122575) \| 0.465038 / 0.323480 (0.141558) \| 0.005580 / 0.007986 (-0.002406) \| 0.003556 / 0.004328 (-0.000773) \| 0.080682 / 0.004250 (0.076431) \| 0.059517 / 0.037052 (0.022464) \| 0.421171 / 0.258489 (0.162682) \| 0.459752 / 0.293841 (0.165911) \| 0.032960 / 0.128546 (-0.095586) \| 0.009107 / 0.075646 (-0.066539) \| 0.086382 / 0.419271 (-0.332889) \| 0.056053 / 0.043533 (0.012520) \| 0.393357 / 0.255139 (0.138218) \| 0.412972 / 0.283200 (0.129772) \| 0.031115 / 0.141683 (-0.110568) \| 1.576961 / 1.452155 (0.124806) \| 1.627249 / 1.492716 (0.134533) \|\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) \| 0.227618 / 0.018006 (0.209612) \| 0.444640 / 0.000490 (0.444150) \| 0.004376 / 0.000200 (0.004176) \| 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) \| 0.030826 / 0.037411 (-0.006586) \| 0.117587 / 0.014526 (0.103062) \| 0.127467 / 0.176557 (-0.049089) \| 0.184440 / 0.737135 (-0.552695) \| 0.133664 / 0.296338 (-0.162675) \|\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) \| 0.443183 / 0.215209 (0.227974) \| 4.408312 / 2.077655 (2.330658) \| 2.132487 / 1.504120 (0.628367) \| 1.923632 / 1.541195 (0.382438) \| 1.967882 / 1.468490 (0.499392) \| 0.552954 / 4.584777 (-4.031823) \| 3.777701 / 3.745712 (0.031989) \| 1.857686 / 5.269862 (-3.412176) \| 1.104847 / 4.565676 (-3.460829) \| 0.068350 / 0.424275 (-0.355925) \| 0.012437 / 0.007607 (0.004830) \| 0.559258 / 0.226044 (0.333214) \| 5.593258 / 2.268929 (3.324330) \| 2.648059 / 55.444624 (-52.796565) \| 2.277428 / 6.876477 (-4.599049) \| 2.351685 / 2.142072 (0.209612) \| 0.678750 / 4.805227 (-4.126477) \| 0.145550 / 6.500664 (-6.355114) \| 0.066556 / 0.075469 (-0.008913) \|\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) \| 1.327128 / 1.841788 (-0.514659) \| 15.649079 / 8.074308 (7.574771) \| 14.478659 / 10.191392 (4.287267) \| 0.147633 / 0.680424 (-0.532791) \| 0.018502 / 0.534201 (-0.515699) \| 0.438556 / 0.579283 (-0.140727) \| 0.433381 / 0.434364 (-0.000983) \| 0.514367 / 0.540337 (-0.025970) \| 0.618347 / 1.386936 (-0.768589) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16aa1c886c5b499641a4bb3d8ce4a4f7de8244b7 \"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) \| 0.006078 / 0.011353 (-0.005275) \| 0.003914 / 0.011008 (-0.007095) \| 0.102039 / 0.038508 (0.063531) \| 0.037660 / 0.023109 (0.014551) \| 0.348963 / 0.275898 (0.073065) \| 0.407284 / 0.323480 (0.083804) \| 0.004661 / 0.007986 (-0.003324) \| 0.003253 / 0.004328 (-0.001076) \| 0.078276 / 0.004250 (0.074025) \| 0.054144 / 0.037052 (0.017091) \| 0.376715 / 0.258489 (0.118225) \| 0.418499 / 0.293841 (0.124658) \| 0.027627 / 0.128546 (-0.100919) \| 0.008494 / 0.075646 (-0.067152) \| 0.316894 / 0.419271 (-0.102377) \| 0.046560 / 0.043533 (0.003027) \| 0.339835 / 0.255139 (0.084696) \| 0.374628 / 0.283200 (0.091428) \| 0.020729 / 0.141683 (-0.120954) \| 1.502769 / 1.452155 (0.050615) \| 1.548756 / 1.492716 (0.056040) \|\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) \| 0.229192 / 0.018006 (0.211186) \| 0.426245 / 0.000490 (0.425756) \| 0.005190 / 0.000200 (0.004990) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024271 / 0.037411 (-0.013140) \| 0.098869 / 0.014526 (0.084343) \| 0.105079 / 0.176557 (-0.071477) \| 0.164707 / 0.737135 (-0.572428) \| 0.110337 / 0.296338 (-0.186002) \|\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) \| 0.426593 / 0.215209 (0.211383) \| 4.293977 / 2.077655 (2.216323) \| 1.928502 / 1.504120 (0.424382) \| 1.728623 / 1.541195 (0.187428) \| 1.792084 / 1.468490 (0.323594) \| 0.568737 / 4.584777 (-4.016040) \| 3.438534 / 3.745712 (-0.307178) \| 1.797798 / 5.269862 (-3.472063) \| 1.054078 / 4.565676 (-3.511598) \| 0.068711 / 0.424275 (-0.355564) \| 0.011250 / 0.007607 (0.003643) \| 0.529299 / 0.226044 (0.303255) \| 5.283965 / 2.268929 (3.015037) \| 2.358274 / 55.444624 (-53.086350) \| 2.012818 / 6.876477 (-4.863659) \| 2.109923 / 2.142072 (-0.032149) \| 0.679556 / 4.805227 (-4.125671) \| 0.138346 / 6.500664 (-6.362318) \| 0.066349 / 0.075469 (-0.009120) \|\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) \| 1.193994 / 1.841788 (-0.647794) \| 14.073158 / 8.074308 (5.998850) \| 13.488525 / 10.191392 (3.297133) \| 0.144536 / 0.680424 (-0.535888) \| 0.016748 / 0.534201 (-0.517453) \| 0.362703 / 0.579283 (-0.216580) \| 0.389511 / 0.434364 (-0.044853) \| 0.427296 / 0.540337 (-0.113041) \| 0.513227 / 1.386936 (-0.873709) \|\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) \| 0.006215 / 0.011353 (-0.005138) \| 0.003834 / 0.011008 (-0.007174) \| 0.078001 / 0.038508 (0.039493) \| 0.036537 / 0.023109 (0.013428) \| 0.369724 / 0.275898 (0.093826) \| 0.426761 / 0.323480 (0.103281) \| 0.003602 / 0.007986 (-0.004383) \| 0.003001 / 0.004328 (-0.001327) \| 0.075989 / 0.004250 (0.071739) \| 0.048618 / 0.037052 (0.011566) \| 0.374296 / 0.258489 (0.115807) \| 0.430330 / 0.293841 (0.136489) \| 0.028299 / 0.128546 (-0.100247) \| 0.008537 / 0.075646 (-0.067109) \| 0.083275 / 0.419271 (-0.335997) \| 0.043136 / 0.043533 (-0.000397) \| 0.359072 / 0.255139 (0.103933) \| 0.387391 / 0.283200 (0.104192) \| 0.021202 / 0.141683 (-0.120481) \| 1.520832 / 1.452155 (0.068677) \| 1.567030 / 1.492716 (0.074313) \|\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) \| 0.230944 / 0.018006 (0.212938) \| 0.422159 / 0.000490 (0.421669) \| 0.003447 / 0.000200 (0.003247) \| 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) \| 0.025442 / 0.037411 (-0.011969) \| 0.103944 / 0.014526 (0.089418) \| 0.110577 / 0.176557 (-0.065979) \| 0.161393 / 0.737135 (-0.575743) \| 0.113482 / 0.296338 (-0.182857) \|\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) \| 0.485765 / 0.215209 (0.270556) \| 4.845737 / 2.077655 (2.768083) \| 2.556732 / 1.504120 (1.052612) \| 2.348638 / 1.541195 (0.807443) \| 2.379289 / 1.468490 (0.910799) \| 0.561261 / 4.584777 (-4.023516) \| 3.482468 / 3.745712 (-0.263244) \| 3.061319 / 5.269862 (-2.208543) \| 1.483938 / 4.565676 (-3.081738) \| 0.067584 / 0.424275 (-0.356691) \| 0.011333 / 0.007607 (0.003726) \| 0.594342 / 0.226044 (0.368297) \| 5.935477 / 2.268929 (3.666548) \| 3.025029 / 55.444624 (-52.419595) \| 2.687032 / 6.876477 (-4.189445) \| 2.752470 / 2.142072 (0.610398) \| 0.674470 / 4.805227 (-4.130757) \| 0.136777 / 6.500664 (-6.363887) \| 0.068335 / 0.075469 (-0.007134) \|\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) \| 1.336456 / 1.841788 (-0.505332) \| 14.376007 / 8.074308 (6.301699) \| 14.171375 / 10.191392 (3.979983) \| 0.159620 / 0.680424 (-0.520804) \| 0.016685 / 0.534201 (-0.517516) \| 0.364344 / 0.579283 (-0.214939) \| 0.395358 / 0.434364 (-0.039006) \| 0.424876 / 0.540337 (-0.115461) \| 0.513267 / 1.386936 (-0.873669) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ed837325cb539a5deb99129e5ad181d0269e050 \"CML watermark\")\n" ]	1,687,365,098,000	1,687,775,683,000	1,687,775,260,000	CONTRIBUTOR	null	For consistency with the JSON builder and Pandas	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5974/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5974/timeline	null	null	0	{ "url": 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https://api.github.com/repos/huggingface/datasets/issues/5972	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5972/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5972/comments	https://api.github.com/repos/huggingface/datasets/issues/5972/events	https://github.com/huggingface/datasets/pull/5972	1,767,897,485	PR_kwDODunzps5TkE7K	5,972	Filter unsupported extensions	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "<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) \| 0.006983 / 0.011353 (-0.004369) \| 0.004473 / 0.011008 (-0.006535) \| 0.105158 / 0.038508 (0.066650) \| 0.048973 / 0.023109 (0.025864) \| 0.358771 / 0.275898 (0.082873) \| 0.432389 / 0.323480 (0.108909) \| 0.005689 / 0.007986 (-0.002297) \| 0.003584 / 0.004328 (-0.000744) \| 0.080852 / 0.004250 (0.076601) \| 0.066133 / 0.037052 (0.029081) \| 0.370981 / 0.258489 (0.112492) \| 0.406942 / 0.293841 (0.113101) \| 0.032123 / 0.128546 (-0.096424) \| 0.009313 / 0.075646 (-0.066333) \| 0.355220 / 0.419271 (-0.064051) \| 0.055768 / 0.043533 (0.012235) \| 0.370545 / 0.255139 (0.115406) \| 0.375619 / 0.283200 (0.092419) \| 0.024258 / 0.141683 (-0.117425) \| 1.559073 / 1.452155 (0.106918) \| 1.616520 / 1.492716 (0.123804) \|\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) \| 0.277893 / 0.018006 (0.259887) \| 0.535447 / 0.000490 (0.534957) \| 0.004877 / 0.000200 (0.004677) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029444 / 0.037411 (-0.007968) \| 0.114366 / 0.014526 (0.099841) \| 0.130957 / 0.176557 (-0.045599) \| 0.189604 / 0.737135 (-0.547531) \| 0.131682 / 0.296338 (-0.164656) \|\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) \| 0.412315 / 0.215209 (0.197106) \| 4.093879 / 2.077655 (2.016225) \| 1.856169 / 1.504120 (0.352050) \| 1.655358 / 1.541195 (0.114164) \| 1.758190 / 1.468490 (0.289699) \| 0.545829 / 4.584777 (-4.038948) \| 3.871436 / 3.745712 (0.125724) \| 1.938244 / 5.269862 (-3.331618) \| 1.122727 / 4.565676 (-3.442950) \| 0.067107 / 0.424275 (-0.357168) \| 0.012012 / 0.007607 (0.004405) \| 0.518868 / 0.226044 (0.292824) \| 5.235081 / 2.268929 (2.966153) \| 2.335115 / 55.444624 (-53.109509) \| 2.013074 / 6.876477 (-4.863402) \| 2.219808 / 2.142072 (0.077735) \| 0.674602 / 4.805227 (-4.130626) \| 0.147051 / 6.500664 (-6.353613) \| 0.068444 / 0.075469 (-0.007025) \|\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) \| 1.245600 / 1.841788 (-0.596188) \| 15.537727 / 8.074308 (7.463419) \| 15.074300 / 10.191392 (4.882908) \| 0.194217 / 0.680424 (-0.486207) \| 0.018536 / 0.534201 (-0.515665) \| 0.437085 / 0.579283 (-0.142198) \| 0.441123 / 0.434364 (0.006759) \| 0.530681 / 0.540337 (-0.009657) \| 0.649154 / 1.386936 (-0.737782) \|\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) \| 0.007243 / 0.011353 (-0.004110) \| 0.004688 / 0.011008 (-0.006320) \| 0.079809 / 0.038508 (0.041301) \| 0.046915 / 0.023109 (0.023805) \| 0.415144 / 0.275898 (0.139246) \| 0.474867 / 0.323480 (0.151388) \| 0.004550 / 0.007986 (-0.003435) \| 0.004585 / 0.004328 (0.000257) \| 0.080837 / 0.004250 (0.076587) \| 0.061667 / 0.037052 (0.024614) \| 0.411321 / 0.258489 (0.152832) \| 0.464195 / 0.293841 (0.170354) \| 0.032510 / 0.128546 (-0.096037) \| 0.009306 / 0.075646 (-0.066340) \| 0.086637 / 0.419271 (-0.332635) \| 0.053335 / 0.043533 (0.009802) \| 0.402302 / 0.255139 (0.147163) \| 0.424864 / 0.283200 (0.141664) \| 0.026573 / 0.141683 (-0.115110) \| 1.566793 / 1.452155 (0.114639) \| 1.628118 / 1.492716 (0.135401) \|\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) \| 0.317802 / 0.018006 (0.299796) \| 0.544593 / 0.000490 (0.544103) \| 0.005690 / 0.000200 (0.005490) \| 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) \| 0.033015 / 0.037411 (-0.004397) \| 0.121940 / 0.014526 (0.107414) \| 0.132920 / 0.176557 (-0.043637) \| 0.191481 / 0.737135 (-0.545655) \| 0.139139 / 0.296338 (-0.157199) \|\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) \| 0.460382 / 0.215209 (0.245173) \| 4.610046 / 2.077655 (2.532392) \| 2.296573 / 1.504120 (0.792453) \| 2.099735 / 1.541195 (0.558540) \| 2.213913 / 1.468490 (0.745423) \| 0.544871 / 4.584777 (-4.039906) \| 3.814174 / 3.745712 (0.068462) \| 3.246397 / 5.269862 (-2.023464) \| 1.480236 / 4.565676 (-3.085440) \| 0.068464 / 0.424275 (-0.355811) \| 0.012651 / 0.007607 (0.005043) \| 0.564989 / 0.226044 (0.338944) \| 5.639188 / 2.268929 (3.370259) \| 2.827601 / 55.444624 (-52.617023) \| 2.473743 / 6.876477 (-4.402734) \| 2.567413 / 2.142072 (0.425340) \| 0.674351 / 4.805227 (-4.130876) \| 0.146248 / 6.500664 (-6.354416) \| 0.067553 / 0.075469 (-0.007916) \|\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) \| 1.346703 / 1.841788 (-0.495085) \| 16.494787 / 8.074308 (8.420479) \| 15.179487 / 10.191392 (4.988095) \| 0.181864 / 0.680424 (-0.498560) \| 0.018857 / 0.534201 (-0.515344) \| 0.437787 / 0.579283 (-0.141496) \| 0.431770 / 0.434364 (-0.002594) \| 0.507116 / 0.540337 (-0.033221) \| 0.608899 / 1.386936 (-0.778037) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fd5b7412f907675e76b183a6e39ef6d176fdcc0 \"CML watermark\")\n", "_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) \| 0.005963 / 0.011353 (-0.005390) \| 0.003743 / 0.011008 (-0.007265) \| 0.098519 / 0.038508 (0.060011) \| 0.037392 / 0.023109 (0.014283) \| 0.322706 / 0.275898 (0.046808) \| 0.380032 / 0.323480 (0.056552) \| 0.004694 / 0.007986 (-0.003292) \| 0.002897 / 0.004328 (-0.001432) \| 0.078664 / 0.004250 (0.074414) \| 0.052646 / 0.037052 (0.015594) \| 0.335523 / 0.258489 (0.077034) \| 0.375464 / 0.293841 (0.081623) \| 0.027537 / 0.128546 (-0.101010) \| 0.008452 / 0.075646 (-0.067194) \| 0.313844 / 0.419271 (-0.105427) \| 0.047368 / 0.043533 (0.003835) \| 0.313833 / 0.255139 (0.058694) \| 0.342284 / 0.283200 (0.059085) \| 0.021136 / 0.141683 (-0.120547) \| 1.544764 / 1.452155 (0.092610) \| 1.563850 / 1.492716 (0.071134) \|\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) \| 0.188609 / 0.018006 (0.170603) \| 0.421686 / 0.000490 (0.421196) \| 0.003336 / 0.000200 (0.003136) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023678 / 0.037411 (-0.013733) \| 0.099191 / 0.014526 (0.084665) \| 0.105819 / 0.176557 (-0.070738) \| 0.169654 / 0.737135 (-0.567481) \| 0.110240 / 0.296338 (-0.186099) \|\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) \| 0.425497 / 0.215209 (0.210288) \| 4.237165 / 2.077655 (2.159510) \| 1.902953 / 1.504120 (0.398833) \| 1.699012 / 1.541195 (0.157818) \| 1.751107 / 1.468490 (0.282617) \| 0.563326 / 4.584777 (-4.021451) \| 3.394189 / 3.745712 (-0.351523) \| 2.706129 / 5.269862 (-2.563732) \| 1.361522 / 4.565676 (-3.204155) \| 0.067776 / 0.424275 (-0.356499) \| 0.010959 / 0.007607 (0.003352) \| 0.530905 / 0.226044 (0.304860) \| 5.322467 / 2.268929 (3.053538) \| 2.384356 / 55.444624 (-53.060269) \| 2.044196 / 6.876477 (-4.832281) \| 2.119837 / 2.142072 (-0.022235) \| 0.682236 / 4.805227 (-4.122991) \| 0.136921 / 6.500664 (-6.363743) \| 0.066784 / 0.075469 (-0.008685) \|\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) \| 1.210642 / 1.841788 (-0.631146) \| 13.804572 / 8.074308 (5.730264) \| 13.309229 / 10.191392 (3.117837) \| 0.154356 / 0.680424 (-0.526068) \| 0.016833 / 0.534201 (-0.517368) \| 0.366503 / 0.579283 (-0.212780) \| 0.385201 / 0.434364 (-0.049163) \| 0.426713 / 0.540337 (-0.113624) \| 0.516795 / 1.386936 (-0.870141) \|\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) \| 0.006144 / 0.011353 (-0.005209) \| 0.003723 / 0.011008 (-0.007285) \| 0.077427 / 0.038508 (0.038919) \| 0.037636 / 0.023109 (0.014527) \| 0.375048 / 0.275898 (0.099150) \| 0.442254 / 0.323480 (0.118774) \| 0.003506 / 0.007986 (-0.004480) \| 0.003751 / 0.004328 (-0.000577) \| 0.076771 / 0.004250 (0.072521) \| 0.047915 / 0.037052 (0.010862) \| 0.378918 / 0.258489 (0.120429) \| 0.435300 / 0.293841 (0.141459) \| 0.028317 / 0.128546 (-0.100230) \| 0.008413 / 0.075646 (-0.067233) \| 0.082774 / 0.419271 (-0.336497) \| 0.043211 / 0.043533 (-0.000321) \| 0.362022 / 0.255139 (0.106883) \| 0.404928 / 0.283200 (0.121728) \| 0.020692 / 0.141683 (-0.120991) \| 1.527303 / 1.452155 (0.075148) \| 1.596091 / 1.492716 (0.103375) \|\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) \| 0.225537 / 0.018006 (0.207530) \| 0.399901 / 0.000490 (0.399412) \| 0.000424 / 0.000200 (0.000224) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026483 / 0.037411 (-0.010928) \| 0.104373 / 0.014526 (0.089847) \| 0.111271 / 0.176557 (-0.065286) \| 0.163872 / 0.737135 (-0.573264) \| 0.113991 / 0.296338 (-0.182347) \|\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) \| 0.456484 / 0.215209 (0.241275) \| 4.572652 / 2.077655 (2.494998) \| 2.374908 / 1.504120 (0.870788) \| 2.207855 / 1.541195 (0.666661) \| 2.260009 / 1.468490 (0.791519) \| 0.562678 / 4.584777 (-4.022099) \| 3.441778 / 3.745712 (-0.303934) \| 1.729006 / 5.269862 (-3.540855) \| 1.024937 / 4.565676 (-3.540739) \| 0.068707 / 0.424275 (-0.355568) \| 0.011334 / 0.007607 (0.003727) \| 0.564293 / 0.226044 (0.338248) \| 5.638367 / 2.268929 (3.369438) \| 2.665654 / 55.444624 (-52.778970) \| 2.320033 / 6.876477 (-4.556444) \| 2.328706 / 2.142072 (0.186634) \| 0.677433 / 4.805227 (-4.127794) \| 0.137190 / 6.500664 (-6.363474) \| 0.068585 / 0.075469 (-0.006885) \|\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) \| 1.312476 / 1.841788 (-0.529312) \| 14.206685 / 8.074308 (6.132377) \| 14.217928 / 10.191392 (4.026536) \| 0.143416 / 0.680424 (-0.537007) \| 0.016647 / 0.534201 (-0.517554) \| 0.361228 / 0.579283 (-0.218055) \| 0.396185 / 0.434364 (-0.038178) \| 0.423275 / 0.540337 (-0.117063) \| 0.512966 / 1.386936 (-0.873970) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b424648fd68bd0b5279eb916cec4836d1220e268 \"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) \| 0.008913 / 0.011353 (-0.002440) \| 0.005142 / 0.011008 (-0.005866) \| 0.133958 / 0.038508 (0.095449) \| 0.049180 / 0.023109 (0.026071) \| 0.389169 / 0.275898 (0.113270) \| 0.481513 / 0.323480 (0.158033) \| 0.006555 / 0.007986 (-0.001430) \| 0.003806 / 0.004328 (-0.000522) \| 0.102056 / 0.004250 (0.097806) \| 0.083259 / 0.037052 (0.046207) \| 0.392536 / 0.258489 (0.134047) \| 0.447503 / 0.293841 (0.153662) \| 0.047472 / 0.128546 (-0.081074) \| 0.014748 / 0.075646 (-0.060899) \| 0.475619 / 0.419271 (0.056348) \| 0.107306 / 0.043533 (0.063773) \| 0.421942 / 0.255139 (0.166803) \| 0.419736 / 0.283200 (0.136536) \| 0.044195 / 0.141683 (-0.097488) \| 1.793840 / 1.452155 (0.341686) \| 1.960204 / 1.492716 (0.467488) \|\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) \| 0.252046 / 0.018006 (0.234040) \| 0.627725 / 0.000490 (0.627236) \| 0.007435 / 0.000200 (0.007235) \| 0.000526 / 0.000054 (0.000472) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034656 / 0.037411 (-0.002755) \| 0.114534 / 0.014526 (0.100008) \| 0.135804 / 0.176557 (-0.040753) \| 0.209309 / 0.737135 (-0.527826) \| 0.140369 / 0.296338 (-0.155969) \|\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) \| 0.636736 / 0.215209 (0.421527) \| 6.039985 / 2.077655 (3.962330) \| 2.640141 / 1.504120 (1.136021) \| 2.284492 / 1.541195 (0.743297) \| 2.324956 / 1.468490 (0.856466) \| 0.934499 / 4.584777 (-3.650278) \| 5.673415 / 3.745712 (1.927703) \| 5.184584 / 5.269862 (-0.085278) \| 2.661911 / 4.565676 (-1.903766) \| 0.150420 / 0.424275 (-0.273855) \| 0.015655 / 0.007607 (0.008048) \| 0.748290 / 0.226044 (0.522246) \| 7.579755 / 2.268929 (5.310827) \| 3.346732 / 55.444624 (-52.097892) \| 2.708212 / 6.876477 (-4.168264) \| 2.682423 / 2.142072 (0.540351) \| 1.170389 / 4.805227 (-3.634838) \| 0.215775 / 6.500664 (-6.284889) \| 0.076360 / 0.075469 (0.000891) \|\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) \| 1.516794 / 1.841788 (-0.324993) \| 18.709117 / 8.074308 (10.634809) \| 22.492542 / 10.191392 (12.301150) \| 0.237978 / 0.680424 (-0.442446) \| 0.027828 / 0.534201 (-0.506373) \| 0.499968 / 0.579283 (-0.079315) \| 0.645899 / 0.434364 (0.211535) \| 0.548599 / 0.540337 (0.008262) \| 0.675428 / 1.386936 (-0.711508) \|\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) \| 0.008469 / 0.011353 (-0.002884) \| 0.005420 / 0.011008 (-0.005589) \| 0.093340 / 0.038508 (0.054832) \| 0.045896 / 0.023109 (0.022786) \| 0.533267 / 0.275898 (0.257369) \| 0.596034 / 0.323480 (0.272555) \| 0.004816 / 0.007986 (-0.003170) \| 0.004379 / 0.004328 (0.000051) \| 0.096356 / 0.004250 (0.092106) \| 0.058339 / 0.037052 (0.021287) \| 0.574464 / 0.258489 (0.315975) \| 0.649301 / 0.293841 (0.355461) \| 0.047599 / 0.128546 (-0.080947) \| 0.013759 / 0.075646 (-0.061887) \| 0.104672 / 0.419271 (-0.314599) \| 0.061658 / 0.043533 (0.018125) \| 0.560956 / 0.255139 (0.305817) \| 0.585328 / 0.283200 (0.302128) \| 0.034137 / 0.141683 (-0.107546) \| 1.844528 / 1.452155 (0.392373) \| 1.971398 / 1.492716 (0.478682) \|\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) \| 0.278666 / 0.018006 (0.260660) \| 0.577342 / 0.000490 (0.576853) \| 0.005496 / 0.000200 (0.005296) \| 0.000131 / 0.000054 (0.000076) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029863 / 0.037411 (-0.007549) \| 0.161703 / 0.014526 (0.147177) \| 0.132279 / 0.176557 (-0.044277) \| 0.227345 / 0.737135 (-0.509791) \| 0.138047 / 0.296338 (-0.158291) \|\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) \| 0.651535 / 0.215209 (0.436326) \| 7.077949 / 2.077655 (5.000295) \| 2.926990 / 1.504120 (1.422871) \| 2.598872 / 1.541195 (1.057678) \| 2.614192 / 1.468490 (1.145702) \| 0.913845 / 4.584777 (-3.670932) \| 5.704301 / 3.745712 (1.958589) \| 2.796914 / 5.269862 (-2.472948) \| 1.836096 / 4.565676 (-2.729580) \| 0.106294 / 0.424275 (-0.317981) \| 0.012705 / 0.007607 (0.005098) \| 0.836336 / 0.226044 (0.610291) \| 8.234079 / 2.268929 (5.965150) \| 3.836410 / 55.444624 (-51.608215) \| 3.116752 / 6.876477 (-3.759724) \| 3.154258 / 2.142072 (1.012186) \| 1.195794 / 4.805227 (-3.609434) \| 0.240491 / 6.500664 (-6.260173) \| 0.087913 / 0.075469 (0.012444) \|\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) \| 1.724723 / 1.841788 (-0.117064) \| 19.492194 / 8.074308 (11.417885) \| 21.443341 / 10.191392 (11.251949) \| 0.245819 / 0.680424 (-0.434605) \| 0.027024 / 0.534201 (-0.507177) \| 0.481071 / 0.579283 (-0.098212) \| 0.596359 / 0.434364 (0.161995) \| 0.646462 / 0.540337 (0.106124) \| 0.706380 / 1.386936 (-0.680556) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ca664e6d5ef137127b238aae1d0aff54e22db2 \"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) \| 0.006634 / 0.011353 (-0.004719) \| 0.004003 / 0.011008 (-0.007005) \| 0.097874 / 0.038508 (0.059365) \| 0.043528 / 0.023109 (0.020419) \| 0.302293 / 0.275898 (0.026395) \| 0.357041 / 0.323480 (0.033561) \| 0.003761 / 0.007986 (-0.004225) \| 0.004312 / 0.004328 (-0.000016) \| 0.076253 / 0.004250 (0.072003) \| 0.062807 / 0.037052 (0.025755) \| 0.316737 / 0.258489 (0.058248) \| 0.356722 / 0.293841 (0.062881) \| 0.030816 / 0.128546 (-0.097730) \| 0.008691 / 0.075646 (-0.066955) \| 0.328366 / 0.419271 (-0.090906) \| 0.062299 / 0.043533 (0.018766) \| 0.293877 / 0.255139 (0.038738) \| 0.319832 / 0.283200 (0.036632) \| 0.024996 / 0.141683 (-0.116687) \| 1.473912 / 1.452155 (0.021758) \| 1.565439 / 1.492716 (0.072723) \|\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) \| 0.208428 / 0.018006 (0.190422) \| 0.435618 / 0.000490 (0.435128) \| 0.000695 / 0.000200 (0.000495) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026253 / 0.037411 (-0.011158) \| 0.106908 / 0.014526 (0.092382) \| 0.117075 / 0.176557 (-0.059482) \| 0.177969 / 0.737135 (-0.559166) \| 0.123400 / 0.296338 (-0.172938) \|\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) \| 0.424970 / 0.215209 (0.209761) \| 4.203233 / 2.077655 (2.125578) \| 2.009679 / 1.504120 (0.505559) \| 1.825691 / 1.541195 (0.284496) \| 1.870639 / 1.468490 (0.402149) \| 0.530758 / 4.584777 (-4.054019) \| 3.718791 / 3.745712 (-0.026921) \| 1.800206 / 5.269862 (-3.469656) \| 1.071651 / 4.565676 (-3.494025) \| 0.065126 / 0.424275 (-0.359149) \| 0.011312 / 0.007607 (0.003704) \| 0.532503 / 0.226044 (0.306458) \| 5.353950 / 2.268929 (3.085021) \| 2.463548 / 55.444624 (-52.981076) \| 2.139832 / 6.876477 (-4.736645) \| 2.238722 / 2.142072 (0.096650) \| 0.655736 / 4.805227 (-4.149492) \| 0.141689 / 6.500664 (-6.358975) \| 0.063282 / 0.075469 (-0.012187) \|\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) \| 1.183523 / 1.841788 (-0.658265) \| 14.146428 / 8.074308 (6.072120) \| 14.312883 / 10.191392 (4.121491) \| 0.169286 / 0.680424 (-0.511138) \| 0.017343 / 0.534201 (-0.516858) \| 0.397934 / 0.579283 (-0.181349) \| 0.417791 / 0.434364 (-0.016573) \| 0.463639 / 0.540337 (-0.076698) \| 0.562787 / 1.386936 (-0.824149) \|\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) \| 0.006594 / 0.011353 (-0.004759) \| 0.004086 / 0.011008 (-0.006922) \| 0.075122 / 0.038508 (0.036614) \| 0.041849 / 0.023109 (0.018740) \| 0.362645 / 0.275898 (0.086747) \| 0.464350 / 0.323480 (0.140870) \| 0.003760 / 0.007986 (-0.004226) \| 0.003327 / 0.004328 (-0.001001) \| 0.076154 / 0.004250 (0.071904) \| 0.053232 / 0.037052 (0.016180) \| 0.407863 / 0.258489 (0.149374) \| 0.460787 / 0.293841 (0.166946) \| 0.031917 / 0.128546 (-0.096630) \| 0.008770 / 0.075646 (-0.066876) \| 0.082612 / 0.419271 (-0.336660) \| 0.051311 / 0.043533 (0.007779) \| 0.354508 / 0.255139 (0.099369) \| 0.419533 / 0.283200 (0.136334) \| 0.023980 / 0.141683 (-0.117703) \| 1.491255 / 1.452155 (0.039100) \| 1.536101 / 1.492716 (0.043384) \|\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) \| 0.178261 / 0.018006 (0.160255) \| 0.444680 / 0.000490 (0.444190) \| 0.013761 / 0.000200 (0.013561) \| 0.000117 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027875 / 0.037411 (-0.009536) \| 0.111269 / 0.014526 (0.096744) \| 0.121096 / 0.176557 (-0.055461) \| 0.174387 / 0.737135 (-0.562749) \| 0.124714 / 0.296338 (-0.171624) \|\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) \| 0.445422 / 0.215209 (0.230213) \| 4.435877 / 2.077655 (2.358222) \| 2.221895 / 1.504120 (0.717775) \| 2.030571 / 1.541195 (0.489376) \| 2.074863 / 1.468490 (0.606373) \| 0.543331 / 4.584777 (-4.041446) \| 3.753615 / 3.745712 (0.007903) \| 3.317074 / 5.269862 (-1.952787) \| 1.630390 / 4.565676 (-2.935286) \| 0.066726 / 0.424275 (-0.357549) \| 0.011556 / 0.007607 (0.003949) \| 0.546985 / 0.226044 (0.320941) \| 5.460634 / 2.268929 (3.191705) \| 2.705945 / 55.444624 (-52.738679) \| 2.373425 / 6.876477 (-4.503052) \| 2.401472 / 2.142072 (0.259399) \| 0.663225 / 4.805227 (-4.142002) \| 0.143694 / 6.500664 (-6.356970) \| 0.065283 / 0.075469 (-0.010186) \|\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) \| 1.264804 / 1.841788 (-0.576983) \| 14.803228 / 8.074308 (6.728919) \| 14.178514 / 10.191392 (3.987122) \| 0.162651 / 0.680424 (-0.517772) \| 0.017586 / 0.534201 (-0.516615) \| 0.398740 / 0.579283 (-0.180543) \| 0.414478 / 0.434364 (-0.019886) \| 0.465442 / 0.540337 (-0.074895) \| 0.563450 / 1.386936 (-0.823486) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76f75a9a3b2aaad05ea0ea5ab77e01fd2ca66760 \"CML watermark\")\n" ]	1,687,362,181,000	1,687,443,809,000	1,687,443,386,000	MEMBER	null	I used a regex to filter the data files based on their extension for packaged builders. I tried and a regex is 10x faster that using `in` to check if the extension is in the list of supported extensions. Supersedes https://github.com/huggingface/datasets/pull/5850 Close https://github.com/huggingface/datasets/issues/5849 I also did a small change to favor the parquet module in case of a draw in the extension counter.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5972/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5972/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5972", "html_url": "https://github.com/huggingface/datasets/pull/5972", "diff_url": "https://github.com/huggingface/datasets/pull/5972.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5972.patch", "merged_at": "2023-06-22T14:16:26" }	true
https://api.github.com/repos/huggingface/datasets/issues/5971	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5971/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5971/comments	https://api.github.com/repos/huggingface/datasets/issues/5971/events	https://github.com/huggingface/datasets/issues/5971	1,767,053,635	I_kwDODunzps5pUxlD	5,971	Docs: make "repository structure" easier to find	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892861, "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation", "name": "documentation", "color": "0075ca", "default": true, "description": "Improvements or additions to documentation" } ]	open	false	{ "login": "benjaminbrown038", "id": 35114142, "node_id": "MDQ6VXNlcjM1MTE0MTQy", "avatar_url": "https://avatars.githubusercontent.com/u/35114142?v=4", "gravatar_id": "", "url": "https://api.github.com/users/benjaminbrown038", "html_url": "https://github.com/benjaminbrown038", "followers_url": "https://api.github.com/users/benjaminbrown038/followers", "following_url": "https://api.github.com/users/benjaminbrown038/following{/other_user}", "gists_url": "https://api.github.com/users/benjaminbrown038/gists{/gist_id}", "starred_url": "https://api.github.com/users/benjaminbrown038/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/benjaminbrown038/subscriptions", "organizations_url": "https://api.github.com/users/benjaminbrown038/orgs", "repos_url": "https://api.github.com/users/benjaminbrown038/repos", "events_url": "https://api.github.com/users/benjaminbrown038/events{/privacy}", "received_events_url": "https://api.github.com/users/benjaminbrown038/received_events", "type": "User", "site_admin": false }	[ { "login": "benjaminbrown038", "id": 35114142, "node_id": "MDQ6VXNlcjM1MTE0MTQy", "avatar_url": "https://avatars.githubusercontent.com/u/35114142?v=4", "gravatar_id": "", "url": "https://api.github.com/users/benjaminbrown038", "html_url": "https://github.com/benjaminbrown038", "followers_url": "https://api.github.com/users/benjaminbrown038/followers", "following_url": "https://api.github.com/users/benjaminbrown038/following{/other_user}", "gists_url": "https://api.github.com/users/benjaminbrown038/gists{/gist_id}", "starred_url": "https://api.github.com/users/benjaminbrown038/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/benjaminbrown038/subscriptions", "organizations_url": "https://api.github.com/users/benjaminbrown038/orgs", "repos_url": "https://api.github.com/users/benjaminbrown038/repos", "events_url": "https://api.github.com/users/benjaminbrown038/events{/privacy}", "received_events_url": "https://api.github.com/users/benjaminbrown038/received_events", "type": "User", "site_admin": false } ]	[ "Loading a local dataset also works the same way when `data_files` are not specified, so I agree we should make this info easier to discover \r\n\r\ncc @stevhliu ", "Is this issue open? If so, I will self assign. ", "@benjaminbrown038 Yes, it is. Maybe @stevhliu can give some pointers on improving this doc page's discoverability.", "I think we can add a version of the [Main use-case](https://huggingface.co/docs/datasets/repository_structure#main-usecase) section to the [Share a dataset to the Hub](https://huggingface.co/docs/datasets/upload_dataset) tutorial. \r\n\r\nCurrently, it doesn't tell you how to structure the repository; it only tells you how to create it. So adding the \"main use-case\" will help bridge the gap and make it easier to find. We should also add a link to the [Structure your repository](https://huggingface.co/docs/datasets/repository_structure) guide for users who want to learn about the other options.", "#self-assign" ]	1,687,336,004,000	1,688,539,898,000	null	CONTRIBUTOR	null	The page https://huggingface.co/docs/datasets/repository_structure explains how to create a simple repository structure without a dataset script. It's the simplest way to create a dataset and should be easier to find, particularly on the docs' first pages.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5971/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5971/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5970	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5970/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5970/comments	https://api.github.com/repos/huggingface/datasets/issues/5970/events	https://github.com/huggingface/datasets/issues/5970	1,766,010,356	I_kwDODunzps5pQy30	5,970	description disappearing from Info when Uploading a Dataset Created with `from_dict`	{ "login": "balisujohn", "id": 20377292, "node_id": "MDQ6VXNlcjIwMzc3Mjky", "avatar_url": "https://avatars.githubusercontent.com/u/20377292?v=4", "gravatar_id": "", "url": "https://api.github.com/users/balisujohn", "html_url": "https://github.com/balisujohn", "followers_url": "https://api.github.com/users/balisujohn/followers", "following_url": "https://api.github.com/users/balisujohn/following{/other_user}", "gists_url": "https://api.github.com/users/balisujohn/gists{/gist_id}", "starred_url": "https://api.github.com/users/balisujohn/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/balisujohn/subscriptions", "organizations_url": "https://api.github.com/users/balisujohn/orgs", "repos_url": "https://api.github.com/users/balisujohn/repos", "events_url": "https://api.github.com/users/balisujohn/events{/privacy}", "received_events_url": "https://api.github.com/users/balisujohn/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Here's a minimal way to reproduce the bug, for the sake of convenience.\r\n````\r\nfrom datasets import Dataset, DatasetInfo, load_dataset\r\n\r\n\r\nepisodes_dict = {\"test\":[1,2,3],\"test2\": [1,2,4]}\r\n\r\nhugging_face_dataset = Dataset.from_dict(\r\n episodes_dict, info=DatasetInfo(description=\"test_str\")\r\n)\r\nprint(hugging_face_dataset.info)\r\n\r\nhugging_face_dataset.push_to_hub(\"balisujohn/minari_test\", private=True)\r\n\r\nredownloaded_dataset= load_dataset(\"balisujohn/minari_test\")[\"train\"]\r\n\r\n\r\nprint(redownloaded_dataset.info)\r\n````\r\n", "Thanks for reporting !\r\n\r\nFor now I would recommend uploading a separate JSON file for your metadata.\r\n\r\nAlternatively you can upload a second configuration of the dataset containing your metadata but this feature is not released yet (though you can already use it from [here](https://github.com/huggingface/datasets/pull/5331), it will be released soon)" ]	1,687,288,706,000	1,687,443,836,000	null	NONE	null	### Describe the bug When uploading a dataset created locally using `from_dict` with a specified `description` field. It appears before upload, but is missing after upload and re-download. ### Steps to reproduce the bug I think the most relevant pattern in the code might be the following lines: ``` description_json_str = json.dumps( { "dataset_id": dataset.spec.dataset_id, "env_name": dataset.spec.env_spec.id, "action_space": serialize_space(dataset.spec.action_space), "observation_space": serialize_space(dataset.spec.observation_space), } ) hugging_face_dataset = Dataset.from_dict( episodes_dict, info=DatasetInfo(description=description_json_str) ) ``` Which comes from this function https://github.com/balisujohn/minarai/blob/8e023727f0a8488c4451651d9f7a79b981412c40/minari/integrations/hugging_face.py#L39 To replicate, clone this branch of my Minari fork https://github.com/balisujohn/minarai/tree/dev-huggingface then run ``` python3.8 -m venv env source env/bin/activate python3 -m pip install -e . python3 -m pip install pytest ``` The change the hugging face repo path in the test called `test_hugging_face_push_and_pull_dataset` in `tests/integrations/test_hugging_face.py` to one you have permissions to write to. Then run: ``` pytest tests/integrations/test_hugging_face.py::test_hugging_face_push_and_pull_dataset ``` ### Expected behavior DATASET INFO BEFORE UPLOADING DatasetInfo(description='{"dataset_id": "dummy-combo-test-v0", "env_name": "DummyComboEnv-v0", "action_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}]}", "observation_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"component_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [-1.0], \\"high\\": [1.0]}, \\"component_2\\": {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"subcomponent_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, \\"subcomponent_2\\": {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}, {\\"type\\": \\"Discrete\\", \\"dtype\\": \\"int64\\", \\"start\\": 0, \\"n\\": 10}]}}}}}]}]}"}', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None) ... DATASET INFO AFTER UPLOADING AND DOWNLOADING DatasetInfo(description='', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits={'train': SplitInfo(name='train', num_bytes=4846, num_examples=60, shard_lengths=None, dataset_name='parquet')}, download_checksums={'https://huggingface.co/datasets/balisujohn/minari_test/resolve/8217b614ff9ba5edc1a30c7df430e92a46f65363/data/train-00000-of-00001-7c5900b93b35745e.parquet': {'num_bytes': 9052, 'checksum': None}}, download_size=9052, post_processing_size=None, dataset_size=4846, size_in_bytes=13898) ... ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5970/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5970/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5969	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5969/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5969/comments	https://api.github.com/repos/huggingface/datasets/issues/5969/events	https://github.com/huggingface/datasets/pull/5969	1,765,529,905	PR_kwDODunzps5Tcgq4	5,969	Add `encoding` and `errors` params to JSON loader	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006770 / 0.011353 (-0.004583) \| 0.004143 / 0.011008 (-0.006865) \| 0.098928 / 0.038508 (0.060420) \| 0.044893 / 0.023109 (0.021783) \| 0.302630 / 0.275898 (0.026732) \| 0.368173 / 0.323480 (0.044693) \| 0.005631 / 0.007986 (-0.002354) \| 0.003397 / 0.004328 (-0.000931) \| 0.075748 / 0.004250 (0.071497) \| 0.062582 / 0.037052 (0.025530) \| 0.329586 / 0.258489 (0.071097) \| 0.362625 / 0.293841 (0.068784) \| 0.033250 / 0.128546 (-0.095296) \| 0.008880 / 0.075646 (-0.066766) \| 0.329683 / 0.419271 (-0.089588) \| 0.054426 / 0.043533 (0.010893) \| 0.297940 / 0.255139 (0.042801) \| 0.319796 / 0.283200 (0.036597) \| 0.023296 / 0.141683 (-0.118387) \| 1.462142 / 1.452155 (0.009987) \| 1.495796 / 1.492716 (0.003079) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201771 / 0.018006 (0.183765) \| 0.454514 / 0.000490 (0.454024) \| 0.003333 / 0.000200 (0.003133) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028084 / 0.037411 (-0.009327) \| 0.109452 / 0.014526 (0.094926) \| 0.119200 / 0.176557 (-0.057357) \| 0.180302 / 0.737135 (-0.556834) \| 0.125653 / 0.296338 (-0.170686) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409819 / 0.215209 (0.194610) \| 4.055117 / 2.077655 (1.977462) \| 1.855279 / 1.504120 (0.351159) \| 1.655281 / 1.541195 (0.114086) \| 1.687938 / 1.468490 (0.219448) \| 0.528352 / 4.584777 (-4.056425) \| 3.750250 / 3.745712 (0.004538) \| 3.386741 / 5.269862 (-1.883121) \| 1.572036 / 4.565676 (-2.993640) \| 0.065125 / 0.424275 (-0.359150) \| 0.011259 / 0.007607 (0.003652) \| 0.513449 / 0.226044 (0.287405) \| 5.139421 / 2.268929 (2.870492) \| 2.316973 / 55.444624 (-53.127651) \| 1.984109 / 6.876477 (-4.892368) \| 2.127915 / 2.142072 (-0.014158) \| 0.653238 / 4.805227 (-4.151989) \| 0.142686 / 6.500664 (-6.357978) \| 0.063666 / 0.075469 (-0.011803) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.185174 / 1.841788 (-0.656614) \| 14.790282 / 8.074308 (6.715974) \| 13.089222 / 10.191392 (2.897830) \| 0.146055 / 0.680424 (-0.534369) \| 0.017835 / 0.534201 (-0.516366) \| 0.399598 / 0.579283 (-0.179685) \| 0.425296 / 0.434364 (-0.009068) \| 0.478552 / 0.540337 (-0.061786) \| 0.579702 / 1.386936 (-0.807234) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006750 / 0.011353 (-0.004603) \| 0.004156 / 0.011008 (-0.006853) \| 0.074948 / 0.038508 (0.036440) \| 0.043368 / 0.023109 (0.020259) \| 0.355389 / 0.275898 (0.079491) \| 0.429167 / 0.323480 (0.105687) \| 0.003911 / 0.007986 (-0.004075) \| 0.004340 / 0.004328 (0.000012) \| 0.075940 / 0.004250 (0.071689) \| 0.054293 / 0.037052 (0.017241) \| 0.400317 / 0.258489 (0.141827) \| 0.432001 / 0.293841 (0.138160) \| 0.032340 / 0.128546 (-0.096206) \| 0.008876 / 0.075646 (-0.066770) \| 0.082284 / 0.419271 (-0.336987) \| 0.050819 / 0.043533 (0.007286) \| 0.351994 / 0.255139 (0.096855) \| 0.375917 / 0.283200 (0.092717) \| 0.022466 / 0.141683 (-0.119217) \| 1.538824 / 1.452155 (0.086669) \| 1.563995 / 1.492716 (0.071279) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227330 / 0.018006 (0.209323) \| 0.446380 / 0.000490 (0.445890) \| 0.000408 / 0.000200 (0.000208) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028534 / 0.037411 (-0.008878) \| 0.113467 / 0.014526 (0.098941) \| 0.123590 / 0.176557 (-0.052966) \| 0.174309 / 0.737135 (-0.562827) \| 0.130631 / 0.296338 (-0.165707) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441020 / 0.215209 (0.225811) \| 4.386564 / 2.077655 (2.308909) \| 2.100704 / 1.504120 (0.596584) \| 1.901484 / 1.541195 (0.360289) \| 1.963494 / 1.468490 (0.495004) \| 0.536838 / 4.584777 (-4.047939) \| 3.739071 / 3.745712 (-0.006642) \| 3.278981 / 5.269862 (-1.990881) \| 1.515476 / 4.565676 (-3.050201) \| 0.066388 / 0.424275 (-0.357887) \| 0.011857 / 0.007607 (0.004250) \| 0.545507 / 0.226044 (0.319463) \| 5.441479 / 2.268929 (3.172550) \| 2.602144 / 55.444624 (-52.842480) \| 2.235583 / 6.876477 (-4.640894) \| 2.293458 / 2.142072 (0.151385) \| 0.658535 / 4.805227 (-4.146692) \| 0.141327 / 6.500664 (-6.359337) \| 0.063726 / 0.075469 (-0.011743) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.247819 / 1.841788 (-0.593968) \| 15.234524 / 8.074308 (7.160216) \| 14.592700 / 10.191392 (4.401308) \| 0.141952 / 0.680424 (-0.538472) \| 0.017747 / 0.534201 (-0.516454) \| 0.396819 / 0.579283 (-0.182465) \| 0.415902 / 0.434364 (-0.018462) \| 0.464619 / 0.540337 (-0.075718) \| 0.560866 / 1.386936 (-0.826070) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b7f6c59deb868e21f295917548fa2df10dd0158 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008278 / 0.011353 (-0.003075) \| 0.005044 / 0.011008 (-0.005964) \| 0.123382 / 0.038508 (0.084874) \| 0.054039 / 0.023109 (0.030929) \| 0.382338 / 0.275898 (0.106440) \| 0.453287 / 0.323480 (0.129807) \| 0.006342 / 0.007986 (-0.001644) \| 0.003930 / 0.004328 (-0.000398) \| 0.094039 / 0.004250 (0.089789) \| 0.076525 / 0.037052 (0.039472) \| 0.394066 / 0.258489 (0.135577) \| 0.445600 / 0.293841 (0.151759) \| 0.039348 / 0.128546 (-0.089199) \| 0.010485 / 0.075646 (-0.065161) \| 0.433730 / 0.419271 (0.014459) \| 0.082671 / 0.043533 (0.039138) \| 0.375250 / 0.255139 (0.120111) \| 0.416269 / 0.283200 (0.133070) \| 0.038397 / 0.141683 (-0.103286) \| 1.864834 / 1.452155 (0.412680) \| 2.010453 / 1.492716 (0.517737) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240008 / 0.018006 (0.222002) \| 0.470975 / 0.000490 (0.470485) \| 0.004001 / 0.000200 (0.003801) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031107 / 0.037411 (-0.006304) \| 0.129371 / 0.014526 (0.114846) \| 0.141559 / 0.176557 (-0.034997) \| 0.205571 / 0.737135 (-0.531564) \| 0.144611 / 0.296338 (-0.151728) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506972 / 0.215209 (0.291763) \| 5.055951 / 2.077655 (2.978296) \| 2.397438 / 1.504120 (0.893318) \| 2.170435 / 1.541195 (0.629240) \| 2.240296 / 1.468490 (0.771806) \| 0.641559 / 4.584777 (-3.943218) \| 4.644772 / 3.745712 (0.899060) \| 4.064200 / 5.269862 (-1.205662) \| 1.946991 / 4.565676 (-2.618685) \| 0.086413 / 0.424275 (-0.337862) \| 0.015082 / 0.007607 (0.007475) \| 0.670413 / 0.226044 (0.444369) \| 6.331346 / 2.268929 (4.062418) \| 2.965813 / 55.444624 (-52.478812) \| 2.547952 / 6.876477 (-4.328524) \| 2.718390 / 2.142072 (0.576318) \| 0.796657 / 4.805227 (-4.008571) \| 0.173229 / 6.500664 (-6.327435) \| 0.079606 / 0.075469 (0.004137) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.568761 / 1.841788 (-0.273026) \| 18.485432 / 8.074308 (10.411124) \| 15.758513 / 10.191392 (5.567121) \| 0.170427 / 0.680424 (-0.509997) \| 0.021421 / 0.534201 (-0.512780) \| 0.518623 / 0.579283 (-0.060660) \| 0.525887 / 0.434364 (0.091523) \| 0.640331 / 0.540337 (0.099993) \| 0.766748 / 1.386936 (-0.620188) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007680 / 0.011353 (-0.003673) \| 0.005289 / 0.011008 (-0.005719) \| 0.093773 / 0.038508 (0.055265) \| 0.054997 / 0.023109 (0.031888) \| 0.456277 / 0.275898 (0.180379) \| 0.500642 / 0.323480 (0.177162) \| 0.005935 / 0.007986 (-0.002050) \| 0.004375 / 0.004328 (0.000047) \| 0.094131 / 0.004250 (0.089881) \| 0.063399 / 0.037052 (0.026347) \| 0.470546 / 0.258489 (0.212057) \| 0.504989 / 0.293841 (0.211148) \| 0.038541 / 0.128546 (-0.090006) \| 0.010403 / 0.075646 (-0.065244) \| 0.102469 / 0.419271 (-0.316802) \| 0.063105 / 0.043533 (0.019572) \| 0.466005 / 0.255139 (0.210866) \| 0.458677 / 0.283200 (0.175477) \| 0.028407 / 0.141683 (-0.113276) \| 1.893829 / 1.452155 (0.441675) \| 1.917954 / 1.492716 (0.425238) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272760 / 0.018006 (0.254754) \| 0.476159 / 0.000490 (0.475669) \| 0.008467 / 0.000200 (0.008267) \| 0.000146 / 0.000054 (0.000091) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035755 / 0.037411 (-0.001656) \| 0.145038 / 0.014526 (0.130512) \| 0.148322 / 0.176557 (-0.028235) \| 0.210193 / 0.737135 (-0.526943) \| 0.156547 / 0.296338 (-0.139792) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.541204 / 0.215209 (0.325995) \| 5.382746 / 2.077655 (3.305091) \| 2.704229 / 1.504120 (1.200109) \| 2.468422 / 1.541195 (0.927227) \| 2.522672 / 1.468490 (1.054182) \| 0.644899 / 4.584777 (-3.939878) \| 4.654401 / 3.745712 (0.908689) \| 2.159223 / 5.269862 (-3.110638) \| 1.280098 / 4.565676 (-3.285578) \| 0.080053 / 0.424275 (-0.344222) \| 0.014383 / 0.007607 (0.006776) \| 0.662770 / 0.226044 (0.436725) \| 6.617651 / 2.268929 (4.348722) \| 3.234347 / 55.444624 (-52.210277) \| 2.861417 / 6.876477 (-4.015059) \| 2.888928 / 2.142072 (0.746856) \| 0.792854 / 4.805227 (-4.012374) \| 0.172553 / 6.500664 (-6.328111) \| 0.078402 / 0.075469 (0.002933) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.565351 / 1.841788 (-0.276436) \| 18.681916 / 8.074308 (10.607608) \| 17.264473 / 10.191392 (7.073081) \| 0.168461 / 0.680424 (-0.511963) \| 0.021353 / 0.534201 (-0.512848) \| 0.517843 / 0.579283 (-0.061440) \| 0.519907 / 0.434364 (0.085543) \| 0.623687 / 0.540337 (0.083350) \| 0.761796 / 1.386936 (-0.625140) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bbf58747f734a46e75937bdbcbc05b06ade0224a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006750 / 0.011353 (-0.004603) \| 0.004268 / 0.011008 (-0.006741) \| 0.098644 / 0.038508 (0.060136) \| 0.044643 / 0.023109 (0.021534) \| 0.309420 / 0.275898 (0.033522) \| 0.379294 / 0.323480 (0.055815) \| 0.005729 / 0.007986 (-0.002256) \| 0.003615 / 0.004328 (-0.000714) \| 0.076086 / 0.004250 (0.071835) \| 0.068994 / 0.037052 (0.031942) \| 0.325653 / 0.258489 (0.067164) \| 0.375187 / 0.293841 (0.081347) \| 0.032546 / 0.128546 (-0.096000) \| 0.009089 / 0.075646 (-0.066557) \| 0.329905 / 0.419271 (-0.089366) \| 0.066832 / 0.043533 (0.023300) \| 0.299247 / 0.255139 (0.044108) \| 0.323460 / 0.283200 (0.040260) \| 0.034226 / 0.141683 (-0.107457) \| 1.475659 / 1.452155 (0.023505) \| 1.556234 / 1.492716 (0.063518) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292305 / 0.018006 (0.274299) \| 0.542584 / 0.000490 (0.542094) \| 0.003047 / 0.000200 (0.002847) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030096 / 0.037411 (-0.007315) \| 0.112341 / 0.014526 (0.097815) \| 0.124965 / 0.176557 (-0.051591) \| 0.183159 / 0.737135 (-0.553976) \| 0.131885 / 0.296338 (-0.164453) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426437 / 0.215209 (0.211228) \| 4.260984 / 2.077655 (2.183330) \| 2.078358 / 1.504120 (0.574238) \| 1.877644 / 1.541195 (0.336449) \| 2.044036 / 1.468490 (0.575546) \| 0.532980 / 4.584777 (-4.051797) \| 3.749573 / 3.745712 (0.003860) \| 1.944155 / 5.269862 (-3.325706) \| 1.090307 / 4.565676 (-3.475370) \| 0.065445 / 0.424275 (-0.358830) \| 0.011237 / 0.007607 (0.003630) \| 0.521448 / 0.226044 (0.295403) \| 5.213118 / 2.268929 (2.944189) \| 2.507829 / 55.444624 (-52.936795) \| 2.177179 / 6.876477 (-4.699297) \| 2.351161 / 2.142072 (0.209088) \| 0.656775 / 4.805227 (-4.148452) \| 0.141207 / 6.500664 (-6.359457) \| 0.063286 / 0.075469 (-0.012183) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.190281 / 1.841788 (-0.651506) \| 15.327424 / 8.074308 (7.253116) \| 13.300695 / 10.191392 (3.109303) \| 0.190484 / 0.680424 (-0.489939) \| 0.017984 / 0.534201 (-0.516217) \| 0.405714 / 0.579283 (-0.173569) \| 0.435915 / 0.434364 (0.001551) \| 0.494083 / 0.540337 (-0.046254) \| 0.600616 / 1.386936 (-0.786320) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006740 / 0.011353 (-0.004613) \| 0.004289 / 0.011008 (-0.006719) \| 0.076532 / 0.038508 (0.038024) \| 0.043305 / 0.023109 (0.020196) \| 0.356111 / 0.275898 (0.080213) \| 0.434121 / 0.323480 (0.110641) \| 0.005599 / 0.007986 (-0.002387) \| 0.003461 / 0.004328 (-0.000868) \| 0.077097 / 0.004250 (0.072847) \| 0.055369 / 0.037052 (0.018317) \| 0.367093 / 0.258489 (0.108604) \| 0.418801 / 0.293841 (0.124960) \| 0.032057 / 0.128546 (-0.096489) \| 0.009048 / 0.075646 (-0.066599) \| 0.082897 / 0.419271 (-0.336374) \| 0.050287 / 0.043533 (0.006754) \| 0.352060 / 0.255139 (0.096921) \| 0.376278 / 0.283200 (0.093078) \| 0.023924 / 0.141683 (-0.117759) \| 1.522780 / 1.452155 (0.070626) \| 1.578938 / 1.492716 (0.086222) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287317 / 0.018006 (0.269311) \| 0.508490 / 0.000490 (0.508000) \| 0.000431 / 0.000200 (0.000231) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031139 / 0.037411 (-0.006272) \| 0.113927 / 0.014526 (0.099401) \| 0.128147 / 0.176557 (-0.048409) \| 0.179712 / 0.737135 (-0.557424) \| 0.134364 / 0.296338 (-0.161975) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452834 / 0.215209 (0.237625) \| 4.507944 / 2.077655 (2.430289) \| 2.287758 / 1.504120 (0.783638) \| 2.091145 / 1.541195 (0.549951) \| 2.196228 / 1.468490 (0.727738) \| 0.539306 / 4.584777 (-4.045471) \| 3.838941 / 3.745712 (0.093228) \| 1.908801 / 5.269862 (-3.361060) \| 1.139235 / 4.565676 (-3.426442) \| 0.066677 / 0.424275 (-0.357599) \| 0.011422 / 0.007607 (0.003815) \| 0.562966 / 0.226044 (0.336921) \| 5.633712 / 2.268929 (3.364784) \| 2.788622 / 55.444624 (-52.656002) \| 2.438465 / 6.876477 (-4.438012) \| 2.523479 / 2.142072 (0.381407) \| 0.668730 / 4.805227 (-4.136498) \| 0.143977 / 6.500664 (-6.356687) \| 0.064661 / 0.075469 (-0.010808) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291708 / 1.841788 (-0.550080) \| 15.573316 / 8.074308 (7.499008) \| 14.435099 / 10.191392 (4.243707) \| 0.147745 / 0.680424 (-0.532679) \| 0.017602 / 0.534201 (-0.516599) \| 0.401560 / 0.579283 (-0.177723) \| 0.429861 / 0.434364 (-0.004502) \| 0.469800 / 0.540337 (-0.070538) \| 0.567515 / 1.386936 (-0.819421) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c340f5dcfd06340f180f6c6ea2d5ef81f49d98 \"CML watermark\")\n" ]	1,687,271,315,000	1,687,354,790,000	1,687,354,342,000	CONTRIBUTOR	null	"Requested" in https://discuss.huggingface.co/t/utf-16-for-datasets/43828/3. `pd.read_json` also has these parameters, so it makes sense to be consistent.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5969/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5969/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5969", "html_url": "https://github.com/huggingface/datasets/pull/5969", "diff_url": "https://github.com/huggingface/datasets/pull/5969.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5969.patch", "merged_at": "2023-06-21T13:32:22" }	true
https://api.github.com/repos/huggingface/datasets/issues/5968	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5968/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5968/comments	https://api.github.com/repos/huggingface/datasets/issues/5968/events	https://github.com/huggingface/datasets/issues/5968	1,765,252,561	I_kwDODunzps5pN53R	5,968	Common Voice datasets still need `use_auth_token=True`	{ "login": "patrickvonplaten", "id": 23423619, "node_id": "MDQ6VXNlcjIzNDIzNjE5", "avatar_url": "https://avatars.githubusercontent.com/u/23423619?v=4", "gravatar_id": "", "url": "https://api.github.com/users/patrickvonplaten", "html_url": "https://github.com/patrickvonplaten", "followers_url": "https://api.github.com/users/patrickvonplaten/followers", "following_url": "https://api.github.com/users/patrickvonplaten/following{/other_user}", "gists_url": "https://api.github.com/users/patrickvonplaten/gists{/gist_id}", "starred_url": "https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/patrickvonplaten/subscriptions", "organizations_url": "https://api.github.com/users/patrickvonplaten/orgs", "repos_url": "https://api.github.com/users/patrickvonplaten/repos", "events_url": "https://api.github.com/users/patrickvonplaten/events{/privacy}", "received_events_url": "https://api.github.com/users/patrickvonplaten/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "cc @pcuenca as well. \r\n\r\nNot super urgent btw", "The issue commes from the dataset itself and is not related to the `datasets` lib\r\n\r\nsee https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1/blob/2c475b3b88e0f2e5828f830a4b91618a25ff20b7/common_voice_6_1.py#L148-L152", "Let's remove these lines in the dataset no? cc @anton-l @Vaibhavs10 " ]	1,687,262,317,000	1,687,342,117,000	null	MEMBER	null	### Describe the bug We don't need to pass `use_auth_token=True` anymore to download gated datasets or models, so the following should work if correctly logged in. ```py from datasets import load_dataset load_dataset("mozilla-foundation/common_voice_6_1", "tr", split="train+validation") ``` However it throws an error - probably because something weird is hardcoded into the dataset loading script. ### Steps to reproduce the bug 1.) ``` huggingface-cli login ``` 2.) Make sure that you have accepted the license here: https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1 3.) Run: ```py from datasets import load_dataset load_dataset("mozilla-foundation/common_voice_6_1", "tr", split="train+validation") ``` 4.) You'll get: ``` File ~/hf/lib/python3.10/site-packages/datasets/builder.py:963, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 961 split_dict = SplitDict(dataset_name=self.name) 962 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 963 split_generators = self._split_generators(dl_manager, split_generators_kwargs) 965 # Checksums verification 966 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_1/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/common_voice_6_1.py:150, in CommonVoice._split_generators(self, dl_manager) 148 hf_auth_token = dl_manager.download_config.use_auth_token 149 if hf_auth_token is None: --> 150 raise ConnectionError( 151 "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset" 152 ) 154 bundle_url_template = STATS["bundleURLTemplate"] 155 bundle_version = bundle_url_template.split("/")[0] ConnectionError: Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset ``` ### Expected behavior One should not have to pass `use_auth_token=True`. Also see discussion here: https://github.com/huggingface/blog/pull/1243#discussion_r1235131150 ### Environment info ``` - `datasets` version: 2.13.0 - Platform: Linux-6.2.0-76060200-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.16.0.dev0 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5968/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5968/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5967	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5967/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5967/comments	https://api.github.com/repos/huggingface/datasets/issues/5967/events	https://github.com/huggingface/datasets/issues/5967	1,763,926,520	I_kwDODunzps5pI2H4	5,967	Config name / split name lost after map with multiproc	{ "login": "sanchit-gandhi", "id": 93869735, "node_id": "U_kgDOBZhWpw", "avatar_url": "https://avatars.githubusercontent.com/u/93869735?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sanchit-gandhi", "html_url": "https://github.com/sanchit-gandhi", "followers_url": "https://api.github.com/users/sanchit-gandhi/followers", "following_url": "https://api.github.com/users/sanchit-gandhi/following{/other_user}", "gists_url": "https://api.github.com/users/sanchit-gandhi/gists{/gist_id}", "starred_url": "https://api.github.com/users/sanchit-gandhi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sanchit-gandhi/subscriptions", "organizations_url": "https://api.github.com/users/sanchit-gandhi/orgs", "repos_url": "https://api.github.com/users/sanchit-gandhi/repos", "events_url": "https://api.github.com/users/sanchit-gandhi/events{/privacy}", "received_events_url": "https://api.github.com/users/sanchit-gandhi/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "This must be due to DatasetInfo.from_merge which drops them and is used in `concatenate_datasets`.\r\n\r\nAnd you're experiencing this issue because multiprocessing does concatenate the resulting datasets from each process.\r\n\r\nMaybe they should be kept if all the subdatasets share the same values for config_name and split", "That sounds like a clean workaround!" ]	1,687,195,656,000	1,687,942,525,000	null	CONTRIBUTOR	null	### Describe the bug Performing a `.map` method on a dataset loses it's config name / split name only if run with multiproc ### Steps to reproduce the bug ```python from datasets import Audio, load_dataset from transformers import AutoFeatureExtractor import numpy as np # load dummy dataset libri = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") # make train / test splits libri = libri["validation"].train_test_split(seed=42, shuffle=True, test_size=0.1) # example feature extractor model_id = "ntu-spml/distilhubert" feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, do_normalize=True, return_attention_mask=True) sampling_rate = feature_extractor.sampling_rate libri = libri.cast_column("audio", Audio(sampling_rate=sampling_rate)) max_duration = 30.0 def preprocess_function(examples): audio_arrays = [x["array"] for x in examples["audio"]] inputs = feature_extractor( audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=int(feature_extractor.sampling_rate * max_duration), truncation=True, return_attention_mask=True, ) return inputs # single proc map libri_encoded = libri.map( preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=1 ) print(10 * "=" ,"Single processing", 10 * "=") print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split) print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split) # multi proc map libri_encoded = libri.map( preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=2 ) print(10 * "=" ,"Multi processing", 10 * "=") print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split) print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split) ``` Print Output: ``` ========== Single processing ========== Config name before: clean Split name before: validation Config name after: clean Split name after: validation ========== Multi processing ========== Config name before: clean Split name before: validation Config name after: None Split name after: None ``` => we can see that the config/split names are lost in the multiprocessing setting ### Expected behavior Should retain both config / split names in the multiproc setting ### Environment info - `datasets` version: 2.13.1.dev0 - Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5967/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5967/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5966	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5966/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5966/comments	https://api.github.com/repos/huggingface/datasets/issues/5966/events	https://github.com/huggingface/datasets/pull/5966	1,763,885,914	PR_kwDODunzps5TXBLP	5,966	Fix JSON generation in benchmarks CI	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "<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) \| 0.006186 / 0.011353 (-0.005167) \| 0.003744 / 0.011008 (-0.007264) \| 0.097295 / 0.038508 (0.058787) \| 0.037106 / 0.023109 (0.013997) \| 0.424154 / 0.275898 (0.148256) \| 0.474536 / 0.323480 (0.151057) \| 0.003454 / 0.007986 (-0.004532) \| 0.003865 / 0.004328 (-0.000463) \| 0.077348 / 0.004250 (0.073097) \| 0.051728 / 0.037052 (0.014675) \| 0.437120 / 0.258489 (0.178631) \| 0.478379 / 0.293841 (0.184538) \| 0.028939 / 0.128546 (-0.099608) \| 0.008376 / 0.075646 (-0.067270) \| 0.312002 / 0.419271 (-0.107270) \| 0.053723 / 0.043533 (0.010190) \| 0.424815 / 0.255139 (0.169676) \| 0.446203 / 0.283200 (0.163004) \| 0.026553 / 0.141683 (-0.115130) \| 1.479983 / 1.452155 (0.027828) \| 1.530613 / 1.492716 (0.037896) \|\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) \| 0.196627 / 0.018006 (0.178620) \| 0.422361 / 0.000490 (0.421871) \| 0.003442 / 0.000200 (0.003242) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022913 / 0.037411 (-0.014499) \| 0.096011 / 0.014526 (0.081485) \| 0.104091 / 0.176557 (-0.072466) \| 0.163273 / 0.737135 (-0.573862) \| 0.109142 / 0.296338 (-0.187197) \|\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) \| 0.431032 / 0.215209 (0.215823) \| 4.314391 / 2.077655 (2.236737) \| 2.003812 / 1.504120 (0.499692) \| 1.799538 / 1.541195 (0.258344) \| 1.830026 / 1.468490 (0.361536) \| 0.560131 / 4.584777 (-4.024646) \| 3.368997 / 3.745712 (-0.376715) \| 1.703032 / 5.269862 (-3.566830) \| 1.026949 / 4.565676 (-3.538727) \| 0.067507 / 0.424275 (-0.356768) \| 0.010910 / 0.007607 (0.003303) \| 0.532606 / 0.226044 (0.306562) \| 5.345179 / 2.268929 (3.076250) \| 2.368077 / 55.444624 (-53.076548) \| 2.028913 / 6.876477 (-4.847564) \| 2.147621 / 2.142072 (0.005549) \| 0.675696 / 4.805227 (-4.129531) \| 0.134902 / 6.500664 (-6.365762) \| 0.065004 / 0.075469 (-0.010465) \|\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) \| 1.233412 / 1.841788 (-0.608376) \| 13.767465 / 8.074308 (5.693157) \| 13.933653 / 10.191392 (3.742261) \| 0.129010 / 0.680424 (-0.551414) \| 0.016708 / 0.534201 (-0.517493) \| 0.362341 / 0.579283 (-0.216942) \| 0.390902 / 0.434364 (-0.043462) \| 0.429156 / 0.540337 (-0.111182) \| 0.521166 / 1.386936 (-0.865770) \|\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) \| 0.006169 / 0.011353 (-0.005184) \| 0.003839 / 0.011008 (-0.007169) \| 0.078784 / 0.038508 (0.040276) \| 0.040218 / 0.023109 (0.017109) \| 0.360439 / 0.275898 (0.084541) \| 0.423957 / 0.323480 (0.100477) \| 0.003456 / 0.007986 (-0.004529) \| 0.002900 / 0.004328 (-0.001428) \| 0.078820 / 0.004250 (0.074569) \| 0.047240 / 0.037052 (0.010187) \| 0.372081 / 0.258489 (0.113592) \| 0.424263 / 0.293841 (0.130422) \| 0.027977 / 0.128546 (-0.100569) \| 0.008400 / 0.075646 (-0.067246) \| 0.084399 / 0.419271 (-0.334872) \| 0.043303 / 0.043533 (-0.000230) \| 0.361583 / 0.255139 (0.106444) \| 0.394987 / 0.283200 (0.111787) \| 0.020006 / 0.141683 (-0.121677) \| 1.520208 / 1.452155 (0.068053) \| 1.587335 / 1.492716 (0.094619) \|\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) \| 0.223847 / 0.018006 (0.205840) \| 0.402194 / 0.000490 (0.401704) \| 0.000384 / 0.000200 (0.000184) \| 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) \| 0.024902 / 0.037411 (-0.012509) \| 0.099076 / 0.014526 (0.084550) \| 0.108041 / 0.176557 (-0.068516) \| 0.159385 / 0.737135 (-0.577750) \| 0.111442 / 0.296338 (-0.184896) \|\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) \| 0.446232 / 0.215209 (0.231023) \| 4.464927 / 2.077655 (2.387272) \| 2.155234 / 1.504120 (0.651114) \| 1.953645 / 1.541195 (0.412450) \| 1.965991 / 1.468490 (0.497501) \| 0.553473 / 4.584777 (-4.031304) \| 3.321397 / 3.745712 (-0.424315) \| 1.693761 / 5.269862 (-3.576101) \| 1.006299 / 4.565676 (-3.559378) \| 0.067013 / 0.424275 (-0.357262) \| 0.011116 / 0.007607 (0.003509) \| 0.555014 / 0.226044 (0.328970) \| 5.535694 / 2.268929 (3.266765) \| 2.598339 / 55.444624 (-52.846285) \| 2.249298 / 6.876477 (-4.627179) \| 2.243419 / 2.142072 (0.101347) \| 0.667603 / 4.805227 (-4.137624) \| 0.133322 / 6.500664 (-6.367343) \| 0.065473 / 0.075469 (-0.009996) \|\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) \| 1.293051 / 1.841788 (-0.548737) \| 14.103731 / 8.074308 (6.029423) \| 14.215204 / 10.191392 (4.023812) \| 0.143990 / 0.680424 (-0.536434) \| 0.016805 / 0.534201 (-0.517396) \| 0.363264 / 0.579283 (-0.216019) \| 0.392769 / 0.434364 (-0.041594) \| 0.425291 / 0.540337 (-0.115046) \| 0.515479 / 1.386936 (-0.871457) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e03a58f3f5d7e6f07279fb833e62d859a0babaad \"CML watermark\")\n", "_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) \| 0.006346 / 0.011353 (-0.005006) \| 0.004130 / 0.011008 (-0.006878) \| 0.096898 / 0.038508 (0.058390) \| 0.042564 / 0.023109 (0.019455) \| 0.343748 / 0.275898 (0.067850) \| 0.412515 / 0.323480 (0.089035) \| 0.006153 / 0.007986 (-0.001833) \| 0.003345 / 0.004328 (-0.000984) \| 0.075314 / 0.004250 (0.071064) \| 0.061478 / 0.037052 (0.024426) \| 0.362948 / 0.258489 (0.104459) \| 0.401533 / 0.293841 (0.107692) \| 0.032363 / 0.128546 (-0.096184) \| 0.008780 / 0.075646 (-0.066867) \| 0.328691 / 0.419271 (-0.090580) \| 0.054253 / 0.043533 (0.010721) \| 0.340783 / 0.255139 (0.085644) \| 0.360705 / 0.283200 (0.077505) \| 0.023183 / 0.141683 (-0.118500) \| 1.484078 / 1.452155 (0.031924) \| 1.528581 / 1.492716 (0.035865) \|\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) \| 0.208732 / 0.018006 (0.190726) \| 0.452572 / 0.000490 (0.452082) \| 0.002936 / 0.000200 (0.002737) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024616 / 0.037411 (-0.012795) \| 0.107547 / 0.014526 (0.093021) \| 0.114492 / 0.176557 (-0.062065) \| 0.171770 / 0.737135 (-0.565365) \| 0.122538 / 0.296338 (-0.173800) \|\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) \| 0.406140 / 0.215209 (0.190930) \| 4.062391 / 2.077655 (1.984736) \| 1.865962 / 1.504120 (0.361842) \| 1.682236 / 1.541195 (0.141041) \| 1.738119 / 1.468490 (0.269629) \| 0.532244 / 4.584777 (-4.052533) \| 3.816421 / 3.745712 (0.070709) \| 2.981205 / 5.269862 (-2.288656) \| 1.519497 / 4.565676 (-3.046179) \| 0.065904 / 0.424275 (-0.358371) \| 0.011277 / 0.007607 (0.003670) \| 0.512789 / 0.226044 (0.286745) \| 5.107618 / 2.268929 (2.838690) \| 2.419399 / 55.444624 (-53.025226) \| 2.079262 / 6.876477 (-4.797214) \| 2.150447 / 2.142072 (0.008375) \| 0.696737 / 4.805227 (-4.108490) \| 0.142497 / 6.500664 (-6.358167) \| 0.063521 / 0.075469 (-0.011949) \|\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) \| 1.180692 / 1.841788 (-0.661095) \| 14.343084 / 8.074308 (6.268776) \| 13.303719 / 10.191392 (3.112327) \| 0.164234 / 0.680424 (-0.516190) \| 0.017439 / 0.534201 (-0.516762) \| 0.399712 / 0.579283 (-0.179571) \| 0.428248 / 0.434364 (-0.006115) \| 0.471909 / 0.540337 (-0.068428) \| 0.573853 / 1.386936 (-0.813083) \|\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) \| 0.006210 / 0.011353 (-0.005143) \| 0.004104 / 0.011008 (-0.006905) \| 0.075140 / 0.038508 (0.036632) \| 0.044647 / 0.023109 (0.021538) \| 0.370120 / 0.275898 (0.094222) \| 0.452936 / 0.323480 (0.129457) \| 0.003943 / 0.007986 (-0.004042) \| 0.003285 / 0.004328 (-0.001043) \| 0.075267 / 0.004250 (0.071017) \| 0.055517 / 0.037052 (0.018465) \| 0.396385 / 0.258489 (0.137896) \| 0.447870 / 0.293841 (0.154029) \| 0.031342 / 0.128546 (-0.097204) \| 0.008720 / 0.075646 (-0.066926) \| 0.082702 / 0.419271 (-0.336570) \| 0.051010 / 0.043533 (0.007477) \| 0.350546 / 0.255139 (0.095407) \| 0.425395 / 0.283200 (0.142195) \| 0.024483 / 0.141683 (-0.117200) \| 1.467341 / 1.452155 (0.015186) \| 1.537187 / 1.492716 (0.044471) \|\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) \| 0.218067 / 0.018006 (0.200061) \| 0.441603 / 0.000490 (0.441114) \| 0.003711 / 0.000200 (0.003512) \| 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) \| 0.028669 / 0.037411 (-0.008742) \| 0.112941 / 0.014526 (0.098415) \| 0.122584 / 0.176557 (-0.053972) \| 0.176494 / 0.737135 (-0.560641) \| 0.129369 / 0.296338 (-0.166970) \|\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) \| 0.434543 / 0.215209 (0.219334) \| 4.344056 / 2.077655 (2.266401) \| 2.079286 / 1.504120 (0.575166) \| 1.887264 / 1.541195 (0.346069) \| 1.910386 / 1.468490 (0.441896) \| 0.538824 / 4.584777 (-4.045953) \| 3.844786 / 3.745712 (0.099074) \| 2.902091 / 5.269862 (-2.367770) \| 1.270852 / 4.565676 (-3.294824) \| 0.066324 / 0.424275 (-0.357951) \| 0.011346 / 0.007607 (0.003739) \| 0.537122 / 0.226044 (0.311078) \| 5.367354 / 2.268929 (3.098426) \| 2.533672 / 55.444624 (-52.910952) \| 2.203260 / 6.876477 (-4.673217) \| 2.224310 / 2.142072 (0.082237) \| 0.663806 / 4.805227 (-4.141422) \| 0.142758 / 6.500664 (-6.357906) \| 0.063870 / 0.075469 (-0.011599) \|\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) \| 1.260487 / 1.841788 (-0.581301) \| 14.800106 / 8.074308 (6.725798) \| 13.993488 / 10.191392 (3.802096) \| 0.165829 / 0.680424 (-0.514595) \| 0.017347 / 0.534201 (-0.516854) \| 0.401819 / 0.579283 (-0.177464) \| 0.424577 / 0.434364 (-0.009787) \| 0.475161 / 0.540337 (-0.065176) \| 0.574659 / 1.386936 (-0.812277) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02e1e9ab6df4720f57b2d08c0b800cecac79a7c8 \"CML watermark\")\n" ]	1,687,193,766,000	1,687,195,751,000	1,687,195,330,000	CONTRIBUTOR	null	Related to changes made in https://github.com/iterative/dvc/pull/9475	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5966/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5966/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5966", "html_url": "https://github.com/huggingface/datasets/pull/5966", "diff_url": "https://github.com/huggingface/datasets/pull/5966.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5966.patch", "merged_at": "2023-06-19T17:22:10" }	true
