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selfishark

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hf-issues-dataset-with-comments

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I_kwDODunzps5IYbDt

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

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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'

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### 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

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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

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

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PR_kwDODunzps42pJQW

Add Nerval Metric

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This PR adds readme.md and ner_val.py to metrics. Nerval is a python package that helps evaluate NER models. It creates classification report and confusion matrix at entity level.

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PR_kwDODunzps42oVFE

Fix `convert_file_size_to_int` for kilobits and megabits

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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

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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

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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

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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

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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

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## 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`

open

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

open

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

open

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

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MEMBER

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I_kwDODunzps5IFm7v

Librispeech documentation, clarification on format

open

NONE

https://github.com/huggingface/datasets/blob/cd3ce34ab1604118351e1978d26402de57188901/datasets/librispeech_asr/librispeech_asr.py#L53 > Note that in order to limit the required storage for preparing this dataset, the audio > is stored in the .flac format and is not converted to a float32 array. To convert, the audio > file to a float32 array, please make use of the `.map()` function as follows: > > ```python > import soundfile as sf > def map_to_array(batch): > speech_array, _ = sf.read(batch["file"]) > batch["speech"] = speech_array > return batch > dataset = dataset.map(map_to_array, remove_columns=["file"]) > ``` Is this still true? In my case, `ds["train.100"]` returns: ``` Dataset({ features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'], num_rows: 28539 }) ``` and taking the first instance yields: ``` {'file': '374-180298-0000.flac', 'audio': {'path': '374-180298-0000.flac', 'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ..., -2.74658203e-04, -1.83105469e-04, -3.05175781e-05]), 'sampling_rate': 16000}, 'text': 'CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED', 'speaker_id': 374, 'chapter_id': 180298, 'id': '374-180298-0000'} ``` The `audio` `array` seems to be already decoded. So such convert/decode code as mentioned in the doc is wrong? But I wonder, is it actually stored as flac on disk, and the decoding is done on-the-fly? Or was it decoded already during the preparation and is stored as raw samples on disk? Note that I also used `datasets.load_dataset("librispeech_asr", "clean").save_to_disk(...)` and then `datasets.load_from_disk(...)` in this example. Does this change anything on how it is stored on disk? A small related question: Actually I would prefer to even store it as mp3 or ogg on disk. Is this easy to convert?

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PR_kwDODunzps42cB2j

[Librispeech] Add 'all' config

closed

MEMBER

Add `"all"` config to Librispeech

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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

FLEURS

open

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_kwDODunzps5IAUNQ

Add some iteration method on a dataset column (specific for inference)

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Currently, `dataset["audio"]` will load EVERY element in the dataset in RAM, which can be quite big for an audio dataset. Having an iterator (or sequence) type of object, would make inference with `transformers` 's `pipeline` easier to use and not so memory hungry. **Describe the solution you'd like** A clear and concise description of what you want to happen. For a non breaking change: ```python for audio in dataset.iterate("audio"): # {"array": np.array(...), "sampling_rate":...} ``` For a breaking change solution (not necessary), changing the type of `dataset["audio"]` to a sequence type so that ```python pipe = pipeline(model="...") for out in pipe(dataset["audio"]): # {"text":....} ``` could work **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. ```python def iterate(dataset, key): for item in dataset: yield dataset[key] for out in pipeline(iterate(dataset, "audio")): # {"array": ...} ``` This works but requires the helper function which feels slightly clunky. **Additional context** Add any other context about the feature request here. The context is actually to showcase better integration between `pipeline` and `datasets` in the Quicktour demo: https://github.com/huggingface/transformers/pull/16723/files @lhoestq

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I_kwDODunzps5IAKJe

Dataset librispeech_asr fails to load

open

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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PR_kwDODunzps42Yxca

Adding missing subsets to the `SemEval-2018 Task 1` dataset

open

NONE

This dataset for the [1st task of SemEval-2018](https://competitions.codalab.org/competitions/17751) competition was missing all subtasks except for subtask 5. I added another two subtasks (subtask 1 and 2), which are each comprised of 12 additional data subsets: for each language in En, Es, Ar, there are 4 datasets, broken down by emotions (anger, fear, joy, sadness). ## Remaining questions I wasn't able to find any documentation about how one should make PRs to modify datasets. Because of that, I just did my best to integrate the new data into the code, and tested locally that this worked. I'm sorry if I'm not respecting your contributing guidelines – if they are documented somewhere, I'd appreciate if you could send a pointer! Not sure how `dataset_infos.json` and `dummy` should be updated. My understanding is that they were automatically generated at the time of the original dataset creation?

