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"2022-12-15T10:21:28"

"2022-12-15T11:51:33"

"2022-12-15T11:51:33"

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[ "Thanks for reporting, @shaoyuta.\r\n\r\nWe have checked and yes, apparently there is an issue with the server hosting the data of the \"enron_emails\" subset of \"the_pile\" dataset: http://eaidata.bmk.sh/data/enron_emails.jsonl.zst\r\nIt seems to be down: The connection has timed out.\r\n\r\nPlease note that at the Hugging Face Hub, we are not hosting their data for this dataset, but only a script that downloads the data from their servers. We are updating the data URL to one in another server.\r\n\r\nIn the meantime, please note that you can train your model in the entire \"the_pile\" dataset, by passing the \"all\" config (instead of the \"enron_emails\" one).", "We have transferred this issue to the corresponding dataset Community tab: https://huggingface.co/datasets/the_pile/discussions/2\r\n\r\nPlease, follow the updates there." ]

"2022-12-15T01:23:03"

"2022-12-15T07:45:54"

"2022-12-15T07:45:53"

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[ "You can try something like this ?\r\n```python\r\ndef mapper_function(batch):\r\n return {\"concatenated_audio\": [np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]])]}\r\n\r\ndataset = dataset.map(\r\n mapper_function,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n```", "Thanks for the snippet!\r\n\r\nOne more question. I wonder why those two mappers are working so different that one taking 4 sec while other taking over 1 min :\r\n\r\n```python\r\n%%time\r\ndef mapper_function1(batch):\r\n # list_audio\r\n return {\r\n \"audio\": [\r\n {\r\n \"array\": np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]]),\r\n \"sampling_rate\": 16_000,\r\n }\r\n ]\r\n }\r\n\r\ndataset.map(\r\n mapper_function1,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n\r\n# 100%\r\n# 135/135 [01:13<00:00, 1.93ba/s]\r\n# CPU times: user 1min 10s, sys: 3.21 s, total: 1min 13s\r\n# Wall time: 1min 13s\r\n# Dataset({\r\n# features: ['audio'],\r\n# num_rows: 135\r\n# })\r\n\r\n# --------------------------------\r\n%%time\r\ndef mapper_function2(batch):\r\n # list_audio\r\n return {\"audio\": [np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]])]}\r\n\r\ndataset.map(\r\n mapper_function2,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n\r\n# 100%\r\n# 135/135 [00:03<00:00, 40.69ba/s]\r\n# CPU times: user 1.88 s, sys: 1.48 s, total: 3.36 s\r\n# Wall time: 4.8 s\r\n# Dataset({\r\n# features: ['audio'],\r\n# num_rows: 135\r\n# })\r\n```\r\n", "In the first one you get a dataset with an Audio type, and in the second one you get a dataset with a sequence of floats type.\r\n\r\nThe Audio type encodes the data as WAV to save disk space, so it takes more time to create.\r\nThe Audio type is automatically inferred because you modify the column \"audio\" which was already an Audio type. If you name it to something else, type inference will use a type struct with array and sampling rate fields." ]

"2022-12-14T18:13:55"

"2023-07-21T14:30:51"

"2023-07-21T14:30:51"

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[ "If you use huggingface trainer, you will find the trainer has wrapped a `IterableDatasetShard` to avoid duplication.\r\nSee:\r\nhttps://github.com/huggingface/transformers/blob/dfd818420dcbad68e05a502495cf666d338b2bfb/src/transformers/trainer.py#L835\r\n", "If you want to support it by datasets natively, maybe we also need to change the code in `transformers` ?", "Opened https://github.com/huggingface/transformers/issues/20770 to discuss this :)", "Maybe something like this then ?\r\n```python\r\nfrom datasets.distributed import split_dataset_by_node\r\nds = split_dataset_by_node(ds, rank=rank, world_size=world_size)\r\n```\r\n\r\nFor map-style datasets the implementation is trivial (it can simply use `.shard()`).\r\n\r\nFor iterable datasets we would need to implement a new ExamplesIterable that would only iterate on a subset of the (possibly shuffled and re-shuffled after each epoch) list of shards, based on the rank and world size.", "My plan is to skip examples by default to not end up with duplicates.\r\n\r\nAnd if a dataset has a number of shards that is a factor of the world size, then I'd make it more optimized by distributing the shards evenly across nodes instead.", "Opened a PR here: https://github.com/huggingface/datasets/pull/5369\r\n\r\nfeel free to play with it and share your feedbacks :)", "@lhoestq I add shuffle after split_dataset_by_node, duplicated data still exist. \r\nFor example, we have a directory named `mock_pretraining_data`, which has three files, `part-00000`, `part-00002`,`part-00002`. \r\nText in `part-00000` is like this: \r\n{\"id\": 0}\r\n{\"id\": 1}\r\n{\"id\": 2}\r\n{\"id\": 3}\r\n{\"id\": 4}\r\n{\"id\": 5}\r\n{\"id\": 6}\r\n{\"id\": 7}\r\n{\"id\": 8}\r\n{\"id\": 9}\r\n\r\nand `part-00001`\r\n{\"id\": 10}\r\n{\"id\": 11}\r\n{\"id\": 12}\r\n{\"id\": 13}\r\n{\"id\": 14}\r\n{\"id\": 15}\r\n{\"id\": 16}\r\n{\"id\": 17}\r\n{\"id\": 18}\r\n{\"id\": 19}\r\n\r\nand `part-00002`\r\n{\"id\": 20}\r\n{\"id\": 21}\r\n{\"id\": 22}\r\n{\"id\": 23}\r\n{\"id\": 24}\r\n{\"id\": 25}\r\n{\"id\": 26}\r\n{\"id\": 27}\r\n{\"id\": 28}\r\n{\"id\": 29}\r\n\r\nAnd code in `test_dist.py` like this,\r\n```python\r\nimport torch\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom datasets import load_dataset\r\nimport os\r\nfrom transformers import AutoTokenizer, NezhaForPreTraining\r\nfrom transformers import AdamW, get_linear_schedule_with_warmup\r\nimport torch.nn.functional as F\r\nimport torch.nn as nn\r\nimport torch.distributed as dist\r\nfrom datasets.distributed import split_dataset_by_node\r\nfrom torch.nn.parallel import DistributedDataParallel as DDP\r\n\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '5,6,7'\r\n\r\ndist.init_process_group(\"nccl\")\r\nlocal_rank = int(os.environ['LOCAL_RANK'])\r\nworld_size = torch.distributed.get_world_size()\r\ndevice = torch.device('cuda', local_rank)\r\ndata_dir = './'\r\n\r\ndef load_trainset(train_path):\r\n dataset = load_dataset('json', data_dir=os.path.join(data_dir, train_path), split='train', streaming=True)\r\n return dataset\r\n\r\ndef collate_fn(examples):\r\n input_ids = []\r\n for example in examples:\r\n input_ids.append(example['id'])\r\n return torch.LongTensor(input_ids).to(device)\r\n\r\n\r\ndataset = load_trainset('mock_pretraining_data')\r\ndataset = split_dataset_by_node(dataset, rank=local_rank, world_size=world_size).shuffle(buffer_size=512)\r\n# train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\r\nbatch_size = 3\r\nprint('batch_size: {}'.format(batch_size))\r\ntrain_dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn)\r\n\r\nfor x in train_dataloader:\r\n print({'rank': local_rank, 'id': x})\r\n```\r\nrun `python -m torch.distributed.launch --nproc_per_node=3 test_dist.py`\r\nThe output is\r\n```\r\n{'rank': 1, 'id': tensor([12, 15, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([16, 10, 18], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([17, 13, 19], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([11], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([0, 2, 9], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([4, 8, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([5, 3, 6], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([7], device='cuda:0')}\r\n{'rank': 2, 'id': tensor([13, 15, 14], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([19, 17, 18], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([12, 16, 11], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([10], device='cuda:2')}\r\n```\r\n`part-00001` is loaded twice, `part-00002` isn't loaded.\r\n\r\nIf I run `python -m torch.distributed.launch --nproc_per_node=2 test_dist.py`\r\nThe output is weirder，many numbers appear twice\r\n```\r\n{'rank': 1, 'id': tensor([26, 8, 13], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([22, 19, 20], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([12, 28, 11], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([24, 2, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([ 6, 27, 3], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([ 8, 25, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([20, 4, 12], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([14, 29, 5], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([ 7, 18, 23], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([19, 17, 11], device='cuda:0')}\r\n``` ", "Hi ! Thanks for reporting, you need to pass `seed=` to `shuffle()` or the processes won't use the same seed to shuffle the shards order before assigning each shard to a node.\r\n\r\nThe issue is that the workers are not using the same seed to shuffle the shards before splitting the shards list by node.", "Opened https://github.com/huggingface/datasets/issues/5696", "I have the same issue\r\n```\r\nds['train'] = load_dataset(streaming=True)\r\nds['train'] = split_dataset_by_node(ds['train'], rank=int(os.environ[\"RANK\"]), world_size=int(os.environ[\"WORLD_SIZE\"]))\r\nvectorized_datasets = ds.map(\r\n prepare_dataset,\r\n remove_columns=raw_datasets_features,\r\n).with_format(\"torch\")\r\n\r\nvectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].shuffle(\r\n buffer_size=500,\r\n seed=42,\r\n)\r\n\r\ndef prepare_dataset(batch):\r\n ....\r\n print(f\"sentence: {batch['sentence']}, target_text: {batch['target_text']}\")\r\n return batch\r\n```\r\nWhen using split_dataset_by_node(), the data being read is indeed different for each GPU ID.\r\n\r\n```\r\ntrainer = Trainer(\r\n model=model,\r\n data_collator=data_collator,\r\n args=training_args,\r\n compute_metrics=compute_metrics,\r\n train_dataset=vectorized_datasets[\"train\"] if training_args.do_train else None,\r\n eval_dataset=vectorized_datasets[\"eval\"] if training_args.do_eval else None,\r\n tokenizer=processor,\r\n callbacks=[ShuffleCallback()],\r\n )\r\n...\r\ntrain_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n```\r\nHowever, when I execute trainer.train(), the data being read is different from what I expected.\r\nBecause I print the batch value in prepare_dataset() , I observe that the data is the same for each GPU ID.\r\n\r\nHow should I handle this issue?\r\n\r\n\r\n", "There are two ways an iterable dataset can be split by node:\r\n1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU\r\n2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others.\r\n\r\nIn case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU.\r\n\r\nThis doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end.\r\n\r\nCould you open a new issue so that we can discuss about this and find a solution ?" ]

"2022-12-14T16:06:19"

"2023-06-15T09:51:13"

"2023-01-16T13:33:33"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for your proposed fix, @freddyheppell.\r\n\r\nCurrently the CI fails because in a test we pass a `tuple` instead of a `list`. I would say we should accept `tuple` as a valid input type as well...\r\n\r\nWhat about checking for `Sequence` instead?", "Fixed that @albertvillanova, can you approve CI again please? Had some issues related to Pytorch .so files when running tests on my M1 mac, so wasn't able to test locally first. Have got them working on my desktop now though." ]

"2022-12-13T23:04:06"

"2022-12-22T16:35:49"

