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https://api.github.com/repos/huggingface/datasets/issues/4242

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PR_kwDODunzps425BYf

Update auth when mirroring datasets on the hub

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We don't need to use extraHeaders anymore for rate limits anymore. Anyway extraHeaders was not working with git LFS because it was passing the wrong auth to S3.

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I_kwDODunzps5IkGlG

NonMatchingChecksumError when attempting to download GLUE

closed

NONE

## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5

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PR_kwDODunzps423xRl

Fix yield for crd3

closed

CONTRIBUTOR

Modified the `_generate_examples` function to consider all the turns for a chunk id as a single example Modified the features accordingly ``` "turns": [ { "names": datasets.features.Sequence(datasets.Value("string")), "utterances": datasets.features.Sequence(datasets.Value("string")), "number": datasets.Value("int32"), } ], } ``` I wasn't able to run `datasets-cli dummy_data datasets` command. Is there a workaround for this?

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PR_kwDODunzps423tZr

Small fixes in ROC AUC docs

closed

CONTRIBUTOR

The list of use cases did not render on GitHub with the prepended spacing. Additionally, some typo's we're fixed.

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I_kwDODunzps5IjIL7

Dataset caching policy

closed

NONE

## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```

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I_kwDODunzps5Ii8sU

Common Voice 8 doesn't show datasets viewer

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PR_kwDODunzps423MOc

Replace data URL in big_patent dataset and support streaming

closed

MEMBER

This PR replaces the Google Drive URL with our Hub one, once the data owners have approved to host their data on the Hub. Moreover, this PR makes the dataset streamable. Fix #4217.

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PR_kwDODunzps422Mwn

Autoeval config

closed

CONTRIBUTOR

Added autoeval config to imdb as pilot

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PR_kwDODunzps421r-6

Autoeval

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PR_kwDODunzps421qz4

adding new tag to tasks.json and modified for existing datasets

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PR_kwDODunzps421pUX

Fix invalid url to CC-Aligned dataset

closed

CONTRIBUTOR

The CC-Aligned dataset url has changed to https://data.statmt.org/cc-aligned/, the old address http://www.statmt.org/cc-aligned/ is no longer valid

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PR_kwDODunzps421mjM

new task tag

closed

CONTRIBUTOR

multi-input-text-classification tag for classification datasets that take more than one input

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PR_kwDODunzps421NKL

new task tag

closed

CONTRIBUTOR

multi-input-text-classification tag for classification datasets that take more than one input

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PR_kwDODunzps420-mc

Add f1 metric card, update docstring in py file

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PR_kwDODunzps420kAv

Add pearsonr mc, update functionality to match the original docs

closed

CONTRIBUTOR

- adds pearsonr metric card - adds ability to return p-value - p-value was mentioned in the original docs as a return value, but there was no option to return it. I updated the _compute function slightly to have an option to return the p-value.

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PR_kwDODunzps420LNM

autoeval config

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CONTRIBUTOR

add train eval index for autoeval

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PR_kwDODunzps420KX2

autoeval config

closed

CONTRIBUTOR

add train eval index for autoeval

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PR_kwDODunzps42z0YV

Add Accuracy Metric Card

closed

CONTRIBUTOR

- adds accuracy metric card - updates docstring in accuracy.py - adds .json file with metric card and docstring information

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PR_kwDODunzps42zpcd

Fix description links in dataset cards

closed

MEMBER

I noticed many links were not properly displayed (only text, no link) on the Hub because of wrong syntax, e.g.: https://huggingface.co/datasets/big_patent This PR fixes all description links in dataset cards.

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I_kwDODunzps5IeVUO

Dictionary Feature

closed

NONE

Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance.

