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matteopilotto

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I_kwDODunzps4-QcAF

CI error: Another metric with the same name already exists in Keras 2.7.0

closed

MEMBER

## Describe the bug Release of TensorFlow 2.7.0 contains an incompatibility with Keras. See: - keras-team/keras#15579 This breaks our CI test suite: https://app.circleci.com/pipelines/github/huggingface/datasets/8493/workflows/055c7ae2-43bc-49b4-9f11-8fc71f35a25c/jobs/52363

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PR_kwDODunzps4uEZJe

[WIP] Allow user-defined hash functions via a registry

closed

CONTRIBUTOR

Inspired by the discussion on hashing in https://github.com/huggingface/datasets/issues/3178#issuecomment-959016329, @lhoestq suggested that it would be neat to allow users more control over the hashing process. Specifically, it would be great if users can specify specific hashing functions depending on the **class** of the object. As an example, we found in the linked topic that loaded spaCy models (`Language` objects) have different hashes when `dump`'d, but their byte representation with `Language.to_bytes()` _is_ deterministic. It would therefore be great if we could specify that for `Language` objects, the hasher should hash the objects `to_bytes()` return value instead of the object itself. This PR adds a new, but tiny, dependency to manage the registry, namely [`catalogue`](https://github.com/explosion/catalogue). Two files have been changed (apart from the added dependency in `setup.py`) and one file has been added. **utils.registry** (added) This file defines our custom Registry and builds a registry called "hashers". A Registry is basically dictionary from names (str) to functions. A function can be added to the registry by a decorator, e.g. ```python @hashers.register(spacy.Language) def hash_spacy_language(nlp): return Hasher.hash(nlp.to_bytes()) ``` You'll notice that `spacy.Language` is not a string, even though the registry holds a str->func mapping. To accomplish this with classes in a dynamic way, catalogue.Registry needed to be subclassed and modified as `DatasetsRegistry`. All methods that use a name as an input are now modified so that classes are deterministically converted in strings in such a way that we can later retrieve the actual class from the string (below). **utils.py_utils** (modified) Added two functions to deal with classes and their qualified names, that is, their full descriptive name including the module. On the one hand it allows us to retrieve a string from a given class, e.g. given `Module` class, return `torch.nn.Module` str. Conversly, a function is added to convert such a full qualified name into a class. For instance, given the string `torch.nn.Module`, return the `Module` class. These straightforward methods allow us to interchangeably use classes and strings without any needed user interaction - they can just register a class, and behind the scenes `DatasetsRegistry` converts these to deterministic strings. **fingerprint** (modified) Updated Hasher.hash so that if the object to hash is an instance of a class in the registry, the registered function is used to hash the object instead of the default behavior. To do so we iterate over the registry `hashers` and convert its keys (strings) into classes, and then we can use `isinstance`. ```python # Check if the current object is an instance that is # applicable to the user-defined hashers. If so, hash # with the user-defined function for full_module_name, func in hashers.get_all().items(): registered_cls = get_cls_from_qualname(full_module_name) if isinstance(value, registered_cls): return func(value) ``` **Putting it all together** To test this, you can try the following example with spaCy. First install spaCy from source and checkout a specific commit. ```shell git clone https://github.com/explosion/spaCy.git cd spaCy/ git checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf cd .. git clone https://github.com/BramVanroy/datasets.git cd datasets git checkout registry pip install -e . pip install ../spaCy spacy download en_core_web_sm ``` Now you can run the following script. By default it will use the custom hasher function for the Language object. You can enable the default behavior by commenting out `@hashers.register...`. ```python import spacy from datasets.fingerprint import Hasher from datasets.utils.registry import hashers # Register a function so that when the Hasher encounters a spacy.Language object # it uses this custom function to hash instead of the default @hashers.register(spacy.Language) def hash_spacy_language(nlp): return Hasher.hash(nlp.to_bytes()) def main(): print(hashers.get_all()) nlp = spacy.load("en_core_web_sm") dump1 = Hasher.hash(nlp) nlp = spacy.load("en_core_web_sm") dump2 = Hasher.hash(nlp) print(dump1) # succeeds when using the registered custom function # fails if using the default assert dump1 == dump2 if __name__ == '__main__': main() ``` To do ==== - The above is just a proof-of-concept. I am open to changes/suggestions - Tests still need to be written - We should consider whether we can make `DatasetsRegistry` very restrictive and ONLY allowing classes. That would make testing easier - otherwise we also need to test for other sorts of objects. - Maybe the `hashers` definition is better suited in `fingerprint`? - Documentation/examples need to be updated - Not sure why the logger is not working in `hash()` - `get_cls_from_qualname` might need a fail-safe: is it possible for a full_qualname to not have a module, and if so how do we deal with that?

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PR_kwDODunzps4uEAlw

Add Multidoc2dial Dataset

closed

CONTRIBUTOR

This PR adds the MultiDoc2Dial dataset introduced in this [paper](https://arxiv.org/pdf/2109.12595v1.pdf )

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I_kwDODunzps4-NbWr

FileNotFoundError for TupleIE dataste

closed

NONE

Hi, `dataset = datasets.load_dataset('tuple_ie', 'all')` returns a FileNotFound error. Is the data not available? Many thanks.

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PR_kwDODunzps4uCNoT

Updated: DaNE - updated URL for download

closed

CONTRIBUTOR

It seems that DaNLP has updated their download URLs and it therefore also needs to be updated in here...

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I_kwDODunzps4-Li1c

Add mIoU metric

open

NONE

**Is your feature request related to a problem? Please describe.** Recently, some semantic segmentation models were added to HuggingFace Transformers, including [SegFormer](https://huggingface.co/transformers/model_doc/segformer.html) and [BEiT](https://huggingface.co/transformers/model_doc/beit.html). Semantic segmentation (which is the task of labeling every pixel of an image with a corresponding class) is typically evaluated using the Mean Intersection and Union (mIoU). Together with the upcoming Image Feature, adding this metric could be very handy when creating example scripts to fine-tune any Transformer-based model on a semantic segmentation dataset. An implementation can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/504965184c3e6bc9ec43af54237129ef21981a5f/mmseg/core/evaluation/metrics.py#L132) for instance.