"2022-12-22T16:32:49"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mariosasko Sorry, I didn't check tests/style after doing a merge from the Git UI last week. Thx for fixing. \r\n\r\nFYI I'm getting \"Only those with [write access](https://docs.github.com/articles/what-are-the-different-access-permissions) to this repository can merge pull requests.\" so it seems somebody else needs to merge this.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008816 / 0.011353 (-0.002536) | 0.004691 / 0.011008 (-0.006317) | 0.100039 / 0.038508 (0.061531) | 0.035422 / 0.023109 (0.012313) | 0.312600 / 0.275898 (0.036702) | 0.378684 / 0.323480 (0.055204) | 0.007593 / 0.007986 (-0.000392) | 0.005183 / 0.004328 (0.000855) | 0.078040 / 0.004250 (0.073790) | 0.041845 / 0.037052 (0.004793) | 0.325251 / 0.258489 (0.066762) | 0.363459 / 0.293841 (0.069618) | 0.038006 / 0.128546 (-0.090540) | 0.011911 / 0.075646 (-0.063735) | 0.335020 / 0.419271 (-0.084251) | 0.048765 / 0.043533 (0.005233) | 0.305913 / 0.255139 (0.050774) | 0.337620 / 0.283200 (0.054420) | 0.101867 / 0.141683 (-0.039816) | 1.450091 / 1.452155 (-0.002064) | 1.437303 / 1.492716 (-0.055413) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225650 / 0.018006 (0.207644) | 0.492480 / 0.000490 (0.491990) | 0.002857 / 0.000200 (0.002658) | 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) | 0.026231 / 0.037411 (-0.011180) | 0.105479 / 0.014526 (0.090953) | 0.118438 / 0.176557 (-0.058119) | 0.167313 / 0.737135 (-0.569822) | 0.119416 / 0.296338 (-0.176923) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396233 / 0.215209 (0.181024) | 3.943325 / 2.077655 (1.865671) | 1.778864 / 1.504120 (0.274744) | 1.587957 / 1.541195 (0.046763) | 1.615404 / 1.468490 (0.146914) | 0.709427 / 4.584777 (-3.875350) | 3.823310 / 3.745712 (0.077598) | 3.461376 / 5.269862 (-1.808486) | 1.888330 / 4.565676 (-2.677346) | 0.086910 / 0.424275 (-0.337365) | 0.012215 / 0.007607 (0.004608) | 0.504877 / 0.226044 (0.278833) | 5.051513 / 2.268929 (2.782584) | 2.249389 / 55.444624 (-53.195235) | 1.890949 / 6.876477 (-4.985528) | 2.015584 / 2.142072 (-0.126489) | 0.862313 / 4.805227 (-3.942914) | 0.166295 / 6.500664 (-6.334369) | 0.061131 / 0.075469 (-0.014338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.201804 / 1.841788 (-0.639984) | 14.589425 / 8.074308 (6.515117) | 13.855522 / 10.191392 (3.664130) | 0.193406 / 0.680424 (-0.487018) | 0.028614 / 0.534201 (-0.505587) | 0.439857 / 0.579283 (-0.139426) | 0.443330 / 0.434364 (0.008966) | 0.514078 / 0.540337 (-0.026259) | 0.608245 / 1.386936 (-0.778691) |\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) | 0.007087 / 0.011353 (-0.004265) | 0.005024 / 0.011008 (-0.005985) | 0.096852 / 0.038508 (0.058344) | 0.032870 / 0.023109 (0.009761) | 0.397790 / 0.275898 (0.121892) | 0.420717 / 0.323480 (0.097237) | 0.005552 / 0.007986 (-0.002434) | 0.003742 / 0.004328 (-0.000586) | 0.074788 / 0.004250 (0.070537) | 0.048030 / 0.037052 (0.010977) | 0.398520 / 0.258489 (0.140031) | 0.460919 / 0.293841 (0.167078) | 0.037652 / 0.128546 (-0.090894) | 0.012249 / 0.075646 (-0.063397) | 0.333077 / 0.419271 (-0.086194) | 0.052364 / 0.043533 (0.008831) | 0.394358 / 0.255139 (0.139219) | 0.414193 / 0.283200 (0.130994) | 0.103569 / 0.141683 (-0.038114) | 1.499208 / 1.452155 (0.047053) | 1.619481 / 1.492716 (0.126764) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229476 / 0.018006 (0.211470) | 0.448670 / 0.000490 (0.448180) | 0.000399 / 0.000200 (0.000199) | 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) | 0.027550 / 0.037411 (-0.009862) | 0.109180 / 0.014526 (0.094654) | 0.118372 / 0.176557 (-0.058185) | 0.153136 / 0.737135 (-0.583999) | 0.122689 / 0.296338 (-0.173650) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445163 / 0.215209 (0.229954) | 4.426350 / 2.077655 (2.348695) | 2.194902 / 1.504120 (0.690782) | 2.019049 / 1.541195 (0.477854) | 2.032795 / 1.468490 (0.564305) | 0.700752 / 4.584777 (-3.884025) | 3.797616 / 3.745712 (0.051903) | 2.046414 / 5.269862 (-3.223447) | 1.345037 / 4.565676 (-3.220639) | 0.085389 / 0.424275 (-0.338886) | 0.012824 / 0.007607 (0.005217) | 0.553875 / 0.226044 (0.327831) | 5.550252 / 2.268929 (3.281323) | 2.702822 / 55.444624 (-52.741803) | 2.346257 / 6.876477 (-4.530220) | 2.410772 / 2.142072 (0.268699) | 0.848271 / 4.805227 (-3.956957) | 0.170787 / 6.500664 (-6.329877) | 0.064344 / 0.075469 (-0.011125) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266222 / 1.841788 (-0.575566) | 14.501194 / 8.074308 (6.426886) | 13.413678 / 10.191392 (3.222286) | 0.589048 / 0.680424 (-0.091375) | 0.018246 / 0.534201 (-0.515955) | 0.425221 / 0.579283 (-0.154062) | 0.425900 / 0.434364 (-0.008464) | 0.494023 / 0.540337 (-0.046314) | 0.604324 / 1.386936 (-0.782612) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]

"2022-12-13T22:35:51"

"2023-01-05T16:46:31"

"2023-01-05T15:59:51"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Need some more time to fix the tests, especially with pickle", "> And I actually don't quite understand the idea - what's the motivation behind making only IterableDataset compatible with torch DataLoader without setting the format explicitly?\r\n\r\nSetting the format to pytorch = set the output types of the dataset to be pytorch tensors. However sometimes your dataset is not made of tensors but you still want to be able to use a pytorch DataLoader", "A bit more context. \r\n\r\nThe arrow-backed `Dataset` supports `DataLoader(ds)` (even if the format is not \"torch\"), and we want to be able to do the same with `IterableDataset` for consistency. However, this is when the PyTorch internals come into play - an iterable dataset needs to be an instance of `torch.utils.data.IterableDataset` due to [this](https://github.com/pytorch/pytorch/blob/abc54f93145830b502400faa92bec86e05422fbd/torch/utils/data/dataloader.py#L276) check (notice there is no check for the map-style version). Hence the explicit subclassing in this PR.", "Exactly :) Btw I just took your comments into account @polinaeterna , so feel free to review again", "@lhoestq just checking, does this change still preserve the fix to the \"data duplicate when setting num_works > 1 with streaming data\" issue from before?\r\n\r\nhttps://github.com/huggingface/datasets/issues/3423", "Yes :)" ]

"2022-12-13T19:39:24"

"2023-01-04T12:45:40"

"2022-12-15T19:15:54"

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

"2022-12-13T18:54:09"

"2022-12-14T17:25:58"

"2022-12-14T17:22:16"

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

"2022-12-13T00:29:47"

"2022-12-13T18:17:56"

"2022-12-13T18:14:42"

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[ "Hi! Linking a comment to provide more info on the issue: https://stackoverflow.com/a/39458225. This means we should replace all (most of) the occurrences of `List` with `Sequence` in function signatures.\r\n\r\n@tranhd95 Would you be interested in submitting a PR?", "Hi all! I tried to reproduce this issue and didn't work for me. Also in your example i noticed that the variables have different names: `list_of_filenames` and `list_of_files`, could this be related to that?\r\n```python\r\n#I found random data in parquet format:\r\n!wget \"https://github.com/Teradata/kylo/raw/master/samples/sample-data/parquet/userdata1.parquet\"\r\n!wget \"https://github.com/Teradata/kylo/raw/master/samples/sample-data/parquet/userdata2.parquet\"\r\n\r\n#Then i try reproduce\r\nlist_of_files = [\"userdata1.parquet\", \"userdata2.parquet\"]\r\nds = Dataset.from_parquet(list_of_files)\r\n```\r\n**My output:**\r\n```python\r\nWARNING:datasets.builder:Using custom data configuration default-e287d097dc54e046\r\nDownloading and preparing dataset parquet/default to /root/.cache/huggingface/datasets/parquet/default-e287d097dc54e046/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\r\nDownloading data files: 100%\r\n1/1 [00:00<00:00, 40.38it/s]\r\nExtracting data files: 100%\r\n1/1 [00:00<00:00, 23.43it/s]\r\nDataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/parquet/default-e287d097dc54e046/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\r\n```\r\nP.S. This is my first experience with open source. So do not judge strictly if I do not understand something)", "@dantema There is indeed a typo in variable names. Nevertheless, I'm sorry if I was not clear but the output is from `mypy` type checker. You can run the code snippet without issues. The problem is with the type checking.", "However, I found out that the type annotation is actually misleading. The [`from_parquet`](https://github.com/huggingface/datasets/blob/5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2/src/datasets/arrow_dataset.py#L1039) method should also accept list of [`PathLike`](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/typing.py#L8) objects which includes [`os.PathLike`](https://docs.python.org/3/library/os.html#os.PathLike). But if I would ran the code snippet below, an exception is thrown.\r\n\r\n**Code**\r\n```py\r\nfrom pathlib import Path\r\n\r\nlist_of_filenames = [Path(\"foo.parquet\"), Path(\"bar.parquet\")]\r\nds = Dataset.from_parquet(list_of_filenames)\r\n```\r\n**Output**\r\n```py\r\n[/usr/local/lib/python3.8/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in from_parquet(path_or_paths, split, features, cache_dir, keep_in_memory, columns, **kwargs)\r\n 1071 from .io.parquet import ParquetDatasetReader\r\n 1072 \r\n-> 1073 return ParquetDatasetReader(\r\n 1074 path_or_paths,\r\n 1075 split=split,\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/io/parquet.py](https://localhost:8080/#) in __init__(self, path_or_paths, split, features, cache_dir, keep_in_memory, streaming, **kwargs)\r\n 35 path_or_paths = path_or_paths if isinstance(path_or_paths, dict) else {self.split: path_or_paths}\r\n 36 hash = _PACKAGED_DATASETS_MODULES[\"parquet\"][1]\r\n---> 37 self.builder = Parquet(\r\n 38 cache_dir=cache_dir,\r\n 39 data_files=path_or_paths,\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in __init__(self, cache_dir, config_name, hash, base_path, info, features, use_auth_token, repo_id, data_files, data_dir, name, **config_kwargs)\r\n 298 \r\n 299 if data_files is not None and not isinstance(data_files, DataFilesDict):\r\n--> 300 data_files = DataFilesDict.from_local_or_remote(\r\n 301 sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n 302 )\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/data_files.py](https://localhost:8080/#) in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n 794 for key, patterns_for_key in patterns.items():\r\n 795 out[key] = (\r\n--> 796 DataFilesList.from_local_or_remote(\r\n 797 patterns_for_key,\r\n 798 base_path=base_path,\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/data_files.py](https://localhost:8080/#) in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n 762 ) -> \"DataFilesList\":\r\n 763 base_path = base_path if base_path is not None else str(Path().resolve())\r\n--> 764 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n 765 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n 766 return cls(data_files, origin_metadata)\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/data_files.py](https://localhost:8080/#) in resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n 357 data_files = []\r\n 358 for pattern in patterns:\r\n--> 359 if is_remote_url(pattern):\r\n 360 data_files.append(Url(pattern))\r\n 361 else:\r\n\r\n[/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py](https://localhost:8080/#) in is_remote_url(url_or_filename)\r\n 62 \r\n 63 def is_remote_url(url_or_filename: str) -> bool:\r\n---> 64 parsed = urlparse(url_or_filename)\r\n 65 return parsed.scheme in (\"http\", \"https\", \"s3\", \"gs\", \"hdfs\", \"ftp\")\r\n 66 \r\n\r\n[/usr/lib/python3.8/urllib/parse.py](https://localhost:8080/#) in urlparse(url, scheme, allow_fragments)\r\n 373 Note that we don't break the components up in smaller bits\r\n 374 (e.g. netloc is a single string) and we don't expand % escapes.\"\"\"\r\n--> 375 url, scheme, _coerce_result = _coerce_args(url, scheme)\r\n 376 splitresult = urlsplit(url, scheme, allow_fragments)\r\n 377 scheme, netloc, url, query, fragment = splitresult\r\n\r\n[/usr/lib/python3.8/urllib/parse.py](https://localhost:8080/#) in _coerce_args(*args)\r\n 125 if str_input:\r\n 126 return args + (_noop,)\r\n--> 127 return _decode_args(args) + (_encode_result,)\r\n 128 \r\n 129 # Result objects are more helpful than simple tuples\r\n\r\n[/usr/lib/python3.8/urllib/parse.py](https://localhost:8080/#) in _decode_args(args, encoding, errors)\r\n 109 def _decode_args(args, encoding=_implicit_encoding,\r\n 110 errors=_implicit_errors):\r\n--> 111 return tuple(x.decode(encoding, errors) if x else '' for x in args)\r\n 112 \r\n 113 def _coerce_args(*args):\r\n\r\n[/usr/lib/python3.8/urllib/parse.py](https://localhost:8080/#) in <genexpr>(.0)\r\n 109 def _decode_args(args, encoding=_implicit_encoding,\r\n 110 errors=_implicit_errors):\r\n--> 111 return tuple(x.decode(encoding, errors) if x else '' for x in args)\r\n 112 \r\n 113 def _coerce_args(*args):\r\n\r\nAttributeError: 'PosixPath' object has no attribute 'decode'\r\n```\r\n\r\n@mariosasko Should I create a new issue? ", "@mariosasko I would like to take this issue up. ", "@avinashsai Hi, I've assigned you the issue.\r\n\r\n@tranhd95 Yes, feel free to report this in a new issue.", "@avinashsai Are you still working on this? If not I would like to give it a try." ]

"2022-12-12T15:39:45"

"2023-07-26T16:25:51"

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[]

[ "Just seen https://github.com/huggingface/datasets/issues/5281" ]

"2022-12-12T13:22:13"

"2022-12-12T13:37:14"

"2022-12-12T13:37:14"

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[ "Hi @J-shel, thanks for reporting.\r\n\r\nI think the issue comes from your call to `load_dataset`. As first argument, you should pass:\r\n- either the name of your dataset (\"mrf\") if this is already published on the Hub\r\n- or the path to the loading script of your dataset (\"path/to/your/local/mrf.py\").", "Hi, following your suggestion, I changed my call to load_dataset. Below is the latest:\r\nreader = load_dataset('data/mrf.py',\"default\", input_size=1024, split=split, streaming=True, keep_in_memory=None)\r\nHowever, I still got the same error.\r\nI have one question that is if I only define input_size=2048 in BUILDER_CONFIGS, may I specify input_size=1024 when loading the dataset? Cause I found that I could only specify name=\"default\" since I only define name=\"default\" in BUILDER_CONFIGS." ]

"2022-12-12T02:52:03"

"2022-12-19T01:38:48"

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[]

