Datasets:

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Francesco-A

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github-issues_huggingface-datasets

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[ "Thanks ! This has been fixed in https://github.com/huggingface/datasets/pull/4377, we can close this PR" ]

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[ "Hi @ArneBinder, we have a few \"generic\" datasets which are intended to load data files with a predefined format:\r\n- csv: https://github.com/huggingface/nlp/tree/master/datasets/csv\r\n- json: https://github.com/huggingface/nlp/tree/master/datasets/json\r\n- text: https://github.com/huggingface/nlp/tree/master/datasets/text\r\n\r\nYou can find more details about this way to load datasets here in the documentation: https://huggingface.co/nlp/loading_datasets.html#from-local-files\r\n\r\nMaybe your brat loading script could be shared in a similar fashion?", "> Maybe your brat loading script could be shared in a similar fashion?\r\n\r\n@thomwolf that was also my first idea and I think I will tackle that in the next days. I separated the code and created a real abstract class `AbstractBrat` to allow to inherit from that (I've just seen that the dataset_loader loads the first non abstract class), now `Brat` is very similar in its functionality to https://github.com/huggingface/nlp/tree/master/datasets/text but inherits from `AbstractBrat`.\r\n\r\nHowever, it is still not clear to me how to add a specific dataset (as explained in https://huggingface.co/nlp/add_dataset.html) to your repo that uses this format/abstract class, i.e. re-using the `features` entry of the `DatasetInfo` object and `_generate_examples()`. Again, by doing so, the only remaining entries/functions to define would be `_DESCRIPTION`, `_CITATION`, `homepage` and `_URL` (which is all copy-paste stuff) and `_split_generators()`.\r\n \r\nIn a lack of better ideas, I tried sth like below, but of course it does not work outside `nlp` (`AbstractBrat` is currently defined in [datasets/brat.py](https://github.com/ArneBinder/nlp/blob/5e81fb8710546ee7be3353a7f02a3045e9a8351e/datasets/brat/brat.py)):\r\n```python\r\nfrom __future__ import absolute_import, division, print_function\r\n\r\nimport os\r\n\r\nimport nlp\r\n\r\nfrom datasets.brat.brat import AbstractBrat\r\n\r\n_CITATION = \"\"\"\r\n@inproceedings{lauscher2018b,\r\n title = {An argument-annotated corpus of scientific publications},\r\n booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},\r\n publisher = {Association for Computational Linguistics},\r\n author = {Lauscher, Anne and Glava\\v{s}, Goran and Ponzetto, Simone Paolo},\r\n address = {Brussels, Belgium},\r\n year = {2018},\r\n pages = {40–46}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThis dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing \r\nfine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific \r\npublications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of \r\nscientific writing.\r\n\"\"\"\r\n\r\n_URL = \"http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip\"\r\n\r\n\r\nclass Sciarg(AbstractBrat):\r\n\r\n VERSION = nlp.Version(\"1.0.0\")\r\n\r\n def _info(self):\r\n\r\n brat_features = super()._info().features\r\n return nlp.DatasetInfo(\r\n # This is the description that will appear on the datasets page.\r\n description=_DESCRIPTION,\r\n # nlp.features.FeatureConnectors\r\n features=brat_features,\r\n # If there's a common (input, target) tuple from the features,\r\n # specify them here. They'll be used if as_supervised=True in\r\n # builder.as_dataset.\r\n #supervised_keys=None,\r\n # Homepage of the dataset for documentation\r\n homepage=\"https://github.com/anlausch/ArguminSci\",\r\n citation=_CITATION,\r\n )\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n # TODO: Downloads the data and defines the splits\r\n # dl_manager is a nlp.download.DownloadManager that can be used to\r\n # download and extract URLs\r\n dl_dir = dl_manager.download_and_extract(_URL)\r\n data_dir = os.path.join(dl_dir, \"compiled_corpus\")\r\n print(f'data_dir: {data_dir}')\r\n return [\r\n nlp.SplitGenerator(\r\n name=nlp.Split.TRAIN,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n \"directory\": data_dir,\r\n },\r\n ),\r\n ]\r\n``` \r\n\r\nNevertheless, many thanks for tackling the dataset accessibility problem with this great library!", "As temporary fix I've created [ArneBinder/nlp-formats](https://github.com/ArneBinder/nlp-formats) (contributions welcome).", "Hi! You can either copy&paste the builder script and import the builder from there or use `datasets.load_dataset_builder` inside the script and call the methods of the returned builder object." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "doc versions are built https://huggingface.co/docs/datasets/index" ]

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[ "LGTM now, thank you =)" ]

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[ "This can happen when a map transform cannot be hashed deterministically (e.g., an object referenced by the transform changes its state after the first call - an issue with fast tokenizers). The solution is to provide `cache_file_name` in the `map` call to check this file for the cached result instead of relying on the default caching mechanism." ]

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[ "similar slow download speed here for nlp.load_dataset('wmt14', 'fr-en')\r\n`\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 658M/658M [1:00:42<00:00, 181kB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 918M/918M [1:39:38<00:00, 154kB/s]\r\nDownloading: 2%|▉ | 40.9M/2.37G [04:48<5:03:06, 128kB/s]\r\n`\r\nCould we just download a specific subdataset in 'wmt14', such as 'newstest14'? ", "> The code runs but the download speed is extremely slow, the same behaviour is not observed on wmt16 and wmt18\r\n\r\nThe original source for the files may provide slow download speeds.\r\nWe can probably host these files ourselves.\r\n\r\n> When trying to download wmt17 zh-en, I got the following error:\r\n> ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz\r\n\r\nLooks like the file`UNv1.0.en-zh.tar.gz` is missing, or the url changed. We need to fix that\r\n\r\n> Could we just download a specific subdataset in 'wmt14', such as 'newstest14'?\r\n\r\nRight now I don't think it's possible. Maybe @patrickvonplaten knows more about it\r\n", "Yeah, the download speed is sadly always extremely slow :-/. \r\nI will try to check out the `wmt17 zh-en` bug :-) ", "Maybe this can be used - https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 ", "These issues seem to be fixed now." ]

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[ "Hi ! Yes this is an issue with `datasets<=1.5.0`\r\nThis issue has been fixed by #2122 , we'll do a new release soon :)\r\nFor now you can test it on the `master` branch.", "Hi, thank you for your answer. I did not realize that my issue stems from the same problem. " ]

[ { "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" } ]

[ "@mariosasko I'd appreciate your feedback on this. " ]

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[ "Summary of CircleCI errors:\r\n\r\n- **sem_eval_2010_task_8**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n- **sms_spam**: `Data Instances` and`Data Splits` are empty.\r\n- **Quora** : Expected some content in section `Citation Information` but it is empty, missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n- **sentiment140**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n\r\nThere are also some timeout errors, I don't really understand the source though :confused: ", "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @cindyxinyiwang,\r\nDid you try adding `.arrow` after `cache_file_name` argument? Here I think they're expecting something like that only for a cache file:\r\nhttps://github.com/huggingface/datasets/blob/e08362256fb157c0b3038437fc0d7a0bbb50de5c/src/datasets/arrow_dataset.py#L1556-L1558", "Hi ! `cache_file_name` is an argument of the `Dataset.map` method. Can you check that your `dataset` is indeed a `Dataset` object ?\r\n\r\nIf you loaded several splits, then it would actually be a `DatasetDict` (one dataset per split, in a dictionary).\r\nIn this case, since there are several datasets in the dict, the `DatasetDict.map` method requires a `cache_file_names` argument (with an 's'), so that you can provide one file name per split.", "I think you are right. I used cache_file_names={data1: name1, data2: name2} and it works. Thank you!" ]

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[ "Dataset card added, so ready for review!" ]

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[ "The original openslr site mixed both names https://openslr.org/72/ :-)", "Yeah, I filed the issue to have it fixed there last year, but it looks like they missed a few." ]

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[ "I still have to compute the dataset_infos, and build + host the indexes", "update: I'm computing the metadata, will update the PR soon", "Finally all green and ready to merge :)" ]

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[ "Great, done!" ]

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[ "Should be fixed now: \r\n\r\n\r\n\r\nNot sure I understand what you mean by the second part?\r\n", "Thank you!\r\n\r\n> Not sure I understand what you mean by the second part?\r\n\r\nCompare the 2:\r\n* https://huggingface.co/datasets/wikihow\r\n* https://huggingface.co/nlp/viewer/?dataset=wikihow&config=all\r\nCan you see the difference? 2nd has formatting, 1st doesn't.\r\n", "For context, those are two different pages (not an old vs new one), one is from the dataset viewer (you can browse data inside the datasets) while the other is just a basic reference page displayed some metadata about the dataset.\r\n\r\nFor the second one, we'll move to markdown parsing soon, so it'll be formatted better.", "I understand. I was just flagging the lack of markup issue." ]

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

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[ "+1 on this request" ]

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[ "Hi ! The `blue` metric doesn't exist, but the `bleu` metric does.\r\nYou can get the full list of metrics [here](https://github.com/huggingface/datasets/tree/master/metrics) or by running\r\n```python\r\nfrom datasets import list_metrics\r\n\r\nprint(list_metrics())\r\n```", "Ah, my mistake. Thanks for correcting" ]