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I_kwDODunzps4-Lhu2

Add GSM8K dataset

open

NONE

## Adding a Dataset - **Name:** GSM8K (short for Grade School Math 8k) - **Description:** GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. - **Paper:** https://openai.com/blog/grade-school-math/ - **Data:** https://github.com/openai/grade-school-math - **Motivation:** The dataset is useful to investigate the reasoning abilities of large Transformer models, such as GPT-3. 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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PR_kwDODunzps4uAZLu

Catch token invalid error in CI

closed

MEMBER

The staging back end sometimes returns invalid token errors when trying to delete a repo. I modified the fixture in the test that uses staging to ignore this error

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PR_kwDODunzps4uAVzQ

Bump huggingface_hub

closed

MEMBER

huggingface_hub just released its first minor version, so we need to update the dependency It was supposed to be part of 1.15.0 but I'm adding it for 1.15.1

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PR_kwDODunzps4t_5G8

Add Multi-Lingual LibriSpeech

closed

MEMBER

Add https://www.openslr.org/94/

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PR_kwDODunzps4t_cry

Fix optimized encoding for arrays

closed

MEMBER

Hi ! #3124 introduced a regression that made the benchmarks CI fail because of a bad array comparison when checking the first encoded element. This PR fixes this by making sure that encoding is applied on all sequence types except lists. cc @eladsegal fyi (no big deal)

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PR_kwDODunzps4t-bxy

QOL improvements: auto-flatten_indices and desc in map calls

closed

CONTRIBUTOR

This PR: * automatically calls `flatten_indices` where needed: in `unique` and `save_to_disk` to avoid saving the indices file * adds descriptions to the map calls Fix #3040

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PR_kwDODunzps4t-ZR0

More robust `None` handling

closed

CONTRIBUTOR

PyArrow has explicit support for `null` values, so it makes sense to support Nones on our side as well. [Colab Notebook with examples](https://colab.research.google.com/drive/1zcK8BnZYnRe3Ao2271u1T19ag9zLEiy3?usp=sharing) Changes: * allow None for the features types with special encoding (`ClassLabel, TranslationVariableLanguages, Value, _ArrayXD`) * handle None in `class_encode_column` (also there is an option to stringify Nones and treat them as a class) * support None sorting in `sort` (use pandas for that) * handle None in align_labels_with_mapping * support for None in ArrayXD (converts `None` to `np.nan` to align the behavior with PyArrow) * support for None in the Audio/Image feature * allow promotion when concatenating tables (`pa.concat_tables(table_list, promote=True)`) and `null` row/~~column~~ broadcasting similar to pandas Additional notes: * use `null` instead of `none` for function arguments for consistency with existing `disable_nullable` * fixes a bug with the `update_metadata_with_features` call in `Dataset.rename_columns` * had to update some tests, let me know if that's ok TODO: - [x] check how the Audio features behaves with Nones - [x] Better None handling in `concatenate_datasets`/`add_item` - [x] Fix formatting with Nones - [x] Add Colab with examples - [x] Tests TODOs for subsequent PRs: - Mention None handling in the docs - Add `drop_null`/`fill_null` to `Dataset`/`DatasetDict` Fix #3181 #3253

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PR_kwDODunzps4t91Eg

Update link to Datasets Tagging app in Spaces

closed

MEMBER

Fix #3193.

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I_kwDODunzps4-Gzet

Update link to datasets-tagging app

closed

MEMBER

Once datasets-tagging has been transferred to Spaces: - huggingface/datasets-tagging#22 We should update the link in Datasets.

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I_kwDODunzps4-ERm2

Multiprocessing filter/map (tests) not working on Windows

open

CONTRIBUTOR

While running the tests, I found that the multiprocessing examples fail on Windows, or rather they do not complete: they cause a deadlock. I haven't dug deep into it, but they do not seem to work as-is. I currently have no time to tests this in detail but at least the tests seem not to run correctly (deadlocking). ## Steps to reproduce the bug ```shell pytest tests/test_arrow_dataset.py -k "test_filter_multiprocessing" pytest tests/test_arrow_dataset.py -k "test_map_multiprocessing" ``` ## Expected results The functionality to work on all platforms. ## Actual results Deadlock. ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.2, also tested with 3.7.9 - PyArrow version: 4.0.1

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I_kwDODunzps4-D9WX

Dataset viewer issue for '*compguesswhat*'

open

NONE

## Dataset viewer issue for '*compguesswhat*' **Link:** https://huggingface.co/datasets/compguesswhat File not found Am I the one who added this dataset ? No

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I_kwDODunzps4-Dr5f

combination of shuffle and filter results in a bug

closed

CONTRIBUTOR

## Describe the bug Hi, I would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq. Thanks. Best regards Rabeeh ## Steps to reproduce the bug ```python import numpy as np import datasets datasets = datasets.load_dataset('super_glue', 'rte', script_version="master") shuffled_data = datasets["train"].shuffle(seed=42) for label in range(2): print("label ", label) data = shuffled_data.filter(lambda example: int(example['label']) == label) print("length ", len(data), np.unique(data['label'])) ``` ## Expected results Filtering per label, should only return the data with that specific label. ## Actual results As you can see, filtered data per label, has still two labels of [0, 1] ``` label 0 length 1249 [0 1] label 1 length 1241 [0 1] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: linux - Python version: 3.7.11 - PyArrow version: 5.0.0

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I_kwDODunzps4-DRX6

conll2003 incorrect label explanation

closed

CONTRIBUTOR

In the [conll2003](https://huggingface.co/datasets/conll2003#data-fields) README, the labels are described as follows > - `id`: a `string` feature. > - `tokens`: a `list` of `string` features. > - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4). > - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4). > - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8). First of all, it would be great if we can get a list of ALL possible pos_tags. Second, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0. EDIT: not really a bug, sorry for mistagging.

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I_kwDODunzps4-DBro

conll2002 issues

closed

CONTRIBUTOR

**Link:** https://huggingface.co/datasets/conll2002 The dataset viewer throws a server error when trying to preview the dataset. ``` Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet ``` In addition, the "point of contact" has encoding issues and does not work when clicked. Am I the one who added this dataset ? No, @lhoestq did

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PR_kwDODunzps4t44Ab

Add ChrF(++) (as implemented in sacrebleu)

