Datasets:

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Francesco-A

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github-issues_huggingface-datasets

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[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[ "Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so that all leading blank spaces in the source dataset are kept, and there is no misalignment between the answer text and the answer_start within the context.", "If you are going to be altering the data cleaning from the source Squad dataset, here is one thing to consider.\r\nThere are occasional double spaces separating words which it might be nice to get rid of. \r\n\r\nEither way, thank you." ]

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

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

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[ "Hi !\r\n\r\nTo fix 1, an you try to run this code ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"squad\", download_mode=\"force_redownload\")\r\n```\r\nMaybe the file your downloaded was corrupted, in this case redownloading this way should fix your issue 1.\r\n\r\nRegarding your 2nd point, you're right that loading the raw json this way doesn't give you a dataset with the column \"context\", \"question\" and \"answers\". Indeed the squad format is a very nested format so you have to preprocess the data. You can do it this way:\r\n```python\r\ndef process_squad(examples):\r\n \"\"\"\r\n Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n \"\"\"\r\n out = {\"context\": [], \"question\": [], \"answers\":[]} \r\n for paragraphs in examples[\"paragraphs\"]: \r\n for paragraph in paragraphs: \r\n for qa in paragraph[\"qas\"]: \r\n answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n out[\"context\"].append(paragraph[\"context\"].strip()) \r\n out[\"question\"].append(qa[\"question\"].strip()) \r\n out[\"answers\"].append(answers) \r\n return out\r\n\r\ndatasets = load_dataset(extension, data_files=data_files, field=\"data\")\r\ncolumn_names = datasets[\"train\"].column_names\r\n\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n```\r\n\r\nHope that helps :)", "Thks for quickly answering！\r\n### 1 I try the first way,but seems not work \r\n```\r\nTraceback (most recent call last):\r\n File \"examples/question-answering/run_qa.py\", line 503, in <module>\r\n main()\r\n File \"examples/question-answering/run_qa.py\", line 218, in main\r\n datasets = load_dataset(data_args.dataset_name, download_mode=\"force_redownload\")\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/load.py\", line 746, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/builder.py\", line 573, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/builder.py\", line 633, in _download_and_prepare\r\n self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 39, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json']\r\n```\r\n### 2 I try the second way,and run the examples/question-answering/run_qa.py,it lead to another bug orz..\r\n```\r\nTraceback (most recent call last):\r\n File \"examples/question-answering/run_qa.py\", line 523, in <module>\r\n main()\r\n File \"examples/question-answering/run_qa.py\", line 379, in main\r\n load_from_cache_file=not data_args.overwrite_cache,\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/arrow_dataset.py\", line 1120, in map\r\n update_data = does_function_return_dict(test_inputs, test_indices)\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/arrow_dataset.py\", line 1091, in does_function_return_dict\r\n function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n File \"examples/question-answering/run_qa.py\", line 339, in prepare_train_features\r\n if len(answers[\"answer_start\"]) == 0:\r\nTypeError: list indices must be integers or slices, not str\r\n```\r\n## may be the function prepare_train_features in run_qa.py need to fix,I think is that the prep\r\n```python\r\nfor i, offsets in enumerate(offset_mapping):\r\n # We will label impossible answers with the index of the CLS token.\r\n input_ids = tokenized_examples[\"input_ids\"][i]\r\n cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n # One example can give several spans, this is the index of the example containing this span of text.\r\n sample_index = sample_mapping[i]\r\n answers = examples[answer_column_name][sample_index]\r\n print(examples,answers)\r\n # If no answers are given, set the cls_index as answer.\r\n if len(answers[\"answer_start\"]) == 0:\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Start/end character index of the answer in the text.\r\n start_char = answers[\"answer_start\"][0]\r\n end_char = start_char + len(answers[\"text\"][0])\r\n\r\n # Start token index of the current span in the text.\r\n token_start_index = 0\r\n while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n token_start_index += 1\r\n\r\n # End token index of the current span in the text.\r\n token_end_index = len(input_ids) - 1\r\n while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n token_end_index -= 1\r\n\r\n # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n # Note: we could go after the last offset if the answer is the last word (edge case).\r\n while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n token_start_index += 1\r\n tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n while offsets[token_end_index][1] >= end_char:\r\n token_end_index -= 1\r\n tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n return tokenized_examples\r\n``` ", "## I have fixed it, @lhoestq \r\n### the first section change as you said and add [\"id\"]\r\n```python\r\ndef process_squad(examples):\r\n \"\"\"\r\n Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n \"\"\"\r\n # print(examples)\r\n out = {\"context\": [], \"question\": [], \"answers\":[],\"id\":[]} \r\n for paragraphs in examples[\"paragraphs\"]: \r\n for paragraph in paragraphs: \r\n for qa in paragraph[\"qas\"]: \r\n answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n out[\"context\"].append(paragraph[\"context\"].strip()) \r\n out[\"question\"].append(qa[\"question\"].strip()) \r\n out[\"answers\"].append(answers) \r\n out[\"id\"].append(qa[\"id\"]) \r\n return out\r\ncolumn_names = datasets[\"train\"].column_names if training_args.do_train else datasets[\"validation\"].column_names\r\n# print(datasets[\"train\"].column_names)\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n# Preprocessing the datasets.\r\n# Preprocessing is slighlty different for training and evaluation.\r\nif training_args.do_train:\r\n column_names = datasets[\"train\"].column_names\r\nelse:\r\n column_names = datasets[\"validation\"].column_names\r\n# print(column_names)\r\nquestion_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\ncontext_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\nanswer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n```\r\n### the second section\r\n```python\r\ndef prepare_train_features(examples):\r\n # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n # in one example possible giving several features when a context is long, each of those features having a\r\n # context that overlaps a bit the context of the previous feature.\r\n tokenized_examples = tokenizer(\r\n examples[question_column_name if pad_on_right else context_column_name],\r\n examples[context_column_name if pad_on_right else question_column_name],\r\n truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n max_length=data_args.max_seq_length,\r\n stride=data_args.doc_stride,\r\n return_overflowing_tokens=True,\r\n return_offsets_mapping=True,\r\n padding=\"max_length\" if data_args.pad_to_max_length else False,\r\n )\r\n\r\n # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n # its corresponding example. This key gives us just that.\r\n sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n # The offset mappings will give us a map from token to character position in the original context. This will\r\n # help us compute the start_positions and end_positions.\r\n offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n # Let's label those examples!\r\n tokenized_examples[\"start_positions\"] = []\r\n tokenized_examples[\"end_positions\"] = []\r\n\r\n for i, offsets in enumerate(offset_mapping):\r\n # We will label impossible answers with the index of the CLS token.\r\n input_ids = tokenized_examples[\"input_ids\"][i]\r\n cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n # One example can give several spans, this is the index of the example containing this span of text.\r\n sample_index = sample_mapping[i]\r\n answers = examples[answer_column_name][sample_index]\r\n # print(examples,answers,offset_mapping,tokenized_examples)\r\n # If no answers are given, set the cls_index as answer.\r\n if len(answers) == 0:#len(answers[\"answer_start\"]) == 0:\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Start/end character index of the answer in the text.\r\n start_char = answers[0][\"answer_start\"]\r\n end_char = start_char + len(answers[0][\"text\"])\r\n\r\n # Start token index of the current span in the text.\r\n token_start_index = 0\r\n while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n token_start_index += 1\r\n\r\n # End token index of the current span in the text.\r\n token_end_index = len(input_ids) - 1\r\n while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n token_end_index -= 1\r\n\r\n # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n # Note: we could go after the last offset if the answer is the last word (edge case).\r\n while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n token_start_index += 1\r\n tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n while offsets[token_end_index][1] >= end_char:\r\n token_end_index -= 1\r\n tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n return tokenized_examples\r\n```", "I'm glad you managed to fix run_qa.py for your case :)\r\n\r\nRegarding the checksum error, I'm not able to reproduce on my side.\r\nThis errors says that the downloaded file doesn't match the expected file.\r\n\r\nCould you try running this and let me know if you get the same output as me ?\r\n```python\r\nfrom datasets.utils.info_utils import get_size_checksum_dict\r\nfrom datasets import cached_path\r\n\r\nget_size_checksum_dict(cached_path(\"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json\"))\r\n# {'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```", "I run the code,and it show below:\r\n```\r\n>>> from datasets.utils.info_utils import get_size_checksum_dict\r\n>>> from datasets import cached_path\r\n>>> get_size_checksum_dict(cached_path(\"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json\"))\r\nDownloading: 30.3MB [04:13, 120kB/s]\r\n{'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```", "Alright ! So in this case redownloading the file with `download_mode=\"force_redownload\"` should fix it. Can you try using `download_mode=\"force_redownload\"` again ?