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PR_kwDODunzps42w5YO

Altered faiss installation comment

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PR_kwDODunzps42v6rE

Add F1 Metric Card

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PR_kwDODunzps42vTA0

Make code for image downloading from image urls cacheable

closed

CONTRIBUTOR

Fix #4199

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I_kwDODunzps5IZquN

Big_Patent dataset broken

closed

NONE

## Dataset viewer issue for '*big_patent*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)* *Unable to view because it says FileNotFound, also cannot download it through the python API* Am I the one who added this dataset ? No

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PR_kwDODunzps42u1_w

Avoid recursion error in map if example is returned as dict value

closed

CONTRIBUTOR

I noticed this bug while answering [this question](https://discuss.huggingface.co/t/correct-way-to-create-a-dataset-from-a-csv-file/15686/11?u=mariosasko). This code replicates the bug: ```python from datasets import Dataset dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]}) dset.map(lambda ex: {"translation": ex}) ``` and this is the fix for it (before this PR): ```python from datasets import Dataset dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]}) dset.map(lambda ex: {"translation": dict(ex)}) ``` Internally, this can be fixed by merging two dicts via dict unpacking (instead of `dict.update) `in `Dataset.map`, which avoids creating recursive dictionaries. P.S. `{**a, **b}` is slightly more performant than `a.update(b)` in my bencmarks.

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PR_kwDODunzps42uuhY

Add `drop_last_batch` to `IterableDataset.map`

closed

CONTRIBUTOR

Addresses this comment: https://github.com/huggingface/datasets/pull/3801#pullrequestreview-901736921

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PR_kwDODunzps42utC5

Skip checksum computation in Imagefolder by default

closed

CONTRIBUTOR

Avoids having to set `ignore_verifications=True` in `load_dataset("imagefolder", ...)` to skip checksum verification and speed up loading. The user can still pass `DownloadConfig(record_checksums=True)` to not skip this part.

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PR_kwDODunzps42uft_

ETT time series dataset

closed

CONTRIBUTOR

Ready for review.

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PR_kwDODunzps42udRf

[Common Voice] Make sure bytes are correctly deleted if `path` exists

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MEMBER

`path` should be set to local path inside audio feature if exist so that bytes can correctly be deleted.

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I_kwDODunzps5IYbDt

DatasetDict containing Datasets with different features when pushed to hub gets remapped features

closed

NONE

Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```

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I_kwDODunzps5IXYeq

TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'

closed

NONE

### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vědět, co je tam uvnitř. ces Kdo o tom chce slyšet? deu Tom sagte, er fühle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rá rögtön. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hören. ``` ### Expected behavior ```shell correctly load train and test files. ```

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PR_kwDODunzps42r7bW

Add CMU MoCap Dataset

closed

CONTRIBUTOR

Resolves #3457 Dataset Request : Add CMU Graphics Lab Motion Capture dataset [#3457](https://github.com/huggingface/datasets/issues/3457) This PR adds the CMU MoCap Dataset. The authors didn't respond even after multiple follow ups, so I ended up crawling the website to get categories, subcategories and description information. Some of the subjects do not have category/subcategory/description as well. I am using a subject to categories, subcategories and description map (metadata file). Currently the loading of the dataset works for "asf/amc" and "avi" formats since they have a single download link. But "c3d" and "mpg" have multiple download links (part archives) and dl_manager.download_and_extract() extracts the files to multiple paths, is there a way to extract these multiple archives into one folder ? Any other way to go about this ? Any suggestions/inputs on this would be helpful. Thank you.

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PR_kwDODunzps42rmbK

[Minor edit] Fix typo in class name

closed

CONTRIBUTOR

Typo: `datasets.DatsetDict` -> `datasets.DatasetDict`

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PR_kwDODunzps42oVFE

Fix `convert_file_size_to_int` for kilobits and megabits

closed

CONTRIBUTOR

Minor change to fully align this function with the recent change in Transformers (https://github.com/huggingface/transformers/pull/16891)

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PR_kwDODunzps42oN0j

Add Recall Metric Card

closed

CONTRIBUTOR

What this PR mainly does: - add metric card for recall metric - update docs in recall python file Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!)

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PR_kwDODunzps42oNrS

Add Precision Metric Card

closed

CONTRIBUTOR

What this PR mainly does: - add metric card for precision metric - update docs in precision python file Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!)