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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

open

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

closed

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

closed

CONTRIBUTOR

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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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I_kwDODunzps5HvI1E

Optional Content Warning for Datasets

open

MEMBER

**Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. We now have hate speech datasets on the hub, like this one: https://huggingface.co/datasets/HannahRoseKirk/HatemojiBuild I'm wondering if there is an option to select a content warning message that appears before the dataset preview? Otherwise, people immediately see hate speech when clicking on this dataset. **Describe the solution you'd like** A clear and concise description of what you want to happen. Implementation of a content warning message that separates users from the dataset preview until they click out of the warning. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. Possibly just a way to remove the dataset preview completely? I think I like the content warning option better, though. **Additional context** Add any other context about the feature request here.

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PR_kwDODunzps42LtGO

Add Conceptual 12M

closed

MEMBER

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PR_kwDODunzps42LEhi

Add Visual Genome

closed

MEMBER

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I_kwDODunzps5Hsfiy

RGBA images not showing

open

CONTRIBUTOR

## Dataset viewer issue for ceyda/smithsonian_butterflies_transparent [**Link:** *link to the dataset viewer page*](https://huggingface.co/datasets/ceyda/smithsonian_butterflies_transparent)  Am I the one who added this dataset ? Yes 👉 More of a general issue of 'RGBA' png images not being supported (the dataset itself is just for the huggan sprint and not that important, consider it just an example)

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PR_kwDODunzps42Izmd

Add `TruthfulQA` dataset

open

CONTRIBUTOR

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PR_kwDODunzps42ITg3

Add AUC ROC Metric

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PR_kwDODunzps42H2Wf

Fix formatting in BLEU metric card

closed

CONTRIBUTOR

Fix #4148

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PR_kwDODunzps42HySw

Adding STSb-TR dataset

open

NONE

Semantic Textual Similarity benchmark Turkish (STSb-TR) dataset introduced in our paper [Semantic Similarity Based Evaluation for Abstractive News Summarization](https://aclanthology.org/2021.gem-1.3.pdf) added.

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PR_kwDODunzps42Hqam

Make HANS dataset streamable

closed

CONTRIBUTOR

Fix #4133

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PR_kwDODunzps42Hh14

Generate tasks.json taxonomy from `huggingface_hub`

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PR_kwDODunzps42HLA8

Adding Text-based NP Enrichment (TNE) dataset

closed

CONTRIBUTOR

Added the [TNE](https://github.com/yanaiela/TNE) dataset to the library

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I_kwDODunzps5HpZXD

ArrayND error in pyarrow 5

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MEMBER

As found in https://github.com/huggingface/datasets/pull/3903, The ArrayND features fail on pyarrow 5: ```python import pyarrow as pa from datasets import Array2D from datasets.table import cast_array_to_feature arr = pa.array([[[0]]]) feature_type = Array2D(shape=(1, 1), dtype="int64") cast_array_to_feature(arr, feature_type) ``` raises ```python --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-8-04610f9fa78c> in <module> ----> 1 cast_array_to_feature(pa.array([[[0]]]), Array2D(shape=(1, 1), dtype="int32")) ~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs) 1672 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1673 else: -> 1674 return func(array, *args, **kwargs) 1675 1676 return wrapper ~/Desktop/hf/datasets/src/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1806 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str) 1807 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1808 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) 1809 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") 1810 ~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs) 1672 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1673 else: -> 1674 return func(array, *args, **kwargs) 1675 1676 return wrapper ~/Desktop/hf/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_number_to_str) 1705 array = array.storage 1706 if isinstance(pa_type, pa.ExtensionType): -> 1707 return pa_type.wrap_array(array) 1708 elif pa.types.is_struct(array.type): 1709 if pa.types.is_struct(pa_type) and ( AttributeError: 'Array2DExtensionType' object has no attribute 'wrap_array' ``` The thing is that `cast_array_to_feature` is called when writing an Arrow file, so creating an Arrow dataset using any ArrayND type currently fails. `wrap_array` has been added in pyarrow 6, so we can either bump the required pyarrow version or fix this for pyarrow 5

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PR_kwDODunzps42GgLu

Add missing label for emotion description

closed

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I_kwDODunzps5HoFSC

Inconsistent splits generation for datasets without loading script (packaged dataset puts everything into a single split)

closed

CONTRIBUTOR

## Describe the bug Splits for dataset loaders without scripts are prepared inconsistently. I think it might be confusing for users. ## Steps to reproduce the bug * If you load a packaged datasets from Hub, it infers splits from directory structure / filenames (check out the data [here](https://huggingface.co/datasets/nateraw/test-imagefolder-dataset)): ```python ds = load_dataset("nateraw/test-imagefolder-dataset") print(ds) ### Output: DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 6 }) test: Dataset({ features: ['image', 'label'], num_rows: 4 }) }) ``` * If you do the same from locally stored data specifying only directory path you'll get the same: ```python ds = load_dataset("/path/to/local/data/test-imagefolder-dataset") print(ds) ### Output: DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 6 }) test: Dataset({ features: ['image', 'label'], num_rows: 4 }) }) ``` * However, if you explicitely specify package name (like `imagefolder`, `csv`, `json`), all the data is put into a single split: ```python ds = load_dataset("imagefolder", data_dir="/path/to/local/data/test-imagefolder-dataset") print(ds) ### Output: DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 10 }) }) ``` ## Expected results For `load_dataset("imagefolder", data_dir="/path/to/local/data/test-imagefolder-dataset")` I expect the same output as of the two first options.