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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==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011635 / 0.011353 (0.000282) | 0.005446 / 0.011008 (-0.005562) | 0.111044 / 0.038508 (0.072536) | 0.034243 / 0.023109 (0.011134) | 0.357560 / 0.275898 (0.081662) | 0.403940 / 0.323480 (0.080460) | 0.008532 / 0.007986 (0.000546) | 0.004327 / 0.004328 (-0.000002) | 0.084659 / 0.004250 (0.080408) | 0.040914 / 0.037052 (0.003861) | 0.367142 / 0.258489 (0.108653) | 0.381651 / 0.293841 (0.087810) | 0.053865 / 0.128546 (-0.074681) | 0.019060 / 0.075646 (-0.056587) | 0.371994 / 0.419271 (-0.047277) | 0.058417 / 0.043533 (0.014884) | 0.357740 / 0.255139 (0.102601) | 0.367423 / 0.283200 (0.084224) | 0.104336 / 0.141683 (-0.037347) | 1.632128 / 1.452155 (0.179974) | 1.676216 / 1.492716 (0.183499) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199649 / 0.018006 (0.181642) | 0.490945 / 0.000490 (0.490455) | 0.001598 / 0.000200 (0.001398) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024541 / 0.037411 (-0.012871) | 0.104713 / 0.014526 (0.090187) | 0.119438 / 0.176557 (-0.057118) | 0.160854 / 0.737135 (-0.576281) | 0.127323 / 0.296338 (-0.169016) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586483 / 0.215209 (0.371274) | 5.771689 / 2.077655 (3.694034) | 2.378962 / 1.504120 (0.874842) | 1.998787 / 1.541195 (0.457592) | 1.993016 / 1.468490 (0.524526) | 1.199169 / 4.584777 (-3.385608) | 5.281648 / 3.745712 (1.535936) | 5.589235 / 5.269862 (0.319373) | 2.715162 / 4.565676 (-1.850514) | 0.153312 / 0.424275 (-0.270963) | 0.014302 / 0.007607 (0.006695) | 0.761185 / 0.226044 (0.535140) | 7.602517 / 2.268929 (5.333589) | 3.095271 / 55.444624 (-52.349354) | 2.407394 / 6.876477 (-4.469083) | 2.519074 / 2.142072 (0.377002) | 1.459270 / 4.805227 (-3.345957) | 0.259578 / 6.500664 (-6.241086) | 0.077356 / 0.075469 (0.001887) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502123 / 1.841788 (-0.339665) | 16.254010 / 8.074308 (8.179702) | 19.971713 / 10.191392 (9.780321) | 0.221491 / 0.680424 (-0.458933) | 0.043959 / 0.534201 (-0.490242) | 0.512566 / 0.579283 (-0.066717) | 0.594724 / 0.434364 (0.160360) | 0.573855 / 0.540337 (0.033518) | 0.680503 / 1.386936 (-0.706433) |\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) | 0.008543 / 0.011353 (-0.002810) | 0.005828 / 0.011008 (-0.005180) | 0.083696 / 0.038508 (0.045188) | 0.036186 / 0.023109 (0.013077) | 0.379777 / 0.275898 (0.103879) | 0.437361 / 0.323480 (0.113881) | 0.006788 / 0.007986 (-0.001197) | 0.005110 / 0.004328 (0.000782) | 0.106075 / 0.004250 (0.101824) | 0.048770 / 0.037052 (0.011718) | 0.390770 / 0.258489 (0.132281) | 0.420813 / 0.293841 (0.126972) | 0.050622 / 0.128546 (-0.077924) | 0.019939 / 0.075646 (-0.055707) | 0.106890 / 0.419271 (-0.312382) | 0.070800 / 0.043533 (0.027267) | 0.406094 / 0.255139 (0.150955) | 0.419796 / 0.283200 (0.136597) | 0.107237 / 0.141683 (-0.034446) | 1.687894 / 1.452155 (0.235739) | 1.735680 / 1.492716 (0.242963) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216403 / 0.018006 (0.198397) | 0.495002 / 0.000490 (0.494512) | 0.004841 / 0.000200 (0.004641) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043774 / 0.037411 (0.006363) | 0.119144 / 0.014526 (0.104618) | 0.143694 / 0.176557 (-0.032862) | 0.195548 / 0.737135 (-0.541587) | 0.151426 / 0.296338 (-0.144912) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.617694 / 0.215209 (0.402485) | 6.216237 / 2.077655 (4.138582) | 2.578341 / 1.504120 (1.074221) | 2.184868 / 1.541195 (0.643673) | 2.244954 / 1.468490 (0.776464) | 1.236072 / 4.584777 (-3.348705) | 5.257919 / 3.745712 (1.512207) | 4.634682 / 5.269862 (-0.635180) | 2.722579 / 4.565676 (-1.843097) | 0.131433 / 0.424275 (-0.292843) | 0.012928 / 0.007607 (0.005321) | 0.768315 / 0.226044 (0.542270) | 7.625277 / 2.268929 (5.356349) | 3.146364 / 55.444624 (-52.298260) | 2.577886 / 6.876477 (-4.298590) | 2.572626 / 2.142072 (0.430554) | 1.468160 / 4.805227 (-3.337067) | 0.252524 / 6.500664 (-6.248140) | 0.083264 / 0.075469 (0.007794) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.452614 / 1.841788 (-0.389174) | 15.906162 / 8.074308 (7.831853) | 17.803630 / 10.191392 (7.612238) | 0.210769 / 0.680424 (-0.469655) | 0.024672 / 0.534201 (-0.509529) | 0.486486 / 0.579283 (-0.092797) | 0.545256 / 0.434364 (0.110892) | 0.598736 / 0.540337 (0.058399) | 0.689083 / 1.386936 (-0.697853) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#189a870b4f0964d77b43c2f4e79c4ca7b799f690 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008806 / 0.011353 (-0.002547) | 0.004947 / 0.011008 (-0.006061) | 0.098559 / 0.038508 (0.060051) | 0.034293 / 0.023109 (0.011183) | 0.311924 / 0.275898 (0.036026) | 0.377501 / 0.323480 (0.054021) | 0.007916 / 0.007986 (-0.000069) | 0.004131 / 0.004328 (-0.000197) | 0.074934 / 0.004250 (0.070684) | 0.043396 / 0.037052 (0.006344) | 0.344788 / 0.258489 (0.086299) | 0.369943 / 0.293841 (0.076102) | 0.036846 / 0.128546 (-0.091700) | 0.011803 / 0.075646 (-0.063843) | 0.331306 / 0.419271 (-0.087965) | 0.047015 / 0.043533 (0.003483) | 0.305890 / 0.255139 (0.050751) | 0.332658 / 0.283200 (0.049459) | 0.101134 / 0.141683 (-0.040549) | 1.485615 / 1.452155 (0.033461) | 1.510230 / 1.492716 (0.017514) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274272 / 0.018006 (0.256266) | 0.514739 / 0.000490 (0.514250) | 0.003433 / 0.000200 (0.003234) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027054 / 0.037411 (-0.010357) | 0.106416 / 0.014526 (0.091890) | 0.118761 / 0.176557 (-0.057796) | 0.156115 / 0.737135 (-0.581021) | 0.123801 / 0.296338 (-0.172537) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403121 / 0.215209 (0.187912) | 4.008806 / 2.077655 (1.931151) | 1.891253 / 1.504120 (0.387133) | 1.698523 / 1.541195 (0.157328) | 1.778533 / 1.468490 (0.310043) | 0.688207 / 4.584777 (-3.896570) | 3.674350 / 3.745712 (-0.071362) | 1.848438 / 5.269862 (-3.421423) | 1.202380 / 4.565676 (-3.363297) | 0.073490 / 0.424275 (-0.350785) | 0.010655 / 0.007607 (0.003048) | 0.446939 / 0.226044 (0.220894) | 4.478489 / 2.268929 (2.209560) | 1.992281 / 55.444624 (-53.452343) | 1.684077 / 6.876477 (-5.192400) | 1.715435 / 2.142072 (-0.426638) | 0.731454 / 4.805227 (-4.073773) | 0.143679 / 6.500664 (-6.356985) | 0.053415 / 0.075469 (-0.022054) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.060583 / 1.841788 (-0.781205) | 13.730462 / 8.074308 (5.656153) | 13.038976 / 10.191392 (2.847583) | 0.144168 / 0.680424 (-0.536256) | 0.025788 / 0.534201 (-0.508413) | 0.393332 / 0.579283 (-0.185951) | 0.409495 / 0.434364 (-0.024869) | 0.523745 / 0.540337 (-0.016592) | 0.601595 / 1.386936 (-0.785341) |\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) | 0.006369 / 0.011353 (-0.004983) | 0.005019 / 0.011008 (-0.005990) | 0.065226 / 0.038508 (0.026718) | 0.029634 / 0.023109 (0.006524) | 0.302871 / 0.275898 (0.026972) | 0.331055 / 0.323480 (0.007575) | 0.005470 / 0.007986 (-0.002516) | 0.005372 / 0.004328 (0.001043) | 0.064930 / 0.004250 (0.060680) | 0.046979 / 0.037052 (0.009927) | 0.305633 / 0.258489 (0.047144) | 0.345305 / 0.293841 (0.051464) | 0.032951 / 0.128546 (-0.095596) | 0.011447 / 0.075646 (-0.064199) | 0.077054 / 0.419271 (-0.342218) | 0.045744 / 0.043533 (0.002211) | 0.303446 / 0.255139 (0.048307) | 0.319837 / 0.283200 (0.036637) | 0.098631 / 0.141683 (-0.043052) | 1.266593 / 1.452155 (-0.185562) | 1.355388 / 1.492716 (-0.137328) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291301 / 0.018006 (0.273295) | 0.537848 / 0.000490 (0.537359) | 0.006697 / 0.000200 (0.006497) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027677 / 0.037411 (-0.009734) | 0.099633 / 0.014526 (0.085107) | 0.110626 / 0.176557 (-0.065931) | 0.144724 / 0.737135 (-0.592412) | 0.114955 / 0.296338 (-0.181383) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.375344 / 0.215209 (0.160135) | 3.717490 / 2.077655 (1.639835) | 1.845886 / 1.504120 (0.341766) | 1.713274 / 1.541195 (0.172079) | 1.761286 / 1.468490 (0.292796) | 0.627924 / 4.584777 (-3.956853) | 3.628154 / 3.745712 (-0.117558) | 3.261851 / 5.269862 (-2.008011) | 1.701008 / 4.565676 (-2.864669) | 0.076703 / 0.424275 (-0.347572) | 0.010839 / 0.007607 (0.003231) | 0.459193 / 0.226044 (0.233148) | 4.589066 / 2.268929 (2.320137) | 2.193972 / 55.444624 (-53.250653) | 1.892115 / 6.876477 (-4.984362) | 1.892453 / 2.142072 (-0.249619) | 0.745727 / 4.805227 (-4.059500) | 0.150232 / 6.500664 (-6.350432) | 0.057245 / 0.075469 (-0.018224) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.114657 / 1.841788 (-0.727131) | 13.595215 / 8.074308 (5.520907) | 12.267177 / 10.191392 (2.075785) | 0.151362 / 0.680424 (-0.529061) | 0.015609 / 0.534201 (-0.518591) | 0.379151 / 0.579283 (-0.200132) | 0.386125 / 0.434364 (-0.048238) | 0.470037 / 0.540337 (-0.070301) | 0.562340 / 1.386936 (-0.824596) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#526578cd473a266fa86643d15905181bf346ecac \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009847 / 0.011353 (-0.001505) | 0.005609 / 0.011008 (-0.005399) | 0.101951 / 0.038508 (0.063443) | 0.038082 / 0.023109 (0.014972) | 0.299933 / 0.275898 (0.024035) | 0.377081 / 0.323480 (0.053601) | 0.008900 / 0.007986 (0.000915) | 0.004608 / 0.004328 (0.000279) | 0.077723 / 0.004250 (0.073473) | 0.048592 / 0.037052 (0.011540) | 0.310789 / 0.258489 (0.052300) | 0.345627 / 0.293841 (0.051787) | 0.038716 / 0.128546 (-0.089830) | 0.012653 / 0.075646 (-0.062993) | 0.336885 / 0.419271 (-0.082387) | 0.048715 / 0.043533 (0.005182) | 0.295336 / 0.255139 (0.040197) | 0.316735 / 0.283200 (0.033536) | 0.115142 / 0.141683 (-0.026541) | 1.480332 / 1.452155 (0.028177) | 1.604972 / 1.492716 (0.112256) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299516 / 0.018006 (0.281510) | 0.525892 / 0.000490 (0.525402) | 0.002246 / 0.000200 (0.002046) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031547 / 0.037411 (-0.005864) | 0.120611 / 0.014526 (0.106085) | 0.124516 / 0.176557 (-0.052041) | 0.166036 / 0.737135 (-0.571100) | 0.131689 / 0.296338 (-0.164650) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400728 / 0.215209 (0.185519) | 4.007027 / 2.077655 (1.929372) | 1.793922 / 1.504120 (0.289803) | 1.596709 / 1.541195 (0.055514) | 1.752130 / 1.468490 (0.283640) | 0.717464 / 4.584777 (-3.867313) | 3.798844 / 3.745712 (0.053132) | 3.685088 / 5.269862 (-1.584774) | 1.914041 / 4.565676 (-2.651636) | 0.086181 / 0.424275 (-0.338094) | 0.012753 / 0.007607 (0.005146) | 0.507984 / 0.226044 (0.281940) | 5.086255 / 2.268929 (2.817326) | 2.280650 / 55.444624 (-53.163974) | 1.929294 / 6.876477 (-4.947183) | 2.057884 / 2.142072 (-0.084188) | 0.852863 / 4.805227 (-3.952364) | 0.165497 / 6.500664 (-6.335168) | 0.063356 / 0.075469 (-0.012113) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212593 / 1.841788 (-0.629194) | 16.270507 / 8.074308 (8.196199) | 15.708406 / 10.191392 (5.517014) | 0.162346 / 0.680424 (-0.518078) | 0.029702 / 0.534201 (-0.504499) | 0.447685 / 0.579283 (-0.131598) | 0.449361 / 0.434364 (0.014997) | 0.530536 / 0.540337 (-0.009801) | 0.613439 / 1.386936 (-0.773497) |\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) | 0.007741 / 0.011353 (-0.003612) | 0.005752 / 0.011008 (-0.005256) | 0.076600 / 0.038508 (0.038092) | 0.034841 / 0.023109 (0.011732) | 0.345106 / 0.275898 (0.069208) | 0.385685 / 0.323480 (0.062205) | 0.006466 / 0.007986 (-0.001519) | 0.005806 / 0.004328 (0.001478) | 0.075110 / 0.004250 (0.070860) | 0.052936 / 0.037052 (0.015883) | 0.343576 / 0.258489 (0.085087) | 0.408749 / 0.293841 (0.114908) | 0.037345 / 0.128546 (-0.091201) | 0.012807 / 0.075646 (-0.062839) | 0.087732 / 0.419271 (-0.331540) | 0.050218 / 0.043533 (0.006685) | 0.338963 / 0.255139 (0.083824) | 0.361629 / 0.283200 (0.078429) | 0.107488 / 0.141683 (-0.034195) | 1.465284 / 1.452155 (0.013130) | 1.562218 / 1.492716 (0.069502) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322496 / 0.018006 (0.304489) | 0.522782 / 0.000490 (0.522292) | 0.006680 / 0.000200 (0.006480) | 0.000144 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031801 / 0.037411 (-0.005611) | 0.116839 / 0.014526 (0.102313) | 0.127552 / 0.176557 (-0.049005) | 0.167670 / 0.737135 (-0.569465) | 0.134170 / 0.296338 (-0.162168) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425449 / 0.215209 (0.210240) | 4.229367 / 2.077655 (2.151713) | 2.014663 / 1.504120 (0.510543) | 1.812981 / 1.541195 (0.271787) | 1.964039 / 1.468490 (0.495549) | 0.703454 / 4.584777 (-3.881323) | 3.786985 / 3.745712 (0.041273) | 2.262377 / 5.269862 (-3.007485) | 1.404868 / 4.565676 (-3.160808) | 0.086234 / 0.424275 (-0.338041) | 0.012616 / 0.007607 (0.005009) | 0.525784 / 0.226044 (0.299739) | 5.268295 / 2.268929 (2.999366) | 2.496674 / 55.444624 (-52.947950) | 2.177773 / 6.876477 (-4.698704) | 2.313677 / 2.142072 (0.171605) | 0.846202 / 4.805227 (-3.959026) | 0.170152 / 6.500664 (-6.330513) | 0.066772 / 0.075469 (-0.008698) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254719 / 1.841788 (-0.587069) | 16.017627 / 8.074308 (7.943319) | 14.560583 / 10.191392 (4.369191) | 0.168275 / 0.680424 (-0.512149) | 0.017935 / 0.534201 (-0.516266) | 0.430806 / 0.579283 (-0.148477) | 0.428737 / 0.434364 (-0.005626) | 0.532001 / 0.540337 (-0.008336) | 0.633680 / 1.386936 (-0.753256) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2c75dff81c3f060cc4731be3416fd962cc6383e \"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" } ]