closed

CONTRIBUTOR

Similar to my [PR for TER](https://github.com/huggingface/datasets/pull/3153), it feels only right to also include ChrF and friends. These are present in Sacrebleu and are therefore very similar to implement as TER and sacrebleu. I tested the implementation with sacrebleu's tests to verify. You can try this below for yourself ```python import datasets EPSILON = 1e-4 chrf = datasets.load_metric(r"path\to\datasets\metrics\chrf") test_cases = [ (["abcdefg"], ["hijklmnop"], 0.0), (["a"], ["b"], 0.0), ([""], ["b"], 0.0), ([""], ["ref"], 0.0), ([""], ["reference"], 0.0), (["aa"], ["ab"], 8.3333), (["a", "b"], ["a", "c"], 8.3333), (["a"], ["a"], 16.6667), (["a b c"], ["a b c"], 50.0), (["a b c"], ["abc"], 50.0), ([" risk assessment must be made of those who are qualified and expertise in the sector - these are the scientists ."], ["risk assessment has to be undertaken by those who are qualified and expert in that area - that is the scientists ."], 63.361730), ([" Die Beziehung zwischen Obama und Netanjahu ist nicht gerade freundlich. "], ["Das Verhältnis zwischen Obama und Netanyahu ist nicht gerade freundschaftlich."], 64.1302698), (["Niemand hat die Absicht, eine Mauer zu errichten"], ["Niemand hat die Absicht, eine Mauer zu errichten"], 100.0), ] for hyp, ref, score in test_cases: # Note the reference transformation which is different from scarebleu's input format results = chrf.compute(predictions=hyp, references=[[r] for r in ref], char_order=6, word_order=0, beta=3, eps_smoothing=True) if abs(score - results["score"]) > EPSILON: print(f"expected {score}, got {results['score']} for {hyp} - {ref}") test_cases_effective_order = [ (["a"], ["a"], 100.0), ([""], ["reference"], 0.0), (["a b c"], ["a b c"], 100.0), (["a b c"], ["abc"], 100.0), ([""], ["c"], 0.0), (["a", "b"], ["a", "c"], 50.0), (["aa"], ["ab"], 25.0), ] for hyp, ref, score in test_cases_effective_order: # Note the reference transformation which is different from scarebleu's input format results = chrf.compute(predictions=hyp, references=[[r] for r in ref], char_order=6, word_order=0, beta=3, eps_smoothing=False) if abs(score - results["score"]) > EPSILON: print(f"expected {score}, got {results['score']} for {hyp} - {ref}") test_cases_keep_whitespace = [ ( ["Die Beziehung zwischen Obama und Netanjahu ist nicht gerade freundlich."], ["Das Verhältnis zwischen Obama und Netanyahu ist nicht gerade freundschaftlich."], 67.3481606, ), ( ["risk assessment must be made of those who are qualified and expertise in the sector - these are the scientists ."], ["risk assessment has to be undertaken by those who are qualified and expert in that area - that is the scientists ."], 65.2414427, ), ] for hyp, ref, score in test_cases_keep_whitespace: # Note the reference transformation which is different from scarebleu's input format results = chrf.compute(predictions=hyp, references=[[r] for r in ref], char_order=6, word_order=0, beta=3, whitespace=True) if abs(score - results["score"]) > EPSILON: print(f"expected {score}, got {results['score']} for {hyp} - {ref}") predictions = ["The relationship between Obama and Netanyahu is not exactly friendly."] references = [["The ties between Obama and Netanyahu are not particularly friendly."]] print(chrf.compute(predictions=predictions, references=references)) ```

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I_kwDODunzps4-Asb1

Dataset viewer for nli_tr

open

CONTRIBUTOR

## Dataset viewer issue for '*nli_tr*' **Link:** https://huggingface.co/datasets/nli_tr Hello, Thank you for the new dataset preview feature that will help the users to view the datasets online. We just noticed that the dataset viewer widget in the `nli_tr` dataset shows the error below. The error must be due to a temporary problem that may have blocked access to the dataset through the dataset viewer. But the dataset is currently accessible through the link in the error message. May we kindly ask if it would be possible to rerun the job so that it can access the dataset for the dataset viewer function? Thank you. Emrah ------------------------------------------ Server Error Status code: 404 Exception: FileNotFoundError Message: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_train.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip ------------------------------------------ Am I the one who added this dataset ? Yes

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I_kwDODunzps4-AZh5

7z dataset preview not implemented?

open

NONE

## Dataset viewer issue for dataset 'samsum' **Link:** https://huggingface.co/datasets/samsum Server Error Status code: 400 Exception: NotImplementedError Message: Extraction protocol '7z' for file at 'https://arxiv.org/src/1911.12237v2/anc/corpus.7z' is not implemented yet

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PR_kwDODunzps4t4J61

RONEC v2

closed

CONTRIBUTOR

Hi, as we've recently finished with the new RONEC (Romanian Named Entity Corpus), we'd like to update the dataset here as well. It's actually essential as links to V1 are no longer valid. In reality we'd like to replace completely v1, as v2 is a full re-annotation of v1 with additional data (up to 2x size vs v1). I've run the make style and all the dummy and real data test, and they passed. I hope it's okay to merge the new RONEC v2 in the datasets. Thanks!

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PR_kwDODunzps4t3Dag

Add missing docstring to DownloadConfig

closed

CONTRIBUTOR

Document the `use_etag` and `num_proc` attributes in `DownloadConig`.

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PR_kwDODunzps4t2-9J

Don't memoize strings when hashing since two identical strings may have different python ids

closed

MEMBER

When hashing an object that has several times the same string, the hashing could return a different hash if the identical strings share the same python `id()` or not. Here is an example code that shows how the issue can affect the caching: ```python import json import pyarrow as pa from datasets.features import Features from datasets.fingerprint import Hasher schema = pa.schema([pa.field("some_string", pa.string()), pa.field("another_string", pa.string())]) features_from_schema = Features.from_arrow_schema(schema) Hasher.hash(features_from_schema) # dffa9dca9a73fd8c features_dict = json.loads('{"some_string": {"dtype": "string", "id": null, "_type": "Value"}, "another_string": {"dtype": "string", "id": null, "_type": "Value"}}') features_from_json = Features.from_dict(features_dict) Hasher.hash(features_from_json) # 3812e76b15e6420e features_from_schema == features_from_json # True ``` This is because in `features_dict`, some strings like "dtype" are repeated but don't share the same id, contrary to the ones in `features_from_schema`. I fixed that by disabling memoization for strings. This could be optimized in the future by implementing a smarter memoization with a special handling for strings.

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I_kwDODunzps49-Eox

`None` converted to `"None"` when loading a dataset

closed

CONTRIBUTOR

## Describe the bug When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`. ## Steps to reproduce the bug ```python from datasets import load_dataset qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists") print(qasper[60]["full_text"]["section_name"]) ``` When installing version 1.1.40, the output is `[None, 'Introduction', 'Benchmark Datasets', ...]` When installing from the master branch, the output is `['None', 'Introduction', 'Benchmark Datasets', ...]` Notice how the first element was changed from `NoneType` to `str`. ## Expected results `None` should stay as is. ## Actual results `None` is converted to a string. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: master - Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 4.0.1

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PR_kwDODunzps4t2qQn

fix label mapping

closed

MEMBER

Fixing label mapping for hlgd. 0 correponds to same event and 1 corresponds to different event <img width="642" alt="Capture d’écran 2021-10-29 à 10 39 58 AM" src="https://user-images.githubusercontent.com/16107619/139454810-1f225e3d-ad48-44a8-b8b1-9205c9533839.png"> <img width="638" alt="Capture d’écran 2021-10-29 à 10 40 09 AM" src="https://user-images.githubusercontent.com/16107619/139454813-93066a3c-7d33-4f56-b133-2f1a7661e438.png"> nt