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PR_kwDODunzps42n278

Fix some type annotation in doc

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PR_kwDODunzps42nIRm

Update GH template for dataset viewer issues

closed

MEMBER

Update template to use new issue forms instead. With this PR we can check if this new feature is useful for us. Once validated, we can update the other templates. CC: @severo

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PR_kwDODunzps42mz0w

Add to docs how to load from local script

closed

MEMBER

This option was missing from the docs guide (it was only explained in the docstring of `load_dataset`). Although this is an infrequent use case, there might be some users interested in it. Related to #4192 CC: @stevhliu

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I_kwDODunzps5IPPCc

Cache miss during reload for datasets using image fetch utilities through map

closed

MEMBER

## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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I_kwDODunzps5INVwv

There is no dataset

closed

NONE

## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No

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PR_kwDODunzps42kyXD

Add remove_columns=True

closed

MEMBER

This should fix all the issue we have with in place operations in mapping functions. This is crucial as where we do some weird things like: ``` def apply(batch): batch_size = len(batch["id"]) batch["text"] = ["potato" for _ range(batch_size)] return {} # Columns are: {"id": int} dset.map(apply, batched=True, remove_columns="text") # crashes because `text` is not in the original columns dset.map(apply, batched=True) # mapped datasets has `text` column ``` In this PR we suggest to have `remove_columns=True` so that we ignore the input completely, and just use the output to generate mapped dataset. This means that inplace operations won't have any effects anymore.

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I_kwDODunzps5IMohd

Embed image and audio files in `save_to_disk`

closed

MEMBER

Following https://github.com/huggingface/datasets/pull/4184, currently a dataset saved using `save_to_disk` doesn't actually contain the bytes of the image or audio files. Instead it stores the path to your local files. Adding `embed_external_files` and set it to True by default to save_to_disk would be kind of a breaking change since some users will get bigger Arrow files when updating the lib, but the advantages are nice: - the resulting dataset is self contained, in case you want to delete your cache for example or share it with someone else - users also upload these Arrow files to cloud storage via the fs parameter, and in this case they would expect to upload a self-contained dataset - consistency with push_to_hub This can be implemented at the same time as sharding for `save_to_disk` for efficiency, and reuse the helpers from `push_to_hub` to embed the external files. cc @mariosasko

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PR_kwDODunzps42jjD3

Support lists of multi-dimensional numpy arrays

closed

MEMBER

Fix #4191. CC: @SaulLu

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PR_kwDODunzps42izQG

Document save_to_disk and push_to_hub on images and audio files

closed

MEMBER

Following https://github.com/huggingface/datasets/pull/4187, I explained in the documentation of `save_to_disk` and `push_to_hub` how they handle image and audio data.

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I_kwDODunzps5IKbPK

load_dataset can't load local dataset,Unable to find ...

closed

NONE

Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained   the code is in the model.py,why I can't use the load_dataset function to load my local dataset?