[ "Hi @andreybond, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to able to reproduce your issue:\r\n```python\r\nIn [4]: from datasets import load_dataset\r\n ...: datasets = load_dataset(\"nielsr/XFUN\", \"xfun.ja\")\r\n\r\nIn [5]: datasets\r\nOut[5]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'],\r\n num_rows: 194\r\n })\r\n validation: Dataset({\r\n features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'],\r\n num_rows: 71\r\n })\r\n})\r\n```\r\n\r\nThe only reason I can imagine this issue may arise is if your default encoding is not \"UTF-8\" (and it is ASCII instead). This is usually the case on Windows machines; but you say your environment is a Linux machine. Maybe you change your machine default encoding?\r\n\r\nCould you please check this?\r\n```python\r\nIn [6]: import sys\r\n\r\nIn [7]: sys.getdefaultencoding()\r\nOut[7]: 'utf-8'\r\n```", "I opened a PR in the original dataset loading script:\r\n- microsoft/unilm#677\r\n\r\nand fixed the corresponding dataset script on the Hub:\r\n- https://huggingface.co/datasets/nielsr/XFUN/commit/73ba5e026621e05fb756ae0f267eb49971f70ebd", "import sys\r\nsys.getdefaultencoding()\r\n\r\nreturned: 'utf-8'\r\n\r\n---------------------\r\n\r\nI've just cloned master branch - your fix works! Thank you!" ]