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I_kwDODunzps499pvY

Cannot load dataset when the config name is "special"

closed

CONTRIBUTOR

## Describe the bug After https://github.com/huggingface/datasets/pull/3159, we can get the config name of "Check/region_1", which is "Check___region_1". But now we cannot load the dataset (not sure it's related to the above PR though). It's the case for all the similar datasets, listed in https://github.com/huggingface/datasets-preview-backend/issues/78 ## Steps to reproduce the bug ```python >>> from datasets import get_dataset_config_names >>> get_dataset_config_names("Check/region_1") ['Check___region_1'] >>> load_dataset("Check/region_1") Using custom data configuration Check___region_1-d2b3bc48f11c9be2 Downloading and preparing dataset json/Check___region_1 to /home/slesage/.cache/huggingface/datasets/json/Check___region_1-d2b3bc48f11c9be2/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426... 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 4443.12it/s] 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1277.19it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split writer.write_table(table) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in <listcomp> pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1249, in pyarrow.lib.Table.__getitem__ File "pyarrow/table.pxi", line 1825, in pyarrow.lib.Table.column File "pyarrow/table.pxi", line 1800, in pyarrow.lib.Table._ensure_integer_index KeyError: 'Field "builder_name" does not exist in table schema' ``` Loading in streaming mode also returns something strange: ```python >>> list(load_dataset("Check/region_1", streaming=True, split="train")) Using custom data configuration Check___region_1-d2b3bc48f11c9be2 [{'builder_name': None, 'citation': '', 'config_name': None, 'dataset_size': None, 'description': '', 'download_checksums': None, 'download_size': None, 'features': {'speech': {'feature': {'dtype': 'float64', 'id': None, '_type': 'Value'}, 'length': -1, 'id': None, '_type': 'Sequence'}, 'sampling_rate': {'dtype': 'int64', 'id': None, '_type': 'Value'}, 'label': {'dtype': 'string', 'id': None, '_type': 'Value'}}, 'homepage': '', 'license': '', 'post_processed': None, 'post_processing_size': None, 'size_in_bytes': None, 'splits': None, 'supervised_keys': None, 'task_templates': None, 'version': None}, {'_data_files': [{'filename': 'dataset.arrow'}], '_fingerprint': 'f1702bb5533c549c', '_format_columns': ['speech', 'sampling_rate', 'label'], '_format_kwargs': {}, '_format_type': None, '_indexes': {}, '_indices_data_files': None, '_output_all_columns': False, '_split': None}] ``` ## Expected results The dataset should be loaded ## Actual results An error occurs ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: Linux-5.11.0-1020-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 4.0.1

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I_kwDODunzps499huE

"Property couldn't be hashed properly" even though fully picklable

open

CONTRIBUTOR

## Describe the bug I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable. ## Steps to reproduce the bug Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below). ```python import pickle from datasets import load_dataset import spacy class Processor: def __init__(self): self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"]) @staticmethod def collate(batch): return [d["en"] for d in batch] def parse(self, batch): batch = batch["translation"] return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]} def process(self): ds = load_dataset("wmt16", "de-en", split="train[:10%]") ds = ds.map(self.parse, batched=True, num_proc=6) if __name__ == '__main__': pr = Processor() # succeeds with open("temp.pkl", "wb") as f: pickle.dump(pr, f) print("Successfully pickled!") pr.process() ``` --- Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object. ```python from datasets.fingerprint import Hasher import pickle from datasets import load_dataset import spacy class Processor: def __init__(self): self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"]) @staticmethod def collate(batch): return [d["en"] for d in batch] def parse(self, batch): batch = batch["translation"] return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]} def process(self): ds = load_dataset("wmt16", "de-en", split="train[:10]") return ds.map(self.parse, batched=True) if __name__ == '__main__': pr = Processor() # succeeds with open("temp.pkl", "wb") as f: pickle.dump(pr, f) print("Successfully pickled class instance!") # succeeds with open("temp.pkl", "wb") as f: pickle.dump(pr.nlp, f) print("Successfully pickled nlp!") # fails print(Hasher.hash(pr.nlp)) pr.process() ``` ## Expected results This to be picklable, working (fingerprinted), and no warning. ## Actual results In the first snippet, I get this warning Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. In the second, I get this traceback which directs to the `Hasher.hash` line. ``` Traceback (most recent call last): File " \Python\Python36\lib\pickle.py", line 918, in save_global obj2, parent = _getattribute(module, name) File " \Python\Python36\lib\pickle.py", line 266, in _getattribute .format(name, obj)) AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0> During handling of the above exception, another exception occurred: Traceback (most recent call last): File " scratch_4.py", line 40, in <module> print(Hasher.hash(pr.nlp)) File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash return cls.hash_default(value) File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default return cls.hash_bytes(dumps(value)) File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps dump(obj, file) File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump Pickler(file, recurse=True).dump(obj) File " \lib\site-packages\dill\_dill.py", line 498, in dump StockPickler.dump(self, obj) File " \Python\Python36\lib\pickle.py", line 409, in dump self.save(obj) File " \Python\Python36\lib\pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File " \Python\Python36\lib\pickle.py", line 634, in save_reduce save(state) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict StockPickler.save_dict(pickler, obj) File " \Python\Python36\lib\pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems save(v) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 781, in save_list self._batch_appends(obj) File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends save(x) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 736, in save_tuple save(element) File " \Python\Python36\lib\pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File " \Python\Python36\lib\pickle.py", line 634, in save_reduce save(state) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 736, in save_tuple save(element) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict StockPickler.save_dict(pickler, obj) File " \Python\Python36\lib\pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems save(v) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0 pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj) File " \Python\Python36\lib\pickle.py", line 610, in save_reduce save(args) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 736, in save_tuple save(element) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function obj=obj, File " \Python\Python36\lib\pickle.py", line 610, in save_reduce save(args) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 751, in save_tuple save(element) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict StockPickler.save_dict(pickler, obj) File " \Python\Python36\lib\pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems save(v) File " \Python\Python36\lib\pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File " \Python\Python36\lib\pickle.py", line 605, in save_reduce save(cls) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 1439, in save_type StockPickler.save_global(pickler, obj, name=name) File " \Python\Python36\lib\pickle.py", line 922, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes ``` ## Environment info Tried on both Linux and Windows - `datasets` version: 1.14.0 - Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12 - PyArrow version: 6.0.0

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I_kwDODunzps499VMk

More control over TQDM when using map/filter with multiple processes

open

CONTRIBUTOR

It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets. ```python dataset.map(lambda examples: tokenize(examples["text"]), batched=True, num_proc=6) ``` The above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down. ``` #0: 0%| | 0/13 [00:00<?, ?ba/s] #1: 0%| | 0/13 [00:00<?, ?ba/s] #2: 0%| | 0/13 [00:00<?, ?ba/s] #3: 0%| | 0/13 [00:00<?, ?ba/s] #4: 0%| | 0/13 [00:00<?, ?ba/s] #5: 0%| | 0/13 [00:00<?, ?ba/s] #0: 8%| | 1/13 [00:00<?, ?ba/s] #1: 8%| | 1/13 [00:00<?, ?ba/s] ... ``` Instead, it would be welcome if we had the option to only show the progress of rank 0.