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I_kwDODunzps5IH5A6

feat: create an `Array3D` column from a list of arrays of dimension 2

closed

NONE

**Is your feature request related to a problem? Please describe.** It is possible to create an `Array2D` column from a list of arrays of dimension 1. Similarly, I think it might be nice to be able to create a `Array3D` column from a list of lists of arrays of dimension 1. To illustrate my proposal, let's take the following toy dataset t: ```python import numpy as np from datasets import Dataset, features data_map = { 1: np.array([[0.2, 0,4],[0.19, 0,3]]), 2: np.array([[0.1, 0,4],[0.19, 0,3]]), } def create_toy_ds(): my_dict = {"id":[1, 2]} return Dataset.from_dict(my_dict) ds = create_toy_ds() ``` The following 2D processing works without any errors raised: ```python def prepare_dataset_2D(batch): batch["pixel_values"] = [data_map[index] for index in batch["id"]] return batch ds_2D = ds.map( prepare_dataset_2D, batched=True, remove_columns=ds.column_names, features=features.Features({"pixel_values": features.Array2D(shape=(2, 3), dtype="float32")}) ) ``` The following 3D processing doesn't work: ```python def prepare_dataset_3D(batch): batch["pixel_values"] = [[data_map[index]] for index in batch["id"]] return batch ds_3D = ds.map( prepare_dataset_3D, batched=True, remove_columns=ds.column_names, features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3, dtype="float32")}) ) ``` The error raised is: ``` --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) [<ipython-input-6-676547e4cd41>](https://localhost:8080/#) in <module>() 3 batched=True, 4 remove_columns=ds.column_names, ----> 5 features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")}) 6 ) 12 frames [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 1971 new_fingerprint=new_fingerprint, 1972 disable_tqdm=disable_tqdm, -> 1973 desc=desc, 1974 ) 1975 else: [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs) 518 self: "Dataset" = kwargs.pop("self") 519 # apply actual function --> 520 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 521 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 522 for dataset in datasets: [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs) 485 } 486 # apply actual function --> 487 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 488 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 489 # re-apply format to the output [/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py](https://localhost:8080/#) in wrapper(*args, **kwargs) 456 # Call actual function 457 --> 458 out = func(self, *args, **kwargs) 459 460 # Update fingerprint of in-place transforms + update in-place history of transforms [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2354 writer.write_table(batch) 2355 else: -> 2356 writer.write_batch(batch) 2357 if update_data and writer is not None: 2358 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file [/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_batch(self, batch_examples, writer_batch_size) 505 col_try_type = try_features[col] if try_features is not None and col in try_features else None 506 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 507 arrays.append(pa.array(typed_sequence)) 508 inferred_features[col] = typed_sequence.get_inferred_type() 509 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() [/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in __arrow_array__(self, type) 175 storage = list_of_np_array_to_pyarrow_listarray(data, type=pa_type.value_type) 176 else: --> 177 storage = pa.array(data, pa_type.storage_dtype) 178 return pa.ExtensionArray.from_storage(pa_type, storage) 179 /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Can only convert 1-dimensional array values ``` **Describe the solution you'd like** No error in the second scenario and an identical result to the following snippets. **Describe alternatives you've considered** There are other alternatives that work such as: ```python def prepare_dataset_3D_bis(batch): batch["pixel_values"] = [[data_map[index].tolist()] for index in batch["id"]] return batch ds_3D_bis = ds.map( prepare_dataset_3D_bis, batched=True, remove_columns=ds.column_names, features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")}) ) ``` or ```python def prepare_dataset_3D_ter(batch): batch["pixel_values"] = [data_map[index][np.newaxis, :, :] for index in batch["id"]] return batch ds_3D_ter = ds.map( prepare_dataset_3D_ter, batched=True, remove_columns=ds.column_names, features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")}) ) ``` But both solutions require the user to be aware that `data_map[index]` is an `np.array` type. cc @lhoestq as we discuss this offline :smile:

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PR_kwDODunzps42gK3y

Deprecate `shard_size` in `push_to_hub` in favor of `max_shard_size`

closed

CONTRIBUTOR

This PR adds a `max_shard_size` param to `push_to_hub` and deprecates `shard_size` in favor of this new param to have a more descriptive name (a shard has at most the `shard_size` bytes in `push_to_hub`) for the param and to align the API with [Transformers](https://github.com/huggingface/transformers/blob/ff06b177917384137af2d9585697d2d76c40cdfc/src/transformers/modeling_utils.py#L1350).

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PR_kwDODunzps42gGv5

Document how to use FAISS index for special operations

closed

MEMBER

Document how to use FAISS index for special operations, by accessing the index itself. Close #4029.

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PR_kwDODunzps42fpMv

Support streaming cnn_dailymail dataset

closed

MEMBER

Support streaming cnn_dailymail dataset. Fix #3969. CC: @severo

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PR_kwDODunzps42flGp

Don't duplicate data when encoding audio or image

closed

MEMBER

Right now if you pass both the `bytes` and a local `path` for audio or image data, then the `bytes` are unnecessarily written in the Arrow file, while we could just keep the local `path`. This PR discards the `bytes` when the audio or image file exists locally. In particular it's common for audio datasets builders to provide both the bytes and the local path in order to work for both streaming (using the bytes) and non-streaming mode (using a local file - which is often required for audio). cc @patrickvonplaten