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[ "Hi! `DatasetBuilder` is a parent class for concrete builders: `GeneratorBasedBuilder`, `ArrowBasedBuilder` and `BeamBasedBuilder`. When writing a builder script, these classes are the ones you should inherit from. And since all of them implement `_prepare_split`, you only have to implement the three methods mentioned above.", "Thanks so much @mariosasko for the fast response! I've been referencing [this page in the docs](https://huggingface.co/docs/datasets/v2.4.0/en/about_dataset_load) because it it pretty comprehensive in terms of what we have to do and I figured since we subclass the `BuilderConfig` the same pattern would hold, but I've also seen the page with those sub-classed builders as well, so that fills in a knowledge gap for me.", "cc @stevhliu who may have some ideas on how to improve this part of the docs.", "one more question for my understanding @mariosasko. the requirement of a loading script has always seemed counterintuitive to me. if i have to provide a script with every dataset, what is the point of using `datasets` if we're doing all the work of loading it, I can just do that in my code and skip the datasets integration (this of course discounts other potential benefits around metadata management, etc., my example is just simplest use case though for the sake of discussion).\r\n\r\nso i figured I would implement my own `BuilderConfig` and `DatasetBuilder` to handle that portion of it and not have to make a script. i _thought_ this would result in `datasets` (via `download_and_prepare`) then making me something that I could load using `load_dataset` moving forward.\r\n\r\nConcretely, i envisioned this pattern being possible:\r\n\r\n ```\r\nclass MyBuilderConfig(BuilderConfig):\r\n def __init__(self, name=\"my_named_dataset\", ...):\r\n super().__init__(name, ...)\r\n\r\nclass MyDatasetBuilder(GeneratorBasedBuilder):\r\n BUILDER_CONFIG_CLASS = MyBuilderConfig\r\n ....\r\n\r\nmy_builder = MyDatasetBuilder(...)\r\n\r\n# this doesn't exactly work like I thought; I don't get a dataset back, but NoneType instead\r\n# though I can see it loading the files and it generates the cache, etc.\r\nmy_dataset = my_builder.download_and_prepare()\r\n\r\n# load the dataset in the future by referencing it by name and loading from the cached arrow version\r\nnew_instance_of_my_dataset = load_dataset(\"my_named_dataset\")\r\n```\r\n\r\nI've seen references to the `save_to_disk` method which might be the next step I need in order to load it by name, in which case, that makes sense, then i just need to debug why `download_and_prepare` isn't returning me a dataset, but I feel like I still have a larger conceptual knowledge gap on how to use the library correctly.\r\n\r\nThanks again in advance!", "> the requirement of a loading script has always seemed counterintuitive to me\r\n\r\nThis is a requirement only for datasets not stored in standard formats such as CSV, JSON, SQL, Parquet, ImageFolder, etc. \r\n\r\n> if i have to provide a script with every dataset, what is the point of using datasets if we're doing all the work of loading it, I can just do that in my code and skip the datasets integration (this of course discounts other potential benefits around metadata management, etc., my example is just simplest use case though for the sake of discussion)\r\n\r\nOur README/documentation lists the main features... \r\n\r\nOne of the main ones is that our library makes it easy to work with datasets larger than RAM (thanks to Arrow and the caching mechanism), and this is not trivial to implement.\r\n\r\nRegarding the step-by-step builder, this is the pattern:\r\n```python\r\nfrom datasets import load_dataset_builder\r\nbuilder = load_dataset_builder(\"path/to/script\") # or direct instantiation with MyDatasetBuilder(...)\r\nbuilder.download_and_prepare()\r\ndset = builder.as_dataset()\r\n```", "ok, that makes sense. thank you @mariosasko. I realized i'd never looked on the hub at any of the files associated with any datasets. just did that now and it appears that i'll need to have a script regardless _but_ that will just contain my custom config and builder classes, so without realizing it I was already making my script, I just need to wrap that in a file that sits alongside my data (I looked at Glue and realized I was already doing what I thought didn't make sense to have to do, lol).\r\n\r\n`download_and_prepare` isn't returning me a dataset though, but I'll look into that and open another issue if I can't figure it out.", "`download_and_prepare` downloads and prepares the arrow files. You need to call `as_dataset` on the builder to get the dataset.", "ok, I think I was assigning the output of `builder.download_and_prepare` but it's an inplace op, so that explains the `NoneType` i was getting back. Now I'm getting:\r\n\r\n```\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-7-3ed50fb87c70> in <module>\r\n----> 1 ds = dataset_builder.as_dataset()\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/builder.py in as_dataset(self, split, run_post_process, ignore_verifications, in_memory)\r\n 1020 \r\n 1021 # Create a dataset for each of the given splits\r\n-> 1022 datasets = map_nested(\r\n 1023 partial(\r\n 1024 self._build_single_dataset,\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 442 num_proc = 1\r\n 443 if num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 444 mapped = [\r\n 445 _single_map_nested((function, obj, types, None, True, None))\r\n 446 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)\r\n 443 if num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 444 mapped = [\r\n--> 445 _single_map_nested((function, obj, types, None, True, None))\r\n 446 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 447 ]\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)\r\n 344 # Singleton first to spare some computation\r\n 345 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 346 return function(data_struct)\r\n 347 \r\n 348 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/builder.py in _build_single_dataset(self, split, run_post_process, ignore_verifications, in_memory)\r\n 1051 \r\n 1052 # Build base dataset\r\n-> 1053 ds = self._as_dataset(\r\n 1054 split=split,\r\n 1055 in_memory=in_memory,\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/builder.py in _as_dataset(self, split, in_memory)\r\n 1120 \"\"\"\r\n 1121 cache_dir = self._fs._strip_protocol(self._output_dir)\r\n-> 1122 dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n 1123 name=self.name,\r\n 1124 instructions=split,\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in read(self, name, instructions, split_infos, in_memory)\r\n 236 msg = f'Instruction \"{instructions}\" corresponds to no data!'\r\n 237 raise ValueError(msg)\r\n--> 238 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n 239 \r\n 240 def read_files(\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in read_files(self, files, original_instructions, in_memory)\r\n 257 \"\"\"\r\n 258 # Prepend path to filename\r\n--> 259 pa_table = self._read_files(files, in_memory=in_memory)\r\n 260 # If original_instructions is not None, convert it to a human-readable NamedSplit\r\n 261 if original_instructions is not None:\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in _read_files(self, files, in_memory)\r\n 192 f[\"filename\"] = os.path.join(self._path, f[\"filename\"])\r\n 193 for f_dict in files:\r\n--> 194 pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n 195 pa_tables.append(pa_table)\r\n 196 pa_tables = [t for t in pa_tables if len(t) > 0]\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in _get_table_from_filename(self, filename_skip_take, in_memory)\r\n 327 filename_skip_take[\"take\"] if \"take\" in filename_skip_take else None,\r\n 328 )\r\n--> 329 table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n 330 if take == -1:\r\n 331 take = len(table) - skip\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/arrow_reader.py in read_table(filename, in_memory)\r\n 348 \"\"\"\r\n 349 table_cls = InMemoryTable if in_memory else MemoryMappedTable\r\n--> 350 return table_cls.from_file(filename)\r\n 351 \r\n 352 \r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/table.py in from_file(cls, filename, replays)\r\n 1034 @classmethod\r\n 1035 def from_file(cls, filename: str, replays=None):\r\n-> 1036 table = _memory_mapped_arrow_table_from_file(filename)\r\n 1037 table = cls._apply_replays(table, replays)\r\n 1038 return cls(table, filename, replays)\r\n\r\n/databricks/python/lib/python3.8/site-packages/datasets/table.py in _memory_mapped_arrow_table_from_file(filename)\r\n 48 def _memory_mapped_arrow_table_from_file(filename: str) -> pa.Table:\r\n 49 memory_mapped_stream = pa.memory_map(filename)\r\n---> 50 opened_stream = pa.ipc.open_stream(memory_mapped_stream)\r\n 51 pa_table = opened_stream.read_all()\r\n 52 return pa_table\r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/ipc.py in open_stream(source)\r\n 152 reader : RecordBatchStreamReader\r\n 153 \"\"\"\r\n--> 154 return RecordBatchStreamReader(source)\r\n 155 \r\n 156 \r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/ipc.py in __init__(self, source)\r\n 43 \r\n 44 def __init__(self, source):\r\n---> 45 self._open(source)\r\n 46 \r\n 47 \r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/ipc.pxi in pyarrow.lib._RecordBatchStreamReader._open()\r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n/databricks/python/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Tried reading schema message, was null or length 0\r\n```\r\n\r\n", "looks like my arrow files are all empty @mariosasko \r\n\r\n\r\n\r\n\r\ni also see the `incomplete_info.lock` file a level up too. seems like the data isn't being persisted to disk when I call `download_and_prepare`. is there something else i need to do before then, perhaps?", "quick update @mariosasko. i got it working! i had to downgrade to `datasets==2.4.0`. testing other versions now and will let you know the results.", "I've tested with every version of `datasets>2.4.0` and i get the same error with all of them." ]

"2022-12-12T01:38:54"

"2022-12-20T18:20:57"

"2022-12-12T16:48:56"

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

"2022-12-09T22:25:30"

"2022-12-12T17:27:20"

"2022-12-12T17:24:01"

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

"2022-12-09T20:17:15"

"2022-12-12T17:27:17"

"2022-12-12T17:24:13"

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[ "note, that `datasets` already did some of that umask fixing in the past and also at the hub - the recent work on the hub about the same: https://github.com/huggingface/huggingface_hub/pull/1220\r\n\r\nAlso I noticed that each file has a .json counterpart and the latter always has the correct perms:\r\n\r\n```\r\n-rw------- 1 uue59kq cnw 173M Dec 9 01:37 537596e64721e2ae3d98785b91d30fda0360c196a8224e29658ad629e7303a4d\r\n-rw-rw---- 1 uue59kq cnw 101 Dec 9 01:37 537596e64721e2ae3d98785b91d30fda0360c196a8224e29658ad629e7303a4d.json\r\n```\r\n\r\nso perhaps cheating is possible and syncing the perms between the 2 will do the trick." ]

"2022-12-09T15:46:27"

"2022-12-09T17:21:26"

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[ "cc @polinaeterna", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5347). All of your documentation changes will be reflected on that endpoint.", "Cool ! Feel free to add a comment in the code to explain that and we can merge :)", "I'm not sure if this is a good change since we plan to get rid of `torchaudio` in the next couple of months...", "What do you think @polinaeterna @patrickvonplaten ? Models are usually using float32 (e.g. Wev2vec2 in `transformers`) IIRC", "IMO we can safely assume that float32 is always good enough when using audio models in inference or training. Nevertheless there might be use cases for audio datasets in the future where float64 is needed. \r\n\r\n=> I would by default always cast to float32, but then possible allow the user to cast to float64 ", "> I'm not sure if this is a good change since we plan to get rid of torchaudio in the next couple of months...\r\n\r\n@mariosasko I agree but who knows how long we will have to wait until we are really able to do so (https://github.com/bastibe/libsndfile-binaries/pull/17 is a draft. so as @patrickvonplaten is okay with float32, I'd merge.\r\n\r\n\r\n", "@polinaeterna Can you comment on the linked PR to see why it's still a draft? Maybe we can help somehow to get this merged finally.\r\n\r\nI think it's weird to align `soundfile` with `torchaudio` when the latter is only used for MP3 (and prob for not much longer). " ]

"2022-12-09T15:10:24"

"2023-01-17T16:12:49"

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[ "As the survey is finished, can we close this issue, @LysandreJik ?", "Yes! I'll post a public summary on the forums shortly.", "Is the summary available? I would be interested in reading your findings." ]

"2022-12-09T14:48:02"

"2023-06-02T20:24:44"

"2023-01-25T19:35:40"

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[ "After some more investigation, this is due to [this line of code](https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L279). The function `sf.read(file)` should be updated to `sf.read(file, dtype=\"float32\")`\r\n\r\nIndeed, the default value in soundfile is `float64` ([see here](https://pysoundfile.readthedocs.io/en/latest/#soundfile.read)). \r\n", "@qmeeus I agree, decoding of different audio formats should return the same dtypes indeed!\r\n\r\nBut note that here you are concatenating datasets with different sampling rates: 48000 for CommonVoice and 16000 for Voxpopuli. So you should cast them to the same sampling rate value before interleaving, for example:\r\n```\r\ncv = cv.cast_column(\"audio\", Audio(sampling_rate=16000))\r\n```\r\notherwise you would get the same error because features of the same column (\"audio\") are not the same.\r\n\r\nAlso, the error you get is unexpected. Could you please confirm that you use the latest main version of the `datasets`? We had an issue that could lead to an error like this after using `rename_column` method, but it was fixed in https://github.com/huggingface/datasets/pull/5287 ", "Hi Polina,\r\nSorry for the late answer\r\nIt is possible that the issue was due to a bug that is now fixed. I installed an editable version of datasets from github, but I don't recall whether I had updated it at the time of the issue. My research led me to other directions so I did not follow through on the interleave datasets.\r\n" ]

"2022-12-09T11:05:11"

"2023-02-10T14:39:28"

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

"2022-12-09T00:02:08"

"2022-12-13T00:56:07"

"2022-12-13T00:53:02"

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"2022-12-08T19:48:46"

"2022-12-08T19:57:17"

"2022-12-08T19:57:17"