https://api.github.com/repos/huggingface/datasets/issues/5965	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5965/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5965/comments	https://api.github.com/repos/huggingface/datasets/issues/5965/events	https://github.com/huggingface/datasets/issues/5965	1,763,648,540	I_kwDODunzps5pHyQc	5,965	"Couldn't cast array of type" in complex datasets	{ "login": "piercefreeman", "id": 1712066, "node_id": "MDQ6VXNlcjE3MTIwNjY=", "avatar_url": "https://avatars.githubusercontent.com/u/1712066?v=4", "gravatar_id": "", "url": "https://api.github.com/users/piercefreeman", "html_url": "https://github.com/piercefreeman", "followers_url": "https://api.github.com/users/piercefreeman/followers", "following_url": "https://api.github.com/users/piercefreeman/following{/other_user}", "gists_url": "https://api.github.com/users/piercefreeman/gists{/gist_id}", "starred_url": "https://api.github.com/users/piercefreeman/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/piercefreeman/subscriptions", "organizations_url": "https://api.github.com/users/piercefreeman/orgs", "repos_url": "https://api.github.com/users/piercefreeman/repos", "events_url": "https://api.github.com/users/piercefreeman/events{/privacy}", "received_events_url": "https://api.github.com/users/piercefreeman/received_events", "type": "User", "site_admin": false }	[]	open	false	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[ { "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false } ]	[ "Thanks for reporting! \r\n\r\nSpecifying the target features explicitly should avoid this error:\r\n```python\r\ndataset = dataset.map(\r\n batch_process,\r\n batched=True,\r\n batch_size=1,\r\n num_proc=1,\r\n remove_columns=dataset.column_names,\r\n features=datasets.Features({\"texts\": datasets.Sequence(datasets.Value(\"string\"))})\r\n)\r\n```\r\n\r\nThis error stems from our type promotion not handling the nested case. But this promotion/casting allocates memory in most scenarios, which can be problematic for large datasets, so explicitly passing the features is the optimal solution.", "Hi @mariosasko thanks for the context, this is helpful to know. Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nFeels like something that would be easy to implement and could save memory / deal with this case in a standardized way.", "> . Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nInteresting proposal! Yes, we could consider doing this if the (return) type hint is `TypedDict`, and raise an error that type hints are incorrect if the cast using the inferred types fails." ]	1,687,184,174,000	1,687,377,603,000	null	NONE	null	### Describe the bug When doing a map of a dataset with complex types, sometimes `datasets` is unable to interpret the valid schema of a returned datasets.map() function. This often comes from conflicting types, like when both empty lists and filled lists are competing for the same field value. This is prone to happen in batch mapping, when the mapper returns a sequence of null/empty values and other batches are non-null. A workaround is to manually cast the new batch to a pyarrow table (like implemented in this [workaround](https://github.com/piercefreeman/lassen/pull/3)) but it feels like this ideally should be solved at the core library level. Note that the reproduction case only throws this error if the first datapoint has the empty list. If it is processed later, datasets already detects its representation as list-type and therefore allows the empty list to be provided. ### Steps to reproduce the bug A trivial reproduction case: ```python from typing import Iterator, Any import pandas as pd from datasets import Dataset def batch_to_examples(batch: dict[str, list[Any]]) -> Iterator[dict[str, Any]]: for i in range(next(iter(lengths))): yield {feature: values[i] for feature, values in batch.items()} def examples_to_batch(examples) -> dict[str, list[Any]]: batch = {} for example in examples: for feature, value in example.items(): if feature not in batch: batch[feature] = [] batch[feature].append(value) return batch def batch_process(examples, explicit_schema: bool): new_examples = [] for example in batch_to_examples(examples): new_examples.append(dict(texts=example["raw_text"].split())) return examples_to_batch(new_examples) df = pd.DataFrame( [ {"raw_text": ""}, {"raw_text": "This is a test"}, {"raw_text": "This is another test"}, ] ) dataset = Dataset.from_pandas(df) # datasets won't be able to typehint a dataset that starts with an empty example. with pytest.raises(TypeError, match="Couldn't cast array of type"): dataset = dataset.map( batch_process, batched=True, batch_size=1, num_proc=1, remove_columns=dataset.column_names, ) ``` This results in crashes like: ```bash File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 2109, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1998, in array_cast raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") TypeError: Couldn't cast array of type string to null ``` ### Expected behavior The code should successfully map and create a new dataset without error. ### Environment info Mac OSX, Linux	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5965/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5965/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5964	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5964/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5964/comments	https://api.github.com/repos/huggingface/datasets/issues/5964/events	https://github.com/huggingface/datasets/pull/5964	1,763,513,574	PR_kwDODunzps5TVweZ	5,964	Always return list in `list_datasets`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006795 / 0.011353 (-0.004558) \| 0.004170 / 0.011008 (-0.006838) \| 0.098698 / 0.038508 (0.060190) \| 0.045393 / 0.023109 (0.022284) \| 0.309205 / 0.275898 (0.033307) \| 0.361333 / 0.323480 (0.037853) \| 0.006009 / 0.007986 (-0.001977) \| 0.003334 / 0.004328 (-0.000995) \| 0.075071 / 0.004250 (0.070821) \| 0.062587 / 0.037052 (0.025535) \| 0.322395 / 0.258489 (0.063906) \| 0.360499 / 0.293841 (0.066659) \| 0.032243 / 0.128546 (-0.096303) \| 0.008768 / 0.075646 (-0.066878) \| 0.329799 / 0.419271 (-0.089472) \| 0.062261 / 0.043533 (0.018728) \| 0.298112 / 0.255139 (0.042973) \| 0.322815 / 0.283200 (0.039615) \| 0.032348 / 0.141683 (-0.109335) \| 1.445807 / 1.452155 (-0.006347) \| 1.528768 / 1.492716 (0.036051) \|\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) \| 0.195701 / 0.018006 (0.177695) \| 0.437042 / 0.000490 (0.436552) \| 0.003867 / 0.000200 (0.003667) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026713 / 0.037411 (-0.010698) \| 0.109548 / 0.014526 (0.095022) \| 0.119216 / 0.176557 (-0.057341) \| 0.178947 / 0.737135 (-0.558188) \| 0.125224 / 0.296338 (-0.171114) \|\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) \| 0.400885 / 0.215209 (0.185676) \| 3.991223 / 2.077655 (1.913568) \| 1.818449 / 1.504120 (0.314329) \| 1.609285 / 1.541195 (0.068090) \| 1.666675 / 1.468490 (0.198184) \| 0.531486 / 4.584777 (-4.053291) \| 3.770142 / 3.745712 (0.024430) \| 3.057189 / 5.269862 (-2.212673) \| 1.517491 / 4.565676 (-3.048186) \| 0.065782 / 0.424275 (-0.358493) \| 0.011251 / 0.007607 (0.003644) \| 0.504277 / 0.226044 (0.278233) \| 5.038979 / 2.268929 (2.770050) \| 2.254717 / 55.444624 (-53.189908) \| 1.929743 / 6.876477 (-4.946734) \| 2.080051 / 2.142072 (-0.062022) \| 0.656831 / 4.805227 (-4.148396) \| 0.142860 / 6.500664 (-6.357804) \| 0.063057 / 0.075469 (-0.012412) \|\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) \| 1.208819 / 1.841788 (-0.632969) \| 14.456966 / 8.074308 (6.382658) \| 12.839799 / 10.191392 (2.648407) \| 0.164361 / 0.680424 (-0.516063) \| 0.017330 / 0.534201 (-0.516871) \| 0.397384 / 0.579283 (-0.181899) \| 0.422704 / 0.434364 (-0.011660) \| 0.472065 / 0.540337 (-0.068273) \| 0.576960 / 1.386936 (-0.809976) \|\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) \| 0.006950 / 0.011353 (-0.004403) \| 0.004012 / 0.011008 (-0.006997) \| 0.076050 / 0.038508 (0.037542) \| 0.046646 / 0.023109 (0.023537) \| 0.353813 / 0.275898 (0.077915) \| 0.417111 / 0.323480 (0.093631) \| 0.005422 / 0.007986 (-0.002564) \| 0.003356 / 0.004328 (-0.000972) \| 0.076662 / 0.004250 (0.072411) \| 0.055018 / 0.037052 (0.017966) \| 0.371561 / 0.258489 (0.113072) \| 0.410471 / 0.293841 (0.116630) \| 0.031860 / 0.128546 (-0.096686) \| 0.008754 / 0.075646 (-0.066893) \| 0.083192 / 0.419271 (-0.336079) \| 0.050479 / 0.043533 (0.006946) \| 0.351725 / 0.255139 (0.096586) \| 0.371596 / 0.283200 (0.088396) \| 0.023042 / 0.141683 (-0.118641) \| 1.480533 / 1.452155 (0.028379) \| 1.545970 / 1.492716 (0.053254) \|\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) \| 0.220095 / 0.018006 (0.202089) \| 0.441550 / 0.000490 (0.441061) \| 0.000375 / 0.000200 (0.000175) \| 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) \| 0.029527 / 0.037411 (-0.007884) \| 0.111645 / 0.014526 (0.097119) \| 0.125732 / 0.176557 (-0.050825) \| 0.177322 / 0.737135 (-0.559813) \| 0.128620 / 0.296338 (-0.167718) \|\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) \| 0.432415 / 0.215209 (0.217206) \| 4.314381 / 2.077655 (2.236726) \| 2.079450 / 1.504120 (0.575331) \| 1.893139 / 1.541195 (0.351944) \| 1.951363 / 1.468490 (0.482873) \| 0.531466 / 4.584777 (-4.053311) \| 3.716860 / 3.745712 (-0.028852) \| 1.850111 / 5.269862 (-3.419750) \| 1.100676 / 4.565676 (-3.465000) \| 0.066247 / 0.424275 (-0.358028) \| 0.011503 / 0.007607 (0.003896) \| 0.537208 / 0.226044 (0.311164) \| 5.367560 / 2.268929 (3.098631) \| 2.543697 / 55.444624 (-52.900927) \| 2.221670 / 6.876477 (-4.654806) \| 2.252009 / 2.142072 (0.109937) \| 0.658509 / 4.805227 (-4.146718) \| 0.142345 / 6.500664 (-6.358319) \| 0.064701 / 0.075469 (-0.010768) \|\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) \| 1.266442 / 1.841788 (-0.575346) \| 15.105953 / 8.074308 (7.031645) \| 14.288229 / 10.191392 (4.096837) \| 0.161182 / 0.680424 (-0.519242) \| 0.017074 / 0.534201 (-0.517127) \| 0.399464 / 0.579283 (-0.179819) \| 0.419459 / 0.434364 (-0.014905) \| 0.467553 / 0.540337 (-0.072784) \| 0.566337 / 1.386936 (-0.820599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53ac2d9662f9e5923ae7c52199eaa620d82f0043 \"CML watermark\")\n" ]	1,687,180,028,000	1,687,195,777,000	1,687,195,361,000	CONTRIBUTOR	null	Fix #5925 Plus, deprecate `list_datasets`/`inspect_dataset` in favor of `huggingface_hub.list_datasets`/"git clone workflow" (downloads data files)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5964/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5964/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5964", "html_url": "https://github.com/huggingface/datasets/pull/5964", "diff_url": "https://github.com/huggingface/datasets/pull/5964.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5964.patch", "merged_at": "2023-06-19T17:22:41" }	true
https://api.github.com/repos/huggingface/datasets/issues/5963	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5963/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5963/comments	https://api.github.com/repos/huggingface/datasets/issues/5963/events	https://github.com/huggingface/datasets/issues/5963	1,762,774,457	I_kwDODunzps5pEc25	5,963	Got an error _pickle.PicklingError use Dataset.from_spark.	{ "login": "yanzia12138", "id": 112800614, "node_id": "U_kgDOBrkzZg", "avatar_url": "https://avatars.githubusercontent.com/u/112800614?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yanzia12138", "html_url": "https://github.com/yanzia12138", "followers_url": "https://api.github.com/users/yanzia12138/followers", "following_url": "https://api.github.com/users/yanzia12138/following{/other_user}", "gists_url": "https://api.github.com/users/yanzia12138/gists{/gist_id}", "starred_url": "https://api.github.com/users/yanzia12138/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yanzia12138/subscriptions", "organizations_url": "https://api.github.com/users/yanzia12138/orgs", "repos_url": "https://api.github.com/users/yanzia12138/repos", "events_url": "https://api.github.com/users/yanzia12138/events{/privacy}", "received_events_url": "https://api.github.com/users/yanzia12138/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "i got error using method from_spark when using multi-node Spark cluster. seems could only use \"from_spark\" in local?", "@lhoestq ", "cc @maddiedawson it looks like there an issue with `_validate_cache_dir` ?\r\n\r\nIt looks like the function passed to mapPartitions has a reference to the Spark dataset builder, and therefore contains the SparkContext itself.\r\n\r\nI think it can be fixed by defining `create_cache_and_write_probe` outside the Spark dataset builder, and pass a `partial(create_cache_and_write_probe, cache_dir=self._cache_dir)` to `mapPartitions`", "Just saw this; thanks for flagging! Your proposed solution sounds good. I can prepare a PR", "@maddiedawson can you show me the demo ,so i can test in local .before your PR" ]	1,687,152,635,000	1,688,003,906,000	null	NONE	null	python 3.9.2 Got an error _pickle.PicklingError use Dataset.from_spark. Did the dataset import load data from spark dataframe using multi-node Spark cluster df = spark.read.parquet(args.input_data).repartition(50) ds = Dataset.from_spark(df, keep_in_memory=True, cache_dir="/pnc-data/data/nuplan/t5_spark/cache_data") ds.save_to_disk(args.output_data) Error : _pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforma tion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063. 23/06/16 21:17:20 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.) _Originally posted by @yanzia12138 in https://github.com/huggingface/datasets/issues/5701#issuecomment-1594674306_ W Traceback (most recent call last): File "/home/work/main.py", line 100, in <module> run(args) File "/home/work/main.py", line 80, in run ds = Dataset.from_spark(df1, keep_in_memory=True, File "/home/work/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1281, in from_spark return SparkDatasetReader( File "/home/work/.local/lib/python3.9/site-packages/datasets/io/spark.py", line 53, in read self.builder.download_and_prepare( File "/home/work/.local/lib/python3.9/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/work/.local/lib/python3.9/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/work/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py", line 254, in _prepare_split self._validate_cache_dir() File "/home/work/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py", line 122, in _validate_cache_dir self._spark.sparkContext.parallelize(range(1), 1).mapPartitions(create_cache_and_write_probe).collect() File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 950, in collect sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2951, in _jrdd wrapped_func = _wrap_function(self.ctx, self.func, self._prev_jrdd_deserializer, File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2830, in _wrap_function pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command) File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2816, in _prepare_for_python_RDD pickled_command = ser.dumps(command) File "/home/work/.local/lib/python3.9/site-packages/pyspark/serializers.py", line 447, in dumps raise pickle.PicklingError(msg) _pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. S parkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063. 23/06/19 13:51:21 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5963/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5963/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5962	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5962/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5962/comments	https://api.github.com/repos/huggingface/datasets/issues/5962/events	https://github.com/huggingface/datasets/issues/5962	1,761,589,882	I_kwDODunzps5o_7p6	5,962	Issue with train_test_split maintaining the same underlying PyArrow Table	{ "login": "Oziel14", "id": 70730520, "node_id": "MDQ6VXNlcjcwNzMwNTIw", "avatar_url": "https://avatars.githubusercontent.com/u/70730520?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Oziel14", "html_url": "https://github.com/Oziel14", "followers_url": "https://api.github.com/users/Oziel14/followers", "following_url": "https://api.github.com/users/Oziel14/following{/other_user}", "gists_url": "https://api.github.com/users/Oziel14/gists{/gist_id}", "starred_url": "https://api.github.com/users/Oziel14/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Oziel14/subscriptions", "organizations_url": "https://api.github.com/users/Oziel14/orgs", "repos_url": "https://api.github.com/users/Oziel14/repos", "events_url": "https://api.github.com/users/Oziel14/events{/privacy}", "received_events_url": "https://api.github.com/users/Oziel14/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[]	1,686,968,398,000	1,686,968,398,000	null	NONE	null	### Describe the bug I've been using the train_test_split method in the datasets module to split my HuggingFace Dataset into separate training, validation, and testing subsets. However, I've noticed an issue where the split datasets appear to maintain the same underlying PyArrow Table. ### Steps to reproduce the bug 1. Load any dataset ```dataset = load_dataset("lhoestq/demo1")``` 2. Try the next code: ```python from datasets import Dataset, DatasetDict train_size = 0.6 split_train = dataset["train"].train_test_split( train_size=train_size, ) separate_dataset_dict = DatasetDict({ "train": split_train["train"], "test": split_train["test"], }) ``` 3. The next code ```print(separate_dataset_dict)``` when printing the dataset it gives the indication that they have 3 and 2 rows respectively. 4. But the next code: ```python print(len(separate_dataset_dict["train"].data['id'])) print(len(separate_dataset_dict["test"].data['id'])) ``` Indicates that both tables still have 5 rows. ### Expected behavior However, I've noticed that train_test_split["train"].data, test_val_split["train"].data, and test_val_split["test"].data are identical, suggesting that they all point to the same underlying PyArrow Table. This means that the split datasets are not independent, as I expected. I believe this is a bug in the train_test_split implementation, as I would expect this function to return datasets with separate underlying PyArrow Tables. Could you please help me understand if this is expected behavior, or if there's a workaround to create truly independent split datasets? I would appreciate any assistance with this issue. Thank you. ### Environment info I tried in Colab: - `datasets` version: 2.13.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.10.11 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1 and my PC: - `datasets` version: 2.13.0 - Platform: Linux-5.15.107+-x86_64-with-glibc2.31 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5962/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5962/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5961	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5961/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5961/comments	https://api.github.com/repos/huggingface/datasets/issues/5961/events	https://github.com/huggingface/datasets/issues/5961	1,758,525,111	I_kwDODunzps5o0Pa3	5,961	IterableDataset: split by node and map may preprocess samples that will be skipped anyway	{ "login": "johnchienbronci", "id": 27708347, "node_id": "MDQ6VXNlcjI3NzA4MzQ3", "avatar_url": "https://avatars.githubusercontent.com/u/27708347?v=4", "gravatar_id": "", "url": "https://api.github.com/users/johnchienbronci", "html_url": "https://github.com/johnchienbronci", "followers_url": "https://api.github.com/users/johnchienbronci/followers", "following_url": "https://api.github.com/users/johnchienbronci/following{/other_user}", "gists_url": "https://api.github.com/users/johnchienbronci/gists{/gist_id}", "starred_url": "https://api.github.com/users/johnchienbronci/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/johnchienbronci/subscriptions", "organizations_url": "https://api.github.com/users/johnchienbronci/orgs", "repos_url": "https://api.github.com/users/johnchienbronci/repos", "events_url": "https://api.github.com/users/johnchienbronci/events{/privacy}", "received_events_url": "https://api.github.com/users/johnchienbronci/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Does \"number of shards\" refer to the total number of data?\r\n\r\nmy config:\r\nnproc_per_node=2\r\nds=ds['train'] = load_dataset(streaming=True).take(50000)\r\n\r\nI'm test again: in prepare_data(), data have the same for each GPU\r\n", "The number of shards is `ds.n_shards`. It corresponds generally to the number of files the dataset is made of, to be able to distribute to several nodes.\r\n\r\nYou don't end up with the same data per GPU. But all the samples are going through your preprocessing function you pass to map. They are just skipped afterwards to only keep 1 sample out of n(GPUs)", "For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end. \r\nIs my understanding correct?\r\n\r\nWhere can I print the actual training data for each GPU?", "> For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\nIs my understanding correct?\r\n\r\nYes exactly :)\r\n\r\n> Where can I print the actual training data for each GPU?\r\n\r\nYou should call print in the data_collator", "I print out n_shards, and under multiple GPUs, this value is always 1.\r\nIs this value correct?", "Yes it's correct, and it explains why you always have the same data passed to your map function (the data can't be split).\r\n\r\nBut after being passed to `map`, each GPU keeps one example out of n(GPUs) so that you don't end up with duplicate data across GPUs", "> > For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\n> > Is my understanding correct?\r\n> \r\n> Yes exactly :)\r\n> \r\n> > Where can I print the actual training data for each GPU?\r\n> \r\n> You should call print in the data_collator\r\n\r\nOK, when printing the train data in the data collator, each GPU sees different data.\r\n\r\nThanks for your reply" ]	1,686,824,950,000	1,687,224,640,000	null	NONE	null	There are two ways an iterable dataset can be split by node: 1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU 2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others. In case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU. This doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end. Could you open a new issue so that we can discuss about this and find a solution ? _Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/5360#issuecomment-1592729051_	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5961/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5961/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5959	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5959/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5959/comments	https://api.github.com/repos/huggingface/datasets/issues/5959/events	https://github.com/huggingface/datasets/issues/5959	1,757,397,507	I_kwDODunzps5ov8ID	5,959	read metric glue.py from local file	{ "login": "JiazhaoLi", "id": 31148397, "node_id": "MDQ6VXNlcjMxMTQ4Mzk3", "avatar_url": "https://avatars.githubusercontent.com/u/31148397?v=4", "gravatar_id": "", "url": "https://api.github.com/users/JiazhaoLi", "html_url": "https://github.com/JiazhaoLi", "followers_url": "https://api.github.com/users/JiazhaoLi/followers", "following_url": "https://api.github.com/users/JiazhaoLi/following{/other_user}", "gists_url": "https://api.github.com/users/JiazhaoLi/gists{/gist_id}", "starred_url": "https://api.github.com/users/JiazhaoLi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JiazhaoLi/subscriptions", "organizations_url": "https://api.github.com/users/JiazhaoLi/orgs", "repos_url": "https://api.github.com/users/JiazhaoLi/repos", "events_url": "https://api.github.com/users/JiazhaoLi/events{/privacy}", "received_events_url": "https://api.github.com/users/JiazhaoLi/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Sorry, I solve this by call `evaluate.load('glue_metric.py','sst-2')`\r\n" ]	1,686,765,575,000	1,686,765,856,000	1,686,765,856,000	NONE	null	### Describe the bug Currently, The server is off-line. I am using the glue metric from the local file downloaded from the hub. I download / cached datasets using `load_dataset('glue','sst2', cache_dir='/xxx')` to cache them and then in the off-line mode, I use `load_dataset('xxx/glue.py','sst2', cache_dir='/xxx')`. I can successfully reuse cached datasets. My problem is about the load_metric. When I run `load_dataset('xxx/glue_metric.py','sst2',cache_dir='/xxx')` , it returns ` File "xx/lib64/python3.9/site-packages/datasets/utils/deprecation_utils.py", line 46, in wrapper return deprecated_function(args, *kwargs) File "xx//lib64/python3.9/site-packages/datasets/load.py", line 1392, in load_metric metric = metric_cls( TypeError: 'NoneType' object is not callable` Thanks in advance for help! ### Steps to reproduce the bug N/A ### Expected behavior N/A ### Environment info `datasets == 2.12.0`	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5959/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5959/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5958	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5958/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5958/comments	https://api.github.com/repos/huggingface/datasets/issues/5958/events	https://github.com/huggingface/datasets/pull/5958	1,757,265,971	PR_kwDODunzps5TA3__	5,958	set dev version	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5958). 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) \| 0.006232 / 0.011353 (-0.005121) \| 0.003788 / 0.011008 (-0.007220) \| 0.100014 / 0.038508 (0.061506) \| 0.036488 / 0.023109 (0.013379) \| 0.306255 / 0.275898 (0.030357) \| 0.363337 / 0.323480 (0.039857) \| 0.004765 / 0.007986 (-0.003221) \| 0.002935 / 0.004328 (-0.001394) \| 0.078897 / 0.004250 (0.074647) \| 0.052221 / 0.037052 (0.015169) \| 0.315169 / 0.258489 (0.056680) \| 0.353050 / 0.293841 (0.059209) \| 0.029059 / 0.128546 (-0.099488) \| 0.008599 / 0.075646 (-0.067047) \| 0.318770 / 0.419271 (-0.100502) \| 0.046631 / 0.043533 (0.003098) \| 0.303728 / 0.255139 (0.048589) \| 0.332379 / 0.283200 (0.049180) \| 0.021164 / 0.141683 (-0.120519) \| 1.576963 / 1.452155 (0.124808) \| 1.629575 / 1.492716 (0.136859) \|\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) \| 0.204246 / 0.018006 (0.186240) \| 0.426600 / 0.000490 (0.426110) \| 0.004336 / 0.000200 (0.004136) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024039 / 0.037411 (-0.013372) \| 0.098240 / 0.014526 (0.083715) \| 0.108889 / 0.176557 (-0.067668) \| 0.170827 / 0.737135 (-0.566308) \| 0.111288 / 0.296338 (-0.185051) \|\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) \| 0.418103 / 0.215209 (0.202894) \| 4.190759 / 2.077655 (2.113104) \| 1.875978 / 1.504120 (0.371858) \| 1.679198 / 1.541195 (0.138003) \| 1.737965 / 1.468490 (0.269474) \| 0.556660 / 4.584777 (-4.028117) \| 3.413800 / 3.745712 (-0.331912) \| 3.004999 / 5.269862 (-2.264862) \| 1.464030 / 4.565676 (-3.101647) \| 0.067338 / 0.424275 (-0.356937) \| 0.011486 / 0.007607 (0.003879) \| 0.522589 / 0.226044 (0.296544) \| 5.214653 / 2.268929 (2.945724) \| 2.316903 / 55.444624 (-53.127722) \| 1.991941 / 6.876477 (-4.884536) \| 2.110601 / 2.142072 (-0.031471) \| 0.665400 / 4.805227 (-4.139828) \| 0.135755 / 6.500664 (-6.364910) \| 0.065980 / 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) \| 1.197269 / 1.841788 (-0.644519) \| 14.085205 / 8.074308 (6.010897) \| 14.083360 / 10.191392 (3.891968) \| 0.148054 / 0.680424 (-0.532369) \| 0.016548 / 0.534201 (-0.517653) \| 0.371538 / 0.579283 (-0.207745) \| 0.391068 / 0.434364 (-0.043296) \| 0.430589 / 0.540337 (-0.109748) \| 0.529319 / 1.386936 (-0.857617) \|\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) \| 0.006214 / 0.011353 (-0.005138) \| 0.003846 / 0.011008 (-0.007162) \| 0.078559 / 0.038508 (0.040051) \| 0.037855 / 0.023109 (0.014745) \| 0.437479 / 0.275898 (0.161581) \| 0.497588 / 0.323480 (0.174108) \| 0.003491 / 0.007986 (-0.004494) \| 0.003900 / 0.004328 (-0.000428) \| 0.078443 / 0.004250 (0.074193) \| 0.048019 / 0.037052 (0.010967) \| 0.452076 / 0.258489 (0.193587) \| 0.494597 / 0.293841 (0.200756) \| 0.028127 / 0.128546 (-0.100419) \| 0.008549 / 0.075646 (-0.067098) \| 0.082977 / 0.419271 (-0.336295) \| 0.043133 / 0.043533 (-0.000400) \| 0.441342 / 0.255139 (0.186203) \| 0.464339 / 0.283200 (0.181139) \| 0.020110 / 0.141683 (-0.121573) \| 1.485181 / 1.452155 (0.033026) \| 1.532019 / 1.492716 (0.039302) \|\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) \| 0.228014 / 0.018006 (0.210007) \| 0.416887 / 0.000490 (0.416397) \| 0.001133 / 0.000200 (0.000933) \| 0.000108 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026452 / 0.037411 (-0.010960) \| 0.104328 / 0.014526 (0.089802) \| 0.110045 / 0.176557 (-0.066511) \| 0.164725 / 0.737135 (-0.572410) \| 0.116348 / 0.296338 (-0.179990) \|\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) \| 0.483502 / 0.215209 (0.268293) \| 4.829814 / 2.077655 (2.752159) \| 2.505271 / 1.504120 (1.001151) \| 2.305819 / 1.541195 (0.764624) \| 2.348633 / 1.468490 (0.880143) \| 0.562316 / 4.584777 (-4.022461) \| 3.426425 / 3.745712 (-0.319287) \| 1.737934 / 5.269862 (-3.531927) \| 1.042616 / 4.565676 (-3.523061) \| 0.068088 / 0.424275 (-0.356187) \| 0.011735 / 0.007607 (0.004128) \| 0.586339 / 0.226044 (0.360295) \| 5.861283 / 2.268929 (3.592354) \| 2.953956 / 55.444624 (-52.490668) \| 2.626611 / 6.876477 (-4.249865) \| 2.687978 / 2.142072 (0.545906) \| 0.672748 / 4.805227 (-4.132479) \| 0.137231 / 6.500664 (-6.363433) \| 0.068149 / 0.075469 (-0.007320) \|\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) \| 1.323139 / 1.841788 (-0.518649) \| 14.503102 / 8.074308 (6.428794) \| 14.092102 / 10.191392 (3.900710) \| 0.165395 / 0.680424 (-0.515028) \| 0.016898 / 0.534201 (-0.517303) \| 0.366905 / 0.579283 (-0.212378) \| 0.396671 / 0.434364 (-0.037692) \| 0.421831 / 0.540337 (-0.118506) \| 0.514075 / 1.386936 (-0.872861) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d4238c132dd44b9a6e1dfe7101228bdeb538d57 \"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) \| 0.004624 / 0.011008 (-0.006384) \| 0.123426 / 0.038508 (0.084918) \| 0.052209 / 0.023109 (0.029100) \| 0.341084 / 0.275898 (0.065186) \| 0.421905 / 0.323480 (0.098425) \| 0.005768 / 0.007986 (-0.002217) \| 0.003647 / 0.004328 (-0.000682) \| 0.085569 / 0.004250 (0.081319) \| 0.070473 / 0.037052 (0.033421) \| 0.356626 / 0.258489 (0.098136) \| 0.407413 / 0.293841 (0.113572) \| 0.038800 / 0.128546 (-0.089746) \| 0.010289 / 0.075646 (-0.065357) \| 0.462707 / 0.419271 (0.043436) \| 0.060390 / 0.043533 (0.016858) \| 0.349805 / 0.255139 (0.094666) \| 0.355288 / 0.283200 (0.072088) \| 0.025364 / 0.141683 (-0.116318) \| 1.745720 / 1.452155 (0.293565) \| 1.852764 / 1.492716 (0.360048) \|\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) \| 0.290582 / 0.018006 (0.272576) \| 0.480044 / 0.000490 (0.479554) \| 0.007658 / 0.000200 (0.007458) \| 0.000100 / 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) \| 0.031529 / 0.037411 (-0.005882) \| 0.130441 / 0.014526 (0.115915) \| 0.147653 / 0.176557 (-0.028904) \| 0.215935 / 0.737135 (-0.521200) \| 0.149871 / 0.296338 (-0.146467) \|\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) \| 0.461662 / 0.215209 (0.246453) \| 4.570353 / 2.077655 (2.492698) \| 2.104416 / 1.504120 (0.600297) \| 1.936974 / 1.541195 (0.395779) \| 2.139167 / 1.468490 (0.670677) \| 0.645100 / 4.584777 (-3.939677) \| 4.361536 / 3.745712 (0.615824) \| 2.155960 / 5.269862 (-3.113902) \| 1.207854 / 4.565676 (-3.357822) \| 0.080162 / 0.424275 (-0.344113) \| 0.014265 / 0.007607 (0.006658) \| 0.606294 / 0.226044 (0.380250) \| 5.928093 / 2.268929 (3.659165) \| 2.701811 / 55.444624 (-52.742813) \| 2.344490 / 6.876477 (-4.531987) \| 2.435997 / 2.142072 (0.293925) \| 0.761020 / 4.805227 (-4.044207) \| 0.165860 / 6.500664 (-6.334804) \| 0.075666 / 0.075469 (0.000197) \|\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) \| 1.427318 / 1.841788 (-0.414469) \| 17.327468 / 8.074308 (9.253160) \| 15.323065 / 10.191392 (5.131673) \| 0.178518 / 0.680424 (-0.501905) \| 0.020888 / 0.534201 (-0.513313) \| 0.497891 / 0.579283 (-0.081393) \| 0.487717 / 0.434364 (0.053353) \| 0.581430 / 0.540337 (0.041093) \| 0.703430 / 1.386936 (-0.683506) \|\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) \| 0.007954 / 0.011353 (-0.003399) \| 0.004442 / 0.011008 (-0.006566) \| 0.090950 / 0.038508 (0.052442) \| 0.054282 / 0.023109 (0.031173) \| 0.424474 / 0.275898 (0.148576) \| 0.531770 / 0.323480 (0.208290) \| 0.004492 / 0.007986 (-0.003493) \| 0.004745 / 0.004328 (0.000416) \| 0.088213 / 0.004250 (0.083962) \| 0.063967 / 0.037052 (0.026914) \| 0.454256 / 0.258489 (0.195767) \| 0.502870 / 0.293841 (0.209029) \| 0.038203 / 0.128546 (-0.090343) \| 0.010327 / 0.075646 (-0.065319) \| 0.097809 / 0.419271 (-0.321463) \| 0.062136 / 0.043533 (0.018604) \| 0.426148 / 0.255139 (0.171009) \| 0.467812 / 0.283200 (0.184612) \| 0.029148 / 0.141683 (-0.112535) \| 1.762307 / 1.452155 (0.310152) \| 1.814238 / 1.492716 (0.321521) \|\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) \| 0.195676 / 0.018006 (0.177670) \| 0.475382 / 0.000490 (0.474892) \| 0.003070 / 0.000200 (0.002870) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033945 / 0.037411 (-0.003466) \| 0.134666 / 0.014526 (0.120140) \| 0.147585 / 0.176557 (-0.028971) \| 0.209472 / 0.737135 (-0.527664) \| 0.154471 / 0.296338 (-0.141867) \|\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) \| 0.518132 / 0.215209 (0.302923) \| 5.103423 / 2.077655 (3.025768) \| 2.565207 / 1.504120 (1.061087) \| 2.389454 / 1.541195 (0.848259) \| 2.391706 / 1.468490 (0.923216) \| 0.606463 / 4.584777 (-3.978314) \| 4.392227 / 3.745712 (0.646515) \| 2.067121 / 5.269862 (-3.202741) \| 1.217551 / 4.565676 (-3.348125) \| 0.074304 / 0.424275 (-0.349971) \| 0.013418 / 0.007607 (0.005811) \| 0.623327 / 0.226044 (0.397282) \| 6.340233 / 2.268929 (4.071304) \| 3.153948 / 55.444624 (-52.290677) \| 2.824548 / 6.876477 (-4.051929) \| 2.938402 / 2.142072 (0.796329) \| 0.774305 / 4.805227 (-4.030922) \| 0.170681 / 6.500664 (-6.329983) \| 0.075895 / 0.075469 (0.000426) \|\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) \| 1.473491 / 1.841788 (-0.368296) \| 17.372294 / 8.074308 (9.297986) \| 15.550201 / 10.191392 (5.358809) \| 0.191402 / 0.680424 (-0.489022) \| 0.021401 / 0.534201 (-0.512800) \| 0.484377 / 0.579283 (-0.094906) \| 0.488844 / 0.434364 (0.054480) \| 0.563336 / 0.540337 (0.022999) \| 0.694210 / 1.386936 (-0.692726) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96da7f51d81e52d7b587685f820b5e55f71e07d \"CML watermark\")\n" ]	1,686,759,994,000	1,686,760,495,000	1,686,760,011,000	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5958/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5958/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5958", "html_url": "https://github.com/huggingface/datasets/pull/5958", "diff_url": "https://github.com/huggingface/datasets/pull/5958.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5958.patch", "merged_at": "2023-06-14T16:26:51" }	true
https://api.github.com/repos/huggingface/datasets/issues/5957	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5957/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5957/comments	https://api.github.com/repos/huggingface/datasets/issues/5957/events	https://github.com/huggingface/datasets/pull/5957	1,757,252,466	PR_kwDODunzps5TA1EB	5,957	Release: 2.13.0	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "<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) \| 0.003970 / 0.011008 (-0.007038) \| 0.099242 / 0.038508 (0.060734) \| 0.044363 / 0.023109 (0.021254) \| 0.313900 / 0.275898 (0.038002) \| 0.386562 / 0.323480 (0.063082) \| 0.003837 / 0.007986 (-0.004149) \| 0.004203 / 0.004328 (-0.000125) \| 0.076191 / 0.004250 (0.071940) \| 0.058823 / 0.037052 (0.021771) \| 0.333838 / 0.258489 (0.075349) \| 0.368235 / 0.293841 (0.074394) \| 0.030774 / 0.128546 (-0.097772) \| 0.008787 / 0.075646 (-0.066860) \| 0.326474 / 0.419271 (-0.092798) \| 0.050903 / 0.043533 (0.007370) \| 0.303928 / 0.255139 (0.048789) \| 0.321532 / 0.283200 (0.038333) \| 0.024162 / 0.141683 (-0.117520) \| 1.479662 / 1.452155 (0.027507) \| 1.520300 / 1.492716 (0.027584) \|\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) \| 0.212403 / 0.018006 (0.194397) \| 0.448019 / 0.000490 (0.447529) \| 0.005465 / 0.000200 (0.005265) \| 0.000388 / 0.000054 (0.000334) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027533 / 0.037411 (-0.009878) \| 0.117477 / 0.014526 (0.102952) \| 0.121182 / 0.176557 (-0.055374) \| 0.181150 / 0.737135 (-0.555985) \| 0.128557 / 0.296338 (-0.167782) \|\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) \| 0.397763 / 0.215209 (0.182554) \| 3.959460 / 2.077655 (1.881805) \| 1.822057 / 1.504120 (0.317937) \| 1.627020 / 1.541195 (0.085826) \| 1.695394 / 1.468490 (0.226904) \| 0.536848 / 4.584777 (-4.047929) \| 3.765205 / 3.745712 (0.019493) \| 3.196300 / 5.269862 (-2.073561) \| 1.623583 / 4.565676 (-2.942094) \| 0.065823 / 0.424275 (-0.358452) \| 0.011062 / 0.007607 (0.003455) \| 0.500428 / 0.226044 (0.274384) \| 5.008816 / 2.268929 (2.739888) \| 2.314660 / 55.444624 (-53.129965) \| 2.007429 / 6.876477 (-4.869047) \| 2.141438 / 2.142072 (-0.000635) \| 0.656697 / 4.805227 (-4.148530) \| 0.143555 / 6.500664 (-6.357109) \| 0.063928 / 0.075469 (-0.011541) \|\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) \| 1.169038 / 1.841788 (-0.672750) \| 15.027186 / 8.074308 (6.952878) \| 13.571484 / 10.191392 (3.380092) \| 0.166437 / 0.680424 (-0.513986) \| 0.017656 / 0.534201 (-0.516545) \| 0.397725 / 0.579283 (-0.181558) \| 0.451019 / 0.434364 (0.016655) \| 0.469134 / 0.540337 (-0.071203) \| 0.575885 / 1.386936 (-0.811051) \|\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) \| 0.006887 / 0.011353 (-0.004465) \| 0.004166 / 0.011008 (-0.006842) \| 0.077137 / 0.038508 (0.038629) \| 0.055631 / 0.023109 (0.032522) \| 0.397658 / 0.275898 (0.121760) \| 0.473981 / 0.323480 (0.150502) \| 0.005365 / 0.007986 (-0.002621) \| 0.003401 / 0.004328 (-0.000928) \| 0.076481 / 0.004250 (0.072231) \| 0.056014 / 0.037052 (0.018961) \| 0.415253 / 0.258489 (0.156764) \| 0.457620 / 0.293841 (0.163779) \| 0.031850 / 0.128546 (-0.096696) \| 0.008869 / 0.075646 (-0.066777) \| 0.083475 / 0.419271 (-0.335796) \| 0.049232 / 0.043533 (0.005699) \| 0.392947 / 0.255139 (0.137808) \| 0.417243 / 0.283200 (0.134043) \| 0.024554 / 0.141683 (-0.117129) \| 1.508081 / 1.452155 (0.055926) \| 1.541845 / 1.492716 (0.049129) \|\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) \| 0.228470 / 0.018006 (0.210464) \| 0.450933 / 0.000490 (0.450443) \| 0.001508 / 0.000200 (0.001308) \| 0.000083 / 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) \| 0.030189 / 0.037411 (-0.007222) \| 0.118853 / 0.014526 (0.104327) \| 0.124809 / 0.176557 (-0.051747) \| 0.175066 / 0.737135 (-0.562069) \| 0.129819 / 0.296338 (-0.166519) \|\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) \| 0.451830 / 0.215209 (0.236621) \| 4.505352 / 2.077655 (2.427698) \| 2.309303 / 1.504120 (0.805183) \| 2.120983 / 1.541195 (0.579789) \| 2.198808 / 1.468490 (0.730317) \| 0.543836 / 4.584777 (-4.040940) \| 3.836650 / 3.745712 (0.090938) \| 1.872293 / 5.269862 (-3.397568) \| 1.122335 / 4.565676 (-3.443342) \| 0.067463 / 0.424275 (-0.356812) \| 0.012143 / 0.007607 (0.004536) \| 0.553674 / 0.226044 (0.327630) \| 5.572101 / 2.268929 (3.303173) \| 2.772151 / 55.444624 (-52.672473) \| 2.451557 / 6.876477 (-4.424920) \| 2.521241 / 2.142072 (0.379169) \| 0.665799 / 4.805227 (-4.139428) \| 0.143842 / 6.500664 (-6.356822) \| 0.065373 / 0.075469 (-0.010096) \|\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) \| 1.271013 / 1.841788 (-0.570775) \| 15.290054 / 8.074308 (7.215746) \| 14.807044 / 10.191392 (4.615652) \| 0.163767 / 0.680424 (-0.516657) \| 0.017383 / 0.534201 (-0.516818) \| 0.393046 / 0.579283 (-0.186237) \| 0.423056 / 0.434364 (-0.011308) \| 0.459193 / 0.540337 (-0.081145) \| 0.559964 / 1.386936 (-0.826972) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#011b75f044ef7fa6b8981ef3496615296aeb315b \"CML watermark\")\n", "_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) \| 0.006112 / 0.011353 (-0.005241) \| 0.003712 / 0.011008 (-0.007297) \| 0.099996 / 0.038508 (0.061488) \| 0.037526 / 0.023109 (0.014417) \| 0.305834 / 0.275898 (0.029936) \| 0.361368 / 0.323480 (0.037888) \| 0.004849 / 0.007986 (-0.003136) \| 0.002912 / 0.004328 (-0.001417) \| 0.077729 / 0.004250 (0.073479) \| 0.053203 / 0.037052 (0.016151) \| 0.318088 / 0.258489 (0.059599) \| 0.371745 / 0.293841 (0.077904) \| 0.029384 / 0.128546 (-0.099162) \| 0.008504 / 0.075646 (-0.067142) \| 0.318472 / 0.419271 (-0.100799) \| 0.046043 / 0.043533 (0.002510) \| 0.310418 / 0.255139 (0.055279) \| 0.335044 / 0.283200 (0.051844) \| 0.020364 / 0.141683 (-0.121319) \| 1.503201 / 1.452155 (0.051047) \| 1.556408 / 1.492716 (0.063692) \|\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) \| 0.210245 / 0.018006 (0.192239) \| 0.418918 / 0.000490 (0.418428) \| 0.002552 / 0.000200 (0.002352) \| 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) \| 0.022295 / 0.037411 (-0.015116) \| 0.099534 / 0.014526 (0.085008) \| 0.106432 / 0.176557 (-0.070124) \| 0.165110 / 0.737135 (-0.572026) \| 0.109851 / 0.296338 (-0.186488) \|\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) \| 0.423947 / 0.215209 (0.208738) \| 4.232978 / 2.077655 (2.155323) \| 2.004849 / 1.504120 (0.500729) \| 1.814345 / 1.541195 (0.273151) \| 1.809192 / 1.468490 (0.340702) \| 0.561146 / 4.584777 (-4.023631) \| 3.385043 / 3.745712 (-0.360669) \| 1.708265 / 5.269862 (-3.561597) \| 1.030290 / 4.565676 (-3.535387) \| 0.067095 / 0.424275 (-0.357180) \| 0.011052 / 0.007607 (0.003445) \| 0.522416 / 0.226044 (0.296371) \| 5.207003 / 2.268929 (2.938075) \| 2.367067 / 55.444624 (-53.077558) \| 1.998705 / 6.876477 (-4.877772) \| 2.068633 / 2.142072 (-0.073439) \| 0.672396 / 4.805227 (-4.132831) \| 0.135818 / 6.500664 (-6.364846) \| 0.065229 / 0.075469 (-0.010240) \|\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) \| 1.187079 / 1.841788 (-0.654709) \| 13.893153 / 8.074308 (5.818845) \| 13.951328 / 10.191392 (3.759936) \| 0.142519 / 0.680424 (-0.537905) \| 0.016546 / 0.534201 (-0.517655) \| 0.364008 / 0.579283 (-0.215275) \| 0.385957 / 0.434364 (-0.048407) \| 0.425218 / 0.540337 (-0.115120) \| 0.519586 / 1.386936 (-0.867350) \|\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) \| 0.005914 / 0.011353 (-0.005439) \| 0.003619 / 0.011008 (-0.007389) \| 0.077806 / 0.038508 (0.039298) \| 0.037254 / 0.023109 (0.014144) \| 0.378976 / 0.275898 (0.103078) \| 0.433620 / 0.323480 (0.110140) \| 0.003291 / 0.007986 (-0.004694) \| 0.004523 / 0.004328 (0.000194) \| 0.077604 / 0.004250 (0.073353) \| 0.047493 / 0.037052 (0.010441) \| 0.396027 / 0.258489 (0.137538) \| 0.453345 / 0.293841 (0.159504) \| 0.028170 / 0.128546 (-0.100376) \| 0.008431 / 0.075646 (-0.067215) \| 0.083985 / 0.419271 (-0.335286) \| 0.045149 / 0.043533 (0.001617) \| 0.369364 / 0.255139 (0.114225) \| 0.407191 / 0.283200 (0.123991) \| 0.024033 / 0.141683 (-0.117649) \| 1.516838 / 1.452155 (0.064683) \| 1.564260 / 1.492716 (0.071544) \|\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) \| 0.200848 / 0.018006 (0.182842) \| 0.407818 / 0.000490 (0.407328) \| 0.003971 / 0.000200 (0.003771) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025033 / 0.037411 (-0.012378) \| 0.103585 / 0.014526 (0.089059) \| 0.108741 / 0.176557 (-0.067816) \| 0.161061 / 0.737135 (-0.576075) \| 0.112763 / 0.296338 (-0.183576) \|\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) \| 0.479913 / 0.215209 (0.264704) \| 4.801904 / 2.077655 (2.724249) \| 2.511433 / 1.504120 (1.007313) \| 2.307523 / 1.541195 (0.766328) \| 2.338343 / 1.468490 (0.869853) \| 0.557731 / 4.584777 (-4.027046) \| 3.386261 / 3.745712 (-0.359451) \| 2.999978 / 5.269862 (-2.269883) \| 1.463058 / 4.565676 (-3.102619) \| 0.067645 / 0.424275 (-0.356630) \| 0.011224 / 0.007607 (0.003617) \| 0.596854 / 0.226044 (0.370810) \| 5.940946 / 2.268929 (3.672017) \| 2.980194 / 55.444624 (-52.464430) \| 2.634961 / 6.876477 (-4.241516) \| 2.648160 / 2.142072 (0.506088) \| 0.669728 / 4.805227 (-4.135499) \| 0.135536 / 6.500664 (-6.365128) \| 0.066865 / 0.075469 (-0.008604) \|\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) \| 1.287151 / 1.841788 (-0.554637) \| 14.491681 / 8.074308 (6.417373) \| 14.185752 / 10.191392 (3.994360) \| 0.129391 / 0.680424 (-0.551032) \| 0.016650 / 0.534201 (-0.517551) \| 0.380111 / 0.579283 (-0.199172) \| 0.392877 / 0.434364 (-0.041487) \| 0.439402 / 0.540337 (-0.100935) \| 0.530865 / 1.386936 (-0.856071) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"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) \| 0.011446 / 0.011353 (0.000093) \| 0.006623 / 0.011008 (-0.004386) \| 0.131915 / 0.038508 (0.093407) \| 0.047364 / 0.023109 (0.024255) \| 0.369203 / 0.275898 (0.093305) \| 0.451509 / 0.323480 (0.128029) \| 0.006265 / 0.007986 (-0.001720) \| 0.004072 / 0.004328 (-0.000257) \| 0.098626 / 0.004250 (0.094375) \| 0.079523 / 0.037052 (0.042470) \| 0.406038 / 0.258489 (0.147549) \| 0.450564 / 0.293841 (0.156723) \| 0.050793 / 0.128546 (-0.077753) \| 0.014667 / 0.075646 (-0.060979) \| 0.401359 / 0.419271 (-0.017913) \| 0.072299 / 0.043533 (0.028767) \| 0.404456 / 0.255139 (0.149317) \| 0.396223 / 0.283200 (0.113023) \| 0.037048 / 0.141683 (-0.104635) \| 1.869123 / 1.452155 (0.416968) \| 1.953621 / 1.492716 (0.460905) \|\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) \| 0.237246 / 0.018006 (0.219240) \| 0.533207 / 0.000490 (0.532717) \| 0.007392 / 0.000200 (0.007192) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029458 / 0.037411 (-0.007954) \| 0.112438 / 0.014526 (0.097912) \| 0.139115 / 0.176557 (-0.037441) \| 0.215225 / 0.737135 (-0.521911) \| 0.134440 / 0.296338 (-0.161898) \|\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) \| 0.616783 / 0.215209 (0.401574) \| 6.113925 / 2.077655 (4.036270) \| 2.403465 / 1.504120 (0.899345) \| 1.967523 / 1.541195 (0.426329) \| 2.042144 / 1.468490 (0.573654) \| 0.927447 / 4.584777 (-3.657330) \| 5.280413 / 3.745712 (1.534701) \| 2.715335 / 5.269862 (-2.554527) \| 1.755640 / 4.565676 (-2.810036) \| 0.114370 / 0.424275 (-0.309905) \| 0.013583 / 0.007607 (0.005976) \| 0.761701 / 0.226044 (0.535657) \| 7.466049 / 2.268929 (5.197120) \| 3.041943 / 55.444624 (-52.402682) \| 2.314477 / 6.876477 (-4.562000) \| 2.469285 / 2.142072 (0.327213) \| 1.216055 / 4.805227 (-3.589172) \| 0.214205 / 6.500664 (-6.286459) \| 0.080901 / 0.075469 (0.005432) \|\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) \| 1.565185 / 1.841788 (-0.276603) \| 18.387986 / 8.074308 (10.313678) \| 19.665109 / 10.191392 (9.473717) \| 0.226670 / 0.680424 (-0.453754) \| 0.028430 / 0.534201 (-0.505771) \| 0.510526 / 0.579283 (-0.068757) \| 0.623178 / 0.434364 (0.188814) \| 0.592039 / 0.540337 (0.051702) \| 0.728462 / 1.386936 (-0.658474) \|\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) \| 0.009161 / 0.011353 (-0.002192) \| 0.004891 / 0.011008 (-0.006117) \| 0.106502 / 0.038508 (0.067994) \| 0.048234 / 0.023109 (0.025125) \| 0.451173 / 0.275898 (0.175275) \| 0.557948 / 0.323480 (0.234468) \| 0.005350 / 0.007986 (-0.002635) \| 0.004559 / 0.004328 (0.000230) \| 0.110393 / 0.004250 (0.106142) \| 0.060624 / 0.037052 (0.023572) \| 0.459265 / 0.258489 (0.200776) \| 0.575302 / 0.293841 (0.281461) \| 0.051379 / 0.128546 (-0.077167) \| 0.015576 / 0.075646 (-0.060070) \| 0.116650 / 0.419271 (-0.302621) \| 0.065534 / 0.043533 (0.022001) \| 0.461431 / 0.255139 (0.206292) \| 0.487677 / 0.283200 (0.204477) \| 0.037773 / 0.141683 (-0.103910) \| 1.992416 / 1.452155 (0.540261) \| 1.991280 / 1.492716 (0.498564) \|\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) \| 0.233607 / 0.018006 (0.215601) \| 0.507539 / 0.000490 (0.507049) \| 0.001307 / 0.000200 (0.001107) \| 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) \| 0.032897 / 0.037411 (-0.004514) \| 0.126549 / 0.014526 (0.112023) \| 0.137893 / 0.176557 (-0.038663) \| 0.192124 / 0.737135 (-0.545012) \| 0.147300 / 0.296338 (-0.149038) \|\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) \| 0.679371 / 0.215209 (0.464162) \| 6.673249 / 2.077655 (4.595595) \| 2.979141 / 1.504120 (1.475022) \| 2.568789 / 1.541195 (1.027594) \| 2.537540 / 1.468490 (1.069050) \| 0.973555 / 4.584777 (-3.611222) \| 5.313536 / 3.745712 (1.567824) \| 2.693283 / 5.269862 (-2.576579) \| 1.819483 / 4.565676 (-2.746194) \| 0.111644 / 0.424275 (-0.312631) \| 0.013218 / 0.007607 (0.005611) \| 0.776114 / 0.226044 (0.550070) \| 7.758907 / 2.268929 (5.489978) \| 3.417611 / 55.444624 (-52.027013) \| 2.859502 / 6.876477 (-4.016975) \| 2.927726 / 2.142072 (0.785653) \| 1.163671 / 4.805227 (-3.641556) \| 0.228636 / 6.500664 (-6.272028) \| 0.082077 / 0.075469 (0.006607) \|\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) \| 1.746150 / 1.841788 (-0.095637) \| 17.961955 / 8.074308 (9.887647) \| 21.590545 / 10.191392 (11.399153) \| 0.210017 / 0.680424 (-0.470406) \| 0.028435 / 0.534201 (-0.505766) \| 0.509253 / 0.579283 (-0.070030) \| 0.606993 / 0.434364 (0.172629) \| 0.587189 / 0.540337 (0.046851) \| 0.684023 / 1.386936 (-0.702913) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"CML watermark\")\n" ]	1,686,759,446,000	1,686,760,419,000	1,686,759,879,000	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5957/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5957/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5957", "html_url": "https://github.com/huggingface/datasets/pull/5957", "diff_url": "https://github.com/huggingface/datasets/pull/5957.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5957.patch", "merged_at": "2023-06-14T16:24:39" }	true