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PR_kwDODunzps42evF5

Fix outdated docstring about default dataset config

closed

MEMBER

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PR_kwDODunzps42cB2j

[Librispeech] Add 'all' config

closed

MEMBER

Add `"all"` config to Librispeech Closed #4179

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PR_kwDODunzps42bjXn

Document librispeech configs

closed

MEMBER

Added an example of how to load one config or the other

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I_kwDODunzps5IBPgz

Zenodo.org download is not responding

closed

CONTRIBUTOR

## Describe the bug Source download_url from zenodo.org does not respond. `_DOWNLOAD_URL = "https://zenodo.org/record/2787612/files/SICK.zip?download=1"` Other datasets also use zenodo.org to store data and they cannot be downloaded as well. It would be better to actually use more reliable way to store original data like s3 bucket. ## Steps to reproduce the bug ```python load_dataset("sick") ``` ## Expected results Dataset should be downloaded. ## Actual results ConnectionError: Couldn't reach https://zenodo.org/record/2787612/files/SICK.zip?download=1 (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out. (read timeout=100)"))) ## Environment info - `datasets` version: 2.1.0 - Platform: Darwin-21.4.0-x86_64-i386-64bit - Python version: 3.7.11 - PyArrow version: 7.0.0 - Pandas version: 1.3.5

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I_kwDODunzps5IA5b1

Support streaming FLEURS dataset

closed

MEMBER

## Dataset viewer issue for '*name of the dataset*' https://huggingface.co/datasets/google/fleurs ``` Status code: 400 Exception: NotImplementedError Message: Extraction protocol for TAR archives like 'https://storage.googleapis.com/xtreme_translations/FLEURS/af_za.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead. ``` Am I the one who added this dataset ? Yes Can I fix this somehow in the script? @lhoestq @severo

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I_kwDODunzps5IAKJe

Dataset librispeech_asr fails to load

closed

NONE

## Describe the bug The dataset librispeech_asr (standard Librispeech) fails to load. ## Steps to reproduce the bug ```python datasets.load_dataset("librispeech_asr") ``` ## Expected results It should download and prepare the whole dataset (all subsets). In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other). However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want. Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training. ## Actual results ``` ... File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators line: archive_path = dl_manager.download(_DL_URLS[self.config.name]) locals: archive_path = <not found> dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160> dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>> _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'... self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310> self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None) self.config.name = <local> 'default', len = 7 KeyError: 'default' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31 - Python version: 3.9.9 - PyArrow version: 6.0.1 - Pandas version: 1.4.2

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PR_kwDODunzps42ZfFN

[feat] Add ImageNet dataset

closed

MEMBER

To use the dataset download the tar file [imagenet_object_localization_patched2019.tar.gz](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=imagenet_object_localization_patched2019.tar.gz) from Kaggle and then point the datasets library to it by using: ```py from datasets import load_dataset dataset = load_dataset("imagenet", data_dir="/path/to/imagenet_object_localization_patched2019.tar.gz") ``` Currently train and validation splits are supported.

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I_kwDODunzps5H6fdr

Very slow between two operations

closed

NONE

Hello, in the processing stage, I use two operations. The first one : map + filter, is very fast and it uses the full cores, while the socond step is very slow and did not use full cores. Also, there is a significant lag between them. Am I missing something ? ``` raw_datasets = raw_datasets.map(split_func, batched=False, num_proc=args.preprocessing_num_workers, load_from_cache_file=not args.overwrite_cache, desc = "running split para ==>")\ .filter(lambda example: example['text1']!='' and example['text2']!='', num_proc=args.preprocessing_num_workers, desc="filtering ==>") processed_datasets = raw_datasets.map( preprocess_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on dataset===>", ) ```

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PR_kwDODunzps42SqF-

Add WIT Dataset

closed

MEMBER

closes #2981 #2810 @nateraw @hassiahk I've listed you guys as co-author as you've contributed previously to this dataset

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PR_kwDODunzps42SnJS

Fix when map function modifies input in-place

closed

MEMBER

When `function` modifies input in-place, the guarantee that columns in `remove_columns` are contained in `input` doesn't hold true anymore. Therefore we need to relax way we pop elements by checking if that column exists.