\r\n\r\nNot sure why it didn't work for you the first time though :/" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007039 / 0.011353 (-0.004314) | 0.004175 / 0.011008 (-0.006833) | 0.085426 / 0.038508 (0.046918) | 0.079818 / 0.023109 (0.056709) | 0.321924 / 0.275898 (0.046026) | 0.345482 / 0.323480 (0.022002) | 0.005510 / 0.007986 (-0.002475) | 0.003452 / 0.004328 (-0.000877) | 0.065158 / 0.004250 (0.060907) | 0.058843 / 0.037052 (0.021791) | 0.316280 / 0.258489 (0.057791) | 0.351666 / 0.293841 (0.057825) | 0.031190 / 0.128546 (-0.097357) | 0.008500 / 0.075646 (-0.067147) | 0.289595 / 0.419271 (-0.129676) | 0.053798 / 0.043533 (0.010265) | 0.315804 / 0.255139 (0.060665) | 0.334957 / 0.283200 (0.051757) | 0.024350 / 0.141683 (-0.117332) | 1.515753 / 1.452155 (0.063599) | 1.556215 / 1.492716 (0.063499) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210378 / 0.018006 (0.192372) | 0.469309 / 0.000490 (0.468820) | 0.002890 / 0.000200 (0.002690) | 0.000086 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030214 / 0.037411 (-0.007197) | 0.088492 / 0.014526 (0.073966) | 0.098684 / 0.176557 (-0.077873) | 0.156077 / 0.737135 (-0.581058) | 0.098814 / 0.296338 (-0.197525) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404548 / 0.215209 (0.189339) | 4.026173 / 2.077655 (1.948518) | 2.043216 / 1.504120 (0.539096) | 1.880997 / 1.541195 (0.339802) | 1.975205 / 1.468490 (0.506715) | 0.489395 / 4.584777 (-4.095382) | 3.684097 / 3.745712 (-0.061615) | 5.126934 / 5.269862 (-0.142928) | 3.092153 / 4.565676 (-1.473524) | 0.057668 / 0.424275 (-0.366607) | 0.007372 / 0.007607 (-0.000235) | 0.479647 / 0.226044 (0.253603) | 4.780207 / 2.268929 (2.511278) | 2.533457 / 55.444624 (-52.911168) | 2.182126 / 6.876477 (-4.694351) | 2.431834 / 2.142072 (0.289761) | 0.591760 / 4.805227 (-4.213467) | 0.135450 / 6.500664 (-6.365214) | 0.063218 / 0.075469 (-0.012251) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262053 / 1.841788 (-0.579734) | 20.246992 / 8.074308 (12.172684) | 14.638222 / 10.191392 (4.446830) | 0.150021 / 0.680424 (-0.530403) | 0.018680 / 0.534201 (-0.515521) | 0.395215 / 0.579283 (-0.184068) | 0.421270 / 0.434364 (-0.013094) | 0.458845 / 0.540337 (-0.081492) | 0.634488 / 1.386936 (-0.752448) |\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) | 0.007080 / 0.011353 (-0.004273) | 0.004112 / 0.011008 (-0.006896) | 0.066426 / 0.038508 (0.027918) | 0.090088 / 0.023109 (0.066978) | 0.400191 / 0.275898 (0.124293) | 0.429614 / 0.323480 (0.106134) | 0.005428 / 0.007986 (-0.002558) | 0.003501 / 0.004328 (-0.000827) | 0.065056 / 0.004250 (0.060806) | 0.061643 / 0.037052 (0.024590) | 0.398619 / 0.258489 (0.140130) | 0.445497 / 0.293841 (0.151657) | 0.031703 / 0.128546 (-0.096843) | 0.008708 / 0.075646 (-0.066938) | 0.071561 / 0.419271 (-0.347711) | 0.050684 / 0.043533 (0.007151) | 0.385361 / 0.255139 (0.130222) | 0.409349 / 0.283200 (0.126149) | 0.027388 / 0.141683 (-0.114295) | 1.473021 / 1.452155 (0.020866) | 1.525246 / 1.492716 (0.032529) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237710 / 0.018006 (0.219704) | 0.468719 / 0.000490 (0.468230) | 0.000385 / 0.000200 (0.000185) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032539 / 0.037411 (-0.004872) | 0.095324 / 0.014526 (0.080798) | 0.102248 / 0.176557 (-0.074308) | 0.156096 / 0.737135 (-0.581039) | 0.103458 / 0.296338 (-0.192881) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416226 / 0.215209 (0.201017) | 4.141044 / 2.077655 (2.063389) | 2.143732 / 1.504120 (0.639612) | 2.001020 / 1.541195 (0.459825) | 2.091194 / 1.468490 (0.622704) | 0.489977 / 4.584777 (-4.094800) | 3.579615 / 3.745712 (-0.166097) | 3.438082 / 5.269862 (-1.831780) | 2.069031 / 4.565676 (-2.496645) | 0.056994 / 0.424275 (-0.367281) | 0.007362 / 0.007607 (-0.000245) | 0.493077 / 0.226044 (0.267033) | 4.922622 / 2.268929 (2.653694) | 2.627083 / 55.444624 (-52.817541) | 2.301141 / 6.876477 (-4.575336) | 2.356794 / 2.142072 (0.214722) | 0.583792 / 4.805227 (-4.221436) | 0.133707 / 6.500664 (-6.366958) | 0.062892 / 0.075469 (-0.012577) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.364908 / 1.841788 (-0.476880) | 20.641219 / 8.074308 (12.566911) | 14.848528 / 10.191392 (4.657136) | 0.174207 / 0.680424 (-0.506217) | 0.018206 / 0.534201 (-0.515995) | 0.413742 / 0.579283 (-0.165541) | 0.419940 / 0.434364 (-0.014424) | 0.458543 / 0.540337 (-0.081794) | 0.616518 / 1.386936 (-0.770418) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18b2202c3e7cdde05920078f01864964556427da \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006875 / 0.011353 (-0.004478) | 0.003489 / 0.011008 (-0.007519) | 0.082077 / 0.038508 (0.043569) | 0.103011 / 0.023109 (0.079902) | 0.370572 / 0.275898 (0.094674) | 0.416400 / 0.323480 (0.092920) | 0.004048 / 0.007986 (-0.003938) | 0.003563 / 0.004328 (-0.000765) | 0.062666 / 0.004250 (0.058416) | 0.063664 / 0.037052 (0.026612) | 0.374206 / 0.258489 (0.115717) | 0.425590 / 0.293841 (0.131749) | 0.028174 / 0.128546 (-0.100373) | 0.007906 / 0.075646 (-0.067741) | 0.266251 / 0.419271 (-0.153020) | 0.045923 / 0.043533 (0.002390) | 0.376746 / 0.255139 (0.121607) | 0.401950 / 0.283200 (0.118750) | 0.024628 / 0.141683 (-0.117054) | 1.441903 / 1.452155 (-0.010252) | 1.537494 / 1.492716 (0.044777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214696 / 0.018006 (0.196690) | 0.425626 / 0.000490 (0.425137) | 0.003370 / 0.000200 (0.003170) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023133 / 0.037411 (-0.014279) | 0.072374 / 0.014526 (0.057848) | 0.081255 / 0.176557 (-0.095301) | 0.146960 / 0.737135 (-0.590175) | 0.081748 / 0.296338 (-0.214590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390683 / 0.215209 (0.175473) | 3.893166 / 2.077655 (1.815511) | 1.884321 / 1.504120 (0.380201) | 1.701899 / 1.541195 (0.160704) | 1.737839 / 1.468490 (0.269349) | 0.497008 / 4.584777 (-4.087769) | 3.041211 / 3.745712 (-0.704501) | 3.519947 / 5.269862 (-1.749915) | 2.015085 / 4.565676 (-2.550592) | 0.057685 / 0.424275 (-0.366590) | 0.006415 / 0.007607 (-0.001192) | 0.465565 / 0.226044 (0.239520) | 4.635224 / 2.268929 (2.366295) | 2.297941 / 55.444624 (-53.146683) | 1.946670 / 6.876477 (-4.929807) | 2.078527 / 2.142072 (-0.063546) | 0.584101 / 4.805227 (-4.221126) | 0.126488 / 6.500664 (-6.374176) | 0.060819 / 0.075469 (-0.014650) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223400 / 1.841788 (-0.618388) | 17.960923 / 8.074308 (9.886615) | 13.187683 / 10.191392 (2.996291) | 0.129258 / 0.680424 (-0.551166) | 0.016601 / 0.534201 (-0.517600) | 0.330028 / 0.579283 (-0.249255) | 0.353861 / 0.434364 (-0.080503) | 0.376022 / 0.540337 (-0.164315) | 0.518145 / 1.386936 (-0.868791) |\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) | 0.006015 / 0.011353 (-0.005338) | 0.003605 / 0.011008 (-0.007403) | 0.062169 / 0.038508 (0.023661) | 0.056094 / 0.023109 (0.032985) | 0.353085 / 0.275898 (0.077187) | 0.393744 / 0.323480 (0.070265) | 0.004672 / 0.007986 (-0.003313) | 0.002859 / 0.004328 (-0.001469) | 0.062992 / 0.004250 (0.058742) | 0.049767 / 0.037052 (0.012714) | 0.356850 / 0.258489 (0.098361) | 0.403731 / 0.293841 (0.109890) | 0.026664 / 0.128546 (-0.101882) | 0.008026 / 0.075646 (-0.067621) | 0.067944 / 0.419271 (-0.351327) | 0.042133 / 0.043533 (-0.001400) | 0.353865 / 0.255139 (0.098726) | 0.383461 / 0.283200 (0.100261) | 0.021250 / 0.141683 (-0.120433) | 1.428102 / 1.452155 (-0.024053) | 1.481061 / 1.492716 (-0.011655) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223552 / 0.018006 (0.205546) | 0.402390 / 0.000490 (0.401900) | 0.000721 / 0.000200 (0.000521) | 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) | 0.025065 / 0.037411 (-0.012347) | 0.075537 / 0.014526 (0.061011) | 0.083519 / 0.176557 (-0.093037) | 0.137068 / 0.737135 (-0.600068) | 0.084165 / 0.296338 (-0.212173) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420176 / 0.215209 (0.204967) | 4.206226 / 2.077655 (2.128571) | 2.168089 / 1.504120 (0.663969) | 1.987299 / 1.541195 (0.446104) | 2.029489 / 1.468490 (0.560999) | 0.495822 / 4.584777 (-4.088955) | 3.106580 / 3.745712 (-0.639132) | 3.833215 / 5.269862 (-1.436647) | 2.450450 / 4.565676 (-2.115226) | 0.056979 / 0.424275 (-0.367296) | 0.006514 / 0.007607 (-0.001093) | 0.503646 / 0.226044 (0.277601) | 5.035035 / 2.268929 (2.766106) | 2.608245 / 55.444624 (-52.836379) | 2.245492 / 6.876477 (-4.630985) | 2.262868 / 2.142072 (0.120795) | 0.590736 / 4.805227 (-4.214491) | 0.124637 / 6.500664 (-6.376027) | 0.061442 / 0.075469 (-0.014027) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316736 / 1.841788 (-0.525052) | 17.948635 / 8.074308 (9.874327) | 13.752442 / 10.191392 (3.561050) | 0.144107 / 0.680424 (-0.536317) | 0.017112 / 0.534201 (-0.517089) | 0.336537 / 0.579283 (-0.242746) | 0.347832 / 0.434364 (-0.086532) | 0.392944 / 0.540337 (-0.147393) | 0.534455 / 1.386936 (-0.852481) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#406b2212263c0d33f267e35b917f410ff6b3bc00 \"CML watermark\")\n" ]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6056). All of your documentation changes will be reflected on that endpoint.", "@lhoestq Reading the filenames is something I tried earlier, but I decided to use the yaml direction because:\r\n\r\n1. The yaml file name is constructed to retain information about the shard_size, and total number of shards, hence ensuring that the files uploaded are not just files that have the same name but actually represent a different configuration of shard_size, and total number of shards. \r\n2. Remembering the total file size is done easily in the yaml, whereas alternatively I am not sure how one could access the file size of the uploaded files without downloading them.\r\n3. I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it. \r\n\r\nIf 1 and 2 can be achieved without an additional yaml, then I would be willing to make those changes. Let me know of any ideas. 1. could be done by changing the data file names, but I'd rather not do that as to prevent breaking existing datasets that try to upload updates to their data. ", "If the file name depends on the shard's fingerprint **before** mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n\r\n> I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n\r\nwhat was the issue ?" ]