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[ "Hi @cbarond there's already an open issue at https://github.com/dair-ai/emotion_dataset/issues/5, as the data seems to be missing now, so check that issue instead 👍🏻 ", "Thanks @cbarond for reporting and @alvarobartt for pointing to the issue we opened in the author's repo.\r\n\r\nIndeed, this issue was first raised in the \"emotion\" dataset Community tab: https://huggingface.co/datasets/emotion/discussions/3\r\n\r\nI'm closing this issue and leave the issue above for the subsequent updates.\r\n\r\nDuplicate of: https://huggingface.co/datasets/emotion/discussions/3", "try using \"SetFit/emotion\" instead", "> try using \"SetFit/emotion\" instead\r\n\r\nI' replaced \"emotion\" with \"SetFit/Emotion\", but the code is getting stuck at\r\n\r\n`emotions = load_dataset(\"SetFit/emotion\")`\r\n\r\nI pause execution using the debugger, and it takes me to filelock.py:226\r\n\r\n`with self._thread_lock:`\r\n\r\nDo you know a way to get past this issue?", "thanks @honeyimholm - worked for me", "> try using \"SetFit/emotion\" instead\r\n\r\nIt really helps a lot, thank you!", "The dataset loading script has been fixed: https://huggingface.co/datasets/emotion/discussions/4" ]

"2022-12-08T19:07:09"

"2023-02-23T19:13:19"

"2022-12-09T10:46:11"

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

"2022-12-08T11:04:35"

"2022-12-09T12:26:21"

"2022-12-09T12:23:20"

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

"2022-12-08T00:17:53"

"2022-12-08T19:33:14"

"2022-12-08T19:30:10"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5339). All of your documentation changes will be reflected on that endpoint.", "@lhoestq I think I need some serious help with the tests 😅...I started this locally but it got too time consuming.\n\nOne issue I remember running into is with lossless audio encoding/decoding. I started thinking of using the underlying Audio feature instead of PyAV so I didn't have to rewrite similar logic here...but assumed that would turn into a mess w/ underlying logic" ]

"2022-12-07T20:48:34"

"2023-01-05T23:54:12"

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[ "Hi !\r\n\r\n> It starts using all the cores (I am not sure why because I did not pass num_proc)\r\n\r\nThe tokenizer uses Rust code that is multithreaded. And maybe the `feature_extractor` might run some things in parallel as well - but I'm not super familiar with its internals.\r\n\r\n> then progress bar stops at every 1k steps. (starts using a single core)\r\n\r\nEvery 1000 examples we flush the processed examples to disk. It is this way because Arrow is a columnar format: you must write data chunk by chunk. The processing in on hold while writing right now - maybe this can be improved in the future.", "Hi @lhoestq \r\nThanks for the explanation! it was so helpful! Let me check why `feature_extractor` is running on multiple cpus." ]

"2022-12-07T19:09:40"

"2022-12-10T00:39:29"

"2022-12-10T00:39:28"

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[ "I like the idea of having `webdataset` as an optional dependency to ensure our loader generates web datasets the same way as the main project.", "Webdataset is the one of the most popular dataset formats for large scale computer vision tasks. Upvote for this issue. ", "Any updates on this?", "We haven't had the bandwidth to implement it so far, but if someone wants to give it a shot please don't hesitate ^^" ]

"2022-12-07T11:32:25"

"2023-05-26T10:34:45"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5336). All of your documentation changes will be reflected on that endpoint.", "Hi @mariosasko, @lhoestq I was wondering whether we should include `batched` as a `pytest.mark` param for the functions testing `IterableDataset.map` so as to ensure that the changes done in this PR work fine without breaking anything of the actual functionality.\r\n\r\nI've pushed updated tests just for one of the unit testing functions to be run as `pytest tests/test_iterable_dataset.py::test_mapped_examples_iterable -s --durations 0`, but some are still missing `batched` param, it was just to ask you whether we're supposed to do this for the rest of the functions or not, if it's a yes I'll push the commit as it's ready, but didn't want to push extra stuff that may be discarded later!\r\n\r\nThanks :hugs:", "Thanks for the feedback @lhoestq, I agree with keeping `Optional` instead of `Union[type, None]` for now 👍🏻" ]

"2022-12-06T17:08:10"

"2022-12-07T14:14:56"

"2022-12-07T14:06:27"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I think the only place where we need to add it is here https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts\r\n\r\nAnd I think we can remove tasks.json completely from this repo", "Isn't tasks.json used anymore in this repo?", "> I think the only place where we need to add it is here https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts\r\n> \r\n> And I think we can remove tasks.json completely from this repo\r\n\r\nWhat about the warning I mentioned in https://github.com/huggingface/datasets/issues/5255#issuecomment-1339013527? Also, the depth estimation entry is already present in https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts. ", "The update is based on what I received in the output of the export job (c.f. https://github.com/huggingface/datasets/issues/5255#issuecomment-1339107195). \r\n\r\nEdit: Oh, are you referring to the dataset card of NYU Depth V2?", "Yes, my suggestion was for the dataset card: you got the error message because you tried to set `depth-estimation` in `class_ids` instead of `class_categories`.", "> What about the warning I mentioned in https://github.com/huggingface/datasets/issues/5255#issuecomment-1339013527? Also, the depth estimation entry is already present in https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts.\r\n\r\nif you place it in `task_categories` you should be good :)", "yes i would suggest rm'ing tasks.json here for clarity", "Closing it. ", "It's not clear if we can remove it btw, since old versions of `evaluate` rely on it (see https://github.com/huggingface/evaluate/pull/309)\r\n\r\ncc @lvwerra ", "Actually it can be removed without incidence in old versions of evaluate since we kept an hardcoded `known_task_ids` that is marked \"DEPRECATED\"" ]

"2022-12-06T11:37:57"

"2022-12-08T11:05:33"

"2022-12-07T12:46:03"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks ! Let us know if we can help :)\r\n\r\nSmall pref for having multiple PRs", "Awesome, thanks! Sorry this one is a little big, I'll open some smaller ones next :)" ]

"2022-12-05T20:56:08"

"2022-12-09T01:44:25"

"2022-12-09T01:41:44"

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

"2022-12-05T16:06:09"

"2022-12-06T08:25:17"

"2022-12-06T08:22:49"

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[ "Should `datasets` allow `ClassLabel` input parameter to be an `np.array` even though internally we need to cast it to a Python list? @lhoestq @mariosasko ", "Hi! No, I don't think so. The `names` parameter is [annotated](https://github.com/huggingface/datasets/blob/582236640b9109988e5f7a16a8353696ffa09a16/src/datasets/features/features.py#L892) as `List[str]` (**NumPy arrays are not lists**), and considering that type checking is not a common practice in Python, I think we can leave the code as-is.", "I appreciate it is the wrong type, and that type checking is not common, but I think there's a few circumstances that make it a good idea from a usability perspective.\r\n\r\nIt's quite a difficult error to debug because it comes from a utility function (so it's not immediately obvious which parameter caused it). What makes it even more difficult is the exception happens when the features instance is used to instantiate the dataset, **not** when when the wrong type is actually passed when the features is instantiated. When I was debugging the error, I didn't really consider it could be an issue with the features instance because it had instantiated fine. It's also not one of the more common exceptions caused by trying to use a non-list as a list.\r\n\r\nIt's also relatively easy to accidentally get a numpy array of class types (e.g. calling `unique()` on a pandas dataframe column). Additionally, passing in a `set` instead of the list (again, relatively easy because people may run `set(classes)` to generate uniques) causes an error when the features instance is used, albeit a slightly more obvious one.\r\n\r\nThe names list is already being processed and validated in the `__post_init__` method anyway, so it would not really be adding any complexity to check it is actually a list here too. I'm happy to contribute this change if you change your mind about whether it's worthwhile.", "I agree that it's not easy to debug this issue, so perhaps we could add some basic type checking (e.g. `not isinstance(names, list)` -> error) to make debugging easier. Feel free to submit a PR.\r\n\r\n> Additionally, passing in a set instead of the list (again, relatively easy because people may run set(classes) to generate uniques) causes an error when the features instance is used, albeit a slightly more obvious one.\r\n\r\n`set` is an unordered structure (it's ordered in Python 3.6+, but this is CPython's implementation detail), and the order of ClassLabel `names` matters, so this doesn't require a fix.", "What about checking for `Sequence` instead? I think users can pass a list or a tuple as well." ]

"2022-12-05T12:59:03"

"2022-12-22T16:32:50"

"2022-12-22T16:32:50"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "feel free to merge `main` into your PR to fix the CI :)", "Let me see if I can fix the pattern thing ^^'", "Hmm I think it would be easier to specify the `data_files` in the end, because having a split pattern like `{split}-...` at the root of the repository can lead to unexpected behaviors IMO, and we probably don't want to have a different behavior for `data_files` depending if it's inside a `data_dir` or not\r\n\r\nMaybe something like\r\n```yaml\r\nbuilder_config:\r\n data_dir: data_dir\r\n data_files:\r\n - split: train\r\n pattern: train-[0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*\r\n```", " > Also, I'm not sure if it's a good idea to have this field in the YAML metadata - Transformers use this part of the card only for Hub-related stuff (widgets, tags, CO2 emission, etc.), and I think we should aim to do the same in Datasets. We could achieve this by having these kwargs in a special file (they can be seen as a faster way of defining a builder (builder script) that subclasses a packaged builder) and removing the dataset_info field (the only useful info there seem to be features and we can fetch those directly from a dataset script/Parquet files).\r\n\r\nSomething like `config.json`?\r\n\r\n```json\r\n{\r\n \"data_dir\": \"data\"\r\n \"data_files\": {\r\n \"train\": \"train-[0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*\"\r\n }\r\n}\r\n```\r\n\r\nwe could also support lists for several configs", "opened https://github.com/huggingface/datasets/issues/5694", "I opened a PR to this PR to add data_files in YAML: https://github.com/polinaeterna/datasets/pull/1\r\n\r\n```yaml\r\nbuilder_config:\r\n data_files:\r\n - split: train\r\n pattern: data/train-*\r\n```", "Let me open a PR to see if I can move the data files resolution outside of the MetadataConfigs to not modify it in-place", "I wonder if we can make the cache backward compatible: we could just check if the cache directory with the old path exists. It will be useful for the research team which has a big datasets cache", "> I wonder if we can make the cache backward compatible: we could just check if the cache directory with the old path exists. It will be useful for the research team which has a big datasets cache\r\n\r\n\r\n\r\nIn the next PR maybe? :D \r\nIt's possible but requires some additional logic to correctly pass old `config_kwargs` (which used to include `data_files` but now it's `None` for builders from metadata) to generate the hash which is used to create the path.", "If we only consider datasets that were pushed to hub, it's just a matter of using `\"{username}__parquet\"` instead of `\"{username}__{dataset_name}\"` in the cache directory name. The hashes stay the same :)\r\n\r\nEDIT: and the config name\r\nEDIT2: and the arrow file names", "Did a small PR for backward compatibility, it was easy to add in the end: https://github.com/polinaeterna/datasets/pull/3", "Just created a branch [dev-3.0](https://github.com/huggingface/datasets/tree/dev-3.0) in which we can merge this one and the other datasets 3.0 related PRs", "@lhoestq why can't we merge it in main?", "We can, it was just in case we had other things to merge after @mariosasko or @albertvillanova 's reviews", "@lhoestq @albertvillanova @mariosasko we agreed on having `configs` (in plural) as a metadata field in readme but apparently Hub's yaml validation doesn't allow it to be not a list :D \r\n\r\n(with `config` (in singular) it works)\r\n\r\nedit: and now the tests for hub datasets with metadata configs are failing because I cannot change the yaml there...", "> we agreed on having configs (in plural) as a metadata field in readme but apparently Hub's yaml validation doesn't allow it to be not a list :D\r\n\r\nIf the `configs` field is specified in the YAML, the Hub can use it to [improve](https://github.com/huggingface/moon-landing/blob/97aca4cac32fbb7d84ce5eba9b18afad87968c4a/server/views/components/DatasetLibraryModal/datasetLibrarySnippets.ts#L11) the `Use in dataset library` snippet by listing the possible config values in `load_dataset`. So I think this needs to be fixed on the Hub side.\r\n\r\nPS: I couldn't find an instance of someone using this field on the Hub, so I think using it for this feature is OK.", "> I couldn't find an instance of someone using this field on the Hub, so I think using it for this feature is OK.\r\n\r\n@mariosasko I think it's because @lhoestq renamed `configs` to `config_names` in all canonical datasets :D so yes, `configs` field is now supposed to include custom configuration parameters introduced in this PR, and `config_names` is used (not really used lol) for list of strings of config names. It's being fixed on the Hub's side https://github.com/huggingface/moon-landing/pull/6490", "after more thought I agree it's maybe overkill to do a major release for this one, since we have a good backward compatibility", "There is one edge case I forgot to mention in the reviews - I think it's a good idea to support passing config params that are functions (Pandas uses them a lot) using this API (e.g. `converters` in the CSV config for converting a string column into a sequence). I see two solutions: string blocks with Python code in YAML or PyYAML [tags](https://pyyaml.org/wiki/PyYAMLDocumentation#yaml-tags-and-python-types). \r\n\r\nBut I think this can be addressed later.", "I'm resolving the conflicts and writing some docs :) let's merge this soon !", "<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) | 0.005868 / 0.011353 (-0.005485) | 0.003544 / 0.011008 (-0.007464) | 0.080329 / 0.038508 (0.041821) | 0.061072 / 0.023109 (0.037963) | 0.307802 / 0.275898 (0.031904) | 0.340353 / 0.323480 (0.016873) | 0.004665 / 0.007986 (-0.003321) | 0.002779 / 0.004328 (-0.001550) | 0.062065 / 0.004250 (0.057815) | 0.046350 / 0.037052 (0.009297) | 0.312045 / 0.258489 (0.053556) | 0.353524 / 0.293841 (0.059683) | 0.026965 / 0.128546 (-0.101581) | 0.007906 / 0.075646 (-0.067740) | 0.260678 / 0.419271 (-0.158593) | 0.044167 / 0.043533 (0.000634) | 0.309757 / 0.255139 (0.054618) | 0.340188 / 0.283200 (0.056988) | 0.020440 / 0.141683 (-0.121243) | 1.486886 / 1.452155 (0.034732) | 1.548330 / 1.492716 (0.055614) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188658 / 0.018006 (0.170652) | 0.422204 / 0.000490 (0.421715) | 0.003508 / 0.000200 (0.003308) | 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) | 0.025173 / 0.037411 (-0.012238) | 0.072868 / 0.014526 (0.058343) | 0.084817 / 0.176557 (-0.091739) | 0.151667 / 0.737135 (-0.585468) | 0.085632 / 0.296338 (-0.210706) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400998 / 0.215209 (0.185789) | 4.022274 / 2.077655 (1.944619) | 2.025768 / 1.504120 (0.521648) | 1.874193 / 1.541195 (0.332998) | 2.006537 / 1.468490 (0.538047) | 0.501799 / 4.584777 (-4.082978) | 2.987487 / 3.745712 (-0.758225) | 4.552295 / 5.269862 (-0.717566) | 2.775859 / 4.565676 (-1.789817) | 0.057596 / 0.424275 (-0.366679) | 0.006449 / 0.007607 (-0.001158) | 0.470776 / 0.226044 (0.244732) | 4.725933 / 2.268929 (2.457005) | 2.480130 / 55.444624 (-52.964494) | 2.183919 / 6.876477 (-4.692558) | 2.408052 / 2.142072 (0.265979) | 0.584038 / 4.805227 (-4.221190) | 0.124964 / 6.500664 (-6.375701) | 0.060939 / 0.075469 (-0.014530) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221263 / 1.841788 (-0.620524) | 18.326372 / 8.074308 (10.252064) | 13.398937 / 10.191392 (3.207545) | 0.149153 / 0.680424 (-0.531271) | 0.016941 / 0.534201 (-0.517260) | 0.332106 / 0.579283 (-0.247177) | 0.339958 / 0.434364 (-0.094406) | 0.378125 / 0.540337 (-0.162212) | 0.517787 / 1.386936 (-0.869149) |\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) | 0.005927 / 0.011353 (-0.005426) | 0.003607 / 0.011008 (-0.007402) | 0.062925 / 0.038508 (0.024417) | 0.058676 / 0.023109 (0.035566) | 0.362129 / 0.275898 (0.086231) | 0.395864 / 0.323480 (0.072384) | 0.004652 / 0.007986 (-0.003334) | 0.002893 / 0.004328 (-0.001435) | 0.062696 / 0.004250 (0.058445) | 0.049988 / 0.037052 (0.012935) | 0.365366 / 0.258489 (0.106877) | 0.412326 / 0.293841 (0.118485) | 0.027118 / 0.128546 (-0.101429) | 0.008179 / 0.075646 (-0.067467) | 0.068048 / 0.419271 (-0.351223) | 0.041065 / 0.043533 (-0.002468) | 0.359858 / 0.255139 (0.104719) | 0.386589 / 0.283200 (0.103390) | 0.020467 / 0.141683 (-0.121216) | 1.438070 / 1.452155 (-0.014084) | 1.479617 / 1.492716 (-0.013099) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231516 / 0.018006 (0.213510) | 0.413407 / 0.000490 (0.412917) | 0.000358 / 0.000200 (0.000158) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026071 / 0.037411 (-0.011340) | 0.076486 / 0.014526 (0.061960) | 0.085943 / 0.176557 (-0.090613) | 0.138087 / 0.737135 (-0.599048) | 0.087466 / 0.296338 (-0.208872) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417711 / 0.215209 (0.202502) | 4.171915 / 2.077655 (2.094260) | 2.140677 / 1.504120 (0.636557) | 1.960164 / 1.541195 (0.418969) | 2.002134 / 1.468490 (0.533644) | 0.499699 / 4.584777 (-4.085078) | 2.991814 / 3.745712 (-0.753898) | 2.906589 / 5.269862 (-2.363272) | 1.842305 / 4.565676 (-2.723372) | 0.057633 / 0.424275 (-0.366642) | 0.006465 / 0.007607 (-0.001142) | 0.492874 / 0.226044 (0.266830) | 4.931613 / 2.268929 (2.662684) | 2.623161 / 55.444624 (-52.821463) | 2.310624 / 6.876477 (-4.565853) | 2.483146 / 2.142072 (0.341074) | 0.586910 / 4.805227 (-4.218317) | 0.124681 / 6.500664 (-6.375983) | 0.061561 / 0.075469 (-0.013908) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.319111 / 1.841788 (-0.522677) | 18.637326 / 8.074308 (10.563018) | 13.803912 / 10.191392 (3.612520) | 0.143989 / 0.680424 (-0.536435) | 0.017025 / 0.534201 (-0.517176) | 0.333156 / 0.579283 (-0.246127) | 0.342163 / 0.434364 (-0.092201) | 0.380357 / 0.540337 (-0.159981) | 0.512261 / 1.386936 (-0.874675) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f49a16346dc35e5eabeec39778d0f2e4e850dfd7 \"CML watermark\")\n" ]