https://api.github.com/repos/huggingface/datasets/issues/5956	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5956/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5956/comments	https://api.github.com/repos/huggingface/datasets/issues/5956/events	https://github.com/huggingface/datasets/pull/5956	1,756,959,367	PR_kwDODunzps5S_1o2	5,956	Fix ArrowExamplesIterable.shard_data_sources	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.005893 / 0.011353 (-0.005460) \| 0.003682 / 0.011008 (-0.007327) \| 0.098358 / 0.038508 (0.059850) \| 0.028130 / 0.023109 (0.005020) \| 0.305960 / 0.275898 (0.030062) \| 0.334869 / 0.323480 (0.011390) \| 0.003522 / 0.007986 (-0.004463) \| 0.003683 / 0.004328 (-0.000645) \| 0.079418 / 0.004250 (0.075168) \| 0.037662 / 0.037052 (0.000609) \| 0.310893 / 0.258489 (0.052404) \| 0.341347 / 0.293841 (0.047506) \| 0.027450 / 0.128546 (-0.101096) \| 0.008381 / 0.075646 (-0.067265) \| 0.316020 / 0.419271 (-0.103252) \| 0.045079 / 0.043533 (0.001546) \| 0.307806 / 0.255139 (0.052667) \| 0.331804 / 0.283200 (0.048604) \| 0.091806 / 0.141683 (-0.049877) \| 1.492611 / 1.452155 (0.040457) \| 1.551762 / 1.492716 (0.059046) \|\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) \| 0.201640 / 0.018006 (0.183634) \| 0.422776 / 0.000490 (0.422286) \| 0.003734 / 0.000200 (0.003535) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025429 / 0.037411 (-0.011982) \| 0.104699 / 0.014526 (0.090173) \| 0.110505 / 0.176557 (-0.066051) \| 0.171252 / 0.737135 (-0.565883) \| 0.113131 / 0.296338 (-0.183208) \|\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) \| 0.419914 / 0.215209 (0.204705) \| 4.184414 / 2.077655 (2.106760) \| 1.999263 / 1.504120 (0.495143) \| 1.828669 / 1.541195 (0.287474) \| 1.940366 / 1.468490 (0.471876) \| 0.556939 / 4.584777 (-4.027838) \| 3.389164 / 3.745712 (-0.356548) \| 1.796323 / 5.269862 (-3.473538) \| 1.048843 / 4.565676 (-3.516833) \| 0.067315 / 0.424275 (-0.356960) \| 0.011531 / 0.007607 (0.003923) \| 0.517226 / 0.226044 (0.291182) \| 5.167255 / 2.268929 (2.898326) \| 2.431129 / 55.444624 (-53.013495) \| 2.133913 / 6.876477 (-4.742564) \| 2.359021 / 2.142072 (0.216948) \| 0.666390 / 4.805227 (-4.138838) \| 0.135147 / 6.500664 (-6.365517) \| 0.064855 / 0.075469 (-0.010614) \|\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) \| 1.166530 / 1.841788 (-0.675258) \| 14.060551 / 8.074308 (5.986242) \| 14.171663 / 10.191392 (3.980271) \| 0.285821 / 0.680424 (-0.394603) \| 0.016867 / 0.534201 (-0.517334) \| 0.369102 / 0.579283 (-0.210181) \| 0.393580 / 0.434364 (-0.040784) \| 0.423721 / 0.540337 (-0.116616) \| 0.512559 / 1.386936 (-0.874377) \|\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) \| 0.006674 / 0.011353 (-0.004679) \| 0.004006 / 0.011008 (-0.007002) \| 0.080160 / 0.038508 (0.041652) \| 0.032508 / 0.023109 (0.009399) \| 0.378168 / 0.275898 (0.102270) \| 0.417796 / 0.323480 (0.094316) \| 0.003706 / 0.007986 (-0.004280) \| 0.002995 / 0.004328 (-0.001333) \| 0.079275 / 0.004250 (0.075025) \| 0.043690 / 0.037052 (0.006638) \| 0.377717 / 0.258489 (0.119228) \| 0.439801 / 0.293841 (0.145961) \| 0.028438 / 0.128546 (-0.100108) \| 0.008661 / 0.075646 (-0.066985) \| 0.085280 / 0.419271 (-0.333991) \| 0.043716 / 0.043533 (0.000183) \| 0.370086 / 0.255139 (0.114947) \| 0.403763 / 0.283200 (0.120563) \| 0.095022 / 0.141683 (-0.046661) \| 1.534376 / 1.452155 (0.082221) \| 1.597658 / 1.492716 (0.104942) \|\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) \| 0.240229 / 0.018006 (0.222223) \| 0.496281 / 0.000490 (0.495792) \| 0.002165 / 0.000200 (0.001965) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025330 / 0.037411 (-0.012081) \| 0.102414 / 0.014526 (0.087888) \| 0.112733 / 0.176557 (-0.063824) \| 0.161181 / 0.737135 (-0.575955) \| 0.114196 / 0.296338 (-0.182143) \|\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) \| 0.456808 / 0.215209 (0.241599) \| 4.534937 / 2.077655 (2.457283) \| 2.318834 / 1.504120 (0.814714) \| 2.074085 / 1.541195 (0.532890) \| 2.117409 / 1.468490 (0.648919) \| 0.559110 / 4.584777 (-4.025667) \| 3.371695 / 3.745712 (-0.374017) \| 2.543154 / 5.269862 (-2.726708) \| 1.360552 / 4.565676 (-3.205125) \| 0.067602 / 0.424275 (-0.356674) \| 0.011396 / 0.007607 (0.003789) \| 0.561666 / 0.226044 (0.335622) \| 5.607666 / 2.268929 (3.338737) \| 2.802775 / 55.444624 (-52.641849) \| 2.486162 / 6.876477 (-4.390315) \| 2.390885 / 2.142072 (0.248813) \| 0.667407 / 4.805227 (-4.137820) \| 0.135948 / 6.500664 (-6.364717) \| 0.067272 / 0.075469 (-0.008197) \|\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) \| 1.279664 / 1.841788 (-0.562124) \| 15.188099 / 8.074308 (7.113791) \| 14.380355 / 10.191392 (4.188963) \| 0.140344 / 0.680424 (-0.540080) \| 0.016832 / 0.534201 (-0.517369) \| 0.364631 / 0.579283 (-0.214652) \| 0.400306 / 0.434364 (-0.034058) \| 0.430793 / 0.540337 (-0.109545) \| 0.525923 / 1.386936 (-0.861013) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48ca19cf1f4d1c99765a1f847c1f6b849496d99d \"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) \| 0.008502 / 0.011353 (-0.002851) \| 0.005946 / 0.011008 (-0.005062) \| 0.131279 / 0.038508 (0.092771) \| 0.035400 / 0.023109 (0.012291) \| 0.423240 / 0.275898 (0.147342) \| 0.470248 / 0.323480 (0.146768) \| 0.004949 / 0.007986 (-0.003037) \| 0.004544 / 0.004328 (0.000215) \| 0.106856 / 0.004250 (0.102605) \| 0.046579 / 0.037052 (0.009527) \| 0.441135 / 0.258489 (0.182646) \| 0.470401 / 0.293841 (0.176561) \| 0.047231 / 0.128546 (-0.081315) \| 0.017278 / 0.075646 (-0.058368) \| 0.401937 / 0.419271 (-0.017335) \| 0.067151 / 0.043533 (0.023619) \| 0.453908 / 0.255139 (0.198769) \| 0.422171 / 0.283200 (0.138971) \| 0.123583 / 0.141683 (-0.018100) \| 1.852895 / 1.452155 (0.400740) \| 1.827282 / 1.492716 (0.334566) \|\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) \| 0.246419 / 0.018006 (0.228413) \| 0.576930 / 0.000490 (0.576440) \| 0.007511 / 0.000200 (0.007312) \| 0.000165 / 0.000054 (0.000111) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032732 / 0.037411 (-0.004680) \| 0.130266 / 0.014526 (0.115740) \| 0.150537 / 0.176557 (-0.026019) \| 0.218554 / 0.737135 (-0.518582) \| 0.148572 / 0.296338 (-0.147766) \|\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) \| 0.598611 / 0.215209 (0.383402) \| 6.181219 / 2.077655 (4.103564) \| 2.473468 / 1.504120 (0.969348) \| 2.206374 / 1.541195 (0.665179) \| 2.216707 / 1.468490 (0.748217) \| 0.981295 / 4.584777 (-3.603482) \| 5.716384 / 3.745712 (1.970672) \| 5.882327 / 5.269862 (0.612466) \| 2.761081 / 4.565676 (-1.804595) \| 0.113544 / 0.424275 (-0.310731) \| 0.015131 / 0.007607 (0.007524) \| 0.850939 / 0.226044 (0.624894) \| 8.046611 / 2.268929 (5.777682) \| 3.340542 / 55.444624 (-52.104083) \| 2.673692 / 6.876477 (-4.202785) \| 2.926330 / 2.142072 (0.784257) \| 1.176164 / 4.805227 (-3.629064) \| 0.226745 / 6.500664 (-6.273919) \| 0.085910 / 0.075469 (0.010441) \|\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) \| 1.483792 / 1.841788 (-0.357995) \| 18.895009 / 8.074308 (10.820701) \| 20.982461 / 10.191392 (10.791069) \| 0.253085 / 0.680424 (-0.427339) \| 0.031284 / 0.534201 (-0.502917) \| 0.516569 / 0.579283 (-0.062714) \| 0.635781 / 0.434364 (0.201417) \| 0.604359 / 0.540337 (0.064022) \| 0.725278 / 1.386936 (-0.661658) \|\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) \| 0.009220 / 0.011353 (-0.002133) \| 0.005792 / 0.011008 (-0.005216) \| 0.099795 / 0.038508 (0.061287) \| 0.033812 / 0.023109 (0.010703) \| 0.459386 / 0.275898 (0.183488) \| 0.518067 / 0.323480 (0.194587) \| 0.005083 / 0.007986 (-0.002902) \| 0.004145 / 0.004328 (-0.000183) \| 0.103506 / 0.004250 (0.099255) \| 0.050429 / 0.037052 (0.013377) \| 0.478149 / 0.258489 (0.219660) \| 0.531280 / 0.293841 (0.237440) \| 0.047373 / 0.128546 (-0.081173) \| 0.013647 / 0.075646 (-0.061999) \| 0.115174 / 0.419271 (-0.304098) \| 0.061099 / 0.043533 (0.017566) \| 0.455002 / 0.255139 (0.199863) \| 0.507765 / 0.283200 (0.224565) \| 0.112219 / 0.141683 (-0.029464) \| 1.873591 / 1.452155 (0.421436) \| 1.952061 / 1.492716 (0.459345) \|\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) \| 0.283587 / 0.018006 (0.265581) \| 0.587562 / 0.000490 (0.587073) \| 0.001252 / 0.000200 (0.001052) \| 0.000095 / 0.000054 (0.000040) \|\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 (-0.004705) \| 0.137715 / 0.014526 (0.123189) \| 0.131932 / 0.176557 (-0.044625) \| 0.200042 / 0.737135 (-0.537094) \| 0.159327 / 0.296338 (-0.137011) \|\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) \| 0.624061 / 0.215209 (0.408852) \| 6.386235 / 2.077655 (4.308580) \| 2.908786 / 1.504120 (1.404666) \| 2.589855 / 1.541195 (1.048660) \| 2.387988 / 1.468490 (0.919498) \| 0.952625 / 4.584777 (-3.632152) \| 5.571641 / 3.745712 (1.825929) \| 2.711154 / 5.269862 (-2.558708) \| 1.788015 / 4.565676 (-2.777662) \| 0.104488 / 0.424275 (-0.319787) \| 0.015213 / 0.007607 (0.007606) \| 0.798446 / 0.226044 (0.572401) \| 8.011614 / 2.268929 (5.742686) \| 3.711951 / 55.444624 (-51.732673) \| 2.896881 / 6.876477 (-3.979595) \| 3.172116 / 2.142072 (1.030043) \| 1.136816 / 4.805227 (-3.668411) \| 0.239254 / 6.500664 (-6.261410) \| 0.081136 / 0.075469 (0.005667) \|\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) \| 1.798246 / 1.841788 (-0.043542) \| 19.497108 / 8.074308 (11.422800) \| 23.450258 / 10.191392 (13.258866) \| 0.250021 / 0.680424 (-0.430403) \| 0.029138 / 0.534201 (-0.505063) \| 0.532984 / 0.579283 (-0.046299) \| 0.638161 / 0.434364 (0.203797) \| 0.615720 / 0.540337 (0.075382) \| 0.770621 / 1.386936 (-0.616315) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d8345c5f8a844ff44cfbb30cbda514ffe89bfd7 \"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) \| 0.009120 / 0.011353 (-0.002233) \| 0.005381 / 0.011008 (-0.005627) \| 0.139719 / 0.038508 (0.101211) \| 0.037229 / 0.023109 (0.014120) \| 0.414633 / 0.275898 (0.138734) \| 0.480313 / 0.323480 (0.156833) \| 0.005027 / 0.007986 (-0.002959) \| 0.005015 / 0.004328 (0.000687) \| 0.108513 / 0.004250 (0.104263) \| 0.056167 / 0.037052 (0.019115) \| 0.407588 / 0.258489 (0.149099) \| 0.518899 / 0.293841 (0.225058) \| 0.048857 / 0.128546 (-0.079689) \| 0.013694 / 0.075646 (-0.061952) \| 0.418035 / 0.419271 (-0.001237) \| 0.067755 / 0.043533 (0.024222) \| 0.417740 / 0.255139 (0.162601) \| 0.478622 / 0.283200 (0.195422) \| 0.118290 / 0.141683 (-0.023393) \| 1.901473 / 1.452155 (0.449319) \| 1.978126 / 1.492716 (0.485409) \|\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) \| 0.271960 / 0.018006 (0.253954) \| 0.602745 / 0.000490 (0.602255) \| 0.005371 / 0.000200 (0.005171) \| 0.000102 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029620 / 0.037411 (-0.007791) \| 0.122402 / 0.014526 (0.107877) \| 0.132645 / 0.176557 (-0.043911) \| 0.212635 / 0.737135 (-0.524500) \| 0.136901 / 0.296338 (-0.159438) \|\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) \| 0.644017 / 0.215209 (0.428808) \| 6.597151 / 2.077655 (4.519496) \| 2.454471 / 1.504120 (0.950351) \| 2.151357 / 1.541195 (0.610163) \| 2.290748 / 1.468490 (0.822258) \| 0.970194 / 4.584777 (-3.614583) \| 5.475275 / 3.745712 (1.729563) \| 2.772658 / 5.269862 (-2.497204) \| 1.785311 / 4.565676 (-2.780366) \| 0.114503 / 0.424275 (-0.309772) \| 0.015374 / 0.007607 (0.007767) \| 0.768413 / 0.226044 (0.542368) \| 7.956219 / 2.268929 (5.687290) \| 3.272138 / 55.444624 (-52.172486) \| 2.539638 / 6.876477 (-4.336839) \| 2.713526 / 2.142072 (0.571454) \| 1.181221 / 4.805227 (-3.624006) \| 0.236327 / 6.500664 (-6.264337) \| 0.089815 / 0.075469 (0.014345) \|\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) \| 1.521805 / 1.841788 (-0.319983) \| 18.196529 / 8.074308 (10.122221) \| 20.287324 / 10.191392 (10.095932) \| 0.256959 / 0.680424 (-0.423465) \| 0.028846 / 0.534201 (-0.505355) \| 0.522354 / 0.579283 (-0.056929) \| 0.600216 / 0.434364 (0.165852) \| 0.607668 / 0.540337 (0.067331) \| 0.762101 / 1.386936 (-0.624835) \|\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) \| 0.009227 / 0.011353 (-0.002126) \| 0.005398 / 0.011008 (-0.005610) \| 0.094998 / 0.038508 (0.056490) \| 0.036633 / 0.023109 (0.013524) \| 0.493317 / 0.275898 (0.217419) \| 0.517216 / 0.323480 (0.193736) \| 0.005510 / 0.007986 (-0.002476) \| 0.004249 / 0.004328 (-0.000079) \| 0.107936 / 0.004250 (0.103685) \| 0.050223 / 0.037052 (0.013171) \| 0.580275 / 0.258489 (0.321786) \| 0.551477 / 0.293841 (0.257636) \| 0.048758 / 0.128546 (-0.079788) \| 0.013954 / 0.075646 (-0.061692) \| 0.107021 / 0.419271 (-0.312250) \| 0.064416 / 0.043533 (0.020884) \| 0.485225 / 0.255139 (0.230086) \| 0.513862 / 0.283200 (0.230663) \| 0.118848 / 0.141683 (-0.022835) \| 1.755396 / 1.452155 (0.303241) \| 1.970349 / 1.492716 (0.477633) \|\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) \| 0.290743 / 0.018006 (0.272737) \| 0.603293 / 0.000490 (0.602803) \| 0.006814 / 0.000200 (0.006614) \| 0.000156 / 0.000054 (0.000101) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029862 / 0.037411 (-0.007550) \| 0.136530 / 0.014526 (0.122005) \| 0.133728 / 0.176557 (-0.042829) \| 0.194709 / 0.737135 (-0.542427) \| 0.151080 / 0.296338 (-0.145258) \|\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) \| 0.649202 / 0.215209 (0.433993) \| 6.637578 / 2.077655 (4.559923) \| 3.040135 / 1.504120 (1.536015) \| 2.671308 / 1.541195 (1.130113) \| 2.722412 / 1.468490 (1.253922) \| 0.953029 / 4.584777 (-3.631748) \| 5.805002 / 3.745712 (2.059290) \| 5.049939 / 5.269862 (-0.219922) \| 2.284053 / 4.565676 (-2.281623) \| 0.130399 / 0.424275 (-0.293876) \| 0.014726 / 0.007607 (0.007119) \| 0.932570 / 0.226044 (0.706526) \| 8.576693 / 2.268929 (6.307765) \| 4.032738 / 55.444624 (-51.411886) \| 3.274715 / 6.876477 (-3.601762) \| 3.513788 / 2.142072 (1.371716) \| 1.130624 / 4.805227 (-3.674603) \| 0.219597 / 6.500664 (-6.281067) \| 0.081425 / 0.075469 (0.005956) \|\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) \| 1.735312 / 1.841788 (-0.106476) \| 18.438587 / 8.074308 (10.364279) \| 21.582310 / 10.191392 (11.390918) \| 0.224040 / 0.680424 (-0.456384) \| 0.027590 / 0.534201 (-0.506611) \| 0.503598 / 0.579283 (-0.075685) \| 0.624379 / 0.434364 (0.190015) \| 0.571911 / 0.540337 (0.031574) \| 0.723215 / 1.386936 (-0.663721) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e40d28f2b0060a429c70827191fa5ff3ce8cf27 \"CML watermark\")\n" ]	1,686,750,638,000	1,686,753,792,000	1,686,753,225,000	MEMBER	null	ArrowExamplesIterable.shard_data_sources was outdated I also fixed a warning message by not using format_type= in with_format()	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5956/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5956/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5956", "html_url": "https://github.com/huggingface/datasets/pull/5956", "diff_url": "https://github.com/huggingface/datasets/pull/5956.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5956.patch", "merged_at": "2023-06-14T14:33:45" }	true
https://api.github.com/repos/huggingface/datasets/issues/5955	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5955/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5955/comments	https://api.github.com/repos/huggingface/datasets/issues/5955/events	https://github.com/huggingface/datasets/issues/5955	1,756,827,133	I_kwDODunzps5otw39	5,955	Strange bug in loading local JSON files, using load_dataset	{ "login": "Night-Quiet", "id": 73934131, "node_id": "MDQ6VXNlcjczOTM0MTMx", "avatar_url": "https://avatars.githubusercontent.com/u/73934131?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Night-Quiet", "html_url": "https://github.com/Night-Quiet", "followers_url": "https://api.github.com/users/Night-Quiet/followers", "following_url": "https://api.github.com/users/Night-Quiet/following{/other_user}", "gists_url": "https://api.github.com/users/Night-Quiet/gists{/gist_id}", "starred_url": "https://api.github.com/users/Night-Quiet/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Night-Quiet/subscriptions", "organizations_url": "https://api.github.com/users/Night-Quiet/orgs", "repos_url": "https://api.github.com/users/Night-Quiet/repos", "events_url": "https://api.github.com/users/Night-Quiet/events{/privacy}", "received_events_url": "https://api.github.com/users/Night-Quiet/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "This is the actual error:\r\n```\r\nFailed to read file '/home/lakala/hjc/code/pycode/glm/temp.json' with error <class 'pyarrow.lib.ArrowInvalid'>: cannot mix list and non-list, non-null values\r\n```\r\nWhich means some samples are incorrectly formatted.\r\n\r\nPyArrow, a storage backend that we use under the hood, requires that all the list elements have the same level of nesting (same number of dimensions) or are `None`.\r\n```python\r\nimport pyarrow as pa\r\npa.array([[1, 2, 3], 2]) # ArrowInvalid: cannot mix list and non-list, non-null values\r\npa.array([[1, 2, 3], [2]]) # works\r\n``` ", "@mariosasko \r\nI used the same operation to check the original data before and after slicing.\r\nThis is reflected in my code.\r\n160000 is not a specific number.\r\nI can also get output using 150000.\r\nThis doesn't seem to align very well with what you said.\r\nBecause if only some sample formats are incorrect.\r\nSo there should be an error in one of the front and back slices.\r\nthank you for your reply.", "Our JSON loader does the following in your case:\r\n\r\n```python\r\nimport json\r\nimport pyarrow as pa\r\n\r\nwith open(file, encoding=\"utf-8\") as f:\r\n dataset = json.load(f)\r\nkeys = set().union(*[row.keys() for row in dataset])\r\nmapping = {col: [row.get(col) for row in dataset] for col in keys}\r\npa_table = pa.Table.from_pydict(mapping) # the ArrowInvalid error comes from here\r\n```\r\n\r\nSo if this code throws an error with correctly-formatted JSON, then this is an Arrow bug and should be reported in their repo.\r\n\r\n> I used the same operation to check the original data before and after slicing.\r\nThis is reflected in my code.\r\n160000 is not a specific number.\r\nI can also get output using 150000.\r\nThis doesn't seem to align very well with what you said.\r\nBecause if only some sample formats are incorrect.\r\nSo there should be an error in one of the front and back slices.\r\n\r\nYou should shuffle the data to make sure that's not the case", "@mariosasko \r\nThank you.\r\nI will try again." ]	1,686,746,760,000	1,687,358,535,000	1,687,358,535,000	NONE	null	### Describe the bug I am using 'load_dataset 'loads a JSON file, but I found a strange bug: an error will be reported when the length of the JSON file exceeds 160000 (uncertain exact number). I have checked the data through the following code and there are no issues. So I cannot determine the true reason for this error. The data is a list containing a dictionary. As follows: [ {'input': 'someting...', 'target': 'someting...', 'type': 'someting...', 'history': ['someting...', ...]}, ... ] ### Steps to reproduce the bug ``` import json from datasets import load_dataset path = "target.json" temp_path = "temp.json" with open(path, "r") as f: data = json.load(f) print(f"\n-------the JSON file length is: {len(data)}-------\n") with open(temp_path, "w") as f: json.dump(data[:160000], f) dataset = load_dataset("json", data_files=temp_path) print("\n-------This works when the JSON file length is 160000-------\n") with open(temp_path, "w") as f: json.dump(data[160000:], f) dataset = load_dataset("json", data_files=temp_path) print("\n-------This works and eliminates data issues-------\n") with open(temp_path, "w") as f: json.dump(data[:170000], f) dataset = load_dataset("json", data_files=temp_path) ``` ### Expected behavior ``` -------the JSON file length is: 173049------- Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-acf3c7f418c5f4b4/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... Downloading data files: 100%\|███████████████████\| 1/1 [00:00<00:00, 3328.81it/s] Extracting data files: 100%\|█████████████████████\| 1/1 [00:00<00:00, 639.47it/s] Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-acf3c7f418c5f4b4/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data. 100%\|████████████████████████████████████████████\| 1/1 [00:00<00:00, 265.85it/s] -------This works when the JSON file length is 160000------- Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-a42f04b263ceea6a/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... Downloading data files: 100%\|███████████████████\| 1/1 [00:00<00:00, 2038.05it/s] Extracting data files: 100%\|█████████████████████\| 1/1 [00:00<00:00, 794.83it/s] Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-a42f04b263ceea6a/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data. 100%\|████████████████████████████████████████████\| 1/1 [00:00<00:00, 681.00it/s] -------This works and eliminates data issues------- Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-63f391c89599c7b0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... Downloading data files: 100%\|███████████████████\| 1/1 [00:00<00:00, 3682.44it/s] Extracting data files: 100%\|█████████████████████\| 1/1 [00:00<00:00, 788.70it/s] Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/home/lakala/hjc/code/pycode/glm/temp.json' with error <class 'pyarrow.lib.ArrowInvalid'>: cannot mix list and non-list, non-null values Traceback (most recent call last): File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 1858, in _prepare_split_single for _, table in generator: File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables raise ValueError(f"Not able to read records in the JSON file at {file}.") from None ValueError: Not able to read records in the JSON file at /home/lakala/hjc/code/pycode/glm/temp.json. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/lakala/hjc/code/pycode/glm/test.py", line 22, in <module> dataset = load_dataset("json", data_files=temp_path) File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/load.py", line 1797, in load_dataset builder_instance.download_and_prepare( File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 890, in download_and_prepare self._download_and_prepare( File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 985, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 1746, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 1891, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info ``` Ubuntu==22.04 python==3.8 pytorch-transformers==1.2.0 transformers== 4.27.1 datasets==2.12.0 numpy==1.24.3 pandas==1.5.3 ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5955/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5955/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5954	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5954/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5954/comments	https://api.github.com/repos/huggingface/datasets/issues/5954/events	https://github.com/huggingface/datasets/pull/5954	1,756,572,994	PR_kwDODunzps5S-hSP	5,954	Better filenotfound for gated	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006374 / 0.011353 (-0.004979) \| 0.004100 / 0.011008 (-0.006909) \| 0.104031 / 0.038508 (0.065523) \| 0.035186 / 0.023109 (0.012076) \| 0.328904 / 0.275898 (0.053006) \| 0.361409 / 0.323480 (0.037929) \| 0.003855 / 0.007986 (-0.004130) \| 0.004140 / 0.004328 (-0.000189) \| 0.080406 / 0.004250 (0.076156) \| 0.045658 / 0.037052 (0.008606) \| 0.341133 / 0.258489 (0.082644) \| 0.372688 / 0.293841 (0.078847) \| 0.032025 / 0.128546 (-0.096521) \| 0.008877 / 0.075646 (-0.066769) \| 0.354784 / 0.419271 (-0.064488) \| 0.068874 / 0.043533 (0.025341) \| 0.335441 / 0.255139 (0.080302) \| 0.356498 / 0.283200 (0.073298) \| 0.113367 / 0.141683 (-0.028316) \| 1.522458 / 1.452155 (0.070304) \| 1.608046 / 1.492716 (0.115329) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231653 / 0.018006 (0.213647) \| 0.446678 / 0.000490 (0.446188) \| 0.003246 / 0.000200 (0.003046) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025299 / 0.037411 (-0.012112) \| 0.111440 / 0.014526 (0.096914) \| 0.118758 / 0.176557 (-0.057799) \| 0.175037 / 0.737135 (-0.562098) \| 0.124583 / 0.296338 (-0.171755) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418694 / 0.215209 (0.203484) \| 4.174695 / 2.077655 (2.097041) \| 1.890323 / 1.504120 (0.386203) \| 1.683300 / 1.541195 (0.142106) \| 1.781954 / 1.468490 (0.313464) \| 0.546131 / 4.584777 (-4.038645) \| 3.768055 / 3.745712 (0.022343) \| 1.839878 / 5.269862 (-3.429983) \| 1.111877 / 4.565676 (-3.453800) \| 0.068568 / 0.424275 (-0.355707) \| 0.011950 / 0.007607 (0.004343) \| 0.527469 / 0.226044 (0.301425) \| 5.274887 / 2.268929 (3.005958) \| 2.391274 / 55.444624 (-53.053351) \| 2.063837 / 6.876477 (-4.812640) \| 2.140627 / 2.142072 (-0.001445) \| 0.681508 / 4.805227 (-4.123719) \| 0.148203 / 6.500664 (-6.352461) \| 0.064456 / 0.075469 (-0.011013) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.221478 / 1.841788 (-0.620310) \| 14.713705 / 8.074308 (6.639397) \| 14.674184 / 10.191392 (4.482792) \| 0.148411 / 0.680424 (-0.532012) \| 0.017858 / 0.534201 (-0.516343) \| 0.436166 / 0.579283 (-0.143117) \| 0.437290 / 0.434364 (0.002926) \| 0.521994 / 0.540337 (-0.018343) \| 0.635488 / 1.386936 (-0.751448) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006108 / 0.011353 (-0.005245) \| 0.003888 / 0.011008 (-0.007120) \| 0.078424 / 0.038508 (0.039916) \| 0.033618 / 0.023109 (0.010509) \| 0.376284 / 0.275898 (0.100386) \| 0.396957 / 0.323480 (0.073477) \| 0.003799 / 0.007986 (-0.004187) \| 0.003160 / 0.004328 (-0.001168) \| 0.078358 / 0.004250 (0.074107) \| 0.045597 / 0.037052 (0.008545) \| 0.386396 / 0.258489 (0.127907) \| 0.412985 / 0.293841 (0.119144) \| 0.031610 / 0.128546 (-0.096936) \| 0.008720 / 0.075646 (-0.066926) \| 0.085944 / 0.419271 (-0.333328) \| 0.050780 / 0.043533 (0.007247) \| 0.378099 / 0.255139 (0.122960) \| 0.381894 / 0.283200 (0.098694) \| 0.098926 / 0.141683 (-0.042756) \| 1.513842 / 1.452155 (0.061688) \| 1.595040 / 1.492716 (0.102323) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208169 / 0.018006 (0.190163) \| 0.431653 / 0.000490 (0.431163) \| 0.000935 / 0.000200 (0.000735) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029600 / 0.037411 (-0.007812) \| 0.116936 / 0.014526 (0.102410) \| 0.125603 / 0.176557 (-0.050953) \| 0.177007 / 0.737135 (-0.560129) \| 0.130602 / 0.296338 (-0.165736) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457158 / 0.215209 (0.241949) \| 4.563254 / 2.077655 (2.485599) \| 2.303549 / 1.504120 (0.799429) \| 2.107269 / 1.541195 (0.566074) \| 2.130861 / 1.468490 (0.662371) \| 0.548931 / 4.584777 (-4.035846) \| 3.745578 / 3.745712 (-0.000134) \| 1.820372 / 5.269862 (-3.449490) \| 1.099316 / 4.565676 (-3.466361) \| 0.068218 / 0.424275 (-0.356057) \| 0.012336 / 0.007607 (0.004728) \| 0.569721 / 0.226044 (0.343676) \| 5.691312 / 2.268929 (3.422384) \| 2.797483 / 55.444624 (-52.647141) \| 2.422621 / 6.876477 (-4.453855) \| 2.426187 / 2.142072 (0.284115) \| 0.674777 / 4.805227 (-4.130451) \| 0.144855 / 6.500664 (-6.355809) \| 0.065805 / 0.075469 (-0.009664) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.305078 / 1.841788 (-0.536709) \| 14.874315 / 8.074308 (6.800007) \| 14.541301 / 10.191392 (4.349909) \| 0.175818 / 0.680424 (-0.504606) \| 0.018169 / 0.534201 (-0.516032) \| 0.435836 / 0.579283 (-0.143447) \| 0.458397 / 0.434364 (0.024033) \| 0.506232 / 0.540337 (-0.034106) \| 0.605306 / 1.386936 (-0.781630) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e0c1ceab96821c7c6557482d25a9bd2078d716a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006138 / 0.011353 (-0.005215) \| 0.003792 / 0.011008 (-0.007216) \| 0.099417 / 0.038508 (0.060908) \| 0.028739 / 0.023109 (0.005630) \| 0.302835 / 0.275898 (0.026937) \| 0.336397 / 0.323480 (0.012918) \| 0.003537 / 0.007986 (-0.004449) \| 0.002973 / 0.004328 (-0.001355) \| 0.077461 / 0.004250 (0.073211) \| 0.039493 / 0.037052 (0.002440) \| 0.302367 / 0.258489 (0.043878) \| 0.344936 / 0.293841 (0.051095) \| 0.027813 / 0.128546 (-0.100733) \| 0.008591 / 0.075646 (-0.067055) \| 0.318975 / 0.419271 (-0.100297) \| 0.045971 / 0.043533 (0.002438) \| 0.301672 / 0.255139 (0.046533) \| 0.328202 / 0.283200 (0.045003) \| 0.091400 / 0.141683 (-0.050282) \| 1.487215 / 1.452155 (0.035060) \| 1.557730 / 1.492716 (0.065014) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208343 / 0.018006 (0.190336) \| 0.426764 / 0.000490 (0.426275) \| 0.001196 / 0.000200 (0.000996) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024332 / 0.037411 (-0.013079) \| 0.101861 / 0.014526 (0.087335) \| 0.108669 / 0.176557 (-0.067888) \| 0.172042 / 0.737135 (-0.565093) \| 0.113048 / 0.296338 (-0.183290) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421419 / 0.215209 (0.206210) \| 4.200816 / 2.077655 (2.123162) \| 1.913516 / 1.504120 (0.409396) \| 1.712167 / 1.541195 (0.170972) \| 1.762129 / 1.468490 (0.293639) \| 0.561616 / 4.584777 (-4.023161) \| 3.398122 / 3.745712 (-0.347590) \| 1.744323 / 5.269862 (-3.525538) \| 1.036023 / 4.565676 (-3.529653) \| 0.067658 / 0.424275 (-0.356617) \| 0.011145 / 0.007607 (0.003538) \| 0.522803 / 0.226044 (0.296759) \| 5.226245 / 2.268929 (2.957317) \| 2.355148 / 55.444624 (-53.089476) \| 2.014939 / 6.876477 (-4.861538) \| 2.140028 / 2.142072 (-0.002044) \| 0.695049 / 4.805227 (-4.110178) \| 0.138428 / 6.500664 (-6.362236) \| 0.066721 / 0.075469 (-0.008748) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.219610 / 1.841788 (-0.622177) \| 14.239576 / 8.074308 (6.165268) \| 14.381955 / 10.191392 (4.190563) \| 0.131208 / 0.680424 (-0.549216) \| 0.016698 / 0.534201 (-0.517503) \| 0.361373 / 0.579283 (-0.217910) \| 0.382560 / 0.434364 (-0.051804) \| 0.419427 / 0.540337 (-0.120911) \| 0.508314 / 1.386936 (-0.878622) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006174 / 0.011353 (-0.005179) \| 0.003893 / 0.011008 (-0.007115) \| 0.079614 / 0.038508 (0.041106) \| 0.028685 / 0.023109 (0.005576) \| 0.368627 / 0.275898 (0.092729) \| 0.411599 / 0.323480 (0.088119) \| 0.003573 / 0.007986 (-0.004413) \| 0.002989 / 0.004328 (-0.001340) \| 0.078653 / 0.004250 (0.074402) \| 0.041146 / 0.037052 (0.004094) \| 0.362387 / 0.258489 (0.103898) \| 0.417234 / 0.293841 (0.123393) \| 0.027958 / 0.128546 (-0.100589) \| 0.008695 / 0.075646 (-0.066952) \| 0.084637 / 0.419271 (-0.334635) \| 0.044188 / 0.043533 (0.000655) \| 0.358514 / 0.255139 (0.103375) \| 0.392314 / 0.283200 (0.109114) \| 0.093986 / 0.141683 (-0.047697) \| 1.535366 / 1.452155 (0.083212) \| 1.605978 / 1.492716 (0.113262) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196215 / 0.018006 (0.178209) \| 0.429403 / 0.000490 (0.428913) \| 0.003736 / 0.000200 (0.003536) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025281 / 0.037411 (-0.012130) \| 0.104325 / 0.014526 (0.089799) \| 0.111548 / 0.176557 (-0.065009) \| 0.162326 / 0.737135 (-0.574809) \| 0.113853 / 0.296338 (-0.182486) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.447600 / 0.215209 (0.232391) \| 4.463422 / 2.077655 (2.385767) \| 2.168028 / 1.504120 (0.663908) \| 1.968699 / 1.541195 (0.427504) \| 2.035531 / 1.468490 (0.567041) \| 0.564575 / 4.584777 (-4.020202) \| 3.435338 / 3.745712 (-0.310374) \| 2.981930 / 5.269862 (-2.287932) \| 1.492172 / 4.565676 (-3.073505) \| 0.067981 / 0.424275 (-0.356294) \| 0.011254 / 0.007607 (0.003647) \| 0.544385 / 0.226044 (0.318340) \| 5.441694 / 2.268929 (3.172765) \| 2.650168 / 55.444624 (-52.794456) \| 2.333974 / 6.876477 (-4.542503) \| 2.383424 / 2.142072 (0.241351) \| 0.669814 / 4.805227 (-4.135414) \| 0.135456 / 6.500664 (-6.365209) \| 0.067067 / 0.075469 (-0.008402) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.313275 / 1.841788 (-0.528513) \| 14.527636 / 8.074308 (6.453328) \| 14.470957 / 10.191392 (4.279565) \| 0.144361 / 0.680424 (-0.536063) \| 0.016847 / 0.534201 (-0.517354) \| 0.365158 / 0.579283 (-0.214125) \| 0.393809 / 0.434364 (-0.040555) \| 0.428527 / 0.540337 (-0.111810) \| 0.515816 / 1.386936 (-0.871120) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7845d4c3c301226b3f8941ac90aaa123bfd7c69e \"CML watermark\")\n" ]	1,686,738,790,000	1,686,746,007,000	1,686,745,591,000	MEMBER	null	close https://github.com/huggingface/datasets/issues/5953 <img width="1292" alt="image" src="https://github.com/huggingface/datasets/assets/42851186/270fe5bc-1739-4878-b7bc-ab6d35336d4d">	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5954/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5954/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5954", "html_url": "https://github.com/huggingface/datasets/pull/5954", "diff_url": "https://github.com/huggingface/datasets/pull/5954.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5954.patch", "merged_at": "2023-06-14T12:26:31" }	true
https://api.github.com/repos/huggingface/datasets/issues/5953	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5953/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5953/comments	https://api.github.com/repos/huggingface/datasets/issues/5953/events	https://github.com/huggingface/datasets/issues/5953	1,756,520,523	I_kwDODunzps5osmBL	5,953	Bad error message when trying to download gated dataset	{ "login": "patrickvonplaten", "id": 23423619, "node_id": "MDQ6VXNlcjIzNDIzNjE5", "avatar_url": "https://avatars.githubusercontent.com/u/23423619?v=4", "gravatar_id": "", "url": "https://api.github.com/users/patrickvonplaten", "html_url": "https://github.com/patrickvonplaten", "followers_url": "https://api.github.com/users/patrickvonplaten/followers", "following_url": "https://api.github.com/users/patrickvonplaten/following{/other_user}", "gists_url": "https://api.github.com/users/patrickvonplaten/gists{/gist_id}", "starred_url": "https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/patrickvonplaten/subscriptions", "organizations_url": "https://api.github.com/users/patrickvonplaten/orgs", "repos_url": "https://api.github.com/users/patrickvonplaten/repos", "events_url": "https://api.github.com/users/patrickvonplaten/events{/privacy}", "received_events_url": "https://api.github.com/users/patrickvonplaten/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "cc @sanchit-gandhi @Vaibhavs10 @lhoestq - this is mainly for demos that use Common Voice datasets as done here: https://github.com/facebookresearch/fairseq/tree/main/examples/mms#-transformers\r\n", "Hi ! the error for me is\r\n\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/mozilla-foundation/common_voice_13_0/common_voice_13_0.py or any data file in the same directory. Couldn't find 'mozilla-foundation/common_voice_13_0' on the Hugging Face Hub either: FileNotFoundError: Dataset 'mozilla-foundation/common_voice_13_0' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```\r\n\r\nAnd tbh idk how you managed to get your error. \"n_shards.json\" is not even a thing in `datasets`", "Okay, I am able to reproduce @patrickvonplaten's original error: https://github.com/Vaibhavs10/scratchpad/blob/main/cv13_datasets_test.ipynb\r\n\r\nAlso not sure why it looks for `n_shards.json`", "Ok I see, this file is downloaded from the CV dataset script - let me investigate", "Ok I see: when you log out you no longer have access to the repository.\r\n\r\nTherefore the dataset script is loaded from cache:\r\n```\r\nWARNING:datasets.load:Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_13_0/22809012aac1fc9803eaffc44122e4149043748e93933935d5ea19898587e4d7 (last modified on Wed Jun 14 10:13:17 2023) since it couldn't be found locally at mozilla-foundation/common_voice_13_0., or remotely on the Hugging Face Hub.\r\n```\r\n\r\nand the script tries to download the n_shards.json but fails", "Is this ok for you https://github.com/huggingface/datasets/pull/5954 ?\r\n\r\nI'll do a release this afternoon", "Cool! ", "this is included in the new release 2.13.0" ]	1,686,737,019,000	1,686,760,611,000	1,686,745,592,000	MEMBER	null	### Describe the bug When I attempt to download a model from the Hub that is gated without being logged in, I get a nice error message. E.g.: E.g. ```sh Repository Not Found for url: https://huggingface.co/api/models/DeepFloyd/IF-I-XL-v1.0. Please make sure you specified the correct `repo_id` and `repo_type`. If you are trying to access a private or gated repo, make sure you are authenticated. Invalid username or password.. Will try to load from local cache. ``` If I do the same for a gated dataset on the Hub, I'm not gated a nice error message IMO: ```sh File ~/hf/lib/python3.10/site-packages/fsspec/implementations/http.py:430, in HTTPFileSystem._info(self, url, kwargs) 427 except Exception as exc: 428 if policy == "get": 429 # If get failed, then raise a FileNotFoundError --> 430 raise FileNotFoundError(url) from exc 431 logger.debug(str(exc)) 433 return {"name": url, "size": None, info, "type": "file"} FileNotFoundError: https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0/resolve/main/n_shards.json ``` ### Steps to reproduce the bug ``` huggingface-cli logout ``` and then: ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # Swahili stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "sw", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) sw_sample = next(iter(stream_data))["audio"]["array"] ``` ### Expected behavior Better error message ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.12.0 - Platform: Linux-6.2.0-76060200-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.16.0.dev0 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5953/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5953/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5952	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5952/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5952/comments	https://api.github.com/repos/huggingface/datasets/issues/5952/events	https://github.com/huggingface/datasets/pull/5952	1,756,481,591	PR_kwDODunzps5S-OIh	5,952	Add Arrow builder docs	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006522 / 0.011353 (-0.004831) \| 0.004319 / 0.011008 (-0.006690) \| 0.099280 / 0.038508 (0.060772) \| 0.033117 / 0.023109 (0.010007) \| 0.339392 / 0.275898 (0.063494) \| 0.366219 / 0.323480 (0.042739) \| 0.003896 / 0.007986 (-0.004090) \| 0.003412 / 0.004328 (-0.000916) \| 0.076655 / 0.004250 (0.072404) \| 0.045203 / 0.037052 (0.008150) \| 0.355800 / 0.258489 (0.097311) \| 0.372533 / 0.293841 (0.078692) \| 0.032318 / 0.128546 (-0.096229) \| 0.009030 / 0.075646 (-0.066616) \| 0.328701 / 0.419271 (-0.090571) \| 0.052891 / 0.043533 (0.009358) \| 0.341131 / 0.255139 (0.085992) \| 0.351593 / 0.283200 (0.068393) \| 0.105136 / 0.141683 (-0.036546) \| 1.475953 / 1.452155 (0.023798) \| 1.566074 / 1.492716 (0.073357) \|\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) \| 0.216671 / 0.018006 (0.198664) \| 0.446952 / 0.000490 (0.446462) \| 0.006340 / 0.000200 (0.006140) \| 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) \| 0.028293 / 0.037411 (-0.009118) \| 0.112298 / 0.014526 (0.097773) \| 0.118634 / 0.176557 (-0.057923) \| 0.175542 / 0.737135 (-0.561593) \| 0.124773 / 0.296338 (-0.171565) \|\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) \| 0.435209 / 0.215209 (0.220000) \| 4.344361 / 2.077655 (2.266706) \| 2.128943 / 1.504120 (0.624823) \| 1.945465 / 1.541195 (0.404271) \| 2.049932 / 1.468490 (0.581442) \| 0.547126 / 4.584777 (-4.037651) \| 3.768698 / 3.745712 (0.022986) \| 1.924441 / 5.269862 (-3.345420) \| 1.146364 / 4.565676 (-3.419312) \| 0.067466 / 0.424275 (-0.356809) \| 0.011175 / 0.007607 (0.003568) \| 0.540978 / 0.226044 (0.314933) \| 5.393120 / 2.268929 (3.124191) \| 2.639027 / 55.444624 (-52.805597) \| 2.327216 / 6.876477 (-4.549261) \| 2.500532 / 2.142072 (0.358460) \| 0.679120 / 4.805227 (-4.126107) \| 0.148824 / 6.500664 (-6.351840) \| 0.064195 / 0.075469 (-0.011274) \|\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) \| 1.158387 / 1.841788 (-0.683401) \| 14.880751 / 8.074308 (6.806443) \| 14.725249 / 10.191392 (4.533857) \| 0.149785 / 0.680424 (-0.530639) \| 0.017338 / 0.534201 (-0.516863) \| 0.390980 / 0.579283 (-0.188303) \| 0.425611 / 0.434364 (-0.008753) \| 0.458851 / 0.540337 (-0.081487) \| 0.559209 / 1.386936 (-0.827727) \|\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) \| 0.006835 / 0.011353 (-0.004518) \| 0.004318 / 0.011008 (-0.006690) \| 0.076715 / 0.038508 (0.038207) \| 0.033528 / 0.023109 (0.010419) \| 0.411986 / 0.275898 (0.136087) \| 0.438752 / 0.323480 (0.115272) \| 0.004039 / 0.007986 (-0.003947) \| 0.003509 / 0.004328 (-0.000819) \| 0.077924 / 0.004250 (0.073673) \| 0.049519 / 0.037052 (0.012467) \| 0.420595 / 0.258489 (0.162106) \| 0.450536 / 0.293841 (0.156695) \| 0.032817 / 0.128546 (-0.095729) \| 0.008963 / 0.075646 (-0.066684) \| 0.083818 / 0.419271 (-0.335454) \| 0.057591 / 0.043533 (0.014058) \| 0.404605 / 0.255139 (0.149466) \| 0.423661 / 0.283200 (0.140462) \| 0.110698 / 0.141683 (-0.030984) \| 1.512515 / 1.452155 (0.060361) \| 1.569207 / 1.492716 (0.076490) \|\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) \| 0.200795 / 0.018006 (0.182789) \| 0.448853 / 0.000490 (0.448363) \| 0.003657 / 0.000200 (0.003457) \| 0.000102 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031612 / 0.037411 (-0.005799) \| 0.116712 / 0.014526 (0.102186) \| 0.126162 / 0.176557 (-0.050395) \| 0.180522 / 0.737135 (-0.556614) \| 0.129768 / 0.296338 (-0.166570) \|\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) \| 0.433797 / 0.215209 (0.218588) \| 4.353099 / 2.077655 (2.275444) \| 2.117582 / 1.504120 (0.613462) \| 1.934487 / 1.541195 (0.393292) \| 2.016988 / 1.468490 (0.548498) \| 0.531387 / 4.584777 (-4.053390) \| 3.843520 / 3.745712 (0.097807) \| 1.879560 / 5.269862 (-3.390301) \| 1.129445 / 4.565676 (-3.436231) \| 0.065952 / 0.424275 (-0.358323) \| 0.011566 / 0.007607 (0.003959) \| 0.533949 / 0.226044 (0.307904) \| 5.327447 / 2.268929 (3.058518) \| 2.572202 / 55.444624 (-52.872422) \| 2.240723 / 6.876477 (-4.635753) \| 2.329290 / 2.142072 (0.187217) \| 0.662162 / 4.805227 (-4.143066) \| 0.143191 / 6.500664 (-6.357473) \| 0.065273 / 0.075469 (-0.010196) \|\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) \| 1.274945 / 1.841788 (-0.566843) \| 15.444511 / 8.074308 (7.370203) \| 14.793524 / 10.191392 (4.602132) \| 0.175607 / 0.680424 (-0.504817) \| 0.017324 / 0.534201 (-0.516877) \| 0.396172 / 0.579283 (-0.183111) \| 0.437334 / 0.434364 (0.002970) \| 0.472621 / 0.540337 (-0.067716) \| 0.574888 / 1.386936 (-0.812048) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b4ab1b3ed7257b0e0ad075d7271a51835f320a5e \"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) \| 0.006976 / 0.011353 (-0.004377) \| 0.004541 / 0.011008 (-0.006467) \| 0.106085 / 0.038508 (0.067577) \| 0.029148 / 0.023109 (0.006039) \| 0.306386 / 0.275898 (0.030488) \| 0.351474 / 0.323480 (0.027994) \| 0.003924 / 0.007986 (-0.004062) \| 0.004588 / 0.004328 (0.000260) \| 0.090479 / 0.004250 (0.086229) \| 0.041195 / 0.037052 (0.004142) \| 0.346020 / 0.258489 (0.087531) \| 0.362526 / 0.293841 (0.068685) \| 0.041020 / 0.128546 (-0.087526) \| 0.012536 / 0.075646 (-0.063110) \| 0.333247 / 0.419271 (-0.086024) \| 0.059786 / 0.043533 (0.016253) \| 0.318094 / 0.255139 (0.062955) \| 0.343879 / 0.283200 (0.060679) \| 0.110083 / 0.141683 (-0.031600) \| 1.514027 / 1.452155 (0.061872) \| 1.551435 / 1.492716 (0.058719) \|\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) \| 0.235401 / 0.018006 (0.217395) \| 0.544292 / 0.000490 (0.543803) \| 0.005284 / 0.000200 (0.005084) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025008 / 0.037411 (-0.012403) \| 0.102235 / 0.014526 (0.087709) \| 0.105523 / 0.176557 (-0.071034) \| 0.180846 / 0.737135 (-0.556289) \| 0.107078 / 0.296338 (-0.189261) \|\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) \| 0.502374 / 0.215209 (0.287165) \| 5.224254 / 2.077655 (3.146600) \| 1.987193 / 1.504120 (0.483073) \| 1.694680 / 1.541195 (0.153485) \| 1.663907 / 1.468490 (0.195417) \| 0.786470 / 4.584777 (-3.798307) \| 4.977895 / 3.745712 (1.232183) \| 4.713451 / 5.269862 (-0.556410) \| 2.298763 / 4.565676 (-2.266913) \| 0.090225 / 0.424275 (-0.334051) \| 0.011427 / 0.007607 (0.003820) \| 0.640686 / 0.226044 (0.414641) \| 6.351727 / 2.268929 (4.082798) \| 2.636912 / 55.444624 (-52.807712) \| 2.075566 / 6.876477 (-4.800911) \| 2.080260 / 2.142072 (-0.061812) \| 0.952727 / 4.805227 (-3.852500) \| 0.188651 / 6.500664 (-6.312013) \| 0.068997 / 0.075469 (-0.006472) \|\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) \| 1.258878 / 1.841788 (-0.582910) \| 15.444724 / 8.074308 (7.370416) \| 17.521918 / 10.191392 (7.330526) \| 0.189732 / 0.680424 (-0.490692) \| 0.031084 / 0.534201 (-0.503117) \| 0.445150 / 0.579283 (-0.134133) \| 0.575844 / 0.434364 (0.141480) \| 0.498162 / 0.540337 (-0.042176) \| 0.635885 / 1.386936 (-0.751051) \|\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) \| 0.007402 / 0.011353 (-0.003951) \| 0.005058 / 0.011008 (-0.005950) \| 0.077659 / 0.038508 (0.039151) \| 0.034934 / 0.023109 (0.011825) \| 0.373139 / 0.275898 (0.097241) \| 0.411857 / 0.323480 (0.088377) \| 0.003751 / 0.007986 (-0.004235) \| 0.003634 / 0.004328 (-0.000695) \| 0.075914 / 0.004250 (0.071663) \| 0.037555 / 0.037052 (0.000503) \| 0.387482 / 0.258489 (0.128993) \| 0.434407 / 0.293841 (0.140566) \| 0.040540 / 0.128546 (-0.088006) \| 0.013458 / 0.075646 (-0.062189) \| 0.096129 / 0.419271 (-0.323143) \| 0.055369 / 0.043533 (0.011836) \| 0.386564 / 0.255139 (0.131425) \| 0.410417 / 0.283200 (0.127218) \| 0.093265 / 0.141683 (-0.048418) \| 1.432841 / 1.452155 (-0.019314) \| 1.533180 / 1.492716 (0.040463) \|\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) \| 0.281051 / 0.018006 (0.263045) \| 0.547635 / 0.000490 (0.547146) \| 0.004434 / 0.000200 (0.004234) \| 0.000105 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026409 / 0.037411 (-0.011002) \| 0.098586 / 0.014526 (0.084060) \| 0.109223 / 0.176557 (-0.067334) \| 0.165958 / 0.737135 (-0.571177) \| 0.111751 / 0.296338 (-0.184587) \|\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) \| 0.542717 / 0.215209 (0.327508) \| 5.530075 / 2.077655 (3.452420) \| 2.351141 / 1.504120 (0.847022) \| 2.021659 / 1.541195 (0.480464) \| 1.964900 / 1.468490 (0.496410) \| 0.819698 / 4.584777 (-3.765079) \| 4.917412 / 3.745712 (1.171700) \| 2.425149 / 5.269862 (-2.844712) \| 1.561953 / 4.565676 (-3.003724) \| 0.098417 / 0.424275 (-0.325858) \| 0.012594 / 0.007607 (0.004986) \| 0.717212 / 0.226044 (0.491168) \| 6.994833 / 2.268929 (4.725904) \| 2.997347 / 55.444624 (-52.447277) \| 2.388366 / 6.876477 (-4.488111) \| 2.502913 / 2.142072 (0.360841) \| 1.030545 / 4.805227 (-3.774682) \| 0.184844 / 6.500664 (-6.315820) \| 0.076889 / 0.075469 (0.001420) \|\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) \| 1.371647 / 1.841788 (-0.470141) \| 15.522995 / 8.074308 (7.448687) \| 17.349823 / 10.191392 (7.158431) \| 0.229709 / 0.680424 (-0.450714) \| 0.023303 / 0.534201 (-0.510898) \| 0.413874 / 0.579283 (-0.165409) \| 0.567552 / 0.434364 (0.133188) \| 0.491722 / 0.540337 (-0.048615) \| 0.590640 / 1.386936 (-0.796296) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1911ffa5d1f58f509d04fe1ddeb9d00a63f94d5 \"CML watermark\")\n" ]	1,686,735,766,000	1,686,753,751,000	1,686,753,279,000	MEMBER	null	following https://github.com/huggingface/datasets/pull/5944	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5952/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5952/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5952", "html_url": "https://github.com/huggingface/datasets/pull/5952", "diff_url": "https://github.com/huggingface/datasets/pull/5952.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5952.patch", "merged_at": "2023-06-14T14:34:39" }	true
https://api.github.com/repos/huggingface/datasets/issues/5951	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5951/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5951/comments	https://api.github.com/repos/huggingface/datasets/issues/5951/events	https://github.com/huggingface/datasets/issues/5951	1,756,363,546	I_kwDODunzps5or_sa	5,951	What is the Right way to use discofuse dataset??	{ "login": "akesh1235", "id": 125154243, "node_id": "U_kgDOB3Wzww", "avatar_url": "https://avatars.githubusercontent.com/u/125154243?v=4", "gravatar_id": "", "url": "https://api.github.com/users/akesh1235", "html_url": "https://github.com/akesh1235", "followers_url": "https://api.github.com/users/akesh1235/followers", "following_url": "https://api.github.com/users/akesh1235/following{/other_user}", "gists_url": "https://api.github.com/users/akesh1235/gists{/gist_id}", "starred_url": "https://api.github.com/users/akesh1235/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/akesh1235/subscriptions", "organizations_url": "https://api.github.com/users/akesh1235/orgs", "repos_url": "https://api.github.com/users/akesh1235/repos", "events_url": "https://api.github.com/users/akesh1235/events{/privacy}", "received_events_url": "https://api.github.com/users/akesh1235/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Thanks for opening https://huggingface.co/datasets/discofuse/discussions/3, let's continue the discussion over there if you don't mind", "I have posted there also sir, please check\r\n@lhoestq" ]	1,686,731,919,000	1,686,749,106,000	1,686,744,616,000	NONE	null	[Click here for Dataset link](https://huggingface.co/datasets/discofuse/viewer/discofuse-wikipedia/train?row=6) Below is the following way, as per my understanding , Is it correct :question: :question: The columns/features from `DiscoFuse dataset` that will be the input to the `encoder` and `decoder` are: [Click here for Dataset link](https://huggingface.co/datasets/discofuse/viewer/discofuse-wikipedia/train?row=6) 1. coherent_first_sentence 2. coherent_second_sentence 3. incoherent_first_sentence 4. incoherent_second_sentence [Click here for Dataset link](https://huggingface.co/datasets/discofuse/viewer/discofuse-wikipedia/train?row=6) The `encoder` will take these four columns as input and encode them into a sequence of hidden states. The `decoder` will then take these hidden states as input and decode them into a new sentence that fuses the two original sentences together. The discourse type, connective_string, has_coref_type_pronoun, and has_coref_type_nominal columns will not be used as input to the encoder or decoder. These columns are used to provide additional information about the dataset, but they are not necessary for the task of sentence fusion. Please correct me if I am wrong; otherwise, if this understanding is right, how shall I implement this task practically?	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5951/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5951/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5950	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5950/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5950/comments	https://api.github.com/repos/huggingface/datasets/issues/5950/events	https://github.com/huggingface/datasets/issues/5950	1,755,197,946	I_kwDODunzps5onjH6	5,950	Support for data with instance-wise dictionary as features	{ "login": "richardwth", "id": 33274336, "node_id": "MDQ6VXNlcjMzMjc0MzM2", "avatar_url": "https://avatars.githubusercontent.com/u/33274336?v=4", "gravatar_id": "", "url": "https://api.github.com/users/richardwth", "html_url": "https://github.com/richardwth", "followers_url": "https://api.github.com/users/richardwth/followers", "following_url": "https://api.github.com/users/richardwth/following{/other_user}", "gists_url": "https://api.github.com/users/richardwth/gists{/gist_id}", "starred_url": "https://api.github.com/users/richardwth/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/richardwth/subscriptions", "organizations_url": "https://api.github.com/users/richardwth/orgs", "repos_url": "https://api.github.com/users/richardwth/repos", "events_url": "https://api.github.com/users/richardwth/events{/privacy}", "received_events_url": "https://api.github.com/users/richardwth/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	[ "Hi ! We use the Arrow columnar format under the hood, which doesn't support such dictionaries: each field must have a fixed type and exist in each sample.\r\n\r\nInstead you can restructure your data like\r\n```\r\n{\r\n \"index\": 0,\r\n \"keys\": [\"2 * x + y >= 3\"],\r\n \"values\": [[\"2 * x + y >= 3\", \"4 * x + 2 * y >= 6\"]],\r\n }\r\n},\r\n...\r\n{\r\n \"index\": 9999,\r\n \"keys\": [\"x >= 6\"],\r\n \"values\": [[\"x >= 6\", \"x >= 0\", \"x >= -1\"]],\r\n},\r\n...\r\n```" ]	1,686,671,340,000	1,686,744,818,000	null	NONE	null	### Feature request I notice that when loading data instances with feature type of python dictionary, the dictionary keys would be broadcast so that every instance has the same set of keys. Please see an example in the Motivation section. It is possible to avoid this behavior, i.e., load dictionary features as it is and do not broadcast the keys among instances? Please note that these dictionaries would have to be processed dynamically at each training iteration into strings (and tokenized). ### Motivation I am trying to load a dataset from a json file. Each instance of the dataset has a feature that is a dictionary but its keys depend on the instance. Every two instances may have different keys. For example, imagine a dataset that contains a set of math expressions from a bunch of mutually redundant expressions: ``` { "index": 0, "feature": { "2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"], ... } }, ... { "index": 9999, "feature": { "x >= 6": ["x >= 6", "x >= 0", "x >= -1"], ... } }, ... ``` When directly loading the dataset using `data = load_dataset("json", data_files=file_paths, split='train')`, each instance would have all the keys from other instances and None as values. That is, instance of index 0 becomes: ``` { "index": 0, "feature": { "2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"], ... "x >= 6": None, # keys from other instances ... } }, ``` This is not desirable. Moreover, issue would be raised if I attempt to combine two such datasets using `data = concatenate_datasets(multi_datasets)`, perhaps because their dictionary features contain different keys. A solution I can think of is to store the dictionary features as a long string, and evaluate it later. Please kindly suggest any other solution using existing methods of datasets. ### Your contribution N/A	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5950/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5950/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5949	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5949/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5949/comments	https://api.github.com/repos/huggingface/datasets/issues/5949/events	https://github.com/huggingface/datasets/pull/5949	1,754,843,717	PR_kwDODunzps5S4oPC	5,949	Replace metadata utils with `huggingface_hub`'s RepoCard API	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006635 / 0.011353 (-0.004718) \| 0.004439 / 0.011008 (-0.006570) \| 0.107831 / 0.038508 (0.069323) \| 0.035664 / 0.023109 (0.012555) \| 0.393733 / 0.275898 (0.117835) \| 0.418336 / 0.323480 (0.094856) \| 0.005739 / 0.007986 (-0.002247) \| 0.005737 / 0.004328 (0.001408) \| 0.079820 / 0.004250 (0.075569) \| 0.045402 / 0.037052 (0.008349) \| 0.396108 / 0.258489 (0.137619) \| 0.422951 / 0.293841 (0.129110) \| 0.030506 / 0.128546 (-0.098040) \| 0.009785 / 0.075646 (-0.065861) \| 0.375302 / 0.419271 (-0.043969) \| 0.054355 / 0.043533 (0.010823) \| 0.399652 / 0.255139 (0.144513) \| 0.410825 / 0.283200 (0.127625) \| 0.109238 / 0.141683 (-0.032445) \| 1.687532 / 1.452155 (0.235378) \| 1.736829 / 1.492716 (0.244113) \|\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) \| 0.226514 / 0.018006 (0.208508) \| 0.487010 / 0.000490 (0.486520) \| 0.006436 / 0.000200 (0.006236) \| 0.000102 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029097 / 0.037411 (-0.008315) \| 0.122979 / 0.014526 (0.108453) \| 0.129454 / 0.176557 (-0.047103) \| 0.194006 / 0.737135 (-0.543129) \| 0.137968 / 0.296338 (-0.158370) \|\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) \| 0.466425 / 0.215209 (0.251216) \| 4.627307 / 2.077655 (2.549652) \| 2.108840 / 1.504120 (0.604720) \| 1.882547 / 1.541195 (0.341353) \| 1.891077 / 1.468490 (0.422587) \| 0.590646 / 4.584777 (-3.994131) \| 4.176918 / 3.745712 (0.431205) \| 2.071475 / 5.269862 (-3.198386) \| 1.173815 / 4.565676 (-3.391862) \| 0.075330 / 0.424275 (-0.348945) \| 0.012944 / 0.007607 (0.005337) \| 0.587080 / 0.226044 (0.361036) \| 5.827053 / 2.268929 (3.558125) \| 2.694258 / 55.444624 (-52.750366) \| 2.276997 / 6.876477 (-4.599480) \| 2.329678 / 2.142072 (0.187605) \| 0.721860 / 4.805227 (-4.083367) \| 0.159238 / 6.500664 (-6.341426) \| 0.073013 / 0.075469 (-0.002456) \|\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) \| 1.345396 / 1.841788 (-0.496391) \| 16.619283 / 8.074308 (8.544975) \| 14.754754 / 10.191392 (4.563362) \| 0.180784 / 0.680424 (-0.499639) \| 0.020376 / 0.534201 (-0.513825) \| 0.451010 / 0.579283 (-0.128273) \| 0.481524 / 0.434364 (0.047160) \| 0.564777 / 0.540337 (0.024440) \| 0.683232 / 1.386936 (-0.703704) \|\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) \| 0.007243 / 0.011353 (-0.004110) \| 0.005262 / 0.011008 (-0.005746) \| 0.084090 / 0.038508 (0.045581) \| 0.037429 / 0.023109 (0.014320) \| 0.404038 / 0.275898 (0.128140) \| 0.445040 / 0.323480 (0.121560) \| 0.006220 / 0.007986 (-0.001766) \| 0.004256 / 0.004328 (-0.000072) \| 0.083794 / 0.004250 (0.079544) \| 0.052655 / 0.037052 (0.015603) \| 0.414083 / 0.258489 (0.155594) \| 0.458190 / 0.293841 (0.164349) \| 0.032719 / 0.128546 (-0.095828) \| 0.010063 / 0.075646 (-0.065583) \| 0.092281 / 0.419271 (-0.326990) \| 0.053888 / 0.043533 (0.010355) \| 0.407813 / 0.255139 (0.152674) \| 0.431692 / 0.283200 (0.148493) \| 0.119799 / 0.141683 (-0.021884) \| 1.709853 / 1.452155 (0.257698) \| 1.771592 / 1.492716 (0.278876) \|\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) \| 0.246540 / 0.018006 (0.228534) \| 0.483199 / 0.000490 (0.482709) \| 0.002514 / 0.000200 (0.002315) \| 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) \| 0.031576 / 0.037411 (-0.005835) \| 0.130020 / 0.014526 (0.115495) \| 0.140285 / 0.176557 (-0.036272) \| 0.196164 / 0.737135 (-0.540972) \| 0.143924 / 0.296338 (-0.152414) \|\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) \| 0.488549 / 0.215209 (0.273340) \| 4.888055 / 2.077655 (2.810400) \| 2.389163 / 1.504120 (0.885043) \| 2.184626 / 1.541195 (0.643431) \| 2.260227 / 1.468490 (0.791737) \| 0.601331 / 4.584777 (-3.983446) \| 4.386159 / 3.745712 (0.640447) \| 3.345814 / 5.269862 (-1.924048) \| 1.734360 / 4.565676 (-2.831317) \| 0.073199 / 0.424275 (-0.351076) \| 0.012397 / 0.007607 (0.004790) \| 0.601411 / 0.226044 (0.375366) \| 6.135000 / 2.268929 (3.866072) \| 2.930169 / 55.444624 (-52.514456) \| 2.532631 / 6.876477 (-4.343845) \| 2.619351 / 2.142072 (0.477279) \| 0.740954 / 4.805227 (-4.064274) \| 0.162936 / 6.500664 (-6.337728) \| 0.073885 / 0.075469 (-0.001585) \|\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) \| 1.502493 / 1.841788 (-0.339294) \| 17.026756 / 8.074308 (8.952448) \| 15.880958 / 10.191392 (5.689566) \| 0.167261 / 0.680424 (-0.513163) \| 0.020347 / 0.534201 (-0.513854) \| 0.452902 / 0.579283 (-0.126381) \| 0.481614 / 0.434364 (0.047250) \| 0.539893 / 0.540337 (-0.000445) \| 0.653401 / 1.386936 (-0.733535) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a5781212e968e2515afdf29370a6eab6f657120 \"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) \| 0.008268 / 0.011353 (-0.003084) \| 0.005538 / 0.011008 (-0.005470) \| 0.126136 / 0.038508 (0.087628) \| 0.046100 / 0.023109 (0.022991) \| 0.366882 / 0.275898 (0.090984) \| 0.408912 / 0.323480 (0.085432) \| 0.007090 / 0.007986 (-0.000895) \| 0.004820 / 0.004328 (0.000491) \| 0.091432 / 0.004250 (0.087181) \| 0.058390 / 0.037052 (0.021338) \| 0.368787 / 0.258489 (0.110298) \| 0.419429 / 0.293841 (0.125588) \| 0.034958 / 0.128546 (-0.093588) \| 0.010526 / 0.075646 (-0.065120) \| 0.463063 / 0.419271 (0.043791) \| 0.070544 / 0.043533 (0.027011) \| 0.366182 / 0.255139 (0.111043) \| 0.390851 / 0.283200 (0.107652) \| 0.128377 / 0.141683 (-0.013306) \| 1.819385 / 1.452155 (0.367231) \| 1.928834 / 1.492716 (0.436117) \|\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) \| 0.228413 / 0.018006 (0.210407) \| 0.485511 / 0.000490 (0.485021) \| 0.005395 / 0.000200 (0.005195) \| 0.000119 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035209 / 0.037411 (-0.002203) \| 0.144492 / 0.014526 (0.129967) \| 0.150467 / 0.176557 (-0.026089) \| 0.223861 / 0.737135 (-0.513274) \| 0.156363 / 0.296338 (-0.139975) \|\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) \| 0.517751 / 0.215209 (0.302542) \| 5.150438 / 2.077655 (3.072783) \| 2.483601 / 1.504120 (0.979481) \| 2.279786 / 1.541195 (0.738592) \| 2.374510 / 1.468490 (0.906020) \| 0.637547 / 4.584777 (-3.947230) \| 4.845393 / 3.745712 (1.099681) \| 2.241554 / 5.269862 (-3.028307) \| 1.290105 / 4.565676 (-3.275572) \| 0.079791 / 0.424275 (-0.344484) \| 0.014915 / 0.007607 (0.007308) \| 0.640468 / 0.226044 (0.414423) \| 6.394810 / 2.268929 (4.125881) \| 3.012748 / 55.444624 (-52.431876) \| 2.625565 / 6.876477 (-4.250912) \| 2.792435 / 2.142072 (0.650363) \| 0.782284 / 4.805227 (-4.022944) \| 0.171628 / 6.500664 (-6.329036) \| 0.081714 / 0.075469 (0.006245) \|\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) \| 1.592411 / 1.841788 (-0.249377) \| 18.999604 / 8.074308 (10.925295) \| 18.469946 / 10.191392 (8.278554) \| 0.200878 / 0.680424 (-0.479546) \| 0.021595 / 0.534201 (-0.512606) \| 0.519247 / 0.579283 (-0.060036) \| 0.534940 / 0.434364 (0.100576) \| 0.656325 / 0.540337 (0.115987) \| 0.789658 / 1.386936 (-0.597278) \|\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) \| 0.008093 / 0.011353 (-0.003260) \| 0.005524 / 0.011008 (-0.005484) \| 0.092339 / 0.038508 (0.053831) \| 0.045619 / 0.023109 (0.022510) \| 0.449376 / 0.275898 (0.173478) \| 0.478587 / 0.323480 (0.155107) \| 0.006978 / 0.007986 (-0.001007) \| 0.004622 / 0.004328 (0.000294) \| 0.090618 / 0.004250 (0.086368) \| 0.059321 / 0.037052 (0.022269) \| 0.450989 / 0.258489 (0.192500) \| 0.491652 / 0.293841 (0.197811) \| 0.033308 / 0.128546 (-0.095238) \| 0.010677 / 0.075646 (-0.064969) \| 0.099836 / 0.419271 (-0.319435) \| 0.055937 / 0.043533 (0.012404) \| 0.440560 / 0.255139 (0.185421) \| 0.475305 / 0.283200 (0.192105) \| 0.130829 / 0.141683 (-0.010854) \| 1.857943 / 1.452155 (0.405789) \| 1.989534 / 1.492716 (0.496818) \|\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) \| 0.244715 / 0.018006 (0.226709) \| 0.482866 / 0.000490 (0.482377) \| 0.001100 / 0.000200 (0.000900) \| 0.000095 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036288 / 0.037411 (-0.001124) \| 0.147903 / 0.014526 (0.133377) \| 0.154141 / 0.176557 (-0.022416) \| 0.221863 / 0.737135 (-0.515272) \| 0.162319 / 0.296338 (-0.134019) \|\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) \| 0.536972 / 0.215209 (0.321763) \| 5.382866 / 2.077655 (3.305211) \| 2.719575 / 1.504120 (1.215456) \| 2.516596 / 1.541195 (0.975401) \| 2.699602 / 1.468490 (1.231112) \| 0.639886 / 4.584777 (-3.944891) \| 5.109746 / 3.745712 (1.364034) \| 2.260206 / 5.269862 (-3.009656) \| 1.305506 / 4.565676 (-3.260170) \| 0.080262 / 0.424275 (-0.344013) \| 0.014801 / 0.007607 (0.007194) \| 0.661228 / 0.226044 (0.435184) \| 6.596485 / 2.268929 (4.327557) \| 3.226114 / 55.444624 (-52.218510) \| 2.859776 / 6.876477 (-4.016701) \| 3.059355 / 2.142072 (0.917282) \| 0.793413 / 4.805227 (-4.011814) \| 0.176521 / 6.500664 (-6.324143) \| 0.084062 / 0.075469 (0.008593) \|\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) \| 1.642085 / 1.841788 (-0.199703) \| 20.355459 / 8.074308 (12.281151) \| 17.979620 / 10.191392 (7.788228) \| 0.229329 / 0.680424 (-0.451094) \| 0.025681 / 0.534201 (-0.508520) \| 0.534142 / 0.579283 (-0.045141) \| 0.623439 / 0.434364 (0.189075) \| 0.621938 / 0.540337 (0.081601) \| 0.759038 / 1.386936 (-0.627898) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a98ff43225df344139023a5b7eb9caef610b677 \"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) \| 0.007703 / 0.011353 (-0.003649) \| 0.005362 / 0.011008 (-0.005646) \| 0.113111 / 0.038508 (0.074602) \| 0.038891 / 0.023109 (0.015782) \| 0.348938 / 0.275898 (0.073040) \| 0.398079 / 0.323480 (0.074599) \| 0.006707 / 0.007986 (-0.001278) \| 0.004489 / 0.004328 (0.000160) \| 0.087194 / 0.004250 (0.082943) \| 0.054268 / 0.037052 (0.017216) \| 0.359949 / 0.258489 (0.101460) \| 0.402959 / 0.293841 (0.109118) \| 0.032508 / 0.128546 (-0.096038) \| 0.010224 / 0.075646 (-0.065422) \| 0.387007 / 0.419271 (-0.032264) \| 0.058971 / 0.043533 (0.015439) \| 0.345085 / 0.255139 (0.089946) \| 0.384306 / 0.283200 (0.101107) \| 0.122253 / 0.141683 (-0.019430) \| 1.706353 / 1.452155 (0.254199) \| 1.840780 / 1.492716 (0.348063) \|\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) \| 0.254374 / 0.018006 (0.236368) \| 0.497387 / 0.000490 (0.496897) \| 0.012294 / 0.000200 (0.012094) \| 0.000108 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030902 / 0.037411 (-0.006509) \| 0.132098 / 0.014526 (0.117573) \| 0.140311 / 0.176557 (-0.036245) \| 0.205887 / 0.737135 (-0.531249) \| 0.143992 / 0.296338 (-0.152347) \|\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) \| 0.467367 / 0.215209 (0.252158) \| 4.669936 / 2.077655 (2.592281) \| 2.155358 / 1.504120 (0.651238) \| 1.984132 / 1.541195 (0.442937) \| 2.102352 / 1.468490 (0.633861) \| 0.607014 / 4.584777 (-3.977763) \| 4.396479 / 3.745712 (0.650767) \| 4.666056 / 5.269862 (-0.603806) \| 2.176649 / 4.565676 (-2.389028) \| 0.072657 / 0.424275 (-0.351619) \| 0.012367 / 0.007607 (0.004759) \| 0.569706 / 0.226044 (0.343661) \| 5.749083 / 2.268929 (3.480154) \| 2.640824 / 55.444624 (-52.803801) \| 2.310253 / 6.876477 (-4.566224) \| 2.486748 / 2.142072 (0.344676) \| 0.737891 / 4.805227 (-4.067336) \| 0.163507 / 6.500664 (-6.337157) \| 0.075776 / 0.075469 (0.000307) \|\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) \| 1.362710 / 1.841788 (-0.479078) \| 17.010705 / 8.074308 (8.936396) \| 15.084231 / 10.191392 (4.892839) \| 0.218274 / 0.680424 (-0.462150) \| 0.019555 / 0.534201 (-0.514646) \| 0.456013 / 0.579283 (-0.123270) \| 0.502772 / 0.434364 (0.068408) \| 0.581480 / 0.540337 (0.041142) \| 0.686952 / 1.386936 (-0.699984) \|\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) \| 0.007976 / 0.011353 (-0.003377) \| 0.005141 / 0.011008 (-0.005868) \| 0.086629 / 0.038508 (0.048121) \| 0.039553 / 0.023109 (0.016444) \| 0.433028 / 0.275898 (0.157130) \| 0.463444 / 0.323480 (0.139964) \| 0.006967 / 0.007986 (-0.001018) \| 0.005814 / 0.004328 (0.001485) \| 0.086266 / 0.004250 (0.082015) \| 0.055384 / 0.037052 (0.018332) \| 0.428733 / 0.258489 (0.170243) \| 0.475670 / 0.293841 (0.181829) \| 0.032872 / 0.128546 (-0.095674) \| 0.010664 / 0.075646 (-0.064983) \| 0.094357 / 0.419271 (-0.324915) \| 0.058386 / 0.043533 (0.014854) \| 0.431114 / 0.255139 (0.175975) \| 0.441728 / 0.283200 (0.158528) \| 0.131942 / 0.141683 (-0.009740) \| 1.782214 / 1.452155 (0.330060) \| 1.843185 / 1.492716 (0.350469) \|\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) \| 0.247047 / 0.018006 (0.229041) \| 0.488931 / 0.000490 (0.488441) \| 0.002657 / 0.000200 (0.002457) \| 0.000106 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033893 / 0.037411 (-0.003518) \| 0.131021 / 0.014526 (0.116495) \| 0.142892 / 0.176557 (-0.033665) \| 0.200955 / 0.737135 (-0.536180) \| 0.151329 / 0.296338 (-0.145010) \|\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) \| 0.521138 / 0.215209 (0.305929) \| 5.085207 / 2.077655 (3.007552) \| 2.652901 / 1.504120 (1.148781) \| 2.401545 / 1.541195 (0.860350) \| 2.553461 / 1.468490 (1.084971) \| 0.615347 / 4.584777 (-3.969430) \| 4.448038 / 3.745712 (0.702326) \| 2.049997 / 5.269862 (-3.219865) \| 1.190602 / 4.565676 (-3.375075) \| 0.073356 / 0.424275 (-0.350919) \| 0.013685 / 0.007607 (0.006078) \| 0.626705 / 0.226044 (0.400660) \| 6.391941 / 2.268929 (4.123012) \| 3.218864 / 55.444624 (-52.225760) \| 2.858808 / 6.876477 (-4.017669) \| 3.005808 / 2.142072 (0.863736) \| 0.740725 / 4.805227 (-4.064502) \| 0.161904 / 6.500664 (-6.338760) \| 0.073727 / 0.075469 (-0.001742) \|\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) \| 1.488623 / 1.841788 (-0.353164) \| 17.584367 / 8.074308 (9.510059) \| 16.281818 / 10.191392 (6.090426) \| 0.164482 / 0.680424 (-0.515942) \| 0.020197 / 0.534201 (-0.514003) \| 0.456750 / 0.579283 (-0.122533) \| 0.501156 / 0.434364 (0.066792) \| 0.549779 / 0.540337 (0.009442) \| 0.650156 / 1.386936 (-0.736780) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b6cc63b868ea4ee60502845ebec68abb943958b \"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) \| 0.008337 / 0.011353 (-0.003016) \| 0.005911 / 0.011008 (-0.005097) \| 0.129037 / 0.038508 (0.090529) \| 0.046071 / 0.023109 (0.022962) \| 0.418657 / 0.275898 (0.142759) \| 0.490340 / 0.323480 (0.166860) \| 0.006387 / 0.007986 (-0.001598) \| 0.004724 / 0.004328 (0.000396) \| 0.097953 / 0.004250 (0.093702) \| 0.069025 / 0.037052 (0.031972) \| 0.431178 / 0.258489 (0.172689) \| 0.458363 / 0.293841 (0.164522) \| 0.049341 / 0.128546 (-0.079205) \| 0.014637 / 0.075646 (-0.061009) \| 0.439800 / 0.419271 (0.020529) \| 0.069905 / 0.043533 (0.026373) \| 0.406775 / 0.255139 (0.151636) \| 0.441989 / 0.283200 (0.158790) \| 0.046009 / 0.141683 (-0.095674) \| 1.847630 / 1.452155 (0.395475) \| 1.904067 / 1.492716 (0.411351) \|\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) \| 0.288305 / 0.018006 (0.270299) \| 0.594547 / 0.000490 (0.594058) \| 0.005600 / 0.000200 (0.005400) \| 0.000106 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033847 / 0.037411 (-0.003564) \| 0.125139 / 0.014526 (0.110613) \| 0.147982 / 0.176557 (-0.028574) \| 0.208396 / 0.737135 (-0.528739) \| 0.144005 / 0.296338 (-0.152334) \|\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) \| 0.669175 / 0.215209 (0.453966) \| 6.605289 / 2.077655 (4.527634) \| 2.720468 / 1.504120 (1.216348) \| 2.341355 / 1.541195 (0.800160) \| 2.402069 / 1.468490 (0.933578) \| 0.939303 / 4.584777 (-3.645474) \| 5.718545 / 3.745712 (1.972833) \| 2.856235 / 5.269862 (-2.413627) \| 1.821555 / 4.565676 (-2.744121) \| 0.105473 / 0.424275 (-0.318802) \| 0.014490 / 0.007607 (0.006883) \| 0.774349 / 0.226044 (0.548305) \| 8.065048 / 2.268929 (5.796120) \| 3.508482 / 55.444624 (-51.936143) \| 2.822881 / 6.876477 (-4.053596) \| 2.962947 / 2.142072 (0.820875) \| 1.138944 / 4.805227 (-3.666284) \| 0.248414 / 6.500664 (-6.252250) \| 0.095665 / 0.075469 (0.020196) \|\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) \| 1.688231 / 1.841788 (-0.153557) \| 18.673305 / 8.074308 (10.598997) \| 22.768663 / 10.191392 (12.577271) \| 0.211238 / 0.680424 (-0.469186) \| 0.031380 / 0.534201 (-0.502821) \| 0.517175 / 0.579283 (-0.062108) \| 0.626437 / 0.434364 (0.192073) \| 0.624225 / 0.540337 (0.083888) \| 0.743746 / 1.386936 (-0.643191) \|\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) \| 0.008888 / 0.011353 (-0.002464) \| 0.005491 / 0.011008 (-0.005517) \| 0.105013 / 0.038508 (0.066505) \| 0.049456 / 0.023109 (0.026347) \| 0.528989 / 0.275898 (0.253091) \| 0.651871 / 0.323480 (0.328391) \| 0.006683 / 0.007986 (-0.001302) \| 0.004365 / 0.004328 (0.000037) \| 0.098161 / 0.004250 (0.093911) \| 0.075615 / 0.037052 (0.038563) \| 0.543746 / 0.258489 (0.285257) \| 0.650855 / 0.293841 (0.357014) \| 0.050220 / 0.128546 (-0.078327) \| 0.014471 / 0.075646 (-0.061175) \| 0.115903 / 0.419271 (-0.303368) \| 0.065925 / 0.043533 (0.022392) \| 0.527797 / 0.255139 (0.272658) \| 0.543834 / 0.283200 (0.260634) \| 0.043005 / 0.141683 (-0.098678) \| 1.842846 / 1.452155 (0.390691) \| 1.970615 / 1.492716 (0.477899) \|\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) \| 0.287350 / 0.018006 (0.269343) \| 0.591139 / 0.000490 (0.590649) \| 0.006423 / 0.000200 (0.006223) \| 0.000107 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034594 / 0.037411 (-0.002818) \| 0.137155 / 0.014526 (0.122629) \| 0.154662 / 0.176557 (-0.021894) \| 0.217834 / 0.737135 (-0.519301) \| 0.159642 / 0.296338 (-0.136696) \|\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) \| 0.664288 / 0.215209 (0.449079) \| 6.926912 / 2.077655 (4.849257) \| 3.028957 / 1.504120 (1.524837) \| 2.625178 / 1.541195 (1.083983) \| 2.725316 / 1.468490 (1.256826) \| 1.015715 / 4.584777 (-3.569062) \| 5.834694 / 3.745712 (2.088982) \| 5.105269 / 5.269862 (-0.164593) \| 2.316194 / 4.565676 (-2.249483) \| 0.113802 / 0.424275 (-0.310473) \| 0.014079 / 0.007607 (0.006472) \| 0.893727 / 0.226044 (0.667683) \| 8.577701 / 2.268929 (6.308772) \| 3.706907 / 55.444624 (-51.737717) \| 3.087530 / 6.876477 (-3.788947) \| 3.295004 / 2.142072 (1.152931) \| 1.204172 / 4.805227 (-3.601055) \| 0.248720 / 6.500664 (-6.251944) \| 0.107208 / 0.075469 (0.031739) \|\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) \| 1.800058 / 1.841788 (-0.041730) \| 19.253646 / 8.074308 (11.179338) \| 22.590804 / 10.191392 (12.399412) \| 0.270687 / 0.680424 (-0.409737) \| 0.028678 / 0.534201 (-0.505522) \| 0.534670 / 0.579283 (-0.044613) \| 0.642881 / 0.434364 (0.208518) \| 0.615521 / 0.540337 (0.075184) \| 0.723733 / 1.386936 (-0.663203) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2591cd45a002a06bd551343ec785abf16f1433e2 \"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) \| 0.017236 / 0.011353 (0.005883) \| 0.005341 / 0.011008 (-0.005667) \| 0.131471 / 0.038508 (0.092963) \| 0.048868 / 0.023109 (0.025758) \| 0.448942 / 0.275898 (0.173044) \| 0.498721 / 0.323480 (0.175241) \| 0.006825 / 0.007986 (-0.001161) \| 0.004587 / 0.004328 (0.000259) \| 0.104142 / 0.004250 (0.099891) \| 0.075521 / 0.037052 (0.038469) \| 0.439538 / 0.258489 (0.181049) \| 0.498720 / 0.293841 (0.204879) \| 0.051352 / 0.128546 (-0.077194) \| 0.015070 / 0.075646 (-0.060576) \| 0.441752 / 0.419271 (0.022480) \| 0.089166 / 0.043533 (0.045633) \| 0.428909 / 0.255139 (0.173770) \| 0.446648 / 0.283200 (0.163448) \| 0.042371 / 0.141683 (-0.099312) \| 1.993948 / 1.452155 (0.541793) \| 2.065756 / 1.492716 (0.573039) \|\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) \| 0.257279 / 0.018006 (0.239273) \| 0.575453 / 0.000490 (0.574964) \| 0.004120 / 0.000200 (0.003920) \| 0.000114 / 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) \| 0.034012 / 0.037411 (-0.003399) \| 0.141737 / 0.014526 (0.127211) \| 0.145241 / 0.176557 (-0.031316) \| 0.226196 / 0.737135 (-0.510939) \| 0.149526 / 0.296338 (-0.146813) \|\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) \| 0.665762 / 0.215209 (0.450553) \| 6.683737 / 2.077655 (4.606083) \| 2.869485 / 1.504120 (1.365365) \| 2.462808 / 1.541195 (0.921613) \| 2.526808 / 1.468490 (1.058318) \| 0.957518 / 4.584777 (-3.627259) \| 5.926261 / 3.745712 (2.180548) \| 5.027822 / 5.269862 (-0.242040) \| 2.643185 / 4.565676 (-1.922491) \| 0.117014 / 0.424275 (-0.307261) \| 0.015142 / 0.007607 (0.007535) \| 0.835694 / 0.226044 (0.609650) \| 8.427356 / 2.268929 (6.158427) \| 3.649597 / 55.444624 (-51.795027) \| 2.989607 / 6.876477 (-3.886870) \| 3.043160 / 2.142072 (0.901088) \| 1.158872 / 4.805227 (-3.646355) \| 0.240456 / 6.500664 (-6.260208) \| 0.089196 / 0.075469 (0.013726) \|\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) \| 1.689361 / 1.841788 (-0.152427) \| 18.842158 / 8.074308 (10.767850) \| 22.604249 / 10.191392 (12.412857) \| 0.248487 / 0.680424 (-0.431936) \| 0.029668 / 0.534201 (-0.504533) \| 0.536283 / 0.579283 (-0.043001) \| 0.663253 / 0.434364 (0.228890) \| 0.622973 / 0.540337 (0.082635) \| 0.735297 / 1.386936 (-0.651639) \|\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) \| 0.009296 / 0.011353 (-0.002057) \| 0.005955 / 0.011008 (-0.005053) \| 0.105723 / 0.038508 (0.067215) \| 0.051184 / 0.023109 (0.028074) \| 0.527095 / 0.275898 (0.251197) \| 0.631697 / 0.323480 (0.308217) \| 0.006577 / 0.007986 (-0.001408) \| 0.004452 / 0.004328 (0.000124) \| 0.105921 / 0.004250 (0.101670) \| 0.071951 / 0.037052 (0.034899) \| 0.572518 / 0.258489 (0.314029) \| 0.623957 / 0.293841 (0.330116) \| 0.050861 / 0.128546 (-0.077686) \| 0.014897 / 0.075646 (-0.060749) \| 0.122013 / 0.419271 (-0.297258) \| 0.067194 / 0.043533 (0.023661) \| 0.530352 / 0.255139 (0.275213) \| 0.563912 / 0.283200 (0.280712) \| 0.034756 / 0.141683 (-0.106927) \| 1.961580 / 1.452155 (0.509425) \| 2.052412 / 1.492716 (0.559696) \|\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) \| 0.304996 / 0.018006 (0.286990) \| 0.584899 / 0.000490 (0.584409) \| 0.010444 / 0.000200 (0.010244) \| 0.000134 / 0.000054 (0.000080) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032540 / 0.037411 (-0.004871) \| 0.137349 / 0.014526 (0.122823) \| 0.146233 / 0.176557 (-0.030323) \| 0.206978 / 0.737135 (-0.530157) \| 0.154380 / 0.296338 (-0.141959) \|\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) \| 0.705438 / 0.215209 (0.490229) \| 7.042159 / 2.077655 (4.964504) \| 3.285501 / 1.504120 (1.781381) \| 2.904710 / 1.541195 (1.363515) \| 2.952838 / 1.468490 (1.484348) \| 0.987784 / 4.584777 (-3.596993) \| 5.949550 / 3.745712 (2.203838) \| 2.927148 / 5.269862 (-2.342714) \| 1.870054 / 4.565676 (-2.695622) \| 0.119548 / 0.424275 (-0.304727) \| 0.014565 / 0.007607 (0.006958) \| 0.858311 / 0.226044 (0.632266) \| 8.721679 / 2.268929 (6.452750) \| 4.100825 / 55.444624 (-51.343800) \| 3.358093 / 6.876477 (-3.518383) \| 3.499637 / 2.142072 (1.357564) \| 1.208932 / 4.805227 (-3.596295) \| 0.232961 / 6.500664 (-6.267703) \| 0.089727 / 0.075469 (0.014258) \|\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) \| 1.780143 / 1.841788 (-0.061645) \| 19.074991 / 8.074308 (11.000683) \| 21.218487 / 10.191392 (11.027095) \| 0.258690 / 0.680424 (-0.421734) \| 0.029514 / 0.534201 (-0.504687) \| 0.541764 / 0.579283 (-0.037519) \| 0.640603 / 0.434364 (0.206239) \| 0.635336 / 0.540337 (0.094999) \| 0.756309 / 1.386936 (-0.630627) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b525c199e6352aa8aac55f1dcddeb55a80db373 \"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) \| 0.009619 / 0.011353 (-0.001734) \| 0.005683 / 0.011008 (-0.005325) \| 0.136971 / 0.038508 (0.098463) \| 0.051607 / 0.023109 (0.028497) \| 0.439716 / 0.275898 (0.163818) \| 0.486193 / 0.323480 (0.162713) \| 0.006304 / 0.007986 (-0.001681) \| 0.004489 / 0.004328 (0.000160) \| 0.103837 / 0.004250 (0.099587) \| 0.082954 / 0.037052 (0.045901) \| 0.447286 / 0.258489 (0.188797) \| 0.495434 / 0.293841 (0.201593) \| 0.049244 / 0.128546 (-0.079302) \| 0.015176 / 0.075646 (-0.060470) \| 0.444406 / 0.419271 (0.025134) \| 0.074766 / 0.043533 (0.031233) \| 0.438585 / 0.255139 (0.183446) \| 0.438232 / 0.283200 (0.155032) \| 0.043372 / 0.141683 (-0.098311) \| 2.057286 / 1.452155 (0.605131) \| 2.049540 / 1.492716 (0.556824) \|\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) \| 0.298038 / 0.018006 (0.280031) \| 0.630771 / 0.000490 (0.630281) \| 0.008287 / 0.000200 (0.008087) \| 0.000123 / 0.000054 (0.000068) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033637 / 0.037411 (-0.003775) \| 0.128327 / 0.014526 (0.113801) \| 0.150672 / 0.176557 (-0.025885) \| 0.228521 / 0.737135 (-0.508614) \| 0.142733 / 0.296338 (-0.153606) \|\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) \| 0.629072 / 0.215209 (0.413863) \| 6.612047 / 2.077655 (4.534392) \| 2.715594 / 1.504120 (1.211474) \| 2.327823 / 1.541195 (0.786628) \| 2.417508 / 1.468490 (0.949018) \| 0.959134 / 4.584777 (-3.625643) \| 5.669921 / 3.745712 (1.924209) \| 2.977920 / 5.269862 (-2.291941) \| 1.814564 / 4.565676 (-2.751112) \| 0.120233 / 0.424275 (-0.304042) \| 0.015859 / 0.007607 (0.008252) \| 0.822618 / 0.226044 (0.596574) \| 8.440306 / 2.268929 (6.171377) \| 3.721611 / 55.444624 (-51.723013) \| 2.954867 / 6.876477 (-3.921610) \| 3.135364 / 2.142072 (0.993292) \| 1.226475 / 4.805227 (-3.578752) \| 0.246658 / 6.500664 (-6.254006) \| 0.093920 / 0.075469 (0.018451) \|\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) \| 1.665631 / 1.841788 (-0.176157) \| 19.136369 / 8.074308 (11.062061) \| 23.659564 / 10.191392 (13.468172) \| 0.273430 / 0.680424 (-0.406994) \| 0.028180 / 0.534201 (-0.506021) \| 0.559588 / 0.579283 (-0.019695) \| 0.649203 / 0.434364 (0.214840) \| 0.647113 / 0.540337 (0.106776) \| 0.737978 / 1.386936 (-0.648958) \|\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) \| 0.009104 / 0.011353 (-0.002249) \| 0.006838 / 0.011008 (-0.004171) \| 0.104516 / 0.038508 (0.066008) \| 0.047986 / 0.023109 (0.024877) \| 0.521849 / 0.275898 (0.245951) \| 0.586281 / 0.323480 (0.262801) \| 0.006225 / 0.007986 (-0.001760) \| 0.005713 / 0.004328 (0.001384) \| 0.111507 / 0.004250 (0.107257) \| 0.072320 / 0.037052 (0.035267) \| 0.551061 / 0.258489 (0.292572) \| 0.628034 / 0.293841 (0.334193) \| 0.055417 / 0.128546 (-0.073129) \| 0.019613 / 0.075646 (-0.056034) \| 0.123958 / 0.419271 (-0.295314) \| 0.066132 / 0.043533 (0.022600) \| 0.504461 / 0.255139 (0.249322) \| 0.560428 / 0.283200 (0.277229) \| 0.036098 / 0.141683 (-0.105585) \| 1.927398 / 1.452155 (0.475243) \| 2.015952 / 1.492716 (0.523235) \|\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) \| 0.313065 / 0.018006 (0.295059) \| 0.609174 / 0.000490 (0.608684) \| 0.008755 / 0.000200 (0.008555) \| 0.000120 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.040042 / 0.037411 (0.002630) \| 0.136053 / 0.014526 (0.121527) \| 0.143406 / 0.176557 (-0.033150) \| 0.213080 / 0.737135 (-0.524055) \| 0.154730 / 0.296338 (-0.141609) \|\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) \| 0.692706 / 0.215209 (0.477497) \| 6.952968 / 2.077655 (4.875314) \| 3.232023 / 1.504120 (1.727903) \| 2.835450 / 1.541195 (1.294256) \| 2.933821 / 1.468490 (1.465331) \| 0.984712 / 4.584777 (-3.600065) \| 6.127651 / 3.745712 (2.381939) \| 2.956781 / 5.269862 (-2.313081) \| 1.879928 / 4.565676 (-2.685748) \| 0.111069 / 0.424275 (-0.313206) \| 0.014598 / 0.007607 (0.006991) \| 0.871486 / 0.226044 (0.645442) \| 8.588500 / 2.268929 (6.319572) \| 3.910740 / 55.444624 (-51.533885) \| 3.115781 / 6.876477 (-3.760695) \| 3.222367 / 2.142072 (1.080294) \| 1.229680 / 4.805227 (-3.575547) \| 0.232092 / 6.500664 (-6.268572) \| 0.097717 / 0.075469 (0.022248) \|\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) \| 1.774193 / 1.841788 (-0.067595) \| 19.863087 / 8.074308 (11.788779) \| 24.058856 / 10.191392 (13.867464) \| 0.214917 / 0.680424 (-0.465507) \| 0.028771 / 0.534201 (-0.505430) \| 0.544548 / 0.579283 (-0.034735) \| 0.655882 / 0.434364 (0.221518) \| 0.629110 / 0.540337 (0.088773) \| 0.749246 / 1.386936 (-0.637690) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f4a5ea6a42dcfef1577288b51beeccc0eb124cee \"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) \| 0.007075 / 0.011353 (-0.004278) \| 0.005195 / 0.011008 (-0.005813) \| 0.113043 / 0.038508 (0.074535) \| 0.038442 / 0.023109 (0.015333) \| 0.336310 / 0.275898 (0.060412) \| 0.381888 / 0.323480 (0.058409) \| 0.005990 / 0.007986 (-0.001996) \| 0.003893 / 0.004328 (-0.000435) \| 0.093123 / 0.004250 (0.088872) \| 0.058449 / 0.037052 (0.021397) \| 0.359463 / 0.258489 (0.100974) \| 0.427485 / 0.293841 (0.133644) \| 0.041454 / 0.128546 (-0.087092) \| 0.013016 / 0.075646 (-0.062630) \| 0.372849 / 0.419271 (-0.046422) \| 0.059386 / 0.043533 (0.015853) \| 0.381398 / 0.255139 (0.126259) \| 0.367603 / 0.283200 (0.084403) \| 0.033907 / 0.141683 (-0.107775) \| 1.628903 / 1.452155 (0.176749) \| 1.764131 / 1.492716 (0.271415) \|\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) \| 0.298329 / 0.018006 (0.280322) \| 0.593030 / 0.000490 (0.592540) \| 0.007653 / 0.000200 (0.007453) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025445 / 0.037411 (-0.011966) \| 0.112062 / 0.014526 (0.097536) \| 0.119863 / 0.176557 (-0.056693) \| 0.178389 / 0.737135 (-0.558746) \| 0.129934 / 0.296338 (-0.166404) \|\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) \| 0.532834 / 0.215209 (0.317625) \| 5.250908 / 2.077655 (3.173253) \| 2.086920 / 1.504120 (0.582800) \| 1.799745 / 1.541195 (0.258550) \| 1.909648 / 1.468490 (0.441158) \| 0.825382 / 4.584777 (-3.759395) \| 5.268304 / 3.745712 (1.522592) \| 2.533347 / 5.269862 (-2.736515) \| 1.730187 / 4.565676 (-2.835490) \| 0.099824 / 0.424275 (-0.324451) \| 0.012969 / 0.007607 (0.005362) \| 0.732234 / 0.226044 (0.506189) \| 6.989066 / 2.268929 (4.720138) \| 2.873486 / 55.444624 (-52.571138) \| 2.274351 / 6.876477 (-4.602125) \| 2.311060 / 2.142072 (0.168987) \| 1.125366 / 4.805227 (-3.679861) \| 0.214522 / 6.500664 (-6.286142) \| 0.077579 / 0.075469 (0.002110) \|\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) \| 1.670950 / 1.841788 (-0.170838) \| 18.131528 / 8.074308 (10.057220) \| 21.277823 / 10.191392 (11.086431) \| 0.238807 / 0.680424 (-0.441617) \| 0.032251 / 0.534201 (-0.501950) \| 0.503859 / 0.579283 (-0.075424) \| 0.604825 / 0.434364 (0.170461) \| 0.555623 / 0.540337 (0.015286) \| 0.647301 / 1.386936 (-0.739635) \|\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) \| 0.010857 / 0.011353 (-0.000496) \| 0.005581 / 0.011008 (-0.005427) \| 0.094346 / 0.038508 (0.055838) \| 0.053084 / 0.023109 (0.029975) \| 0.457586 / 0.275898 (0.181688) \| 0.545475 / 0.323480 (0.221995) \| 0.006761 / 0.007986 (-0.001225) \| 0.005094 / 0.004328 (0.000765) \| 0.095509 / 0.004250 (0.091258) \| 0.077182 / 0.037052 (0.040130) \| 0.498717 / 0.258489 (0.240228) \| 0.542433 / 0.293841 (0.248592) \| 0.051547 / 0.128546 (-0.076999) \| 0.014633 / 0.075646 (-0.061014) \| 0.106843 / 0.419271 (-0.312428) \| 0.068459 / 0.043533 (0.024926) \| 0.435793 / 0.255139 (0.180654) \| 0.475484 / 0.283200 (0.192285) \| 0.039495 / 0.141683 (-0.102188) \| 1.684906 / 1.452155 (0.232751) \| 1.798693 / 1.492716 (0.305976) \|\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) \| 0.279853 / 0.018006 (0.261847) \| 0.601016 / 0.000490 (0.600526) \| 0.002055 / 0.000200 (0.001855) \| 0.000219 / 0.000054 (0.000165) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030935 / 0.037411 (-0.006477) \| 0.121197 / 0.014526 (0.106671) \| 0.143360 / 0.176557 (-0.033197) \| 0.200862 / 0.737135 (-0.536274) \| 0.138656 / 0.296338 (-0.157683) \|\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) \| 0.613904 / 0.215209 (0.398695) \| 6.155422 / 2.077655 (4.077767) \| 2.777238 / 1.504120 (1.273118) \| 2.473045 / 1.541195 (0.931851) \| 2.604470 / 1.468490 (1.135980) \| 0.898871 / 4.584777 (-3.685906) \| 5.739666 / 3.745712 (1.993954) \| 4.719822 / 5.269862 (-0.550040) \| 2.727354 / 4.565676 (-1.838322) \| 0.108232 / 0.424275 (-0.316043) \| 0.013632 / 0.007607 (0.006025) \| 0.771802 / 0.226044 (0.545757) \| 7.987466 / 2.268929 (5.718537) \| 3.609856 / 55.444624 (-51.834768) \| 2.974421 / 6.876477 (-3.902056) \| 2.956567 / 2.142072 (0.814495) \| 1.093792 / 4.805227 (-3.711435) \| 0.213369 / 6.500664 (-6.287295) \| 0.084486 / 0.075469 (0.009017) \|\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) \| 1.693855 / 1.841788 (-0.147933) \| 18.055027 / 8.074308 (9.980719) \| 21.397964 / 10.191392 (11.206571) \| 0.240549 / 0.680424 (-0.439875) \| 0.031212 / 0.534201 (-0.502989) \| 0.513657 / 0.579283 (-0.065626) \| 0.651348 / 0.434364 (0.216985) \| 0.603740 / 0.540337 (0.063402) \| 0.752287 / 1.386936 (-0.634649) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f38d00dd40a444ae54c18caa28304ae36b9c3 \"CML watermark\")\n" ]	1,686,661,399,000	1,687,884,471,000	1,687,883,912,000	CONTRIBUTOR	null	Use `huggingface_hub`'s RepoCard API instead of `DatasetMetadata` for modifying the card's YAML, and deprecate `datasets.utils.metadata` and `datasets.utils.readme`. After removing these modules, we can also delete `datasets.utils.resources` since the moon landing repo now stores its own version of these resources for the metadata UI. PS: this change requires bumping `huggingface_hub` to 0.13.0 (Transformers requires 0.14.0, so should be ok)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5949/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5949/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5949", "html_url": "https://github.com/huggingface/datasets/pull/5949", "diff_url": "https://github.com/huggingface/datasets/pull/5949.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5949.patch", "merged_at": "2023-06-27T16:38:32" }	true
https://api.github.com/repos/huggingface/datasets/issues/5948	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5948/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5948/comments	https://api.github.com/repos/huggingface/datasets/issues/5948/events	https://github.com/huggingface/datasets/pull/5948	1,754,794,611	PR_kwDODunzps5S4dUt	5,948	Fix sequence of array support for most dtype	{ "login": "qgallouedec", "id": 45557362, "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "gravatar_id": "", "url": "https://api.github.com/users/qgallouedec", "html_url": "https://github.com/qgallouedec", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "repos_url": "https://api.github.com/users/qgallouedec/repos", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.007220 / 0.011353 (-0.004133) \| 0.004558 / 0.011008 (-0.006451) \| 0.116647 / 0.038508 (0.078139) \| 0.046845 / 0.023109 (0.023736) \| 0.352429 / 0.275898 (0.076531) \| 0.429739 / 0.323480 (0.106259) \| 0.006620 / 0.007986 (-0.001366) \| 0.003731 / 0.004328 (-0.000597) \| 0.088683 / 0.004250 (0.084433) \| 0.070583 / 0.037052 (0.033530) \| 0.366699 / 0.258489 (0.108210) \| 0.420730 / 0.293841 (0.126889) \| 0.037342 / 0.128546 (-0.091204) \| 0.010041 / 0.075646 (-0.065605) \| 0.383477 / 0.419271 (-0.035795) \| 0.060279 / 0.043533 (0.016746) \| 0.349988 / 0.255139 (0.094849) \| 0.371423 / 0.283200 (0.088224) \| 0.026725 / 0.141683 (-0.114958) \| 1.736886 / 1.452155 (0.284731) \| 1.812874 / 1.492716 (0.320157) \|\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) \| 0.253256 / 0.018006 (0.235250) \| 0.563470 / 0.000490 (0.562980) \| 0.010475 / 0.000200 (0.010275) \| 0.000164 / 0.000054 (0.000110) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030518 / 0.037411 (-0.006893) \| 0.133324 / 0.014526 (0.118798) \| 0.137095 / 0.176557 (-0.039461) \| 0.202227 / 0.737135 (-0.534909) \| 0.144195 / 0.296338 (-0.152143) \|\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) \| 0.480870 / 0.215209 (0.265661) \| 4.822713 / 2.077655 (2.745058) \| 2.124183 / 1.504120 (0.620064) \| 1.910733 / 1.541195 (0.369538) \| 1.970266 / 1.468490 (0.501776) \| 0.624695 / 4.584777 (-3.960082) \| 4.459659 / 3.745712 (0.713947) \| 2.210123 / 5.269862 (-3.059739) \| 1.300520 / 4.565676 (-3.265157) \| 0.077096 / 0.424275 (-0.347180) \| 0.013333 / 0.007607 (0.005726) \| 0.596841 / 0.226044 (0.370797) \| 5.917397 / 2.268929 (3.648469) \| 2.699397 / 55.444624 (-52.745228) \| 2.274833 / 6.876477 (-4.601644) \| 2.525376 / 2.142072 (0.383304) \| 0.755718 / 4.805227 (-4.049510) \| 0.163587 / 6.500664 (-6.337077) \| 0.072817 / 0.075469 (-0.002653) \|\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) \| 1.524306 / 1.841788 (-0.317481) \| 18.843312 / 8.074308 (10.769004) \| 15.694644 / 10.191392 (5.503252) \| 0.177400 / 0.680424 (-0.503024) \| 0.020104 / 0.534201 (-0.514097) \| 0.466421 / 0.579283 (-0.112862) \| 0.537274 / 0.434364 (0.102910) \| 0.576920 / 0.540337 (0.036583) \| 0.718889 / 1.386936 (-0.668047) \|\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) \| 0.007671 / 0.011353 (-0.003682) \| 0.004850 / 0.011008 (-0.006158) \| 0.090085 / 0.038508 (0.051576) \| 0.052023 / 0.023109 (0.028914) \| 0.508575 / 0.275898 (0.232677) \| 0.590024 / 0.323480 (0.266544) \| 0.004564 / 0.007986 (-0.003422) \| 0.005345 / 0.004328 (0.001017) \| 0.087904 / 0.004250 (0.083653) \| 0.064446 / 0.037052 (0.027394) \| 0.525625 / 0.258489 (0.267136) \| 0.584307 / 0.293841 (0.290466) \| 0.037221 / 0.128546 (-0.091325) \| 0.010588 / 0.075646 (-0.065059) \| 0.098612 / 0.419271 (-0.320659) \| 0.059597 / 0.043533 (0.016064) \| 0.488064 / 0.255139 (0.232925) \| 0.522330 / 0.283200 (0.239131) \| 0.030004 / 0.141683 (-0.111679) \| 1.732512 / 1.452155 (0.280357) \| 1.809027 / 1.492716 (0.316310) \|\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) \| 0.218741 / 0.018006 (0.200735) \| 0.494946 / 0.000490 (0.494456) \| 0.004580 / 0.000200 (0.004380) \| 0.000104 / 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) \| 0.034916 / 0.037411 (-0.002495) \| 0.133695 / 0.014526 (0.119169) \| 0.147964 / 0.176557 (-0.028592) \| 0.213210 / 0.737135 (-0.523926) \| 0.148850 / 0.296338 (-0.147488) \|\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) \| 0.508855 / 0.215209 (0.293646) \| 5.065088 / 2.077655 (2.987433) \| 2.473110 / 1.504120 (0.968990) \| 2.259765 / 1.541195 (0.718570) \| 2.359189 / 1.468490 (0.890699) \| 0.639082 / 4.584777 (-3.945695) \| 4.768195 / 3.745712 (1.022482) \| 2.253803 / 5.269862 (-3.016059) \| 1.442996 / 4.565676 (-3.122680) \| 0.078761 / 0.424275 (-0.345514) \| 0.013936 / 0.007607 (0.006329) \| 0.625977 / 0.226044 (0.399933) \| 6.260817 / 2.268929 (3.991888) \| 3.149640 / 55.444624 (-52.294985) \| 2.753555 / 6.876477 (-4.122921) \| 2.831872 / 2.142072 (0.689799) \| 0.781294 / 4.805227 (-4.023933) \| 0.169109 / 6.500664 (-6.331555) \| 0.075810 / 0.075469 (0.000341) \|\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) \| 1.533282 / 1.841788 (-0.308506) \| 19.460579 / 8.074308 (11.386271) \| 17.250424 / 10.191392 (7.059032) \| 0.193485 / 0.680424 (-0.486939) \| 0.020650 / 0.534201 (-0.513551) \| 0.472110 / 0.579283 (-0.107173) \| 0.532276 / 0.434364 (0.097912) \| 0.613152 / 0.540337 (0.072814) \| 0.684684 / 1.386936 (-0.702252) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#650a86ee122209d4a8c8e8068c01ebfd3ba553f5 \"CML watermark\")\n" ]	1,686,659,939,000	1,686,755,515,000	1,686,755,013,000	CONTRIBUTOR	null	Fixes #5936 Also, a related fix to #5927	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5948/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5948/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5948", "html_url": "https://github.com/huggingface/datasets/pull/5948", "diff_url": "https://github.com/huggingface/datasets/pull/5948.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5948.patch", "merged_at": "2023-06-14T15:03:33" }	true
https://api.github.com/repos/huggingface/datasets/issues/5947	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5947/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5947/comments	https://api.github.com/repos/huggingface/datasets/issues/5947/events	https://github.com/huggingface/datasets/issues/5947	1,754,359,316	I_kwDODunzps5okWYU	5,947	Return the audio filename when decoding fails due to corrupt files	{ "login": "wetdog", "id": 8949105, "node_id": "MDQ6VXNlcjg5NDkxMDU=", "avatar_url": "https://avatars.githubusercontent.com/u/8949105?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wetdog", "html_url": "https://github.com/wetdog", "followers_url": "https://api.github.com/users/wetdog/followers", "following_url": "https://api.github.com/users/wetdog/following{/other_user}", "gists_url": "https://api.github.com/users/wetdog/gists{/gist_id}", "starred_url": "https://api.github.com/users/wetdog/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/wetdog/subscriptions", "organizations_url": "https://api.github.com/users/wetdog/orgs", "repos_url": "https://api.github.com/users/wetdog/repos", "events_url": "https://api.github.com/users/wetdog/events{/privacy}", "received_events_url": "https://api.github.com/users/wetdog/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	[ "Hi ! The audio data don't always exist as files on disk - the blobs are often stored in the Arrow files. For now I'd suggest disabling decoding with `.cast_column(\"audio\", Audio(decode=False))` and apply your own decoding that handles corrupted files (maybe to filter them out ?)\r\n\r\ncc @sanchit-gandhi since it's related to our discussion about allowing users to make decoding return `None` and show a warning when there are corrupted files", "Thanks @lhoestq, I wasn't aware of the decode flag. It makes more sense as you say to show a warning when there are corrupted files together with some metadata of the file that allows to filter them from the dataset.\r\n\r\nMy workaround was to catch the LibsndfileError and generate a dummy audio with an unsual sample rate to filter it later. However returning `None` seems better. \r\n\r\n`try:\r\n array, sampling_rate = sf.read(file)\r\nexcept sf.LibsndfileError:\r\n print(\"bad file\")\r\n array = np.array([0.0])\r\n sampling_rate = 99.000` \r\n\r\n" ]	1,686,645,849,000	1,686,746,701,000	null	NONE	null	### Feature request Return the audio filename when the audio decoding fails. Although currently there are some checks for mp3 and opus formats with the library version there are still cases when the audio decoding could fail, eg. Corrupt file. ### Motivation When you try to load an object file dataset and the decoding fails you can't know which file is corrupt ``` raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name)) soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7f5ab7e38290>: Format not recognised. ``` ### Your contribution Make a PR to Add exceptions for LIbsndfileError to return the audio filename or path when soundfile decoding fails.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5947/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5947/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5946	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5946/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5946/comments	https://api.github.com/repos/huggingface/datasets/issues/5946/events	https://github.com/huggingface/datasets/issues/5946	1,754,234,469	I_kwDODunzps5oj35l	5,946	IndexError Not Solving -> IndexError: Invalid key: ?? is out of bounds for size 0 or ??	{ "login": "syngokhan", "id": 70565543, "node_id": "MDQ6VXNlcjcwNTY1NTQz", "avatar_url": "https://avatars.githubusercontent.com/u/70565543?v=4", "gravatar_id": "", "url": "https://api.github.com/users/syngokhan", "html_url": "https://github.com/syngokhan", "followers_url": "https://api.github.com/users/syngokhan/followers", "following_url": "https://api.github.com/users/syngokhan/following{/other_user}", "gists_url": "https://api.github.com/users/syngokhan/gists{/gist_id}", "starred_url": "https://api.github.com/users/syngokhan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/syngokhan/subscriptions", "organizations_url": "https://api.github.com/users/syngokhan/orgs", "repos_url": "https://api.github.com/users/syngokhan/repos", "events_url": "https://api.github.com/users/syngokhan/events{/privacy}", "received_events_url": "https://api.github.com/users/syngokhan/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "https://colab.research.google.com/#scrollTo=AQ_HCYruWIHU&fileId=https%3A//huggingface.co/dfurman/falcon-40b-chat-oasst1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb\r\n\r\nI ran the same administration exactly the same but got the same error", "Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq", "> Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq\n\nThe problem has not been solved, I have tried this before, but the problem is the same" ]	1,686,641,655,000	1,686,988,351,000	null	NONE	null	### Describe the bug in <cell line: 1>:1 │ │ │ │ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1537 in train │ │ │ │ 1534 │ │ inner_training_loop = find_executable_batch_size( │ │ 1535 │ │ │ self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size │ │ 1536 │ │ ) │ │ ❱ 1537 │ │ return inner_training_loop( │ │ 1538 │ │ │ args=args, │ │ 1539 │ │ │ resume_from_checkpoint=resume_from_checkpoint, │ │ 1540 │ │ │ trial=trial, │ │ │ │ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1789 in _inner_training_loop │ │ │ │ 1786 │ │ │ │ rng_to_sync = True │ │ 1787 │ │ │ │ │ 1788 │ │ │ step = -1 │ │ ❱ 1789 │ │ │ for step, inputs in enumerate(epoch_iterator): │ │ 1790 │ │ │ │ total_batched_samples += 1 │ │ 1791 │ │ │ │ if rng_to_sync: │ │ 1792 │ │ │ │ │ self._load_rng_state(resume_from_checkpoint) │ │ │ │ /usr/local/lib/python3.10/dist-packages/accelerate/data_loader.py:377 in __iter__ │ │ │ │ 374 │ │ dataloader_iter = super().__iter__() │ │ 375 │ │ # We iterate one batch ahead to check when we are at the end │ │ 376 │ │ try: │ │ ❱ 377 │ │ │ current_batch = next(dataloader_iter) │ │ 378 │ │ except StopIteration: │ │ 379 │ │ │ yield │ │ 380 │ │ │ │ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:633 in __next__ │ │ │ │ 630 │ │ │ if self._sampler_iter is None: │ │ 631 │ │ │ │ # TODO(https://github.com/pytorch/pytorch/issues/76750) │ │ 632 │ │ │ │ self._reset() # type: ignore[call-arg] │ │ ❱ 633 │ │ │ data = self._next_data() │ │ 634 │ │ │ self._num_yielded += 1 │ │ 635 │ │ │ if self._dataset_kind == _DatasetKind.Iterable and \ │ │ 636 │ │ │ │ │ self._IterableDataset_len_called is not None and \ │ │ │ │ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:677 in _next_data │ │ │ │ 674 │ │ │ 675 │ def _next_data(self): │ │ 676 │ │ index = self._next_index() # may raise StopIteration │ │ ❱ 677 │ │ data = self._dataset_fetcher.fetch(index) # may raise StopIteration │ │ 678 │ │ if self._pin_memory: │ │ 679 │ │ │ data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) │ │ 680 │ │ return data │ │ │ │ /usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py:49 in fetch │ │ │ │ 46 │ def fetch(self, possibly_batched_index): │ │ 47 │ │ if self.auto_collation: │ │ 48 │ │ │ if hasattr(self.dataset, "__getitems__") and self.dataset.__getitems__: │ │ ❱ 49 │ │ │ │ data = self.dataset.__getitems__(possibly_batched_index) │ │ 50 │ │ │ else: │ │ 51 │ │ │ │ data = [self.dataset[idx] for idx in possibly_batched_index] │ │ 52 │ │ else: │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2782 in __getitems__ │ │ │ │ 2779 │ │ │ 2780 │ def __getitems__(self, keys: List) -> List: │ │ 2781 │ │ """Can be used to get a batch using a list of integers indices.""" │ │ ❱ 2782 │ │ batch = self.__getitem__(keys) │ │ 2783 │ │ n_examples = len(batch[next(iter(batch))]) │ │ 2784 │ │ return [{col: array[i] for col, array in batch.items()} for i in range(n_example │ │ 2785 │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2778 in __getitem__ │ │ │ │ 2775 │ │ │ 2776 │ def __getitem__(self, key): # noqa: F811 │ │ 2777 │ │ """Can be used to index columns (by string names) or rows (by integer index or i │ │ ❱ 2778 │ │ return self._getitem(key) │ │ 2779 │ │ │ 2780 │ def __getitems__(self, keys: List) -> List: │ │ 2781 │ │ """Can be used to get a batch using a list of integers indices.""" │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2762 in _getitem │ │ │ │ 2759 │ │ format_kwargs = kwargs["format_kwargs"] if "format_kwargs" in kwargs else self._ │ │ 2760 │ │ format_kwargs = format_kwargs if format_kwargs is not None else {} │ │ 2761 │ │ formatter = get_formatter(format_type, features=self._info.features, *format_kw │ │ ❱ 2762 │ │ pa_subtable = query_table(self._data, key, indices=self._indices if self._indice │ │ 2763 │ │ formatted_output = format_table( │ │ 2764 │ │ │ pa_subtable, key, formatter=formatter, format_columns=format_columns, output │ │ 2765 │ │ ) │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:578 in query_table │ │ │ │ 575 │ │ _check_valid_column_key(key, table.column_names) │ │ 576 │ else: │ │ 577 │ │ size = indices.num_rows if indices is not None else table.num_rows │ │ ❱ 578 │ │ _check_valid_index_key(key, size) │ │ 579 │ # Query the main table │ │ 580 │ if indices is None: │ │ 581 │ │ pa_subtable = _query_table(table, key) │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:531 in │ │ _check_valid_index_key │ │ │ │ 528 │ │ │ _check_valid_index_key(min(key), size=size) │ │ 529 │ elif isinstance(key, Iterable): │ │ 530 │ │ if len(key) > 0: │ │ ❱ 531 │ │ │ _check_valid_index_key(int(max(key)), size=size) │ │ 532 │ │ │ _check_valid_index_key(int(min(key)), size=size) │ │ 533 │ else: │ │ 534 │ │ _raise_bad_key_type(key) │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:521 in │ │ _check_valid_index_key │ │ │ │ 518 def _check_valid_index_key(key: Union[int, slice, range, Iterable], size: int) -> None: │ │ 519 │ if isinstance(key, int): │ │ 520 │ │ if (key < 0 and key + size < 0) or (key >= size): │ │ ❱ 521 │ │ │ raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") │ │ 522 │ │ return │ │ 523 │ elif isinstance(key, slice): │ │ 524 │ │ pass ### Steps to reproduce the bug `` import json import os from pprint import pprint import bitsandbytes as bnb import pandas as pd import torch import torch.nn as nn import transformers from datasets import Dataset,load_dataset from peft import ( LoraConfig, PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training ) from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, ) os.environ["CUDA_VISIBLE_DEVICES"] = "0" def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} \|\| all params: {all_param} \|\| trainable%: {100 trainable_params / all_param}" ) MODEL_NAME = "tiiuae/falcon-7b" bnb_config = BitsAndBytesConfig( load_in_4bit = True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ) model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, device_map = "auto", trust_remote_code = True, quantization_config = bnb_config ) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) tokenizer.pad_token = tokenizer.eos_token model.gradient_checkpointing_enable() model = prepare_model_for_kbit_training(model) config = LoraConfig( r = 16, lora_alpha = 32, target_modules = ["query_key_value"], lora_dropout = 0.05, bias = "none", task_type = "CASUAL_LM" ) model = get_peft_model(model,config) print_trainable_parameters(model) def generate_prompt(data_point): return f""" <human>: {data_point["question"]} <assistant>: {data_point["answer"]} """.strip() def generate_and_tokenize_prompt(data_point): full_prompt = generate_prompt(data_point) tokenized_full_prompt = tokenizer(full_prompt, padding = True, truncation = True,return_tensors = None) return dict({ "input_ids" : tokenized_full_prompt["input_ids"], "attention_mask" : tokenized_full_prompt["attention_mask"] }) data = data["train"].shuffle().map(generate_and_tokenize_prompt, batched = False) OUTPUT_DIR = "experiments" trainings_args = transformers.TrainingArguments( per_device_train_batch_size = 1, gradient_accumulation_steps = 4, num_train_epochs = 1, learning_rate = 2e-4, fp16 = True, save_total_limit = 3, logging_steps = 1, output_dir = OUTPUT_DIR, max_steps = 80, optim = "paged_adamw_8bit", lr_scheduler_type = "cosine", warmup_ratio = 0.05, #remove_unused_columns=True ) trainer = transformers.Trainer( model = model, train_dataset = data, args = trainings_args, data_collator = transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False), ) model.config.use_cache = False trainer.train() IndexError: Invalid key: 32 is out of bounds for size 0 DataSet Format is like : [{"question": "How can I create an account?", "answer": "To create an account, click on the 'Sign Up' button on the top right corner of our website and follow the instructions to complete the registration process."}, .... ] ### Expected behavior - ### Environment info !pip install -q pip !pip install -q bitsandbytes==0.39.0 !pip install -q torch==2.0.1 !pip install -q git+https://github.com/huggingface/transformers.git !pip install -q git+https://github.com/huggingface/peft.git !pip install -q git+https://github.com/huggingface/accelerate.git !pip install -q datasets !pip install -q loralib==0.1.1 !pip install -q einops==0.6.1 import json import os from pprint import pprint import bitsandbytes as bnb import pandas as pd import torch import torch.nn as nn import transformers from datasets import Dataset,load_dataset from peft import ( LoraConfig, PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training ) from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, ) os.environ["CUDA_VISIBLE_DEVICES"] = "0"	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5946/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5946/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5945	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5945/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5945/comments	https://api.github.com/repos/huggingface/datasets/issues/5945/events	https://github.com/huggingface/datasets/issues/5945	1,754,084,577	I_kwDODunzps5ojTTh	5,945	Failing to upload dataset to the hub	{ "login": "Ar770", "id": 77382661, "node_id": "MDQ6VXNlcjc3MzgyNjYx", "avatar_url": "https://avatars.githubusercontent.com/u/77382661?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Ar770", "html_url": "https://github.com/Ar770", "followers_url": "https://api.github.com/users/Ar770/followers", "following_url": "https://api.github.com/users/Ar770/following{/other_user}", "gists_url": "https://api.github.com/users/Ar770/gists{/gist_id}", "starred_url": "https://api.github.com/users/Ar770/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Ar770/subscriptions", "organizations_url": "https://api.github.com/users/Ar770/orgs", "repos_url": "https://api.github.com/users/Ar770/repos", "events_url": "https://api.github.com/users/Ar770/events{/privacy}", "received_events_url": "https://api.github.com/users/Ar770/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Hi ! Feel free to re-run your code later, it will resume automatically where you left", "Tried many times in the last 2 weeks, problem remains.", "Alternatively you can save your dataset in parquet files locally and upload them to the hub manually\r\n\r\n```python\r\nfrom tqdm import tqdm\r\nnum_shards = 60\r\nfor index in tqdm(range(num_shards)):\r\n ds.shard(num_shards=num_shards, index=index, contiguous=True).to_parquet(f\"{index:05d}.parquet\")\r\n````" ]	1,686,635,206,000	1,687,172,095,000	null	NONE	null	### Describe the bug Trying to upload a dataset of hundreds of thousands of audio samples (the total volume is not very large, 60 gb) to the hub with push_to_hub, it doesn't work. From time to time one piece of the data (parquet) gets pushed and then I get RemoteDisconnected even though my internet is stable. Please help. I'm trying to upload the dataset for almost a week. Thanks ### Steps to reproduce the bug not relevant ### Expected behavior Be able to upload thedataset ### Environment info python: 3.9	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5945/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5945/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5944	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5944/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5944/comments	https://api.github.com/repos/huggingface/datasets/issues/5944/events	https://github.com/huggingface/datasets/pull/5944	1,752,882,200	PR_kwDODunzps5Sx7O4	5,944	Arrow dataset builder to be able to load and stream Arrow datasets	{ "login": "mariusz-jachimowicz-83", "id": 10278877, "node_id": "MDQ6VXNlcjEwMjc4ODc3", "avatar_url": "https://avatars.githubusercontent.com/u/10278877?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariusz-jachimowicz-83", "html_url": "https://github.com/mariusz-jachimowicz-83", "followers_url": "https://api.github.com/users/mariusz-jachimowicz-83/followers", "following_url": "https://api.github.com/users/mariusz-jachimowicz-83/following{/other_user}", "gists_url": "https://api.github.com/users/mariusz-jachimowicz-83/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariusz-jachimowicz-83/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariusz-jachimowicz-83/subscriptions", "organizations_url": "https://api.github.com/users/mariusz-jachimowicz-83/orgs", "repos_url": "https://api.github.com/users/mariusz-jachimowicz-83/repos", "events_url": "https://api.github.com/users/mariusz-jachimowicz-83/events{/privacy}", "received_events_url": "https://api.github.com/users/mariusz-jachimowicz-83/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq tips applied. Thanks for a review. :smile: It's a lot of fun to improve this project. ", "Let's add some documentation in a subsequent PR :)\r\n\r\nIn particular @mariosasko and I think it's important to note to users that local arrow data are copied to cache according to the way load_dataset works, but if they want they can use Dataset.from_file instead", "<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) \| 0.006384 / 0.011353 (-0.004969) \| 0.003788 / 0.011008 (-0.007220) \| 0.098524 / 0.038508 (0.060016) \| 0.031786 / 0.023109 (0.008677) \| 0.307799 / 0.275898 (0.031901) \| 0.337329 / 0.323480 (0.013849) \| 0.003650 / 0.007986 (-0.004336) \| 0.003731 / 0.004328 (-0.000598) \| 0.076816 / 0.004250 (0.072566) \| 0.041888 / 0.037052 (0.004835) \| 0.310702 / 0.258489 (0.052213) \| 0.343846 / 0.293841 (0.050005) \| 0.027841 / 0.128546 (-0.100705) \| 0.008312 / 0.075646 (-0.067334) \| 0.320230 / 0.419271 (-0.099042) \| 0.047378 / 0.043533 (0.003845) \| 0.308683 / 0.255139 (0.053544) \| 0.335129 / 0.283200 (0.051930) \| 0.096294 / 0.141683 (-0.045389) \| 1.485521 / 1.452155 (0.033366) \| 1.559868 / 1.492716 (0.067152) \|\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) \| 0.197376 / 0.018006 (0.179370) \| 0.430461 / 0.000490 (0.429972) \| 0.004152 / 0.000200 (0.003953) \| 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) \| 0.023660 / 0.037411 (-0.013751) \| 0.103128 / 0.014526 (0.088602) \| 0.107549 / 0.176557 (-0.069008) \| 0.175934 / 0.737135 (-0.561201) \| 0.112210 / 0.296338 (-0.184129) \|\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) \| 0.415804 / 0.215209 (0.200595) \| 4.216333 / 2.077655 (2.138679) \| 1.910354 / 1.504120 (0.406234) \| 1.712689 / 1.541195 (0.171494) \| 1.754705 / 1.468490 (0.286215) \| 0.554647 / 4.584777 (-4.030130) \| 3.393592 / 3.745712 (-0.352120) \| 1.737504 / 5.269862 (-3.532358) \| 1.021213 / 4.565676 (-3.544464) \| 0.066908 / 0.424275 (-0.357367) \| 0.011446 / 0.007607 (0.003839) \| 0.524630 / 0.226044 (0.298585) \| 5.243005 / 2.268929 (2.974077) \| 2.349685 / 55.444624 (-53.094939) \| 2.027457 / 6.876477 (-4.849020) \| 2.131053 / 2.142072 (-0.011020) \| 0.669070 / 4.805227 (-4.136157) \| 0.136317 / 6.500664 (-6.364347) \| 0.065924 / 0.075469 (-0.009545) \|\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) \| 1.254102 / 1.841788 (-0.587686) \| 13.790492 / 8.074308 (5.716184) \| 14.197772 / 10.191392 (4.006380) \| 0.143989 / 0.680424 (-0.536434) \| 0.016577 / 0.534201 (-0.517624) \| 0.375437 / 0.579283 (-0.203846) \| 0.398995 / 0.434364 (-0.035369) \| 0.445287 / 0.540337 (-0.095050) \| 0.538632 / 1.386936 (-0.848304) \|\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) \| 0.006251 / 0.011353 (-0.005101) \| 0.004019 / 0.011008 (-0.006989) \| 0.077985 / 0.038508 (0.039477) \| 0.028705 / 0.023109 (0.005596) \| 0.417360 / 0.275898 (0.141462) \| 0.463964 / 0.323480 (0.140484) \| 0.003489 / 0.007986 (-0.004497) \| 0.003032 / 0.004328 (-0.001296) \| 0.077953 / 0.004250 (0.073702) \| 0.040104 / 0.037052 (0.003051) \| 0.405242 / 0.258489 (0.146753) \| 0.475029 / 0.293841 (0.181188) \| 0.028113 / 0.128546 (-0.100433) \| 0.008610 / 0.075646 (-0.067036) \| 0.084847 / 0.419271 (-0.334424) \| 0.048227 / 0.043533 (0.004694) \| 0.417235 / 0.255139 (0.162096) \| 0.450470 / 0.283200 (0.167270) \| 0.096978 / 0.141683 (-0.044705) \| 1.514688 / 1.452155 (0.062533) \| 1.560205 / 1.492716 (0.067488) \|\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) \| 0.235125 / 0.018006 (0.217119) \| 0.409904 / 0.000490 (0.409414) \| 0.002474 / 0.000200 (0.002275) \| 0.000074 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025152 / 0.037411 (-0.012259) \| 0.103517 / 0.014526 (0.088991) \| 0.110154 / 0.176557 (-0.066402) \| 0.161431 / 0.737135 (-0.575704) \| 0.114891 / 0.296338 (-0.181448) \|\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) \| 0.456077 / 0.215209 (0.240868) \| 4.541171 / 2.077655 (2.463517) \| 2.297912 / 1.504120 (0.793792) \| 2.079337 / 1.541195 (0.538143) \| 2.121291 / 1.468490 (0.652801) \| 0.560172 / 4.584777 (-4.024605) \| 3.421122 / 3.745712 (-0.324590) \| 1.764675 / 5.269862 (-3.505186) \| 1.043482 / 4.565676 (-3.522195) \| 0.067652 / 0.424275 (-0.356623) \| 0.011181 / 0.007607 (0.003574) \| 0.557232 / 0.226044 (0.331188) \| 5.607851 / 2.268929 (3.338922) \| 2.783715 / 55.444624 (-52.660909) \| 2.380943 / 6.876477 (-4.495534) \| 2.378316 / 2.142072 (0.236244) \| 0.674356 / 4.805227 (-4.130871) \| 0.135912 / 6.500664 (-6.364752) \| 0.067009 / 0.075469 (-0.008460) \|\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) \| 1.309002 / 1.841788 (-0.532786) \| 14.464073 / 8.074308 (6.389765) \| 14.418727 / 10.191392 (4.227335) \| 0.148486 / 0.680424 (-0.531938) \| 0.016650 / 0.534201 (-0.517551) \| 0.368786 / 0.579283 (-0.210497) \| 0.395026 / 0.434364 (-0.039338) \| 0.433565 / 0.540337 (-0.106772) \| 0.526603 / 1.386936 (-0.860333) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#443fc92700b4f9e12421e8082e205535314a67d5 \"CML watermark\")\n" ]	1,686,579,709,000	1,686,677,762,000	1,686,677,341,000	CONTRIBUTOR	null	This adds a Arrow dataset builder to be able to load and stream from already preprocessed Arrow files. It's related to https://github.com/huggingface/datasets/issues/3035	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5944/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5944/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5944", "html_url": "https://github.com/huggingface/datasets/pull/5944", "diff_url": "https://github.com/huggingface/datasets/pull/5944.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5944.patch", "merged_at": "2023-06-13T17:29:01" }	true
https://api.github.com/repos/huggingface/datasets/issues/5942	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5942/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5942/comments	https://api.github.com/repos/huggingface/datasets/issues/5942/events	https://github.com/huggingface/datasets/pull/5942	1,752,021,681	PR_kwDODunzps5Su-V4	5,942	Pass datasets-cli additional args as kwargs to DatasetBuilder in `run_beam.py`	{ "login": "graelo", "id": 84066822, "node_id": "MDQ6VXNlcjg0MDY2ODIy", "avatar_url": "https://avatars.githubusercontent.com/u/84066822?v=4", "gravatar_id": "", "url": "https://api.github.com/users/graelo", "html_url": "https://github.com/graelo", "followers_url": "https://api.github.com/users/graelo/followers", "following_url": "https://api.github.com/users/graelo/following{/other_user}", "gists_url": "https://api.github.com/users/graelo/gists{/gist_id}", "starred_url": "https://api.github.com/users/graelo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/graelo/subscriptions", "organizations_url": "https://api.github.com/users/graelo/orgs", "repos_url": "https://api.github.com/users/graelo/repos", "events_url": "https://api.github.com/users/graelo/events{/privacy}", "received_events_url": "https://api.github.com/users/graelo/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[]	1,686,552,650,000	1,688,116,500,000	null	NONE	null	Hi, Following this <https://discuss.huggingface.co/t/how-to-preprocess-a-wikipedia-dataset-using-dataflowrunner/41991/3>, here is a simple PR to pass any additional args to datasets-cli as kwargs in the DatasetBuilder in `run_beam.py`. I also took the liberty to add missing setup steps to the `beam.mdx` docs in order to help everyone. @lhoestq	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5942/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5942/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5942", "html_url": "https://github.com/huggingface/datasets/pull/5942", "diff_url": "https://github.com/huggingface/datasets/pull/5942.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5942.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/5941	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5941/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5941/comments	https://api.github.com/repos/huggingface/datasets/issues/5941/events	https://github.com/huggingface/datasets/issues/5941	1,751,838,897	I_kwDODunzps5oavCx	5,941	Load Data Sets Too Slow In Train Seq2seq Model	{ "login": "xyx361100238", "id": 19569322, "node_id": "MDQ6VXNlcjE5NTY5MzIy", "avatar_url": "https://avatars.githubusercontent.com/u/19569322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/xyx361100238", "html_url": "https://github.com/xyx361100238", "followers_url": "https://api.github.com/users/xyx361100238/followers", "following_url": "https://api.github.com/users/xyx361100238/following{/other_user}", "gists_url": "https://api.github.com/users/xyx361100238/gists{/gist_id}", "starred_url": "https://api.github.com/users/xyx361100238/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xyx361100238/subscriptions", "organizations_url": "https://api.github.com/users/xyx361100238/orgs", "repos_url": "https://api.github.com/users/xyx361100238/repos", "events_url": "https://api.github.com/users/xyx361100238/events{/privacy}", "received_events_url": "https://api.github.com/users/xyx361100238/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Hi ! you can speed it up using multiprocessing by passing `num_proc=` to `load_dataset()`", "already did，but not useful for step Generating train split，it works in step \"Resolving data files\" & \"Downloading data files\" ", "@mariosasko some advice ， thanks！", "I met the same problem, terrible experience" ]	1,686,542,323,000	1,687,021,258,000	null	NONE	null	### Describe the bug step 'Generating train split' in load_dataset is too slow： ![image](https://github.com/huggingface/datasets/assets/19569322/d9b08eee-95fe-4741-a346-b70416c948f8) ### Steps to reproduce the bug Data： own data，16K16B Mono wav Oficial Script:[ run_speech_recognition_seq2seq.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) Add Code： if data_args.data_path is not None: print(data_args.data_path) raw_datasets = load_dataset("audiofolder", data_dir=data_args.data_path, cache_dir=model_args.cache_dir) raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000)) raw_datasets = raw_datasets["train"].train_test_split(test_size=0.005, shuffle=True) （change cache_dir to other path ，ex:/DATA/cache） ### Expected behavior load data fast,at least 1000+ `Generating train split: 387875 examples [32:24:45, 1154.83 examples/s]` ### Environment info - `transformers` version: 4.28.0.dev0 - Platform: Linux-5.4.0-149-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.16 - Huggingface_hub version: 0.13.2 - PyTorch version (GPU?): 1.13.1+cu116 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in>	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5941/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5941/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5990	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5990/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5990/comments	https://api.github.com/repos/huggingface/datasets/issues/5990/events	https://github.com/huggingface/datasets/issues/5990	1,774,389,854	I_kwDODunzps5pwwpe	5,990	Pushing a large dataset on the hub consistently hangs	{ "login": "AntreasAntoniou", "id": 10792502, "node_id": "MDQ6VXNlcjEwNzkyNTAy", "avatar_url": "https://avatars.githubusercontent.com/u/10792502?v=4", "gravatar_id": "", "url": "https://api.github.com/users/AntreasAntoniou", "html_url": "https://github.com/AntreasAntoniou", "followers_url": "https://api.github.com/users/AntreasAntoniou/followers", "following_url": "https://api.github.com/users/AntreasAntoniou/following{/other_user}", "gists_url": "https://api.github.com/users/AntreasAntoniou/gists{/gist_id}", "starred_url": "https://api.github.com/users/AntreasAntoniou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/AntreasAntoniou/subscriptions", "organizations_url": "https://api.github.com/users/AntreasAntoniou/orgs", "repos_url": "https://api.github.com/users/AntreasAntoniou/repos", "events_url": "https://api.github.com/users/AntreasAntoniou/events{/privacy}", "received_events_url": "https://api.github.com/users/AntreasAntoniou/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" } ]	open	false	null	[]	[ "Hi @AntreasAntoniou , sorry to know you are facing this issue. To help debugging it, could you tell me:\r\n- What is the total dataset size?\r\n- Is it always failing on the same shard or is the hanging problem happening randomly?\r\n- Were you able to save the dataset as parquet locally? This would help us determine if the problem comes from the upload or the file generation.\r\n\r\nI'm cc-ing @lhoestq who might have some insights from a `datasets` perspective.", "One trick that can also help is to check the traceback when you kill your python process: it will show where in the code it was hanging", "Right. So I did the trick @lhoestq suggested. Here is where things seem to hang\r\n\r\n```\r\nError while uploading 'data/train-00120-of-00195-466c2dbab2eb9989.parquet' to the Hub. \r\nPushing split train to the Hub. \r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.15s/ba]\r\nUpload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:52<00:00, 52.12s/it]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.08s/ba]\r\nUpload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:45<00:00, 45.54s/it]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.08s/ba]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.03s/ba^Upload 1 LFS files: 0%\| \| 0/1 [\r\n21:27:35<?, ?it/s] \r\nPushing dataset shards to the dataset hub: 63%\|█████████████████████████████████████████████████████████████▎ \| 122/195 [23:37:11<14:07:59, 696.98s/it]\r\n^CError in sys.excepthook: \r\nTraceback (most recent call last): \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1699, in print \r\n extend(render(renderable, render_options)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1335, in render \r\n yield from self.render(render_output, _options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/constrain.py\", line 29, in __rich_console__ \r\n yield from console.render(self.renderable, child_options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/panel.py\", line 220, in __rich_console__ \r\n lines = console.render_lines(renderable, child_options, style=style) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1371, in render_lines \r\n lines = list( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 292, in split_and_crop_lines \r\n for segment in segments: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/padding.py\", line 97, in __rich_console__ \r\n lines = console.render_lines( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1371, in render_lines \r\n lines = list( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 292, in split_and_crop_lines \r\n for segment in segments: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1335, in render \r\n yield from self.render(render_output, _options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/syntax.py\", line 611, in __rich_console__ \r\n segments = Segments(self._get_syntax(console, options)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 668, in __init__ \r\n self.segments = list(segments) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/syntax.py\", line 674, in _get_syntax \r\n lines: Union[List[Text], Lines] = text.split(\"\\n\", allow_blank=ends_on_nl) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 1042, in split \r\n lines = Lines( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/containers.py\", line 70, in __init__ \r\n self._lines: List[\"Text\"] = list(lines) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 1043, in <genexpr> \r\n line for line in self.divide(flatten_spans()) if line.plain != separator \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 385, in plain \r\n if len(self._text) != 1: \r\nKeyboardInterrupt \r\n \r\nOriginal exception was: \r\nTraceback (most recent call last): \r\n File \"/opt/conda/envs/main/lib/python3.10/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 \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/std.py\", line 1178, in __iter__ \r\n for obj in iterable: \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 621, in result_iterator \r\n yield _result_or_cancel(fs.pop()) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 319, in _result_or_cancel \r\n return fut.result(timeout) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 453, in result \r\n self._condition.wait(timeout) \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 320, in wait \r\n waiter.acquire() \r\nKeyboardInterrupt \r\n \r\nDuring handling of the above exception, another exception occurred: \r\n \r\nTraceback (most recent call last): \r\n File \"/TALI/tali/scripts/validate_dataset.py\", line 127, in <module> \r\n train_dataset.push_to_hub(repo_id=\"Antreas/TALI-base\", max_shard_size=\"5GB\") \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/dataset_dict.py\", line 1583, in push_to_hub \r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 5275, in _push_parquet_shards_to_hub \r\n _retry( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/utils/file_utils.py\", line 282, in _retry \r\n return func(func_args, *func_kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 826, in _inner \r\n return fn(self, args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 3205, in upload_file \r\n commit_info = self.create_commit( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 826, in _inner \r\n return fn(self, args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 2680, in create_commit \r\n upload_lfs_files( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/_commit_api.py\", line 353, in upload_lfs_files \r\n thread_map( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map \r\n return _executor_map(ThreadPoolExecutor, fn, iterables, *tqdm_kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 49, in _executor_map \r\n with PoolExecutor(max_workers=max_workers, initializer=tqdm_class.set_lock, \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 649, in __exit__ \r\n self.shutdown(wait=True) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/thread.py\", line 235, in shutdown \r\n t.join() \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 1096, in join \r\n self._wait_for_tstate_lock() \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 1116, in _wait_for_tstate_lock \r\n if lock.acquire(block, timeout): \r\nKeyboardInterrupt \r\n```", "@Wauplin \r\n\r\n>What is the total dataset size?\r\n\r\nThere are three variants, and the random hanging happens on all three. The sizes are 2TB, 1TB, and 200GB. \r\n\r\n>Is it always failing on the same shard or is the hanging problem happening randomly?\r\n\r\nIt seems to be very much random, as restarting can help move past the previous hang, only to find a new one, or not. \r\n\r\n>Were you able to save the dataset as parquet locally? This would help us determine if the problem comes from the upload or the file generation.