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PR_kwDODunzps4t00xS

OpenSLR dataset: update generate_examples to properly extract data for SLR83

closed

CONTRIBUTOR

Fixed #3168. The SLR38 indices are CSV files and there wasn't any code in openslr.py to process these files properly. The end result was an empty table. I've added code to properly process these CSV files.

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PR_kwDODunzps4t0bXw

Add docs for `to_tf_dataset`

closed

MEMBER

This PR adds some documentation for new features released in v1.13.0, with the main addition being `to_tf_dataset`: - Show how to use `to_tf_dataset` in the tutorial, and move `set_format(type='tensorflow'...)` to the Process section (let me know if I'm missing anything @Rocketknight1 😅). - Add an example for loading dataset from multiple zipped CSV files to the Load section. - Add an example for removing columns for an `IterableDataset`. - Add graphic for visualizing streaming.

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PR_kwDODunzps4tyuQ_

Asserts replaced by exceptions (huggingface#3171)

closed

CONTRIBUTOR

I've replaced two asserts with their proper exceptions following the guidelines described in issue #3171 by following the contributing guidelines. PS: This is one of my first PRs, hoping I don't break anything!

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PR_kwDODunzps4typcA

Fix issue with filelock filename being too long on encrypted filesystems

closed

CONTRIBUTOR

Infer max filename length in filelock on Unix-like systems. Should fix problems on encrypted filesystems such as eCryptfs. Fix #2924 cc: @lmmx

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I_kwDODunzps494_zj

`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`

closed

NONE

## Describe the bug I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`. I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. ```python class Dataset() ... def __del__(self): if hasattr(self, "_data"): _ = self._data # <- ugly trick that allows avoiding the issue. del self._data if hasattr(self, "_indices"): del self._indices ``` ## Steps to reproduce the bug ```python # Unfortunately I couldn't isolate the bug. ``` ## Expected results Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback. ## Actual results ``` Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s] Traceback (most recent call last): File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__ del self._data File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler sys.exit(signum) SystemExit: 15 ``` ## Environment info Tested on 2 environments: **Environment 1.** - `datasets` version: 1.14.0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.8 - PyArrow version: 6.0.0 **Environment 2.** - `datasets` version: 1.14.0 - Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.7 - PyArrow version: 6.0.0

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I_kwDODunzps492nk7

Raise exceptions instead of using assertions for control flow

closed

CONTRIBUTOR

Motivated by https://github.com/huggingface/transformers/issues/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks. Currently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR.

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PR_kwDODunzps4twDUo

Preserve ordering in `zip_dict`

closed

CONTRIBUTOR

Replace `set` with the `unique_values` generator in `zip_dict`. This PR fixes the problem with the different ordering of the example keys across different Python sessions caused by the `zip_dict` call in `Features.decode_example`.

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PR_kwDODunzps4ttYmZ

Configurable max filename length in file locks

closed

NONE

Resolve #2924 (https://github.com/huggingface/datasets/issues/2924#issuecomment-952330956) wherein the assumption of file lock maximum filename length to be 255 raises an OSError on encrypted drives (ecryptFS on Linux uses part of the lower filename, reducing the maximum filename size to 143). Allowing this limit to be set in the config module allows this to be modified by users. Will not affect Windows users, as their class passes 255 on init explicitly. Reproduced with the following example ([the first few lines of a script from Lightning Flash](https://lightning-flash.readthedocs.io/en/latest/reference/speech_recognition.html), fine-tuning a HF model): ```py import torch import flash from flash.audio import SpeechRecognition, SpeechRecognitionData from flash.core.data.utils import download_data # 1. Create the DataModule download_data("https://pl-flash-data.s3.amazonaws.com/timit_data.zip", "./data") datamodule = SpeechRecognitionData.from_json( input_fields="file", target_fields="text", train_file="data/timit/train.json", test_file="data/timit/test.json", ) ``` Which gave this traceback: ```py Traceback (most recent call last): File "lf_ft.py", line 10, in <module> datamodule = SpeechRecognitionData.from_json( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 1005, in from_json return cls.from_data_source( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 571, in from_data_source train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 307, in to_datasets train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 344, in generate_dataset data = load_data(data, mock_dataset) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py", line 103, in load_data dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)}) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py", line 1599, in load_dataset builder_instance = load_dataset_builder( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py", line 1457, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/builder.py", line 285, in __init__ with FileLock(lock_path): File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 323, in __enter__ self.acquire() File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 272, in acquire self._acquire() File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 403, in _acquire fd = os.open(self._lock_file, open_mode) OSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock' ``` Note the filename is 145 chars long: ``` >>> len("_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock") 145 ``` After installing datasets as an editable local package and modifying the script I was running to first include: ```py import datasets datasets.config.MAX_DATASET_CONFIG_ID_READABLE_LENGTH = 143 ``` The error goes away. If I instead deliberately set the value incorrectly as 144, the OSError returns: ``` Traceback (most recent call last): File "lf_ft.py", line 14, in <module> datamodule = SpeechRecognitionData.from_json( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 1005, in from_json return cls.from_data_source( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 571, in from_data_source train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 307, in to_datasets train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 344, in generate_dataset data = load_data(data, mock_dataset) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py", line 103, in load_data dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)}) File "/home/louis/dev/hf_datasets/src/datasets/load.py", line 1605, in load_dataset builder_instance = load_dataset_builder( File "/home/louis/dev/hf_datasets/src/datasets/load.py", line 1463, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "/home/louis/dev/hf_datasets/src/datasets/builder.py", line 285, in __init__ with FileLock(lock_path): File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 326, in __enter__ self.acquire() File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 275, in acquire self._acquire() File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 406, in _acquire fd = os.open(self._lock_file, open_mode) OSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-32c812b5c1272d64_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279...-5794079643713042223.lock' ```