"2022-12-02T16:43:44"

"2023-07-24T15:49:54"

"2023-07-13T13:27:56"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I think it's also reasonable to add the same note to the AudioFolder decription", "Thank you ! I think \"regular\" is more appropriate than \"small\". It can easily scale to a few thousands of images - just not millions x)", "Replaced \"small\" with \"several thousand\" since what is considered \"regular\" and even \"small\" can be kind of vague!" ]

"2022-12-01T21:47:29"

"2022-12-06T17:20:04"

"2022-12-06T17:16:53"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "EDIT\r\nAt least the docs for ~~main~~ PR branch are now built:\r\n- https://github.com/huggingface/datasets/actions/runs/3594847760/jobs/6053620813", "Build documentation for main branch was triggered after this PR being merged: https://github.com/huggingface/datasets/actions/runs/3603370082/jobs/6071482470" ]

"2022-12-01T17:07:45"

"2022-12-02T16:29:00"

"2022-12-02T16:26:00"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5327). All of your documentation changes will be reflected on that endpoint." ]

"2022-12-01T17:05:23"

"2023-01-23T12:48:29"

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"2022-12-01T16:50:58"

"2022-12-02T16:26:01"

"2022-12-02T16:26:01"

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[ "Hi! I agree it makes sense for `IterableDataset.map` to support the `batch_size=None` case. This should be super easy to fix.", "@mariosasko as this is something simple maybe I can include it as part of https://github.com/huggingface/datasets/pull/5311? Let me know :+1:", "#self-assign", "Feel free to close this @lhoestq as part of https://github.com/huggingface/datasets/pull/5336 :hugs:", "Thanks again :)\r\n\r\n> For practical usages, an alternative to this would be to convert from an iterable dataset to a map-style dataset, but it is not obvious how to do this.\r\n\r\nThis is interesting as well, if anyone wants to explore" ]

"2022-12-01T15:43:42"

"2022-12-07T15:54:43"

"2022-12-07T15:54:42"

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[ "I agree we have a mess with docstrings...", "Ok, I believe we've cleaned up most of the old syntax we were using for the user-facing docs! There are still a couple of `:obj:`'s and `:class:` floating around in the docstrings we don't expose that I'll track down :)" ]

"2022-12-01T15:34:53"

"2022-12-13T19:03:55"

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[ "Thanks for reporting, @liaeh.\r\n\r\nWe are having a look at it. ", "I have transferred the discussion to the Community tab of the dataset: https://huggingface.co/datasets/taskmaster2/discussions/1" ]

"2022-12-01T15:31:06"

"2022-12-01T16:26:06"

"2022-12-01T16:26:06"

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

"2022-12-01T15:19:28"

"2022-12-14T16:37:16"

"2022-12-14T16:33:30"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> Do you know why this stopped working?\r\n\r\nIt comes from the changes in https://github.com/huggingface/datasets/pull/5107/files#diff-355ae5c229f95f86895404b72378ecd6e966c41cbeebb674af6fe6e9611bc126" ]

"2022-12-01T14:39:06"

"2022-12-01T16:10:09"

"2022-12-01T16:07:02"

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

"2022-12-01T13:55:49"

"2022-12-01T15:36:44"

"2022-12-01T15:33:58"

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

"2022-12-01T09:08:09"

"2022-12-01T15:21:44"

"2022-12-01T15:19:00"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "please review :) @lhoestq @ola13 thankoo", "Thanks :) I just updated the test to make sure it works even when there's a column missing, and did a minor change to json.py to add the missing columns for the other kinds of JSON files as well (I moved the code to`self._cast_table`)", "Thanks Unso! If @lhoestq is happy then I'm also happy :D", "When I noticed the ping, this PR had already been merged...\r\n\r\nLuckily, PyArrow's `read_json` behaves the same when `explicit_schema` is given via `ParseOptions`, so I'm okay with this change (our JSON loader doesn't use `read_json` for decoding JSON in some scenarios, so this manual approach is the right one).\r\n" ]

"2022-12-01T06:18:39"

"2022-12-12T19:06:42"

"2022-12-04T05:49:39"

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[ "Hi ! ImageFolder is made for small scale datasets indeed. For large scale image datasets you better group your images in TAR archives or Arrow/Parquet files. This is true not just for ImageFolder loading performance, but also because having millions of files is not ideal for your filesystem or when moving the data around.\r\n\r\nOption 1. use TAR archives\r\n\r\nI'd suggest you to take a look at how we load [Imagenet](https://huggingface.co/datasets/imagenet-1k/tree/main) for example. The dataset is sharded in multiple TAR archives and there is a [script](https://huggingface.co/datasets/imagenet-1k/blob/main/imagenet-1k.py) that iterates over the archives to load the images.\r\n\r\nOption 2. use Arrow/Parquet\r\n\r\nYou can load your images as an Arrow Dataset with\r\n```python\r\nfrom datasets import Dataset, Image, load_from_disk, load_dataset\r\n\r\nds = Dataset.from_dict({\"image\": list(glob.glob(\"path/to/dir/**/*.jpg\"))})\r\n\r\ndef add_metadata(example):\r\n ...\r\n\r\nds = ds.map(add_metadata, num_proc=16) # num_proc for multiprocessing\r\nds = ds.cast_column(\"image\", Image())\r\n\r\n# save as Arrow locally\r\nds.save_to_disk(\"output_dir\")\r\nreloaded = load_from_disk(\"output_dir\")\r\n\r\n# OR save as Parquet on the HF Hub\r\nds.push_to_hub(\"username/dataset_name\")\r\nreloaded = load_dataset(\"username/dataset_name\")\r\n# reloaded = load_dataset(\"username/dataset_name\", num_proc=16) # to use multiprocessing\r\n```\r\n\r\nPS: maybe we can actually have something similar to ImageFolder but for image archives at one point ?", "@lhoestq Thanks!\r\n\r\nPerhaps it'd be worth adding a note on the documentation that `ImageFolder` is not intended for large datasets? This limitation is not intuitively obvious to someone who has not used it before, I think.", "Thanks for the feedback @salieri! I opened #5329 to make it clear `ImageFolder` is not intended for large datasets. Please feel free to comment if you have any other feedback! 🙂 " ]

"2022-12-01T00:04:21"

"2022-12-01T21:49:26"

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[ "Thanks for reporting, @adampauls.\r\n\r\nWe are having a look at it. " ]

"2022-11-30T19:24:13"

"2022-12-01T15:19:02"

"2022-12-01T15:19:02"

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[ "EDIT:\r\nI think in this case, the metadata files (either README or JSON) should not be read (i.e. `self.info.splits` should be None).\r\n\r\nOne idea: \r\n- I think ideally we should set this behavior when we pass `--save_info` to the CLI `test`\r\n- However, currently, the builder is unaware of this: `save_info` arg is not passed to it", "> I think in this case\r\n\r\n@albertvillanova You mean in cases when the script was changed? \r\n\r\nI suggest that we:\r\n* add a check on the slice (like 'split_name[n%]) kind of format here: https://github.com/huggingface/datasets/blob/main/src/datasets/splits.py#L523 to catch things like this. \r\n* Error here happens before splits verification, but in `_prepare_split`, and `_prepare_split` doesn't perform any verification and don't know about it. so we can pass this parameter and take splits from `split_generator`, not from `split.info` in case when `verify_infos` is False\r\n* we can check if split **names** from split_generators and self.info.splits are the same **before** preparing splits (if `verify_info=True`) so that we don't spend time on generating unwanted data. \r\n* provide some user-friendly warnings about `ignore_verifications` parameter so that users know that if something is not matching they can ignore it\r\n\r\nI started it here: https://github.com/huggingface/datasets/pull/5327/files\r\n\r\nWhat do you think @albertvillanova ?", "I edited my previous comment:\r\n- First I proposed setting `self.info.splits` to None when `ignore_verifications=True`\r\n - I thought it was the easiest implementation because `ignore_verifications` is passed to `DatasetBuilder.download_and_prepare`\r\n - However, afterwards, I realized this might not be a good idea for this use case:\r\n - A user wants to optimize the loading of the dataset, and passes `ignore_verifications=False` to avoid all the verifications\r\n - In this case, we want `self.info.splits` to be read from metadata file\r\n- Then, I thought that it might be better to set `self.info.splits` to None when we pass `--save_info` to the CLI test: if we are going to save the info to the metadata file, it makes no sense to read the info from the metadata file\r\n - This implementation is not so easy because the Builder knows nothing about `--save_info`\r\n\r\nI agree with you there are 2 things to be addressed here:\r\n- One is what I have just commented: `self.info.splits` should be None in this case\r\n- The other, a validation should be implemented when calling `make_file_instructions` and/or `SplitDict.__getitem__`, so that when passing \"training\" to it, we get a more descriptive error other than `TypeError: expected str, bytes or os.PathLike object, not NoneType` " ]