\r\n\r\nYes. The dataset seems to be locally stored as parquet. ", "Hmm it looks like an issue with TQDM lock. Maybe you can try updating TQDM ?", "I am using the latest version of tqdm\r\n\r\n```\r\n⬢ [Docker] ❯ pip install tqdm --upgrade\r\nRequirement already satisfied: tqdm in /opt/conda/envs/main/lib/python3.10/site-packages (4.65.0)\r\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\r\n```", "I tried trying to catch the hanging issue in action again\r\n\r\n```\r\nPushing dataset shards to the dataset hub: 65%\|█████████████████████████████████████████████████████████████████▊ \| 127/195 [2:28:02<1:19:15, 69.94s/it] \r\nError while uploading 'data/train-00127-of-00195-3f8d036ade107c27.parquet' to the Hub. \r\nPushing split train to the Hub. \r\nPushing dataset shards to the dataset hub: 64%\|████████████████████████████████████████████████████████████████▏ \| 124/195 [2:06:10<1:12:14, 61.05s/it]C^[^C^C^C \r\n╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ \r\n│ /TALI/tali/scripts/validate_dataset.py:127 in <module> │ \r\n│ │ \r\n│ 124 │ │ \r\n│ 125 │ while not succesful_competion: │ \r\n│ 126 │ │ try: │ \r\n│ ❱ 127 │ │ │ train_dataset.push_to_hub(repo_id=\"Antreas/TALI-base\", max_shard_size=\"5GB\") │ \r\n│ 128 │ │ │ succesful_competion = True │ \r\n│ 129 │ │ except Exception as e: │ \r\n│ 130 │ │ │ print(e) │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/dataset_dict.py:1583 in push_to_hub │ \r\n│ │ \r\n│ 1580 │ │ for split in self.keys(): │ \r\n│ 1581 │ │ │ logger.warning(f\"Pushing split {split} to the Hub.\") │ \r\n│ 1582 │ │ │ # The split=key needs to be removed before merging │ \r\n│ ❱ 1583 │ │ │ repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parq │ \r\n│ 1584 │ │ │ │ repo_id, │ \r\n│ 1585 │ │ │ │ split=split, │ \r\n│ 1586 │ │ │ │ private=private, │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:5263 in │ \r\n│ _push_parquet_shards_to_hub │ \r\n│ │ \r\n│ 5260 │ │ │ \r\n│ 5261 │ │ uploaded_size = 0 │ \r\n│ 5262 │ │ shards_path_in_repo = [] │ \r\n│ ❱ 5263 │ │ for index, shard in logging.tqdm( │ \r\n│ 5264 │ │ │ enumerate(itertools.chain([first_shard], shards_iter)), │ \r\n│ 5265 │ │ │ desc=\"Pushing dataset shards to the dataset hub\", │ \r\n│ 5266 │ │ │ total=num_shards, │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/tqdm/std.py:1178 in __iter__ │ \r\n│ │ \r\n│ 1175 │ │ time = self._time │ \r\n│ 1176 │ │ │ \r\n│ 1177 │ │ try: │\r\n│ ❱ 1178 │ │ │ for obj in iterable: │\r\n│ 1179 │ │ │ │ yield obj │\r\n│ 1180 │ │ │ │ # Update and possibly print the progressbar. │\r\n│ 1181 │ │ │ │ # Note: does not call self.update(1) for speed optimisation. │\r\n│ │\r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:5238 in │\r\n│ shards_with_embedded_external_files │\r\n│ │\r\n│ 5235 │ │ │ │ for shard in shards: │\r\n│ 5236 │ │ │ │ │ format = shard.format │\r\n│ 5237 │ │ │ │ │ shard = shard.with_format(\"arrow\") │\r\n│ ❱ 5238 │ │ │ │ │ shard = shard.map( │\r\n│ 5239 │ │ │ │ │ │ embed_table_storage, │\r\n│ 5240 │ │ │ │ │ │ batched=True, │\r\n│ 5241 │ │ │ │ │ │ batch_size=1000, │\r\n│ │\r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:578 in wrapper │\r\n│ │\r\n│ 575 │ │ else: │\r\n│ 576 │ │ │ self: \"Dataset\" = kwargs.pop(\"self\") │\r\n│ 577 │ │ # apply actual function │\r\n│ ❱ 578 │ │ out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, *kwargs) │ \r\n│ 579 │ │ datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [ou │ \r\n│ 580 │ │ for dataset in datasets: │ \r\n│ 581 │ │ │ # Remove task templates if a column mapping of the template is no longer val │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:543 in wrapper │ \r\n│ │ \r\n│ 540 │ │ │ \"output_all_columns\": self._output_all_columns, │ \r\n│ 541 │ │ } │ \r\n│ 542 │ │ # apply actual function │ \r\n│ ❱ 543 │ │ out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs) │ \r\n│ 544 │ │ datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [ou │ \r\n│ 545 │ │ # re-apply format to the output │ \r\n│ 546 │ │ for dataset in datasets: │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:3073 in map │ \r\n│ │ \r\n│ 3070 │ │ │ │ │ leave=False, │ \r\n│ 3071 │ │ │ │ │ desc=desc or \"Map\", │ \r\n│ 3072 │ │ │ │ ) as pbar: │ \r\n│ ❱ 3073 │ │ │ │ │ for rank, done, content in Dataset._map_single(dataset_kwargs): │ \r\n│ 3074 │ │ │ │ │ │ if done: │ \r\n│ 3075 │ │ │ │ │ │ │ shards_done += 1 │ \r\n│ 3076 │ │ │ │ │ │ │ logger.debug(f\"Finished processing shard number {rank} of {n │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:3464 in _map_single │ \r\n│ │ \r\n│ 3461 │ │ │ │ │ │ │ │ buf_writer, writer, tmp_file = init_buffer_and_writer() │ \r\n│ 3462 │ │ │ │ │ │ │ │ stack.enter_context(writer) │ \r\n│ 3463 │ │ │ │ │ │ │ if isinstance(batch, pa.Table): │ \r\n│ ❱ 3464 │ │ │ │ │ │ │ │ writer.write_table(batch) │ \r\n│ 3465 │ │ │ │ │ │ │ else: │ \r\n│ 3466 │ │ │ │ │ │ │ │ writer.write_batch(batch) │ \r\n│ 3467 │ │ │ │ │ │ num_examples_progress_update += num_examples_in_batch │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_writer.py:567 in write_table │ \r\n│ │ \r\n│ 564 │ │ │ writer_batch_size = self.writer_batch_size │ \r\n│ 565 │ │ if self.pa_writer is None: │ \r\n│ 566 │ │ │ self._build_writer(inferred_schema=pa_table.schema) │ \r\n│ ❱ 567 │ │ pa_table = pa_table.combine_chunks() │ \r\n│ 568 │ │ pa_table = table_cast(pa_table, self._schema) │ \r\n│ 569 │ │ if self.embed_local_files: │ \r\n│ 570 │ │ │ pa_table = embed_table_storage(pa_table) │ \r\n╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ \r\nKeyboardInterrupt \r\n```", "I'm on my phone so can't help that much. What I'd advice to do is to [save_to_disk](https://huggingface.co/docs/datasets/package_reference/main_classes#save_to_disk) if it's not already done and then upload the files/folder to the Hub separately. You can find what you need in the [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload). It might not help finding the exact issue for now but at least it can unblock you. ", "In your last stacktrace it interrupted while embedding external content - in case your dataset in made of images or audio files that live on your disk. Is it the case ?", "Yeah, the dataset has images, audio, video and text. ", "It's maybe related to https://github.com/apache/arrow/issues/34455: are you using ArrayND features ?\r\n\r\nAlso what's your `pyarrow` version ? Could you try updating to >= 12.0.1 ?", "I was using pyarrow == 12.0.0\r\n\r\nI am not explicitly using ArrayND features, unless the hub API automatically converts my files to such. ", "I have now updated to pyarrow == 12.0.1 and retrying", "You can also try to reduce the `max_shard_size` - Sometimes parquet has a hard time working with data bigger than 2GB", "So, updating the pyarrow seems to help. It can still throw errors here and there but I can retry when that happens. It's better than hanging. \r\n\r\nHowever, I am a bit confused about something. I have uploaded my datasets, but while earlier I could see all three sets, now I can only see 1. What's going on? \r\nhttps://huggingface.co/datasets/Antreas/TALI-base\r\n\r\nI have seen this happen before as well, so I deleted and reuploaded, but this dataset is way too large for me to do this. ", "It's a bug on our side, I'll update the dataset viewer ;)\r\n\r\nThanks for reporting !", "Apparently this happened because of bad modifications in the README.md split metadata.\r\n\r\nI fixed them in this PR: https://huggingface.co/datasets/Antreas/TALI-base/discussions/1", "@lhoestq It's a bit odd that when uploading a dataset, one set at a time \"train\", \"val\", \"test\", the push_to_hub function overwrites the readme and removes differently named sets from previous commits. i.e., you push \"val\", all is well. Then you push \"test\", and the \"val\" entry disappears from the readme, while the data remain intact. ", "Also, just found another related issue. One of the many that make things hang or fail when pushing to hub. \r\n\r\nIn the following code:\r\n\r\n```python\r\ntrain_generator = lambda: data_generator(\"train\", percentage=1.0)\r\n val_generator = lambda: data_generator(\"val\")\r\n test_generator = lambda: data_generator(\"test\")\r\n\r\n train_data = datasets.Dataset.from_generator(\r\n train_generator,\r\n num_proc=mp.cpu_count(),\r\n writer_batch_size=5000,\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n val_data = datasets.Dataset.from_generator(\r\n val_generator,\r\n writer_batch_size=5000,\r\n num_proc=mp.cpu_count(),\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n test_data = datasets.Dataset.from_generator(\r\n test_generator,\r\n writer_batch_size=5000,\r\n num_proc=mp.cpu_count(),\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n print(f\"Pushing TALI-large to hub\")\r\n\r\n dataset = datasets.DatasetDict(\r\n {\"train\": train_data, \"val\": val_data, \"test\": test_data}\r\n )\r\n succesful_competion = False\r\n\r\n while not succesful_competion:\r\n try:\r\n dataset.push_to_hub(repo_id=\"Antreas/TALI-large\", max_shard_size=\"2GB\")\r\n succesful_competion = True\r\n except Exception as e:\r\n print(e)\r\n ```\r\n \r\n \r\n Things keep failing in the push_to_repo step, at random places, with the following error:\r\n \r\n ```bash\r\n Pushing dataset shards to the dataset hub: 7%\|██████████▋ \| 67/950 [42:41<9:22:37, 38.23s/it]\r\nError while uploading 'data/train-00067-of-00950-a4d179ed5a593486.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:01<00:00, 1.81ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.20s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.48ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:15<00:00, 15.30s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.39ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.52s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.47ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.39s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.26ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:38<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 7%\|███████████▎ \| 71/950 [44:37<9:12:28, 37.71s/it]\r\nError while uploading 'data/train-00071-of-00950-72bab6e5cb223aee.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.18ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.94s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.36ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.67s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.57ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.16s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.68ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:09<00:00, 9.63s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.36ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.67s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.37ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:39<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 8%\|████████████ \| 76/950 [46:21<8:53:08, 36.60s/it]\r\nError while uploading 'data/train-00076-of-00950-b90e4e3b433db179.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.21ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:25<00:00, 25.40s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:01<00:00, 1.56ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.40s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.49ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:23<00:00, 23.53s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.27ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.25s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.42ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.03s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.39ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:39<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%\|████████████▊ \| 81/950 [48:30<8:40:22, 35.93s/it]\r\nError while uploading 'data/train-00081-of-00950-84b0450a1df093a9.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.18ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.65s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:01<00:00, 1.92ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:38<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%\|█████████████ \| 82/950 [48:55<8:37:57, 35.80s/it]\r\nError while uploading 'data/train-00082-of-00950-0a1f52da35653e08.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.31ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:26<00:00, 26.29s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.42ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.57s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.64ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.35s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.64ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.74s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.31ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:40<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%\|█████████████▋ \| 86/950 [50:48<8:30:25, 35.45s/it]\r\nError while uploading 'data/train-00086-of-00950-e1cc80dd17191b20.parquet' to the Hub.\r\n```\r\n\r\nI have a while loop that forces retries, but it seems that the progress itself is randomly getting lost as well. Any ideas on how to improve this? It has been blocking me for way too long. \r\n\r\nShould I build the parquet manually and then push manually as well? If I do things manually, how can I ensure my dataset works properly with \"stream=True\"? \r\n\r\nThank you for your help and time. ", "> @lhoestq It's a bit odd that when uploading a dataset, one set at a time \"train\", \"val\", \"test\", the push_to_hub function overwrites the readme and removes differently named sets from previous commits. i.e., you push \"val\", all is well. Then you push \"test\", and the \"val\" entry disappears from the readme, while the data remain intact.\r\n\r\nHmm this shouldn't happen. What code did you run exactly ? Using which version of `datasets` ?", "> I have a while loop that forces retries, but it seems that the progress itself is randomly getting lost as well. Any ideas on how to improve this? It has been blocking me for way too long.\r\n\r\nCould you also print the cause of the error (`e.__cause__`) ? Or show the full stack trace when the error happens ?\r\nThis would give more details about why it failed and would help investigate.", "> Should I build the parquet manually and then push manually as well? If I do things manually, how can I ensure my dataset works properly with \"stream=True\"?\r\n\r\nParquet is supported out of the box ^^\r\n\r\nIf you want to make sure it works as expected you can try locally first:\r\n```python\r\nds = load_dataset(\"path/to/local\", streaming=True)\r\n```", "@lhoestq @AntreasAntoniou I transferred this issue to the `datasets` repository as the questions and answers are more related to this repo. Hope it can help other users find the bug and fixes more easily (like updating [tqdm](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120204) and [pyarrow](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120278) or [setting a lower `max_shard_size`](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120328)).\r\n\r\n~For the initial \"pushing large dataset consistently hangs\"-issue, I still think it's best to try to `save_to_disk` first and then upload it manually/with a script (see [upload_folder](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder)). It's not the most satisfying solution but at least it would confirm from where the problem comes from.~\r\n\r\nEDIT: removed suggestion about saving to disk first (see https://github.com/huggingface/datasets/issues/5990#issuecomment-1607186914).", "> @lhoestq @AntreasAntoniou I transferred this issue to the datasets repository as the questions and answers are more related to this repo. Hope it can help other users find the bug and fixes more easily (like updating https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120204 and https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120278 or https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120328).\r\n\r\nthanks :)\r\n\r\n> For the initial \"pushing large dataset consistently hangs\"-issue, I still think it's best to try to save_to_disk first and then upload it manually/with a script (see [upload_folder](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder)). It's not the most satisfying solution but at least it would confirm from where the problem comes from.\r\n\r\nAs I've already said in other discussions, I would not recommend pushing files saved with `save_to_disk` to the Hub but save to parquet shards and upload them instead. The Hub does not support datasets saved with `save_to_disk`, which is meant for disk only.", "> As I've already said in other discussions, I would not recommend pushing files saved with save_to_disk to the Hub but save to parquet shards and upload them instead. The Hub does not support datasets saved with save_to_disk, which is meant for disk only.\r\n\r\nWell noted, thanks. That part was not clear to me :)", "Sorry for not replying in a few days, I was on leave. :) \r\n\r\nSo, here are more information as to the error that causes some of the delay\r\n\r\n```bash\r\nPushing Antreas/TALI-tiny to hub\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:24<00:00, 4.06s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:24<00:00, 4.15s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:26<00:00, 4.45s/ba]\r\n/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/lfs.py:310: UserWarning: hf_transfer is enabled but does not support uploading from bytes or BinaryIO, falling back to regular upload\r\n warnings.warn(\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:25<00:00, 4.26s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:27<00:00, 4.58s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:24<00:00, 4.10s/ba]\r\nPushing dataset shards to the dataset hub: 22%\|████████████████████████▎ \| 5/23 [52:23<3:08:37, 628.74s/it]\r\nException: Error while uploading 'data/train-00005-of-00023-e224d901fd65e062.parquet' to the Hub., with stacktrace: <traceback object at 0x7f745458d0c0>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/7c/d3/7cd385d9324302dc13e3986331d72d9be6fa0174c63dcfe0e08cd474f7f1e8b7/3415166ae28c0beccbbc692f38742b8dea2c197f5c805321104e888d21d7eb90?X-Amz-Algorithm=AWS4-HMAC-SHA256\r\n&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230627%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230627T003349Z&X-Amz-Expires=86400&X-Amz-Signature=5a12ff96f2\r\n91f644134170992a6628e5f3c4e7b2e7fc3e940b4378fe11ae5390&X-Amz-SignedHeaders=host&partNumber=1&uploadId=JSsK8r63XSF.VlKQx3Vf8OW4DEVp5YIIY7LPnuapNIegsxs5EHgM1p4u0.Nn6_wlPlQnvxm8HKMxZhczKE9KB74t0etB\r\noLcxqBIvsgey3uXBTZMAEGwU6y7CDUADiEIO&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\n```\r\n\r\nOne issue is that the uploading does not continue from the chunk it failed off. It often continues from a very old chunk. e.g. if it failed on chunk 192/250, it will continue from say 53/250, and this behaviour appears almost random. ", "Are you using a proxy of some sort ?", "I am using a kubernetes cluster built into a university VPN. ", "So, other than the random connection drops here and there, any idea why the progress does not continue where it left off?\r\n\r\n```bash\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 10.79ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.65ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.39ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.04ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.52ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 12.28ba/s]\r\nPushing dataset shards to the dataset hub: 20%\|██████████████████████ \| 75/381 [1:34:39<6:26:11, 75.72s/it]\r\nException: Error while uploading 'data/train-00075-of-00381-1614bc251b778766.parquet' to the Hub., with stacktrace: <traceback object at 0x7fab6d9a4980>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/3b/31/3b311464573d8d63b137fcd5b40af1e7a5b1306843c88e80372d0117157504e5/ed8dae933fb79ae1ef5fb1f698f5125d3e1c02977ac69438631f152bb3bfdd1e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-\r\nAmz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230629%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230629T053004Z&X-Amz-Expires=86400&X-Amz-Signature=da2b26270edfd6d0\r\nd069c015a5a432031107a8664c3f0917717e5e40c688183c&X-Amz-SignedHeaders=host&partNumber=1&uploadId=2erWGHTh3ICqBLU_QvHfnygZ2tkMWbL0rEqpJdYohCKHUHnfwMjvoBIg0TI_KSGn4rSKxUxOyqSIzFUFSRSzixZeLeneaXJOw.Qx8\r\nzLKSV5xV7HRQDj4RBesNve6cSoo&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 12.09ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 11.51ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 10.77ba/s]\r\nPushing dataset shards to the dataset hub: 20%\|██████████████████████▋ \| 77/381 [1:32:50<6:06:34, 72.35s/it]\r\nException: Error while uploading 'data/train-00077-of-00381-368b2327a9908aab.parquet' to the Hub., with stacktrace: <traceback object at 0x7fab45b27f80>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/3b/31/3b311464573d8d63b137fcd5b40af1e7a5b1306843c88e80372d0117157504e5/9462ff2c5e61283b53b091984a22de2f41a2f6e37b681171e2eca4a998f979cb?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-\r\nAmz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230629%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230629T070510Z&X-Amz-Expires=86400&X-Amz-Signature=9ab8487b93d443cd\r\n21f05476405855d46051a0771b4986bbb20f770ded21b1a4&X-Amz-SignedHeaders=host&partNumber=1&uploadId=UiHX1B.DcoAO2QmIHpWpCuNPwhXU_o1dsTkTGPqZt1P51o9k0yz.EsFD9eKpQMwgAST3jOatRG78I_JWRBeLBDYYVNp8r0TpIdeSg\r\neUg8uwPZOCPw9y5mWOw8MWJrnBo&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nPushing dataset shards to the dataset hub: 8%\|████████▋ \| 29/381 [27:39<5:50:03, 59.67s/it]\r\nMap: 36%\|████████████████████████████████████████████████████ \| 1000/2764 [00:35<00:34, 51.63 examples/Map: 72%\|████████████████████████████████████████████████████████████████████████████████████████████████████████▏ \| 2000/2764 [00:40<00:15, 49.06 examples/Map: 72%\|████████████████████████████████████████████████████████████████████████████████████████████████████████▏ \| 2000/2764 [00:55<00:15, 49.06 examples/Map: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2764/2764 [00:56<00:00, 48.82 examples/Pushing dataset shards to the dataset hub: 8%\|████████▉ \| 30/381 [28:35<5:43:03, 58.64s/iPushing dataset shards to the dataset hub: 8%\|█████████▎ \| 31/381 [29:40<5:52:18, 60.40s/iPushing dataset shards to the dataset hub: 8%\|█████████▌ \| 32/381 [30:46<6:02:20, 62.29s/it] \r\nMap: 36%\|███████████████████████████████████████████████████▎ \r\n```\r\n\r\nThis is actually the issue that wastes the most time for me, and I need it fixed. Please advice on how I can go about it.\r\n\r\nNotice how the progress goes from \r\n\| 77/381 to 30/381", "If the any shard is missing on the Hub, it will re-upload it. It looks like the 30th shard was missing on the Hub in your case. \r\n\r\nIt also means that the other files up to the 77th that were successfully uploaded won't be uploaded again.\r\n\r\ncc @mariosasko who might know better" ]	1,686,408,407,000	1,688,133,460,000	null	NONE	null	### Describe the bug Once I have locally built a large dataset that I want to push to hub, I use the recommended approach of .push_to_hub to get the dataset on the hub, and after pushing a few shards, it consistently hangs. This has happened over 40 times over the past week, and despite my best efforts to try and catch this happening and kill a process and restart, it seems to be extremely time wasting -- so I came to you to report this and to seek help. I already tried installing hf_transfer, but it doesn't support Byte file uploads so I uninstalled it. ### Reproduction ```python import multiprocessing as mp import pathlib from math import ceil import datasets import numpy as np from tqdm.auto import tqdm from tali.data.data import select_subtitles_between_timestamps from tali.utils import load_json tali_dataset_dir = "/data/" if __name__ == "__main__": full_dataset = datasets.load_dataset( "Antreas/TALI", num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir ) def data_generator(set_name, percentage: float = 1.0): dataset = full_dataset[set_name] for item in tqdm(dataset): video_list = item["youtube_content_video"] video_list = np.random.choice( video_list, int(ceil(len(video_list) * percentage)) ) if len(video_list) == 0: continue captions = item["youtube_subtitle_text"] captions = select_subtitles_between_timestamps( subtitle_dict=load_json( captions.replace( "/data/", tali_dataset_dir, ) ), starting_timestamp=0, ending_timestamp=100000000, ) for video_path in video_list: temp_path = video_path.replace("/data/", tali_dataset_dir) video_path_actual: pathlib.Path = pathlib.Path(temp_path) if video_path_actual.exists(): item["youtube_content_video"] = open(video_path_actual, "rb").read() item["youtube_subtitle_text"] = captions yield item train_generator = lambda: data_generator("train", percentage=0.1) val_generator = lambda: data_generator("val") test_generator = lambda: data_generator("test") train_data = datasets.Dataset.from_generator( train_generator, num_proc=mp.cpu_count(), writer_batch_size=5000, cache_dir=tali_dataset_dir, ) val_data = datasets.Dataset.from_generator( val_generator, writer_batch_size=5000, num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir, ) test_data = datasets.Dataset.from_generator( test_generator, writer_batch_size=5000, num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir, ) dataset = datasets.DatasetDict( { "train": train_data, "val": val_data, "test": test_data, } ) succesful_competion = False while not succesful_competion: try: dataset.push_to_hub(repo_id="Antreas/TALI-small", max_shard_size="5GB") succesful_competion = True except Exception as e: print(e) ``` ### Logs ```shell Pushing dataset shards to the dataset hub: 33%\|██████████████████████████████████████▎ \| 7/21 [24:33<49:06, 210.45s/it] Error while uploading 'data/val-00007-of-00021-6b216a984af1a4c8.parquet' to the Hub. Pushing split train to the Hub. Resuming upload of the dataset shards. Pushing dataset shards to the dataset hub: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 46/46 [42:10<00:00, 55.01s/it] Pushing split val to the Hub. Resuming upload of the dataset shards. Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:01<00:00, 1.55ba/s] Upload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:23<00:00, 23.51s/it] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.39ba/s] Upload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:30<00:00, 30.19s/it] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.28ba/s] Upload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:24<00:00, 24.08s/it] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.42ba/s] Upload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:23<00:00, 23.97s/it] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.49ba/s] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.54ba/s^ Upload 1 LFS files: 0%\| \| 0/1 [04:42<?, ?it/s] Pushing dataset shards to the dataset hub: 52%\|████████████████████████████████████████████████████████████▏ \| 11/21 [17:23<15:48, 94.82s/it] That's where it got stuck ``` ### System info ```shell - huggingface_hub version: 0.15.1 - Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Running in iPython ?: No - Running in notebook ?: No - Running in Google Colab ?: No - Token path ?: /root/.cache/huggingface/token - Has saved token ?: True - Who am I ?: Antreas - Configured git credential helpers: store - FastAI: N/A - Tensorflow: N/A - Torch: 2.1.0.dev20230606+cu121 - Jinja2: 3.1.2 - Graphviz: N/A - Pydot: N/A - Pillow: 9.5.0 - hf_transfer: N/A - gradio: N/A - numpy: 1.24.3 - ENDPOINT: https://huggingface.co - HUGGINGFACE_HUB_CACHE: /root/.cache/huggingface/hub - HUGGINGFACE_ASSETS_CACHE: /root/.cache/huggingface/assets - HF_TOKEN_PATH: /root/.cache/huggingface/token - HF_HUB_OFFLINE: False - HF_HUB_DISABLE_TELEMETRY: False - HF_HUB_DISABLE_PROGRESS_BARS: None - HF_HUB_DISABLE_SYMLINKS_WARNING: False - HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False - HF_HUB_DISABLE_IMPLICIT_TOKEN: False - HF_HUB_ENABLE_HF_TRANSFER: False ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5990/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5990/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5939	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5939/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5939/comments	https://api.github.com/repos/huggingface/datasets/issues/5939/events	https://github.com/huggingface/datasets/issues/5939	1,749,955,883	I_kwDODunzps5oTjUr	5,939	.	{ "login": "flckv", "id": 103381497, "node_id": "U_kgDOBil5-Q", "avatar_url": "https://avatars.githubusercontent.com/u/103381497?v=4", "gravatar_id": "", "url": "https://api.github.com/users/flckv", "html_url": "https://github.com/flckv", "followers_url": "https://api.github.com/users/flckv/followers", "following_url": "https://api.github.com/users/flckv/following{/other_user}", "gists_url": "https://api.github.com/users/flckv/gists{/gist_id}", "starred_url": "https://api.github.com/users/flckv/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/flckv/subscriptions", "organizations_url": "https://api.github.com/users/flckv/orgs", "repos_url": "https://api.github.com/users/flckv/repos", "events_url": "https://api.github.com/users/flckv/events{/privacy}", "received_events_url": "https://api.github.com/users/flckv/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[]	1,686,319,294,000	1,686,572,374,000	1,686,572,359,000	NONE	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5939/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5939/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5938	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5938/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5938/comments	https://api.github.com/repos/huggingface/datasets/issues/5938/events	https://github.com/huggingface/datasets/pull/5938	1,749,462,851	PR_kwDODunzps5SmbkI	5,938	Make get_from_cache use custom temp filename that is locked	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.007241 / 0.011353 (-0.004112) \| 0.004574 / 0.011008 (-0.006434) \| 0.120481 / 0.038508 (0.081973) \| 0.040492 / 0.023109 (0.017383) \| 0.391399 / 0.275898 (0.115501) \| 0.422844 / 0.323480 (0.099365) \| 0.004441 / 0.007986 (-0.003545) \| 0.004544 / 0.004328 (0.000216) \| 0.089482 / 0.004250 (0.085231) \| 0.052939 / 0.037052 (0.015887) \| 0.393649 / 0.258489 (0.135160) \| 0.433852 / 0.293841 (0.140011) \| 0.035882 / 0.128546 (-0.092664) \| 0.010172 / 0.075646 (-0.065474) \| 0.410331 / 0.419271 (-0.008940) \| 0.061481 / 0.043533 (0.017948) \| 0.405066 / 0.255139 (0.149927) \| 0.417732 / 0.283200 (0.134532) \| 0.121647 / 0.141683 (-0.020035) \| 1.790624 / 1.452155 (0.338469) \| 1.863398 / 1.492716 (0.370681) \|\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) \| 0.250650 / 0.018006 (0.232644) \| 0.489044 / 0.000490 (0.488554) \| 0.010421 / 0.000200 (0.010222) \| 0.000106 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030340 / 0.037411 (-0.007071) \| 0.128318 / 0.014526 (0.113792) \| 0.140463 / 0.176557 (-0.036093) \| 0.205762 / 0.737135 (-0.531373) \| 0.147996 / 0.296338 (-0.148342) \|\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) \| 0.493158 / 0.215209 (0.277949) \| 4.858346 / 2.077655 (2.780691) \| 2.242942 / 1.504120 (0.738822) \| 2.010092 / 1.541195 (0.468897) \| 2.076765 / 1.468490 (0.608275) \| 0.636669 / 4.584777 (-3.948108) \| 4.478027 / 3.745712 (0.732314) \| 2.157843 / 5.269862 (-3.112019) \| 1.305133 / 4.565676 (-3.260543) \| 0.079220 / 0.424275 (-0.345055) \| 0.013858 / 0.007607 (0.006251) \| 0.604501 / 0.226044 (0.378457) \| 5.950071 / 2.268929 (3.681143) \| 2.738373 / 55.444624 (-52.706251) \| 2.380275 / 6.876477 (-4.496201) \| 2.517108 / 2.142072 (0.375035) \| 0.772249 / 4.805227 (-4.032979) \| 0.169874 / 6.500664 (-6.330790) \| 0.078026 / 0.075469 (0.002557) \|\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) \| 1.450200 / 1.841788 (-0.391588) \| 17.810965 / 8.074308 (9.736657) \| 15.518998 / 10.191392 (5.327606) \| 0.200469 / 0.680424 (-0.479954) \| 0.020777 / 0.534201 (-0.513424) \| 0.504556 / 0.579283 (-0.074727) \| 0.518493 / 0.434364 (0.084129) \| 0.615335 / 0.540337 (0.074998) \| 0.754065 / 1.386936 (-0.632871) \|\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) \| 0.007224 / 0.011353 (-0.004129) \| 0.004663 / 0.011008 (-0.006345) \| 0.092151 / 0.038508 (0.053643) \| 0.038359 / 0.023109 (0.015250) \| 0.486413 / 0.275898 (0.210515) \| 0.521596 / 0.323480 (0.198116) \| 0.004207 / 0.007986 (-0.003778) \| 0.003745 / 0.004328 (-0.000583) \| 0.089840 / 0.004250 (0.085589) \| 0.050996 / 0.037052 (0.013943) \| 0.498090 / 0.258489 (0.239601) \| 0.533647 / 0.293841 (0.239806) \| 0.035151 / 0.128546 (-0.093395) \| 0.010293 / 0.075646 (-0.065354) \| 0.099056 / 0.419271 (-0.320215) \| 0.057365 / 0.043533 (0.013833) \| 0.470652 / 0.255139 (0.215513) \| 0.509801 / 0.283200 (0.226602) \| 0.115650 / 0.141683 (-0.026033) \| 1.810860 / 1.452155 (0.358705) \| 1.896775 / 1.492716 (0.404059) \|\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) \| 0.261887 / 0.018006 (0.243880) \| 0.489919 / 0.000490 (0.489430) \| 0.006117 / 0.000200 (0.005917) \| 0.000134 / 0.000054 (0.000079) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035033 / 0.037411 (-0.002378) \| 0.141093 / 0.014526 (0.126567) \| 0.152613 / 0.176557 (-0.023943) \| 0.218351 / 0.737135 (-0.518785) \| 0.158366 / 0.296338 (-0.137972) \|\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) \| 0.542219 / 0.215209 (0.327010) \| 5.479358 / 2.077655 (3.401703) \| 2.749586 / 1.504120 (1.245466) \| 2.537686 / 1.541195 (0.996491) \| 2.582351 / 1.468490 (1.113861) \| 0.636750 / 4.584777 (-3.948027) \| 4.537501 / 3.745712 (0.791789) \| 2.141392 / 5.269862 (-3.128469) \| 1.279711 / 4.565676 (-3.285965) \| 0.079227 / 0.424275 (-0.345048) \| 0.014141 / 0.007607 (0.006534) \| 0.662070 / 0.226044 (0.436025) \| 6.572144 / 2.268929 (4.303215) \| 3.321349 / 55.444624 (-52.123275) \| 2.928219 / 6.876477 (-3.948258) \| 3.002732 / 2.142072 (0.860659) \| 0.773808 / 4.805227 (-4.031419) \| 0.166017 / 6.500664 (-6.334647) \| 0.076424 / 0.075469 (0.000955) \|\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) \| 1.584325 / 1.841788 (-0.257463) \| 18.359247 / 8.074308 (10.284938) \| 16.977875 / 10.191392 (6.786483) \| 0.195381 / 0.680424 (-0.485043) \| 0.021048 / 0.534201 (-0.513153) \| 0.512237 / 0.579283 (-0.067047) \| 0.511435 / 0.434364 (0.077071) \| 0.592856 / 0.540337 (0.052518) \| 0.711905 / 1.386936 (-0.675031) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d536e37b21a6dd5c122b6d8113994ec50846c5b5 \"CML watermark\")\n" ]	1,686,301,273,000	1,686,749,738,000	1,686,749,244,000	MEMBER	null	This PR ensures that the temporary filename created is the same as the one that is locked, while writing to the cache. This PR stops using `tempfile` to generate the temporary filename. Additionally, the behavior now is aligned for both `resume_download` `True` and `False`. Refactor temp_file_manager so that it uses the filename that is locked: - Use: `cache_path + ".incomplete"`, when the locked one is `cache_path + ".lock"` Before it was using `tempfile` inside `cache_dir`, which was not locked: although very improbable name collision (8 random characters), this was not impossible when huge number of multiple processes. Maybe related to "Stale file handle" issues caused by `tempfile`: - [ ] https://huggingface.co/datasets/tapaco/discussions/4 - [ ] https://huggingface.co/datasets/xcsr/discussions/1 - [ ] https://huggingface.co/datasets/covost2/discussions/3 ``` Error code: ConfigNamesError Exception: OSError Message: [Errno 116] Stale file handle Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 61, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, use_auth_token=use_auth_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1219, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1188, in dataset_module_factory return HubDatasetModuleFactoryWithScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 907, in get_module dataset_readme_path = self.download_dataset_readme_file() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 896, in download_dataset_readme_file return cached_path( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 183, in cached_path output_path = get_from_cache( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 611, in get_from_cache http_get( File "/usr/local/lib/python3.9/tempfile.py", line 496, in __exit__ result = self.file.__exit__(exc, value, tb) OSError: [Errno 116] Stale file handle ``` - the stale file handle error can be raised when `tempfile` tries to close (when exiting its context manager) a filename that has been already closed by other process - note that `tempfile` filenames are randomly generated but not locked in our code CC: @severo	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5938/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5938/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5938", "html_url": "https://github.com/huggingface/datasets/pull/5938", "diff_url": "https://github.com/huggingface/datasets/pull/5938.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5938.patch", "merged_at": "2023-06-14T13:27:24" }	true
https://api.github.com/repos/huggingface/datasets/issues/5937	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5937/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5937/comments	https://api.github.com/repos/huggingface/datasets/issues/5937/events	https://github.com/huggingface/datasets/pull/5937	1,749,388,597	PR_kwDODunzps5SmLIs	5,937	Avoid parallel redownload in cache	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006157 / 0.011353 (-0.005196) \| 0.003790 / 0.011008 (-0.007219) \| 0.097889 / 0.038508 (0.059381) \| 0.029038 / 0.023109 (0.005929) \| 0.306918 / 0.275898 (0.031020) \| 0.339637 / 0.323480 (0.016157) \| 0.003526 / 0.007986 (-0.004460) \| 0.003102 / 0.004328 (-0.001227) \| 0.076908 / 0.004250 (0.072658) \| 0.039254 / 0.037052 (0.002201) \| 0.309197 / 0.258489 (0.050708) \| 0.345635 / 0.293841 (0.051794) \| 0.027954 / 0.128546 (-0.100593) \| 0.008510 / 0.075646 (-0.067136) \| 0.314674 / 0.419271 (-0.104598) \| 0.057102 / 0.043533 (0.013569) \| 0.307495 / 0.255139 (0.052356) \| 0.329501 / 0.283200 (0.046302) \| 0.098450 / 0.141683 (-0.043233) \| 1.480102 / 1.452155 (0.027948) \| 1.550554 / 1.492716 (0.057838) \|\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) \| 0.207440 / 0.018006 (0.189434) \| 0.426560 / 0.000490 (0.426071) \| 0.003250 / 0.000200 (0.003050) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023777 / 0.037411 (-0.013634) \| 0.103905 / 0.014526 (0.089379) \| 0.108324 / 0.176557 (-0.068233) \| 0.167223 / 0.737135 (-0.569913) \| 0.113529 / 0.296338 (-0.182810) \|\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) \| 0.426770 / 0.215209 (0.211561) \| 4.251806 / 2.077655 (2.174151) \| 2.010426 / 1.504120 (0.506306) \| 1.858630 / 1.541195 (0.317435) \| 1.941318 / 1.468490 (0.472828) \| 0.558056 / 4.584777 (-4.026721) \| 3.399107 / 3.745712 (-0.346606) \| 1.758386 / 5.269862 (-3.511476) \| 1.036305 / 4.565676 (-3.529372) \| 0.067094 / 0.424275 (-0.357182) \| 0.011167 / 0.007607 (0.003560) \| 0.526705 / 0.226044 (0.300661) \| 5.250319 / 2.268929 (2.981390) \| 2.496723 / 55.444624 (-52.947902) \| 2.154013 / 6.876477 (-4.722464) \| 2.394724 / 2.142072 (0.252652) \| 0.669723 / 4.805227 (-4.135504) \| 0.136367 / 6.500664 (-6.364297) \| 0.067080 / 0.075469 (-0.008389) \|\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) \| 1.269700 / 1.841788 (-0.572088) \| 14.099775 / 8.074308 (6.025467) \| 14.422936 / 10.191392 (4.231544) \| 0.132344 / 0.680424 (-0.548080) \| 0.016744 / 0.534201 (-0.517457) \| 0.378286 / 0.579283 (-0.200997) \| 0.392282 / 0.434364 (-0.042082) \| 0.437648 / 0.540337 (-0.102689) \| 0.528554 / 1.386936 (-0.858382) \|\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) \| 0.006086 / 0.011353 (-0.005267) \| 0.003769 / 0.011008 (-0.007239) \| 0.077414 / 0.038508 (0.038906) \| 0.027806 / 0.023109 (0.004697) \| 0.360333 / 0.275898 (0.084434) \| 0.404725 / 0.323480 (0.081245) \| 0.003443 / 0.007986 (-0.004543) \| 0.004434 / 0.004328 (0.000106) \| 0.077309 / 0.004250 (0.073059) \| 0.040441 / 0.037052 (0.003388) \| 0.358627 / 0.258489 (0.100138) \| 0.415246 / 0.293841 (0.121405) \| 0.027718 / 0.128546 (-0.100829) \| 0.008495 / 0.075646 (-0.067151) \| 0.082874 / 0.419271 (-0.336397) \| 0.042323 / 0.043533 (-0.001210) \| 0.354895 / 0.255139 (0.099756) \| 0.390032 / 0.283200 (0.106832) \| 0.092377 / 0.141683 (-0.049306) \| 1.492817 / 1.452155 (0.040662) \| 1.551859 / 1.492716 (0.059143) \|\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) \| 0.198921 / 0.018006 (0.180915) \| 0.417699 / 0.000490 (0.417209) \| 0.001349 / 0.000200 (0.001149) \| 0.000071 / 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) \| 0.026349 / 0.037411 (-0.011062) \| 0.105712 / 0.014526 (0.091186) \| 0.111792 / 0.176557 (-0.064765) \| 0.163677 / 0.737135 (-0.573459) \| 0.116864 / 0.296338 (-0.179474) \|\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) \| 0.447532 / 0.215209 (0.232323) \| 4.468770 / 2.077655 (2.391116) \| 2.403820 / 1.504120 (0.899700) \| 2.273640 / 1.541195 (0.732445) \| 2.337505 / 1.468490 (0.869015) \| 0.560729 / 4.584777 (-4.024048) \| 3.389165 / 3.745712 (-0.356547) \| 2.697614 / 5.269862 (-2.572247) \| 1.351909 / 4.565676 (-3.213768) \| 0.068089 / 0.424275 (-0.356186) \| 0.011639 / 0.007607 (0.004032) \| 0.555277 / 0.226044 (0.329233) \| 5.559291 / 2.268929 (3.290363) \| 2.657609 / 55.444624 (-52.787015) \| 2.346667 / 6.876477 (-4.529809) \| 2.615823 / 2.142072 (0.473751) \| 0.668662 / 4.805227 (-4.136566) \| 0.136593 / 6.500664 (-6.364071) \| 0.068384 / 0.075469 (-0.007085) \|\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) \| 1.312089 / 1.841788 (-0.529699) \| 14.477510 / 8.074308 (6.403202) \| 14.231432 / 10.191392 (4.040040) \| 0.132015 / 0.680424 (-0.548409) \| 0.016908 / 0.534201 (-0.517293) \| 0.368315 / 0.579283 (-0.210968) \| 0.397964 / 0.434364 (-0.036400) \| 0.432446 / 0.540337 (-0.107891) \| 0.526349 / 1.386936 (-0.860587) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78b4d55c3cfc60e309eb033d3ed0aba5e796b6ce \"CML watermark\")\n" ]	1,686,298,716,000	1,686,745,859,000	1,686,745,437,000	MEMBER	null	Avoid parallel redownload in cache by retrying inside the lock if path exists.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5937/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5937/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5937", "html_url": "https://github.com/huggingface/datasets/pull/5937", "diff_url": "https://github.com/huggingface/datasets/pull/5937.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5937.patch", "merged_at": "2023-06-14T12:23:57" }	true
https://api.github.com/repos/huggingface/datasets/issues/5936	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5936/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5936/comments	https://api.github.com/repos/huggingface/datasets/issues/5936/events	https://github.com/huggingface/datasets/issues/5936	1,748,424,388	I_kwDODunzps5oNtbE	5,936	Sequence of array not supported for most dtype	{ "login": "qgallouedec", "id": 45557362, "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "gravatar_id": "", "url": "https://api.github.com/users/qgallouedec", "html_url": "https://github.com/qgallouedec", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "repos_url": "https://api.github.com/users/qgallouedec/repos", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Related, `float16` is the only dtype not supported by `Array2D` (probably by every `ArrayND`):\r\n\r\n```python\r\nfrom datasets import Array2D, Features, Dataset\r\n\r\nimport numpy as np\r\n\r\nfor dtype in [\r\n \"bool\", # ok\r\n \"int8\", # ok\r\n \"int16\", # ok\r\n \"int32\", # ok\r\n \"int64\", # ok\r\n \"uint8\", # ok\r\n \"uint16\", # ok\r\n \"uint32\", # ok\r\n \"uint64\", # ok\r\n \"float16\", # failed\r\n \"float32\", # ok\r\n \"float64\", # ok\r\n]:\r\n features = Features({\"foo\": Array2D(dtype=dtype, shape=(3, 4))})\r\n array = np.zeros((3, 4), dtype=dtype)\r\n try:\r\n dataset = Dataset.from_dict({\"foo\": [array]}, features=features)\r\n except Exception as e:\r\n print(f\"Failed for dtype={dtype}\")\r\n```", "Here's something I can't explain:\r\n\r\nWhen an array is encoded in the `from_dict` method, the numpy array is converted to a list (thus losing the original dtype, which is transfromed to the nearest builtin Python type)\r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/features/features.py#L524-L525\r\n\r\nHowever, later on, this same data is written to memory, and it seems authorized that the data is an array (or in this case, a list of arrays). \r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/arrow_writer.py#L185-L186\r\n\r\nSo the question is: why convert it to a Python list? This seems to be quite expensive both in terms of write time (all data is copied) and memory (e.g., an int8 is converted to an int64).\r\n\r\nFinally, if I try to remove this step, it solves all the previous problems, and it seems to me that it doesn't break anything (the CI passes without problem).", "Arrow only support 1d numpy arrays, so we convert multidim arrays to lists of 1s arrays (and keep the dtype).\r\n\r\nThough you noticed that it's concerting to lists and lose the dtype. If it's the case then it's a bug.", "Ok the conversion to list shouldn't be there indeed ! Could you open a PR to remove it ?" ]	1,686,248,287,000	1,686,755,014,000	1,686,755,014,000	CONTRIBUTOR	null	### Describe the bug Create a dataset composed of sequence of array fails for most dtypes (see code below). ### Steps to reproduce the bug ```python from datasets import Sequence, Array2D, Features, Dataset import numpy as np for dtype in [ "bool", # ok "int8", # failed "int16", # failed "int32", # failed "int64", # ok "uint8", # failed "uint16", # failed "uint32", # failed "uint64", # failed "float16", # failed "float32", # failed "float64", # ok ]: features = Features({"foo": Sequence(Array2D(dtype=dtype, shape=(2, 2)))}) sequence = [ [[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]], ] array = np.array(sequence, dtype=dtype) try: dataset = Dataset.from_dict({"foo": [array]}, features=features) except Exception as e: print(f"Failed for dtype={dtype}") ``` Traceback for `dtype="int8"`: ``` Traceback (most recent call last): File "/home/qgallouedec/datasets/a.py", line 29, in <module> raise e File "/home/qgallouedec/datasets/a.py", line 26, in <module> dataset = Dataset.from_dict({"foo": [array]}, features=features) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 899, in from_dict pa_table = InMemoryTable.from_pydict(mapping=mapping) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 799, in from_pydict return cls(pa.Table.from_pydict(args, kwargs)) File "pyarrow/table.pxi", line 3725, in pyarrow.lib.Table.from_pydict File "pyarrow/table.pxi", line 5254, in pyarrow.lib._from_pydict File "pyarrow/array.pxi", line 350, in pyarrow.lib.asarray File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_writer.py", line 204, in __arrow_array__ out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper return func(array, args, *kwargs) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2091, in cast_array_to_feature casted_values = _c(array.values, feature.feature) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper return func(array, args, *kwargs) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2139, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper return func(array, args, **kwargs) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1967, in array_cast return pa_type.wrap_array(array) File "pyarrow/types.pxi", line 879, in pyarrow.lib.BaseExtensionType.wrap_array TypeError: Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: int8>>, got list<item: list<item: int64>> ``` ### Expected behavior Not to fail. ### Environment info - Python 3.10.6 - datasets: master branch - Numpy: 1.23.4	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5936/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5936/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5935	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5935/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5935/comments	https://api.github.com/repos/huggingface/datasets/issues/5935/events	https://github.com/huggingface/datasets/pull/5935	1,748,090,220	PR_kwDODunzps5Sh9Mg	5,935	Better row group size in push_to_hub	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "<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) \| 0.007489 / 0.011353 (-0.003864) \| 0.004914 / 0.011008 (-0.006095) \| 0.111626 / 0.038508 (0.073117) \| 0.037920 / 0.023109 (0.014811) \| 0.350571 / 0.275898 (0.074673) \| 0.389667 / 0.323480 (0.066187) \| 0.006309 / 0.007986 (-0.001676) \| 0.005488 / 0.004328 (0.001160) \| 0.083962 / 0.004250 (0.079712) \| 0.050728 / 0.037052 (0.013675) \| 0.360997 / 0.258489 (0.102508) \| 0.392736 / 0.293841 (0.098895) \| 0.031975 / 0.128546 (-0.096571) \| 0.009941 / 0.075646 (-0.065705) \| 0.379840 / 0.419271 (-0.039432) \| 0.056522 / 0.043533 (0.012989) \| 0.359379 / 0.255139 (0.104240) \| 0.384487 / 0.283200 (0.101287) \| 0.117523 / 0.141683 (-0.024160) \| 1.683639 / 1.452155 (0.231485) \| 1.791645 / 1.492716 (0.298929) \|\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) \| 0.236862 / 0.018006 (0.218856) \| 0.481208 / 0.000490 (0.480719) \| 0.007455 / 0.000200 (0.007255) \| 0.000111 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030854 / 0.037411 (-0.006557) \| 0.126892 / 0.014526 (0.112367) \| 0.139207 / 0.176557 (-0.037350) \| 0.206447 / 0.737135 (-0.530689) \| 0.143095 / 0.296338 (-0.153244) \|\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) \| 0.474677 / 0.215209 (0.259468) \| 4.699534 / 2.077655 (2.621879) \| 2.152102 / 1.504120 (0.647983) \| 1.934815 / 1.541195 (0.393620) \| 1.986448 / 1.468490 (0.517958) \| 0.607184 / 4.584777 (-3.977593) \| 4.480385 / 3.745712 (0.734673) \| 2.074729 / 5.269862 (-3.195132) \| 1.182383 / 4.565676 (-3.383294) \| 0.075624 / 0.424275 (-0.348651) \| 0.014046 / 0.007607 (0.006439) \| 0.598859 / 0.226044 (0.372814) \| 5.959551 / 2.268929 (3.690622) \| 2.700851 / 55.444624 (-52.743773) \| 2.303775 / 6.876477 (-4.572702) \| 2.456441 / 2.142072 (0.314369) \| 0.747185 / 4.805227 (-4.058042) \| 0.165787 / 6.500664 (-6.334878) \| 0.075817 / 0.075469 (0.000348) \|\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) \| 1.411859 / 1.841788 (-0.429928) \| 17.375495 / 8.074308 (9.301187) \| 15.187098 / 10.191392 (4.995706) \| 0.169953 / 0.680424 (-0.510471) \| 0.020204 / 0.534201 (-0.513997) \| 0.461424 / 0.579283 (-0.117859) \| 0.494443 / 0.434364 (0.060080) \| 0.544583 / 0.540337 (0.004246) \| 0.648231 / 1.386936 (-0.738705) \|\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) \| 0.007785 / 0.011353 (-0.003568) \| 0.005314 / 0.011008 (-0.005694) \| 0.087273 / 0.038508 (0.048765) \| 0.037810 / 0.023109 (0.014701) \| 0.425473 / 0.275898 (0.149575) \| 0.459976 / 0.323480 (0.136497) \| 0.007270 / 0.007986 (-0.000716) \| 0.004631 / 0.004328 (0.000303) \| 0.087063 / 0.004250 (0.082812) \| 0.052630 / 0.037052 (0.015578) \| 0.432384 / 0.258489 (0.173895) \| 0.500291 / 0.293841 (0.206450) \| 0.033144 / 0.128546 (-0.095402) \| 0.010101 / 0.075646 (-0.065545) \| 0.096068 / 0.419271 (-0.323204) \| 0.062750 / 0.043533 (0.019217) \| 0.419308 / 0.255139 (0.164169) \| 0.437099 / 0.283200 (0.153900) \| 0.122289 / 0.141683 (-0.019394) \| 1.737829 / 1.452155 (0.285674) \| 1.851481 / 1.492716 (0.358765) \|\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) \| 0.014277 / 0.018006 (-0.003729) \| 0.489835 / 0.000490 (0.489345) \| 0.008423 / 0.000200 (0.008223) \| 0.000188 / 0.000054 (0.000134) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032966 / 0.037411 (-0.004445) \| 0.130069 / 0.014526 (0.115544) \| 0.144372 / 0.176557 (-0.032185) \| 0.200400 / 0.737135 (-0.536735) \| 0.149384 / 0.296338 (-0.146954) \|\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) \| 0.511542 / 0.215209 (0.296333) \| 5.093879 / 2.077655 (3.016225) \| 2.572088 / 1.504120 (1.067968) \| 2.339118 / 1.541195 (0.797923) \| 2.441637 / 1.468490 (0.973147) \| 0.614818 / 4.584777 (-3.969959) \| 4.724441 / 3.745712 (0.978729) \| 5.431978 / 5.269862 (0.162116) \| 2.257794 / 4.565676 (-2.307883) \| 0.078109 / 0.424275 (-0.346166) \| 0.013821 / 0.007607 (0.006214) \| 0.639232 / 0.226044 (0.413188) \| 6.424623 / 2.268929 (4.155694) \| 3.163018 / 55.444624 (-52.281606) \| 2.756786 / 6.876477 (-4.119690) \| 2.808655 / 2.142072 (0.666583) \| 0.745843 / 4.805227 (-4.059385) \| 0.165562 / 6.500664 (-6.335102) \| 0.076610 / 0.075469 (0.001141) \|\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) \| 1.738630 / 1.841788 (-0.103158) \| 18.073573 / 8.074308 (9.999265) \| 16.482820 / 10.191392 (6.291428) \| 0.213233 / 0.680424 (-0.467191) \| 0.022839 / 0.534201 (-0.511362) \| 0.487043 / 0.579283 (-0.092240) \| 0.512518 / 0.434364 (0.078154) \| 0.549365 / 0.540337 (0.009028) \| 0.656612 / 1.386936 (-0.730324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#288e92b03bd4ec91c10c8a529b32631cfaba9fb7 \"CML watermark\")\n", "Good idea!\r\n\r\nI was wondering: if we want to optimize the balance between the size of downloading a row group, and the number of rows in the group, would it make sense to compute the row group size by checking the average size of the rows?\r\n\r\neg. 32x32 images could have a larger row group size than full HD images, no? Relying on the size would even remove the need to check the column types.\r\n\r\n(in this proposal, we could use the computed row group size, eg 837, or use the nearest row group size in a list of values: 10, 100, 1000, 10000)", "Probably, but I would go for a simpler solution first :p", "Sure! I wanted to understand if the idea made sense or not, but it's not for this PR.", "I think it will be more useful for people who use the viewer and won't impact sequential io that much.", "DuckDB [paragraph](https://duckdb.org/docs/data/parquet/tips.html#selecting-a-row_group_size) that explains how to choose the `row_group_size`. Our default shard size is 500 MB in `push_to_hub`, so, ideally, we should aim for 64 MB row groups (and make this part configurable for power users 🙂).\r\n\r\nSo, before merging this PR, let's add a TODO or open an issue as a reminder that this can be improved.", "I moved the config values, improved the features check and mentioned the improvements we could do in the docstring :)", "_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) \| 0.006211 / 0.011353 (-0.005141) \| 0.004244 / 0.011008 (-0.006764) \| 0.097941 / 0.038508 (0.059433) \| 0.028564 / 0.023109 (0.005455) \| 0.299651 / 0.275898 (0.023753) \| 0.340694 / 0.323480 (0.017214) \| 0.005161 / 0.007986 (-0.002824) \| 0.004764 / 0.004328 (0.000435) \| 0.075505 / 0.004250 (0.071255) \| 0.039656 / 0.037052 (0.002603) \| 0.309242 / 0.258489 (0.050753) \| 0.350783 / 0.293841 (0.056942) \| 0.025145 / 0.128546 (-0.103401) \| 0.008498 / 0.075646 (-0.067148) \| 0.317657 / 0.419271 (-0.101615) \| 0.043926 / 0.043533 (0.000394) \| 0.305915 / 0.255139 (0.050776) \| 0.331630 / 0.283200 (0.048430) \| 0.088564 / 0.141683 (-0.053119) \| 1.533175 / 1.452155 (0.081021) \| 1.581017 / 1.492716 (0.088301) \|\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) \| 0.206032 / 0.018006 (0.188025) \| 0.433446 / 0.000490 (0.432956) \| 0.003955 / 0.000200 (0.003755) \| 0.000095 / 0.000054 (0.000040) \|\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) \| 0.103292 / 0.014526 (0.088766) \| 0.107234 / 0.176557 (-0.069322) \| 0.168525 / 0.737135 (-0.568610) \| 0.113218 / 0.296338 (-0.183120) \|\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) \| 0.431085 / 0.215209 (0.215875) \| 4.302082 / 2.077655 (2.224427) \| 2.068290 / 1.504120 (0.564171) \| 1.850718 / 1.541195 (0.309523) \| 1.964261 / 1.468490 (0.495771) \| 0.547562 / 4.584777 (-4.037215) \| 3.410739 / 3.745712 (-0.334974) \| 1.779640 / 5.269862 (-3.490221) \| 1.005466 / 4.565676 (-3.560210) \| 0.066250 / 0.424275 (-0.358025) \| 0.011877 / 0.007607 (0.004270) \| 0.525185 / 0.226044 (0.299141) \| 5.234786 / 2.268929 (2.965857) \| 2.398045 / 55.444624 (-53.046580) \| 2.073020 / 6.876477 (-4.803457) \| 2.210753 / 2.142072 (0.068680) \| 0.654897 / 4.805227 (-4.150331) \| 0.134639 / 6.500664 (-6.366025) \| 0.067050 / 0.075469 (-0.008419) \|\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) \| 1.180210 / 1.841788 (-0.661577) \| 13.613091 / 8.074308 (5.538783) \| 13.441837 / 10.191392 (3.250445) \| 0.146048 / 0.680424 (-0.534376) \| 0.016505 / 0.534201 (-0.517696) \| 0.363210 / 0.579283 (-0.216073) \| 0.405484 / 0.434364 (-0.028880) \| 0.428712 / 0.540337 (-0.111625) \| 0.522300 / 1.386936 (-0.864636) \|\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) \| 0.006147 / 0.011353 (-0.005206) \| 0.004161 / 0.011008 (-0.006847) \| 0.075861 / 0.038508 (0.037353) \| 0.027948 / 0.023109 (0.004839) \| 0.362466 / 0.275898 (0.086568) \| 0.398227 / 0.323480 (0.074747) \| 0.005014 / 0.007986 (-0.002972) \| 0.004772 / 0.004328 (0.000444) \| 0.075674 / 0.004250 (0.071423) \| 0.039158 / 0.037052 (0.002106) \| 0.363567 / 0.258489 (0.105078) \| 0.410378 / 0.293841 (0.116537) \| 0.025510 / 0.128546 (-0.103036) \| 0.008528 / 0.075646 (-0.067118) \| 0.081803 / 0.419271 (-0.337468) \| 0.040954 / 0.043533 (-0.002579) \| 0.358492 / 0.255139 (0.103353) \| 0.381345 / 0.283200 (0.098145) \| 0.092347 / 0.141683 (-0.049336) \| 1.567695 / 1.452155 (0.115540) \| 1.668412 / 1.492716 (0.175696) \|\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) \| 0.203367 / 0.018006 (0.185360) \| 0.424642 / 0.000490 (0.424152) \| 0.002451 / 0.000200 (0.002251) \| 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) \| 0.026129 / 0.037411 (-0.011282) \| 0.102564 / 0.014526 (0.088039) \| 0.110583 / 0.176557 (-0.065973) \| 0.164332 / 0.737135 (-0.572804) \| 0.115706 / 0.296338 (-0.180632) \|\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) \| 0.468925 / 0.215209 (0.253716) \| 4.657266 / 2.077655 (2.579612) \| 2.423280 / 1.504120 (0.919160) \| 2.236284 / 1.541195 (0.695089) \| 2.323019 / 1.468490 (0.854529) \| 0.548120 / 4.584777 (-4.036657) \| 3.455602 / 3.745712 (-0.290110) \| 1.730421 / 5.269862 (-3.539441) \| 1.006089 / 4.565676 (-3.559588) \| 0.067478 / 0.424275 (-0.356797) \| 0.011465 / 0.007607 (0.003857) \| 0.574235 / 0.226044 (0.348190) \| 5.744404 / 2.268929 (3.475475) \| 2.882225 / 55.444624 (-52.562400) \| 2.618246 / 6.876477 (-4.258231) \| 2.642920 / 2.142072 (0.500847) \| 0.661441 / 4.805227 (-4.143787) \| 0.137358 / 6.500664 (-6.363306) \| 0.070372 / 0.075469 (-0.005097) \|\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) \| 1.333815 / 1.841788 (-0.507973) \| 14.689667 / 8.074308 (6.615359) \| 14.362294 / 10.191392 (4.170902) \| 0.152011 / 0.680424 (-0.528413) \| 0.016869 / 0.534201 (-0.517332) \| 0.370433 / 0.579283 (-0.208851) \| 0.399642 / 0.434364 (-0.034722) \| 0.433759 / 0.540337 (-0.106578) \| 0.525443 / 1.386936 (-0.861493) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09e9f9a88edd9055b5c540e3d83b5a11d48f8ba8 \"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) \| 0.006564 / 0.011353 (-0.004789) \| 0.004350 / 0.011008 (-0.006658) \| 0.096277 / 0.038508 (0.057769) \| 0.032956 / 0.023109 (0.009847) \| 0.303675 / 0.275898 (0.027777) \| 0.336384 / 0.323480 (0.012904) \| 0.005789 / 0.007986 (-0.002197) \| 0.003957 / 0.004328 (-0.000371) \| 0.073990 / 0.004250 (0.069740) \| 0.050974 / 0.037052 (0.013922) \| 0.321754 / 0.258489 (0.063265) \| 0.349489 / 0.293841 (0.055648) \| 0.031138 / 0.128546 (-0.097409) \| 0.009000 / 0.075646 (-0.066646) \| 0.325445 / 0.419271 (-0.093826) \| 0.070173 / 0.043533 (0.026640) \| 0.304706 / 0.255139 (0.049567) \| 0.321803 / 0.283200 (0.038603) \| 0.109405 / 0.141683 (-0.032278) \| 1.489812 / 1.452155 (0.037657) \| 1.577729 / 1.492716 (0.085013) \|\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) \| 0.287187 / 0.018006 (0.269181) \| 0.527625 / 0.000490 (0.527135) \| 0.006533 / 0.000200 (0.006333) \| 0.000090 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026659 / 0.037411 (-0.010752) \| 0.106236 / 0.014526 (0.091710) \| 0.118615 / 0.176557 (-0.057941) \| 0.173156 / 0.737135 (-0.563979) \| 0.122883 / 0.296338 (-0.173456) \|\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) \| 0.407189 / 0.215209 (0.191980) \| 4.055732 / 2.077655 (1.978078) \| 1.865594 / 1.504120 (0.361474) \| 1.664325 / 1.541195 (0.123130) \| 1.668961 / 1.468490 (0.200471) \| 0.521207 / 4.584777 (-4.063570) \| 3.740424 / 3.745712 (-0.005288) \| 3.431973 / 5.269862 (-1.837889) \| 1.636669 / 4.565676 (-2.929008) \| 0.065271 / 0.424275 (-0.359005) \| 0.012151 / 0.007607 (0.004544) \| 0.514233 / 0.226044 (0.288189) \| 5.110150 / 2.268929 (2.841222) \| 2.264340 / 55.444624 (-53.180284) \| 1.940428 / 6.876477 (-4.936049) \| 2.042286 / 2.142072 (-0.099787) \| 0.639200 / 4.805227 (-4.166028) \| 0.139537 / 6.500664 (-6.361127) \| 0.063195 / 0.075469 (-0.012274) \|\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) \| 1.179501 / 1.841788 (-0.662286) \| 14.600133 / 8.074308 (6.525825) \| 14.902137 / 10.191392 (4.710745) \| 0.144509 / 0.680424 (-0.535915) \| 0.017449 / 0.534201 (-0.516752) \| 0.393135 / 0.579283 (-0.186148) \| 0.413103 / 0.434364 (-0.021261) \| 0.459897 / 0.540337 (-0.080440) \| 0.552602 / 1.386936 (-0.834334) \|\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) \| 0.006891 / 0.011353 (-0.004462) \| 0.004633 / 0.011008 (-0.006375) \| 0.073093 / 0.038508 (0.034585) \| 0.032509 / 0.023109 (0.009399) \| 0.348332 / 0.275898 (0.072434) \| 0.381920 / 0.323480 (0.058440) \| 0.005978 / 0.007986 (-0.002007) \| 0.005360 / 0.004328 (0.001032) \| 0.074307 / 0.004250 (0.070056) \| 0.049668 / 0.037052 (0.012615) \| 0.354713 / 0.258489 (0.096224) \| 0.398521 / 0.293841 (0.104681) \| 0.032013 / 0.128546 (-0.096534) \| 0.008890 / 0.075646 (-0.066756) \| 0.080013 / 0.419271 (-0.339259) \| 0.051820 / 0.043533 (0.008288) \| 0.349730 / 0.255139 (0.094591) \| 0.369267 / 0.283200 (0.086067) \| 0.103874 / 0.141683 (-0.037809) \| 1.484148 / 1.452155 (0.031993) \| 1.573927 / 1.492716 (0.081211) \|\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) \| 0.009699 / 0.018006 (-0.008307) \| 0.511176 / 0.000490 (0.510686) \| 0.002938 / 0.000200 (0.002738) \| 0.000109 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027847 / 0.037411 (-0.009564) \| 0.111565 / 0.014526 (0.097039) \| 0.120625 / 0.176557 (-0.055932) \| 0.172130 / 0.737135 (-0.565006) \| 0.125949 / 0.296338 (-0.170389) \|\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) \| 0.430634 / 0.215209 (0.215424) \| 4.315377 / 2.077655 (2.237722) \| 2.070764 / 1.504120 (0.566644) \| 1.881962 / 1.541195 (0.340767) \| 1.904053 / 1.468490 (0.435563) \| 0.524973 / 4.584777 (-4.059804) \| 3.718359 / 3.745712 (-0.027353) \| 3.415344 / 5.269862 (-1.854518) \| 1.224568 / 4.565676 (-3.341108) \| 0.065593 / 0.424275 (-0.358682) \| 0.011643 / 0.007607 (0.004036) \| 0.537050 / 0.226044 (0.311006) \| 5.352155 / 2.268929 (3.083226) \| 2.557361 / 55.444624 (-52.887263) \| 2.217770 / 6.876477 (-4.658707) \| 2.194975 / 2.142072 (0.052902) \| 0.635142 / 4.805227 (-4.170085) \| 0.140642 / 6.500664 (-6.360022) \| 0.064690 / 0.075469 (-0.010779) \|\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 (-0.575663) \| 14.836413 / 8.074308 (6.762105) \| 14.446870 / 10.191392 (4.255478) \| 0.191545 / 0.680424 (-0.488878) \| 0.017433 / 0.534201 (-0.516768) \| 0.392296 / 0.579283 (-0.186987) \| 0.420698 / 0.434364 (-0.013666) \| 0.463225 / 0.540337 (-0.077112) \| 0.556127 / 1.386936 (-0.830809) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7fcbe5b1575c8d162b65b9397b3dfda995a4e048 \"CML watermark\")\n" ]	1,686,236,475,000	1,686,332,857,000	1,686,332,409,000	MEMBER	null	This is a very simple change that improves `to_parquet` to use a more reasonable row group size for image and audio datasets. This is especially useful for `push_to_hub` and will provide a better experience with the dataset viewer on HF	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5935/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5935/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5935", "html_url": "https://github.com/huggingface/datasets/pull/5935", "diff_url": "https://github.com/huggingface/datasets/pull/5935.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5935.patch", "merged_at": "2023-06-09T17:40:09" }	true