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PR_kwDODunzps42Ppnd

Stream private zipped images

closed

MEMBER

As mentioned in https://github.com/huggingface/datasets/issues/4139 it's currently not possible to stream private/gated zipped images from the Hub. This is because `Image.decode_example` does not handle authentication. Indeed decoding requires to access and download the file from the private repository. In this PR I added authentication to `Image.decode_example` via a `token_per_repo_id` optional argument. I first wanted to just pass `use_auth_token` but a single `Image` instance can be responsible of decoding images from a combination of several datasets together (from `interleave_datasets` for example). Therefore I just used a dictionary `repo_id` -> `token` instead. I'm getting the `repo_id` from the dataset builder (I replaced the `namepace` attribute with `repo_id`) I did the same for `Audio.decode_example` cc @SBrandeis @severo

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PR_kwDODunzps42O7LW

Update assin2 dataset_infos.json

closed

MEMBER

Following comments in https://github.com/huggingface/datasets/issues/4003 we found that it was outdated and casing an error when loading the dataset

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PR_kwDODunzps42O2-L

to_tf_dataset rewrite

closed

MEMBER

This PR rewrites almost all of `to_tf_dataset()`, which makes it kind of hard to list all the changes, but the most critical ones are: - Much better stability and no more dropping unexpected column names (Sorry @NielsRogge) - Doesn't clobber custom transforms on the data (Sorry @NielsRogge again) - Much better handling of the situation when the `collate_fn` adds columns that aren't in the dataset. - Better inference of shapes and data types - Lots of hacky special-casing code removed - Can return string columns (as `tf.String`) - Most arguments have default values, calling the method should be much simpler - ~~Can accept a `model` argument and only return columns that are valid inputs to that model~~ - Drops the `dummy_labels` argument - this was a workaround for Keras issues that have been resolved by changes in `transformers`. Also remove it from tests and the Overview notebook. I still have a couple of TODOs remaining and some testing to do, so don't merge yet, but it should be mostly ready for review at this point!

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I_kwDODunzps5Hw4Td

Timit_asr dataset cannot be previewed recently

closed

NONE

## Dataset viewer issue for '*timit_asr*' **Link:** *https://huggingface.co/datasets/timit_asr* Issue: The timit-asr dataset cannot be previewed recently. Am I the one who added this dataset ? Yes-No No

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PR_kwDODunzps42NL6F

Add code examples to API docs

closed

MEMBER

This PR adds code examples for functions related to the base Datasets class to highlight usage. Most of the examples use the `rotten_tomatoes` dataset since it is nice and small. Several things I would appreciate feedback on: - Do you think it is clearer to make every code example fully reproducible so when users copy the code they can actually run it and get an output? This seems quite repetitive - maybe even unnecessary - but it is definitely clearer. Personally, I think we might be able to get away with not including this since users probably want to try the function on their own dataset. For example: ```py >>> from datasets import load_dataset >>> ds = load_dataset("rotten_tomatoes", split="validation") >>> code example goes here ``` - Should we showcase a function with more than one parameter to highlight different use-cases (it's pretty basic right now, but I'd be happy to add more)? - For the `class_encode_column` function, let me know if there is a simpler dataset with fewer columns (currently using `winograd_wsc`) so it is easier for users to see what changed. - Where possible, I try to show the input before and the output after using a function like `flatten` for example. Do you think this is too much and just showing the usage (ie, `>>> ds.flatten()`) will be sufficient? Thanks :)

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PR_kwDODunzps42M1O5

Avoid rate limit in update hub repositories

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MEMBER

use http.extraHeader to avoid rate limit

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PR_kwDODunzps42M0dS

Fix exact match

closed

CONTRIBUTOR

Clarify docs and add clarifying example to the exact_match metric

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PR_kwDODunzps42MubF

Fix google bleu typos, examples

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PR_kwDODunzps42MfxX

Fix duplicate key in multi_news

closed

MEMBER

To merge after this job succeeded: https://github.com/huggingface/datasets/runs/6012207928

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PR_kwDODunzps42LtGO

Add Conceptual 12M

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