[]

[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006173 / 0.011353 (-0.005180) | 0.003773 / 0.011008 (-0.007235) | 0.099499 / 0.038508 (0.060991) | 0.037918 / 0.023109 (0.014809) | 0.321329 / 0.275898 (0.045431) | 0.379739 / 0.323480 (0.056259) | 0.004664 / 0.007986 (-0.003322) | 0.002943 / 0.004328 (-0.001385) | 0.077759 / 0.004250 (0.073509) | 0.055271 / 0.037052 (0.018219) | 0.329428 / 0.258489 (0.070939) | 0.378731 / 0.293841 (0.084890) | 0.027737 / 0.128546 (-0.100810) | 0.008566 / 0.075646 (-0.067081) | 0.313220 / 0.419271 (-0.106052) | 0.047101 / 0.043533 (0.003568) | 0.316211 / 0.255139 (0.061072) | 0.341826 / 0.283200 (0.058626) | 0.020838 / 0.141683 (-0.120845) | 1.550064 / 1.452155 (0.097909) | 1.706518 / 1.492716 (0.213801) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203093 / 0.018006 (0.185087) | 0.425345 / 0.000490 (0.424856) | 0.004800 / 0.000200 (0.004600) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024590 / 0.037411 (-0.012821) | 0.098115 / 0.014526 (0.083589) | 0.108274 / 0.176557 (-0.068282) | 0.170804 / 0.737135 (-0.566332) | 0.110560 / 0.296338 (-0.185778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425251 / 0.215209 (0.210042) | 4.239075 / 2.077655 (2.161421) | 1.955601 / 1.504120 (0.451481) | 1.774796 / 1.541195 (0.233602) | 1.826641 / 1.468490 (0.358150) | 0.558777 / 4.584777 (-4.026000) | 3.361697 / 3.745712 (-0.384015) | 1.764468 / 5.269862 (-3.505394) | 1.032280 / 4.565676 (-3.533396) | 0.067872 / 0.424275 (-0.356403) | 0.010998 / 0.007607 (0.003391) | 0.525682 / 0.226044 (0.299637) | 5.254356 / 2.268929 (2.985427) | 2.384332 / 55.444624 (-53.060292) | 2.045578 / 6.876477 (-4.830898) | 2.170914 / 2.142072 (0.028841) | 0.674782 / 4.805227 (-4.130445) | 0.135351 / 6.500664 (-6.365314) | 0.066591 / 0.075469 (-0.008878) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209181 / 1.841788 (-0.632606) | 14.044518 / 8.074308 (5.970210) | 13.184705 / 10.191392 (2.993313) | 0.130836 / 0.680424 (-0.549588) | 0.016582 / 0.534201 (-0.517619) | 0.360005 / 0.579283 (-0.219279) | 0.379519 / 0.434364 (-0.054845) | 0.422174 / 0.540337 (-0.118164) | 0.515546 / 1.386936 (-0.871390) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006293 / 0.011353 (-0.005060) | 0.003784 / 0.011008 (-0.007224) | 0.079248 / 0.038508 (0.040739) | 0.038452 / 0.023109 (0.015343) | 0.444727 / 0.275898 (0.168829) | 0.500535 / 0.323480 (0.177055) | 0.003455 / 0.007986 (-0.004531) | 0.002873 / 0.004328 (-0.001455) | 0.077439 / 0.004250 (0.073189) | 0.047855 / 0.037052 (0.010803) | 0.448049 / 0.258489 (0.189560) | 0.509517 / 0.293841 (0.215676) | 0.028359 / 0.128546 (-0.100188) | 0.008503 / 0.075646 (-0.067143) | 0.084961 / 0.419271 (-0.334310) | 0.042880 / 0.043533 (-0.000653) | 0.436628 / 0.255139 (0.181489) | 0.456574 / 0.283200 (0.173375) | 0.019539 / 0.141683 (-0.122144) | 1.561273 / 1.452155 (0.109118) | 1.572018 / 1.492716 (0.079301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230250 / 0.018006 (0.212244) | 0.415189 / 0.000490 (0.414700) | 0.003213 / 0.000200 (0.003013) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025541 / 0.037411 (-0.011871) | 0.102326 / 0.014526 (0.087800) | 0.110258 / 0.176557 (-0.066298) | 0.162488 / 0.737135 (-0.574647) | 0.112782 / 0.296338 (-0.183556) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457936 / 0.215209 (0.242727) | 4.581503 / 2.077655 (2.503848) | 2.237659 / 1.504120 (0.733540) | 2.029960 / 1.541195 (0.488765) | 2.082911 / 1.468490 (0.614421) | 0.556485 / 4.584777 (-4.028292) | 3.384418 / 3.745712 (-0.361295) | 1.748809 / 5.269862 (-3.521053) | 1.034759 / 4.565676 (-3.530917) | 0.067500 / 0.424275 (-0.356776) | 0.011425 / 0.007607 (0.003818) | 0.561340 / 0.226044 (0.335295) | 5.623629 / 2.268929 (3.354701) | 2.733587 / 55.444624 (-52.711038) | 2.401578 / 6.876477 (-4.474899) | 2.524569 / 2.142072 (0.382496) | 0.673170 / 4.805227 (-4.132057) | 0.136681 / 6.500664 (-6.363983) | 0.068060 / 0.075469 (-0.007409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318651 / 1.841788 (-0.523137) | 14.362123 / 8.074308 (6.287815) | 14.385964 / 10.191392 (4.194572) | 0.149914 / 0.680424 (-0.530510) | 0.016877 / 0.534201 (-0.517324) | 0.358406 / 0.579283 (-0.220877) | 0.394349 / 0.434364 (-0.040015) | 0.422471 / 0.540337 (-0.117866) | 0.513807 / 1.386936 (-0.873129) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b9ce11d1b94e6178df663ff5fcad029849d10fb \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006272 / 0.011353 (-0.005080) | 0.003903 / 0.011008 (-0.007105) | 0.100180 / 0.038508 (0.061672) | 0.037799 / 0.023109 (0.014690) | 0.385627 / 0.275898 (0.109729) | 0.446518 / 0.323480 (0.123038) | 0.004811 / 0.007986 (-0.003175) | 0.003032 / 0.004328 (-0.001296) | 0.077063 / 0.004250 (0.072812) | 0.055564 / 0.037052 (0.018512) | 0.397346 / 0.258489 (0.138857) | 0.443242 / 0.293841 (0.149401) | 0.027904 / 0.128546 (-0.100642) | 0.008386 / 0.075646 (-0.067260) | 0.315013 / 0.419271 (-0.104259) | 0.047943 / 0.043533 (0.004410) | 0.378443 / 0.255139 (0.123304) | 0.411472 / 0.283200 (0.128272) | 0.020465 / 0.141683 (-0.121218) | 1.526594 / 1.452155 (0.074439) | 1.547018 / 1.492716 (0.054301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219377 / 0.018006 (0.201370) | 0.430254 / 0.000490 (0.429764) | 0.003218 / 0.000200 (0.003018) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023667 / 0.037411 (-0.013744) | 0.099143 / 0.014526 (0.084617) | 0.106044 / 0.176557 (-0.070513) | 0.166186 / 0.737135 (-0.570949) | 0.108736 / 0.296338 (-0.187603) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437971 / 0.215209 (0.222762) | 4.363675 / 2.077655 (2.286021) | 2.011993 / 1.504120 (0.507873) | 1.845189 / 1.541195 (0.303994) | 1.831848 / 1.468490 (0.363358) | 0.562402 / 4.584777 (-4.022375) | 3.365259 / 3.745712 (-0.380453) | 1.781491 / 5.269862 (-3.488371) | 1.023454 / 4.565676 (-3.542223) | 0.067857 / 0.424275 (-0.356418) | 0.011076 / 0.007607 (0.003469) | 0.532267 / 0.226044 (0.306223) | 5.340344 / 2.268929 (3.071415) | 2.388649 / 55.444624 (-53.055976) | 2.055373 / 6.876477 (-4.821104) | 2.205047 / 2.142072 (0.062975) | 0.672909 / 4.805227 (-4.132318) | 0.135244 / 6.500664 (-6.365420) | 0.066184 / 0.075469 (-0.009285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206838 / 1.841788 (-0.634950) | 13.967075 / 8.074308 (5.892767) | 13.143971 / 10.191392 (2.952579) | 0.143991 / 0.680424 (-0.536433) | 0.016673 / 0.534201 (-0.517527) | 0.376180 / 0.579283 (-0.203103) | 0.386550 / 0.434364 (-0.047814) | 0.440590 / 0.540337 (-0.099747) | 0.529974 / 1.386936 (-0.856962) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006299 / 0.011353 (-0.005054) | 0.003784 / 0.011008 (-0.007224) | 0.077875 / 0.038508 (0.039367) | 0.038689 / 0.023109 (0.015580) | 0.421684 / 0.275898 (0.145786) | 0.472649 / 0.323480 (0.149169) | 0.003570 / 0.007986 (-0.004415) | 0.004448 / 0.004328 (0.000120) | 0.077867 / 0.004250 (0.073616) | 0.049514 / 0.037052 (0.012462) | 0.375983 / 0.258489 (0.117494) | 0.470632 / 0.293841 (0.176791) | 0.028238 / 0.128546 (-0.100308) | 0.008462 / 0.075646 (-0.067185) | 0.082452 / 0.419271 (-0.336819) | 0.043617 / 0.043533 (0.000084) | 0.400874 / 0.255139 (0.145735) | 0.426191 / 0.283200 (0.142992) | 0.020602 / 0.141683 (-0.121081) | 1.567658 / 1.452155 (0.115504) | 1.572610 / 1.492716 (0.079893) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246144 / 0.018006 (0.228138) | 0.419402 / 0.000490 (0.418913) | 0.001691 / 0.000200 (0.001491) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026105 / 0.037411 (-0.011306) | 0.104734 / 0.014526 (0.090208) | 0.110257 / 0.176557 (-0.066300) | 0.161429 / 0.737135 (-0.575706) | 0.114367 / 0.296338 (-0.181972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453352 / 0.215209 (0.238143) | 4.537924 / 2.077655 (2.460269) | 2.196193 / 1.504120 (0.692073) | 2.002087 / 1.541195 (0.460892) | 2.041722 / 1.468490 (0.573231) | 0.561643 / 4.584777 (-4.023134) | 3.449108 / 3.745712 (-0.296605) | 2.862800 / 5.269862 (-2.407062) | 1.387895 / 4.565676 (-3.177782) | 0.068076 / 0.424275 (-0.356199) | 0.011568 / 0.007607 (0.003961) | 0.559279 / 0.226044 (0.333235) | 5.598738 / 2.268929 (3.329809) | 2.676649 / 55.444624 (-52.767975) | 2.334588 / 6.876477 (-4.541889) | 2.376215 / 2.142072 (0.234142) | 0.673109 / 4.805227 (-4.132118) | 0.137587 / 6.500664 (-6.363077) | 0.069131 / 0.075469 (-0.006338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307332 / 1.841788 (-0.534456) | 14.536036 / 8.074308 (6.461728) | 14.173734 / 10.191392 (3.982342) | 0.145143 / 0.680424 (-0.535281) | 0.016662 / 0.534201 (-0.517539) | 0.366901 / 0.579283 (-0.212383) | 0.394498 / 0.434364 (-0.039866) | 0.430546 / 0.540337 (-0.109792) | 0.518950 / 1.386936 (-0.867986) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008122 / 0.011353 (-0.003231) | 0.005585 / 0.011008 (-0.005424) | 0.121219 / 0.038508 (0.082711) | 0.047616 / 0.023109 (0.024507) | 0.440576 / 0.275898 (0.164678) | 0.491053 / 0.323480 (0.167573) | 0.004774 / 0.007986 (-0.003211) | 0.006758 / 0.004328 (0.002430) | 0.103852 / 0.004250 (0.099602) | 0.071560 / 0.037052 (0.034508) | 0.463107 / 0.258489 (0.204618) | 0.516904 / 0.293841 (0.223063) | 0.048052 / 0.128546 (-0.080494) | 0.013679 / 0.075646 (-0.061968) | 0.428383 / 0.419271 (0.009112) | 0.069468 / 0.043533 (0.025936) | 0.432593 / 0.255139 (0.177454) | 0.471810 / 0.283200 (0.188611) | 0.037541 / 0.141683 (-0.104142) | 1.823490 / 1.452155 (0.371335) | 1.922558 / 1.492716 (0.429842) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252315 / 0.018006 (0.234309) | 0.541757 / 0.000490 (0.541267) | 0.000373 / 0.000200 (0.000173) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030361 / 0.037411 (-0.007050) | 0.125928 / 0.014526 (0.111402) | 0.145102 / 0.176557 (-0.031455) | 0.209798 / 0.737135 (-0.527337) | 0.147349 / 0.296338 (-0.148990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627554 / 0.215209 (0.412345) | 5.917422 / 2.077655 (3.839767) | 2.491083 / 1.504120 (0.986963) | 2.147078 / 1.541195 (0.605883) | 2.167511 / 1.468490 (0.699021) | 0.903061 / 4.584777 (-3.681716) | 5.518537 / 3.745712 (1.772825) | 2.654348 / 5.269862 (-2.615514) | 1.645121 / 4.565676 (-2.920556) | 0.103782 / 0.424275 (-0.320493) | 0.013048 / 0.007607 (0.005441) | 0.756732 / 0.226044 (0.530687) | 7.622873 / 2.268929 (5.353945) | 3.122689 / 55.444624 (-52.321936) | 2.537735 / 6.876477 (-4.338742) | 2.640090 / 2.142072 (0.498018) | 1.128635 / 4.805227 (-3.676593) | 0.228089 / 6.500664 (-6.272575) | 0.086207 / 0.075469 (0.010738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561591 / 1.841788 (-0.280197) | 18.110299 / 8.074308 (10.035991) | 20.718017 / 10.191392 (10.526625) | 0.225741 / 0.680424 (-0.454682) | 0.031738 / 0.534201 (-0.502463) | 0.530789 / 0.579283 (-0.048495) | 0.607364 / 0.434364 (0.173000) | 0.581593 / 0.540337 (0.041256) | 0.726033 / 1.386936 (-0.660903) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009323 / 0.011353 (-0.002030) | 0.005360 / 0.011008 (-0.005649) | 0.103608 / 0.038508 (0.065100) | 0.050158 / 0.023109 (0.027049) | 0.499906 / 0.275898 (0.224008) | 0.561005 / 0.323480 (0.237525) | 0.005093 / 0.007986 (-0.002892) | 0.008285 / 0.004328 (0.003956) | 0.103446 / 0.004250 (0.099196) | 0.061478 / 0.037052 (0.024426) | 0.494016 / 0.258489 (0.235527) | 0.537550 / 0.293841 (0.243709) | 0.048829 / 0.128546 (-0.079717) | 0.017032 / 0.075646 (-0.058614) | 0.107748 / 0.419271 (-0.311524) | 0.065607 / 0.043533 (0.022074) | 0.488709 / 0.255139 (0.233570) | 0.512023 / 0.283200 (0.228823) | 0.032067 / 0.141683 (-0.109616) | 1.907585 / 1.452155 (0.455431) | 1.960994 / 1.492716 (0.468278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278378 / 0.018006 (0.260371) | 0.551474 / 0.000490 (0.550985) | 0.006886 / 0.000200 (0.006686) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030674 / 0.037411 (-0.006737) | 0.135179 / 0.014526 (0.120654) | 0.133703 / 0.176557 (-0.042853) | 0.198923 / 0.737135 (-0.538212) | 0.155108 / 0.296338 (-0.141231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.690566 / 0.215209 (0.475357) | 6.789594 / 2.077655 (4.711940) | 2.940668 / 1.504120 (1.436549) | 2.562431 / 1.541195 (1.021236) | 2.554232 / 1.468490 (1.085742) | 0.888470 / 4.584777 (-3.696307) | 5.672318 / 3.745712 (1.926606) | 2.741626 / 5.269862 (-2.528236) | 1.818336 / 4.565676 (-2.747340) | 0.110434 / 0.424275 (-0.313841) | 0.014114 / 0.007607 (0.006507) | 0.830632 / 0.226044 (0.604588) | 8.270787 / 2.268929 (6.001859) | 3.723486 / 55.444624 (-51.721139) | 2.993671 / 6.876477 (-3.882806) | 2.918273 / 2.142072 (0.776201) | 1.105337 / 4.805227 (-3.699891) | 0.222976 / 6.500664 (-6.277688) | 0.085290 / 0.075469 (0.009820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.816027 / 1.841788 (-0.025760) | 18.496850 / 8.074308 (10.422541) | 20.457032 / 10.191392 (10.265640) | 0.243533 / 0.680424 (-0.436891) | 0.027044 / 0.534201 (-0.507157) | 0.500752 / 0.579283 (-0.078531) | 0.620963 / 0.434364 (0.186599) | 0.607995 / 0.540337 (0.067658) | 0.722915 / 1.386936 (-0.664021) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n" ]