https://api.github.com/repos/huggingface/datasets/issues/5934	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5934/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5934/comments	https://api.github.com/repos/huggingface/datasets/issues/5934/events	https://github.com/huggingface/datasets/pull/5934	1,747,904,840	PR_kwDODunzps5ShUxQ	5,934	Modify levels of some logging messages	{ "login": "Laurent2916", "id": 21087104, "node_id": "MDQ6VXNlcjIxMDg3MTA0", "avatar_url": "https://avatars.githubusercontent.com/u/21087104?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Laurent2916", "html_url": "https://github.com/Laurent2916", "followers_url": "https://api.github.com/users/Laurent2916/followers", "following_url": "https://api.github.com/users/Laurent2916/following{/other_user}", "gists_url": "https://api.github.com/users/Laurent2916/gists{/gist_id}", "starred_url": "https://api.github.com/users/Laurent2916/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Laurent2916/subscriptions", "organizations_url": "https://api.github.com/users/Laurent2916/orgs", "repos_url": "https://api.github.com/users/Laurent2916/repos", "events_url": "https://api.github.com/users/Laurent2916/events{/privacy}", "received_events_url": "https://api.github.com/users/Laurent2916/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[]	1,686,231,104,000	1,686,231,104,000	null	CONTRIBUTOR	null	Some warning messages didn't quite sound like warnings so I modified their logging levels to info.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5934/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5934/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5934", "html_url": "https://github.com/huggingface/datasets/pull/5934", "diff_url": "https://github.com/huggingface/datasets/pull/5934.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5934.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/5933	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5933/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5933/comments	https://api.github.com/repos/huggingface/datasets/issues/5933/events	https://github.com/huggingface/datasets/pull/5933	1,747,382,500	PR_kwDODunzps5Sfi5J	5,933	Fix `to_numpy` when None values in the sequence	{ "login": "qgallouedec", "id": 45557362, "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "gravatar_id": "", "url": "https://api.github.com/users/qgallouedec", "html_url": "https://github.com/qgallouedec", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "repos_url": "https://api.github.com/users/qgallouedec/repos", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "I just added the same test with dynamic shape", "_The documentation is not available anymore as the PR was closed or merged._", "Awesome ! I'm merging now if you don't mind :)\r\nWe should probably give you permissions to merge your own PRs when you have an approval", "<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) \| 0.009980 / 0.011353 (-0.001373) \| 0.005709 / 0.011008 (-0.005300) \| 0.132185 / 0.038508 (0.093677) \| 0.039299 / 0.023109 (0.016190) \| 0.400168 / 0.275898 (0.124270) \| 0.470582 / 0.323480 (0.147102) \| 0.007753 / 0.007986 (-0.000233) \| 0.005196 / 0.004328 (0.000868) \| 0.093698 / 0.004250 (0.089448) \| 0.052631 / 0.037052 (0.015579) \| 0.430347 / 0.258489 (0.171858) \| 0.460162 / 0.293841 (0.166321) \| 0.057511 / 0.128546 (-0.071035) \| 0.013944 / 0.075646 (-0.061702) \| 0.459008 / 0.419271 (0.039737) \| 0.075532 / 0.043533 (0.031999) \| 0.405165 / 0.255139 (0.150026) \| 0.456142 / 0.283200 (0.172942) \| 0.117309 / 0.141683 (-0.024374) \| 1.945787 / 1.452155 (0.493633) \| 2.067162 / 1.492716 (0.574446) \|\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) \| 0.285755 / 0.018006 (0.267749) \| 0.619965 / 0.000490 (0.619476) \| 0.005071 / 0.000200 (0.004871) \| 0.000114 / 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) \| 0.031112 / 0.037411 (-0.006299) \| 0.128514 / 0.014526 (0.113988) \| 0.137161 / 0.176557 (-0.039396) \| 0.211363 / 0.737135 (-0.525772) \| 0.151045 / 0.296338 (-0.145293) \|\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) \| 0.609361 / 0.215209 (0.394152) \| 6.124844 / 2.077655 (4.047189) \| 2.440757 / 1.504120 (0.936637) \| 2.034495 / 1.541195 (0.493300) \| 2.047192 / 1.468490 (0.578702) \| 0.883171 / 4.584777 (-3.701606) \| 5.470552 / 3.745712 (1.724840) \| 4.401696 / 5.269862 (-0.868165) \| 2.378674 / 4.565676 (-2.187003) \| 0.108065 / 0.424275 (-0.316210) \| 0.013239 / 0.007607 (0.005632) \| 0.830957 / 0.226044 (0.604913) \| 8.090659 / 2.268929 (5.821731) \| 3.289203 / 55.444624 (-52.155422) \| 2.500777 / 6.876477 (-4.375700) \| 2.561440 / 2.142072 (0.419367) \| 1.064893 / 4.805227 (-3.740334) \| 0.220486 / 6.500664 (-6.280178) \| 0.079507 / 0.075469 (0.004038) \|\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) \| 1.544334 / 1.841788 (-0.297454) \| 17.878997 / 8.074308 (9.804689) \| 18.952191 / 10.191392 (8.760799) \| 0.245166 / 0.680424 (-0.435258) \| 0.028022 / 0.534201 (-0.506179) \| 0.517828 / 0.579283 (-0.061455) \| 0.618988 / 0.434364 (0.184624) \| 0.589742 / 0.540337 (0.049405) \| 0.670902 / 1.386936 (-0.716034) \|\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) \| 0.009616 / 0.011353 (-0.001737) \| 0.006098 / 0.011008 (-0.004911) \| 0.100301 / 0.038508 (0.061793) \| 0.037792 / 0.023109 (0.014683) \| 0.484667 / 0.275898 (0.208769) \| 0.519286 / 0.323480 (0.195806) \| 0.007427 / 0.007986 (-0.000558) \| 0.007172 / 0.004328 (0.002844) \| 0.104429 / 0.004250 (0.100179) \| 0.056567 / 0.037052 (0.019515) \| 0.502641 / 0.258489 (0.244152) \| 0.549629 / 0.293841 (0.255788) \| 0.049574 / 0.128546 (-0.078972) \| 0.015223 / 0.075646 (-0.060424) \| 0.113947 / 0.419271 (-0.305324) \| 0.064585 / 0.043533 (0.021053) \| 0.512962 / 0.255139 (0.257823) \| 0.507218 / 0.283200 (0.224019) \| 0.122194 / 0.141683 (-0.019488) \| 1.927821 / 1.452155 (0.475667) \| 2.051161 / 1.492716 (0.558445) \|\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) \| 0.291350 / 0.018006 (0.273344) \| 0.588099 / 0.000490 (0.587610) \| 0.001368 / 0.000200 (0.001168) \| 0.000153 / 0.000054 (0.000099) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030604 / 0.037411 (-0.006807) \| 0.126810 / 0.014526 (0.112285) \| 0.139309 / 0.176557 (-0.037248) \| 0.208030 / 0.737135 (-0.529105) \| 0.138985 / 0.296338 (-0.157353) \|\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) \| 0.681254 / 0.215209 (0.466045) \| 6.753856 / 2.077655 (4.676201) \| 2.780704 / 1.504120 (1.276585) \| 2.475205 / 1.541195 (0.934010) \| 2.486784 / 1.468490 (1.018294) \| 0.879223 / 4.584777 (-3.705554) \| 5.662294 / 3.745712 (1.916582) \| 2.698705 / 5.269862 (-2.571156) \| 1.660620 / 4.565676 (-2.905057) \| 0.112218 / 0.424275 (-0.312057) \| 0.014211 / 0.007607 (0.006604) \| 0.796957 / 0.226044 (0.570913) \| 8.180897 / 2.268929 (5.911969) \| 3.540419 / 55.444624 (-51.904205) \| 2.899467 / 6.876477 (-3.977010) \| 2.870306 / 2.142072 (0.728233) \| 1.069537 / 4.805227 (-3.735690) \| 0.211281 / 6.500664 (-6.289383) \| 0.078898 / 0.075469 (0.003429) \|\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) \| 1.666790 / 1.841788 (-0.174998) \| 18.302127 / 8.074308 (10.227819) \| 21.317546 / 10.191392 (11.126153) \| 0.242795 / 0.680424 (-0.437629) \| 0.026754 / 0.534201 (-0.507447) \| 0.493375 / 0.579283 (-0.085908) \| 0.605400 / 0.434364 (0.171036) \| 0.586888 / 0.540337 (0.046550) \| 0.722809 / 1.386936 (-0.664127) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce2328e7b1d62998b22510492530af55d4493b73 \"CML watermark\")\n" ]	1,686,213,536,000	1,686,318,581,000	1,686,317,028,000	CONTRIBUTOR	null	Closes #5927 I've realized that the error was overlooked during testing due to the presence of only one None value in the sequence. Unfortunately, it was the only case where the function works as expected. When the sequence contained more than one None value, the function failed. Consequently, I've updated the tests to include sequences with multiple None values.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5933/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5933/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5933", "html_url": "https://github.com/huggingface/datasets/pull/5933", "diff_url": "https://github.com/huggingface/datasets/pull/5933.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5933.patch", "merged_at": "2023-06-09T13:23:48" }	true
https://api.github.com/repos/huggingface/datasets/issues/5932	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5932/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5932/comments	https://api.github.com/repos/huggingface/datasets/issues/5932/events	https://github.com/huggingface/datasets/pull/5932	1,746,249,161	PR_kwDODunzps5Sbrzo	5,932	[doc build] Use secrets	{ "login": "mishig25", "id": 11827707, "node_id": "MDQ6VXNlcjExODI3NzA3", "avatar_url": "https://avatars.githubusercontent.com/u/11827707?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mishig25", "html_url": "https://github.com/mishig25", "followers_url": "https://api.github.com/users/mishig25/followers", "following_url": "https://api.github.com/users/mishig25/following{/other_user}", "gists_url": "https://api.github.com/users/mishig25/gists{/gist_id}", "starred_url": "https://api.github.com/users/mishig25/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mishig25/subscriptions", "organizations_url": "https://api.github.com/users/mishig25/orgs", "repos_url": "https://api.github.com/users/mishig25/repos", "events_url": "https://api.github.com/users/mishig25/events{/privacy}", "received_events_url": "https://api.github.com/users/mishig25/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "<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) \| 0.008499 / 0.011353 (-0.002854) \| 0.006155 / 0.011008 (-0.004853) \| 0.124032 / 0.038508 (0.085524) \| 0.037337 / 0.023109 (0.014228) \| 0.389274 / 0.275898 (0.113376) \| 0.427736 / 0.323480 (0.104257) \| 0.006929 / 0.007986 (-0.001057) \| 0.005017 / 0.004328 (0.000689) \| 0.096356 / 0.004250 (0.092105) \| 0.055694 / 0.037052 (0.018642) \| 0.391417 / 0.258489 (0.132928) \| 0.448098 / 0.293841 (0.154257) \| 0.042442 / 0.128546 (-0.086105) \| 0.013456 / 0.075646 (-0.062190) \| 0.423502 / 0.419271 (0.004230) \| 0.062919 / 0.043533 (0.019386) \| 0.384317 / 0.255139 (0.129178) \| 0.410851 / 0.283200 (0.127652) \| 0.112807 / 0.141683 (-0.028875) \| 1.746050 / 1.452155 (0.293895) \| 1.977974 / 1.492716 (0.485257) \|\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) \| 0.306382 / 0.018006 (0.288375) \| 0.620310 / 0.000490 (0.619820) \| 0.009309 / 0.000200 (0.009109) \| 0.000106 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026900 / 0.037411 (-0.010511) \| 0.140125 / 0.014526 (0.125599) \| 0.136295 / 0.176557 (-0.040261) \| 0.207721 / 0.737135 (-0.529414) \| 0.146328 / 0.296338 (-0.150011) \|\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) \| 0.616712 / 0.215209 (0.401503) \| 6.237820 / 2.077655 (4.160166) \| 2.503809 / 1.504120 (0.999689) \| 2.129739 / 1.541195 (0.588544) \| 2.160768 / 1.468490 (0.692277) \| 0.971273 / 4.584777 (-3.613504) \| 5.687161 / 3.745712 (1.941449) \| 2.738148 / 5.269862 (-2.531713) \| 1.692695 / 4.565676 (-2.872981) \| 0.113701 / 0.424275 (-0.310574) \| 0.014809 / 0.007607 (0.007202) \| 0.774795 / 0.226044 (0.548750) \| 7.660012 / 2.268929 (5.391083) \| 3.253036 / 55.444624 (-52.191588) \| 2.607498 / 6.876477 (-4.268979) \| 2.681678 / 2.142072 (0.539606) \| 1.095275 / 4.805227 (-3.709952) \| 0.239078 / 6.500664 (-6.261586) \| 0.081034 / 0.075469 (0.005565) \|\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) \| 1.574547 / 1.841788 (-0.267240) \| 18.323566 / 8.074308 (10.249258) \| 19.274482 / 10.191392 (9.083090) \| 0.210275 / 0.680424 (-0.470149) \| 0.031843 / 0.534201 (-0.502358) \| 0.514843 / 0.579283 (-0.064440) \| 0.633782 / 0.434364 (0.199418) \| 0.588569 / 0.540337 (0.048232) \| 0.721401 / 1.386936 (-0.665535) \|\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) \| 0.008866 / 0.011353 (-0.002487) \| 0.006460 / 0.011008 (-0.004548) \| 0.121337 / 0.038508 (0.082829) \| 0.033896 / 0.023109 (0.010786) \| 0.455702 / 0.275898 (0.179804) \| 0.509685 / 0.323480 (0.186205) \| 0.007650 / 0.007986 (-0.000336) \| 0.005578 / 0.004328 (0.001250) \| 0.098505 / 0.004250 (0.094255) \| 0.056122 / 0.037052 (0.019069) \| 0.478483 / 0.258489 (0.219994) \| 0.560008 / 0.293841 (0.266167) \| 0.044926 / 0.128546 (-0.083620) \| 0.014562 / 0.075646 (-0.061085) \| 0.115027 / 0.419271 (-0.304244) \| 0.066494 / 0.043533 (0.022961) \| 0.463434 / 0.255139 (0.208296) \| 0.513856 / 0.283200 (0.230656) \| 0.126436 / 0.141683 (-0.015247) \| 1.874729 / 1.452155 (0.422575) \| 1.925080 / 1.492716 (0.432364) \|\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) \| 0.012672 / 0.018006 (-0.005334) \| 0.615797 / 0.000490 (0.615307) \| 0.001606 / 0.000200 (0.001406) \| 0.000118 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031104 / 0.037411 (-0.006307) \| 0.130107 / 0.014526 (0.115581) \| 0.140587 / 0.176557 (-0.035970) \| 0.205081 / 0.737135 (-0.532054) \| 0.144068 / 0.296338 (-0.152270) \|\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) \| 0.646549 / 0.215209 (0.431340) \| 6.403962 / 2.077655 (4.326307) \| 2.812594 / 1.504120 (1.308474) \| 2.478480 / 1.541195 (0.937285) \| 2.552385 / 1.468490 (1.083895) \| 0.991987 / 4.584777 (-3.592790) \| 5.777917 / 3.745712 (2.032205) \| 5.697830 / 5.269862 (0.427969) \| 2.370583 / 4.565676 (-2.195094) \| 0.109905 / 0.424275 (-0.314370) \| 0.013801 / 0.007607 (0.006193) \| 0.799932 / 0.226044 (0.573888) \| 8.155672 / 2.268929 (5.886743) \| 3.711662 / 55.444624 (-51.732963) \| 3.042164 / 6.876477 (-3.834312) \| 3.073549 / 2.142072 (0.931477) \| 1.137515 / 4.805227 (-3.667712) \| 0.231266 / 6.500664 (-6.269398) \| 0.080893 / 0.075469 (0.005424) \|\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) \| 1.669210 / 1.841788 (-0.172577) \| 18.747144 / 8.074308 (10.672836) \| 21.084589 / 10.191392 (10.893197) \| 0.241379 / 0.680424 (-0.439045) \| 0.029473 / 0.534201 (-0.504728) \| 0.524605 / 0.579283 (-0.054678) \| 0.622852 / 0.434364 (0.188488) \| 0.604941 / 0.540337 (0.064604) \| 0.715978 / 1.386936 (-0.670958) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#142484a60b1330359d7713e906fc9e5e30aa9f64 \"CML watermark\")\n", "Cool ! what about `.github/workflows/build_pr_documentation.yml` and `.github/workflows/delete_doc_comment.yml` ?", "<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) \| 0.005973 / 0.011353 (-0.005380) \| 0.004389 / 0.011008 (-0.006620) \| 0.096076 / 0.038508 (0.057568) \| 0.031569 / 0.023109 (0.008460) \| 0.328300 / 0.275898 (0.052402) \| 0.359356 / 0.323480 (0.035876) \| 0.005378 / 0.007986 (-0.002607) \| 0.003703 / 0.004328 (-0.000625) \| 0.075251 / 0.004250 (0.071000) \| 0.042340 / 0.037052 (0.005287) \| 0.346103 / 0.258489 (0.087614) \| 0.379896 / 0.293841 (0.086055) \| 0.027493 / 0.128546 (-0.101053) \| 0.009033 / 0.075646 (-0.066613) \| 0.327829 / 0.419271 (-0.091442) \| 0.064074 / 0.043533 (0.020541) \| 0.337703 / 0.255139 (0.082564) \| 0.355335 / 0.283200 (0.072136) \| 0.101179 / 0.141683 (-0.040504) \| 1.471738 / 1.452155 (0.019584) \| 1.539031 / 1.492716 (0.046315) \|\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) \| 0.194097 / 0.018006 (0.176091) \| 0.434190 / 0.000490 (0.433701) \| 0.005730 / 0.000200 (0.005530) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025634 / 0.037411 (-0.011778) \| 0.105080 / 0.014526 (0.090555) \| 0.116508 / 0.176557 (-0.060049) \| 0.173867 / 0.737135 (-0.563269) \| 0.117749 / 0.296338 (-0.178590) \|\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) \| 0.401566 / 0.215209 (0.186357) \| 4.003558 / 2.077655 (1.925903) \| 1.802756 / 1.504120 (0.298636) \| 1.604222 / 1.541195 (0.063027) \| 1.656617 / 1.468490 (0.188127) \| 0.523385 / 4.584777 (-4.061392) \| 3.744292 / 3.745712 (-0.001420) \| 1.794295 / 5.269862 (-3.475567) \| 1.044690 / 4.565676 (-3.520987) \| 0.064992 / 0.424275 (-0.359284) \| 0.011542 / 0.007607 (0.003935) \| 0.507830 / 0.226044 (0.281785) \| 5.061574 / 2.268929 (2.792645) \| 2.252896 / 55.444624 (-53.191729) \| 1.912551 / 6.876477 (-4.963926) \| 2.073510 / 2.142072 (-0.068562) \| 0.642148 / 4.805227 (-4.163079) \| 0.140151 / 6.500664 (-6.360513) \| 0.062623 / 0.075469 (-0.012846) \|\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) \| 1.180367 / 1.841788 (-0.661421) \| 14.263475 / 8.074308 (6.189167) \| 12.917251 / 10.191392 (2.725859) \| 0.143815 / 0.680424 (-0.536608) \| 0.017286 / 0.534201 (-0.516915) \| 0.388411 / 0.579283 (-0.190872) \| 0.430512 / 0.434364 (-0.003851) \| 0.466595 / 0.540337 (-0.073742) \| 0.564545 / 1.386936 (-0.822391) \|\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) \| 0.006059 / 0.011353 (-0.005294) \| 0.004419 / 0.011008 (-0.006590) \| 0.074206 / 0.038508 (0.035697) \| 0.031180 / 0.023109 (0.008071) \| 0.380031 / 0.275898 (0.104133) \| 0.410373 / 0.323480 (0.086893) \| 0.005397 / 0.007986 (-0.002589) \| 0.003952 / 0.004328 (-0.000376) \| 0.074426 / 0.004250 (0.070176) \| 0.046256 / 0.037052 (0.009203) \| 0.385543 / 0.258489 (0.127054) \| 0.430724 / 0.293841 (0.136883) \| 0.028052 / 0.128546 (-0.100494) \| 0.008810 / 0.075646 (-0.066836) \| 0.080749 / 0.419271 (-0.338522) \| 0.046746 / 0.043533 (0.003214) \| 0.380325 / 0.255139 (0.125186) \| 0.398901 / 0.283200 (0.115701) \| 0.099607 / 0.141683 (-0.042076) \| 1.433343 / 1.452155 (-0.018812) \| 1.520447 / 1.492716 (0.027730) \|\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) \| 0.202232 / 0.018006 (0.184225) \| 0.431342 / 0.000490 (0.430852) \| 0.001020 / 0.000200 (0.000820) \| 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) \| 0.028762 / 0.037411 (-0.008649) \| 0.111777 / 0.014526 (0.097251) \| 0.119283 / 0.176557 (-0.057273) \| 0.168151 / 0.737135 (-0.568985) \| 0.126093 / 0.296338 (-0.170245) \|\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) \| 0.442689 / 0.215209 (0.227480) \| 4.369202 / 2.077655 (2.291547) \| 2.167703 / 1.504120 (0.663583) \| 1.960580 / 1.541195 (0.419385) \| 2.001459 / 1.468490 (0.532969) \| 0.527169 / 4.584777 (-4.057608) \| 3.738987 / 3.745712 (-0.006726) \| 1.819002 / 5.269862 (-3.450860) \| 1.082786 / 4.565676 (-3.482891) \| 0.066209 / 0.424275 (-0.358066) \| 0.011549 / 0.007607 (0.003942) \| 0.545959 / 0.226044 (0.319915) \| 5.466655 / 2.268929 (3.197727) \| 2.671448 / 55.444624 (-52.773176) \| 2.340968 / 6.876477 (-4.535509) \| 2.358805 / 2.142072 (0.216733) \| 0.649456 / 4.805227 (-4.155771) \| 0.142009 / 6.500664 (-6.358655) \| 0.064199 / 0.075469 (-0.011270) \|\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) \| 1.259819 / 1.841788 (-0.581969) \| 14.456988 / 8.074308 (6.382680) \| 14.478982 / 10.191392 (4.287590) \| 0.163156 / 0.680424 (-0.517268) \| 0.017090 / 0.534201 (-0.517111) \| 0.391339 / 0.579283 (-0.187944) \| 0.422021 / 0.434364 (-0.012343) \| 0.465340 / 0.540337 (-0.074997) \| 0.564517 / 1.386936 (-0.822419) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#97358c88f996a65f49923ec215358044e4146a95 \"CML watermark\")\n", "> .github/workflows/delete_doc_comment.yml \r\n\r\nis already updated https://github.com/huggingface/datasets/pull/5932/files\r\n\r\n> .github/workflows/build_pr_documentation.yml\r\n\r\nindeed no changes are needed" ]	1,686,154,179,000	1,686,305,818,000	1,686,304,396,000	CONTRIBUTOR	null	Companion pr to https://github.com/huggingface/doc-builder/pull/379	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5932/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5932/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5932", "html_url": "https://github.com/huggingface/datasets/pull/5932", "diff_url": "https://github.com/huggingface/datasets/pull/5932.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5932.patch", "merged_at": "2023-06-09T09:53:16" }	true
https://api.github.com/repos/huggingface/datasets/issues/5931	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5931/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5931/comments	https://api.github.com/repos/huggingface/datasets/issues/5931/events	https://github.com/huggingface/datasets/issues/5931	1,745,408,784	I_kwDODunzps5oCNMQ	5,931	`datasets.map` not reusing cached copy by default	{ "login": "bhavitvyamalik", "id": 19718818, "node_id": "MDQ6VXNlcjE5NzE4ODE4", "avatar_url": "https://avatars.githubusercontent.com/u/19718818?v=4", "gravatar_id": "", "url": "https://api.github.com/users/bhavitvyamalik", "html_url": "https://github.com/bhavitvyamalik", "followers_url": "https://api.github.com/users/bhavitvyamalik/followers", "following_url": "https://api.github.com/users/bhavitvyamalik/following{/other_user}", "gists_url": "https://api.github.com/users/bhavitvyamalik/gists{/gist_id}", "starred_url": "https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bhavitvyamalik/subscriptions", "organizations_url": "https://api.github.com/users/bhavitvyamalik/orgs", "repos_url": "https://api.github.com/users/bhavitvyamalik/repos", "events_url": "https://api.github.com/users/bhavitvyamalik/events{/privacy}", "received_events_url": "https://api.github.com/users/bhavitvyamalik/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "This can happen when a map transform cannot be hashed deterministically (e.g., an object referenced by the transform changes its state after the first call - an issue with fast tokenizers). The solution is to provide `cache_file_name` in the `map` call to check this file for the cached result instead of relying on the default caching mechanism." ]	1,686,128,613,000	1,687,364,140,000	1,687,364,140,000	CONTRIBUTOR	null	### Describe the bug When I load the dataset from local directory, it's cached copy is picked up after first time. However, for `map` operation, the operation is applied again and cached copy is not picked up. Is there any way to pick cached copy instead of processing it again? The only solution I could think of was to use `save_to_disk` after my last transform and then use that in my DataLoader pipeline. Are there any other solutions for the same? One more thing, my dataset is occupying 6GB storage memory after I use `map`, is there any way I can reduce that memory usage? ### Steps to reproduce the bug ``` # make sure that dataset decodes audio with correct sampling rate dataset_sampling_rate = next(iter(self.raw_datasets.values())).features["audio"].sampling_rate if dataset_sampling_rate != self.feature_extractor.sampling_rate: self.raw_datasets = self.raw_datasets.cast_column( "audio", datasets.features.Audio(sampling_rate=self.feature_extractor.sampling_rate) ) vectorized_datasets = self.raw_datasets.map( self.prepare_dataset, remove_columns=next(iter(self.raw_datasets.values())).column_names, num_proc=self.num_workers, desc="preprocess datasets", ) # filter data that is longer than max_input_length self.vectorized_datasets = vectorized_datasets.filter( self.is_audio_in_length_range, num_proc=self.num_workers, input_columns=["input_length"], ) def prepare_dataset(self, batch): # load audio sample = batch["audio"] inputs = self.feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) batch["labels"] = self.tokenizer(batch["target_text"]).input_ids return batch ``` ### Expected behavior `map` to use cached copy and if possible an alternative technique to reduce memory usage after using `map` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-3.10.0-1160.71.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5931/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5931/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5930	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5930/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5930/comments	https://api.github.com/repos/huggingface/datasets/issues/5930/events	https://github.com/huggingface/datasets/issues/5930	1,745,184,395	I_kwDODunzps5oBWaL	5,930	loading private custom dataset script - authentication error	{ "login": "flckv", "id": 103381497, "node_id": "U_kgDOBil5-Q", "avatar_url": "https://avatars.githubusercontent.com/u/103381497?v=4", "gravatar_id": "", "url": "https://api.github.com/users/flckv", "html_url": "https://github.com/flckv", "followers_url": "https://api.github.com/users/flckv/followers", "following_url": "https://api.github.com/users/flckv/following{/other_user}", "gists_url": "https://api.github.com/users/flckv/gists{/gist_id}", "starred_url": "https://api.github.com/users/flckv/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/flckv/subscriptions", "organizations_url": "https://api.github.com/users/flckv/orgs", "repos_url": "https://api.github.com/users/flckv/repos", "events_url": "https://api.github.com/users/flckv/events{/privacy}", "received_events_url": "https://api.github.com/users/flckv/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "This issue seems to have been resolved, so I'm closing it." ]	1,686,121,103,000	1,686,840,561,000	1,686,840,560,000	NONE	null	### Describe the bug Train model with my custom dataset stored in HuggingFace and loaded with the loading script requires authentication but I am not sure how ? I am logged in in the terminal, in the browser. I receive this error: /python3.8/site-packages/datasets/utils/file_utils.py", line 566, in get_from_cache raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})") ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels `(ConnectionError('Unauthorized for URL `https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels. Please use the parameter ``use_auth_token=True`` after logging in with ``huggingface-cli login``')) when I added: `use_auth_token=True` and logged in via terminal then I received error: or the same error in different format: raise ConnectionError(f"`Couldn't reach {url} (error {response.status_code}`)") ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels (`error 401`) ### Steps to reproduce the bug 1. cloned transformers library locally: https://huggingface.co/docs/transformers/v4.15.0/examples : > git clone https://github.com/huggingface/transformers > cd transformers > pip install . > cd /transformers/examples/pytorch/audio-classification > pip install -r requirements.txt 2. created loading script > https://huggingface.co/docs/datasets/dataset_script added next to dataset: 3. uploaded private custom dataset with loading script to HuggingFace > https://huggingface.co/docs/datasets/dataset_script 4. added dataset loading script to local directory in the above cloned transformers library: > cd /transformers/examples/pytorch/audio-classification 5. logged in to HuggingFace on local terminal with : > huggingface-cli login 6. run the model with the custom dataset stored on HuggingFace with code: https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/README.md cd /transformers/examples/pytorch/audio-classification > python run_audio_classification.py \ > --model_name_or_path facebook/wav2vec2-base \ > --output_dir l/users/flck/outputs/wav2vec2-base-s \ > --overwrite_output_dir \ > --dataset_name s \ > --dataset_config_name s \ > --remove_unused_columns False \ > --do_train \ > --do_eval \ > --fp16 \ > --learning_rate 3e-5 \ > --max_length_seconds 1 \ > --attention_mask False \ > --warmup_ratio 0.1 \ > --num_train_epochs 5 \ > --per_device_train_batch_size 32 \ > --gradient_accumulation_steps 4 \ > --per_device_eval_batch_size 32 \ > --dataloader_num_workers 4 \ > --logging_strategy steps \ > --logging_steps 10 \ > --evaluation_strategy epoch \ > --save_strategy epoch \ > --load_best_model_at_end True \ > --metric_for_best_model accuracy \ > --save_total_limit 3 \ > --seed 0 \ > --push_to_hub \ > --use_auth_token=True ### Expected behavior Be able to train a model the https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/ run_audio_classification.py with private custom dataset stored on HuggingFace. ### Environment info - datasets version: 2.12.0 - `transformers` version: 4.30.0.dev0 - Platform: Linux-5.4.204-ql-generic-12.0-19-x86_64-with-glibc2.17 - Python version: 3.8.12 - Huggingface_hub version: 0.15.1 - Safetensors version: 0.3.1 - PyTorch version (GPU?): 2.0.1+cu117 (True) Versions of relevant libraries: [pip3] numpy==1.24.3 [pip3] torch==2.0.1 [pip3] torchaudio==2.0.2 [conda] numpy 1.24.3 pypi_0 pypi [conda] torch 2.0.1 pypi_0 pypi [conda] torchaudio 2.0.2 pypi_0 pypi	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5930/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5930/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5929	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5929/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5929/comments	https://api.github.com/repos/huggingface/datasets/issues/5929/events	https://github.com/huggingface/datasets/issues/5929	1,744,478,456	I_kwDODunzps5n-qD4	5,929	Importing PyTorch reduces multiprocessing performance for map	{ "login": "Maxscha", "id": 12814709, "node_id": "MDQ6VXNlcjEyODE0NzA5", "avatar_url": "https://avatars.githubusercontent.com/u/12814709?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Maxscha", "html_url": "https://github.com/Maxscha", "followers_url": "https://api.github.com/users/Maxscha/followers", "following_url": "https://api.github.com/users/Maxscha/following{/other_user}", "gists_url": "https://api.github.com/users/Maxscha/gists{/gist_id}", "starred_url": "https://api.github.com/users/Maxscha/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Maxscha/subscriptions", "organizations_url": "https://api.github.com/users/Maxscha/orgs", "repos_url": "https://api.github.com/users/Maxscha/repos", "events_url": "https://api.github.com/users/Maxscha/events{/privacy}", "received_events_url": "https://api.github.com/users/Maxscha/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Hi! The times match when I run this code locally or on Colab.\r\n\r\nAlso, we use `multiprocess`, not `multiprocessing`, for parallelization, and torch's `__init__.py` (executed on `import torch` ) slightly modifies the latter.", "Hey Mariosasko,\r\n\r\nThanks for looking into it. We further did some investigations after your comment and figured out it's only affecting some hardware/software configurations with the `pytorch` installation of `conda-forge`. Based on this we found the following issue in PyTorch: https://github.com/pytorch/pytorch/issues/102269 with a quick fix for now.\r\n\r\nSince it seems to be a deeper issue with forking processes, the difference between`multiprocess` and `multiprocessing` didn't make a difference.\r\n\r\nClosing this, since the issue comes from `pytorch` not `dataset`. \r\n" ]	1,686,080,545,000	1,686,920,952,000	1,686,920,952,000	NONE	null	### Describe the bug I noticed that the performance of my dataset preprocessing with `map(...,num_proc=32)` decreases when PyTorch is imported. ### Steps to reproduce the bug I created two example scripts to reproduce this behavior: ``` import datasets datasets.disable_caching() from datasets import Dataset import time PROC=32 if __name__ == "__main__": dataset = [True] * 10000000 dataset = Dataset.from_dict({'train': dataset}) start = time.time() dataset.map(lambda x: x, num_proc=PROC) end = time.time() print(end - start) ``` Takes around 4 seconds on my machine. While the same code, but with an `import torch`: ``` import datasets datasets.disable_caching() from datasets import Dataset import time import torch PROC=32 if __name__ == "__main__": dataset = [True] * 10000000 dataset = Dataset.from_dict({'train': dataset}) start = time.time() dataset.map(lambda x: x, num_proc=PROC) end = time.time() print(end - start) ``` takes around 22 seconds. ### Expected behavior I would expect that the import of torch to not have such a significant effect on the performance of map using multiprocessing. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.35 - Python version: 3.11.3 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2 - torch: 2.0.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5929/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5929/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5928	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5928/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5928/comments	https://api.github.com/repos/huggingface/datasets/issues/5928/events	https://github.com/huggingface/datasets/pull/5928	1,744,098,371	PR_kwDODunzps5SUXPC	5,928	Fix link to quickstart docs in README.md	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006693 / 0.011353 (-0.004660) \| 0.004331 / 0.011008 (-0.006677) \| 0.098022 / 0.038508 (0.059514) \| 0.032764 / 0.023109 (0.009654) \| 0.295812 / 0.275898 (0.019914) \| 0.325029 / 0.323480 (0.001550) \| 0.005779 / 0.007986 (-0.002206) \| 0.005381 / 0.004328 (0.001052) \| 0.075785 / 0.004250 (0.071535) \| 0.048759 / 0.037052 (0.011707) \| 0.308986 / 0.258489 (0.050497) \| 0.348000 / 0.293841 (0.054159) \| 0.027686 / 0.128546 (-0.100860) \| 0.008839 / 0.075646 (-0.066807) \| 0.328389 / 0.419271 (-0.090883) \| 0.062173 / 0.043533 (0.018640) \| 0.312257 / 0.255139 (0.057119) \| 0.325024 / 0.283200 (0.041824) \| 0.103886 / 0.141683 (-0.037797) \| 1.440215 / 1.452155 (-0.011940) \| 1.528665 / 1.492716 (0.035948) \|\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) \| 0.210082 / 0.018006 (0.192076) \| 0.442480 / 0.000490 (0.441990) \| 0.006559 / 0.000200 (0.006359) \| 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) \| 0.026774 / 0.037411 (-0.010637) \| 0.108362 / 0.014526 (0.093837) \| 0.117631 / 0.176557 (-0.058926) \| 0.176657 / 0.737135 (-0.560478) \| 0.124154 / 0.296338 (-0.172184) \|\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) \| 0.428136 / 0.215209 (0.212927) \| 4.270287 / 2.077655 (2.192632) \| 2.014728 / 1.504120 (0.510608) \| 1.806772 / 1.541195 (0.265577) \| 1.946284 / 1.468490 (0.477794) \| 0.525542 / 4.584777 (-4.059235) \| 3.667025 / 3.745712 (-0.078687) \| 1.878751 / 5.269862 (-3.391111) \| 1.048321 / 4.565676 (-3.517356) \| 0.065550 / 0.424275 (-0.358725) \| 0.011881 / 0.007607 (0.004274) \| 0.529873 / 0.226044 (0.303829) \| 5.289641 / 2.268929 (3.020712) \| 2.489403 / 55.444624 (-52.955221) \| 2.141037 / 6.876477 (-4.735440) \| 2.230735 / 2.142072 (0.088662) \| 0.639781 / 4.805227 (-4.165447) \| 0.141410 / 6.500664 (-6.359254) \| 0.064374 / 0.075469 (-0.011095) \|\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) \| 1.159462 / 1.841788 (-0.682325) \| 14.524730 / 8.074308 (6.450422) \| 13.578070 / 10.191392 (3.386678) \| 0.152138 / 0.680424 (-0.528286) \| 0.017255 / 0.534201 (-0.516946) \| 0.387607 / 0.579283 (-0.191676) \| 0.413652 / 0.434364 (-0.020712) \| 0.453644 / 0.540337 (-0.086693) \| 0.550051 / 1.386936 (-0.836885) \|\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) \| 0.006668 / 0.011353 (-0.004685) \| 0.004677 / 0.011008 (-0.006331) \| 0.075950 / 0.038508 (0.037442) \| 0.032439 / 0.023109 (0.009329) \| 0.381839 / 0.275898 (0.105941) \| 0.419411 / 0.323480 (0.095931) \| 0.005813 / 0.007986 (-0.002172) \| 0.004090 / 0.004328 (-0.000238) \| 0.075052 / 0.004250 (0.070802) \| 0.048453 / 0.037052 (0.011401) \| 0.388076 / 0.258489 (0.129587) \| 0.431793 / 0.293841 (0.137952) \| 0.028408 / 0.128546 (-0.100138) \| 0.009028 / 0.075646 (-0.066618) \| 0.082569 / 0.419271 (-0.336702) \| 0.046772 / 0.043533 (0.003239) \| 0.380182 / 0.255139 (0.125043) \| 0.401828 / 0.283200 (0.118629) \| 0.105388 / 0.141683 (-0.036294) \| 1.453356 / 1.452155 (0.001201) \| 1.561483 / 1.492716 (0.068767) \|\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) \| 0.008922 / 0.018006 (-0.009084) \| 0.444112 / 0.000490 (0.443623) \| 0.002756 / 0.000200 (0.002556) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030408 / 0.037411 (-0.007003) \| 0.112924 / 0.014526 (0.098399) \| 0.124625 / 0.176557 (-0.051932) \| 0.176915 / 0.737135 (-0.560220) \| 0.129141 / 0.296338 (-0.167198) \|\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) \| 0.448197 / 0.215209 (0.232987) \| 4.476548 / 2.077655 (2.398893) \| 2.243977 / 1.504120 (0.739857) \| 2.054060 / 1.541195 (0.512865) \| 2.130680 / 1.468490 (0.662190) \| 0.526815 / 4.584777 (-4.057962) \| 3.759312 / 3.745712 (0.013600) \| 3.333618 / 5.269862 (-1.936244) \| 1.579611 / 4.565676 (-2.986065) \| 0.065714 / 0.424275 (-0.358561) \| 0.011939 / 0.007607 (0.004332) \| 0.550313 / 0.226044 (0.324269) \| 5.476946 / 2.268929 (3.208018) \| 2.726521 / 55.444624 (-52.718104) \| 2.364977 / 6.876477 (-4.511499) \| 2.450624 / 2.142072 (0.308551) \| 0.647174 / 4.805227 (-4.158053) \| 0.141265 / 6.500664 (-6.359399) \| 0.065493 / 0.075469 (-0.009976) \|\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) \| 1.249702 / 1.841788 (-0.592085) \| 15.205647 / 8.074308 (7.131338) \| 14.678310 / 10.191392 (4.486918) \| 0.141539 / 0.680424 (-0.538884) \| 0.017323 / 0.534201 (-0.516878) \| 0.387602 / 0.579283 (-0.191681) \| 0.415106 / 0.434364 (-0.019258) \| 0.458146 / 0.540337 (-0.082192) \| 0.553318 / 1.386936 (-0.833618) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#55127d7bf399fd2f3a8713db9822e8cb47cdbbed \"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) \| 0.008567 / 0.011353 (-0.002786) \| 0.005245 / 0.011008 (-0.005763) \| 0.115074 / 0.038508 (0.076566) \| 0.032567 / 0.023109 (0.009458) \| 0.352297 / 0.275898 (0.076399) \| 0.393403 / 0.323480 (0.069923) \| 0.006402 / 0.007986 (-0.001583) \| 0.004353 / 0.004328 (0.000025) \| 0.087903 / 0.004250 (0.083653) \| 0.048424 / 0.037052 (0.011372) \| 0.370078 / 0.258489 (0.111588) \| 0.410192 / 0.293841 (0.116351) \| 0.042396 / 0.128546 (-0.086150) \| 0.014426 / 0.075646 (-0.061220) \| 0.411358 / 0.419271 (-0.007914) \| 0.059546 / 0.043533 (0.016013) \| 0.364721 / 0.255139 (0.109582) \| 0.385100 / 0.283200 (0.101901) \| 0.100572 / 0.141683 (-0.041111) \| 1.741457 / 1.452155 (0.289302) \| 1.933134 / 1.492716 (0.440418) \|\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) \| 0.217177 / 0.018006 (0.199171) \| 0.510399 / 0.000490 (0.509909) \| 0.005542 / 0.000200 (0.005342) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026852 / 0.037411 (-0.010559) \| 0.125580 / 0.014526 (0.111054) \| 0.132164 / 0.176557 (-0.044392) \| 0.189073 / 0.737135 (-0.548063) \| 0.135980 / 0.296338 (-0.160358) \|\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) \| 0.601924 / 0.215209 (0.386715) \| 5.891397 / 2.077655 (3.813743) \| 2.389494 / 1.504120 (0.885375) \| 2.044013 / 1.541195 (0.502818) \| 2.019367 / 1.468490 (0.550877) \| 0.883807 / 4.584777 (-3.700970) \| 5.141349 / 3.745712 (1.395636) \| 2.607415 / 5.269862 (-2.662446) \| 1.567268 / 4.565676 (-2.998409) \| 0.102738 / 0.424275 (-0.321537) \| 0.013480 / 0.007607 (0.005873) \| 0.744979 / 0.226044 (0.518934) \| 7.404182 / 2.268929 (5.135254) \| 2.983406 / 55.444624 (-52.461219) \| 2.331847 / 6.876477 (-4.544630) \| 2.465119 / 2.142072 (0.323047) \| 1.106725 / 4.805227 (-3.698502) \| 0.205779 / 6.500664 (-6.294885) \| 0.081019 / 0.075469 (0.005550) \|\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) \| 1.527840 / 1.841788 (-0.313947) \| 16.989487 / 8.074308 (8.915179) \| 18.016123 / 10.191392 (7.824731) \| 0.216157 / 0.680424 (-0.464266) \| 0.025393 / 0.534201 (-0.508808) \| 0.496743 / 0.579283 (-0.082540) \| 0.575365 / 0.434364 (0.141002) \| 0.559978 / 0.540337 (0.019641) \| 0.677474 / 1.386936 (-0.709462) \|\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) \| 0.008913 / 0.011353 (-0.002440) \| 0.005540 / 0.011008 (-0.005469) \| 0.100001 / 0.038508 (0.061493) \| 0.034432 / 0.023109 (0.011323) \| 0.419824 / 0.275898 (0.143926) \| 0.443566 / 0.323480 (0.120086) \| 0.006372 / 0.007986 (-0.001614) \| 0.004405 / 0.004328 (0.000077) \| 0.094927 / 0.004250 (0.090677) \| 0.050300 / 0.037052 (0.013248) \| 0.424806 / 0.258489 (0.166317) \| 0.480793 / 0.293841 (0.186952) \| 0.050869 / 0.128546 (-0.077677) \| 0.015899 / 0.075646 (-0.059747) \| 0.111413 / 0.419271 (-0.307859) \| 0.058093 / 0.043533 (0.014560) \| 0.430575 / 0.255139 (0.175436) \| 0.483786 / 0.283200 (0.200586) \| 0.106878 / 0.141683 (-0.034805) \| 1.763576 / 1.452155 (0.311422) \| 1.837750 / 1.492716 (0.345033) \|\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) \| 0.011565 / 0.018006 (-0.006441) \| 0.484411 / 0.000490 (0.483922) \| 0.004869 / 0.000200 (0.004669) \| 0.000111 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030706 / 0.037411 (-0.006706) \| 0.126901 / 0.014526 (0.112375) \| 0.130367 / 0.176557 (-0.046190) \| 0.206568 / 0.737135 (-0.530567) \| 0.146505 / 0.296338 (-0.149834) \|\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) \| 0.627266 / 0.215209 (0.412057) \| 6.314049 / 2.077655 (4.236394) \| 2.582920 / 1.504120 (1.078800) \| 2.249401 / 1.541195 (0.708206) \| 2.244960 / 1.468490 (0.776470) \| 0.907770 / 4.584777 (-3.677007) \| 5.349622 / 3.745712 (1.603910) \| 4.591244 / 5.269862 (-0.678618) \| 2.301612 / 4.565676 (-2.264064) \| 0.108813 / 0.424275 (-0.315462) \| 0.013187 / 0.007607 (0.005580) \| 0.806071 / 0.226044 (0.580027) \| 7.843903 / 2.268929 (5.574974) \| 3.405968 / 55.444624 (-52.038656) \| 2.564301 / 6.876477 (-4.312176) \| 2.652208 / 2.142072 (0.510135) \| 1.168142 / 4.805227 (-3.637086) \| 0.218551 / 6.500664 (-6.282113) \| 0.078120 / 0.075469 (0.002651) \|\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) \| 1.562517 / 1.841788 (-0.279271) \| 17.519325 / 8.074308 (9.445017) \| 20.727083 / 10.191392 (10.535691) \| 0.207135 / 0.680424 (-0.473288) \| 0.028208 / 0.534201 (-0.505993) \| 0.496157 / 0.579283 (-0.083126) \| 0.569239 / 0.434364 (0.134875) \| 0.566137 / 0.540337 (0.025799) \| 0.704208 / 1.386936 (-0.682728) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8eb3f34d876da98e722d866be90d7f26135ea9e3 \"CML watermark\")\n" ]	1,686,064,981,000	1,686,066,754,000	1,686,066,233,000	CONTRIBUTOR	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5928/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5928/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5928", "html_url": "https://github.com/huggingface/datasets/pull/5928", "diff_url": "https://github.com/huggingface/datasets/pull/5928.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5928.patch", "merged_at": "2023-06-06T15:43:53" }	true
https://api.github.com/repos/huggingface/datasets/issues/5927	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5927/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5927/comments	https://api.github.com/repos/huggingface/datasets/issues/5927/events	https://github.com/huggingface/datasets/issues/5927	1,744,009,032	I_kwDODunzps5n83dI	5,927	`IndexError` when indexing `Sequence` of `Array2D` with `None` values	{ "login": "qgallouedec", "id": 45557362, "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "gravatar_id": "", "url": "https://api.github.com/users/qgallouedec", "html_url": "https://github.com/qgallouedec", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "repos_url": "https://api.github.com/users/qgallouedec/repos", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Easy fix would be to add:\r\n\r\n```python\r\nnull_indices -= np.arange(len(null_indices))\r\n```\r\n\r\nbefore L279, but I'm not sure it's the most intuitive way to fix it.", "Same issue here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7fcbe5b1575c8d162b65b9397b3dfda995a4e048/src/datasets/features/features.py#L1398\r\n\r\nFixed in #5948 " ]	1,686,062,182,000	1,686,659,979,000	1,686,317,030,000	CONTRIBUTOR	null	### Describe the bug Having `None` values in a `Sequence` of `ArrayND` fails. ### Steps to reproduce the bug ```python from datasets import Array2D, Dataset, Features, Sequence data = [ [ [[0]], None, None, ] ] feature = Sequence(Array2D((1, 1), dtype="int64")) dataset = Dataset.from_dict({"a": data}, features=Features({"a": feature})) dataset[0] # error raised only when indexing ``` ``` Traceback (most recent call last): File "/Users/quentingallouedec/gia/c.py", line 13, in <module> dataset[0] # error raised only when indexing File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2658, in __getitem__ return self._getitem(key) File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2643, in _getitem formatted_output = format_table( File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 634, in format_table return formatter(pa_table, query_type=query_type) File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 406, in __call__ return self.format_row(pa_table) File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 441, in format_row row = self.python_arrow_extractor().extract_row(pa_table) File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 144, in extract_row return _unnest(pa_table.to_pydict()) File "pyarrow/table.pxi", line 4146, in pyarrow.lib.Table.to_pydict File "pyarrow/table.pxi", line 1312, in pyarrow.lib.ChunkedArray.to_pylist File "pyarrow/array.pxi", line 1521, in pyarrow.lib.Array.to_pylist File "pyarrow/scalar.pxi", line 675, in pyarrow.lib.ListScalar.as_py File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/features/features.py", line 760, in to_pylist return self.to_numpy(zero_copy_only=zero_copy_only).tolist() File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/features/features.py", line 725, in to_numpy numpy_arr = np.insert(numpy_arr.astype(np.float64), null_indices, np.nan, axis=0) File "<__array_function__ internals>", line 200, in insert File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/numpy/lib/function_base.py", line 5426, in insert old_mask[indices] = False IndexError: index 3 is out of bounds for axis 0 with size 3 ``` AFAIK, the problem only occurs when you use a `Sequence` of `ArrayND`. I strongly suspect that the problem comes from this line, or `np.insert` is misused: https://github.com/huggingface/datasets/blob/02ee418831aba68d0be93227bce8b3f42ef8980f/src/datasets/features/features.py#L729 To put t simply, you want something that do that: ```python import numpy as np numpy_arr = np.zeros((1, 1, 1)) null_indices = np.array([1, 2]) np.insert(numpy_arr, null_indices, np.nan, axis=0) # raise an error, instead of outputting # array([[[ 0.]], # [[nan]], # [[nan]]]) ``` ### Expected behavior The previous code should not raise an error. ### Environment info - Python 3.10.11 - datasets 2.10.0 - pyarrow 12.0.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5927/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5927/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5926	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5926/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5926/comments	https://api.github.com/repos/huggingface/datasets/issues/5926/events	https://github.com/huggingface/datasets/issues/5926	1,743,922,028	I_kwDODunzps5n8iNs	5,926	Uncaught exception when generating the splits from a dataset that miss data	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[]	open	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	[ "Thanks for reporting, @severo.\r\n\r\nThis is a known issue with `fsspec`:\r\n- #5862\r\n- https://github.com/fsspec/filesystem_spec/issues/1265" ]	1,686,059,461,000	1,686,124,396,000	null	CONTRIBUTOR	null	### Describe the bug Dataset https://huggingface.co/datasets/blog_authorship_corpus has an issue with its hosting platform, since https://drive.google.com/u/0/uc?id=1cGy4RNDV87ZHEXbiozABr9gsSrZpPaPz&export=download returns 404 error. But when trying to generate the split names, we get an exception which is now correctly caught. Seen originally in https://github.com/huggingface/datasets-server/blob/adbdcd6710ffed4e2eb2e4cd905b5e0dff530a15/services/worker/src/worker/job_runners/config/parquet_and_info.py#L435 ### Steps to reproduce the bug ```python >>> from datasets import StreamingDownloadManager, load_dataset_builder >>> builder = load_dataset_builder(path="blog_authorship_corpus") Downloading builder script: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 5.60k/5.60k [00:00<00:00, 23.1MB/s] Downloading metadata: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.81k/2.81k [00:00<00:00, 14.7MB/s] Downloading readme: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 7.30k/7.30k [00:00<00:00, 30.8MB/s] >>> dl_manager = StreamingDownloadManager(base_path=builder.base_path) >>> builder._split_generators(dl_manager) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/blog_authorship_corpus/6f5d78241afd8313111956f877a57db7a0e9fc6718255dc85df0928197feb683/blog_authorship_corpus.py", line 79, in _split_generators data = dl_manager.download_and_extract(_DATA_URL) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1087, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1039, in extract urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 435, in map_nested return function(data_struct) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1044, in _extract protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 433, in _get_extraction_protocol with fsspec.open(urlpath, **kwargs) as f: File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 439, in open return open_files( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 194, in __getitem__ out = super().__getitem__(item) IndexError: list index out of range ``` ### Expected behavior We should have an Exception raised by the datasets library. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.19.0-1026-aws-x86_64-with-glibc2.35 - Python version: 3.9.15 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5926/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5926/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5925	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5925/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5925/comments	https://api.github.com/repos/huggingface/datasets/issues/5925/events	https://github.com/huggingface/datasets/issues/5925	1,741,941,436	I_kwDODunzps5n0-q8	5,925	Breaking API change in datasets.list_datasets caused by change in HfApi.list_datasets	{ "login": "mtkinit", "id": 78868366, "node_id": "MDQ6VXNlcjc4ODY4MzY2", "avatar_url": "https://avatars.githubusercontent.com/u/78868366?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mtkinit", "html_url": "https://github.com/mtkinit", "followers_url": "https://api.github.com/users/mtkinit/followers", "following_url": "https://api.github.com/users/mtkinit/following{/other_user}", "gists_url": "https://api.github.com/users/mtkinit/gists{/gist_id}", "starred_url": "https://api.github.com/users/mtkinit/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mtkinit/subscriptions", "organizations_url": "https://api.github.com/users/mtkinit/orgs", "repos_url": "https://api.github.com/users/mtkinit/repos", "events_url": "https://api.github.com/users/mtkinit/events{/privacy}", "received_events_url": "https://api.github.com/users/mtkinit/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[]	1,685,976,364,000	1,687,195,363,000	1,687,195,363,000	NONE	null	### Describe the bug Hi all, after an update of the `datasets` library, we observer crashes in our code. We relied on `datasets.list_datasets` returning a `list`. Now, after the API of the HfApi.list_datasets was changed and it returns a `list` instead of an `Iterable`, the `datasets.list_datasets` now sometimes returns a `list` and somesimes an `Iterable`. It would be helpful to indicate that by the return type of the `datasets.list_datasets` function. Thanks, Martin ### Steps to reproduce the bug Here, the code crashed after we updated the `datasets` library: ```python # list_datasets no longer returns a list, which leads to an error when one tries to slice it for datasets.list_datasets(with_details=True)[:limit]: ... ``` ### Expected behavior It would be helpful to indicate that by the return type of the `datasets.list_datasets` function. ### Environment info Ubuntu 22.04 datasets 2.12.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5925/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5925/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5924	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5924/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5924/comments	https://api.github.com/repos/huggingface/datasets/issues/5924/events	https://github.com/huggingface/datasets/pull/5924	1,738,889,236	PR_kwDODunzps5SCiFv	5,924	Add parallel module using joblib for Spark	{ "login": "es94129", "id": 12763339, "node_id": "MDQ6VXNlcjEyNzYzMzM5", "avatar_url": "https://avatars.githubusercontent.com/u/12763339?v=4", "gravatar_id": "", "url": "https://api.github.com/users/es94129", "html_url": "https://github.com/es94129", "followers_url": "https://api.github.com/users/es94129/followers", "following_url": "https://api.github.com/users/es94129/following{/other_user}", "gists_url": "https://api.github.com/users/es94129/gists{/gist_id}", "starred_url": "https://api.github.com/users/es94129/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/es94129/subscriptions", "organizations_url": "https://api.github.com/users/es94129/orgs", "repos_url": "https://api.github.com/users/es94129/repos", "events_url": "https://api.github.com/users/es94129/events{/privacy}", "received_events_url": "https://api.github.com/users/es94129/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Hi @lhoestq, I added the `parallel` part according to the discussion we had. Could you take a look to see if this is aligned with your proposal?\r\n\r\nMeanwhile I'm working on adding a `parallel_backend` parameter to `load_datasets` so that it can be used like:\r\n```python\r\nwith parallel_backend('spark', steps=['downloading']) as backend:\r\n ds = load_dataset(..., parallel_backend=backend)\r\n```\r\nwhere `parallel_backend` is a `ParallelBackend` class.", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq Thanks for the comments!\r\nWith your suggestion, no changes made to `load_dataset` and I validated that downloading with spark is working now with this:\r\n```py\r\nwith parallel_backend('spark', steps=[\"download\"]):\r\n dataset = load_dataset(..., num_proc=2)\r\n```", "@lhoestq Can a maintainer help trigger the tests again?\r\n> One idea is to decorate the download method to set the current global step to \"download\", and then only use joblib if the current step is one of the steps provided in parallel_backend.\r\n\r\nYes I think this is doable in a subsequent PR.\r\nFor throwing `NotImplementedError` I also think it can be done in a subsequent PR, because I'm not sure if `Dataset.map` is the only function that a user would expect to run using `with parallel_backend`.", "Just triggered the tests :)\r\n\r\n> Yes I think this is doable in a subsequent PR.\r\nFor throwing NotImplementedError I also think it can be done in a subsequent PR, because I'm not sure if Dataset.map is the only function that a user would expect to run using with parallel_backend.\r\n\r\nI think any Dataset method that has a `num_proc` argument: Dataset.map (the other methods like filter or cast or based on map), and later we can see for the to_xxx methods (to_csv, to_parquet, etc.)", "Hi maintainers, I've just addressed most of the comments, please take another look, thank you.", "<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) \| 0.008422 / 0.011353 (-0.002931) \| 0.005658 / 0.011008 (-0.005350) \| 0.135372 / 0.038508 (0.096864) \| 0.044766 / 0.023109 (0.021657) \| 0.417876 / 0.275898 (0.141978) \| 0.462785 / 0.323480 (0.139305) \| 0.005485 / 0.007986 (-0.002501) \| 0.005640 / 0.004328 (0.001311) \| 0.105020 / 0.004250 (0.100770) \| 0.049114 / 0.037052 (0.012062) \| 0.490450 / 0.258489 (0.231961) \| 0.467693 / 0.293841 (0.173852) \| 0.050929 / 0.128546 (-0.077617) \| 0.014644 / 0.075646 (-0.061002) \| 0.452373 / 0.419271 (0.033101) \| 0.074897 / 0.043533 (0.031364) \| 0.425816 / 0.255139 (0.170677) \| 0.420415 / 0.283200 (0.137215) \| 0.134121 / 0.141683 (-0.007561) \| 1.927744 / 1.452155 (0.475589) \| 2.014417 / 1.492716 (0.521701) \|\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) \| 0.254811 / 0.018006 (0.236805) \| 0.550011 / 0.000490 (0.549521) \| 0.004913 / 0.000200 (0.004714) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032644 / 0.037411 (-0.004768) \| 0.135672 / 0.014526 (0.121146) \| 0.158984 / 0.176557 (-0.017572) \| 0.218267 / 0.737135 (-0.518869) \| 0.150348 / 0.296338 (-0.145991) \|\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) \| 0.625723 / 0.215209 (0.410514) \| 6.247559 / 2.077655 (4.169905) \| 2.626785 / 1.504120 (1.122666) \| 2.195224 / 1.541195 (0.654030) \| 2.232140 / 1.468490 (0.763650) \| 0.943082 / 4.584777 (-3.641695) \| 5.799262 / 3.745712 (2.053550) \| 2.849411 / 5.269862 (-2.420450) \| 1.744160 / 4.565676 (-2.821516) \| 0.119056 / 0.424275 (-0.305219) \| 0.014233 / 0.007607 (0.006626) \| 0.795238 / 0.226044 (0.569194) \| 7.569586 / 2.268929 (5.300657) \| 3.179481 / 55.444624 (-52.265143) \| 2.519772 / 6.876477 (-4.356704) \| 2.714570 / 2.142072 (0.572498) \| 1.107197 / 4.805227 (-3.698030) \| 0.229986 / 6.500664 (-6.270678) \| 0.087993 / 0.075469 (0.012524) \|\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) \| 1.535610 / 1.841788 (-0.306178) \| 18.639369 / 8.074308 (10.565061) \| 21.081844 / 10.191392 (10.890452) \| 0.253247 / 0.680424 (-0.427177) \| 0.026711 / 0.534201 (-0.507490) \| 0.503790 / 0.579283 (-0.075493) \| 0.600124 / 0.434364 (0.165760) \| 0.617944 / 0.540337 (0.077607) \| 0.766947 / 1.386936 (-0.619989) \|\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) \| 0.007885 / 0.011353 (-0.003468) \| 0.004761 / 0.011008 (-0.006248) \| 0.097995 / 0.038508 (0.059487) \| 0.033624 / 0.023109 (0.010515) \| 0.504307 / 0.275898 (0.228409) \| 0.534803 / 0.323480 (0.211323) \| 0.006048 / 0.007986 (-0.001937) \| 0.005042 / 0.004328 (0.000714) \| 0.102288 / 0.004250 (0.098038) \| 0.048695 / 0.037052 (0.011643) \| 0.559086 / 0.258489 (0.300597) \| 0.553233 / 0.293841 (0.259392) \| 0.044596 / 0.128546 (-0.083950) \| 0.013696 / 0.075646 (-0.061950) \| 0.109875 / 0.419271 (-0.309397) \| 0.059993 / 0.043533 (0.016460) \| 0.485579 / 0.255139 (0.230440) \| 0.519835 / 0.283200 (0.236635) \| 0.123504 / 0.141683 (-0.018179) \| 1.820506 / 1.452155 (0.368351) \| 1.963448 / 1.492716 (0.470732) \|\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) \| 0.292663 / 0.018006 (0.274656) \| 0.557783 / 0.000490 (0.557293) \| 0.001330 / 0.000200 (0.001130) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036890 / 0.037411 (-0.000522) \| 0.140373 / 0.014526 (0.125847) \| 0.140176 / 0.176557 (-0.036381) \| 0.237378 / 0.737135 (-0.499757) \| 0.160186 / 0.296338 (-0.136152) \|\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) \| 0.673599 / 0.215209 (0.458390) \| 6.510280 / 2.077655 (4.432625) \| 2.981617 / 1.504120 (1.477497) \| 2.684664 / 1.541195 (1.143469) \| 2.760471 / 1.468490 (1.291981) \| 0.975413 / 4.584777 (-3.609364) \| 5.708933 / 3.745712 (1.963220) \| 2.772069 / 5.269862 (-2.497793) \| 1.763627 / 4.565676 (-2.802049) \| 0.111632 / 0.424275 (-0.312643) \| 0.013223 / 0.007607 (0.005616) \| 0.791545 / 0.226044 (0.565500) \| 8.063287 / 2.268929 (5.794359) \| 3.671920 / 55.444624 (-51.772704) \| 3.057248 / 6.876477 (-3.819229) \| 3.083569 / 2.142072 (0.941497) \| 1.118136 / 4.805227 (-3.687092) \| 0.214655 / 6.500664 (-6.286009) \| 0.083074 / 0.075469 (0.007605) \|\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) \| 1.761731 / 1.841788 (-0.080056) \| 18.874200 / 8.074308 (10.799892) \| 22.383693 / 10.191392 (12.192301) \| 0.240292 / 0.680424 (-0.440132) \| 0.028850 / 0.534201 (-0.505351) \| 0.557334 / 0.579283 (-0.021949) \| 0.627732 / 0.434364 (0.193369) \| 0.634484 / 0.540337 (0.094146) \| 0.767372 / 1.386936 (-0.619564) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#accaaf2e69fbb5dc5e50229d2eb1591b8ad982b6 \"CML watermark\")\n" ]	1,685,744,725,000	1,686,738,310,000	1,686,737,746,000	CONTRIBUTOR	null	Discussion in https://github.com/huggingface/datasets/issues/5798	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5924/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5924/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5924", "html_url": "https://github.com/huggingface/datasets/pull/5924", "diff_url": "https://github.com/huggingface/datasets/pull/5924.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5924.patch", "merged_at": "2023-06-14T10:15:46" }	true