[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]

[]

[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]

[ "Hi @yjernite I am interested in adding this dataset. \r\nIn the repo they have also added a code mixed MT task from English to Hinglish [here](https://github.com/microsoft/GLUECoS#code-mixed-machine-translation-task). I think this could be a good dataset addition in itself and then I can add the rest of the GLUECoS tasks as one dataset. What do you think?" ]

[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]

[ "Cool, love it ! :)\r\n\r\nFeel free to add a paragraph saying how to load the dataset:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"stevhliu/demo\")\r\n\r\n# or to separate each csv file into several splits\r\ndata_files = {\"train\": \"train.csv\", \"test\": \"test.csv\"}\r\ndataset = load_dataset(\"stevhliu/demo\", data_files=data_files)\r\nprint(dataset[\"train\"][0])\r\n```", "Perfect, feel free to mark this PR ready for review :)\r\n\r\ncc @albertvillanova do you have any comment ? You can check the tutorial here:\r\nhttps://47389-250213286-gh.circle-artifacts.com/0/docs/_build/html/no_code_upload.html\r\n\r\nMaybe we can just add a list of supported file types:\r\n- csv\r\n- json\r\n- json lines\r\n- text\r\n- parquet", "I just added a mention of the login for private datasets. Don't hesitate to edit or comment.\r\n\r\nOtherwise I think it's all good, feel free to merge it @stevhliu if you don't have other changes to make :)" ]

[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" }, { "color": "7057ff", "default": true, "description": "Good for newcomers", "id": 1935892877, "name": "good first issue", "node_id": "MDU6TGFiZWwxOTM1ODkyODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue" } ]

[ "#take", "Thanks @hamid-vakilzadeh ! Let us know if you have some questions or if we can help", "Thank you! I certainly will reach out if I need any help." ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[ "Hi @arymbe, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your problem.\r\n\r\nCould you please write the complete stack trace? That way we will be able to see which package originates the exception.", "Hello, thank you for your fast replied. this is the complete error that I got\r\n\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError Traceback (most recent call last)\r\n\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError Traceback (most recent call last)\r\n\r\nInput In [27], in <module>\r\n----> 1 from datasets import load_dataset\r\n\r\nvenv/lib/python3.8/site-packages/datasets/__init__.py:39, in <module>\r\n 37 from .arrow_dataset import Dataset, concatenate_datasets\r\n 38 from .arrow_reader import ReadInstruction\r\n---> 39 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n 40 from .combine import interleave_datasets\r\n 41 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\nvenv/lib/python3.8/site-packages/datasets/builder.py:40, in <module>\r\n 32 from .arrow_reader import (\r\n 33 HF_GCP_BASE_URL,\r\n 34 ArrowReader,\r\n (...)\r\n 37 ReadInstruction,\r\n 38 )\r\n 39 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 40 from .data_files import DataFilesDict, sanitize_patterns\r\n 41 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n 42 from .features import Features\r\n\r\nvenv/lib/python3.8/site-packages/datasets/data_files.py:297, in <module>\r\n 292 except FileNotFoundError:\r\n 293 raise FileNotFoundError(f\"The directory at {base_path} doesn't contain any data file\") from None\r\n 296 def _resolve_single_pattern_in_dataset_repository(\r\n--> 297 dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n 298 pattern: str,\r\n 299 allowed_extensions: Optional[list] = None,\r\n 300 ) -> List[PurePath]:\r\n 301 data_files_ignore = FILES_TO_IGNORE\r\n 302 fs = HfFileSystem(repo_info=dataset_info)\r\n\r\nAttributeError: module 'huggingface_hub' has no attribute 'hf_api'", "This is weird... It is long ago that the package `huggingface_hub` has a submodule called `hf_api`.\r\n\r\nMaybe you have a problem with your installed `huggingface_hub`...\r\n\r\nCould you please try to update it?\r\n```shell\r\npip install -U huggingface_hub\r\n```", "Yap, I've updated several times. Then, I've tried numeral combination of datasets and huggingface_hub versions. However, I think your point is right that there is a problem with my huggingface_hub installation. I'll try another way to find the solution. I'll update it later when I get the solution. Thank you :)", "I'm sorry I can't reproduce your problem.\r\n\r\nMaybe you could try to create a new Python virtual environment and install all dependencies there from scratch. You can use either:\r\n- Python venv: https://docs.python.org/3/library/venv.html\r\n- or conda venv (if you are using conda): https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html", "Facing the same issue.\r\n\r\nResponse from `pip show datasets`\r\n```\r\nName: datasets\r\nVersion: 1.15.1\r\nSummary: HuggingFace community-driven open-source library of datasets\r\nHome-page: https://github.com/huggingface/datasets\r\nAuthor: HuggingFace Inc.\r\nAuthor-email: [email protected]\r\nLicense: Apache 2.0\r\nLocation: /usr/local/lib/python3.8/dist-packages\r\nRequires: aiohttp, dill, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, requests, tqdm, xxhash\r\nRequired-by: lm-eval\r\n```\r\n\r\nResponse from `pip show huggingface_hub`\r\n\r\n```\r\nName: huggingface-hub\r\nVersion: 0.8.1\r\nSummary: Client library to download and publish models, datasets and other repos on the huggingface.co hub\r\nHome-page: https://github.com/huggingface/huggingface_hub\r\nAuthor: Hugging Face, Inc.\r\nAuthor-email: [email protected]\r\nLicense: Apache\r\nLocation: /usr/local/lib/python3.8/dist-packages\r\nRequires: filelock, packaging, pyyaml, requests, tqdm, typing-extensions\r\nRequired-by: datasets\r\n```\r\n\r\nresponse from `datasets-cli env`\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/datasets-cli\", line 5, in <module>\r\n from datasets.commands.datasets_cli import main\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/__init__.py\", line 37, in <module>\r\n from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 44, in <module>\r\n from .data_files import DataFilesDict, _sanitize_patterns\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/data_files.py\", line 120, in <module>\r\n dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n File \"/usr/local/lib/python3.8/dist-packages/huggingface_hub/__init__.py\", line 105, in __getattr__\r\n raise AttributeError(f\"No {package_name} attribute {name}\")\r\nAttributeError: No huggingface_hub attribute hf_api\r\n```", "A workaround: \r\nI changed lines around Line 125 in `__init__.py` of `huggingface_hub` to something like\r\n```\r\n__getattr__, __dir__, __all__ = _attach(\r\n __name__,\r\n submodules=['hf_api'],\r\n```\r\nand it works ( which gives `datasets` direct access to `huggingface_hub.hf_api` ).", "I was getting the same issue. After trying a few versions, following combination worked for me.\r\ndataset==2.3.2\r\nhuggingface_hub==0.7.0\r\n\r\nIn another environment, I just installed latest repos from pip through `pip install -U transformers datasets tokenizers evaluate`, resulting in following versions. This also worked. Hope it helps someone. \r\n\r\ndatasets-2.3.2 evaluate-0.1.2 huggingface-hub-0.8.1 responses-0.18.0 tokenizers-0.12.1 transformers-4.20.1", "For layoutlm_v3 finetune\r\ndatasets-2.3.2 evaluate-0.1.2 huggingface-hub-0.8.1 responses-0.18.0 tokenizers-0.12.1 transformers-4.12.5", "(For layoutlmv3 fine-tuning) In my case, modifying `requirements.txt` as below worked.\r\n\r\n- python = 3.7\r\n\r\n```\r\ndatasets==2.3.2\r\nevaluate==0.1.2\r\nhuggingface-hub==0.8.1\r\nresponse==0.5.0\r\ntokenizers==0.10.1\r\ntransformers==4.12.5\r\nseqeval==1.2.2\r\ndeepspeed==0.5.7\r\ntensorboard==2.7.0\r\nseqeval==1.2.2\r\nsentencepiece\r\ntimm==0.4.12\r\nPillow\r\neinops\r\ntextdistance\r\nshapely\r\n```", "> For layoutlm_v3 finetune datasets-2.3.2 evaluate-0.1.2 huggingface-hub-0.8.1 responses-0.18.0 tokenizers-0.12.1 transformers-4.12.5\r\n\r\nGOOD!! Thanks!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3943). All of your documentation changes will be reflected on that endpoint." ]

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[ "Already supported." ]

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[ "So much better - thank you for doing that, @lhoestq!", "Also fixed the `uncorpus` urls for wmt19 ru-en and zh-en for https://github.com/huggingface/datasets/issues/1893", "Thanks!\r\nCan this be merged sooner? \r\nI manually update it and it works well." ]