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I_kwDODunzps5Hm76l

load_dataset for winoground returning decoding error

closed

CONTRIBUTOR

## Describe the bug I am trying to use datasets to load winoground and I'm getting a JSON decoding error. ## Steps to reproduce the bug ```python from datasets import load_dataset token = 'hf_XXXXX' # my HF access token datasets = load_dataset('facebook/winoground', use_auth_token=token) ``` ## Expected results I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing ```python import json with open('examples.jsonl', 'r') as f: examples = f.read().split('\n') # Thinking this would error if the JSON is not utf-8 encoded json_data = [json.loads(x) for x in examples] print(json_data[-1]) ``` and I see ```python {'caption_0': 'someone is overdoing it', 'caption_1': 'someone is doing it over', 'collapsed_tag': 'Relation', 'id': 399, 'image_0': 'ex_399_img_0', 'image_1': 'ex_399_img_1', 'num_main_preds': 1, 'secondary_tag': 'Morpheme-Level', 'tag': 'Scope, Preposition'} ``` so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text. ## Actual results During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity). ``` datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files) ... UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 7.0.0

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I_kwDODunzps5HmGNa

fix confusing bleu metric example

closed

NONE

**Is your feature request related to a problem? Please describe.** I would like to see the example in "Metric Card for BLEU" changed. The 0th element in the predictions list is not closed in square brackets, and the 1st list is missing a comma. The BLEU score are calculated correctly, but it is difficult to understand, so it would be helpful if you could correct this. ``` >> predictions = [ ... ["hello", "there", "general", "kenobi", # <- no closing square bracket. ... ["foo", "bar" "foobar"] # <- no comma between "bar" and "foobar" ... ] >>> references = [ ... [["hello", "there", "general", "kenobi"]], ... [["foo", "bar", "foobar"]] ... ] >>> bleu = datasets.load_metric("bleu") >>> results = bleu.compute(predictions=predictions, references=references) >>> print(results) {'bleu': 0.6370964381207871, ... ``` **Describe the solution you'd like** ``` >> predictions = [ ... ["hello", "there", "general", "kenobi", # <- no closing square bracket. ... ["foo", "bar" "foobar"] # <- no comma between "bar" and "foobar" ... ] # and >>> print(results) {'bleu':1.0, ... ```

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PR_kwDODunzps42CtPl

Adjust path to datasets tutorial in How-To

closed

MEMBER

The link in the How-To overview page to the Datasets tutorials is currently broken. This is just a small adjustment to make it match the format used in https://github.com/huggingface/datasets/blob/master/docs/source/tutorial.md. (Edit to add: The link in the PR deployment (https://moon-ci-docs.huggingface.co/docs/datasets/pr_4147/en/how_to) is also broken since it's actually hardcoded to `master` and not dynamic to the branch name, but other links seem to behave similarly.)

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I_kwDODunzps5Hidbt

SAMSum dataset viewer not working

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_kwDODunzps42A6Rt

Redirect TIMIT download from LDC

closed

MEMBER

LDC data is protected under US copyright laws and under various legal agreements between the Linguistic Data Consortium/the University of Pennsylvania and data providers which prohibit redistribution of that data by anyone other than LDC. Similarly, LDC's membership agreements, non-member user agreement and various corpus-specific license agreements specifically state that users cannot publish, retransmit, disclose, copy, reproduce or redistribute LDC databases to others outside their organizations. LDC explicitly asked us to remove the download script for the TIMIT dataset. In this PR I remove all means to download the dataset, and redirect users to download the data from https://catalog.ldc.upenn.edu/LDC93S1

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PR_kwDODunzps42ARmu

Fix splits in local packaged modules, local datasets without script and hub datasets without script

closed

CONTRIBUTOR

fixes #4150 I suggest to infer splits structure from files when `data_dir` is passed with `get_patterns_locally`, analogous to what's done in `LocalDatasetModuleFactoryWithoutScript` with `self.path`, instead of generating files with `data_dir/**` patterns and putting them all into a single default (train) split. I would also suggest to align `HubDatasetModuleFactoryWithoutScript` and `LocalDatasetModuleFactoryWithoutScript` with this logic (remove `data_files = os.path.join(data_dir, "**")`). It's not reflected in the current code now as I'd like to discuss it cause I might be unaware of some use cases. @lhoestq @mariosasko @albertvillanova WDYT?