https://api.github.com/repos/huggingface/datasets/issues/5923	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5923/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5923/comments	https://api.github.com/repos/huggingface/datasets/issues/5923/events	https://github.com/huggingface/datasets/issues/5923	1,737,436,227	I_kwDODunzps5njyxD	5,923	Cannot import datasets - ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility	{ "login": "ehuangc", "id": 71412682, "node_id": "MDQ6VXNlcjcxNDEyNjgy", "avatar_url": "https://avatars.githubusercontent.com/u/71412682?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ehuangc", "html_url": "https://github.com/ehuangc", "followers_url": "https://api.github.com/users/ehuangc/followers", "following_url": "https://api.github.com/users/ehuangc/following{/other_user}", "gists_url": "https://api.github.com/users/ehuangc/gists{/gist_id}", "starred_url": "https://api.github.com/users/ehuangc/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ehuangc/subscriptions", "organizations_url": "https://api.github.com/users/ehuangc/orgs", "repos_url": "https://api.github.com/users/ehuangc/repos", "events_url": "https://api.github.com/users/ehuangc/events{/privacy}", "received_events_url": "https://api.github.com/users/ehuangc/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Based on https://github.com/rapidsai/cudf/issues/10187, this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n\r\nCan you please execute the following commands in the terminal and paste the output here?\r\n```\r\nconda list \| grep arrow\r\n``` \r\n```\r\npython -c \"import pyarrow; print(pyarrow.__file__)\"\r\n```\r\n\r\n\r\n", "> Based on [rapidsai/cudf#10187](https://github.com/rapidsai/cudf/issues/10187), this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n> \r\n> Can you please execute the following commands in the terminal and paste the output here?\r\n> \r\n> ```\r\n> conda list \| grep arrow\r\n> ```\r\n> \r\n> ```\r\n> python -c \"import pyarrow; print(pyarrow.__file__)\"\r\n> ```\r\n\r\n\r\nHere is the output to the first command:\r\n```\r\narrow-cpp 11.0.0 py39h7f74497_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n```\r\nand the second:\r\n```\r\n/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/__init__.py\r\n```\r\nThanks!\r\n\r\n\r\n\r\n", "after installing pytesseract 0.3.10, I got the above error. FYI ", "RuntimeError: Failed to import transformers.trainer because of the following error (look up to see its traceback):\r\npyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject", "I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n\r\nDo we need to update dependencies? ", "Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291", "For conda with python3.8.16 this solved my problem! thanks!\r\n\r\n> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies? I can work on that if no one else is working on it.\r\n\r\n", "Thanks for replying. I am not sure about those environments but it seems like pyarrow-12.0.0 does not work for conda with python 3.8.16. \r\n\r\n> Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291\r\n\r\n", "Got the same error with:\r\n\r\n```\r\narrow-cpp 11.0.0 py310h7516544_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n\r\npython 3.10.11 h7a1cb2a_2 \r\n\r\ndatasets 2.13.0 pyhd8ed1ab_0 conda-forge\r\n```", "> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies?\r\n\r\nThis solved the issue for me as well.", "> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies?\r\n\r\nSolved it for me also" ]	1,685,679,392,000	1,687,799,689,000	null	NONE	null	### Describe the bug When trying to import datasets, I get a pyarrow ValueError: Traceback (most recent call last): File "/Users/edward/test/test.py", line 1, in <module> import datasets File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 65, in <module> from .arrow_reader import ArrowReader File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/arrow_reader.py", line 28, in <module> import pyarrow.parquet as pq File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/parquet/__init__.py", line 20, in <module> from .core import * File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 45, in <module> from pyarrow.fs import (LocalFileSystem, FileSystem, FileType, File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/fs.py", line 49, in <module> from pyarrow._gcsfs import GcsFileSystem # noqa File "pyarrow/_gcsfs.pyx", line 1, in init pyarrow._gcsfs ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Steps to reproduce the bug `import datasets` ### Expected behavior Successful import ### Environment info Conda environment, MacOS python 3.9.12 datasets 2.12.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5923/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5923/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5922	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5922/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5922/comments	https://api.github.com/repos/huggingface/datasets/issues/5922/events	https://github.com/huggingface/datasets/issues/5922	1,736,898,953	I_kwDODunzps5nhvmJ	5,922	Length of table does not accurately reflect the split	{ "login": "amogkam", "id": 8068268, "node_id": "MDQ6VXNlcjgwNjgyNjg=", "avatar_url": "https://avatars.githubusercontent.com/u/8068268?v=4", "gravatar_id": "", "url": "https://api.github.com/users/amogkam", "html_url": "https://github.com/amogkam", "followers_url": "https://api.github.com/users/amogkam/followers", "following_url": "https://api.github.com/users/amogkam/following{/other_user}", "gists_url": "https://api.github.com/users/amogkam/gists{/gist_id}", "starred_url": "https://api.github.com/users/amogkam/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/amogkam/subscriptions", "organizations_url": "https://api.github.com/users/amogkam/orgs", "repos_url": "https://api.github.com/users/amogkam/repos", "events_url": "https://api.github.com/users/amogkam/events{/privacy}", "received_events_url": "https://api.github.com/users/amogkam/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892913, "node_id": "MDU6TGFiZWwxOTM1ODkyOTEz", "url": "https://api.github.com/repos/huggingface/datasets/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": "This will not be worked on" } ]	closed	false	null	[]	[ "As already replied by @lhoestq (private channel):\r\n> `.train_test_split` (as well as `.shard`, `.select`) doesn't create a new arrow table to save time and disk space. Instead, it uses an indices mapping on top of the table that locate which examples are part of train or test.", "This is an optimization that we don't plan to \"fix\", so I'm closing this issue." ]	1,685,645,786,000	1,685,722,411,000	1,685,722,411,000	NONE	null	### Describe the bug I load a Huggingface Dataset and do `train_test_split`. I'm expecting the underlying table for the dataset to also be split, but it's not. ### Steps to reproduce the bug ![image](https://github.com/huggingface/datasets/assets/8068268/83e5768f-8b4c-422a-945c-832a7585afff) ### Expected behavior The expected behavior is when `len(hf_dataset["train"].data)` should match the length of the train split, and not be the entire unsplit dataset. ### Environment info datasets 2.10.1 python 3.10.11	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5922/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5922/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5921	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5921/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5921/comments	https://api.github.com/repos/huggingface/datasets/issues/5921/events	https://github.com/huggingface/datasets/pull/5921	1,736,563,023	PR_kwDODunzps5R6j-y	5,921	Fix streaming parquet with image feature in schema	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.007088 / 0.011353 (-0.004265) \| 0.005216 / 0.011008 (-0.005793) \| 0.097572 / 0.038508 (0.059064) \| 0.036510 / 0.023109 (0.013401) \| 0.316885 / 0.275898 (0.040987) \| 0.348541 / 0.323480 (0.025061) \| 0.006513 / 0.007986 (-0.001473) \| 0.004579 / 0.004328 (0.000251) \| 0.073779 / 0.004250 (0.069529) \| 0.057500 / 0.037052 (0.020448) \| 0.329840 / 0.258489 (0.071351) \| 0.357530 / 0.293841 (0.063690) \| 0.028515 / 0.128546 (-0.100031) \| 0.009156 / 0.075646 (-0.066491) \| 0.328340 / 0.419271 (-0.090932) \| 0.068400 / 0.043533 (0.024867) \| 0.313692 / 0.255139 (0.058553) \| 0.329170 / 0.283200 (0.045971) \| 0.111969 / 0.141683 (-0.029714) \| 1.422096 / 1.452155 (-0.030059) \| 1.550042 / 1.492716 (0.057326) \|\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) \| 0.285113 / 0.018006 (0.267107) \| 0.546788 / 0.000490 (0.546298) \| 0.006992 / 0.000200 (0.006792) \| 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) \| 0.026841 / 0.037411 (-0.010570) \| 0.108413 / 0.014526 (0.093887) \| 0.118375 / 0.176557 (-0.058181) \| 0.174889 / 0.737135 (-0.562246) \| 0.122781 / 0.296338 (-0.173558) \|\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) \| 0.404187 / 0.215209 (0.188978) \| 4.039673 / 2.077655 (1.962019) \| 1.894616 / 1.504120 (0.390496) \| 1.729182 / 1.541195 (0.187987) \| 1.772917 / 1.468490 (0.304427) \| 0.524046 / 4.584777 (-4.060731) \| 3.628111 / 3.745712 (-0.117601) \| 1.866075 / 5.269862 (-3.403787) \| 1.026435 / 4.565676 (-3.539242) \| 0.065328 / 0.424275 (-0.358947) \| 0.012717 / 0.007607 (0.005110) \| 0.505821 / 0.226044 (0.279777) \| 5.049518 / 2.268929 (2.780589) \| 2.338486 / 55.444624 (-53.106139) \| 2.002874 / 6.876477 (-4.873602) \| 2.193049 / 2.142072 (0.050976) \| 0.664638 / 4.805227 (-4.140589) \| 0.151323 / 6.500664 (-6.349341) \| 0.063774 / 0.075469 (-0.011695) \|\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) \| 1.168168 / 1.841788 (-0.673620) \| 15.289200 / 8.074308 (7.214891) \| 13.614249 / 10.191392 (3.422857) \| 0.167950 / 0.680424 (-0.512474) \| 0.017522 / 0.534201 (-0.516679) \| 0.393480 / 0.579283 (-0.185803) \| 0.420549 / 0.434364 (-0.013815) \| 0.461425 / 0.540337 (-0.078912) \| 0.563583 / 1.386936 (-0.823353) \|\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) \| 0.006859 / 0.011353 (-0.004493) \| 0.004864 / 0.011008 (-0.006144) \| 0.075084 / 0.038508 (0.036576) \| 0.033989 / 0.023109 (0.010880) \| 0.372512 / 0.275898 (0.096614) \| 0.394725 / 0.323480 (0.071246) \| 0.006382 / 0.007986 (-0.001604) \| 0.004521 / 0.004328 (0.000193) \| 0.076422 / 0.004250 (0.072172) \| 0.055383 / 0.037052 (0.018331) \| 0.400974 / 0.258489 (0.142485) \| 0.411570 / 0.293841 (0.117729) \| 0.028264 / 0.128546 (-0.100282) \| 0.009123 / 0.075646 (-0.066523) \| 0.081257 / 0.419271 (-0.338015) \| 0.048147 / 0.043533 (0.004614) \| 0.390735 / 0.255139 (0.135596) \| 0.376426 / 0.283200 (0.093226) \| 0.108164 / 0.141683 (-0.033518) \| 1.429667 / 1.452155 (-0.022488) \| 1.556291 / 1.492716 (0.063575) \|\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) \| 0.289514 / 0.018006 (0.271508) \| 0.532860 / 0.000490 (0.532370) \| 0.003810 / 0.000200 (0.003611) \| 0.000121 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031292 / 0.037411 (-0.006119) \| 0.116530 / 0.014526 (0.102005) \| 0.127624 / 0.176557 (-0.048932) \| 0.178276 / 0.737135 (-0.558859) \| 0.133742 / 0.296338 (-0.162597) \|\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) \| 0.431505 / 0.215209 (0.216296) \| 4.309206 / 2.077655 (2.231551) \| 2.174779 / 1.504120 (0.670659) \| 1.998122 / 1.541195 (0.456927) \| 2.126478 / 1.468490 (0.657988) \| 0.528971 / 4.584777 (-4.055806) \| 3.797608 / 3.745712 (0.051895) \| 1.876275 / 5.269862 (-3.393586) \| 1.087458 / 4.565676 (-3.478218) \| 0.066940 / 0.424275 (-0.357335) \| 0.012432 / 0.007607 (0.004825) \| 0.538346 / 0.226044 (0.312301) \| 5.370968 / 2.268929 (3.102039) \| 2.613718 / 55.444624 (-52.830906) \| 2.246585 / 6.876477 (-4.629892) \| 2.375695 / 2.142072 (0.233622) \| 0.652227 / 4.805227 (-4.153001) \| 0.143246 / 6.500664 (-6.357418) \| 0.066163 / 0.075469 (-0.009306) \|\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) \| 1.291263 / 1.841788 (-0.550524) \| 16.532281 / 8.074308 (8.457973) \| 15.038471 / 10.191392 (4.847079) \| 0.168139 / 0.680424 (-0.512285) \| 0.017724 / 0.534201 (-0.516477) \| 0.391636 / 0.579283 (-0.187648) \| 0.429690 / 0.434364 (-0.004674) \| 0.474941 / 0.540337 (-0.065396) \| 0.579461 / 1.386936 (-0.807475) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db690affa0373b08f7cef04e25fe2113ee831ef5 \"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) \| 0.006083 / 0.011353 (-0.005269) \| 0.004085 / 0.011008 (-0.006923) \| 0.098337 / 0.038508 (0.059829) \| 0.027573 / 0.023109 (0.004464) \| 0.305688 / 0.275898 (0.029790) \| 0.341767 / 0.323480 (0.018287) \| 0.005143 / 0.007986 (-0.002842) \| 0.003396 / 0.004328 (-0.000932) \| 0.076925 / 0.004250 (0.072674) \| 0.041027 / 0.037052 (0.003975) \| 0.307877 / 0.258489 (0.049388) \| 0.346559 / 0.293841 (0.052718) \| 0.025183 / 0.128546 (-0.103363) \| 0.008575 / 0.075646 (-0.067071) \| 0.319449 / 0.419271 (-0.099823) \| 0.043378 / 0.043533 (-0.000154) \| 0.304563 / 0.255139 (0.049424) \| 0.332019 / 0.283200 (0.048819) \| 0.087725 / 0.141683 (-0.053958) \| 1.484904 / 1.452155 (0.032749) \| 1.582780 / 1.492716 (0.090064) \|\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) \| 0.197503 / 0.018006 (0.179497) \| 0.410370 / 0.000490 (0.409880) \| 0.003840 / 0.000200 (0.003640) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024179 / 0.037411 (-0.013232) \| 0.098876 / 0.014526 (0.084350) \| 0.106189 / 0.176557 (-0.070367) \| 0.168964 / 0.737135 (-0.568171) \| 0.109723 / 0.296338 (-0.186616) \|\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) \| 0.429453 / 0.215209 (0.214244) \| 4.295584 / 2.077655 (2.217929) \| 2.014330 / 1.504120 (0.510210) \| 1.841119 / 1.541195 (0.299924) \| 1.928378 / 1.468490 (0.459888) \| 0.554571 / 4.584777 (-4.030206) \| 3.431769 / 3.745712 (-0.313943) \| 1.716204 / 5.269862 (-3.553658) \| 0.995054 / 4.565676 (-3.570622) \| 0.067374 / 0.424275 (-0.356902) \| 0.012557 / 0.007607 (0.004950) \| 0.533785 / 0.226044 (0.307740) \| 5.363360 / 2.268929 (3.094431) \| 2.535190 / 55.444624 (-52.909434) \| 2.191646 / 6.876477 (-4.684831) \| 2.400799 / 2.142072 (0.258727) \| 0.663961 / 4.805227 (-4.141266) \| 0.135992 / 6.500664 (-6.364672) \| 0.067378 / 0.075469 (-0.008092) \|\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) \| 1.235110 / 1.841788 (-0.606678) \| 13.820695 / 8.074308 (5.746387) \| 13.667202 / 10.191392 (3.475810) \| 0.143025 / 0.680424 (-0.537399) \| 0.016757 / 0.534201 (-0.517444) \| 0.356262 / 0.579283 (-0.223021) \| 0.401871 / 0.434364 (-0.032493) \| 0.423928 / 0.540337 (-0.116410) \| 0.514598 / 1.386936 (-0.872338) \|\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) \| 0.006260 / 0.011353 (-0.005093) \| 0.004159 / 0.011008 (-0.006850) \| 0.076780 / 0.038508 (0.038272) \| 0.027899 / 0.023109 (0.004789) \| 0.412756 / 0.275898 (0.136858) \| 0.455145 / 0.323480 (0.131665) \| 0.005029 / 0.007986 (-0.002956) \| 0.003482 / 0.004328 (-0.000847) \| 0.076148 / 0.004250 (0.071898) \| 0.038969 / 0.037052 (0.001917) \| 0.429975 / 0.258489 (0.171486) \| 0.465880 / 0.293841 (0.172039) \| 0.025555 / 0.128546 (-0.102991) \| 0.008612 / 0.075646 (-0.067034) \| 0.082604 / 0.419271 (-0.336667) \| 0.039690 / 0.043533 (-0.003842) \| 0.403644 / 0.255139 (0.148505) \| 0.440438 / 0.283200 (0.157238) \| 0.090984 / 0.141683 (-0.050699) \| 1.465915 / 1.452155 (0.013760) \| 1.564227 / 1.492716 (0.071511) \|\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) \| 0.010502 / 0.018006 (-0.007504) \| 0.410573 / 0.000490 (0.410083) \| 0.000384 / 0.000200 (0.000184) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025726 / 0.037411 (-0.011686) \| 0.101760 / 0.014526 (0.087235) \| 0.110102 / 0.176557 (-0.066454) \| 0.161321 / 0.737135 (-0.575815) \| 0.112507 / 0.296338 (-0.183832) \|\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) \| 0.469925 / 0.215209 (0.254716) \| 4.718740 / 2.077655 (2.641085) \| 2.466272 / 1.504120 (0.962152) \| 2.267357 / 1.541195 (0.726162) \| 2.331343 / 1.468490 (0.862853) \| 0.553448 / 4.584777 (-4.031329) \| 3.464228 / 3.745712 (-0.281484) \| 3.060957 / 5.269862 (-2.208905) \| 1.387261 / 4.565676 (-3.178415) \| 0.067989 / 0.424275 (-0.356286) \| 0.012349 / 0.007607 (0.004741) \| 0.575046 / 0.226044 (0.349001) \| 5.740322 / 2.268929 (3.471394) \| 2.925666 / 55.444624 (-52.518958) \| 2.606535 / 6.876477 (-4.269942) \| 2.658144 / 2.142072 (0.516072) \| 0.655157 / 4.805227 (-4.150071) \| 0.138520 / 6.500664 (-6.362144) \| 0.069442 / 0.075469 (-0.006027) \|\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) \| 1.306523 / 1.841788 (-0.535265) \| 14.400380 / 8.074308 (6.326072) \| 14.231519 / 10.191392 (4.040127) \| 0.146194 / 0.680424 (-0.534230) \| 0.016632 / 0.534201 (-0.517569) \| 0.361151 / 0.579283 (-0.218132) \| 0.388838 / 0.434364 (-0.045526) \| 0.419337 / 0.540337 (-0.121001) \| 0.500483 / 1.386936 (-0.886453) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0429e9806bf7065d03dc5858c039a30c5af716c \"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) \| 0.009430 / 0.011353 (-0.001923) \| 0.006673 / 0.011008 (-0.004335) \| 0.125151 / 0.038508 (0.086643) \| 0.038258 / 0.023109 (0.015149) \| 0.426383 / 0.275898 (0.150485) \| 0.432327 / 0.323480 (0.108847) \| 0.006964 / 0.007986 (-0.001022) \| 0.005140 / 0.004328 (0.000811) \| 0.100767 / 0.004250 (0.096517) \| 0.058663 / 0.037052 (0.021610) \| 0.424709 / 0.258489 (0.166220) \| 0.453049 / 0.293841 (0.159208) \| 0.051042 / 0.128546 (-0.077505) \| 0.015291 / 0.075646 (-0.060355) \| 0.456549 / 0.419271 (0.037278) \| 0.067106 / 0.043533 (0.023573) \| 0.408959 / 0.255139 (0.153820) \| 0.445067 / 0.283200 (0.161867) \| 0.115590 / 0.141683 (-0.026092) \| 1.929439 / 1.452155 (0.477284) \| 2.045709 / 1.492716 (0.552992) \|\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) \| 0.250726 / 0.018006 (0.232720) \| 0.598976 / 0.000490 (0.598486) \| 0.007542 / 0.000200 (0.007342) \| 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) \| 0.030317 / 0.037411 (-0.007094) \| 0.133177 / 0.014526 (0.118651) \| 0.152761 / 0.176557 (-0.023795) \| 0.233708 / 0.737135 (-0.503428) \| 0.147303 / 0.296338 (-0.149036) \|\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) \| 0.633562 / 0.215209 (0.418353) \| 6.235021 / 2.077655 (4.157366) \| 2.652573 / 1.504120 (1.148454) \| 2.223363 / 1.541195 (0.682168) \| 2.231022 / 1.468490 (0.762531) \| 0.942218 / 4.584777 (-3.642559) \| 6.068661 / 3.745712 (2.322949) \| 2.778604 / 5.269862 (-2.491257) \| 1.787939 / 4.565676 (-2.777737) \| 0.117749 / 0.424275 (-0.306526) \| 0.015613 / 0.007607 (0.008006) \| 0.810222 / 0.226044 (0.584177) \| 7.931509 / 2.268929 (5.662581) \| 3.260679 / 55.444624 (-52.183945) \| 2.609085 / 6.876477 (-4.267391) \| 2.867838 / 2.142072 (0.725766) \| 1.144672 / 4.805227 (-3.660555) \| 0.224379 / 6.500664 (-6.276285) \| 0.084490 / 0.075469 (0.009021) \|\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) \| 1.650608 / 1.841788 (-0.191179) \| 18.919748 / 8.074308 (10.845440) \| 20.163162 / 10.191392 (9.971770) \| 0.229427 / 0.680424 (-0.450997) \| 0.033090 / 0.534201 (-0.501111) \| 0.535549 / 0.579283 (-0.043734) \| 0.658629 / 0.434364 (0.224265) \| 0.631526 / 0.540337 (0.091189) \| 0.748701 / 1.386936 (-0.638235) \|\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) \| 0.009157 / 0.011353 (-0.002196) \| 0.006153 / 0.011008 (-0.004856) \| 0.106294 / 0.038508 (0.067786) \| 0.040947 / 0.023109 (0.017837) \| 0.493242 / 0.275898 (0.217344) \| 0.563525 / 0.323480 (0.240045) \| 0.007256 / 0.007986 (-0.000730) \| 0.006757 / 0.004328 (0.002429) \| 0.105151 / 0.004250 (0.100901) \| 0.056262 / 0.037052 (0.019209) \| 0.573341 / 0.258489 (0.314852) \| 0.591125 / 0.293841 (0.297284) \| 0.047935 / 0.128546 (-0.080611) \| 0.015385 / 0.075646 (-0.060262) \| 0.119457 / 0.419271 (-0.299814) \| 0.066510 / 0.043533 (0.022977) \| 0.485622 / 0.255139 (0.230483) \| 0.540929 / 0.283200 (0.257730) \| 0.132619 / 0.141683 (-0.009064) \| 1.916905 / 1.452155 (0.464750) \| 2.152722 / 1.492716 (0.660006) \|\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) \| 0.294823 / 0.018006 (0.276817) \| 0.569371 / 0.000490 (0.568882) \| 0.000642 / 0.000200 (0.000442) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034321 / 0.037411 (-0.003090) \| 0.134165 / 0.014526 (0.119639) \| 0.157871 / 0.176557 (-0.018685) \| 0.210753 / 0.737135 (-0.526382) \| 0.152961 / 0.296338 (-0.143377) \|\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) \| 0.686810 / 0.215209 (0.471601) \| 6.890432 / 2.077655 (4.812778) \| 3.182875 / 1.504120 (1.678755) \| 2.770836 / 1.541195 (1.229641) \| 2.790785 / 1.468490 (1.322295) \| 0.938145 / 4.584777 (-3.646632) \| 5.861093 / 3.745712 (2.115381) \| 2.719862 / 5.269862 (-2.550000) \| 1.760834 / 4.565676 (-2.804842) \| 0.111317 / 0.424275 (-0.312958) \| 0.015722 / 0.007607 (0.008115) \| 0.863032 / 0.226044 (0.636988) \| 8.482433 / 2.268929 (6.213504) \| 3.892621 / 55.444624 (-51.552003) \| 3.207370 / 6.876477 (-3.669106) \| 3.344412 / 2.142072 (1.202339) \| 1.133903 / 4.805227 (-3.671324) \| 0.223456 / 6.500664 (-6.277209) \| 0.084335 / 0.075469 (0.008866) \|\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) \| 1.794116 / 1.841788 (-0.047672) \| 19.077447 / 8.074308 (11.003139) \| 23.102309 / 10.191392 (12.910917) \| 0.268806 / 0.680424 (-0.411617) \| 0.027709 / 0.534201 (-0.506492) \| 0.540488 / 0.579283 (-0.038796) \| 0.658478 / 0.434364 (0.224114) \| 0.604769 / 0.540337 (0.064431) \| 0.722768 / 1.386936 (-0.664168) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e52021c66666e6953d5be0bd45a079e3ddb8c3f \"CML watermark\")\n" ]	1,685,632,990,000	1,685,700,174,000	1,685,699,591,000	MEMBER	null	It was not reading the feature type from the parquet arrow schema	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5921/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5921/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5921", "html_url": "https://github.com/huggingface/datasets/pull/5921", "diff_url": "https://github.com/huggingface/datasets/pull/5921.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5921.patch", "merged_at": "2023-06-02T09:53:11" }	true
https://api.github.com/repos/huggingface/datasets/issues/5920	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5920/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5920/comments	https://api.github.com/repos/huggingface/datasets/issues/5920/events	https://github.com/huggingface/datasets/pull/5920	1,736,196,991	PR_kwDODunzps5R5TRB	5,920	Optimize IterableDataset.from_file using ArrowExamplesIterable	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.007439 / 0.011353 (-0.003914) \| 0.004884 / 0.011008 (-0.006124) \| 0.098750 / 0.038508 (0.060242) \| 0.040723 / 0.023109 (0.017613) \| 0.347242 / 0.275898 (0.071344) \| 0.381202 / 0.323480 (0.057722) \| 0.006814 / 0.007986 (-0.001171) \| 0.004543 / 0.004328 (0.000215) \| 0.075338 / 0.004250 (0.071088) \| 0.058976 / 0.037052 (0.021924) \| 0.344746 / 0.258489 (0.086257) \| 0.406761 / 0.293841 (0.112920) \| 0.028961 / 0.128546 (-0.099585) \| 0.009531 / 0.075646 (-0.066115) \| 0.337324 / 0.419271 (-0.081947) \| 0.051071 / 0.043533 (0.007538) \| 0.341251 / 0.255139 (0.086112) \| 0.362773 / 0.283200 (0.079573) \| 0.109423 / 0.141683 (-0.032260) \| 1.457420 / 1.452155 (0.005266) \| 1.588824 / 1.492716 (0.096108) \|\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) \| 0.288620 / 0.018006 (0.270614) \| 0.568975 / 0.000490 (0.568485) \| 0.003350 / 0.000200 (0.003150) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028732 / 0.037411 (-0.008680) \| 0.117820 / 0.014526 (0.103294) \| 0.120180 / 0.176557 (-0.056376) \| 0.178736 / 0.737135 (-0.558399) \| 0.126399 / 0.296338 (-0.169939) \|\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) \| 0.428357 / 0.215209 (0.213148) \| 4.251989 / 2.077655 (2.174334) \| 2.005239 / 1.504120 (0.501119) \| 1.784009 / 1.541195 (0.242815) \| 1.883763 / 1.468490 (0.415272) \| 0.555429 / 4.584777 (-4.029348) \| 3.868146 / 3.745712 (0.122434) \| 2.081896 / 5.269862 (-3.187965) \| 1.126047 / 4.565676 (-3.439629) \| 0.069496 / 0.424275 (-0.354779) \| 0.012926 / 0.007607 (0.005318) \| 0.536989 / 0.226044 (0.310944) \| 5.256052 / 2.268929 (2.987124) \| 2.526802 / 55.444624 (-52.917822) \| 2.233346 / 6.876477 (-4.643131) \| 2.389063 / 2.142072 (0.246990) \| 0.677107 / 4.805227 (-4.128120) \| 0.147212 / 6.500664 (-6.353452) \| 0.067061 / 0.075469 (-0.008408) \|\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) \| 1.210651 / 1.841788 (-0.631137) \| 17.236898 / 8.074308 (9.162589) \| 14.427301 / 10.191392 (4.235909) \| 0.207194 / 0.680424 (-0.473229) \| 0.018079 / 0.534201 (-0.516122) \| 0.398355 / 0.579283 (-0.180929) \| 0.462453 / 0.434364 (0.028089) \| 0.484544 / 0.540337 (-0.055794) \| 0.590119 / 1.386936 (-0.796817) \|\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) \| 0.007392 / 0.011353 (-0.003961) \| 0.005614 / 0.011008 (-0.005394) \| 0.075587 / 0.038508 (0.037079) \| 0.040429 / 0.023109 (0.017320) \| 0.389901 / 0.275898 (0.114003) \| 0.429466 / 0.323480 (0.105986) \| 0.006790 / 0.007986 (-0.001196) \| 0.006627 / 0.004328 (0.002299) \| 0.075227 / 0.004250 (0.070976) \| 0.060298 / 0.037052 (0.023246) \| 0.391905 / 0.258489 (0.133416) \| 0.449385 / 0.293841 (0.155544) \| 0.028794 / 0.128546 (-0.099753) \| 0.009461 / 0.075646 (-0.066185) \| 0.083386 / 0.419271 (-0.335886) \| 0.057968 / 0.043533 (0.014435) \| 0.377327 / 0.255139 (0.122188) \| 0.402825 / 0.283200 (0.119626) \| 0.125477 / 0.141683 (-0.016206) \| 1.462986 / 1.452155 (0.010832) \| 1.595959 / 1.492716 (0.103243) \|\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) \| 0.304179 / 0.018006 (0.286173) \| 0.543113 / 0.000490 (0.542623) \| 0.004136 / 0.000200 (0.003936) \| 0.000109 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032617 / 0.037411 (-0.004794) \| 0.123596 / 0.014526 (0.109070) \| 0.128714 / 0.176557 (-0.047842) \| 0.176344 / 0.737135 (-0.560792) \| 0.132525 / 0.296338 (-0.163813) \|\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) \| 0.446041 / 0.215209 (0.230832) \| 4.438799 / 2.077655 (2.361144) \| 2.210815 / 1.504120 (0.706695) \| 2.052025 / 1.541195 (0.510830) \| 2.204687 / 1.468490 (0.736197) \| 0.535219 / 4.584777 (-4.049558) \| 3.858407 / 3.745712 (0.112695) \| 3.826043 / 5.269862 (-1.443819) \| 1.334149 / 4.565676 (-3.231527) \| 0.067454 / 0.424275 (-0.356821) \| 0.012566 / 0.007607 (0.004958) \| 0.551597 / 0.226044 (0.325553) \| 5.520054 / 2.268929 (3.251126) \| 2.817976 / 55.444624 (-52.626649) \| 2.528074 / 6.876477 (-4.348403) \| 2.622391 / 2.142072 (0.480319) \| 0.657632 / 4.805227 (-4.147595) \| 0.147039 / 6.500664 (-6.353625) \| 0.069603 / 0.075469 (-0.005866) \|\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) \| 1.300140 / 1.841788 (-0.541648) \| 17.303907 / 8.074308 (9.229599) \| 15.657887 / 10.191392 (5.466495) \| 0.168991 / 0.680424 (-0.511433) \| 0.021332 / 0.534201 (-0.512869) \| 0.487261 / 0.579283 (-0.092022) \| 0.450073 / 0.434364 (0.015709) \| 0.465865 / 0.540337 (-0.074473) \| 0.565501 / 1.386936 (-0.821435) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1723ab75a6b3a5e156ea0a41651e80e91fa9cc6 \"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) \| 0.006536 / 0.011353 (-0.004817) \| 0.004254 / 0.011008 (-0.006755) \| 0.095387 / 0.038508 (0.056878) \| 0.032885 / 0.023109 (0.009776) \| 0.298580 / 0.275898 (0.022682) \| 0.319771 / 0.323480 (-0.003709) \| 0.005510 / 0.007986 (-0.002476) \| 0.003891 / 0.004328 (-0.000437) \| 0.073763 / 0.004250 (0.069513) \| 0.041625 / 0.037052 (0.004573) \| 0.294896 / 0.258489 (0.036407) \| 0.341308 / 0.293841 (0.047467) \| 0.027898 / 0.128546 (-0.100648) \| 0.008837 / 0.075646 (-0.066809) \| 0.325055 / 0.419271 (-0.094216) \| 0.050652 / 0.043533 (0.007119) \| 0.298756 / 0.255139 (0.043617) \| 0.318261 / 0.283200 (0.035061) \| 0.098927 / 0.141683 (-0.042756) \| 1.450356 / 1.452155 (-0.001798) \| 1.508034 / 1.492716 (0.015318) \|\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) \| 0.209009 / 0.018006 (0.191003) \| 0.439154 / 0.000490 (0.438665) \| 0.004299 / 0.000200 (0.004099) \| 0.000142 / 0.000054 (0.000087) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025938 / 0.037411 (-0.011473) \| 0.105954 / 0.014526 (0.091429) \| 0.113858 / 0.176557 (-0.062698) \| 0.168887 / 0.737135 (-0.568249) \| 0.121292 / 0.296338 (-0.175046) \|\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) \| 0.402050 / 0.215209 (0.186841) \| 4.002310 / 2.077655 (1.924655) \| 1.816190 / 1.504120 (0.312070) \| 1.634404 / 1.541195 (0.093209) \| 1.713632 / 1.468490 (0.245142) \| 0.519633 / 4.584777 (-4.065144) \| 3.740291 / 3.745712 (-0.005421) \| 1.787602 / 5.269862 (-3.482260) \| 1.038844 / 4.565676 (-3.526833) \| 0.064973 / 0.424275 (-0.359302) \| 0.012475 / 0.007607 (0.004868) \| 0.498152 / 0.226044 (0.272108) \| 4.970941 / 2.268929 (2.702013) \| 2.287429 / 55.444624 (-53.157195) \| 1.998050 / 6.876477 (-4.878427) \| 2.091903 / 2.142072 (-0.050169) \| 0.630363 / 4.805227 (-4.174864) \| 0.138623 / 6.500664 (-6.362041) \| 0.063293 / 0.075469 (-0.012176) \|\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) \| 1.201802 / 1.841788 (-0.639986) \| 14.073836 / 8.074308 (5.999528) \| 12.968665 / 10.191392 (2.777273) \| 0.144653 / 0.680424 (-0.535771) \| 0.017613 / 0.534201 (-0.516588) \| 0.392067 / 0.579283 (-0.187216) \| 0.416955 / 0.434364 (-0.017409) \| 0.471492 / 0.540337 (-0.068845) \| 0.554576 / 1.386936 (-0.832360) \|\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) \| 0.006408 / 0.011353 (-0.004945) \| 0.004452 / 0.011008 (-0.006556) \| 0.073648 / 0.038508 (0.035140) \| 0.032536 / 0.023109 (0.009427) \| 0.358546 / 0.275898 (0.082648) \| 0.387330 / 0.323480 (0.063850) \| 0.005542 / 0.007986 (-0.002444) \| 0.003882 / 0.004328 (-0.000447) \| 0.073867 / 0.004250 (0.069617) \| 0.044798 / 0.037052 (0.007746) \| 0.362303 / 0.258489 (0.103814) \| 0.400496 / 0.293841 (0.106655) \| 0.028244 / 0.128546 (-0.100302) \| 0.008931 / 0.075646 (-0.066715) \| 0.080617 / 0.419271 (-0.338654) \| 0.046575 / 0.043533 (0.003043) \| 0.364283 / 0.255139 (0.109145) \| 0.373215 / 0.283200 (0.090015) \| 0.100080 / 0.141683 (-0.041603) \| 1.430047 / 1.452155 (-0.022108) \| 1.530957 / 1.492716 (0.038240) \|\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) \| 0.221061 / 0.018006 (0.203055) \| 0.441753 / 0.000490 (0.441263) \| 0.003626 / 0.000200 (0.003426) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029509 / 0.037411 (-0.007902) \| 0.109578 / 0.014526 (0.095053) \| 0.121009 / 0.176557 (-0.055548) \| 0.168950 / 0.737135 (-0.568185) \| 0.124475 / 0.296338 (-0.171864) \|\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) \| 0.431355 / 0.215209 (0.216146) \| 4.295507 / 2.077655 (2.217852) \| 2.167514 / 1.504120 (0.663394) \| 2.013073 / 1.541195 (0.471879) \| 1.973730 / 1.468490 (0.505240) \| 0.529778 / 4.584777 (-4.054999) \| 3.794702 / 3.745712 (0.048989) \| 3.062940 / 5.269862 (-2.206922) \| 1.503426 / 4.565676 (-3.062251) \| 0.066692 / 0.424275 (-0.357583) \| 0.011682 / 0.007607 (0.004075) \| 0.539311 / 0.226044 (0.313266) \| 5.406342 / 2.268929 (3.137414) \| 2.652709 / 55.444624 (-52.791916) \| 2.260066 / 6.876477 (-4.616410) \| 2.295752 / 2.142072 (0.153680) \| 0.647199 / 4.805227 (-4.158029) \| 0.142981 / 6.500664 (-6.357683) \| 0.065082 / 0.075469 (-0.010387) \|\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) \| 1.279788 / 1.841788 (-0.562000) \| 14.982845 / 8.074308 (6.908536) \| 14.277166 / 10.191392 (4.085774) \| 0.145082 / 0.680424 (-0.535342) \| 0.017885 / 0.534201 (-0.516316) \| 0.392071 / 0.579283 (-0.187212) \| 0.420425 / 0.434364 (-0.013939) \| 0.461244 / 0.540337 (-0.079093) \| 0.559956 / 1.386936 (-0.826980) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#651d96c1c4083a206c65f11602712d75f1f0453d \"CML watermark\")\n" ]	1,685,621,676,000	1,685,623,330,000	1,685,622,914,000	MEMBER	null	following https://github.com/huggingface/datasets/pull/5893	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5920/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5920/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5920", "html_url": "https://github.com/huggingface/datasets/pull/5920", "diff_url": "https://github.com/huggingface/datasets/pull/5920.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5920.patch", "merged_at": "2023-06-01T12:35:14" }	true
https://api.github.com/repos/huggingface/datasets/issues/5919	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5919/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5919/comments	https://api.github.com/repos/huggingface/datasets/issues/5919/events	https://github.com/huggingface/datasets/pull/5919	1,735,519,227	PR_kwDODunzps5R2_EK	5,919	add support for storage_options for load_dataset API	{ "login": "janineguo", "id": 59083384, "node_id": "MDQ6VXNlcjU5MDgzMzg0", "avatar_url": "https://avatars.githubusercontent.com/u/59083384?v=4", "gravatar_id": "", "url": "https://api.github.com/users/janineguo", "html_url": "https://github.com/janineguo", "followers_url": "https://api.github.com/users/janineguo/followers", "following_url": "https://api.github.com/users/janineguo/following{/other_user}", "gists_url": "https://api.github.com/users/janineguo/gists{/gist_id}", "starred_url": "https://api.github.com/users/janineguo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/janineguo/subscriptions", "organizations_url": "https://api.github.com/users/janineguo/orgs", "repos_url": "https://api.github.com/users/janineguo/repos", "events_url": "https://api.github.com/users/janineguo/events{/privacy}", "received_events_url": "https://api.github.com/users/janineguo/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "hi @lhoestq,\r\nI saw some errors in my test and found all the failed reasons are `FileNotFoundError` about `test_load_streaming_private_dataset_with_zipped_data` and `test_load_dataset_private_zipped_images` in `test_load.py `, I run pytest on my own Wins and Ubuntu system all the test in `test_load.py ` are succeed. could you help me to check the test environment of our server?\r\n\r\n`2023-06-08T16:50:48.0828281Z FAILED tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_txt_data-16862429577813\\repo_zipped_txt_data-16862429577813.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813`\r\n`2023-06-08T16:50:48.0830602Z FAILED tests/test_load.py::test_load_dataset_private_zipped_images[False-False] - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_img_data-16862429594168\\repo_zipped_img_data-16862429594168.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168`", "I just re-ran the CI, hopefully it's fixed", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5919). All of your documentation changes will be reflected on that endpoint.", "> I just re-ran the CI, hopefully it's fixed\r\n\r\nI just checked, still has the same error, maybe need someone to fix it", "I think the issue comes from this PR somehow, since the CI fail is related to loading private repositories and this PR touches authentication related code. Let me check what's the issue, and I'll also review your PR later (sorry I don't have a ton of bandwidth atm)" ]	1,685,598,752,000	1,686,808,764,000	null	NONE	null	to solve the issue in #5880 1. add s3 support in the link check step, previous we only check `http` and `https`, 2. change the parameter of `use_auth_token` to `download_config` to support both `storage_options` and `use_auth_token` parameter when trying to handle(list, open, read, etc,.) the remote files. 3. integrate the check part's duplicate code to make adding or deleting other sources easier.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5919/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5919/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5919", "html_url": "https://github.com/huggingface/datasets/pull/5919", "diff_url": "https://github.com/huggingface/datasets/pull/5919.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5919.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/5918	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5918/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5918/comments	https://api.github.com/repos/huggingface/datasets/issues/5918/events	https://github.com/huggingface/datasets/issues/5918	1,735,313,549	I_kwDODunzps5nbsiN	5,918	File not found for audio dataset	{ "login": "RobertBaruch", "id": 1783950, "node_id": "MDQ6VXNlcjE3ODM5NTA=", "avatar_url": "https://avatars.githubusercontent.com/u/1783950?v=4", "gravatar_id": "", "url": "https://api.github.com/users/RobertBaruch", "html_url": "https://github.com/RobertBaruch", "followers_url": "https://api.github.com/users/RobertBaruch/followers", "following_url": "https://api.github.com/users/RobertBaruch/following{/other_user}", "gists_url": "https://api.github.com/users/RobertBaruch/gists{/gist_id}", "starred_url": "https://api.github.com/users/RobertBaruch/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/RobertBaruch/subscriptions", "organizations_url": "https://api.github.com/users/RobertBaruch/orgs", "repos_url": "https://api.github.com/users/RobertBaruch/repos", "events_url": "https://api.github.com/users/RobertBaruch/events{/privacy}", "received_events_url": "https://api.github.com/users/RobertBaruch/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "load_dataset () did not work for loading local files either " ]	1,685,585,729,000	1,686,463,345,000	null	NONE	null	### Describe the bug After loading an audio dataset, and looking at a sample entry, the `path` element, which is supposed to be the path to the audio file, doesn't actually exist. ### Steps to reproduce the bug Run bug.py: ```py import os.path from datasets import load_dataset def run() -> None: cv13 = load_dataset( "mozilla-foundation/common_voice_13_0", "hi", split="train", ) print(cv13[0]) audio_file = cv13[0]["path"] if not os.path.exists(audio_file): raise ValueError(f'File {audio_file} does not exist.') if __name__ == "__main__": run() ``` The result (on my machine): ```json {'client_id': '0f018a99663f33afbb7d38aee281fb1afcfd07f9e7acd00383f604e1e17c38d6ed8adf1bd2ccbf927a52c5adefb8ac4b158ce27a7c2ed9581e71202eb302dfb3', 'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'audio': {'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'array': array([ 6.46234854e-26, -1.35709319e-25, -8.07793567e-26, ..., 1.06425944e-07, 4.46417090e-08, 2.61451660e-09]), 'sampling_rate': 48000}, 'sentence': 'हमने उसका जन्मदिन मनाया।', 'up_votes': 2, 'down_votes': 0, 'age': '', 'gender': '', 'accent': '', 'locale': 'hi', 'segment': '' ', 'variant': ''} ``` ```txt Traceback (most recent call last): File "F:\eo-reco\bug.py", line 18, in <module> run() File "F:\eo-reco\bug.py", line 15, in run raise ValueError(f'File {audio_file} does not exist.') ValueError: File C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\common_voice_hi_26008353.mp3 does not exist. ``` ### Expected behavior The `path` element points to the correct file, which happens to be: ``` C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\hi_train_0\common_voice_hi_26008353.mp3 ``` That is, there's an extra directory `hi_train_0` that is not in the `path` element. ### Environment info - `datasets` version: 2.12.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.11.3 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1 -	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5918/reactions", "total_count": 2, "+1": 2, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5918/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5917	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5917/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5917/comments	https://api.github.com/repos/huggingface/datasets/issues/5917/events	https://github.com/huggingface/datasets/pull/5917	1,733,661,588	PR_kwDODunzps5RwoRU	5,917	Refactor extensions	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.008358 / 0.011353 (-0.002995) \| 0.005673 / 0.011008 (-0.005335) \| 0.124034 / 0.038508 (0.085526) \| 0.037550 / 0.023109 (0.014441) \| 0.331301 / 0.275898 (0.055403) \| 0.383542 / 0.323480 (0.060062) \| 0.006940 / 0.007986 (-0.001046) \| 0.005959 / 0.004328 (0.001631) \| 0.084670 / 0.004250 (0.080419) \| 0.054214 / 0.037052 (0.017162) \| 0.359897 / 0.258489 (0.101408) \| 0.383260 / 0.293841 (0.089419) \| 0.047642 / 0.128546 (-0.080904) \| 0.013902 / 0.075646 (-0.061744) \| 0.380232 / 0.419271 (-0.039040) \| 0.077790 / 0.043533 (0.034257) \| 0.376648 / 0.255139 (0.121509) \| 0.387536 / 0.283200 (0.104336) \| 0.104644 / 0.141683 (-0.037038) \| 1.618560 / 1.452155 (0.166406) \| 1.742569 / 1.492716 (0.249853) \|\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) \| 0.257218 / 0.018006 (0.239212) \| 0.636801 / 0.000490 (0.636311) \| 0.000634 / 0.000200 (0.000434) \| 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) \| 0.037874 / 0.037411 (0.000462) \| 0.107454 / 0.014526 (0.092928) \| 0.117855 / 0.176557 (-0.058702) \| 0.204067 / 0.737135 (-0.533068) \| 0.134029 / 0.296338 (-0.162310) \|\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) \| 0.583657 / 0.215209 (0.368447) \| 5.761289 / 2.077655 (3.683635) \| 2.280201 / 1.504120 (0.776081) \| 2.033442 / 1.541195 (0.492247) \| 2.035343 / 1.468490 (0.566853) \| 0.868122 / 4.584777 (-3.716655) \| 5.352591 / 3.745712 (1.606879) \| 2.432814 / 5.269862 (-2.837047) \| 1.560765 / 4.565676 (-3.004911) \| 0.098793 / 0.424275 (-0.325482) \| 0.017327 / 0.007607 (0.009720) \| 0.734676 / 0.226044 (0.508631) \| 7.070318 / 2.268929 (4.801390) \| 2.972701 / 55.444624 (-52.471924) \| 2.442189 / 6.876477 (-4.434288) \| 2.604379 / 2.142072 (0.462307) \| 1.028853 / 4.805227 (-3.776374) \| 0.210390 / 6.500664 (-6.290274) \| 0.069329 / 0.075469 (-0.006140) \|\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) \| 1.469586 / 1.841788 (-0.372202) \| 16.570305 / 8.074308 (8.495997) \| 19.187845 / 10.191392 (8.996453) \| 0.219162 / 0.680424 (-0.461262) \| 0.026356 / 0.534201 (-0.507845) \| 0.447370 / 0.579283 (-0.131913) \| 0.555893 / 0.434364 (0.121529) \| 0.574958 / 0.540337 (0.034621) \| 0.639166 / 1.386936 (-0.747770) \|\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) \| 0.008166 / 0.011353 (-0.003187) \| 0.005577 / 0.011008 (-0.005431) \| 0.103578 / 0.038508 (0.065070) \| 0.040563 / 0.023109 (0.017454) \| 0.441996 / 0.275898 (0.166098) \| 0.483594 / 0.323480 (0.160114) \| 0.007329 / 0.007986 (-0.000657) \| 0.004546 / 0.004328 (0.000218) \| 0.090471 / 0.004250 (0.086220) \| 0.052740 / 0.037052 (0.015688) \| 0.442197 / 0.258489 (0.183708) \| 0.524310 / 0.293841 (0.230469) \| 0.042487 / 0.128546 (-0.086060) \| 0.012917 / 0.075646 (-0.062730) \| 0.103992 / 0.419271 (-0.315280) \| 0.060570 / 0.043533 (0.017037) \| 0.441956 / 0.255139 (0.186817) \| 0.477084 / 0.283200 (0.193885) \| 0.103815 / 0.141683 (-0.037868) \| 1.696963 / 1.452155 (0.244809) \| 1.747849 / 1.492716 (0.255132) \|\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) \| 0.292465 / 0.018006 (0.274458) \| 0.571518 / 0.000490 (0.571028) \| 0.000476 / 0.000200 (0.000276) \| 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) \| 0.028697 / 0.037411 (-0.008714) \| 0.111671 / 0.014526 (0.097145) \| 0.138826 / 0.176557 (-0.037731) \| 0.189697 / 0.737135 (-0.547439) \| 0.125454 / 0.296338 (-0.170884) \|\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) \| 0.619273 / 0.215209 (0.404064) \| 6.138669 / 2.077655 (4.061015) \| 2.558622 / 1.504120 (1.054502) \| 2.201550 / 1.541195 (0.660356) \| 2.279034 / 1.468490 (0.810544) \| 0.850752 / 4.584777 (-3.734025) \| 5.438185 / 3.745712 (1.692473) \| 2.529343 / 5.269862 (-2.740518) \| 1.572178 / 4.565676 (-2.993499) \| 0.100768 / 0.424275 (-0.323507) \| 0.013902 / 0.007607 (0.006295) \| 0.726660 / 0.226044 (0.500616) \| 7.794918 / 2.268929 (5.525990) \| 3.311695 / 55.444624 (-52.132930) \| 2.729167 / 6.876477 (-4.147310) \| 2.630984 / 2.142072 (0.488911) \| 1.018534 / 4.805227 (-3.786693) \| 0.194602 / 6.500664 (-6.306062) \| 0.070876 / 0.075469 (-0.004593) \|\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) \| 1.573005 / 1.841788 (-0.268783) \| 17.042710 / 8.074308 (8.968401) \| 19.615320 / 10.191392 (9.423928) \| 0.229405 / 0.680424 (-0.451019) \| 0.027560 / 0.534201 (-0.506641) \| 0.447984 / 0.579283 (-0.131299) \| 0.598392 / 0.434364 (0.164028) \| 0.571769 / 0.540337 (0.031431) \| 0.653025 / 1.386936 (-0.733911) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9dca2ff89a8589595313e9535d16597ce10e3700 \"CML watermark\")\n" ]	1,685,521,982,000	1,685,540,075,000	1,685,539,557,000	MEMBER	null	Related to: - #5850	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5917/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5917/timeline	null	null	0	{ "url": 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https://api.github.com/repos/huggingface/datasets/issues/5916	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5916/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5916/comments	https://api.github.com/repos/huggingface/datasets/issues/5916/events	https://github.com/huggingface/datasets/pull/5916	1,732,456,392	PR_kwDODunzps5RskTb	5,916	Unpin responses	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006113 / 0.011353 (-0.005239) \| 0.004195 / 0.011008 (-0.006813) \| 0.098103 / 0.038508 (0.059595) \| 0.027970 / 0.023109 (0.004860) \| 0.300992 / 0.275898 (0.025094) \| 0.335402 / 0.323480 (0.011922) \| 0.005079 / 0.007986 (-0.002906) \| 0.003516 / 0.004328 (-0.000813) \| 0.077311 / 0.004250 (0.073061) \| 0.037863 / 0.037052 (0.000810) \| 0.302638 / 0.258489 (0.044149) \| 0.346554 / 0.293841 (0.052713) \| 0.025218 / 0.128546 (-0.103328) \| 0.008630 / 0.075646 (-0.067017) \| 0.319748 / 0.419271 (-0.099523) \| 0.049182 / 0.043533 (0.005650) \| 0.306233 / 0.255139 (0.051094) \| 0.331040 / 0.283200 (0.047840) \| 0.089203 / 0.141683 (-0.052480) \| 1.496104 / 1.452155 (0.043949) \| 1.567878 / 1.492716 (0.075162) \|\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) \| 0.215774 / 0.018006 (0.197768) \| 0.436810 / 0.000490 (0.436320) \| 0.000307 / 0.000200 (0.000107) \| 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) \| 0.024102 / 0.037411 (-0.013310) \| 0.095459 / 0.014526 (0.080933) \| 0.106564 / 0.176557 (-0.069992) \| 0.169894 / 0.737135 (-0.567241) \| 0.109152 / 0.296338 (-0.187186) \|\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) \| 0.429066 / 0.215209 (0.213857) \| 4.297385 / 2.077655 (2.219730) \| 2.054854 / 1.504120 (0.550734) \| 1.846844 / 1.541195 (0.305649) \| 1.840807 / 1.468490 (0.372317) \| 0.553193 / 4.584777 (-4.031584) \| 3.366788 / 3.745712 (-0.378924) \| 1.727337 / 5.269862 (-3.542525) \| 0.994357 / 4.565676 (-3.571319) \| 0.067790 / 0.424275 (-0.356485) \| 0.012002 / 0.007607 (0.004395) \| 0.533335 / 0.226044 (0.307291) \| 5.341341 / 2.268929 (3.072412) \| 2.543581 / 55.444624 (-52.901043) \| 2.220374 / 6.876477 (-4.656103) \| 2.321656 / 2.142072 (0.179583) \| 0.654408 / 4.805227 (-4.150819) \| 0.134693 / 6.500664 (-6.365971) \| 0.066926 / 0.075469 (-0.008544) \|\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) \| 1.209463 / 1.841788 (-0.632325) \| 13.568221 / 8.074308 (5.493913) \| 13.965418 / 10.191392 (3.774026) \| 0.145049 / 0.680424 (-0.535375) \| 0.016936 / 0.534201 (-0.517265) \| 0.371587 / 0.579283 (-0.207696) \| 0.386363 / 0.434364 (-0.048001) \| 0.437137 / 0.540337 (-0.103201) \| 0.514779 / 1.386936 (-0.872157) \|\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) \| 0.006245 / 0.011353 (-0.005108) \| 0.004232 / 0.011008 (-0.006776) \| 0.075682 / 0.038508 (0.037174) \| 0.027858 / 0.023109 (0.004749) \| 0.425325 / 0.275898 (0.149427) \| 0.466732 / 0.323480 (0.143253) \| 0.005240 / 0.007986 (-0.002745) \| 0.003506 / 0.004328 (-0.000823) \| 0.075294 / 0.004250 (0.071044) \| 0.041677 / 0.037052 (0.004624) \| 0.426552 / 0.258489 (0.168063) \| 0.469452 / 0.293841 (0.175611) \| 0.025443 / 0.128546 (-0.103104) \| 0.008526 / 0.075646 (-0.067120) \| 0.082190 / 0.419271 (-0.337081) \| 0.040906 / 0.043533 (-0.002626) \| 0.428406 / 0.255139 (0.173267) \| 0.446795 / 0.283200 (0.163595) \| 0.093837 / 0.141683 (-0.047846) \| 1.518639 / 1.452155 (0.066484) \| 1.620214 / 1.492716 (0.127498) \|\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) \| 0.223259 / 0.018006 (0.205253) \| 0.425077 / 0.000490 (0.424588) \| 0.001980 / 0.000200 (0.001780) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025813 / 0.037411 (-0.011599) \| 0.103062 / 0.014526 (0.088536) \| 0.108958 / 0.176557 (-0.067598) \| 0.161591 / 0.737135 (-0.575544) \| 0.112130 / 0.296338 (-0.184209) \|\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) \| 0.472843 / 0.215209 (0.257634) \| 4.713281 / 2.077655 (2.635626) \| 2.458216 / 1.504120 (0.954096) \| 2.272467 / 1.541195 (0.731273) \| 2.324456 / 1.468490 (0.855965) \| 0.554686 / 4.584777 (-4.030091) \| 3.445079 / 3.745712 (-0.300634) \| 3.451896 / 5.269862 (-1.817966) \| 1.431065 / 4.565676 (-3.134612) \| 0.067868 / 0.424275 (-0.356407) \| 0.012093 / 0.007607 (0.004486) \| 0.573571 / 0.226044 (0.347526) \| 5.820452 / 2.268929 (3.551523) \| 2.934858 / 55.444624 (-52.509767) \| 2.602719 / 6.876477 (-4.273758) \| 2.645999 / 2.142072 (0.503927) \| 0.660688 / 4.805227 (-4.144540) \| 0.137490 / 6.500664 (-6.363174) \| 0.068311 / 0.075469 (-0.007158) \|\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) \| 1.321709 / 1.841788 (-0.520079) \| 14.592346 / 8.074308 (6.518038) \| 14.520748 / 10.191392 (4.329356) \| 0.132689 / 0.680424 (-0.547735) \| 0.016422 / 0.534201 (-0.517779) \| 0.370071 / 0.579283 (-0.209212) \| 0.397091 / 0.434364 (-0.037273) \| 0.431979 / 0.540337 (-0.108358) \| 0.509965 / 1.386936 (-0.876971) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8bcd061ab2082a0862f30329bc52f6e0d321805c \"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) \| 0.006182 / 0.011353 (-0.005171) \| 0.004153 / 0.011008 (-0.006855) \| 0.095715 / 0.038508 (0.057207) \| 0.032457 / 0.023109 (0.009347) \| 0.314961 / 0.275898 (0.039063) \| 0.353696 / 0.323480 (0.030216) \| 0.005256 / 0.007986 (-0.002729) \| 0.004870 / 0.004328 (0.000541) \| 0.072442 / 0.004250 (0.068192) \| 0.046102 / 0.037052 (0.009050) \| 0.324410 / 0.258489 (0.065921) \| 0.366861 / 0.293841 (0.073020) \| 0.027088 / 0.128546 (-0.101458) \| 0.008572 / 0.075646 (-0.067075) \| 0.325988 / 0.419271 (-0.093284) \| 0.049494 / 0.043533 (0.005961) \| 0.311221 / 0.255139 (0.056082) \| 0.359720 / 0.283200 (0.076521) \| 0.095101 / 0.141683 (-0.046581) \| 1.472821 / 1.452155 (0.020667) \| 1.516157 / 1.492716 (0.023441) \|\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) \| 0.210456 / 0.018006 (0.192450) \| 0.439440 / 0.000490 (0.438950) \| 0.003764 / 0.000200 (0.003564) \| 0.000087 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024076 / 0.037411 (-0.013335) \| 0.104886 / 0.014526 (0.090360) \| 0.114164 / 0.176557 (-0.062393) \| 0.167289 / 0.737135 (-0.569847) \| 0.116457 / 0.296338 (-0.179882) \|\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) \| 0.400039 / 0.215209 (0.184830) \| 3.973243 / 2.077655 (1.895588) \| 1.801991 / 1.504120 (0.297871) \| 1.592017 / 1.541195 (0.050822) \| 1.612564 / 1.468490 (0.144074) \| 0.527475 / 4.584777 (-4.057302) \| 3.676246 / 3.745712 (-0.069466) \| 1.806423 / 5.269862 (-3.463438) \| 1.176921 / 4.565676 (-3.388756) \| 0.065902 / 0.424275 (-0.358373) \| 0.012245 / 0.007607 (0.004638) \| 0.490883 / 0.226044 (0.264838) \| 4.905270 / 2.268929 (2.636341) \| 2.218694 / 55.444624 (-53.225930) \| 1.903074 / 6.876477 (-4.973403) \| 1.979505 / 2.142072 (-0.162567) \| 0.644415 / 4.805227 (-4.160812) \| 0.142433 / 6.500664 (-6.358231) \| 0.063564 / 0.075469 (-0.011905) \|\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) \| 1.193756 / 1.841788 (-0.648032) \| 14.673103 / 8.074308 (6.598795) \| 13.410951 / 10.191392 (3.219559) \| 0.159175 / 0.680424 (-0.521249) \| 0.017076 / 0.534201 (-0.517125) \| 0.388880 / 0.579283 (-0.190403) \| 0.409974 / 0.434364 (-0.024390) \| 0.454494 / 0.540337 (-0.085844) \| 0.556873 / 1.386936 (-0.830063) \|\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) \| 0.006107 / 0.011353 (-0.005246) \| 0.004433 / 0.011008 (-0.006575) \| 0.073892 / 0.038508 (0.035384) \| 0.032386 / 0.023109 (0.009277) \| 0.370339 / 0.275898 (0.094441) \| 0.388996 / 0.323480 (0.065516) \| 0.005438 / 0.007986 (-0.002548) \| 0.003875 / 0.004328 (-0.000454) \| 0.073867 / 0.004250 (0.069617) \| 0.048350 / 0.037052 (0.011298) \| 0.380328 / 0.258489 (0.121839) \| 0.411373 / 0.293841 (0.117532) \| 0.028183 / 0.128546 (-0.100363) \| 0.008924 / 0.075646 (-0.066723) \| 0.082484 / 0.419271 (-0.336787) \| 0.047321 / 0.043533 (0.003788) \| 0.371702 / 0.255139 (0.116563) \| 0.380535 / 0.283200 (0.097335) \| 0.100772 / 0.141683 (-0.040911) \| 1.475038 / 1.452155 (0.022883) \| 1.564293 / 1.492716 (0.071577) \|\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) \| 0.214589 / 0.018006 (0.196583) \| 0.437193 / 0.000490 (0.436703) \| 0.003676 / 0.000200 (0.003476) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027991 / 0.037411 (-0.009421) \| 0.111154 / 0.014526 (0.096628) \| 0.120365 / 0.176557 (-0.056191) \| 0.173601 / 0.737135 (-0.563535) \| 0.126244 / 0.296338 (-0.170094) \|\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) \| 0.442848 / 0.215209 (0.227639) \| 4.398336 / 2.077655 (2.320681) \| 2.217058 / 1.504120 (0.712938) \| 2.011155 / 1.541195 (0.469960) \| 2.123086 / 1.468490 (0.654596) \| 0.525857 / 4.584777 (-4.058920) \| 3.730191 / 3.745712 (-0.015521) \| 3.517680 / 5.269862 (-1.752181) \| 1.557940 / 4.565676 (-3.007736) \| 0.066309 / 0.424275 (-0.357967) \| 0.011788 / 0.007607 (0.004181) \| 0.548506 / 0.226044 (0.322462) \| 5.483615 / 2.268929 (3.214687) \| 2.663784 / 55.444624 (-52.780840) \| 2.325744 / 6.876477 (-4.550732) \| 2.344179 / 2.142072 (0.202106) \| 0.644217 / 4.805227 (-4.161010) \| 0.141546 / 6.500664 (-6.359118) \| 0.063730 / 0.075469 (-0.011739) \|\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) \| 1.296032 / 1.841788 (-0.545756) \| 14.903729 / 8.074308 (6.829421) \| 14.505409 / 10.191392 (4.314017) \| 0.170478 / 0.680424 (-0.509946) \| 0.017876 / 0.534201 (-0.516325) \| 0.401047 / 0.579283 (-0.178236) \| 0.417855 / 0.434364 (-0.016509) \| 0.472138 / 0.540337 (-0.068200) \| 0.570859 / 1.386936 (-0.816077) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a4d530965eb35c66955ef89df79210c66b7f5e6 \"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) \| 0.008495 / 0.011353 (-0.002858) \| 0.005322 / 0.011008 (-0.005686) \| 0.125471 / 0.038508 (0.086962) \| 0.034604 / 0.023109 (0.011495) \| 0.419831 / 0.275898 (0.143933) \| 0.415707 / 0.323480 (0.092227) \| 0.007471 / 0.007986 (-0.000515) \| 0.005441 / 0.004328 (0.001112) \| 0.095412 / 0.004250 (0.091162) \| 0.053865 / 0.037052 (0.016812) \| 0.375257 / 0.258489 (0.116768) \| 0.438114 / 0.293841 (0.144273) \| 0.046183 / 0.128546 (-0.082363) \| 0.013663 / 0.075646 (-0.061984) \| 0.438317 / 0.419271 (0.019045) \| 0.065665 / 0.043533 (0.022133) \| 0.387640 / 0.255139 (0.132501) \| 0.431350 / 0.283200 (0.148150) \| 0.112841 / 0.141683 (-0.028842) \| 1.778639 / 1.452155 (0.326484) \| 1.891948 / 1.492716 (0.399232) \|\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) \| 0.284371 / 0.018006 (0.266365) \| 0.598247 / 0.000490 (0.597758) \| 0.013674 / 0.000200 (0.013474) \| 0.000483 / 0.000054 (0.000428) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032437 / 0.037411 (-0.004974) \| 0.120547 / 0.014526 (0.106021) \| 0.129845 / 0.176557 (-0.046711) \| 0.203455 / 0.737135 (-0.533680) \| 0.140039 / 0.296338 (-0.156300) \|\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) \| 0.596549 / 0.215209 (0.381340) \| 6.138766 / 2.077655 (4.061111) \| 2.515506 / 1.504120 (1.011386) \| 2.124472 / 1.541195 (0.583277) \| 2.160812 / 1.468490 (0.692322) \| 0.898965 / 4.584777 (-3.685812) \| 5.588152 / 3.745712 (1.842440) \| 2.717580 / 5.269862 (-2.552282) \| 1.683641 / 4.565676 (-2.882036) \| 0.108045 / 0.424275 (-0.316230) \| 0.014089 / 0.007607 (0.006481) \| 0.749567 / 0.226044 (0.523523) \| 7.518051 / 2.268929 (5.249123) \| 3.198238 / 55.444624 (-52.246386) \| 2.575156 / 6.876477 (-4.301321) \| 2.725818 / 2.142072 (0.583745) \| 1.149338 / 4.805227 (-3.655889) \| 0.220443 / 6.500664 (-6.280221) \| 0.081452 / 0.075469 (0.005983) \|\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) \| 1.624462 / 1.841788 (-0.217325) \| 18.204963 / 8.074308 (10.130655) \| 21.379169 / 10.191392 (11.187777) \| 0.248520 / 0.680424 (-0.431903) \| 0.030121 / 0.534201 (-0.504080) \| 0.499542 / 0.579283 (-0.079741) \| 0.599783 / 0.434364 (0.165419) \| 0.597642 / 0.540337 (0.057305) \| 0.681948 / 1.386936 (-0.704988) \|\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) \| 0.008431 / 0.011353 (-0.002921) \| 0.006143 / 0.011008 (-0.004865) \| 0.107531 / 0.038508 (0.069023) \| 0.036308 / 0.023109 (0.013199) \| 0.480555 / 0.275898 (0.204657) \| 0.556407 / 0.323480 (0.232927) \| 0.007614 / 0.007986 (-0.000372) \| 0.004749 / 0.004328 (0.000421) \| 0.105734 / 0.004250 (0.101484) \| 0.051619 / 0.037052 (0.014567) \| 0.514821 / 0.258489 (0.256332) \| 0.562143 / 0.293841 (0.268302) \| 0.042957 / 0.128546 (-0.085589) \| 0.015142 / 0.075646 (-0.060505) \| 0.143161 / 0.419271 (-0.276111) \| 0.061910 / 0.043533 (0.018377) \| 0.496923 / 0.255139 (0.241784) \| 0.556302 / 0.283200 (0.273102) \| 0.136700 / 0.141683 (-0.004983) \| 1.886184 / 1.452155 (0.434029) \| 2.004087 / 1.492716 (0.511371) \|\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) \| 0.235530 / 0.018006 (0.217523) \| 0.600796 / 0.000490 (0.600306) \| 0.009074 / 0.000200 (0.008874) \| 0.000203 / 0.000054 (0.000149) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036345 / 0.037411 (-0.001066) \| 0.126112 / 0.014526 (0.111586) \| 0.143369 / 0.176557 (-0.033188) \| 0.211381 / 0.737135 (-0.525755) \| 0.151095 / 0.296338 (-0.145243) \|\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) \| 0.695022 / 0.215209 (0.479813) \| 6.685981 / 2.077655 (4.608326) \| 3.104521 / 1.504120 (1.600401) \| 2.758323 / 1.541195 (1.217128) \| 2.706286 / 1.468490 (1.237796) \| 0.941182 / 4.584777 (-3.643595) \| 5.715839 / 3.745712 (1.970127) \| 5.089636 / 5.269862 (-0.180226) \| 2.594739 / 4.565676 (-1.970937) \| 0.112621 / 0.424275 (-0.311655) \| 0.014001 / 0.007607 (0.006394) \| 0.812990 / 0.226044 (0.586945) \| 8.060890 / 2.268929 (5.791961) \| 3.832506 / 55.444624 (-51.612119) \| 3.148051 / 6.876477 (-3.728425) \| 3.110096 / 2.142072 (0.968023) \| 1.105050 / 4.805227 (-3.700178) \| 0.219835 / 6.500664 (-6.280829) \| 0.078600 / 0.075469 (0.003131) \|\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) \| 1.707551 / 1.841788 (-0.134237) \| 19.238194 / 8.074308 (11.163885) \| 22.167076 / 10.191392 (11.975684) \| 0.233458 / 0.680424 (-0.446966) \| 0.025131 / 0.534201 (-0.509070) \| 0.525241 / 0.579283 (-0.054042) \| 0.649666 / 0.434364 (0.215303) \| 0.602941 / 0.540337 (0.062603) \| 0.718472 / 1.386936 (-0.668464) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac3a42c525d91cb630273702a0c110a71c9bf54b \"CML watermark\")\n" ]	1,685,458,788,000	1,685,469,790,000	1,685,469,209,000	CONTRIBUTOR	null	Fix #5906	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5916/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5916/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5916", "html_url": "https://github.com/huggingface/datasets/pull/5916", "diff_url": "https://github.com/huggingface/datasets/pull/5916.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5916.patch", "merged_at": "2023-05-30T17:53:29" }	true