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[ "Yes that would be awesome. Not only the download speeds are awful, but also some files are missing.\r\nWe list all the URLs in the datasets/wmt19/wmt_utils.py so we can make a script to download them all and host on S3.\r\nAlso I think most of the materials are under the CC BY-NC-SA 3.0 license (must double check) so it should be possible to redistribute the data with no issues.\r\n\r\ncc @patrickvonplaten who knows more about the wmt scripts", "Yeah, the scripts are pretty ugly! A big refactor would make sense here...and I also remember that the datasets were veeery slow to download", "I'm downloading them.\r\nI'm starting with the ones hosted on http://data.statmt.org which are the slowest ones", "@lhoestq better to use our new git-based system than just raw S3, no? (that way we have built-in CDN etc.)", "Closing since the urls were changed to mirror urls in #1912 ", "Hi there! What about mirroring other datasets like [CCAligned](http://www.statmt.org/cc-aligned/) as well? All of them are really slow to download..." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The CI is failing because of the incomplete dataset card - this is unrelated to the goal of this PR so we can ignore it" ]

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[ "This one is ready for review now", "I changed to using features only, instead of info.\r\nLet mw know if it sounds good to you now @thomwolf " ]

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[ "also to fix the check_code_quality CI you have to remove the imports of the unused `csv` and `os`", "@lhoestq Thanks for the review! I have done what you asked, README is also updated. 🤗 \r\nThe CI fails because of the added dependency. I have never used circleCI before, so I am curious how will you solve that?", "I just added `py7zr` to our test dependencies", "merging since the CI is fixed on master", "Thanks! 🤗 " ]

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[ "All the tests pass on my side. Not sure if it is a cache issue or a pytest issue or a circleci issue.\r\nEDIT: I have the same error on google colab. Trying to fix that", "I think I fixed it (sorry didn't notice you were on it as well)" ]

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

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3860). All of your documentation changes will be reflected on that endpoint.", "There are still some `.. code-block:: python` (e.g. see [this](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#datasets.Dataset.align_labels_with_mapping)) directives in our codebase, so maybe we can remove those as well as part of this PR." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "We are having an unrelated issue that makes several tests fail. We are working on that. Once fixed, you will be able to merge the main branch into this, so that you get the fix and the tests pass..." ]

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[ "> Later we can add imagenet-21k as a new dataset if we want.\r\n\r\nisn't it what models refer to as `imagenet` already?", "> isn't it what models refer to as imagenet already?\r\n\r\nI wasn't sure, but it looks like it indeed. Therefore having a dataset `imagenet` for ImageNet 21k makes sense actually.\r\n\r\nEDIT: actually not all `imagenet` tag refer to ImageNet 21k - we will need to correct some of them", "_The documentation is not available anymore as the PR was closed or merged._", "should we remove the repo mirror on the hub side or will you do it?" ]

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[ "Thanks! See https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikipedia%20Processing.ipynb for more implementation details / some data around the overhead induced by adding the extra preprocessing steps (stripping link prefixes and magic words)", "Closed by:\r\n- #3435" ]

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[ "~~It seems like #5043 fix (merged recently) is the root cause of such behaviour. When we empty indices mapping (because the dataset length equals to zero), we can no longer get column item like: `ds_filter_2['sentence']` which uses\r\n`ds_filter_1._indices.column(0)`~~\r\n\r\n**UPDATE:**\r\nEmpty datasets are returned without going through partial function on `map` method, which will not work to get indices for `filter`: we need to run `get_indices_from_mask_function` partial function on the dataset to get output = `{\"indices\": []}`. But this is complicated since functions used in args, in particular `get_indices_from_mask_function`, do not support empty datasets.\r\nWe can just handle empty datasets aside on filter method.", "#self-assign", "Thank you for solving this amazingly quickly!" ]

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[ "Reverted the multi-input-text-classification tag from task_categories and added it as task_ids @lhoestq ", "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "This PR is ready :)\r\nIt brings official support for windows.\r\n\r\nSome tests `AWSDatasetTest` are failing.\r\nThis is because I had to fix a few datasets that were not compatible with windows.\r\nThese test will pass once they got merged on master :)" ]

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[ "The CI failures are unrelated to the changes (missing fields in the readme and the CER metric error fixed in #3252)." ]

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[ "@lhoestq I was indeed working on that... to make another commit on this feature branch...", "But as you prefer... nevermind! :)", "Ah what do you have in mind for the tests ? I was thinking of adding a check in the MockDownloadManager used for tests based on dummy data. I'm creating a PR right now, I'd be happy to have your opinion", "Indeed I was thinking of something similar: monckeypatching the HTTP request...", "Therefore, if you agree, I am removing all the rest of `os.path.join`, both from the code and the docs...", "If you spot other `os.path.join` for urls in dataset scripts or metrics scripts feel free to fix them.\r\nIn the library itself (/src/datasets) it should be fine since there are tests and a windows CI, but if you have doubts of some usage of `os.path.join` somewhere, let me know.", "Alright create the test in #905 .\r\nThe windows CI is failing for all the datasets that have bad usage of `os.path.join` for urls.\r\nThere are of course the ones you fixed in this PR (thanks again !) but I found others as well such as pg19 and blimp.\r\nYou can check the full list by looking at the CI failures of the commit 1ce3354", "I am merging this one as well as #906 that should fix all of the datasets.\r\nThen I'll rebase #905 which adds the test that checks for bad urls and make sure it' all green now" ]

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

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[ "Using `dataset = load_dataset(\"kilt_tasks\", \"wow\", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution.", "Hi @slyviacassell, thanks for reporting.\r\n\r\nYes, there is an issue with the checksum verification. I'm fixing it.\r\n\r\nAnd as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`. " ]

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[ "Hi @mskovalova, \r\n\r\nSome datasets have multiple configurations. Therefore, in order to load them, you have to specify both the *dataset name* and the *configuration name*.\r\n\r\nIn the error message you got, you have a usage example:\r\n- To load the 'wikitext-103-raw-v1' configuration of the 'wikitext' dataset, you should use: \r\n ```python\r\n load_dataset('wikitext', 'wikitext-103-raw-v1')\r\n ```\r\n\r\nIn your case, if you would like to load the 'wikitext-2-v1' configuration of the 'wikitext' dataset, please use:\r\n```python\r\nraw_datasets = load_dataset(\"wikitext\", \"wikitext-2-v1\")\r\n```", "Hi, if I want to load the dataset from local file, then how to specify the configuration name?" ]

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[ "Hi !\r\n\r\nAs you noticed, \"big\" datasets like Wikipedia require apache beam to be processed.\r\nHowever users usually don't have an apache beam runtime available (spark, dataflow, etc.) so our goal for this library is to also make available processed versions of these datasets, so that users can just download and use them right away.\r\n\r\nThis is the case for english and french wikipedia right now: we've processed them ourselves and now they are available from our google storage. However we've not processed the german one (yet).", "Hi @lhoestq \r\n\r\nThank you for your quick reply. I thought this might be the case, that the processing was done for some languages and not for others. Is there any set timeline for when other languages (German, Italian) will be processed?\r\n\r\nGiven enough memory, is it possible to process the data ourselves by specifying the `beam_runner`?", "Adding them is definitely in our short term objectives. I'll be working on this early next week :)\r\n\r\nAlthough if you have an apache beam runtime feel free to specify the beam runner. You can find more info [here](https://github.com/huggingface/nlp/blob/master/docs/beam_dataset.md) on how to make it work on Dataflow but you can adapt it for Spark or any other beam runtime (by changing the `runner`).\r\n\r\nHowever if you don't have a beam runtime and even if you have enough memory, I discourage you to use the `DirectRunner` on the german or italian wikipedia. According to Apache Beam documentation it was made for testing purposes and therefore it is memory-inefficient.", "German is [almost] done @gregburman", "I added the German and the Italian Wikipedia to our google cloud storage:\r\nFirst update the `nlp` package to 0.3.0:\r\n```bash\r\npip install nlp --upgrade\r\n```\r\nand then\r\n```python\r\nfrom nlp import load_dataset\r\nwiki_de = load_dataset(\"wikipedia\", \"20200501.de\")\r\nwiki_it = load_dataset(\"wikipedia\", \"20200501.it\")\r\n```\r\nThe datasets are downloaded and directly ready to use (no processing).", "Hi @lhoestq \r\n\r\nWow, thanks so much, that's **really** incredible! I was considering looking at creating my own Beam Dataset, as per the doc you linked, but instead opted to process the data myself using `wikiextractor`. However, now that this is available, I'll definitely switch across and use it.\r\n\r\nThanks so much for the incredible work, this really helps out our team considerably!\r\n\r\nHave a great (and well-deserved ;) weekend ahead!\r\n\r\nP.S. I'm not sure if I should close the issue here - if so I'm happy to do so.", "Thanks for your message, glad I could help :)\r\nClosing this one." ]

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[ "@lhoestq thanks for the review! I've modified the labels to follow other NLI datasets.\r\nPlease review my change and let me know if I miss anything." ]

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[ "Thanks for reporting !\r\nWhich version of transformers are you using ?\r\nIt looks like it doesn't have the PreTrainedTokenizerBase class", "I was using transformer 2.9. And I switch to the latest transformer package. Everything works just fine!!\r\n\r\nThanks for helping! I should look more carefully next time. Didn't realize loading the data part requires using tokenizer.\r\n", "Yes it shouldn’t fail with older version of transformers since this is only a special feature to make caching more efficient when using transformers for tokenization.\r\nWe’ll update this." ]