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I_kwDODunzps49ymDv

OpenSLR/83 is empty

closed

CONTRIBUTOR

## Describe the bug As the summary says, openslr / SLR83 / train is empty. The dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**. ## Steps to reproduce the bug ```python import datasets datasets.load_dataset('openslr', 'SLR83') ``` ## Expected results ``` DatasetDict({ train: Dataset({ features: ['path', 'audio', 'sentence'], num_rows: 17877 }) }) ``` ## Actual results ``` DatasetDict({ train: Dataset({ features: ['path', 'audio', 'sentence'], num_rows: 0 }) }) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.14.1.dev0 (master HEAD) - Platform: Ubuntu 20.04 - Python version: 3.7.10 - PyArrow version: 3.0.0

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I_kwDODunzps49x5Eg

bookcorpusopen no longer works

closed

CONTRIBUTOR

## Describe the bug When using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...). This did not happen with 1.4.1. I tried also `rm -rf ~/.cache/huggingface` but did not help. Changing python version between 3.7, 3.8 and 3.9 did not help too. ## Steps to reproduce the bug ```python import datasets d = datasets.load_dataset('bookcorpusopen') ``` ## 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: 1.14.0 - Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27 - Python version: 3.9.7 - PyArrow version: 4.0.1

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PR_kwDODunzps4tsVQJ

Deprecate prepare_module

closed

MEMBER

In version 1.13, `prepare_module` was deprecated. This PR adds a deprecation warning and removes it from all the library, using `dataset_module_factory` or `metric_module_factory` instead. Fix #3165.

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I_kwDODunzps49xvTm

Deprecate prepare_module

closed

MEMBER

In version 1.13, `prepare_module` was deprecated. Add deprecation warning and remove its usage from all the library.

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I_kwDODunzps49uvXu

Add raw data files to the Hub with GitHub LFS for canonical dataset

closed

NONE

I'm interested in sharing the CaseHOLD dataset (https://arxiv.org/abs/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset](https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!

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PR_kwDODunzps4tpI44

Add Image feature

closed

CONTRIBUTOR

Adds the Image feature. This feature is heavily inspired by the recently added Audio feature (#2324). Currently, this PR is pretty simple. Some considerations that need further discussion: * I've decided to use `Pillow`/`PIL` as the image decoding library. Another candidate I considered is `torchvision`, mostly because of its `accimage` backend, which should be faster for loading `jpeg` images than `Pillow`. However, `torchvision`'s io module only supports png and jpeg images, has `torch` as a hard dependency, and requires magic to work with image bytes ( `torch.ByteTensor(torch.ByteStorage.from_buffer(image_bytes)))`). * Currently, I'm converting `PIL`'s `Image` type to `np.ndarray`. The vision models in Transformers such as ViT prefer the raw `Image` type and not the decoded tensors, so there is a small overhead due to [this conversion](https://github.com/huggingface/transformers/blob/3e8761ab8077e3bb243fe2f78b2a682bd2257cf1/src/transformers/image_utils.py#L62-L73). IMO this is justified to keep this part aligned with the Audio feature, which also returns `np.ndarray`. What do you think? * Still have to work on the channel decoding logic: * PyTorch prefers the channel-first ordering (C, H, W); TF and Flax the channel-last ordering (H, W, C). One cool feature would be adjusting the channel order based on the selected formatter (`torch`, `tf`, `jax`). * By default, `Image.open` returns images of shape (H, W, C). However, ViT's feature extractor expects the format (C, H, W) if the image is passed as an array (explained [here](https://huggingface.co/transformers/model_doc/vit.html#transformers.ViTFeatureExtractor.__call__)), so I'm more inclined to the format (C, H, W). Which one do you prefer, (C, H, W) or (H, W, C)? * Are there any options you'd like to see? (the user could change those via `cast_column`, such as `sampling_rate` in the Audio feature) TODOs: * [x] tests * in subsequent PRs: * docs - a section in the docs, which gives some additional info on the Image and Audio feature and compares them to `ArrayND` * streaming (waiting for #3129 and #3133 to get merged first) * update the image tasks and the datasets to use the new feature * Image/Audio formatting [Colab Notebook](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c?usp=sharing) where you can play with this feature. I'm also adding a link to the [Image](https://github.com/tensorflow/datasets/blob/7ac7d506488d46038a5854961d068926b3f93c7f/tensorflow_datasets/core/features/image_feature.py#L155) feature in TFDS because one of our goals is to parse TFDS scripts eventually, so our Image feature has to (at least) support all the formats theirs does. Feel free to cc anyone who might be interested. P.S. Please ignore the changes in the `datasets/**/*.py` files 😄.

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I_kwDODunzps49t-X4

`datasets-cli test` should work with datasets without scripts

open

NONE

It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not). I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!

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PR_kwDODunzps4tpCsm

Add riddle_sense dataset

closed

CONTRIBUTOR

Adding a new dataset for QA with riddles. I'm confused about the tagging process because it looks like the streamlit app loads data from the current repo, so is it something that should be done after merging or off my fork?

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PR_kwDODunzps4tofO0

Better error msg if `len(predictions)` doesn't match `len(references)` in metrics

closed

CONTRIBUTOR

Improve the error message in `Metric.add_batch` if `len(predictions)` doesn't match `len(references)`. cc: @BramVanroy (feel free to test this code on your examples and review this PR)

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PR_kwDODunzps4toKD5

Make inspect.get_dataset_config_names always return a non-empty list

closed

MEMBER

Make all named configs cases, so that no special unnamed config case needs to be handled differently. Fix #3135.

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PR_kwDODunzps4toGpe

Fix string encoding for Value type

closed

MEMBER

Some metrics have `string` features but currently it fails if users pass integers instead. Indeed feature encoding that handles the conversion of the user's objects to the right python type is missing a case for `string`, while it already works as expected for integers, floats and booleans Here is an example code that didn't work previously, but that works with this fix: ```python import datasets # Note that 'id' is an integer while the SQuAD metric uses strings predictions = [{'prediction_text': '1976', 'id': 5}] references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': 5}] squad_metric = datasets.load_metric("squad") squad_metric.add_batch(predictions=predictions, references=references) results = squad_metric.compute() # {'exact_match': 100.0, 'f1': 100.0} ``` cc @sgugger @philschmid

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PR_kwDODunzps4tm3_I

Fixed: duplicate parameter and missing parameter in docstring

closed

CONTRIBUTOR

changing duplicate parameter `data_files` in `DatasetBuilder.__init__` to the missing parameter `data_dir`

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I_kwDODunzps49qOT8

Rouge and Meteor for multiple references

open

CONTRIBUTOR

Hi, Currently rogue and meteor supports only single references. Can we use these metrics to calculate for multiple references?