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

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

[ { "color": "0e8a16", "default": false, "description": "Contribution to a dataset script", "id": 4564477500, "name": "dataset contribution", "node_id": "LA_kwDODunzps8AAAABEBBmPA", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20contribution" } ]

[ "The CI failures other than a missing dummy data file and missing fields in the card are unrelated to this PR. ", "Thanks a lot for this great work and fixing TFDS based script @mariosasko 🤗 will generate the dummy dataset and write the model card tomorrow!", "@mariosasko I added the dataset card, I'm on the dummy data rn. ", "@merveenoyan Let me know if you need any help with the dummy data.\r\n\r\nI plan to split the current script/dataset into 4 smaller scripts/datasets to make sure they are properly indexed by Papers With Code later on. In this format:\r\n* the `*_image_captioning` configs will form the [COCO Captions](https://paperswithcode.com/sota/image-captioning-on-coco-captions) dataset (also present in TFDS, but only the 2017 version)\r\n* the `stuff_segmentation` config will form the [COCO Stuff](https://paperswithcode.com/dataset/coco-stuff) dataset\r\n* the `desnepose` config will form the [DensePose-COCO](https://paperswithcode.com/dataset/densepose) dataset\r\n* the rest will be [COCO](https://paperswithcode.com/dataset/coco) (+ will add the `minival` and the `valminusminival` splits to COCO 2014)\r\n\r\nAlso, if I find the time, I'll add preprocessing examples that rely on `pycocotools` to the README files.", "@mariosasko I feel like we can just push main COCO and add Captions + Stuff later, WDYT?", "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for your contribution, @mariosasko and @merveenoyan. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some 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" }, { "color": "2edb81", "default": false, "description": "A bug in a dataset script provided in the library", "id": 2067388877, "name": "dataset bug", "node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug" } ]