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[ "Hi @SapirWeissbuch,\r\nWhen you are saying it freezes, at that time it is unzipping the file from the zip file it downloaded. Since it's a very heavy file it'll take some time. It was taking ~11GB after unzipping when it started reading examples for me. Hope that helps!\r\n\r\n", "Hi @bhavitvyamalik \r\nThanks for the reply!\r\nActually I let it run for 30 minutes before I killed the process. In this time, 30GB were extracted (much more than 11GB), I checked the size of the destination directory.\r\n\r\nWhat version of Datasets are you using?\r\n", "I'm using datasets version: 1.1.3. I think you should drop `cache_dir` and use only\r\n`dataset = datasets.load_dataset(\"trivia_qa\", \"rc\")`\r\n\r\nTried that on colab and it's working there too\r\n\r\n", "Train, Validation, and Test splits contain 138384, 18669, and 17210 samples respectively. It takes some time to read the samples. Even in your colab notebook it was reading the samples before you killed the process. Let me know if it works now!", "Hi, it works on colab but it still doesn't work on my computer, same problem as before - overly large and long extraction process.\r\nI have to use a custom 'cache_dir' because I don't have any space left in my home directory where it is defaulted, maybe this could be the issue?", "I tried running this again - More details of the problem:\r\nCode:\r\n```\r\ndatasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir=\"/path/to/cache\")\r\n```\r\n\r\nThe output:\r\n```\r\nDownloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to path/to/cache/trivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d... \r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.67G/2.67G [03:38<00:00, 12.2MB/s]\r\n\r\n```\r\nThe process continues (no progress bar is visible).\r\nI tried `du -sh .` in `path/to/cache`, and the size keeps increasing, reached 35G before I killed the process.\r\n\r\nGoogle Colab with custom `cache_dir` has same issue.\r\nhttps://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing#scrollTo=2G2O0AeNIXan", "1) You can clear the huggingface folder in your `.cache` directory to use default directory for datasets. Speed of extraction and loading of samples depends a lot on your machine's configurations too.\r\n\r\n2) I tried on colab `dataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \"./datasets\")`. After memory usage reached around 42GB (starting from 32GB used already), the dataset was loaded in the memory. Even Your colab notebook shows \r\n\r\nwhich means it's loaded now.", "Facing the same issue.\r\nI am able to download datasets without `cache_dir`, however, when I specify the `cache_dir`, the process hangs indefinitely after partial download. \r\nTried for `data = load_dataset(\"cnn_dailymail\", \"3.0.0\")`", "Hi @ashutoshml,\r\nI tried this and it worked for me:\r\n`data = load_dataset(\"cnn_dailymail\", \"3.0.0\", cache_dir=\"./dummy\")`\r\n\r\nI'm using datasets==1.8.0. It took around 3-4 mins for dataset to unpack and start loading examples.", "Ok. I waited for 20-30 mins, and it still is stuck.\r\nI am using datasets==1.8.0.\r\n\r\nIs there anyway to check what is happening? like a` --verbose` flag?\r\n\r\n\r\n" ]

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[ "Sounds good, thanks !" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3976). All of your documentation changes will be reflected on that endpoint.", "Not sure why some section titles end with `[[datasets.xxx]]`, like this: https://moon-ci-docs.huggingface.co/docs/datasets/pr_3976/en/package_reference/main_classes#datasetdict[[datasets.datasetdict]]", "Thanks ! I think this has been fixed already in https://github.com/huggingface/datasets/pull/3925 though\r\n\r\nI'm closing this one then if it's fine for you" ]

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[ "Nevermind, I restarted my python session and it worked fine...\r\n\r\n---\r\n\r\nI had an authentification error, and I authenticated from another terminal. After that, no more error but it was not working. Restarting the sessions makes it work :)" ]

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[ "[Off topic but related: Is the uptime of S3 provably better than Zenodo's?]", "Hi @dkajtoch, please note that at HuggingFace we are not hosting this dataset: we are just using a script to download their data file and create a dataset from it.\r\n\r\nIt was the dataset owners decision to host their data at Zenodo. You can see this on their website: https://marcobaroni.org/composes/sick.html\r\n\r\nAnd yes, you are right: Zenodo is currently having some incidents and people are reporting problems from it.\r\n\r\nOn the other hand, we could contact the data owners and propose them to host their data at our Hugging Face Hub.\r\n\r\n@julien-c I guess so.\r\n", "Thanks @albertvillanova. I know that the problem lies in the source data. I just wanted to point out that these kind of problems are unavoidable without having one place where data sources are cached. Websites may go down or data sources may move. Having a copy in Hugging Face Hub would be a great solution. ", "Definitely, @dkajtoch! But we have to ask permission to the data owners. And many dataset licenses directly forbid data redistribution: in those cases we are not allowed to host their data on our Hub.", "Ahhh good point! License is the problem :(" ]

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[ "Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.\r\n\r\n" ]

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[ "`pyarrow` 1.0.0 doesn't support some types in parquet, we'll have to bump its minimum version.\r\n\r\nAlso I still need to add dummy data to test the parquet builder.", "I had to bump the minimum pyarrow version to 3.0.0 to properly support parquet.\r\n\r\nEverything is ready for review now :)\r\nI reused pretty much the same tests we had for CSV", "Done !\r\nNow we're still allowing pyarrow>=1.0.0, but when users want to use parquet features they're asked to update to pyarrow>=3.0.0" ]

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

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[ "thanks ! can you also regenerate the dataset_infos.json file please ?" ]

[]

[ "Apparently the merge brought back some tests that use `flatten_()` that we removed recently", "_The documentation is not available anymore as the PR was closed or merged._" ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[]

[]