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I_kwDODunzps49qL2V

Illegal instruction (core dumped) at datasets import

closed

CONTRIBUTOR

## Describe the bug I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)" ## Steps to reproduce the bug ``` conda create --prefix path/to/env conda activate path/to/env conda install -c huggingface -c conda-forge datasets # exits with output "Illegal instruction (core dumped)" python -m datasets ``` ## Environment info When I run "datasets-cli env", I also get "Illegal instruction (core dumped)" If I run the following commands: ``` conda create --prefix path/to/another/new/env conda activate path/to/another/new/env conda install -c huggingface transformers transformers-cli env ``` Then I get: - `transformers` version: 4.11.3 - Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyTorch version (GPU?): not installed (NA) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: No - Using distributed or parallel set-up in script?: No Let me know what additional information you need in order to debug this issue. Thanks in advance!

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I_kwDODunzps49pxvO

Sacrebleu unexpected behaviour/requirement for data format

closed

CONTRIBUTOR

## Describe the bug When comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https://github.com/huggingface/datasets/pull/3153). In the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error. ## Steps to reproduce the bug ```python import sacrebleu import datasets refs = [ ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'], ] hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'] expected_bleu = 48.530827 ds_bleu = datasets.load_metric("sacrebleu") bleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score print(bleu_score_sb, expected_bleu) # works: 48.5308... bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"] print(bleu_score_ds, expected_bleu) # ValueError: Predictions and/or references don't match the expected format. ``` This seems to be related to how datasets forces the features format here: https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99 and then manipulates the references during the compute stage here https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L119-L122 I do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https://github.com/mjpost/sacrebleu/blob/2787185dd0f8d224c72ee5a831d163c2ac711a47/sacrebleu/metrics/base.py#L229). ## Actual results Traceback (most recent call last): File "C:\Users\bramv\AppData\Roaming\JetBrains\PyCharm2020.3\scratches\scratch_23.py", line 23, in <module> bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"] File "C:\dev\python\datasets\src\datasets\metric.py", line 392, in compute self.add_batch(predictions=predictions, references=references) File "C:\dev\python\datasets\src\datasets\metric.py", line 439, in add_batch raise ValueError( ValueError: Predictions and/or references don't match the expected format. Expected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')}, Input predictions: ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'], Input references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']] ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.2 - PyArrow version: 4.0.1

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PR_kwDODunzps4tlEVE

Add TER (as implemented in sacrebleu)

closed

CONTRIBUTOR

Implements TER (Translation Edit Rate) as per its implementation in sacrebleu. Sacrebleu for BLEU scores is already implemented in `datasets` so I thought this would be a nice addition. I started from the sacrebleu implementation, as the two metrics have a lot in common. Verified with sacrebleu's [testing suite](https://github.com/mjpost/sacrebleu/blob/078c440168c6adc89ba75fe6d63f0d922d42bcfe/test/test_ter.py) that this indeed works as intended. ```python import datasets test_cases = [ (['aaaa bbbb cccc dddd'], ['aaaa bbbb cccc dddd'], 0), # perfect match (['dddd eeee ffff'], ['aaaa bbbb cccc'], 1), # no overlap ([''], ['a'], 1), # corner case, empty hypothesis (['d e f g h a b c'], ['a b c d e f g h'], 1 / 8), # a single shift fixes MT ( [ 'wählen Sie " Bild neu berechnen , " um beim Ändern der Bildgröße Pixel hinzuzufügen oder zu entfernen , damit das Bild ungefähr dieselbe Größe aufweist wie die andere Größe .', 'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren möchten , wählen Sie im Menü des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "', 'klicken Sie auf der Registerkarte " Optionen " auf die Schaltfläche " Benutzerdefiniert " und geben Sie Werte für " Fehlerkorrektur-Level " und " Y / X-Verhältnis " ein .', 'Sie können beispielsweise ein Dokument erstellen , das ein Auto über die Bühne enthält .', 'wählen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "', ], [ 'wählen Sie " Bild neu berechnen , " um beim Ändern der Bildgröße Pixel hinzuzufügen oder zu entfernen , damit die Darstellung des Bildes in einer anderen Größe beibehalten wird .', 'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren möchten , wählen Sie im Menü des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "', 'klicken Sie auf der Registerkarte " Optionen " auf die Schaltfläche " Benutzerdefiniert " und geben Sie für " Fehlerkorrektur-Level " und " Y / X-Verhältnis " niedrigere Werte ein .', 'Sie können beispielsweise ein Dokument erstellen , das ein Auto enthalt , das sich über die Bühne bewegt .', 'wählen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "', ], 0.136 # realistic example from WMT dev data (2019) ), ] ter = datasets.load_metric(r"path\to\datasets\metrics\ter") predictions = ["hello there general kenobi", "foo bar foobar"] references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]] print(ter.compute(predictions=predictions, references=references)) for hyp, ref, score in test_cases: # Note the reference transformation which is different from scarebleu's input format results = ter.compute(predictions=hyp, references=[[r] for r in ref]) assert 100*score == results["score"], f"expected {100*score}, got {results['score']}" ```

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PR_kwDODunzps4tkqi-

Fix some typos in the documentation

closed

CONTRIBUTOR

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PR_kwDODunzps4tkL7t

Re-add faiss to windows testing suite

closed

CONTRIBUTOR

In recent versions, `faiss-cpu` seems to be available for Windows as well. See the [PyPi page](https://pypi.org/project/faiss-cpu/#files) to confirm. We can therefore included it for Windows in the setup file. At first tests didn't pass due to problems with permissions as caused by `NamedTemporaryFile` on Windows. This built-in library is notoriously poor in playing nice on Windows. The required change isn't pretty, but it works. First set `delete=False` to not automatically try to delete the file on `exit`. Then, manually delete the file with `unlink`. It's weird, I know, but it works. ```python with tempfile.NamedTemporaryFile(delete=False) as tmp_file: # do stuff os.unlink(tmp_file.name) ``` closes #3150

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I_kwDODunzps49nwRq

Faiss _is_ available on Windows

closed

CONTRIBUTOR

In the setup file, I find the following: https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/setup.py#L171 However, FAISS does install perfectly fine on Windows on my system. You can also confirm this on the [PyPi page](https://pypi.org/project/faiss-cpu/#files), where Windows wheels are available. Maybe this was true for older versions? For current versions, this can be removed I think. (This isn't really a bug but didn't know how else to tag.) If you agree I can do a quick PR and remove that line.