[ "I tried `unshuffled_original_da` and it is also not working" ]

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[ "Hi, it seems I am somewhat stuck here. The failed test `ci/circleci: run_dataset_script_tests_pyarrow_1_WIN` seems to be caused by some broken connection (`ConnectionResetError: [WinError 10054] An existing connection was forcibly closed by the remote host`). Any help on this is appreciated. \r\n\r\nEdit: Seems to be resolved now.", "I guess the `dummy_data.zip` is too large. I can reduce the languages that are contained there, but when testing it, it obviously throws an error, as not all files can be found. I guess I can either i) change the default value regarding which languages are loaded or ii) let the `_generate_examples` silently skip any language for which it cannot find files. Both solutions are not really pretty - is there another way around this?", "Thanks for the review and the constructive comments :) ! I tried to address them, and reduced the number of lines in the dummy data to 1 to reduce its size. " ]

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[ "Good start! Here are some proposed next steps:\r\n- We want the Class structure to reflect the template - so the parser know what section titles to expect and when something has gone wrong\r\n- As a result, we don't need to parse the table of contents, since it will always be the same\r\n- For each section/subsection it would be cool to have a variable saying whether it's filled out or not (when it's either empty or has `[More Information Needed]`)\r\n- `attributes` should probably be `text`", "@yjernite @lhoestq \r\n\r\nI have added basic validation checking in the class. It works based on a YAML string. The YAML string determines the expected structure and which text is to be checked. The `text` can be true or false showing whether the text has to be checked or not for emptiness. Similarly, each subsection is parsed recursively. I have used print statement currently so that all issues are shown.\r\n\r\nPlease let me know your thoughts.\r\n\r\nI haven't added a variable that keeps a track of whether the text is empty or not but it can be done easliy if required.", "This looks like a good start !\r\nMaybe we can use a field named `allow_empty` instead of `text` ?\r\nAlso +1 for keeping track of empty texts\r\n\r\nDo you think you can have a way to collect all the validation fails of a readme and then raise an error showing all the failures instead of using print ?\r\n\r\nThen we can create a `tests/test_dataset_cards.py` test file to make sure all the readmes of the repo are valid !", "Hi @lhoestq \r\n\r\nI have added changes accordingly. I prepared a list which stores all the errors and raises them at the end. I'm not sure if there is a better way.", "Hi @lhoestq @yjernite \r\n\r\nPlease find the output for the existing READMEs here: http://p.ip.fi/2vYU\r\n\r\nThanks,\r\nGunjan", "Hi @lhoestq\r\n\r\nI have added some basic tests, also have restructured `ReadMe` class slightly.\r\n\r\nThere is one print statement currently, I'm not sure how to remove it. Basically, I want to warn but not stop further validation. I can't append to a list because the `error_list` and `warning_list` are both only present in `validate` method, and this print is present in the `parse` method. This is done when someone has repeated a section multiple times. For e.g.:\r\n\r\n```markdown\r\n---\r\n---\r\n\r\n# Dataset Card for FashionMNIST\r\n## Dataset Description\r\n## Dataset Description\r\n```\r\n\r\nIn this case, I check for validation only in the latest entry.\r\n\r\nI can also raise an error (ideal case scenario), but still, it is in the `parse`. Should I add `error_lines` and `warning_lines` as instance variables? That would probably solve the issue.\r\n\r\nIn tests, I'm using a dummy YAML string for structure, we can also make it into a file but I feel that is not a hard requirement. Let me know your thoughts.\r\n\r\nI will add tests for `from_readme` as well.\r\n\r\nHowever, I would love to be able to check the exact message in the test when an error is raised. I checked a couple of methods but couldn't get it working. Let me know if you're aware of a way to do that.", "Hi @lhoestq \r\n\r\nThanks for merging. :)\r\nThanks a lot to you and @yjernite for guiding me and helping me out.\r\n\r\nYes, I'll also use the next PR for combining the readme and tags validation. ^_^" ]

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[ "The beam utilities allow to prepare a dataset as parquet in your cloud storage. From my perspective this CLI is not super easy to use, but we've been working on a new python API to prepare a dataset in your cloud storage:\r\n```python\r\nfrom datasets import load_dataset_builder\r\n\r\nbuilder = load_dataset_builder(\"c4\", \"en\")\r\nbuilder.download_and_prepapre(\"s3://my-bucket/c4\", file_format=\"parquet\")\r\n```\r\n\r\nAnd to use Beam you can do:\r\n```python\r\nbeam_runner = ... # one of \"SparkRunner\", \"DataFlowRunner\", \"DirectRunner\", etc.\r\nbeam_options = ...\r\n\r\nbuilder.download_and_prepapre(\r\n \"s3://my-bucket/c4\",\r\n file_format=\"parquet\",\r\n beam_runner=beam_runner,\r\n beam_options=beam_options\r\n)\r\n```\r\n\r\nThough Beam can be used ONLY if there is a dataset script based on the `BeamBasedBuilder` right now - it doesn't work on an arbitrary dataset (see [wikipedia.py](https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py) for example).", "Thanks! \r\n\r\nWould be nice to have something similar for creating large image datasets. " ]

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[ "@lhoestq I ran `make style` but CI code quality still failing and I don't have access to logs", "It's also likely that due to the previous placement of the field initialization, much of the data about topics etc was simply wrong and carried over from previous entries. Model scores seem to improve significantly with this PR." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks a lot for inputs. I'll use the URL suggested and check.\r\n\r\n> we need to implement the streamable (can't use os.path.join) and the non-streamable versions of _generate_examples.\r\n\r\nSure. I will check the reference and try this out, will get back to you if I face any issues.\r\n\r\n> The labels-only data file URL doesn't work for me, so feel free to ask the authors whether they are OK with us hosting the file on the Hub/S3 (to speed up the streamable version)\r\n\r\nJust checked. The author (Adam Harley) has responded positively and allowed us to host the file. Do I share the file with you for hosting it on Hub/S3 ?", "> Just checked. The author (Adam Harley) has responded positively and allowed us to host the file. Do I share the file with you for hosting it on Hub/S3 ?\r\n\r\nYes, feel free to e-mail me the file. Then I'll create a repo under my namespace and push the file there. We run a GH action on a GH dataset after merging to create its repo on the Hub, so after this PR is merged, I'll push the file to the \"official\" namespace and update the download link.", "> You can use this URL to avoid manual download: https://drive.google.com/uc?export=download&id=0Bz1dfcnrpXM-MUt4cHNzUEFXcmc\r\n\r\nFor some reason, the direct download doesn't seem to work for me even with this URL. \r\n```\r\nDownloading and preparing dataset rvl_cdip/default to ~/.cache/huggingface/datasets/rvl_cdip/default/1.0.0/ea152149e06310d60a9ef3c3020199dd4780bb952a773ba5aac6b57d59f12628...\r\nDownloading data files: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 6307.22it/s]\r\n{'rvl-cdip': '~/.cache/huggingface/datasets/downloads/07ef956a33750078d570d76fefe9fed49f7dc32ecf6e872d690de11e66bbe869'}\r\n```\r\nAnd this directory does not exist. Am I doing something wrong ?\r\nTo verify, I tried using [gdown](https://github.com/wkentaro/gdown) for the above URL, we get the following : \r\n```\r\nAccess denied with the following error:\r\n\r\n Cannot retrieve the public link of the file. You may need to change\r\n the permission to 'Anyone with the link', or have had many accesses. \r\n\r\nYou may still be able to access the file from the browser:\r\n```\r\n----\r\n\r\n> Yes, feel free to e-mail me the file. Then I'll create a repo under my namespace and push the file there. We run a GH action on a GH dataset after merging to create its repo on the Hub, so after this PR is merged, I'll push the file to the \"official\" namespace and update the download link.\r\n\r\nGot it. I've sent you an email with the file. Thank you.", "Actually this URL works for direct download :\r\n`https://drive.google.com/uc?export=download&confirm=pbef&id=0Bz1dfcnrpXM-MUt4cHNzUEFXcmc`\r\nRef : https://github.com/wkentaro/gdown/issues/146#issuecomment-1042382215\r\n\r\nI'm working on the streamable versions of _generate_examples as well, will update you regarding this.", "Google Drive is a tricky host, and it's easy to exceed daily download quota limits, so if we are allowed to host the `rvl-cdip.tar.gz` file, I can push it to the Hub.", "Just checked, the authors have agreed. He mentioned that he had complaints about the GDrive link.\r\nYou can push it to the Hub and share the link. :)", "I have added :\r\n- streaming support for rvl-cdip.tar.gz file. [ Need to test this ]\r\n\r\nIs it possible for you to upload the train.txt, test.txt, val.txt files separately to the Hub instead of labels_only.tar.gz file.\r\nCurrently during the tests in stream mode, we get : \r\n`NotImplementedError: Extraction protocol for TAR archives like 'https://huggingface.co/datasets/mariosasko/rvl_cdip/resolve/main/labels_only.tar.gz' is not implemented in streaming mode. Please use dl_manager.iter_archive instead.`\r\nIf the label files are present as .txt files then we can directly use dl_manager.download.\r\n\r\n\r\n", "The rvl-cdip.tar.gz archive and txt files with the labels are on the Hub!", "- Added 🤗 Hub download links.\r\n- streamable and non-streamable versions of _generate_examples.\r\n- Updated dummy data, both real and dummy dataset tests have passed.\r\n\r\n", "I've removed the extraction of the archive file locally as suggested. Let me know if any other changes are required. :)", "The check for **Update Hub repositories / update-hub-repositories** has failed.\r\n\r\n> https://github.com/huggingface/datasets/runs/6116502392?check_suite_focus=true\r\n\r\n", "Hi ! Thanks for reporting ;) yes this CI job has been failing for a few days. I'm working on fixing it, and I'm manually running it on my side in the meantime", "Great. :D Thank you @lhoestq " ]