[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007703 / 0.011353 (-0.003650) | 0.004699 / 0.011008 (-0.006309) | 0.090195 / 0.038508 (0.051687) | 0.119165 / 0.023109 (0.096056) | 0.361435 / 0.275898 (0.085537) | 0.404429 / 0.323480 (0.080949) | 0.006172 / 0.007986 (-0.001814) | 0.003932 / 0.004328 (-0.000397) | 0.068384 / 0.004250 (0.064133) | 0.066730 / 0.037052 (0.029678) | 0.360978 / 0.258489 (0.102489) | 0.401301 / 0.293841 (0.107460) | 0.032836 / 0.128546 (-0.095710) | 0.010821 / 0.075646 (-0.064825) | 0.294526 / 0.419271 (-0.124745) | 0.068751 / 0.043533 (0.025218) | 0.368427 / 0.255139 (0.113288) | 0.376969 / 0.283200 (0.093770) | 0.040538 / 0.141683 (-0.101145) | 1.509966 / 1.452155 (0.057811) | 1.564885 / 1.492716 (0.072169) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292243 / 0.018006 (0.274237) | 0.662067 / 0.000490 (0.661577) | 0.004966 / 0.000200 (0.004766) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029050 / 0.037411 (-0.008361) | 0.099880 / 0.014526 (0.085354) | 0.109277 / 0.176557 (-0.067280) | 0.167877 / 0.737135 (-0.569258) | 0.110770 / 0.296338 (-0.185569) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395742 / 0.215209 (0.180533) | 3.944152 / 2.077655 (1.866498) | 1.875295 / 1.504120 (0.371175) | 1.705088 / 1.541195 (0.163893) | 1.884443 / 1.468490 (0.415953) | 0.497243 / 4.584777 (-4.087534) | 3.749287 / 3.745712 (0.003575) | 4.418826 / 5.269862 (-0.851035) | 2.481149 / 4.565676 (-2.084528) | 0.058260 / 0.424275 (-0.366015) | 0.007744 / 0.007607 (0.000137) | 0.472531 / 0.226044 (0.246486) | 4.716022 / 2.268929 (2.447094) | 2.480446 / 55.444624 (-52.964179) | 2.163098 / 6.876477 (-4.713379) | 2.217555 / 2.142072 (0.075482) | 0.601965 / 4.805227 (-4.203262) | 0.139364 / 6.500664 (-6.361301) | 0.067097 / 0.075469 (-0.008372) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330537 / 1.841788 (-0.511251) | 22.176270 / 8.074308 (14.101962) | 16.224981 / 10.191392 (6.033589) | 0.173708 / 0.680424 (-0.506715) | 0.019402 / 0.534201 (-0.514799) | 0.401994 / 0.579283 (-0.177289) | 0.432597 / 0.434364 (-0.001767) | 0.489933 / 0.540337 (-0.050404) | 0.672334 / 1.386936 (-0.714602) |\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) | 0.008622 / 0.011353 (-0.002731) | 0.004609 / 0.011008 (-0.006399) | 0.067791 / 0.038508 (0.029283) | 0.112770 / 0.023109 (0.089661) | 0.380939 / 0.275898 (0.105041) | 0.416940 / 0.323480 (0.093460) | 0.006170 / 0.007986 (-0.001815) | 0.003876 / 0.004328 (-0.000452) | 0.066227 / 0.004250 (0.061976) | 0.073132 / 0.037052 (0.036080) | 0.390120 / 0.258489 (0.131631) | 0.420893 / 0.293841 (0.127052) | 0.033235 / 0.128546 (-0.095311) | 0.009659 / 0.075646 (-0.065987) | 0.072668 / 0.419271 (-0.346604) | 0.051333 / 0.043533 (0.007801) | 0.393828 / 0.255139 (0.138689) | 0.412376 / 0.283200 (0.129176) | 0.027760 / 0.141683 (-0.113923) | 1.494369 / 1.452155 (0.042214) | 1.592862 / 1.492716 (0.100145) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.345376 / 0.018006 (0.327369) | 0.609399 / 0.000490 (0.608909) | 0.000546 / 0.000200 (0.000346) | 0.000061 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035601 / 0.037411 (-0.001810) | 0.106527 / 0.014526 (0.092001) | 0.114388 / 0.176557 (-0.062168) | 0.175607 / 0.737135 (-0.561529) | 0.113009 / 0.296338 (-0.183329) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417237 / 0.215209 (0.202028) | 4.136329 / 2.077655 (2.058675) | 2.147134 / 1.504120 (0.643014) | 2.009501 / 1.541195 (0.468306) | 2.139499 / 1.468490 (0.671009) | 0.491593 / 4.584777 (-4.093184) | 3.766734 / 3.745712 (0.021022) | 5.652446 / 5.269862 (0.382585) | 3.021654 / 4.565676 (-1.544022) | 0.058458 / 0.424275 (-0.365817) | 0.008271 / 0.007607 (0.000664) | 0.488229 / 0.226044 (0.262184) | 4.861343 / 2.268929 (2.592415) | 2.694142 / 55.444624 (-52.750482) | 2.489130 / 6.876477 (-4.387346) | 2.679376 / 2.142072 (0.537304) | 0.589959 / 4.805227 (-4.215268) | 0.137939 / 6.500664 (-6.362725) | 0.066833 / 0.075469 (-0.008636) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.444871 / 1.841788 (-0.396916) | 22.874961 / 8.074308 (14.800653) | 15.842130 / 10.191392 (5.650738) | 0.175529 / 0.680424 (-0.504895) | 0.019024 / 0.534201 (-0.515177) | 0.406551 / 0.579283 (-0.172732) | 0.430335 / 0.434364 (-0.004029) | 0.475750 / 0.540337 (-0.064587) | 0.624836 / 1.386936 (-0.762100) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dabbb7467f49fd22ae1a43cc577eb43008d63ee8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006068 / 0.011353 (-0.005285) | 0.003694 / 0.011008 (-0.007315) | 0.080321 / 0.038508 (0.041813) | 0.061738 / 0.023109 (0.038629) | 0.329675 / 0.275898 (0.053777) | 0.364008 / 0.323480 (0.040528) | 0.004722 / 0.007986 (-0.003263) | 0.002857 / 0.004328 (-0.001471) | 0.062447 / 0.004250 (0.058197) | 0.047006 / 0.037052 (0.009953) | 0.335730 / 0.258489 (0.077241) | 0.373047 / 0.293841 (0.079206) | 0.027273 / 0.128546 (-0.101274) | 0.007979 / 0.075646 (-0.067667) | 0.262693 / 0.419271 (-0.156579) | 0.045416 / 0.043533 (0.001883) | 0.340774 / 0.255139 (0.085635) | 0.359667 / 0.283200 (0.076468) | 0.020848 / 0.141683 (-0.120835) | 1.450110 / 1.452155 (-0.002045) | 1.489511 / 1.492716 (-0.003206) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185090 / 0.018006 (0.167084) | 0.429823 / 0.000490 (0.429334) | 0.000703 / 0.000200 (0.000503) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024398 / 0.037411 (-0.013013) | 0.072983 / 0.014526 (0.058457) | 0.084012 / 0.176557 (-0.092544) | 0.146160 / 0.737135 (-0.590975) | 0.084068 / 0.296338 (-0.212270) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432204 / 0.215209 (0.216995) | 4.320593 / 2.077655 (2.242939) | 2.261260 / 1.504120 (0.757140) | 2.087148 / 1.541195 (0.545954) | 2.144520 / 1.468490 (0.676029) | 0.501477 / 4.584777 (-4.083300) | 3.119557 / 3.745712 (-0.626156) | 3.572527 / 5.269862 (-1.697335) | 2.208836 / 4.565676 (-2.356840) | 0.057232 / 0.424275 (-0.367043) | 0.006494 / 0.007607 (-0.001113) | 0.508135 / 0.226044 (0.282091) | 5.090416 / 2.268929 (2.821488) | 2.739800 / 55.444624 (-52.704824) | 2.416105 / 6.876477 (-4.460372) | 2.616037 / 2.142072 (0.473965) | 0.583730 / 4.805227 (-4.221497) | 0.124312 / 6.500664 (-6.376352) | 0.060760 / 0.075469 (-0.014709) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256097 / 1.841788 (-0.585691) | 18.326073 / 8.074308 (10.251765) | 13.859173 / 10.191392 (3.667781) | 0.143639 / 0.680424 (-0.536785) | 0.016649 / 0.534201 (-0.517552) | 0.331671 / 0.579283 (-0.247612) | 0.365370 / 0.434364 (-0.068994) | 0.392753 / 0.540337 (-0.147584) | 0.549302 / 1.386936 (-0.837634) |\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) | 0.006054 / 0.011353 (-0.005299) | 0.003641 / 0.011008 (-0.007367) | 0.063109 / 0.038508 (0.024601) | 0.060482 / 0.023109 (0.037372) | 0.404047 / 0.275898 (0.128149) | 0.425436 / 0.323480 (0.101956) | 0.004603 / 0.007986 (-0.003382) | 0.002905 / 0.004328 (-0.001423) | 0.063207 / 0.004250 (0.058956) | 0.048248 / 0.037052 (0.011196) | 0.404325 / 0.258489 (0.145836) | 0.432652 / 0.293841 (0.138811) | 0.027630 / 0.128546 (-0.100916) | 0.008062 / 0.075646 (-0.067584) | 0.068367 / 0.419271 (-0.350905) | 0.042169 / 0.043533 (-0.001364) | 0.384903 / 0.255139 (0.129764) | 0.418617 / 0.283200 (0.135417) | 0.020767 / 0.141683 (-0.120915) | 1.463606 / 1.452155 (0.011451) | 1.512081 / 1.492716 (0.019365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229601 / 0.018006 (0.211594) | 0.417878 / 0.000490 (0.417388) | 0.000373 / 0.000200 (0.000173) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026354 / 0.037411 (-0.011057) | 0.078100 / 0.014526 (0.063574) | 0.087122 / 0.176557 (-0.089434) | 0.140017 / 0.737135 (-0.597118) | 0.089923 / 0.296338 (-0.206415) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422405 / 0.215209 (0.207196) | 4.237383 / 2.077655 (2.159728) | 2.161104 / 1.504120 (0.656984) | 1.982337 / 1.541195 (0.441142) | 2.050216 / 1.468490 (0.581726) | 0.499281 / 4.584777 (-4.085496) | 2.996953 / 3.745712 (-0.748759) | 5.027069 / 5.269862 (-0.242792) | 2.804703 / 4.565676 (-1.760974) | 0.057707 / 0.424275 (-0.366568) | 0.006809 / 0.007607 (-0.000798) | 0.495196 / 0.226044 (0.269152) | 4.946593 / 2.268929 (2.677665) | 2.598965 / 55.444624 (-52.845660) | 2.349871 / 6.876477 (-4.526606) | 2.451665 / 2.142072 (0.309593) | 0.592314 / 4.805227 (-4.212913) | 0.125685 / 6.500664 (-6.374979) | 0.063252 / 0.075469 (-0.012217) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.325422 / 1.841788 (-0.516366) | 18.521059 / 8.074308 (10.446751) | 14.046757 / 10.191392 (3.855365) | 0.133009 / 0.680424 (-0.547415) | 0.017097 / 0.534201 (-0.517104) | 0.339804 / 0.579283 (-0.239479) | 0.345464 / 0.434364 (-0.088900) | 0.387623 / 0.540337 (-0.152714) | 0.519880 / 1.386936 (-0.867056) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008671 / 0.011353 (-0.002682) | 0.004681 / 0.011008 (-0.006327) | 0.107517 / 0.038508 (0.069008) | 0.078846 / 0.023109 (0.055737) | 0.449745 / 0.275898 (0.173847) | 0.504075 / 0.323480 (0.180596) | 0.005837 / 0.007986 (-0.002148) | 0.004031 / 0.004328 (-0.000297) | 0.092021 / 0.004250 (0.087771) | 0.065954 / 0.037052 (0.028902) | 0.442082 / 0.258489 (0.183593) | 0.529349 / 0.293841 (0.235508) | 0.052527 / 0.128546 (-0.076019) | 0.013854 / 0.075646 (-0.061792) | 0.367315 / 0.419271 (-0.051956) | 0.068731 / 0.043533 (0.025199) | 0.494733 / 0.255139 (0.239594) | 0.472801 / 0.283200 (0.189601) | 0.036791 / 0.141683 (-0.104892) | 1.877648 / 1.452155 (0.425493) | 1.928399 / 1.492716 (0.435683) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231910 / 0.018006 (0.213904) | 0.553464 / 0.000490 (0.552974) | 0.011915 / 0.000200 (0.011715) | 0.000378 / 0.000054 (0.000324) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028232 / 0.037411 (-0.009179) | 0.091441 / 0.014526 (0.076916) | 0.110394 / 0.176557 (-0.066162) | 0.187638 / 0.737135 (-0.549497) | 0.111810 / 0.296338 (-0.184529) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.599987 / 0.215209 (0.384778) | 6.008709 / 2.077655 (3.931054) | 2.518769 / 1.504120 (1.014650) | 2.197029 / 1.541195 (0.655834) | 2.217165 / 1.468490 (0.748675) | 0.894939 / 4.584777 (-3.689837) | 5.001217 / 3.745712 (1.255505) | 4.636482 / 5.269862 (-0.633379) | 3.237613 / 4.565676 (-1.328063) | 0.104227 / 0.424275 (-0.320048) | 0.008504 / 0.007607 (0.000897) | 0.750190 / 0.226044 (0.524145) | 7.514571 / 2.268929 (5.245642) | 3.358003 / 55.444624 (-52.086621) | 2.585649 / 6.876477 (-4.290827) | 2.731129 / 2.142072 (0.589056) | 1.088828 / 4.805227 (-3.716400) | 0.217308 / 6.500664 (-6.283356) | 0.076410 / 0.075469 (0.000941) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.620087 / 1.841788 (-0.221701) | 23.145743 / 8.074308 (15.071435) | 20.583403 / 10.191392 (10.392011) | 0.225467 / 0.680424 (-0.454956) | 0.029063 / 0.534201 (-0.505138) | 0.480563 / 0.579283 (-0.098720) | 0.539083 / 0.434364 (0.104719) | 0.563787 / 0.540337 (0.023449) | 0.782902 / 1.386936 (-0.604034) |\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) | 0.010113 / 0.011353 (-0.001239) | 0.004997 / 0.011008 (-0.006011) | 0.082974 / 0.038508 (0.044466) | 0.090375 / 0.023109 (0.067266) | 0.440273 / 0.275898 (0.164375) | 0.476939 / 0.323480 (0.153459) | 0.005955 / 0.007986 (-0.002031) | 0.004375 / 0.004328 (0.000046) | 0.080459 / 0.004250 (0.076209) | 0.061787 / 0.037052 (0.024734) | 0.477211 / 0.258489 (0.218722) | 0.487164 / 0.293841 (0.193323) | 0.054198 / 0.128546 (-0.074348) | 0.013945 / 0.075646 (-0.061701) | 0.093006 / 0.419271 (-0.326266) | 0.062685 / 0.043533 (0.019152) | 0.461373 / 0.255139 (0.206234) | 0.475766 / 0.283200 (0.192567) | 0.032059 / 0.141683 (-0.109623) | 1.857989 / 1.452155 (0.405834) | 1.837993 / 1.492716 (0.345277) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243048 / 0.018006 (0.225042) | 0.535850 / 0.000490 (0.535360) | 0.007204 / 0.000200 (0.007004) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032584 / 0.037411 (-0.004827) | 0.098151 / 0.014526 (0.083625) | 0.109691 / 0.176557 (-0.066866) | 0.172803 / 0.737135 (-0.564333) | 0.110469 / 0.296338 (-0.185869) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.635086 / 0.215209 (0.419877) | 6.500864 / 2.077655 (4.423210) | 2.996727 / 1.504120 (1.492607) | 2.537218 / 1.541195 (0.996023) | 2.572310 / 1.468490 (1.103820) | 0.870868 / 4.584777 (-3.713909) | 4.989744 / 3.745712 (1.244032) | 4.422174 / 5.269862 (-0.847687) | 2.935874 / 4.565676 (-1.629803) | 0.097118 / 0.424275 (-0.327157) | 0.009360 / 0.007607 (0.001753) | 0.790447 / 0.226044 (0.564403) | 7.859519 / 2.268929 (5.590591) | 3.975616 / 55.444624 (-51.469009) | 3.018271 / 6.876477 (-3.858206) | 3.111173 / 2.142072 (0.969101) | 1.085577 / 4.805227 (-3.719651) | 0.225719 / 6.500664 (-6.274945) | 0.080576 / 0.075469 (0.005107) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.802284 / 1.841788 (-0.039504) | 23.487921 / 8.074308 (15.413613) | 20.595171 / 10.191392 (10.403779) | 0.196610 / 0.680424 (-0.483814) | 0.027483 / 0.534201 (-0.506718) | 0.485840 / 0.579283 (-0.093443) | 0.542661 / 0.434364 (0.108297) | 0.580602 / 0.540337 (0.040265) | 0.768195 / 1.386936 (-0.618741) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"CML watermark\")\n" ]