"2022-11-30T18:02:15"

"2022-12-02T07:02:53"

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[ "This seems similar to https://github.com/huggingface/datasets/issues/2512 Can you try to update seqeval ? ", "@JonathanAlis also note that the metrics are deprecated in our `datasets` library.\r\n\r\nPlease, use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate" ]

"2022-11-30T14:01:03"

"2023-07-21T14:40:31"

"2023-07-21T14:40:31"

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

"2022-11-29T18:00:28"

"2022-12-01T14:55:30"

"2022-12-01T14:00:34"

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[ "The current implementation is what I had in mind, i.e. concatenate all splits by default.\r\n\r\nHowever, I think most tabular datasets would come as a single split. So for that usecase, it wouldn't change UX if we raise when there are more than one splits.\r\n\r\nAnd for multiple splits, the user either passes a list, or they can pass `splits=\"all\"` to have all splits concatenated.", "I think it's better to raise an error in cases when there are multiple splits but no split is specified so that users know for sure with which data they are working. I imagine a case when a user loads a dataset that they don't know much about (like what splits it has), and if they get a concatenation of everything, it might lead to incorrect processing or interpretations and it would be hard to notice it.\r\n(\"explicit is better than implicit\")", "I just changed to raise an error if there are multiple splits. The error shows an example of how to choose a split to convert.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5312). All of your documentation changes will be reflected on that endpoint.", "Thanks for the review, I've updated the type hint and added a line to raise an error on bad splits :)", "Merging https://github.com/huggingface/datasets/pull/5301 would eliminate the need for this PR, no?\r\n\r\nIn the meantime, I find the current API cleaner.", "This solution is simpler than https://github.com/huggingface/datasets/pull/5301 and covers most cases for tabular datasets, so I'm in favor of merging this one and put https://github.com/huggingface/datasets/pull/5301 on stand by", "Let me know if it sounds good to you @mariosasko @albertvillanova :)", "I'm still not convinced. If `DatasetDict` needs this method and there is no other way, then IMO it would make more sense to return a dictionary with the splits converted to `pd.DataFrame`. ", "@mariosasko the issue we're dealing with is that in tabular scenarios, we often don't have splits in the dataset, and imposing that concept to people dealing with the library hampers adoption.", "@adrinjalali This PR proposes a solution inconsistent with the existing API (in other words, a solution that clutters our API 🙂). Moreover, our library primarily focuses on larger-than-RAM datasets, and tabular datasets don't (directly) fall into this group.\r\n\r\nInstead of the temporary \"fix\" proposed here, it makes much more sense to align `load_dataset` with both tabular and DL workflows \"in a consistent way\", so I suggest we continue our discussion from https://github.com/huggingface/datasets/issues/5189 to have this resolved by version 3.0.", "closing this one for now" ]

"2022-11-29T16:30:02"

"2023-01-25T17:33:43"

"2023-01-25T17:33:42"

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

"2022-11-29T11:08:34"

"2022-12-06T15:45:02"

"2022-12-06T15:42:04"

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

"2022-11-29T09:20:47"

"2022-11-30T12:00:09"

"2022-11-30T11:57:16"

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

"2022-11-28T16:59:39"

"2022-12-07T12:55:20"

"2022-12-07T12:52:15"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I like the `kwargs` approach, thanks!" ]

"2022-11-28T14:42:41"

"2022-12-09T12:58:55"

"2022-12-09T12:55:59"

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

"2022-11-28T13:59:10"

"2022-11-28T15:30:37"

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[ "Forgot to actually convert the feature dict to a Feature object. Closing." ]

"2022-11-28T07:55:44"

"2022-11-28T08:11:45"

"2022-11-28T08:11:44"

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[ "Thanks for reporting @JoelNiklaus.\r\n\r\nPlease note that since we moved all dataset loading scripts to the Hub, the issues and pull requests relative to specific datasets are directly handled on the Hub, in their Community tab. I'm transferring this issue there: https://huggingface.co/datasets/joelito/mc4_legal/discussions\r\n\r\nI am also having a look at the bug in your script.", "Issue transferred to: https://huggingface.co/datasets/joelito/mc4_legal/discussions/1" ]

"2022-11-28T00:16:16"

"2022-11-28T07:22:42"

"2022-11-28T07:22:42"

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[ "The [timit_asr.py](https://huggingface.co/datasets/timit_asr/blob/main/timit_asr.py) script iterates over the WAV files per split directory using this:\r\n```python\r\nwav_paths = sorted(Path(data_dir).glob(f\"**/{split}/**/*.wav\"))\r\nwav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f\"**/{split.upper()}/**/*.WAV\"))\r\n```\r\n\r\nCan you check that there is a directory named \"test\" somewhere in your timit data directory ?" ]

"2022-11-26T10:18:22"

"2023-02-10T16:33:21"