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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_3892). All of your documentation changes will be reflected on that endpoint.", "Feel free to merge if it's good for you :)", "I've added a test @lhoestq. Once all green, I'll merge. ", "Last failing tests do not have nothing to do with this PR." ]

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

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[ "@ManuelFay thanks you so much for adding a dataset card, this is such a cool contribution!\r\n\r\nThis looks like it uses an old template for the card we've moved things around a bit and we have an app you should be using to get the tags and the structure of the Data Fields paragraph :) Would you mind moving your text to the newer format (we're also asking contributors to keep the full template structure, even if some sections still have [More Information Needed] for the time being)\r\n\r\nHere's the link to the instructions:\r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card\r\n\r\nOut of curiosity, what was your landing point for filling out the card? Did you follow the \"Update on Github\" when navigating the datasets? Trying to make the instructions as clear as possible :) ", "@yjernite \r\n\r\nPerfect, I'll follow the instructions when I have a bit more time tomorrow ! I was actually browsing the new contributions after the dataset sprint and realized most of the \"old\" datasets were not tagged, so I just copied and pasted the readme from another dataset and was not aware there was precise instructions... Will fix !\r\n\r\nBTW, amazing job with the retriBert work, I used the contrastive + in-batch negative quite a bit for various projects. Probably neither the time nor place to talk about that but I was curious as to why, in your original work, you prefered using a simple projection in the last layer to differentiate the question vs answer embedding, rather than allowing for bias in the dense layer or even just to fine-tune 2 different embedders for question + answer ? ", "Cool! Looking forward to the next version!\r\n\r\nQuick answer for retriBERT is that I expected a simple projection to generalize better and more importantly only having to store the gradients for the proj means training with larger batches :) If you want to keep chatting about it, feel free to send me an email!", "Hi @ManuelFay ! \r\nIf you're still interested in completing the FQuAD dataset card, note that we've generated one that is pre-filled.\r\nTherefore feel free to complete it with the content you already have in your README.md.\r\nThis would be awesome ! And thanks again for your contribution :)", "Yo @lhoestq , just not sure about the tag table at the top, I used @yjernite eli5 template so hope it's okay ! Also want to signal the streamlit app for dataset tagging has a weird behavior with the size categories when filling in the form. \r\n\r\nThanks to you guys for doing that and sorry about the time it took, i completely forgot about it ! \r\n" ]

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[ "@lhoestq Let me make sure we never need it, and if not then I'll remove it entirely in a follow-up PR.", "Thanks ;) it will be less confusing and easier to maintain to not keep unused hacky features" ]

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[ "Hi Apparently I need to shard the data and give one host a chunk, could you provide me please with examples on how to do it? I want to use it jointly with finetune_trainer.py in huggingface repo seq2seq examples. thanks. ", "Hey @rabeehkarimimahabadi I'm actually looking for the same feature. Did you manage to get somewhere?", "@rabeehkarimimahabadi need the same feature", "Hi! I think you can use the `accelerate` library for that, which implements distributed sampling." ]

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

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[ "To give more context: after our discussion, if I understood properly, you are trying to fix a call to `datasets` that takes 15 minutes: https://github.com/huggingface/datasets-preview-backend/issues/17 Is this right?\r\n\r\n", "Yes, that's it. And to do that I'm trying to use https://pypi.org/project/stopit/, which will raise a stopit.TimeoutException exception. But currently, if this exception is raised, it's caught and considered as a \"FileNotFoundError\" while it should not be caught. ", "And what about passing the `timeout` parameter instead?", "It might be a good idea, but I would have to add a timeout argument to several methods, I'm not sure we want that (I want to ensure all my queries in https://github.com/huggingface/datasets-preview-backend/tree/master/src/datasets_preview_backend/queries resolve in a given time, be it with an error in case of timeout, or with the successful response). The methods are `prepare_module`, `import_main_class`, *builder_cls.*`get_all_exported_dataset_infos`, `load_dataset_builder`, and `load_dataset`", "I understand, you are trying to find a fix for your use case. OK.\r\n\r\nJust note that it is also an issue for `datasets` users. Once #2859 fixed in `datasets`, you will no longer have this issue...", "Closing, since 1. my problem is more #2859, and I was asking for that change in order to make a hack work on my side, 2. if we want to change how exceptions are handled, we surely want to do it on all the codebase, not only in this particular case." ]

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[ "Hi ! To help me understand the situation, can you print the values of `load_from_disk(PATH_DATA_CLS_A)['train']._indices_data_files` and `load_from_disk(PATH_DATA_CLS_B)['train']._indices_data_files` ?\r\nThey should both have a path to an arrow file\r\n\r\nAlso note that from #2025 concatenating datasets will no longer have such restrictions.", "Sure, thanks for the fast reply!\r\n\r\nFor dataset A: `[{'filename': 'drive/MyDrive/data_target_task/dataset_a/train/cache-4797266bf4db1eb7.arrow'}]`\r\nFor dataset B: `[]`\r\n\r\nNo clue why for B it returns nothing. `PATH_DATA_CLS_B` is exactly the same in `save_to_disk` and `load_from_disk`... Also I can verify that the folder physically exists under 'drive/MyDrive/data_target_task/dataset_b/'", "In the next release you'll be able to concatenate any kinds of dataset (either from memory or from disk).\r\n\r\nFor now I'd suggest you to flatten the indices of the A and B datasets. This will remove the indices mapping and you will be able to concatenate them. You can flatten the indices with\r\n```python\r\ndataset = dataset.flatten_indices()\r\n```", "Indeed this works. Not the most elegant solution, but it does the trick. Thanks a lot! " ]

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[ "Hi ! The cache files are the results of all the transforms you applied to the dataset using `map` for example.\r\nDid you run a transform that could potentially blow up the size of the dataset ?", "@lhoestq,\r\nI don't remember, but I can't imagine what kind of transform may generate data that grow over 200 times in size. \r\nI think maybe it doesn' matter, it's just cache after all." ]