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[ "I tried to make it run once on the SparkRunner but it seems that this runner has some issues when it is run locally.\r\nFrom my experience the DirectRunner is fine though, even if it's clearly not memory efficient.\r\n\r\nIt would be awesome though to make it work locally on a SparkRunner !\r\nDid you manage to make your processing work ?" ]

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[ "The two configurations don't have the same sizes, I may change that so that they both have 21015300 examples for convenience, even though it's supposed to have 21015324 examples in total.\r\n\r\nOne configuration only has 21015300 examples because it seems that the embeddings of the last 24 examples are missing.", "It's ok to merge now imo. I'll make another PR if we find a way to have the missing embeddings" ]

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[ "<img width=\"519\" alt=\"Capture d’écran 2022-09-20 à 22 33 47\" src=\"https://user-images.githubusercontent.com/1676121/191358952-1220cb7d-745a-4203-a66b-3c707b25038f.png\">\r\n\r\n```\r\nNot found.\r\n\r\nError code: SplitsResponseNotFound\r\n```\r\n\r\nhttps://datasets-server.huggingface.co/splits?dataset=asapp/slue\r\n\r\n```json\r\n{\"error\":\"Not found.\"}\r\n```", "I just launched a refresh. It's weird, I don't see any entry for this dataset in the cache, it's a bug on our side. In order to try to understand what happened, did you change the visibility status from private to public, by any chance?", "The dataset is being refreshed, please retry later.\r\n\r\n<img width=\"802\" alt=\"Capture d’écran 2022-09-20 à 22 39 46\" src=\"https://user-images.githubusercontent.com/1676121/191360072-7cc86486-4e84-4b47-8f9a-4a69fe84a5ac.png\">\r\n", "OK. We now have an issue because the dataset cannot be streamed, and the dataset viewer relies on it.\r\n\r\nMaybe @huggingface/datasets can help:\r\n\r\n```\r\nError code: StreamingRowsError\r\nException: NotImplementedError\r\nMessage: Extraction protocol for TAR archives like 'https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxpopuli_v0.2_blind.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.\r\nTraceback: Traceback (most recent call last):\r\n File \"/src/services/worker/src/worker/responses/first_rows.py\", line 337, in get_first_rows_response\r\n rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)\r\n File \"/src/services/worker/src/worker/utils.py\", line 123, in decorator\r\n return func(*args, **kwargs)\r\n File \"/src/services/worker/src/worker/responses/first_rows.py\", line 65, in get_rows\r\n ds = load_dataset(\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1739, in load_dataset\r\n return builder_instance.as_streaming_dataset(split=split)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1025, in as_streaming_dataset\r\n splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n File \"/tmp/modules-cache/datasets_modules/datasets/asapp--slue/adaa0c78233e1a1df9c2f054e690ec5fc3eaf453bd76b80fe5cbe5728e55d9b1/slue.py\", line 189, in _split_generators\r\n dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name])\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 944, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 907, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 385, in map_nested\r\n return function(data_struct)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 912, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 390, in _get_extraction_protocol\r\n raise NotImplementedError(\r\n NotImplementedError: Extraction protocol for TAR archives like 'https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxpopuli_v0.2_blind.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.\r\n```", "Thanks @severo, \r\n\r\nDo I have to modify the python script to support streaming so that it can be previewed?\r\nIs there a document somewhere that I can follow?\r\n", "Hi @fwu-asapp thanks for reporting, and thanks @severo for the investigation.\r\n\r\nAs explained by @severo, the preview requires that your dataset loading script supports streaming.\r\n\r\nThere are several options here:\r\n- the easiest would be to replace the source files, archived using ZIP instead TAR: the TAR format does not allow random access while streaming, but only sequential access; the ZIP files support streaming out of the box.\r\n- alternatively, to stream TAR archives you can use `dl_manager.iter_archive`: the only prerequisite is that your \"index\" files (.tsv) should have been archived before their corresponding audio files, so while iterating the content of the TAR archive, the metadata files appear first. I think this is the case for voxpopuli tar but not for voxceleb.\r\n- if your .tsv files were not archived before their corresponding audio files (I think this is the case for voxceleb), then you should extract the .tsv files and host them separately (you can host them on the same Hugging Face Hub).\r\n - you can take as example, e.g.: https://huggingface.co/datasets/vivos/blob/main/vivos.py\r\n\r\nAs an advanced approach, you can handle both streaming and non-streaming cases separately.\r\n- as for example: https://huggingface.co/datasets/librispeech_asr/blob/main/librispeech_asr.py or https://huggingface.co/datasets/google/fleurs/blob/main/fleurs.py\r\n\r\nSee related discussion:\r\n- https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492", "Thanks @albertvillanova for your clarification. I'll talk to my collaborators to see if we can replace those files. Let me just close this issue for now.", "FYI, after replacing the source files with the ZIP ones, the dataset viewer works well. Thanks again to @severo and @albertvillanova for your help!", "Great! And thank you for sharing that interesting dataset!" ]

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[ "Hi ! I think there exist tools to dump and reload an index in your elastic search but I'm not super familiar with it.\r\n\r\nAnyway after reloading an index in elastic search you can call `ds.load_elasticsearch_index` which will connect the index to the dataset without re-indexing" ]

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[ "Hello @lhoestq, ```make style``` is applied as asked. :)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4947). All of your documentation changes will be reflected on that endpoint." ]

[]

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

[]