"2023-02-10T16:33:21"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "100% agree that the checksum verification is overkill and not super useful. But I think this PR would also disable the check on num_examples no ?\r\n \r\nAs a user I would like to know if the dataset I'm loading changed significantly.\r\nAnd I also think it can be useful to make sure the metadata are up to date.\r\n\r\nWhat do you think ?\r\n\r\nWe could have a default `ignore_verifications=\"ignore_checksums\"`", "> We could have a default `ignore_verifications=\"ignore_checksums\"`\r\n\r\nAccepting multiple types (booleans and strings) at the same time is not the best design. Maybe we could define an enum for this parameter?", "Yes an enum sounds good !", "so we can have three verification levels, - smth like \"ignore_all\" (to skip both checksums and all other info like num_examples verification), \"ignore_checksums\" (to skip only checksums verification), and \"verify_all\" (to perform all verification)?\r\nand deprecate `ignore_verifications` param.\r\n\r\n@mariosasko if you're not going to work on this PR in the coming days, I can take over it if you want (this PR will help me with [this issue](https://github.com/huggingface/datasets/issues/5315), not super urgent though).", "Okay, I propose deprecating `ignore_verifications` in favor of `verification_mode` (`load_dataset` already has `download_mode`; some other projects use this name for verification control). `verification_mode` would accept the following enum (or strings in the same manner as `download_mode` does):\r\n\r\n```python\r\nclass VerificationMode(enum.Enum):\r\n FULL = \"full\" # runs all verification checks \r\n BASIC = \"basic\" # default, runs only the cheap ones (skips the checksum check)\r\n NONE = \"none\" # skips all the checks\r\n```\r\n\r\nWDTY?", "(copy paste from my message on slack)\r\n\r\nWhat do you think of a config variable in config.py to switch from one verification mode to another ? This way we don’t deprecate anything\r\n\r\nMany users are familiar with ignore_verifications=True, it might be overkill to deprecate it", "@lhoestq So we have \"basic\" verification mode in `config.py` and continue to have `False` as a default \r\nvalue for `ignore_verifications`? That way running all verifications including checksums would not be possible without switching the config var, right? \r\n\r\nI like having a `VerificationMode` enum because it's aligned with `DownloadMode` and sounds more natural to me (`ignore_verifications` feels a bit semantically reverted but this is probably just my feeling) and it's flexible (no need to worry about `config.py`, I'm not sure that users even know it exists, wdyt?).\r\n\r\nThe usage point seems also valid to me, but cases when users are stuck with NonMatchingX errors also happen from time to time and to figure out what's wrong is non-trivial here. \r\n\r\nAs a note aside - I suggest to add instructions to the NonMatchingX error message (how to use `ignore_verifications` / `verification_mode`), this would save users who don't know about this param a lot of time.", "Ok I see. I'm fine with the new parameter then (even though I had a small pref for the config variable) :)", "I like the idea of an enum and the `verification_mode` parameter. \r\n\r\nIn relation with the config parameter, we could additionally add a `DEFAULT_VERIFICATION_MODE`, maybe only if users require it. Note that until now there wasn't any config parameter for a default `ignore_verifications` value: I guess people are explicitly passing `ignore_verifications=True`...\r\n\r\nAs a note aside, I like the suggestion by @polinaeterna: we could give actionable messages when verifying checksums. This could be done in other PR.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012891 / 0.011353 (0.001538) | 0.006474 / 0.011008 (-0.004535) | 0.144038 / 0.038508 (0.105530) | 0.036151 / 0.023109 (0.013042) | 0.404366 / 0.275898 (0.128468) | 0.479988 / 0.323480 (0.156508) | 0.010219 / 0.007986 (0.002233) | 0.005319 / 0.004328 (0.000990) | 0.099705 / 0.004250 (0.095455) | 0.046639 / 0.037052 (0.009586) | 0.398997 / 0.258489 (0.140508) | 0.478431 / 0.293841 (0.184590) | 0.069125 / 0.128546 (-0.059421) | 0.019603 / 0.075646 (-0.056043) | 0.400829 / 0.419271 (-0.018443) | 0.066549 / 0.043533 (0.023016) | 0.398343 / 0.255139 (0.143204) | 0.417928 / 0.283200 (0.134728) | 0.121124 / 0.141683 (-0.020559) | 1.751513 / 1.452155 (0.299358) | 1.821239 / 1.492716 (0.328523) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251603 / 0.018006 (0.233597) | 0.579916 / 0.000490 (0.579427) | 0.003257 / 0.000200 (0.003058) | 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) | 0.031502 / 0.037411 (-0.005909) | 0.134688 / 0.014526 (0.120162) | 0.152306 / 0.176557 (-0.024251) | 0.198943 / 0.737135 (-0.538192) | 0.142551 / 0.296338 (-0.153788) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.634672 / 0.215209 (0.419463) | 6.370215 / 2.077655 (4.292561) | 2.548123 / 1.504120 (1.044003) | 2.184263 / 1.541195 (0.643069) | 2.239026 / 1.468490 (0.770536) | 1.233340 / 4.584777 (-3.351437) | 5.791824 / 3.745712 (2.046112) | 5.093032 / 5.269862 (-0.176830) | 2.849833 / 4.565676 (-1.715844) | 0.143787 / 0.424275 (-0.280488) | 0.015279 / 0.007607 (0.007672) | 0.757984 / 0.226044 (0.531939) | 7.883604 / 2.268929 (5.614675) | 3.321591 / 55.444624 (-52.123033) | 2.671777 / 6.876477 (-4.204700) | 2.685215 / 2.142072 (0.543142) | 1.546709 / 4.805227 (-3.258519) | 0.247186 / 6.500664 (-6.253478) | 0.085117 / 0.075469 (0.009648) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.679809 / 1.841788 (-0.161979) | 18.528893 / 8.074308 (10.454585) | 23.168590 / 10.191392 (12.977198) | 0.277618 / 0.680424 (-0.402806) | 0.045109 / 0.534201 (-0.489092) | 0.568873 / 0.579283 (-0.010410) | 0.695017 / 0.434364 (0.260653) | 0.671024 / 0.540337 (0.130687) | 0.823817 / 1.386936 (-0.563119) |\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) | 0.009809 / 0.011353 (-0.001544) | 0.006890 / 0.011008 (-0.004118) | 0.099211 / 0.038508 (0.060703) | 0.035387 / 0.023109 (0.012278) | 0.507603 / 0.275898 (0.231705) | 0.535553 / 0.323480 (0.212073) | 0.007346 / 0.007986 (-0.000640) | 0.007559 / 0.004328 (0.003231) | 0.099132 / 0.004250 (0.094882) | 0.048048 / 0.037052 (0.010996) | 0.518096 / 0.258489 (0.259607) | 0.561134 / 0.293841 (0.267294) | 0.057580 / 0.128546 (-0.070966) | 0.023665 / 0.075646 (-0.051982) | 0.138409 / 0.419271 (-0.280862) | 0.061989 / 0.043533 (0.018456) | 0.510568 / 0.255139 (0.255429) | 0.552722 / 0.283200 (0.269522) | 0.115990 / 0.141683 (-0.025693) | 1.884900 / 1.452155 (0.432745) | 1.990604 / 1.492716 (0.497888) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.280638 / 0.018006 (0.262632) | 0.592837 / 0.000490 (0.592347) | 0.000465 / 0.000200 (0.000265) | 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) | 0.030253 / 0.037411 (-0.007158) | 0.141580 / 0.014526 (0.127054) | 0.135114 / 0.176557 (-0.041443) | 0.190003 / 0.737135 (-0.547133) | 0.160230 / 0.296338 (-0.136109) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.699762 / 0.215209 (0.484553) | 6.632344 / 2.077655 (4.554689) | 2.718803 / 1.504120 (1.214683) | 2.485294 / 1.541195 (0.944099) | 2.579889 / 1.468490 (1.111399) | 1.268795 / 4.584777 (-3.315982) | 5.777745 / 3.745712 (2.032033) | 3.232551 / 5.269862 (-2.037311) | 2.127699 / 4.565676 (-2.437977) | 0.146570 / 0.424275 (-0.277705) | 0.015971 / 0.007607 (0.008364) | 0.803181 / 0.226044 (0.577137) | 8.377192 / 2.268929 (6.108264) | 3.551242 / 55.444624 (-51.893382) | 2.865228 / 6.876477 (-4.011249) | 2.774869 / 2.142072 (0.632797) | 1.553856 / 4.805227 (-3.251371) | 0.264510 / 6.500664 (-6.236154) | 0.087918 / 0.075469 (0.012449) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.653396 / 1.841788 (-0.188391) | 18.703863 / 8.074308 (10.629555) | 22.067331 / 10.191392 (11.875939) | 0.257424 / 0.680424 (-0.422999) | 0.026448 / 0.534201 (-0.507753) | 0.550100 / 0.579283 (-0.029183) | 0.647296 / 0.434364 (0.212932) | 0.657476 / 0.540337 (0.117138) | 0.781119 / 1.386936 (-0.605817) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c4a9cb95f8742a2850f11d59abbef71d6c1f60c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008889 / 0.011353 (-0.002464) | 0.004563 / 0.011008 (-0.006445) | 0.101627 / 0.038508 (0.063118) | 0.030526 / 0.023109 (0.007417) | 0.297175 / 0.275898 (0.021277) | 0.368454 / 0.323480 (0.044974) | 0.007246 / 0.007986 (-0.000740) | 0.003565 / 0.004328 (-0.000763) | 0.078644 / 0.004250 (0.074394) | 0.038616 / 0.037052 (0.001564) | 0.310521 / 0.258489 (0.052032) | 0.348014 / 0.293841 (0.054173) | 0.033463 / 0.128546 (-0.095083) | 0.011544 / 0.075646 (-0.064102) | 0.323281 / 0.419271 (-0.095990) | 0.040187 / 0.043533 (-0.003346) | 0.298015 / 0.255139 (0.042876) | 0.326392 / 0.283200 (0.043193) | 0.088730 / 0.141683 (-0.052952) | 1.503387 / 1.452155 (0.051233) | 1.548704 / 1.492716 (0.055988) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185983 / 0.018006 (0.167977) | 0.451889 / 0.000490 (0.451400) | 0.001433 / 0.000200 (0.001233) | 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) | 0.023396 / 0.037411 (-0.014015) | 0.118236 / 0.014526 (0.103710) | 0.124594 / 0.176557 (-0.051962) | 0.159089 / 0.737135 (-0.578047) | 0.129369 / 0.296338 (-0.166969) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423161 / 0.215209 (0.207952) | 4.228211 / 2.077655 (2.150556) | 1.853862 / 1.504120 (0.349742) | 1.649471 / 1.541195 (0.108276) | 1.708631 / 1.468490 (0.240141) | 0.697456 / 4.584777 (-3.887321) | 3.473244 / 3.745712 (-0.272468) | 1.942586 / 5.269862 (-3.327275) | 1.291592 / 4.565676 (-3.274084) | 0.082758 / 0.424275 (-0.341517) | 0.012256 / 0.007607 (0.004649) | 0.528355 / 0.226044 (0.302311) | 5.277620 / 2.268929 (3.008691) | 2.299604 / 55.444624 (-53.145020) | 1.954940 / 6.876477 (-4.921537) | 2.055543 / 2.142072 (-0.086529) | 0.814723 / 4.805227 (-3.990505) | 0.149937 / 6.500664 (-6.350727) | 0.064529 / 0.075469 (-0.010941) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266240 / 1.841788 (-0.575547) | 14.144016 / 8.074308 (6.069708) | 14.331733 / 10.191392 (4.140340) | 0.138963 / 0.680424 (-0.541461) | 0.029034 / 0.534201 (-0.505167) | 0.397325 / 0.579283 (-0.181958) | 0.405293 / 0.434364 (-0.029071) | 0.480745 / 0.540337 (-0.059592) | 0.573386 / 1.386936 (-0.813550) |\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) | 0.007214 / 0.011353 (-0.004139) | 0.004569 / 0.011008 (-0.006439) | 0.078718 / 0.038508 (0.040209) | 0.031104 / 0.023109 (0.007995) | 0.342562 / 0.275898 (0.066664) | 0.387802 / 0.323480 (0.064322) | 0.005378 / 0.007986 (-0.002608) | 0.003414 / 0.004328 (-0.000915) | 0.077249 / 0.004250 (0.072999) | 0.044337 / 0.037052 (0.007285) | 0.341397 / 0.258489 (0.082907) | 0.385536 / 0.293841 (0.091695) | 0.033257 / 0.128546 (-0.095289) | 0.011825 / 0.075646 (-0.063821) | 0.086723 / 0.419271 (-0.332549) | 0.045951 / 0.043533 (0.002418) | 0.340914 / 0.255139 (0.085775) | 0.367126 / 0.283200 (0.083926) | 0.096326 / 0.141683 (-0.045357) | 1.608612 / 1.452155 (0.156458) | 1.687251 / 1.492716 (0.194534) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227595 / 0.018006 (0.209589) | 0.418502 / 0.000490 (0.418013) | 0.000392 / 0.000200 (0.000192) | 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) | 0.026232 / 0.037411 (-0.011179) | 0.101020 / 0.014526 (0.086494) | 0.110017 / 0.176557 (-0.066539) | 0.153497 / 0.737135 (-0.583639) | 0.110602 / 0.296338 (-0.185737) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433789 / 0.215209 (0.218579) | 4.329350 / 2.077655 (2.251696) | 2.052136 / 1.504120 (0.548016) | 1.848457 / 1.541195 (0.307262) | 1.936791 / 1.468490 (0.468301) | 0.700609 / 4.584777 (-3.884168) | 3.391983 / 3.745712 (-0.353729) | 1.903220 / 5.269862 (-3.366642) | 1.179463 / 4.565676 (-3.386213) | 0.084025 / 0.424275 (-0.340250) | 0.012743 / 0.007607 (0.005136) | 0.536816 / 0.226044 (0.310772) | 5.420230 / 2.268929 (3.151302) | 2.507438 / 55.444624 (-52.937187) | 2.178907 / 6.876477 (-4.697570) | 2.228586 / 2.142072 (0.086514) | 0.812527 / 4.805227 (-3.992701) | 0.153382 / 6.500664 (-6.347282) | 0.069932 / 0.075469 (-0.005537) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256861 / 1.841788 (-0.584927) | 14.309236 / 8.074308 (6.234928) | 13.740323 / 10.191392 (3.548931) | 0.142698 / 0.680424 (-0.537726) | 0.016998 / 0.534201 (-0.517203) | 0.385489 / 0.579283 (-0.193794) | 0.391515 / 0.434364 (-0.042849) | 0.472704 / 0.540337 (-0.067633) | 0.565042 / 1.386936 (-0.821894) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b0713ddf2e2e7129d9ccda791d265684c96675c \"CML watermark\")\n", "This is ready for review. \r\n\r\nIf `verification_mode` is None, it defaults to `VerificationMode.BASIC` instead of `VerificationMode.NONE`, so maybe we should find a better name for the latter to avoid confusion.\r\n\r\nPS: `ignore_verifications` is still present in the `test`/`run_beam` commands for simplicity. Let me know if you think these commands should support all three modes.", "> I would also prefer to change the name for the NONE verification mode, but don't have really good ideas in mind. maybe smth like SKIP_ALL ?\r\n\r\nI decided to go with the following names:\r\n* `no_checks` (previously `none`)\r\n* `basic_checks` (previously `basic`)\r\n* `all_checks` (previously `full`)\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008900 / 0.011353 (-0.002453) | 0.004492 / 0.011008 (-0.006516) | 0.100957 / 0.038508 (0.062449) | 0.030145 / 0.023109 (0.007036) | 0.302531 / 0.275898 (0.026633) | 0.344072 / 0.323480 (0.020592) | 0.007032 / 0.007986 (-0.000953) | 0.004150 / 0.004328 (-0.000178) | 0.078272 / 0.004250 (0.074021) | 0.034142 / 0.037052 (-0.002910) | 0.310798 / 0.258489 (0.052308) | 0.350077 / 0.293841 (0.056236) | 0.034497 / 0.128546 (-0.094050) | 0.011417 / 0.075646 (-0.064230) | 0.323427 / 0.419271 (-0.095844) | 0.045664 / 0.043533 (0.002132) | 0.304688 / 0.255139 (0.049549) | 0.336591 / 0.283200 (0.053391) | 0.086116 / 0.141683 (-0.055567) | 1.519278 / 1.452155 (0.067123) | 1.576728 / 1.492716 (0.084011) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242482 / 0.018006 (0.224476) | 0.403548 / 0.000490 (0.403058) | 0.001217 / 0.000200 (0.001017) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023466 / 0.037411 (-0.013945) | 0.095220 / 0.014526 (0.080694) | 0.104119 / 0.176557 (-0.072438) | 0.141107 / 0.737135 (-0.596029) | 0.107236 / 0.296338 (-0.189102) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416290 / 0.215209 (0.201081) | 4.159068 / 2.077655 (2.081413) | 1.846014 / 1.504120 (0.341894) | 1.634789 / 1.541195 (0.093594) | 1.724687 / 1.468490 (0.256196) | 0.696887 / 4.584777 (-3.887890) | 3.313861 / 3.745712 (-0.431851) | 1.907239 / 5.269862 (-3.362622) | 1.266815 / 4.565676 (-3.298861) | 0.081660 / 0.424275 (-0.342615) | 0.012290 / 0.007607 (0.004683) | 0.522866 / 0.226044 (0.296822) | 5.237356 / 2.268929 (2.968428) | 2.294645 / 55.444624 (-53.149979) | 1.946407 / 6.876477 (-4.930069) | 1.995441 / 2.142072 (-0.146632) | 0.808340 / 4.805227 (-3.996887) | 0.149670 / 6.500664 (-6.350994) | 0.065162 / 0.075469 (-0.010307) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219476 / 1.841788 (-0.622312) | 13.868709 / 8.074308 (5.794401) | 14.115783 / 10.191392 (3.924391) | 0.149403 / 0.680424 (-0.531021) | 0.028514 / 0.534201 (-0.505686) | 0.398194 / 0.579283 (-0.181089) | 0.410898 / 0.434364 (-0.023466) | 0.485763 / 0.540337 (-0.054574) | 0.574924 / 1.386936 (-0.812012) |\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) | 0.006906 / 0.011353 (-0.004447) | 0.004446 / 0.011008 (-0.006562) | 0.075936 / 0.038508 (0.037428) | 0.027693 / 0.023109 (0.004584) | 0.339505 / 0.275898 (0.063607) | 0.383315 / 0.323480 (0.059835) | 0.005138 / 0.007986 (-0.002847) | 0.004636 / 0.004328 (0.000308) | 0.074829 / 0.004250 (0.070578) | 0.040327 / 0.037052 (0.003274) | 0.340516 / 0.258489 (0.082027) | 0.388569 / 0.293841 (0.094729) | 0.031562 / 0.128546 (-0.096984) | 0.011585 / 0.075646 (-0.064061) | 0.084753 / 0.419271 (-0.334518) | 0.041310 / 0.043533 (-0.002223) | 0.338272 / 0.255139 (0.083133) | 0.367243 / 0.283200 (0.084043) | 0.092653 / 0.141683 (-0.049029) | 1.515973 / 1.452155 (0.063818) | 1.582869 / 1.492716 (0.090152) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229366 / 0.018006 (0.211360) | 0.414404 / 0.000490 (0.413914) | 0.002922 / 0.000200 (0.002723) | 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) | 0.026391 / 0.037411 (-0.011020) | 0.106754 / 0.014526 (0.092228) | 0.110718 / 0.176557 (-0.065839) | 0.145786 / 0.737135 (-0.591350) | 0.113180 / 0.296338 (-0.183159) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446340 / 0.215209 (0.231131) | 4.499756 / 2.077655 (2.422101) | 2.071485 / 1.504120 (0.567365) | 1.873223 / 1.541195 (0.332029) | 1.931562 / 1.468490 (0.463071) | 0.699270 / 4.584777 (-3.885507) | 3.452383 / 3.745712 (-0.293329) | 2.970630 / 5.269862 (-2.299232) | 1.300859 / 4.565676 (-3.264817) | 0.083971 / 0.424275 (-0.340304) | 0.012489 / 0.007607 (0.004882) | 0.544190 / 0.226044 (0.318146) | 5.460097 / 2.268929 (3.191169) | 2.700244 / 55.444624 (-52.744380) | 2.396694 / 6.876477 (-4.479783) | 2.376334 / 2.142072 (0.234262) | 0.812845 / 4.805227 (-3.992382) | 0.154441 / 6.500664 (-6.346223) | 0.069510 / 0.075469 (-0.005959) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.278836 / 1.841788 (-0.562952) | 14.153158 / 8.074308 (6.078850) | 13.821290 / 10.191392 (3.629898) | 0.160464 / 0.680424 (-0.519960) | 0.016742 / 0.534201 (-0.517459) | 0.379840 / 0.579283 (-0.199443) | 0.391903 / 0.434364 (-0.042461) | 0.461646 / 0.540337 (-0.078691) | 0.550691 / 1.386936 (-0.836245) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aeb637daab938d51b8b15ad4d175d06817e99512 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009858 / 0.011353 (-0.001495) | 0.005383 / 0.011008 (-0.005625) | 0.100527 / 0.038508 (0.062019) | 0.037176 / 0.023109 (0.014067) | 0.295204 / 0.275898 (0.019306) | 0.364511 / 0.323480 (0.041031) | 0.008486 / 0.007986 (0.000500) | 0.004273 / 0.004328 (-0.000055) | 0.076538 / 0.004250 (0.072288) | 0.046250 / 0.037052 (0.009197) | 0.307102 / 0.258489 (0.048613) | 0.339313 / 0.293841 (0.045472) | 0.040783 / 0.128546 (-0.087763) | 0.012323 / 0.075646 (-0.063323) | 0.336216 / 0.419271 (-0.083055) | 0.050480 / 0.043533 (0.006947) | 0.293689 / 0.255139 (0.038550) | 0.315034 / 0.283200 (0.031834) | 0.113775 / 0.141683 (-0.027908) | 1.438738 / 1.452155 (-0.013416) | 1.499874 / 1.492716 (0.007157) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202392 / 0.018006 (0.184386) | 0.442784 / 0.000490 (0.442295) | 0.003004 / 0.000200 (0.002804) | 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) | 0.027792 / 0.037411 (-0.009620) | 0.110886 / 0.014526 (0.096360) | 0.121041 / 0.176557 (-0.055515) | 0.166803 / 0.737135 (-0.570333) | 0.127617 / 0.296338 (-0.168722) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409762 / 0.215209 (0.194553) | 4.073297 / 2.077655 (1.995643) | 1.836375 / 1.504120 (0.332255) | 1.651507 / 1.541195 (0.110312) | 1.734134 / 1.468490 (0.265644) | 0.690900 / 4.584777 (-3.893877) | 3.812045 / 3.745712 (0.066333) | 2.101378 / 5.269862 (-3.168483) | 1.438242 / 4.565676 (-3.127434) | 0.083256 / 0.424275 (-0.341020) | 0.012436 / 0.007607 (0.004829) | 0.501702 / 0.226044 (0.275658) | 5.007679 / 2.268929 (2.738751) | 2.315158 / 55.444624 (-53.129466) | 2.003934 / 6.876477 (-4.872543) | 2.154658 / 2.142072 (0.012586) | 0.831749 / 4.805227 (-3.973478) | 0.165058 / 6.500664 (-6.335606) | 0.062166 / 0.075469 (-0.013303) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212435 / 1.841788 (-0.629353) | 15.022673 / 8.074308 (6.948365) | 14.649631 / 10.191392 (4.458239) | 0.172121 / 0.680424 (-0.508303) | 0.028791 / 0.534201 (-0.505410) | 0.440290 / 0.579283 (-0.138993) | 0.437359 / 0.434364 (0.002995) | 0.543603 / 0.540337 (0.003265) | 0.643241 / 1.386936 (-0.743695) |\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) | 0.007572 / 0.011353 (-0.003781) | 0.005207 / 0.011008 (-0.005801) | 0.074427 / 0.038508 (0.035919) | 0.033384 / 0.023109 (0.010275) | 0.334538 / 0.275898 (0.058640) | 0.371556 / 0.323480 (0.048076) | 0.006453 / 0.007986 (-0.001532) | 0.004010 / 0.004328 (-0.000319) | 0.073488 / 0.004250 (0.069238) | 0.048082 / 0.037052 (0.011030) | 0.337325 / 0.258489 (0.078836) | 0.395143 / 0.293841 (0.101302) | 0.036714 / 0.128546 (-0.091832) | 0.012089 / 0.075646 (-0.063557) | 0.086008 / 0.419271 (-0.333263) | 0.049277 / 0.043533 (0.005744) | 0.333848 / 0.255139 (0.078709) | 0.354003 / 0.283200 (0.070803) | 0.105012 / 0.141683 (-0.036671) | 1.450769 / 1.452155 (-0.001386) | 1.554538 / 1.492716 (0.061821) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208407 / 0.018006 (0.190400) | 0.438778 / 0.000490 (0.438288) | 0.000399 / 0.000200 (0.000199) | 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) | 0.030180 / 0.037411 (-0.007232) | 0.115432 / 0.014526 (0.100906) | 0.126106 / 0.176557 (-0.050451) | 0.167508 / 0.737135 (-0.569627) | 0.130566 / 0.296338 (-0.165772) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421408 / 0.215209 (0.206198) | 4.208492 / 2.077655 (2.130838) | 2.024177 / 1.504120 (0.520057) | 1.834356 / 1.541195 (0.293161) | 1.923234 / 1.468490 (0.454744) | 0.699548 / 4.584777 (-3.885229) | 3.933775 / 3.745712 (0.188063) | 2.124526 / 5.269862 (-3.145336) | 1.360934 / 4.565676 (-3.204742) | 0.086568 / 0.424275 (-0.337707) | 0.012351 / 0.007607 (0.004744) | 0.517431 / 0.226044 (0.291387) | 5.175428 / 2.268929 (2.906499) | 2.471031 / 55.444624 (-52.973593) | 2.131529 / 6.876477 (-4.744948) | 2.202512 / 2.142072 (0.060440) | 0.849364 / 4.805227 (-3.955863) | 0.171505 / 6.500664 (-6.329159) | 0.065864 / 0.075469 (-0.009605) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270054 / 1.841788 (-0.571734) | 15.254502 / 8.074308 (7.180194) | 13.874969 / 10.191392 (3.683577) | 0.144131 / 0.680424 (-0.536293) | 0.017743 / 0.534201 (-0.516458) | 0.421990 / 0.579283 (-0.157293) | 0.423924 / 0.434364 (-0.010439) | 0.522560 / 0.540337 (-0.017778) | 0.626159 / 1.386936 (-0.760777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05bd726a575a3c1c337022424fa7d226f1a2ebee \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008643 / 0.011353 (-0.002710) | 0.004479 / 0.011008 (-0.006529) | 0.102372 / 0.038508 (0.063864) | 0.029703 / 0.023109 (0.006594) | 0.301479 / 0.275898 (0.025581) | 0.370970 / 0.323480 (0.047490) | 0.007044 / 0.007986 (-0.000942) | 0.004868 / 0.004328 (0.000540) | 0.079568 / 0.004250 (0.075318) | 0.035344 / 0.037052 (-0.001708) | 0.308091 / 0.258489 (0.049602) | 0.353812 / 0.293841 (0.059971) | 0.033406 / 0.128546 (-0.095140) | 0.011476 / 0.075646 (-0.064170) | 0.324343 / 0.419271 (-0.094929) | 0.040293 / 0.043533 (-0.003240) | 0.300007 / 0.255139 (0.044868) | 0.334410 / 0.283200 (0.051210) | 0.086553 / 0.141683 (-0.055130) | 1.463814 / 1.452155 (0.011659) | 1.501580 / 1.492716 (0.008864) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198032 / 0.018006 (0.180025) | 0.409970 / 0.000490 (0.409480) | 0.001075 / 0.000200 (0.000875) | 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) | 0.022941 / 0.037411 (-0.014471) | 0.097320 / 0.014526 (0.082794) | 0.106445 / 0.176557 (-0.070111) | 0.139073 / 0.737135 (-0.598063) | 0.108408 / 0.296338 (-0.187930) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419315 / 0.215209 (0.204106) | 4.199273 / 2.077655 (2.121618) | 1.877689 / 1.504120 (0.373569) | 1.670442 / 1.541195 (0.129247) | 1.735034 / 1.468490 (0.266544) | 0.694691 / 4.584777 (-3.890086) | 3.323644 / 3.745712 (-0.422069) | 2.884349 / 5.269862 (-2.385513) | 1.518882 / 4.565676 (-3.046794) | 0.082390 / 0.424275 (-0.341886) | 0.012884 / 0.007607 (0.005277) | 0.525103 / 0.226044 (0.299058) | 5.277297 / 2.268929 (3.008369) | 2.328639 / 55.444624 (-53.115985) | 1.983210 / 6.876477 (-4.893267) | 2.037985 / 2.142072 (-0.104088) | 0.809520 / 4.805227 (-3.995707) | 0.150150 / 6.500664 (-6.350514) | 0.065578 / 0.075469 (-0.009891) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221971 / 1.841788 (-0.619817) | 13.692361 / 8.074308 (5.618052) | 13.874582 / 10.191392 (3.683190) | 0.138182 / 0.680424 (-0.542242) | 0.028618 / 0.534201 (-0.505583) | 0.395104 / 0.579283 (-0.184179) | 0.397169 / 0.434364 (-0.037195) | 0.457509 / 0.540337 (-0.082829) | 0.537275 / 1.386936 (-0.849661) |\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) | 0.004585 / 0.011008 (-0.006423) | 0.076877 / 0.038508 (0.038369) | 0.027305 / 0.023109 (0.004196) | 0.349085 / 0.275898 (0.073187) | 0.401416 / 0.323480 (0.077936) | 0.004912 / 0.007986 (-0.003074) | 0.003315 / 0.004328 (-0.001014) | 0.075676 / 0.004250 (0.071425) | 0.038960 / 0.037052 (0.001907) | 0.346196 / 0.258489 (0.087707) | 0.403185 / 0.293841 (0.109344) | 0.032054 / 0.128546 (-0.096493) | 0.011742 / 0.075646 (-0.063905) | 0.086631 / 0.419271 (-0.332640) | 0.041633 / 0.043533 (-0.001900) | 0.343519 / 0.255139 (0.088380) | 0.385413 / 0.283200 (0.102213) | 0.091430 / 0.141683 (-0.050253) | 1.478886 / 1.452155 (0.026731) | 1.546873 / 1.492716 (0.054156) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.167882 / 0.018006 (0.149876) | 0.396464 / 0.000490 (0.395974) | 0.003629 / 0.000200 (0.003429) | 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) | 0.024829 / 0.037411 (-0.012583) | 0.099607 / 0.014526 (0.085081) | 0.106187 / 0.176557 (-0.070370) | 0.142379 / 0.737135 (-0.594756) | 0.109307 / 0.296338 (-0.187032) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442276 / 0.215209 (0.227067) | 4.427099 / 2.077655 (2.349444) | 2.093407 / 1.504120 (0.589287) | 1.880973 / 1.541195 (0.339778) | 1.915592 / 1.468490 (0.447102) | 0.708196 / 4.584777 (-3.876581) | 3.417649 / 3.745712 (-0.328063) | 2.859953 / 5.269862 (-2.409909) | 1.528380 / 4.565676 (-3.037297) | 0.084054 / 0.424275 (-0.340221) | 0.012585 / 0.007607 (0.004978) | 0.537614 / 0.226044 (0.311569) | 5.409915 / 2.268929 (3.140987) | 2.555853 / 55.444624 (-52.888771) | 2.195075 / 6.876477 (-4.681402) | 2.232775 / 2.142072 (0.090703) | 0.814994 / 4.805227 (-3.990233) | 0.152882 / 6.500664 (-6.347782) | 0.067467 / 0.075469 (-0.008002) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306007 / 1.841788 (-0.535780) | 13.923981 / 8.074308 (5.849673) | 13.385881 / 10.191392 (3.194489) | 0.150712 / 0.680424 (-0.529712) | 0.016731 / 0.534201 (-0.517470) | 0.376557 / 0.579283 (-0.202726) | 0.379396 / 0.434364 (-0.054968) | 0.456251 / 0.540337 (-0.084087) | 0.545731 / 1.386936 (-0.841205) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc637d107ef3e3b9948691379312a8099b6476aa \"CML watermark\")\n" ]