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[ "Thanks for reporting !\r\ncc @srush for the empty feature list issue and the encoding issue\r\ncc @julien-c maybe we can update the url and just have a redirection from the old url to the new one ?", "Ok, I redirected on our side to a new url. ⚠️ @srush: if you update the Streamlit config too to `/datasets/viewer`, let me know because I'll need to change our nginx config at the same time", "9", "‏⠀‏‏‏⠀‏‏‏⠀ ‏⠀ ", "‏⠀‏‏‏⠀‏‏‏⠀ ‏⠀ " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5528). All of your documentation changes will be reflected on that endpoint.", "It seems that the parameter `create_pr` is available for [`0.8.0`](https://huggingface.co/docs/huggingface_hub/v0.8.1/en/package_reference/hf_api#huggingface_hub.HfApi.upload_file) (its not here: [`0.7.0`](https://huggingface.co/docs/huggingface_hub/v0.7.0.rc0/en/package_reference/hf_api#huggingface_hub.HfApi.upload_file)) and onwards. I included a warning, informing the user that no PR was created.", "@nateraw you are completely right! Actually, the dataset shards is never added to the created pr, only the metadata, as the code is now. Ill look into you suggestion asap. Thank!", "@nateraw Nothing more to add, that's a perfect usage of `huggingface_hub` as far as I can tell ! :fire: \r\n\r\nA very nit improvement would be to use the [for .. else ... python statement](https://book.pythontips.com/en/latest/for_-_else.html).\r\ni.e:\r\n\r\n```py\r\nif create_pr is True and revision is not None:\r\n for discussion in get_repo_discussions(repo_id, repo_type='dataset'):\r\n if discussion.is_pull_request and discussion.git_reference == revision:\r\n create_pr = False\r\n break\r\n else:\r\n raise ValueError(\"Provided revision not found\")\r\n```\r\nNo need for the `revision_found` temporary flag when do so. Yeah ok, it's niche :wink: ", "I added the suggestions from @nateraw and @Wauplin .", "> Thanks. Some comments/suggestions below...\r\n> \r\n> Why have you removed the test for create_pr? You could add it again and just add a pytest skipif when version of huggingface_hub is lower than 0.8.1.\r\n\r\nI have added the test again. I removed it because i kept getting errors when calling `create_pull_request` with `repo_id=ds_name` where `temporary_repo = ds_name`, and thought i might look more thoroughly at it later. I have added a test called `test_test` showing this, it gives:\r\n```\r\ntests/test_upstream_hub.py:360: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n return fn(*args, **kwargs)\r\n.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3451: in create_pull_request\r\n return self.create_discussion(\r\n.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n return fn(*args, **kwargs)\r\n.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3393: in create_discussion\r\n hf_raise_for_status(resp)\r\n(...)\r\nE huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-63ecd2cb-2cf2557a332c86ad27f687b3)\r\nE \r\nE Repository Not Found for url: https://huggingface.co/api/models/__DUMMY_TRANSFORMERS_USER__/test-16764648321590/discussions.\r\nE Please make sure you specified the correct `repo_id` and `repo_type`.\r\nE If you are trying to access a private or gated repo, make sure you are authenticated.\r\nE Invalid username or password.\r\n```", "> > Thanks. Some comments/suggestions below...\r\n> > Why have you removed the test for create_pr? You could add it again and just add a pytest skipif when version of huggingface_hub is lower than 0.8.1.\r\n> \r\n> I have added the test again. I removed it because i kept getting errors when calling `create_pull_request` with `repo_id=ds_name` where `temporary_repo = ds_name`, and thought i might look more thoroughly at it later. I have added a test called `test_test` showing this, it gives:\r\n> \r\n> ```\r\n> tests/test_upstream_hub.py:360: \r\n> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n> return fn(*args, **kwargs)\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3451: in create_pull_request\r\n> return self.create_discussion(\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n> return fn(*args, **kwargs)\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3393: in create_discussion\r\n> hf_raise_for_status(resp)\r\n> (...)\r\n> E huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-63ecd2cb-2cf2557a332c86ad27f687b3)\r\n> E \r\n> E Repository Not Found for url: https://huggingface.co/api/models/__DUMMY_TRANSFORMERS_USER__/test-16764648321590/discussions.\r\n> E Please make sure you specified the correct `repo_id` and `repo_type`.\r\n> E If you are trying to access a private or gated repo, make sure you are authenticated.\r\n> E Invalid username or password.\r\n> ```\r\n\r\n@albertvillanova, @lhoestq , FYI I have looked at this again, and i haven't figured it out, so the test`test_push_dataset_to_hub_with_pull_request` and the minimal example `test_test` are still failing locally, while the other tests succeed. Do you have any advice?", "I tried to move all of the \"create pr safely\"-logic to a seperate function in `_hf_hub_fixes`. I looked at how the exceptions were raised before `huggingface_hub.utils.RepositoryNotFoundError`existed, and make changes accordingly. ", "`create_pr` was set during `push_to_hub`, even though it was `None` from the outset, hence causing tests to fail for older versions of `huggingface_hub`. This is now fixed.\r\n\r\nWith the implementation of `_hf_hub_fixes.upload_file` the function call expected `commit_message`, `commit_description`. If these are not set we call the function without them, even though we are on a version of `huggingface_hub` where they are not available in `upload_file`.\r\n\r\nWhen `huggingface_hub < 0.5.0` we assume `repo_id` of them form `organisation/name`, so now that we are calling `create_repo` in the tests with `repo_id` not of this form, we need to handle this case, this is now done.\r\n\r\nMany tests failed for `dataset_dict` for the above reasons, so the fixes from `arrow_dataset.py` were also added to `dataset_dict.py`. \r\n\r\n**All tests are now passing locally for `huggingface_hub==0.2.0` and `huggingface_hub==0.12.1`…** Im sorry I should have downgraded and went through this a long time ago, but I didn’t realise the extend of these version fixes until recently…", "Hi ! FYI bumped the `huggingface-hub` dependency to 0.11 and removed the `_hf_hub_fixes.py` - which should make this PR much easier" ]

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[ "the dataset viewer is working on this dataset. I imagine the issue is that we would expect to be able to listen to the audio files in the `Please Record Your Voice file` column, right?\r\n\r\nmaybe @lhoestq or @albertvillanova could help\r\n\r\n<img width=\"1019\" alt=\"Capture d’écran 2022-03-24 à 17 36 20\" src=\"https://user-images.githubusercontent.com/1676121/159966006-57dcf8f7-b65f-4200-ac8c-66859318a8bb.png\">\r\n", "The structure of the dataset is not supported. Only the CSV file is parsed and the audio files are ignored.\r\n\r\nWe're working on supporting audio datasets with a specific structure in #3963 ", "Got it." ]

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[ "@lhoestq I took a shot at addressing your comments but the build scripts seem to be complaining about not being able to open dummy files. How do I resolve those errors without copying the full dataset into the dummy dir?", "Could you check that the names of the dummy data files are right ?\r\nYou can use \r\n```\r\ndatasets-cli dummy_data ./datasets/xum\r\n```\r\nto print the expected file names", "Ok @lhoestq looks like I got the tests to pass :)" ]

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[ "This'll be a really helpful fix, thank you!" ]

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[ "Hi @osanseviero I would like to add this dataset.", "Hey @nandwalritik! Thanks for offering to help!\r\n\r\nThis dataset might be somewhat complex and I'm concerned about it being 65 TB, which would be quite expensive to host. @lhoestq @mariosasko I would love your input if you think it's worth adding this dataset.", "Thanks for proposing this interesting dataset, @osanseviero.\r\n\r\nPlease note that the data files are already hosted in a third-party server: e.g. the index of data files for entity \"6795937\" is at https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/index.html \r\n- audio.tar: https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/audio.tar\r\n- ...\r\n\r\nTherefore, in principle, we don't need to host them on our Hub: it would be enough to just implement a loading script in the corresponding Hub dataset repo, e.g. \"facebook/multiface\"..." ]

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[ "Thanks for contacting @Eytan-S.\r\n\r\nI think @lvwerra could better answer this. ", "That seems to be an oversight - I originally planned to include them in the dataset and for some reason they were in the list of languages but not in the query. Since there is an issue with the deduplication step I'll rerun the pipeline anyway and will double check the query.\r\n\r\nThanks for reporting this @Eytan-S!", "Can confirm that the two languages are indeed missing from the dataset. Here are the file counts per language:\r\n```Python\r\n{'Assembly': 82847,\r\n 'Batchfile': 236755,\r\n 'C': 14127969,\r\n 'C#': 6793439,\r\n 'C++': 7368473,\r\n 'CMake': 175076,\r\n 'CSS': 1733625,\r\n 'Dockerfile': 331966,\r\n 'FORTRAN': 141963,\r\n 'GO': 2259363,\r\n 'Haskell': 340521,\r\n 'HTML': 11165464,\r\n 'Java': 19515696,\r\n 'JavaScript': 11829024,\r\n 'Julia': 58177,\r\n 'Lua': 576279,\r\n 'Makefile': 679338,\r\n 'Markdown': 8454049,\r\n 'PHP': 11181930,\r\n 'Perl': 497490,\r\n 'PowerShell': 136827,\r\n 'Python': 7203553,\r\n 'Ruby': 4479767,\r\n 'Rust': 321765,\r\n 'SQL': 655657,\r\n 'Scala': 0,\r\n 'Shell': 1382786,\r\n 'TypeScript': 0,\r\n 'TeX': 250764,\r\n 'Visual Basic': 155371}\r\n ```", "@Eytan-S check out v1.1 of the `github-code` dataset where issue should be fixed:\r\n\r\n| | Language |File Count| Size (GB)|\r\n|---:|:-------------|---------:|-------:|\r\n| 0 | Java | 19548190 | 107.7 |\r\n| 1 | C | 14143113 | 183.83 |\r\n| 2 | JavaScript | 11839883 | 87.82 |\r\n| 3 | HTML | 11178557 | 118.12 |\r\n| 4 | PHP | 11177610 | 61.41 |\r\n| 5 | Markdown | 8464626 | 23.09 |\r\n| 6 | C++ | 7380520 | 87.73 |\r\n| 7 | Python | 7226626 | 52.03 |\r\n| 8 | C# | 6811652 | 36.83 |\r\n| 9 | Ruby | 4473331 | 10.95 |\r\n| 10 | GO | 2265436 | 19.28 |\r\n| 11 | TypeScript | 1940406 | 24.59 |\r\n| 12 | CSS | 1734406 | 22.67 |\r\n| 13 | Shell | 1385648 | 3.01 |\r\n| 14 | Scala | 835755 | 3.87 |\r\n| 15 | Makefile | 679430 | 2.92 |\r\n| 16 | SQL | 656671 | 5.67 |\r\n| 17 | Lua | 578554 | 2.81 |\r\n| 18 | Perl | 497949 | 4.7 |\r\n| 19 | Dockerfile | 366505 | 0.71 |\r\n| 20 | Haskell | 340623 | 1.85 |\r\n| 21 | Rust | 322431 | 2.68 |\r\n| 22 | TeX | 251015 | 2.15 |\r\n| 23 | Batchfile | 236945 | 0.7 |\r\n| 24 | CMake | 175282 | 0.54 |\r\n| 25 | Visual Basic | 155652 | 1.91 |\r\n| 26 | FORTRAN | 142038 | 1.62 |\r\n| 27 | PowerShell | 136846 | 0.69 |\r\n| 28 | Assembly | 82905 | 0.78 |\r\n| 29 | Julia | 58317 | 0.29 |", "Thanks @lvwerra. " ]

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[ "Sure, it's on the roadmap.", "Closing in favor of https://github.com/huggingface/datasets-server/issues/39." ]

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