[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006031 / 0.011353 (-0.005322) | 0.003579 / 0.011008 (-0.007429) | 0.080862 / 0.038508 (0.042354) | 0.056660 / 0.023109 (0.033551) | 0.388285 / 0.275898 (0.112387) | 0.422270 / 0.323480 (0.098790) | 0.004651 / 0.007986 (-0.003335) | 0.002895 / 0.004328 (-0.001433) | 0.062767 / 0.004250 (0.058517) | 0.046491 / 0.037052 (0.009438) | 0.389918 / 0.258489 (0.131428) | 0.434650 / 0.293841 (0.140809) | 0.027265 / 0.128546 (-0.101281) | 0.007946 / 0.075646 (-0.067701) | 0.261207 / 0.419271 (-0.158065) | 0.045057 / 0.043533 (0.001525) | 0.391977 / 0.255139 (0.136838) | 0.418525 / 0.283200 (0.135326) | 0.020705 / 0.141683 (-0.120978) | 1.459271 / 1.452155 (0.007116) | 1.516935 / 1.492716 (0.024218) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.174659 / 0.018006 (0.156653) | 0.429627 / 0.000490 (0.429137) | 0.003714 / 0.000200 (0.003514) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023255 / 0.037411 (-0.014156) | 0.073463 / 0.014526 (0.058937) | 0.083000 / 0.176557 (-0.093557) | 0.146704 / 0.737135 (-0.590431) | 0.084419 / 0.296338 (-0.211919) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.392222 / 0.215209 (0.177013) | 3.902620 / 2.077655 (1.824966) | 1.903056 / 1.504120 (0.398936) | 1.753423 / 1.541195 (0.212228) | 1.874547 / 1.468490 (0.406057) | 0.495947 / 4.584777 (-4.088829) | 3.084680 / 3.745712 (-0.661032) | 4.235064 / 5.269862 (-1.034797) | 2.626840 / 4.565676 (-1.938837) | 0.057273 / 0.424275 (-0.367002) | 0.006457 / 0.007607 (-0.001150) | 0.466018 / 0.226044 (0.239974) | 4.648264 / 2.268929 (2.379335) | 2.520293 / 55.444624 (-52.924331) | 2.339393 / 6.876477 (-4.537083) | 2.538848 / 2.142072 (0.396775) | 0.592018 / 4.805227 (-4.213210) | 0.125041 / 6.500664 (-6.375623) | 0.061038 / 0.075469 (-0.014431) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244285 / 1.841788 (-0.597503) | 18.411576 / 8.074308 (10.337268) | 13.850100 / 10.191392 (3.658708) | 0.131904 / 0.680424 (-0.548520) | 0.016824 / 0.534201 (-0.517377) | 0.328931 / 0.579283 (-0.250352) | 0.364801 / 0.434364 (-0.069563) | 0.376298 / 0.540337 (-0.164039) | 0.525045 / 1.386936 (-0.861891) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006059 / 0.011353 (-0.005294) | 0.003693 / 0.011008 (-0.007315) | 0.062982 / 0.038508 (0.024473) | 0.062155 / 0.023109 (0.039046) | 0.389467 / 0.275898 (0.113568) | 0.437046 / 0.323480 (0.113566) | 0.004823 / 0.007986 (-0.003163) | 0.002935 / 0.004328 (-0.001393) | 0.062679 / 0.004250 (0.058429) | 0.049676 / 0.037052 (0.012623) | 0.418054 / 0.258489 (0.159565) | 0.442467 / 0.293841 (0.148626) | 0.027652 / 0.128546 (-0.100895) | 0.008146 / 0.075646 (-0.067501) | 0.069414 / 0.419271 (-0.349858) | 0.042884 / 0.043533 (-0.000649) | 0.387167 / 0.255139 (0.132028) | 0.418684 / 0.283200 (0.135484) | 0.022419 / 0.141683 (-0.119264) | 1.460606 / 1.452155 (0.008451) | 1.514204 / 1.492716 (0.021487) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200523 / 0.018006 (0.182517) | 0.415970 / 0.000490 (0.415481) | 0.003202 / 0.000200 (0.003002) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025836 / 0.037411 (-0.011575) | 0.078859 / 0.014526 (0.064333) | 0.088523 / 0.176557 (-0.088034) | 0.141572 / 0.737135 (-0.595563) | 0.090258 / 0.296338 (-0.206080) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416548 / 0.215209 (0.201339) | 4.155278 / 2.077655 (2.077623) | 2.126683 / 1.504120 (0.622563) | 1.963762 / 1.541195 (0.422568) | 2.029018 / 1.468490 (0.560528) | 0.499005 / 4.584777 (-4.085772) | 3.063503 / 3.745712 (-0.682209) | 4.250800 / 5.269862 (-1.019061) | 2.642634 / 4.565676 (-1.923043) | 0.057815 / 0.424275 (-0.366460) | 0.006784 / 0.007607 (-0.000823) | 0.492481 / 0.226044 (0.266437) | 4.914306 / 2.268929 (2.645377) | 2.601582 / 55.444624 (-52.843042) | 2.337863 / 6.876477 (-4.538614) | 2.462854 / 2.142072 (0.320782) | 0.593738 / 4.805227 (-4.211489) | 0.127030 / 6.500664 (-6.373634) | 0.064206 / 0.075469 (-0.011263) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.326919 / 1.841788 (-0.514868) | 18.728929 / 8.074308 (10.654621) | 13.903681 / 10.191392 (3.712289) | 0.162670 / 0.680424 (-0.517754) | 0.016913 / 0.534201 (-0.517288) | 0.337504 / 0.579283 (-0.241779) | 0.339786 / 0.434364 (-0.094577) | 0.384955 / 0.540337 (-0.155383) | 0.514358 / 1.386936 (-0.872578) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5c31b492c45e01c6f4593ada2b84517a75a5c7c \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6085). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007610 / 0.011353 (-0.003743) | 0.004616 / 0.011008 (-0.006392) | 0.100330 / 0.038508 (0.061821) | 0.084450 / 0.023109 (0.061341) | 0.386610 / 0.275898 (0.110712) | 0.418479 / 0.323480 (0.094999) | 0.006085 / 0.007986 (-0.001900) | 0.003800 / 0.004328 (-0.000529) | 0.076248 / 0.004250 (0.071997) | 0.065175 / 0.037052 (0.028122) | 0.387154 / 0.258489 (0.128665) | 0.425484 / 0.293841 (0.131643) | 0.035946 / 0.128546 (-0.092601) | 0.009901 / 0.075646 (-0.065745) | 0.343015 / 0.419271 (-0.076256) | 0.060965 / 0.043533 (0.017432) | 0.390585 / 0.255139 (0.135446) | 0.405873 / 0.283200 (0.122673) | 0.026929 / 0.141683 (-0.114754) | 1.767916 / 1.452155 (0.315761) | 1.893431 / 1.492716 (0.400715) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237888 / 0.018006 (0.219882) | 0.503949 / 0.000490 (0.503459) | 0.004769 / 0.000200 (0.004570) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031553 / 0.037411 (-0.005859) | 0.096950 / 0.014526 (0.082424) | 0.110374 / 0.176557 (-0.066183) | 0.176754 / 0.737135 (-0.560381) | 0.111703 / 0.296338 (-0.184635) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449232 / 0.215209 (0.234023) | 4.510247 / 2.077655 (2.432592) | 2.188547 / 1.504120 (0.684427) | 2.007530 / 1.541195 (0.466335) | 2.095650 / 1.468490 (0.627160) | 0.563262 / 4.584777 (-4.021515) | 4.062412 / 3.745712 (0.316700) | 6.338350 / 5.269862 (1.068489) | 3.844669 / 4.565676 (-0.721008) | 0.064517 / 0.424275 (-0.359758) | 0.008536 / 0.007607 (0.000929) | 0.553872 / 0.226044 (0.327828) | 5.530311 / 2.268929 (3.261383) | 2.835109 / 55.444624 (-52.609516) | 2.493900 / 6.876477 (-4.382577) | 2.728412 / 2.142072 (0.586340) | 0.680161 / 4.805227 (-4.125066) | 0.155831 / 6.500664 (-6.344833) | 0.070359 / 0.075469 (-0.005110) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.504852 / 1.841788 (-0.336936) | 22.806335 / 8.074308 (14.732027) | 16.598386 / 10.191392 (6.406994) | 0.207857 / 0.680424 (-0.472566) | 0.021425 / 0.534201 (-0.512776) | 0.474069 / 0.579283 (-0.105214) | 0.472263 / 0.434364 (0.037899) | 0.542195 / 0.540337 (0.001858) | 0.782871 / 1.386936 (-0.604065) |\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) | 0.007443 / 0.011353 (-0.003910) | 0.004465 / 0.011008 (-0.006544) | 0.076268 / 0.038508 (0.037759) | 0.086607 / 0.023109 (0.063498) | 0.443295 / 0.275898 (0.167397) | 0.472819 / 0.323480 (0.149339) | 0.005841 / 0.007986 (-0.002144) | 0.003727 / 0.004328 (-0.000602) | 0.076015 / 0.004250 (0.071765) | 0.063188 / 0.037052 (0.026136) | 0.450555 / 0.258489 (0.192066) | 0.478532 / 0.293841 (0.184691) | 0.036258 / 0.128546 (-0.092288) | 0.009869 / 0.075646 (-0.065777) | 0.083786 / 0.419271 (-0.335486) | 0.056546 / 0.043533 (0.013013) | 0.449647 / 0.255139 (0.194508) | 0.457588 / 0.283200 (0.174389) | 0.027197 / 0.141683 (-0.114486) | 1.769991 / 1.452155 (0.317836) | 1.859905 / 1.492716 (0.367189) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268637 / 0.018006 (0.250631) | 0.492860 / 0.000490 (0.492370) | 0.008574 / 0.000200 (0.008374) | 0.000140 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037679 / 0.037411 (0.000268) | 0.108258 / 0.014526 (0.093733) | 0.117850 / 0.176557 (-0.058707) | 0.181611 / 0.737135 (-0.555524) | 0.120901 / 0.296338 (-0.175437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485780 / 0.215209 (0.270571) | 4.851289 / 2.077655 (2.773635) | 2.486068 / 1.504120 (0.981948) | 2.299417 / 1.541195 (0.758222) | 2.387093 / 1.468490 (0.918603) | 0.568826 / 4.584777 (-4.015951) | 4.163426 / 3.745712 (0.417713) | 6.224964 / 5.269862 (0.955102) | 3.255619 / 4.565676 (-1.310058) | 0.067081 / 0.424275 (-0.357194) | 0.009065 / 0.007607 (0.001458) | 0.580449 / 0.226044 (0.354405) | 5.786394 / 2.268929 (3.517465) | 3.057780 / 55.444624 (-52.386844) | 2.764339 / 6.876477 (-4.112138) | 2.880718 / 2.142072 (0.738645) | 0.681376 / 4.805227 (-4.123851) | 0.157858 / 6.500664 (-6.342806) | 0.072481 / 0.075469 (-0.002988) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.590704 / 1.841788 (-0.251083) | 23.141929 / 8.074308 (15.067620) | 17.001141 / 10.191392 (6.809749) | 0.203790 / 0.680424 (-0.476634) | 0.021766 / 0.534201 (-0.512435) | 0.475309 / 0.579283 (-0.103974) | 0.466448 / 0.434364 (0.032084) | 0.551470 / 0.540337 (0.011132) | 0.727876 / 1.386936 (-0.659060) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#61b19eea7fc5cf484e8cdf41d6ae035f94d8a671 \"CML watermark\")\n" ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]