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I_kwDODunzps5HhZmp

Unable to download `Wikepedia` 20220301.en version

closed

NONE

## Describe the bug Unable to download `Wikepedia` dataset, 20220301.en version ## Steps to reproduce the bug ```python !pip install apache_beam mwparserfromhell dataset_wikipedia = load_dataset("wikipedia", "20220301.en") ``` ## Actual results ``` ValueError: BuilderConfig 20220301.en not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Ubuntu - Python version: 3.6 - PyArrow version: 6.0.1

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I_kwDODunzps5Hg2o-

Add ObjectFolder 2.0 dataset

open

MEMBER

## Adding a Dataset - **Name:** ObjectFolder 2.0 - **Description:** ObjectFolder 2.0 is a dataset of 1,000 objects in the form of implicit representations. It contains 1,000 Object Files each containing the complete multisensory profile for an object instance. - **Paper:** [*link to the dataset paper if available*](https://arxiv.org/abs/2204.02389) - **Data:** https://github.com/rhgao/ObjectFolder Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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I_kwDODunzps5HgJwF

Why is the dataset not visible under the dataset preview section?

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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I_kwDODunzps5Hfs0E

Error loading arxiv data set

closed

NONE

## Describe the bug A clear and concise description of what the bug is. I met the error below when loading arxiv dataset via `nlp.load_dataset('scientific_papers', 'arxiv',)`. ``` Traceback (most recent call last): File "scripts/summarization.py", line 354, in <module> main(args) File "scripts/summarization.py", line 306, in main model.hf_datasets = nlp.load_dataset('scientific_papers', 'arxiv') File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/load.py", line 549, in load_dataset download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications, File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 463, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 522, in _download_and_prepare self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download'] ``` I then tried to ignore verification steps by `ignore_verifications=True` and there is another error. ``` Traceback (most recent call last): File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 537, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 810, in _prepare_split for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False): File "/opt/conda/envs/longformer/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/datasets/scientific_papers/9e4f2cfe3d8494e9f34a84ce49c3214605b4b52a3d8eb199104430d04c52cc12/scientific_papers.py", line 108, in _generate_examples with open(path, encoding="utf-8") as f: NotADirectoryError: [Errno 20] Not a directory: '/home/username/.cache/huggingface/datasets/downloads/c0deae7af7d9c87f25dfadf621f7126f708d7dcac6d353c7564883084a000076/arxiv-dataset/train.txt' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "scripts/summarization.py", line 354, in <module> main(args) File "scripts/summarization.py", line 306, in main model.hf_datasets = nlp.load_dataset('scientific_papers', 'arxiv', ignore_verifications=True) File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/load.py", line 549, in load_dataset download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications, File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 463, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 539, in _download_and_prepare raise OSError("Cannot find data file. " + (self.manual_download_instructions or "")) OSError: Cannot find data file. ``` ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:

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I_kwDODunzps5Hfg9u

Dataset viewer issue for Winoground

open

NONE

## Dataset viewer issue for 'Winoground' **Link:** [*link to the dataset viewer page*](https://huggingface.co/datasets/facebook/winoground/viewer/facebook--winoground/train) *short description of the issue* Getting 401, message='Unauthorized' The dataset is subject to authorization, but I can access the files from the interface, so I assume I'm granted to access it. I'd assume the permission somehow doesn't propagate to the dataset viewer tool. Am I the one who added this dataset ? No

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I_kwDODunzps5He71S

Incorrect Russian filenames encoding after extraction by datasets.DownloadManager.download_and_extract()

closed

NONE

## Dataset viewer issue for 'MalakhovIlya/RuREBus' **Link:** https://huggingface.co/datasets/MalakhovIlya/RuREBus **Description** Using os.walk(topdown=False) in DatasetBuilder causes following error: Status code: 400 Exception: TypeError Message: xwalk() got an unexpected keyword argument 'topdown' Couldn't find where "xwalk" come from. How can I fix this? Am I the one who added this dataset ? Yes

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PR_kwDODunzps419D6A

Add single dataset citations for TweetEval

closed

CONTRIBUTOR

This PR adds single data citations as per request of the original creators of the TweetEval dataset. This is a recent email from the creator: > Could I ask you a favor? Would you be able to add at the end of the README the citations of the single datasets as well? You can just copy our readme maybe? https://github.com/cardiffnlp/tweeteval#citing-tweeteval (just to be sure that the creator of the single datasets also get credits when tweeteval is used) Please let me know if this looks okay or if any changes are needed. Thanks, Gunjan

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PR_kwDODunzps416-Rn

Support streaming xtreme dataset for PAN-X config

closed

MEMBER

Support streaming xtreme dataset for PAN-X config.

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