"2022-11-25T18:39:09"

"2023-02-13T16:50:42"

"2023-02-13T16:43:47"

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

"2022-11-25T17:09:21"

"2022-12-09T14:20:15"

"2022-12-09T14:17:20"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5301). All of your documentation changes will be reflected on that endpoint.", "Just noticed that now we have to deal with indexed & split datasets. The remaining tests are failing because one should be able to get an indexed dataset when accessing the split of a dataset made of indexed splits (right now the index is just trashed)" ]

"2022-11-25T16:35:54"

"2023-02-21T13:13:13"

"2023-02-21T13:13:13"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I noticed this bug the other day and was going to look into it! \"Where are these processes coming from?\" ;-)" ]

"2022-11-25T15:37:42"

"2022-12-07T12:55:39"

"2022-12-07T12:52:51"

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[]

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

"2022-11-25T15:35:28"

"2022-11-29T08:23:58"

"2022-11-29T08:21:24"

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"2022-11-25T15:21:32"

"2022-11-29T08:21:25"

"2022-11-29T08:21:25"

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

"2022-11-25T13:30:17"

"2022-11-29T08:07:39"

"2022-11-29T08:05:12"

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"2022-11-25T13:29:33"

"2022-11-29T08:05:13"

"2022-11-29T08:05:13"

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[ "Hi ! Thanks for reporting. Indeed the lock file should be placed in a directory with write permission (e.g. in the directory where the archive is extracted).", "I opened https://github.com/huggingface/datasets/pull/5320 to fix this - it places the lock file in the cache directory instead of trying to put in next to the ZIP where it's read-only" ]

"2022-11-25T03:59:43"

"2023-07-21T14:39:09"

"2023-07-21T14:39:09"

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

"2022-11-24T18:04:38"

"2022-11-29T07:09:08"

"2022-11-29T07:06:32"

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"2022-11-24T17:52:08"

"2022-11-29T07:06:33"

"2022-11-29T07:06:33"

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[ "- Build docs for 2.6.2:\r\n - Commit: a6a5a1cf4cdf1e0be65168aed5a327f543001fe8\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539470622/jobs/5941404044\r\n- Build docs for 2.7.1:\r\n - Commit: 5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539574442/jobs/5941636792" ]

"2022-11-24T09:42:10"

"2022-11-24T10:10:02"

"2022-11-24T10:10:02"

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