https://api.github.com/repos/huggingface/datasets/issues/5915	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5915/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5915/comments	https://api.github.com/repos/huggingface/datasets/issues/5915/events	https://github.com/huggingface/datasets/pull/5915	1,732,389,984	PR_kwDODunzps5RsVzj	5,915	Raise error in `DatasetBuilder.as_dataset` when `file_format` is not `"arrow"`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006416 / 0.011353 (-0.004937) \| 0.004278 / 0.011008 (-0.006731) \| 0.097562 / 0.038508 (0.059054) \| 0.029488 / 0.023109 (0.006379) \| 0.308648 / 0.275898 (0.032750) \| 0.339879 / 0.323480 (0.016399) \| 0.005288 / 0.007986 (-0.002697) \| 0.005033 / 0.004328 (0.000704) \| 0.074666 / 0.004250 (0.070416) \| 0.034888 / 0.037052 (-0.002164) \| 0.309960 / 0.258489 (0.051471) \| 0.344276 / 0.293841 (0.050435) \| 0.025564 / 0.128546 (-0.102982) \| 0.008579 / 0.075646 (-0.067067) \| 0.319796 / 0.419271 (-0.099476) \| 0.044786 / 0.043533 (0.001253) \| 0.308888 / 0.255139 (0.053749) \| 0.334001 / 0.283200 (0.050802) \| 0.089917 / 0.141683 (-0.051766) \| 1.456696 / 1.452155 (0.004541) \| 1.542273 / 1.492716 (0.049557) \|\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) \| 0.213236 / 0.018006 (0.195230) \| 0.425139 / 0.000490 (0.424650) \| 0.008831 / 0.000200 (0.008631) \| 0.000209 / 0.000054 (0.000155) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023990 / 0.037411 (-0.013421) \| 0.096787 / 0.014526 (0.082261) \| 0.105783 / 0.176557 (-0.070774) \| 0.167182 / 0.737135 (-0.569954) \| 0.108896 / 0.296338 (-0.187442) \|\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) \| 0.419844 / 0.215209 (0.204635) \| 4.201909 / 2.077655 (2.124254) \| 1.910784 / 1.504120 (0.406664) \| 1.685183 / 1.541195 (0.143988) \| 1.716927 / 1.468490 (0.248437) \| 0.548261 / 4.584777 (-4.036516) \| 3.414168 / 3.745712 (-0.331544) \| 1.695446 / 5.269862 (-3.574415) \| 0.989668 / 4.565676 (-3.576008) \| 0.067328 / 0.424275 (-0.356948) \| 0.012084 / 0.007607 (0.004477) \| 0.523799 / 0.226044 (0.297754) \| 5.240589 / 2.268929 (2.971661) \| 2.331618 / 55.444624 (-53.113007) \| 1.996094 / 6.876477 (-4.880383) \| 2.105450 / 2.142072 (-0.036623) \| 0.654614 / 4.805227 (-4.150613) \| 0.134721 / 6.500664 (-6.365943) \| 0.066227 / 0.075469 (-0.009242) \|\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) \| 1.196266 / 1.841788 (-0.645521) \| 13.990045 / 8.074308 (5.915737) \| 13.928126 / 10.191392 (3.736734) \| 0.142600 / 0.680424 (-0.537824) \| 0.016462 / 0.534201 (-0.517739) \| 0.363113 / 0.579283 (-0.216170) \| 0.428590 / 0.434364 (-0.005773) \| 0.452594 / 0.540337 (-0.087743) \| 0.551678 / 1.386936 (-0.835258) \|\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) \| 0.005992 / 0.011353 (-0.005361) \| 0.004161 / 0.011008 (-0.006847) \| 0.076098 / 0.038508 (0.037589) \| 0.028559 / 0.023109 (0.005450) \| 0.411696 / 0.275898 (0.135798) \| 0.444519 / 0.323480 (0.121040) \| 0.004965 / 0.007986 (-0.003021) \| 0.003452 / 0.004328 (-0.000876) \| 0.075107 / 0.004250 (0.070857) \| 0.037305 / 0.037052 (0.000252) \| 0.429728 / 0.258489 (0.171239) \| 0.444313 / 0.293841 (0.150472) \| 0.025278 / 0.128546 (-0.103268) \| 0.008527 / 0.075646 (-0.067120) \| 0.081502 / 0.419271 (-0.337770) \| 0.041237 / 0.043533 (-0.002296) \| 0.417848 / 0.255139 (0.162709) \| 0.426615 / 0.283200 (0.143415) \| 0.094641 / 0.141683 (-0.047041) \| 1.525141 / 1.452155 (0.072987) \| 1.615608 / 1.492716 (0.122892) \|\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) \| 0.192867 / 0.018006 (0.174861) \| 0.414979 / 0.000490 (0.414490) \| 0.000815 / 0.000200 (0.000615) \| 0.000068 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025354 / 0.037411 (-0.012058) \| 0.102085 / 0.014526 (0.087559) \| 0.107930 / 0.176557 (-0.068626) \| 0.160483 / 0.737135 (-0.576652) \| 0.112341 / 0.296338 (-0.183997) \|\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) \| 0.446938 / 0.215209 (0.231728) \| 4.480057 / 2.077655 (2.402402) \| 2.154825 / 1.504120 (0.650705) \| 1.942774 / 1.541195 (0.401580) \| 1.996418 / 1.468490 (0.527928) \| 0.556728 / 4.584777 (-4.028049) \| 3.441228 / 3.745712 (-0.304484) \| 3.004179 / 5.269862 (-2.265683) \| 1.314104 / 4.565676 (-3.251573) \| 0.068670 / 0.424275 (-0.355606) \| 0.011972 / 0.007607 (0.004365) \| 0.556604 / 0.226044 (0.330560) \| 5.561783 / 2.268929 (3.292855) \| 2.631262 / 55.444624 (-52.813363) \| 2.262143 / 6.876477 (-4.614333) \| 2.364243 / 2.142072 (0.222170) \| 0.660621 / 4.805227 (-4.144607) \| 0.137371 / 6.500664 (-6.363293) \| 0.069104 / 0.075469 (-0.006365) \|\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) \| 1.305706 / 1.841788 (-0.536081) \| 14.015932 / 8.074308 (5.941624) \| 14.353580 / 10.191392 (4.162187) \| 0.146172 / 0.680424 (-0.534251) \| 0.016699 / 0.534201 (-0.517502) \| 0.357970 / 0.579283 (-0.221313) \| 0.389067 / 0.434364 (-0.045297) \| 0.415470 / 0.540337 (-0.124867) \| 0.501359 / 1.386936 (-0.885577) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2b837b4e7267db9e32d2613d8bf8d70d2ce0b47 \"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) \| 0.006800 / 0.011353 (-0.004552) \| 0.004721 / 0.011008 (-0.006287) \| 0.097760 / 0.038508 (0.059252) \| 0.034192 / 0.023109 (0.011083) \| 0.298240 / 0.275898 (0.022342) \| 0.331119 / 0.323480 (0.007639) \| 0.005826 / 0.007986 (-0.002160) \| 0.003968 / 0.004328 (-0.000360) \| 0.073833 / 0.004250 (0.069582) \| 0.046288 / 0.037052 (0.009236) \| 0.303018 / 0.258489 (0.044529) \| 0.342163 / 0.293841 (0.048322) \| 0.028504 / 0.128546 (-0.100042) \| 0.009031 / 0.075646 (-0.066615) \| 0.331617 / 0.419271 (-0.087655) \| 0.060911 / 0.043533 (0.017379) \| 0.304044 / 0.255139 (0.048905) \| 0.328959 / 0.283200 (0.045759) \| 0.113174 / 0.141683 (-0.028509) \| 1.424652 / 1.452155 (-0.027502) \| 1.531392 / 1.492716 (0.038676) \|\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) \| 0.206175 / 0.018006 (0.188169) \| 0.435916 / 0.000490 (0.435426) \| 0.002587 / 0.000200 (0.002387) \| 0.000083 / 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) \| 0.026996 / 0.037411 (-0.010415) \| 0.106722 / 0.014526 (0.092196) \| 0.117655 / 0.176557 (-0.058902) \| 0.176969 / 0.737135 (-0.560166) \| 0.122577 / 0.296338 (-0.173762) \|\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) \| 0.396086 / 0.215209 (0.180877) \| 3.972465 / 2.077655 (1.894811) \| 1.800798 / 1.504120 (0.296678) \| 1.616747 / 1.541195 (0.075552) \| 1.680711 / 1.468490 (0.212221) \| 0.526479 / 4.584777 (-4.058298) \| 3.791528 / 3.745712 (0.045816) \| 2.989518 / 5.269862 (-2.280344) \| 1.463221 / 4.565676 (-3.102455) \| 0.065649 / 0.424275 (-0.358626) \| 0.012155 / 0.007607 (0.004548) \| 0.500241 / 0.226044 (0.274197) \| 5.008895 / 2.268929 (2.739966) \| 2.315288 / 55.444624 (-53.129336) \| 1.959409 / 6.876477 (-4.917067) \| 2.102371 / 2.142072 (-0.039701) \| 0.639611 / 4.805227 (-4.165617) \| 0.140101 / 6.500664 (-6.360563) \| 0.063599 / 0.075469 (-0.011870) \|\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) \| 1.206729 / 1.841788 (-0.635059) \| 15.127250 / 8.074308 (7.052942) \| 14.397228 / 10.191392 (4.205836) \| 0.148802 / 0.680424 (-0.531622) \| 0.017628 / 0.534201 (-0.516573) \| 0.396150 / 0.579283 (-0.183133) \| 0.435826 / 0.434364 (0.001462) \| 0.471215 / 0.540337 (-0.069122) \| 0.559413 / 1.386936 (-0.827523) \|\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) \| 0.006479 / 0.011353 (-0.004874) \| 0.004520 / 0.011008 (-0.006488) \| 0.074395 / 0.038508 (0.035887) \| 0.033400 / 0.023109 (0.010291) \| 0.388411 / 0.275898 (0.112513) \| 0.396714 / 0.323480 (0.073234) \| 0.005736 / 0.007986 (-0.002250) \| 0.004038 / 0.004328 (-0.000291) \| 0.073595 / 0.004250 (0.069345) \| 0.045207 / 0.037052 (0.008155) \| 0.378096 / 0.258489 (0.119607) \| 0.417830 / 0.293841 (0.123989) \| 0.028365 / 0.128546 (-0.100181) \| 0.008887 / 0.075646 (-0.066760) \| 0.080766 / 0.419271 (-0.338505) \| 0.046923 / 0.043533 (0.003390) \| 0.376190 / 0.255139 (0.121051) \| 0.385875 / 0.283200 (0.102675) \| 0.107542 / 0.141683 (-0.034141) \| 1.409257 / 1.452155 (-0.042898) \| 1.518475 / 1.492716 (0.025759) \|\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) \| 0.223299 / 0.018006 (0.205292) \| 0.440640 / 0.000490 (0.440150) \| 0.000397 / 0.000200 (0.000197) \| 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) \| 0.031388 / 0.037411 (-0.006024) \| 0.113078 / 0.014526 (0.098552) \| 0.124398 / 0.176557 (-0.052159) \| 0.173802 / 0.737135 (-0.563333) \| 0.129555 / 0.296338 (-0.166783) \|\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) \| 0.440220 / 0.215209 (0.225011) \| 4.398052 / 2.077655 (2.320398) \| 2.188396 / 1.504120 (0.684276) \| 1.997811 / 1.541195 (0.456616) \| 2.093338 / 1.468490 (0.624847) \| 0.519597 / 4.584777 (-4.065180) \| 3.885795 / 3.745712 (0.140083) \| 2.896327 / 5.269862 (-2.373534) \| 1.245785 / 4.565676 (-3.319891) \| 0.065675 / 0.424275 (-0.358600) \| 0.011729 / 0.007607 (0.004121) \| 0.541526 / 0.226044 (0.315482) \| 5.406763 / 2.268929 (3.137834) \| 2.722914 / 55.444624 (-52.721711) \| 2.471111 / 6.876477 (-4.405366) \| 2.541488 / 2.142072 (0.399415) \| 0.633566 / 4.805227 (-4.171661) \| 0.139622 / 6.500664 (-6.361042) \| 0.064220 / 0.075469 (-0.011249) \|\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) \| 1.296097 / 1.841788 (-0.545690) \| 15.095320 / 8.074308 (7.021012) \| 14.300821 / 10.191392 (4.109429) \| 0.145470 / 0.680424 (-0.534954) \| 0.017496 / 0.534201 (-0.516705) \| 0.400589 / 0.579283 (-0.178694) \| 0.423091 / 0.434364 (-0.011273) \| 0.468258 / 0.540337 (-0.072079) \| 0.570873 / 1.386936 (-0.816063) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aee6c67034d6ff298b2153a2fcdab97f14ee6d66 \"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) \| 0.005918 / 0.011353 (-0.005435) \| 0.004393 / 0.011008 (-0.006615) \| 0.091677 / 0.038508 (0.053169) \| 0.033546 / 0.023109 (0.010437) \| 0.344682 / 0.275898 (0.068784) \| 0.388906 / 0.323480 (0.065426) \| 0.005412 / 0.007986 (-0.002574) \| 0.004909 / 0.004328 (0.000580) \| 0.082589 / 0.004250 (0.078339) \| 0.045242 / 0.037052 (0.008190) \| 0.339191 / 0.258489 (0.080702) \| 0.349673 / 0.293841 (0.055832) \| 0.026805 / 0.128546 (-0.101742) \| 0.007529 / 0.075646 (-0.068117) \| 0.319108 / 0.419271 (-0.100164) \| 0.049482 / 0.043533 (0.005949) \| 0.320013 / 0.255139 (0.064874) \| 0.342059 / 0.283200 (0.058859) \| 0.096623 / 0.141683 (-0.045060) \| 1.458204 / 1.452155 (0.006049) \| 1.571172 / 1.492716 (0.078455) \|\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) \| 0.235171 / 0.018006 (0.217165) \| 0.479678 / 0.000490 (0.479188) \| 0.006627 / 0.000200 (0.006427) \| 0.000257 / 0.000054 (0.000202) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025716 / 0.037411 (-0.011696) \| 0.107730 / 0.014526 (0.093204) \| 0.111595 / 0.176557 (-0.064962) \| 0.171316 / 0.737135 (-0.565819) \| 0.118962 / 0.296338 (-0.177377) \|\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) \| 0.376318 / 0.215209 (0.161109) \| 4.039484 / 2.077655 (1.961829) \| 1.811548 / 1.504120 (0.307428) \| 1.646728 / 1.541195 (0.105533) \| 1.688071 / 1.468490 (0.219581) \| 0.551256 / 4.584777 (-4.033520) \| 4.153931 / 3.745712 (0.408218) \| 3.424154 / 5.269862 (-1.845707) \| 1.734860 / 4.565676 (-2.830816) \| 0.067753 / 0.424275 (-0.356522) \| 0.012699 / 0.007607 (0.005092) \| 0.505722 / 0.226044 (0.279677) \| 4.997321 / 2.268929 (2.728392) \| 2.258755 / 55.444624 (-53.185869) \| 1.954382 / 6.876477 (-4.922095) \| 1.967545 / 2.142072 (-0.174527) \| 0.630489 / 4.805227 (-4.174738) \| 0.138738 / 6.500664 (-6.361926) \| 0.064907 / 0.075469 (-0.010562) \|\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) \| 1.209634 / 1.841788 (-0.632154) \| 15.055062 / 8.074308 (6.980754) \| 12.721606 / 10.191392 (2.530214) \| 0.164908 / 0.680424 (-0.515516) \| 0.019528 / 0.534201 (-0.514673) \| 0.400136 / 0.579283 (-0.179147) \| 0.451640 / 0.434364 (0.017276) \| 0.466272 / 0.540337 (-0.074065) \| 0.553258 / 1.386936 (-0.833679) \|\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) \| 0.006341 / 0.011353 (-0.005011) \| 0.004617 / 0.011008 (-0.006391) \| 0.077953 / 0.038508 (0.039445) \| 0.031104 / 0.023109 (0.007995) \| 0.360328 / 0.275898 (0.084430) \| 0.408403 / 0.323480 (0.084923) \| 0.005704 / 0.007986 (-0.002282) \| 0.003588 / 0.004328 (-0.000741) \| 0.071441 / 0.004250 (0.067190) \| 0.043520 / 0.037052 (0.006468) \| 0.375798 / 0.258489 (0.117309) \| 0.400955 / 0.293841 (0.107114) \| 0.028166 / 0.128546 (-0.100381) \| 0.008578 / 0.075646 (-0.067068) \| 0.086673 / 0.419271 (-0.332598) \| 0.046424 / 0.043533 (0.002891) \| 0.367276 / 0.255139 (0.112137) \| 0.414550 / 0.283200 (0.131351) \| 0.097355 / 0.141683 (-0.044328) \| 1.465191 / 1.452155 (0.013036) \| 1.555028 / 1.492716 (0.062312) \|\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) \| 0.196642 / 0.018006 (0.178636) \| 0.464221 / 0.000490 (0.463731) \| 0.002726 / 0.000200 (0.002526) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028078 / 0.037411 (-0.009333) \| 0.110762 / 0.014526 (0.096236) \| 0.122212 / 0.176557 (-0.054344) \| 0.164758 / 0.737135 (-0.572377) \| 0.133969 / 0.296338 (-0.162370) \|\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) \| 0.448134 / 0.215209 (0.232925) \| 4.339335 / 2.077655 (2.261680) \| 2.129209 / 1.504120 (0.625089) \| 1.957805 / 1.541195 (0.416611) \| 1.994038 / 1.468490 (0.525548) \| 0.497101 / 4.584777 (-4.087676) \| 4.114432 / 3.745712 (0.368720) \| 3.437305 / 5.269862 (-1.832556) \| 1.692810 / 4.565676 (-2.872866) \| 0.071077 / 0.424275 (-0.353198) \| 0.012735 / 0.007607 (0.005128) \| 0.534393 / 0.226044 (0.308348) \| 5.217445 / 2.268929 (2.948517) \| 2.594858 / 55.444624 (-52.849766) \| 2.317464 / 6.876477 (-4.559012) \| 2.337974 / 2.142072 (0.195902) \| 0.622291 / 4.805227 (-4.182936) \| 0.144934 / 6.500664 (-6.355730) \| 0.068524 / 0.075469 (-0.006945) \|\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) \| 1.310601 / 1.841788 (-0.531187) \| 15.771527 / 8.074308 (7.697219) \| 13.952032 / 10.191392 (3.760640) \| 0.212473 / 0.680424 (-0.467951) \| 0.017963 / 0.534201 (-0.516238) \| 0.400755 / 0.579283 (-0.178528) \| 0.439817 / 0.434364 (0.005453) \| 0.472614 / 0.540337 (-0.067724) \| 0.558410 / 1.386936 (-0.828526) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b51429d02a0da1ff798873afe655309136c5689 \"CML watermark\")\n" ]	1,685,456,875,000	1,685,539,881,000	1,685,539,434,000	CONTRIBUTOR	null	Raise an error in `DatasetBuilder.as_dataset` when `file_format != "arrow"` (and fix the docstring) Fix #5874	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5915/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5915/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5915", "html_url": "https://github.com/huggingface/datasets/pull/5915", "diff_url": "https://github.com/huggingface/datasets/pull/5915.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5915.patch", "merged_at": "2023-05-31T13:23:54" }	true
https://api.github.com/repos/huggingface/datasets/issues/5914	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5914/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5914/comments	https://api.github.com/repos/huggingface/datasets/issues/5914/events	https://github.com/huggingface/datasets/issues/5914	1,731,483,996	I_kwDODunzps5nNFlc	5,914	array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size in Datasets	{ "login": "ravenouse", "id": 85110830, "node_id": "MDQ6VXNlcjg1MTEwODMw", "avatar_url": "https://avatars.githubusercontent.com/u/85110830?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ravenouse", "html_url": "https://github.com/ravenouse", "followers_url": "https://api.github.com/users/ravenouse/followers", "following_url": "https://api.github.com/users/ravenouse/following{/other_user}", "gists_url": "https://api.github.com/users/ravenouse/gists{/gist_id}", "starred_url": "https://api.github.com/users/ravenouse/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ravenouse/subscriptions", "organizations_url": "https://api.github.com/users/ravenouse/orgs", "repos_url": "https://api.github.com/users/ravenouse/repos", "events_url": "https://api.github.com/users/ravenouse/events{/privacy}", "received_events_url": "https://api.github.com/users/ravenouse/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[]	1,685,420,700,000	1,685,420,700,000	null	NONE	null	### Describe the bug When using the `filter` or `map` function to preprocess a dataset, a ValueError is encountered with the error message "array is too big; arr.size * arr.dtype.itemsize is larger than the maximum possible size." Detailed error message: Traceback (most recent call last): File "data_processing.py", line 26, in <module> processed_dataset[split] = samromur_children[split].map(prepare_dataset, cache_file_name=cache_dict[split],writer_batch_size = 50) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2405, in map desc=desc, File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 524, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/fingerprint.py", line 480, in wrapper out = func(self, args, *kwargs) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2756, in _map_single example = apply_function_on_filtered_inputs(example, i, offset=offset) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2655, in apply_function_on_filtered_inputs processed_inputs = function(fn_args, additional_args, fn_kwargs) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2347, in decorated result = f(decorated_item, args, *kwargs) File "data_processing.py", line 11, in prepare_dataset audio = batch["audio"] File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 123, in __getitem__ value = decode_nested_example(self.features[key], value) if value is not None else None File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/features.py", line 1260, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/audio.py", line 156, in decode_example array, sampling_rate = self._decode_non_mp3_path_like(path, token_per_repo_id=token_per_repo_id) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/audio.py", line 257, in _decode_non_mp3_path_like array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/librosa/core/audio.py", line 176, in load y, sr_native = __soundfile_load(path, offset, duration, dtype) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/librosa/core/audio.py", line 222, in __soundfile_load y = sf_desc.read(frames=frame_duration, dtype=dtype, always_2d=False).T File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/soundfile.py", line 891, in read out = self._create_empty_array(frames, always_2d, dtype) File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/soundfile.py", line 1323, in _create_empty_array return np.empty(shape, dtype, order='C') ValueError: array is too big; `arr.size arr.dtype.itemsize` is larger than the maximum possible size. ### Steps to reproduce the bug ```python from datasets import load_dataset, DatasetDict from transformers import WhisperFeatureExtractor from transformers import WhisperTokenizer samromur_children= load_dataset("language-and-voice-lab/samromur_children") feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small") tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="icelandic", task="transcribe") def prepare_dataset(batch): # load and resample audio data from 48 to 16kHz audio = batch["audio"] # compute log-Mel input features from input audio array batch["input_features"] = feature_extractor(audio["array"], sampling_rate=16000).input_features[0] # encode target text to label ids batch["labels"] = tokenizer(batch["normalized_text"]).input_ids return batch cache_dict = {"train": "./cache/audio_train.cache", \ "validation": "./cache/audio_validation.cache", \ "test": "./cache/audio_test.cache"} filter_cache_dict = {"train": "./cache/filter_train.arrow", \ "validation": "./cache/filter_validation.arrow", \ "test": "./cache/filter_test.arrow"} print("before filtering") print(samromur_children) #filter the dataset to only include examples with more than 2 seconds of audio samromur_children = samromur_children.filter(lambda example: example["audio"]["array"].shape[0] > 16000*2, cache_file_names=filter_cache_dict) print("after filtering") print(samromur_children) processed_dataset = DatasetDict() # processed_dataset = samromur_children.map(prepare_dataset, cache_file_names=cache_dict, num_proc=10,) for split in ["train", "validation", "test"]: processed_dataset[split] = samromur_children[split].map(prepare_dataset, cache_file_name=cache_dict[split]) ``` ### Expected behavior The dataset is successfully processed and ready to train the model. ### Environment info Python version: 3.7.13 datasets package version: 2.4.0 librosa package version: 0.10.0.post2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5914/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5914/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5913	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5913/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5913/comments	https://api.github.com/repos/huggingface/datasets/issues/5913/events	https://github.com/huggingface/datasets/issues/5913	1,731,427,484	I_kwDODunzps5nM3yc	5,913	I tried to load a custom dataset using the following statement: dataset = load_dataset('json', data_files=data_files). The dataset contains 50 million text-image pairs, but an error occurred.	{ "login": "cjt222", "id": 17508662, "node_id": "MDQ6VXNlcjE3NTA4NjYy", "avatar_url": "https://avatars.githubusercontent.com/u/17508662?v=4", "gravatar_id": "", "url": "https://api.github.com/users/cjt222", "html_url": "https://github.com/cjt222", "followers_url": "https://api.github.com/users/cjt222/followers", "following_url": "https://api.github.com/users/cjt222/following{/other_user}", "gists_url": "https://api.github.com/users/cjt222/gists{/gist_id}", "starred_url": "https://api.github.com/users/cjt222/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/cjt222/subscriptions", "organizations_url": "https://api.github.com/users/cjt222/orgs", "repos_url": "https://api.github.com/users/cjt222/repos", "events_url": "https://api.github.com/users/cjt222/events{/privacy}", "received_events_url": "https://api.github.com/users/cjt222/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Thanks for reporting, @cjt222.\r\n\r\nWhat is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead. ", "> Thanks for reporting, @cjt222.\r\n> \r\n> What is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead.\r\n\r\nThanks! I have encountered similar problems. I modify the json format from list to line and works!" ]	1,685,415,326,000	1,686,385,512,000	null	NONE	null	### Describe the bug File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/builder.py", line 1858, in _prepare_split_single Downloading and preparing dataset json/default to /home/kas/diffusers/examples/dreambooth/cache_data/datasets/json/default-acf423d8c6ef99d0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... Downloading data files: 0%\| \| 0/1 [00:00<?, ?it/s] Downloading data files: 100%\|██████████\| 1/1 [00:00<00:00, 84.35it/s] Extracting data files: 0%\| \| 0/1 [00:00<?, ?it/s] for _, table in generator: File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 114, in _generate_tables io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) File "pyarrow/_json.pyx", line 258, in pyarrow._json.read_json Extracting data files: 100%\|██████████\| 1/1 [00:00<00:00, 27.72it/s] Generating train split: 0 examples [00:00, ? examples/s] File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 125, in pyarrow.lib.check_status pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2390448764 ### Steps to reproduce the bug 1、data_files = ["1.json", "2.json", "3.json"] 2、dataset = load_dataset('json', data_files=data_files) ### Expected behavior Read the dataset normally. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-4.15.0-29-generic-x86_64-with-debian-buster-sid - Python version: 3.7.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 1.3.5	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5913/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5913/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5912	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5912/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5912/comments	https://api.github.com/repos/huggingface/datasets/issues/5912/events	https://github.com/huggingface/datasets/issues/5912	1,730,299,852	I_kwDODunzps5nIkfM	5,912	Missing elements in `map` a batched dataset	{ "login": "sachinruk", "id": 1410927, "node_id": "MDQ6VXNlcjE0MTA5Mjc=", "avatar_url": "https://avatars.githubusercontent.com/u/1410927?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sachinruk", "html_url": "https://github.com/sachinruk", "followers_url": "https://api.github.com/users/sachinruk/followers", "following_url": "https://api.github.com/users/sachinruk/following{/other_user}", "gists_url": "https://api.github.com/users/sachinruk/gists{/gist_id}", "starred_url": "https://api.github.com/users/sachinruk/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sachinruk/subscriptions", "organizations_url": "https://api.github.com/users/sachinruk/orgs", "repos_url": "https://api.github.com/users/sachinruk/repos", "events_url": "https://api.github.com/users/sachinruk/events{/privacy}", "received_events_url": "https://api.github.com/users/sachinruk/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Hi ! in your code batching is only used within `map`, to process examples in batch. The dataset itself however is not batched and returns elements one by one.\r\n\r\nTo iterate on batches, you can do\r\n```python\r\nfor batch in dataset.iter(batch_size=8):\r\n ...\r\n```" ]	1,685,347,759,000	1,685,468,133,000	null	NONE	null	### Describe the bug As outlined [here](https://discuss.huggingface.co/t/length-error-using-map-with-datasets/40969/3?u=sachin), the following collate function drops 5 out of possible 6 elements in the batch (it is 6 because out of the eight, two are bad links in laion). A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here. The weirdest part is when inspecting the sizes of the tensors as shown below, both `tokenized_captions["input_ids"]` and `image_features` show the correct shapes. Simply the output only has one element (with the batch dimension squeezed out). ```python class CollateFn: def get_image(self, url): try: response = requests.get(url) return Image.open(io.BytesIO(response.content)).convert("RGB") except PIL.UnidentifiedImageError: logger.info(f"Reading error: Could not transform f{url}") return None except requests.exceptions.ConnectionError: logger.info(f"Connection error: Could not transform f{url}") return None def __call__(self, batch): images = [self.get_image(url) for url in batch["url"]] captions = [caption for caption, image in zip(batch["caption"], images) if image is not None] images = [image for image in images if image is not None] tokenized_captions = tokenizer( captions, padding="max_length", truncation=True, max_length=tokenizer.model_max_length, return_tensors="pt", ) image_features = torch.stack([torch.Tensor(feature_extractor(image)["pixel_values"][0]) for image in images]) # import pdb; pdb.set_trace() return {"input_ids": tokenized_captions["input_ids"], "images": image_features} collate_fn = CollateFn() laion_ds = datasets.load_dataset("laion/laion400m", split="train", streaming=True) laion_ds_batched = laion_ds.map(collate_fn, batched=True, batch_size=8, remove_columns=next(iter(laion_ds)).keys()) ``` ### Steps to reproduce the bug A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here. ### Expected behavior Would expect `next(iter(laion_ds_batched))` to produce two tensors of shape `(batch_size, 77)` and `batch_size, image_shape`. ### Environment info datasets==2.12.0 python==3.10	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5912/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5912/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5910	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5910/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5910/comments	https://api.github.com/repos/huggingface/datasets/issues/5910/events	https://github.com/huggingface/datasets/issues/5910	1,728,909,790	I_kwDODunzps5nDRHe	5,910	Cannot use both set_format and set_transform	{ "login": "ybouane", "id": 14046002, "node_id": "MDQ6VXNlcjE0MDQ2MDAy", "avatar_url": "https://avatars.githubusercontent.com/u/14046002?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ybouane", "html_url": "https://github.com/ybouane", "followers_url": "https://api.github.com/users/ybouane/followers", "following_url": "https://api.github.com/users/ybouane/following{/other_user}", "gists_url": "https://api.github.com/users/ybouane/gists{/gist_id}", "starred_url": "https://api.github.com/users/ybouane/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ybouane/subscriptions", "organizations_url": "https://api.github.com/users/ybouane/orgs", "repos_url": "https://api.github.com/users/ybouane/repos", "events_url": "https://api.github.com/users/ybouane/events{/privacy}", "received_events_url": "https://api.github.com/users/ybouane/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "Currently, it's not possible to chain `set_format`/`set_transform` calls (plus, this is a breaking change if we decide to implement it), so I see two possible solutions:\r\n* using `set_format`/`set_transform` for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n* implementing and registering a custom formatter (the relevant code is [here](https://github.com/huggingface/datasets/tree/main/src/datasets/formatting))\r\n\r\nBtw, your example requires a single `set_format` call:\r\n```python\r\nds.set_format(\"torch\", columns=[\"image\"], output_all_columns=True, dtype=torch.double)\r\n```", "Hey Mario,\r\nThanks, for getting back to me. the toDouble was just an example my real life case requires many more transforms.\r\n\r\nWhat do you mean by:\r\n> using set_format/set_transform for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n\r\nHow would that go, I thought you can't chain them?\r\n\r\nAs for the custom formatter, is it possible to reference an existing formatter, in my case `torch_formatter` inside of my custom formatter?\r\n\r\nmaybe I can inherit from it and just call `super.recursive_tensorize()`?", "> How would that go, I thought you can't chain them?\r\n\r\nYes, they cannot be chained. This is what I meant:\r\n```python\r\nds.set_transform(first_transform)\r\n# calling the 2nd transform on each accessed batch\r\nsecond_transform(ds[2:3])\r\n```\r\n\r\n> As for the custom formatter, is it possible to reference an existing formatter, in my case torch_formatter inside of my custom formatter?\r\n>\r\n>maybe I can inherit from it and just call super.recursive_tensorize()?\r\n\r\nYes, subclassing makes the most sense.", "Great, thank you for the details." ]	1,685,215,343,000	1,686,926,485,000	1,686,926,484,000	NONE	null	### Describe the bug I need to process some data using the set_transform method but I also need the data to be formatted for pytorch before processing it. I don't see anywhere in the documentation something that says that both methods cannot be used at the same time. ### Steps to reproduce the bug ``` from datasets import load_dataset ds = load_dataset("mnist", split="train") ds.set_format(type="torch") def transform(entry): return entry["image"].double() ds.set_transform(transform) print(ds[0]) ``` ### Expected behavior It should print the pytorch tensor image as a double, but it errors because "entry" in the transform function doesn't receive a pytorch tensor to begin with, it receives a PIL Image -> entry.double() errors because entry isn't a pytorch tensor. ### Environment info Latest versions. ### Note: It would be at least handy to have access to a function that can do the dataset.set_format in the set_transform function. Something like: ``` from datasets import load_dataset, do_format ds = load_dataset("mnist", split="train") def transform(entry): entry = do_format(entry, type="torch") return entry["image"].double() ds.set_transform(transform) print(ds[0]) ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5910/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5910/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5909	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5909/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5909/comments	https://api.github.com/repos/huggingface/datasets/issues/5909/events	https://github.com/huggingface/datasets/pull/5909	1,728,900,068	PR_kwDODunzps5Rgga6	5,909	Use more efficient and idiomatic way to construct list.	{ "login": "ttsugriy", "id": 172294, "node_id": "MDQ6VXNlcjE3MjI5NA==", "avatar_url": "https://avatars.githubusercontent.com/u/172294?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ttsugriy", "html_url": "https://github.com/ttsugriy", "followers_url": "https://api.github.com/users/ttsugriy/followers", "following_url": "https://api.github.com/users/ttsugriy/following{/other_user}", "gists_url": "https://api.github.com/users/ttsugriy/gists{/gist_id}", "starred_url": "https://api.github.com/users/ttsugriy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ttsugriy/subscriptions", "organizations_url": "https://api.github.com/users/ttsugriy/orgs", "repos_url": "https://api.github.com/users/ttsugriy/repos", "events_url": "https://api.github.com/users/ttsugriy/events{/privacy}", "received_events_url": "https://api.github.com/users/ttsugriy/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.008156 / 0.011353 (-0.003197) \| 0.005563 / 0.011008 (-0.005445) \| 0.118319 / 0.038508 (0.079810) \| 0.044305 / 0.023109 (0.021195) \| 0.366221 / 0.275898 (0.090323) \| 0.407585 / 0.323480 (0.084105) \| 0.006961 / 0.007986 (-0.001024) \| 0.004841 / 0.004328 (0.000513) \| 0.089949 / 0.004250 (0.085698) \| 0.062197 / 0.037052 (0.025144) \| 0.360721 / 0.258489 (0.102232) \| 0.415332 / 0.293841 (0.121491) \| 0.035709 / 0.128546 (-0.092837) \| 0.010617 / 0.075646 (-0.065030) \| 0.397454 / 0.419271 (-0.021817) \| 0.063490 / 0.043533 (0.019958) \| 0.374289 / 0.255139 (0.119150) \| 0.382827 / 0.283200 (0.099628) \| 0.121014 / 0.141683 (-0.020669) \| 1.729933 / 1.452155 (0.277779) \| 1.896222 / 1.492716 (0.403506) \|\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) \| 0.254030 / 0.018006 (0.236023) \| 0.491225 / 0.000490 (0.490736) \| 0.018933 / 0.000200 (0.018734) \| 0.000413 / 0.000054 (0.000358) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033085 / 0.037411 (-0.004327) \| 0.132837 / 0.014526 (0.118311) \| 0.143275 / 0.176557 (-0.033282) \| 0.215800 / 0.737135 (-0.521335) \| 0.149802 / 0.296338 (-0.146536) \|\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) \| 0.474688 / 0.215209 (0.259479) \| 4.743223 / 2.077655 (2.665569) \| 2.163107 / 1.504120 (0.658988) \| 1.946396 / 1.541195 (0.405201) \| 2.057538 / 1.468490 (0.589047) \| 0.618836 / 4.584777 (-3.965941) \| 4.605934 / 3.745712 (0.860222) \| 2.201537 / 5.269862 (-3.068324) \| 1.275758 / 4.565676 (-3.289919) \| 0.077782 / 0.424275 (-0.346493) \| 0.014830 / 0.007607 (0.007223) \| 0.593372 / 0.226044 (0.367328) \| 5.927000 / 2.268929 (3.658072) \| 2.687293 / 55.444624 (-52.757331) \| 2.301797 / 6.876477 (-4.574679) \| 2.489928 / 2.142072 (0.347856) \| 0.756779 / 4.805227 (-4.048449) \| 0.168065 / 6.500664 (-6.332600) \| 0.077276 / 0.075469 (0.001807) \|\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) \| 1.608169 / 1.841788 (-0.233619) \| 19.048790 / 8.074308 (10.974482) \| 16.100228 / 10.191392 (5.908836) \| 0.215346 / 0.680424 (-0.465077) \| 0.022293 / 0.534201 (-0.511907) \| 0.535899 / 0.579283 (-0.043384) \| 0.533729 / 0.434364 (0.099365) \| 0.562697 / 0.540337 (0.022360) \| 0.764082 / 1.386936 (-0.622854) \|\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) \| 0.010087 / 0.011353 (-0.001266) \| 0.005357 / 0.011008 (-0.005651) \| 0.092678 / 0.038508 (0.054170) \| 0.041207 / 0.023109 (0.018098) \| 0.437464 / 0.275898 (0.161566) \| 0.527867 / 0.323480 (0.204387) \| 0.006861 / 0.007986 (-0.001125) \| 0.006131 / 0.004328 (0.001802) \| 0.093741 / 0.004250 (0.089490) \| 0.064142 / 0.037052 (0.027090) \| 0.433577 / 0.258489 (0.175088) \| 0.537148 / 0.293841 (0.243307) \| 0.035339 / 0.128546 (-0.093207) \| 0.010432 / 0.075646 (-0.065214) \| 0.102838 / 0.419271 (-0.316434) \| 0.057905 / 0.043533 (0.014372) \| 0.437956 / 0.255139 (0.182817) \| 0.509562 / 0.283200 (0.226362) \| 0.120620 / 0.141683 (-0.021063) \| 1.798686 / 1.452155 (0.346531) \| 2.013290 / 1.492716 (0.520574) \|\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) \| 0.249067 / 0.018006 (0.231061) \| 0.462219 / 0.000490 (0.461729) \| 0.000476 / 0.000200 (0.000276) \| 0.000068 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033988 / 0.037411 (-0.003424) \| 0.135863 / 0.014526 (0.121337) \| 0.144082 / 0.176557 (-0.032474) \| 0.201715 / 0.737135 (-0.535421) \| 0.152079 / 0.296338 (-0.144259) \|\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) \| 0.522820 / 0.215209 (0.307611) \| 5.216723 / 2.077655 (3.139068) \| 2.582355 / 1.504120 (1.078235) \| 2.352799 / 1.541195 (0.811604) \| 2.451943 / 1.468490 (0.983453) \| 0.620381 / 4.584777 (-3.964396) \| 4.537841 / 3.745712 (0.792129) \| 2.206431 / 5.269862 (-3.063431) \| 1.269865 / 4.565676 (-3.295811) \| 0.078744 / 0.424275 (-0.345531) \| 0.014375 / 0.007607 (0.006768) \| 0.648215 / 0.226044 (0.422171) \| 6.482809 / 2.268929 (4.213881) \| 3.210670 / 55.444624 (-52.233954) \| 2.847485 / 6.876477 (-4.028992) \| 2.820946 / 2.142072 (0.678873) \| 0.762711 / 4.805227 (-4.042516) \| 0.171235 / 6.500664 (-6.329429) \| 0.080230 / 0.075469 (0.004761) \|\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) \| 1.646840 / 1.841788 (-0.194948) \| 19.400451 / 8.074308 (11.326142) \| 16.758845 / 10.191392 (6.567453) \| 0.171377 / 0.680424 (-0.509046) \| 0.020400 / 0.534201 (-0.513801) \| 0.467675 / 0.579283 (-0.111608) \| 0.529745 / 0.434364 (0.095381) \| 0.605989 / 0.540337 (0.065652) \| 0.694659 / 1.386936 (-0.692277) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#006bf33ac5c308f9c70f4df4868abd539eb6c366 \"CML watermark\")\n", "It's faster because all the items are the same object, but this also means modifying one of them will alter each unless these items are immutable, and they are in this case (tuples). So we should be careful when using this idiom." ]	1,685,213,687,000	1,685,547,431,000	1,685,539,709,000	CONTRIBUTOR	null	Using `*` is ~2X faster according to [benchmark](https://colab.research.google.com/gist/ttsugriy/c964a2604edf70c41911b10335729b6a/for-vs-mult.ipynb) with just 4 patterns. This doesn't matter much since this tiny difference is not going to be noticeable, but why not?	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5909/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5909/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5909", "html_url": "https://github.com/huggingface/datasets/pull/5909", "diff_url": "https://github.com/huggingface/datasets/pull/5909.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5909.patch", "merged_at": "2023-05-31T13:28:28" }	true
https://api.github.com/repos/huggingface/datasets/issues/5908	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5908/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5908/comments	https://api.github.com/repos/huggingface/datasets/issues/5908/events	https://github.com/huggingface/datasets/issues/5908	1,728,653,935	I_kwDODunzps5nCSpv	5,908	Unbearably slow sorting on big mapped datasets	{ "login": "maximxlss", "id": 29152154, "node_id": "MDQ6VXNlcjI5MTUyMTU0", "avatar_url": "https://avatars.githubusercontent.com/u/29152154?v=4", "gravatar_id": "", "url": "https://api.github.com/users/maximxlss", "html_url": "https://github.com/maximxlss", "followers_url": "https://api.github.com/users/maximxlss/followers", "following_url": "https://api.github.com/users/maximxlss/following{/other_user}", "gists_url": "https://api.github.com/users/maximxlss/gists{/gist_id}", "starred_url": "https://api.github.com/users/maximxlss/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/maximxlss/subscriptions", "organizations_url": "https://api.github.com/users/maximxlss/orgs", "repos_url": "https://api.github.com/users/maximxlss/repos", "events_url": "https://api.github.com/users/maximxlss/events{/privacy}", "received_events_url": "https://api.github.com/users/maximxlss/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Hi ! `shard` currently returns a slow dataset by default, with examples evenly distributed in the dataset.\r\n\r\nYou can get a fast dataset using `contiguous=True` (which should be the default imo):\r\n\r\n```python\r\ndataset = dataset.shard(10, 0, contiguous=True)\r\n```\r\n\r\nThis way you don't need to flatten_indices() and sort should be fast as well", "@lhoestq \r\n\r\n> contiguous=True (which should be the default imo)\r\n\r\nFor `IterableDataset`, it's not possible to implement contiguous sharding without knowing the number of examples in advance, so setting the default value to `contiguous=True` would result in an inconsistency between `Dataset` and `IterableDataset` (when we add `IterableDataset.shard`)", "Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nIf the dataset is made of one shard it's indeed not possible to shard it contiguously though", "> Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nBut sharding an iterable dataset by sharding its `gen_kwargs` would still yield approximate shards(not equal to `Dataset.shard`), no? ", "Yes indeed !", "I understand the issue doesn't exist with non-mapped datasets, but if flattening is so much more efficient than sorting the indices, that's an issue in itself.\n\nThere are plenty of issues people posted for which the root cause turns out to be the same. It seems like mapped datasets are terribly inefficient. I think I saw some issue like that somewhere (about the mapped datasets in general), but can't find it now.\n\nMaybe indices should be flattened before any additional processing, then." ]	1,685,185,712,000	1,686,678,310,000	null	CONTRIBUTOR	null	### Describe the bug For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lines at all, with flattening takes about a minute). ### Steps to reproduce the bug ```Python from datasets import load_dataset import time dataset = load_dataset("xnli", "en", split="train") dataset = dataset.shard(10, 0) print(len(dataset)) t = time.time() # dataset = dataset.flatten_indices() # uncomment this line and it's fast dataset = dataset.sort("label", reverse=True, load_from_cache_file=False) print(f"finished in {time.time() - t:.4f} seconds") ``` ### Expected behavior Expect sorting to take the same or less time than flattening and then sorting. ### Environment info - `datasets` version: 2.12.1.dev0 (same with 2.12.0 too) - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.10.10 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5908/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5908/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5907	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5907/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5907/comments	https://api.github.com/repos/huggingface/datasets/issues/5907/events	https://github.com/huggingface/datasets/pull/5907	1,728,648,560	PR_kwDODunzps5RfqUU	5,907	Add `flatten_indices` to `DatasetDict`	{ "login": "maximxlss", "id": 29152154, "node_id": "MDQ6VXNlcjI5MTUyMTU0", "avatar_url": "https://avatars.githubusercontent.com/u/29152154?v=4", "gravatar_id": "", "url": "https://api.github.com/users/maximxlss", "html_url": "https://github.com/maximxlss", "followers_url": "https://api.github.com/users/maximxlss/followers", "following_url": "https://api.github.com/users/maximxlss/following{/other_user}", "gists_url": "https://api.github.com/users/maximxlss/gists{/gist_id}", "starred_url": "https://api.github.com/users/maximxlss/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/maximxlss/subscriptions", "organizations_url": "https://api.github.com/users/maximxlss/orgs", "repos_url": "https://api.github.com/users/maximxlss/repos", "events_url": "https://api.github.com/users/maximxlss/events{/privacy}", "received_events_url": "https://api.github.com/users/maximxlss/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.006192 / 0.011353 (-0.005161) \| 0.004410 / 0.011008 (-0.006598) \| 0.095990 / 0.038508 (0.057482) \| 0.032662 / 0.023109 (0.009553) \| 0.322827 / 0.275898 (0.046929) \| 0.352542 / 0.323480 (0.029062) \| 0.005398 / 0.007986 (-0.002588) \| 0.003926 / 0.004328 (-0.000403) \| 0.075131 / 0.004250 (0.070880) \| 0.046205 / 0.037052 (0.009153) \| 0.330957 / 0.258489 (0.072468) \| 0.360166 / 0.293841 (0.066325) \| 0.027880 / 0.128546 (-0.100666) \| 0.008813 / 0.075646 (-0.066833) \| 0.327316 / 0.419271 (-0.091955) \| 0.050071 / 0.043533 (0.006539) \| 0.319939 / 0.255139 (0.064800) \| 0.331593 / 0.283200 (0.048393) \| 0.096745 / 0.141683 (-0.044938) \| 1.445165 / 1.452155 (-0.006990) \| 1.515538 / 1.492716 (0.022821) \|\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) \| 0.209365 / 0.018006 (0.191358) \| 0.437007 / 0.000490 (0.436518) \| 0.003207 / 0.000200 (0.003007) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027261 / 0.037411 (-0.010151) \| 0.105101 / 0.014526 (0.090575) \| 0.117163 / 0.176557 (-0.059394) \| 0.176237 / 0.737135 (-0.560898) \| 0.122559 / 0.296338 (-0.173779) \|\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) \| 0.406792 / 0.215209 (0.191583) \| 4.060831 / 2.077655 (1.983176) \| 1.829691 / 1.504120 (0.325571) \| 1.633155 / 1.541195 (0.091960) \| 1.704817 / 1.468490 (0.236327) \| 0.525325 / 4.584777 (-4.059452) \| 3.752907 / 3.745712 (0.007194) \| 1.857513 / 5.269862 (-3.412349) \| 1.222237 / 4.565676 (-3.343439) \| 0.065941 / 0.424275 (-0.358334) \| 0.012498 / 0.007607 (0.004891) \| 0.495009 / 0.226044 (0.268965) \| 4.968074 / 2.268929 (2.699145) \| 2.277898 / 55.444624 (-53.166727) \| 1.936656 / 6.876477 (-4.939821) \| 1.970698 / 2.142072 (-0.171374) \| 0.635221 / 4.805227 (-4.170006) \| 0.140539 / 6.500664 (-6.360125) \| 0.064111 / 0.075469 (-0.011358) \|\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) \| 1.238151 / 1.841788 (-0.603637) \| 14.681262 / 8.074308 (6.606954) \| 13.405525 / 10.191392 (3.214133) \| 0.163225 / 0.680424 (-0.517199) \| 0.017282 / 0.534201 (-0.516918) \| 0.395526 / 0.579283 (-0.183757) \| 0.429156 / 0.434364 (-0.005208) \| 0.470806 / 0.540337 (-0.069531) \| 0.571290 / 1.386936 (-0.815646) \|\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) \| 0.006444 / 0.011353 (-0.004909) \| 0.004388 / 0.011008 (-0.006621) \| 0.075004 / 0.038508 (0.036496) \| 0.032904 / 0.023109 (0.009795) \| 0.375360 / 0.275898 (0.099462) \| 0.413684 / 0.323480 (0.090204) \| 0.005854 / 0.007986 (-0.002132) \| 0.005504 / 0.004328 (0.001175) \| 0.075049 / 0.004250 (0.070799) \| 0.047973 / 0.037052 (0.010920) \| 0.377943 / 0.258489 (0.119454) \| 0.427039 / 0.293841 (0.133198) \| 0.028248 / 0.128546 (-0.100298) \| 0.008972 / 0.075646 (-0.066674) \| 0.081848 / 0.419271 (-0.337424) \| 0.047935 / 0.043533 (0.004402) \| 0.377980 / 0.255139 (0.122841) \| 0.407856 / 0.283200 (0.124656) \| 0.103454 / 0.141683 (-0.038229) \| 1.469051 / 1.452155 (0.016896) \| 1.590657 / 1.492716 (0.097941) \|\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) \| 0.192380 / 0.018006 (0.174374) \| 0.440995 / 0.000490 (0.440505) \| 0.004082 / 0.000200 (0.003882) \| 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) \| 0.029584 / 0.037411 (-0.007828) \| 0.110051 / 0.014526 (0.095525) \| 0.121196 / 0.176557 (-0.055361) \| 0.172249 / 0.737135 (-0.564886) \| 0.125380 / 0.296338 (-0.170958) \|\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) \| 0.435218 / 0.215209 (0.220009) \| 4.354811 / 2.077655 (2.277156) \| 2.102050 / 1.504120 (0.597930) \| 1.913454 / 1.541195 (0.372260) \| 1.974624 / 1.468490 (0.506134) \| 0.529975 / 4.584777 (-4.054802) \| 3.801605 / 3.745712 (0.055893) \| 3.162408 / 5.269862 (-2.107454) \| 1.599576 / 4.565676 (-2.966101) \| 0.066710 / 0.424275 (-0.357565) \| 0.012158 / 0.007607 (0.004551) \| 0.549187 / 0.226044 (0.323142) \| 5.489930 / 2.268929 (3.221002) \| 2.646787 / 55.444624 (-52.797837) \| 2.311915 / 6.876477 (-4.564562) \| 2.335645 / 2.142072 (0.193572) \| 0.641067 / 4.805227 (-4.164160) \| 0.142227 / 6.500664 (-6.358437) \| 0.065303 / 0.075469 (-0.010166) \|\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) \| 1.283209 / 1.841788 (-0.558579) \| 15.241809 / 8.074308 (7.167501) \| 14.131471 / 10.191392 (3.940079) \| 0.143921 / 0.680424 (-0.536503) \| 0.017497 / 0.534201 (-0.516704) \| 0.402236 / 0.579283 (-0.177047) \| 0.418917 / 0.434364 (-0.015447) \| 0.461745 / 0.540337 (-0.078593) \| 0.560212 / 1.386936 (-0.826724) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7098922130cabfbfa6b8a3885ff2e6f032d6203d \"CML watermark\")\n" ]	1,685,184,944,000	1,685,619,995,000	1,685,619,576,000	CONTRIBUTOR	null	Add `flatten_indices` to `DatasetDict` for convinience	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5907/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5907/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5907", "html_url": "https://github.com/huggingface/datasets/pull/5907", "diff_url": "https://github.com/huggingface/datasets/pull/5907.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5907.patch", "merged_at": "2023-06-01T11:39:35" }	true
https://api.github.com/repos/huggingface/datasets/issues/5906	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5906/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5906/comments	https://api.github.com/repos/huggingface/datasets/issues/5906/events	https://github.com/huggingface/datasets/issues/5906	1,728,171,113	I_kwDODunzps5nAcxp	5,906	Could you unpin responses version?	{ "login": "kenimou", "id": 47789026, "node_id": "MDQ6VXNlcjQ3Nzg5MDI2", "avatar_url": "https://avatars.githubusercontent.com/u/47789026?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kenimou", "html_url": "https://github.com/kenimou", "followers_url": "https://api.github.com/users/kenimou/followers", "following_url": "https://api.github.com/users/kenimou/following{/other_user}", "gists_url": "https://api.github.com/users/kenimou/gists{/gist_id}", "starred_url": "https://api.github.com/users/kenimou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kenimou/subscriptions", "organizations_url": "https://api.github.com/users/kenimou/orgs", "repos_url": "https://api.github.com/users/kenimou/repos", "events_url": "https://api.github.com/users/kenimou/events{/privacy}", "received_events_url": "https://api.github.com/users/kenimou/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[]	1,685,131,334,000	1,685,469,211,000	1,685,469,211,000	NONE	null	### Describe the bug Could you unpin [this](https://github.com/huggingface/datasets/blob/main/setup.py#L139) or move it to test requirements? This is a testing library and we also use it for our tests as well. We do not want to use a very outdated version. ### Steps to reproduce the bug could not install this library due to dependency conflict. ### Expected behavior can install datasets ### Environment info linux 64	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5906/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5906/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5905	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5905/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5905/comments	https://api.github.com/repos/huggingface/datasets/issues/5905/events	https://github.com/huggingface/datasets/issues/5905	1,727,541,392	I_kwDODunzps5m-DCQ	5,905	Offer an alternative to Iterable Dataset that allows lazy loading and processing while skipping batches efficiently	{ "login": "Hubert-Bonisseur", "id": 48770768, "node_id": "MDQ6VXNlcjQ4NzcwNzY4", "avatar_url": "https://avatars.githubusercontent.com/u/48770768?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Hubert-Bonisseur", "html_url": "https://github.com/Hubert-Bonisseur", "followers_url": "https://api.github.com/users/Hubert-Bonisseur/followers", "following_url": "https://api.github.com/users/Hubert-Bonisseur/following{/other_user}", "gists_url": "https://api.github.com/users/Hubert-Bonisseur/gists{/gist_id}", "starred_url": "https://api.github.com/users/Hubert-Bonisseur/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Hubert-Bonisseur/subscriptions", "organizations_url": "https://api.github.com/users/Hubert-Bonisseur/orgs", "repos_url": "https://api.github.com/users/Hubert-Bonisseur/repos", "events_url": "https://api.github.com/users/Hubert-Bonisseur/events{/privacy}", "received_events_url": "https://api.github.com/users/Hubert-Bonisseur/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	[ "We plan to improve this eventually (see https://github.com/huggingface/datasets/issues/5454 and https://github.com/huggingface/datasets/issues/5380).\r\n\r\n> Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there.\r\nIf not, I could do it using a plain Pytorch dataset. Then I would need to convert it to a datasets' dataset to get all the features of datasets. Is it something possible ?\r\n\r\nYes, by creating a mapped dataset that stores audio URLs. Indexing a dataset in such format only downloads and decodes the bytes of the accessed samples (without storing them on disk).\r\n\r\nYou can do the following to create this dataset:\r\n```python\r\n\r\ndef gen():\r\n # Generator that yields (audio URL, text) pairs as dict\r\n ...\r\n yield {\"audio\": \"audio_url\", \"text\": \"some text\"}\r\n\r\nfeatures = Features({\"audio\": datasets.Audio(), \"text\": datasets.Value(\"string\")})\r\nds = Dataset.from_generator(gen, features=features)\r\nds[2:5] # downloads and decodes the samples each time they are accessed\r\n```" ]	1,685,104,382,000	1,686,836,058,000	null	CONTRIBUTOR	null	### Feature request I would like a way to resume training from a checkpoint without waiting for a very long time when using an iterable dataset. ### Motivation I am training models on the speech-recognition task. I have very large datasets that I can't comfortably store on a disk and also quite computationally intensive audio processing to do. As a result I want to load data from my remote when it is needed and perform all processing on the fly. I am currently using the iterable dataset feature of _datasets_. It does everything I need with one exception. My issue is that when resuming training at a step n, we have to download all the data and perform the processing of steps < n, just to get the iterable at the right step. In my case it takes almost as long as training for the same steps, which make resuming training from a checkpoint useless in practice. I understand that the nature of iterators make it probably nearly impossible to quickly resume training. I thought about a possible solution nonetheless : I could in fact index my large dataset and make it a mapped dataset. Then I could use set_transform to perform the processing on the fly. Finally, if I'm not mistaken, the _accelerate_ package allows to [skip steps efficiently](https://github.com/huggingface/accelerate/blob/a73898027a211c3f6dc4460351b0ec246aa824aa/src/accelerate/data_loader.py#L827) for a mapped dataset. Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there. If not, I could do it using a plain _Pytorch_ dataset. Then I would need to convert it to a _datasets_' dataset to get all the features of _datasets_. Is it something possible ? ### Your contribution I could provide a PR to allow lazy loading of mapped dataset or the conversion of a mapped _Pytorch_ dataset into a _Datasets_ dataset if you think it is an useful new feature.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5905/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5905/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5904	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5904/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5904/comments	https://api.github.com/repos/huggingface/datasets/issues/5904/events	https://github.com/huggingface/datasets/pull/5904	1,727,415,626	PR_kwDODunzps5Rbfks	5,904	Validate name parameter in make_file_instructions	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	[ "_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) \| 0.007401 / 0.011353 (-0.003952) \| 0.005198 / 0.011008 (-0.005810) \| 0.112317 / 0.038508 (0.073809) \| 0.038406 / 0.023109 (0.015297) \| 0.358008 / 0.275898 (0.082110) \| 0.395350 / 0.323480 (0.071870) \| 0.006201 / 0.007986 (-0.001785) \| 0.004368 / 0.004328 (0.000039) \| 0.087718 / 0.004250 (0.083467) \| 0.055299 / 0.037052 (0.018247) \| 0.350481 / 0.258489 (0.091992) \| 0.419876 / 0.293841 (0.126035) \| 0.032459 / 0.128546 (-0.096087) \| 0.010635 / 0.075646 (-0.065011) \| 0.383282 / 0.419271 (-0.035989) \| 0.059241 / 0.043533 (0.015708) \| 0.365101 / 0.255139 (0.109962) \| 0.378144 / 0.283200 (0.094944) \| 0.114287 / 0.141683 (-0.027396) \| 1.680870 / 1.452155 (0.228715) \| 1.788183 / 1.492716 (0.295467) \|\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) \| 0.242919 / 0.018006 (0.224913) \| 0.489850 / 0.000490 (0.489360) \| 0.011408 / 0.000200 (0.011208) \| 0.000444 / 0.000054 (0.000389) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030742 / 0.037411 (-0.006669) \| 0.123092 / 0.014526 (0.108566) \| 0.138246 / 0.176557 (-0.038311) \| 0.207299 / 0.737135 (-0.529836) \| 0.142647 / 0.296338 (-0.153691) \|\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) \| 0.472553 / 0.215209 (0.257344) \| 4.671763 / 2.077655 (2.594108) \| 2.119986 / 1.504120 (0.615866) \| 1.891851 / 1.541195 (0.350656) \| 1.979094 / 1.468490 (0.510604) \| 0.617956 / 4.584777 (-3.966821) \| 4.969418 / 3.745712 (1.223706) \| 4.672083 / 5.269862 (-0.597779) \| 2.119049 / 4.565676 (-2.446627) \| 0.077466 / 0.424275 (-0.346809) \| 0.014434 / 0.007607 (0.006827) \| 0.580746 / 0.226044 (0.354701) \| 5.805458 / 2.268929 (3.536530) \| 2.622498 / 55.444624 (-52.822126) \| 2.259499 / 6.876477 (-4.616978) \| 2.362078 / 2.142072 (0.220006) \| 0.719911 / 4.805227 (-4.085317) \| 0.164939 / 6.500664 (-6.335725) \| 0.074762 / 0.075469 (-0.000707) \|\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) \| 1.496709 / 1.841788 (-0.345079) \| 18.247499 / 8.074308 (10.173191) \| 15.397075 / 10.191392 (5.205683) \| 0.181163 / 0.680424 (-0.499261) \| 0.022604 / 0.534201 (-0.511597) \| 0.462791 / 0.579283 (-0.116492) \| 0.504473 / 0.434364 (0.070109) \| 0.582254 / 0.540337 (0.041917) \| 0.673849 / 1.386936 (-0.713087) \|\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) \| 0.007633 / 0.011353 (-0.003720) \| 0.004859 / 0.011008 (-0.006149) \| 0.091194 / 0.038508 (0.052686) \| 0.038255 / 0.023109 (0.015146) \| 0.460972 / 0.275898 (0.185074) \| 0.470441 / 0.323480 (0.146961) \| 0.006482 / 0.007986 (-0.001504) \| 0.004500 / 0.004328 (0.000172) \| 0.089998 / 0.004250 (0.085748) \| 0.055470 / 0.037052 (0.018418) \| 0.459188 / 0.258489 (0.200699) \| 0.491255 / 0.293841 (0.197414) \| 0.032200 / 0.128546 (-0.096346) \| 0.010372 / 0.075646 (-0.065274) \| 0.097429 / 0.419271 (-0.321843) \| 0.052469 / 0.043533 (0.008936) \| 0.452492 / 0.255139 (0.197353) \| 0.475210 / 0.283200 (0.192010) \| 0.116976 / 0.141683 (-0.024707) \| 1.752742 / 1.452155 (0.300587) \| 1.849535 / 1.492716 (0.356819) \|\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) \| 0.229822 / 0.018006 (0.211816) \| 0.472259 / 0.000490 (0.471770) \| 0.000455 / 0.000200 (0.000255) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033796 / 0.037411 (-0.003615) \| 0.136151 / 0.014526 (0.121625) \| 0.144015 / 0.176557 (-0.032542) \| 0.199337 / 0.737135 (-0.537798) \| 0.150024 / 0.296338 (-0.146315) \|\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) \| 0.522737 / 0.215209 (0.307528) \| 5.165223 / 2.077655 (3.087568) \| 2.630334 / 1.504120 (1.126214) \| 2.392383 / 1.541195 (0.851188) \| 2.488966 / 1.468490 (1.020476) \| 0.608981 / 4.584777 (-3.975796) \| 4.711545 / 3.745712 (0.965833) \| 2.121537 / 5.269862 (-3.148325) \| 1.205477 / 4.565676 (-3.360199) \| 0.078277 / 0.424275 (-0.345998) \| 0.014175 / 0.007607 (0.006568) \| 0.640720 / 0.226044 (0.414675) \| 6.391173 / 2.268929 (4.122245) \| 3.265131 / 55.444624 (-52.179493) \| 2.939188 / 6.876477 (-3.937289) \| 2.919217 / 2.142072 (0.777145) \| 0.745095 / 4.805227 (-4.060132) \| 0.164065 / 6.500664 (-6.336599) \| 0.076993 / 0.075469 (0.001524) \|\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) \| 1.539971 / 1.841788 (-0.301817) \| 18.597296 / 8.074308 (10.522988) \| 16.899330 / 10.191392 (6.707938) \| 0.169005 / 0.680424 (-0.511419) \| 0.020447 / 0.534201 (-0.513754) \| 0.465862 / 0.579283 (-0.113421) \| 0.522819 / 0.434364 (0.088455) \| 0.547111 / 0.540337 (0.006773) \| 0.657777 / 1.386936 (-0.729159) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56aff9ecb4e565eb95faad525558914648cc22f1 \"CML watermark\")\n" ]	1,685,099,566,000	1,685,519,012,000	1,685,518,497,000	MEMBER	null	Validate `name` parameter in `make_file_instructions`. This way users get more informative error messages, instead of: ```stacktrace .../huggingface/datasets/src/datasets/arrow_reader.py in make_file_instructions(name, split_infos, instruction, filetype_suffix, prefix_path) 110 name2len = {info.name: info.num_examples for info in split_infos} 111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos} --> 112 name2filenames = { 113 info.name: filenames_for_dataset_split( 114 path=prefix_path, .../huggingface/datasets/src/datasets/arrow_reader.py in <dictcomp>(.0) 111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos} 112 name2filenames = { --> 113 info.name: filenames_for_dataset_split( 114 path=prefix_path, 115 dataset_name=name, .../huggingface/datasets/src/datasets/naming.py in filenames_for_dataset_split(path, dataset_name, split, filetype_suffix, shard_lengths) 68 69 def filenames_for_dataset_split(path, dataset_name, split, filetype_suffix=None, shard_lengths=None): ---> 70 prefix = filename_prefix_for_split(dataset_name, split) 71 prefix = os.path.join(path, prefix) 72 .../huggingface/datasets/src/datasets/naming.py in filename_prefix_for_split(name, split) 52 53 def filename_prefix_for_split(name, split): ---> 54 if os.path.basename(name) != name: 55 raise ValueError(f"Should be a dataset name, not a path: {name}") 56 if not re.match(_split_re, split): .../lib/python3.9/posixpath.py in basename(p) 140 def basename(p): 141 """Returns the final component of a pathname""" --> 142 p = os.fspath(p) 143 sep = _get_sep(p) 144 i = p.rfind(sep) + 1 TypeError: expected str, bytes or os.PathLike object, not NoneType ``` Related to #5895.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5904/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5904/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5904", "html_url": "https://github.com/huggingface/datasets/pull/5904", "diff_url": "https://github.com/huggingface/datasets/pull/5904.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5904.patch", "merged_at": "2023-05-31T07:34:57" }	true
https://api.github.com/repos/huggingface/datasets/issues/5903	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5903/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5903/comments	https://api.github.com/repos/huggingface/datasets/issues/5903/events	https://github.com/huggingface/datasets/pull/5903	1,727,372,549	PR_kwDODunzps5RbV82	5,903	Relax `ci.yml` trigger for `pull_request` based on modified paths	{ "login": "alvarobartt", "id": 36760800, "node_id": "MDQ6VXNlcjM2NzYwODAw", "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "gravatar_id": "", "url": "https://api.github.com/users/alvarobartt", "html_url": "https://github.com/alvarobartt", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "repos_url": "https://api.github.com/users/alvarobartt/repos", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	[ "Also this could be extended to the rest of the GitHub Action `yml` files, so let me know whether you want me to have a look into it! 🤗", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5903). All of your documentation changes will be reflected on that endpoint." ]	1,685,098,012,000	1,685,098,297,000	null	CONTRIBUTOR	null	## What's in this PR? As of a previous PR at #5902, I've seen that the CI was automatically trigger on any file, in that case when modifying a Jupyter Notebook (.ipynb), which IMO could be skipped, as the modification on the Jupyter Notebook has no effect/impact on the `ci.yml` outcome. So this PR controls the paths that trigger the `ci.yml` to avoid wasting resources when not needed. ## What's pending in this PR? I would like to confirm whether this should affect both `push` and `pull_request`, since just modifications in those files won't change the `ci.yml` outcome, so maybe it's worth skipping it too in the `push` trigger.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5903/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5903/timeline	null	null	0	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5903", "html_url": "https://github.com/huggingface/datasets/pull/5903", "diff_url": "https://github.com/huggingface/datasets/pull/5903.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5903.patch", "merged_at": null }	true