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PR_kwDODunzps4tjuUt

Add CMU Hinglish DoG Dataset for MT

closed

CONTRIBUTOR

Address part of #2841 Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model. Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two. The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py) The dummy data part is not working properly, it shows ``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ``` when I run without ``--auto_generate``. Please let me know how I can fix that. Thanks

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I_kwDODunzps49nMjY

Streaming with num_workers != 0

open

NONE

## Describe the bug When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch. The code owner is likely @lhoestq ## Steps to reproduce the bug For your convenience, we've prepped a colab notebook that reproduces the bug https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing ```python !pip install datasets==1.14.0 should_freeze_forever = True # ^-- set this to True in order to freeze forever, set to False in order to work normally import torch from datasets import load_dataset data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True) data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True) data = data.shuffle(100, seed=1337) data = data.with_format("torch") loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0) # v-- the code should freeze forever at this line for i, row in enumerate(loader): print(row) if i > 10: break print("DONE!") ``` ## Expected results The code should not freeze forever with num_workers=2 ## Actual results The code freezes forever with num_workers=2 ## Environment info - `datasets` version: 1.14.0 (also found in previous versions) - Platform: google colab (also locally) - Python version: 3.7, (also 3.8) - PyArrow version: 3.0.0

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PR_kwDODunzps4tjRHG

Fix CLI test to ignore verfications when saving infos

closed

MEMBER

Fix #3146.

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I_kwDODunzps49m5M7

CLI test command throws NonMatchingSplitsSizesError when saving infos

closed

MEMBER

When trying to generate a datset JSON metadata, a `NonMatchingSplitsSizesError` is thrown: ``` $ datasets-cli test datasets/arabic_billion_words --save_infos --all_configs Testing builder 'Alittihad' (1/10) Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: Unknown size, post-processed: Unknown size, total: 332.13 MiB) to .cache\arabic_billion_words\Alittihad\1.1.0\8175ff1c9714c6d5d15b1141b6042e5edf048276bb81a9c14e35e149a7a62ae4... Traceback (most recent call last): File "path\huggingface\datasets\.venv\Scripts\datasets-cli-script.py", line 33, in <module> sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')()) File "path\huggingface\datasets\src\datasets\commands\datasets_cli.py", line 33, in main service.run() File "path\huggingface\datasets\src\datasets\commands\test.py", line 144, in run builder.download_and_prepare( File "path\huggingface\datasets\src\datasets\builder.py", line 607, in download_and_prepare self._download_and_prepare( File "path\huggingface\datasets\src\datasets\builder.py", line 709, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "path\huggingface\datasets\src\datasets\utils\info_utils.py", line 74, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words')}] ``` This is due because a previous run generated a wrong `dataset_info.json`. This error can be avoided by passing `--ignore_verifications`, but I think this should be assumed when passing `--save_infos`.

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I_kwDODunzps49my3p

[when Image type will exist] provide a way to get the data as binary + filename

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web. Note: this issue would apply exactly the same for the `Audio` type. **Describe the solution you'd like** If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as: ```python filename: str data: bytes ``` **Describe alternatives you've considered** A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.

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I_kwDODunzps49mxWA

Infer the features if missing

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Some datasets, in particular community datasets, have no info file, thus no features. **Describe the solution you'd like** If a dataset has no features, the first loaded data (5-10 rows) could be used to infer the type. Related: `datasets` would provide a way to load the data, and get the rows AND the features as the result. **Describe alternatives you've considered** The HF hub could also provide some UI to help the dataset maintainers to explicit the types of their rows, or automatically infer them as an initial proposal.

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I_kwDODunzps49mwV3

Provide a way to check if the features (in info) match with the data of a split

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** I understand that currently the data loaded has not always the type described in the info features **Describe the solution you'd like** Provide a way to check if the rows have the type described by info features **Describe alternatives you've considered** Always check it, and raise an error when loading the data if their type doesn't match the features.

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I_kwDODunzps49mvdS

Provide a way to write a streamed dataset to the disk

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again. **Describe the solution you'd like** Provide a way to write the streamed rows of a dataset on the disk, and to load from it later. **Describe alternatives you've considered** Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.

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PR_kwDODunzps4tjGYz

Fix caching bugs

closed

CONTRIBUTOR

This PR fixes some caching bugs (most likely introduced in the latest refactor): * remove ")" added by accident in the dataset dir name * correctly pass the namespace kwargs in `CachedDatasetModuleFactory` * improve the warning message if `HF_DATASETS_OFFLINE is `True`

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I_kwDODunzps49mlOk

Add DER metric

open

MEMBER

Add DER metric for speaker diarization task. This is used by SUPERB beenchmark, for example.

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I_kwDODunzps49mlNv

Fix file/directory deletion on Windows

open

CONTRIBUTOR

Currently, on Windows, some attempts to delete a dataset file/directory will fail with the `PerimissionError`. Examples: - download a dataset, then force redownload it in the same session while keeping a reference to the downloaded dataset ```python from datasets import load_dataset dset = load_dataset("sst", split="train") dset = load_dataset("sst", split="train", download_mode="force_redownload") ``` - try to clean up the cache files while keeping a reference to those files (via the mapped dataset): ```python from datasets import load_dataset dset = load_dataset("sst", split="train") dset_mapped = dset.map(lambda _: {"dummy_col": 1}) dset.cleanup_cache_files() ``` We should fix those.

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I_kwDODunzps49mCCd

More fine-grained taxonomy of error types

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Exceptions like `FileNotFoundError` can be raised by different parts of the code, and it's hard to detect which one did **Describe the solution you'd like** Give a specific exception type for every group of similar errors **Describe alternatives you've considered** Rely on the error message, using regex

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PR_kwDODunzps4tievk

Fix numpy deprecation warning for ragged tensors

closed

MEMBER

Numpy shows a deprecation warning when we call `np.array` on a list of ragged tensors without specifying the `dtype`. If their shapes match, the tensors can be collated together, otherwise the resulting array should have `dtype=np.object`. Fix #3084 cc @Rocketknight1

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PR_kwDODunzps4tieFi

Fix script of Arabic Billion Words dataset to return all data

closed

MEMBER

The script has a bug and only parses and generates a portion of the entire dataset. This PR fixes the loading script so that is properly parses the entire dataset. Current implementation generates the same number of examples as reported in the [original paper](https://arxiv.org/abs/1611.04033) for all configurations except for one: - For "Youm7" we generate more examples (1172136) than the ones reported by the paper (1025027) | | Number of examples | Number of examples according to the source | |:---------------|-------------------:|-----:| | Alittihad | 349342 |349342 | | Almasryalyoum | 291723 |291723 | | Almustaqbal | 446873 |446873 | | Alqabas | 817274 |817274 | | Echoroukonline | 139732 |139732 | | Ryiadh | 858188 | 858188 | | Sabanews | 92149 |92149 | | SaudiYoum | 888068 |888068 | | Techreen | 314597 |314597 | | Youm7 | 1172136 |1025027 | Fix #3126.

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I_kwDODunzps49ltHb

Make inspect.get_dataset_config_names always return a non-empty list of configs

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to **Describe the solution you'd like** In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`). https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161

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I_kwDODunzps49liur

Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py

closed

NONE

datasets version: 1.12.1 `metric = datasets.load_metric('rouge')` The error: > ConnectionError Traceback (most recent call last) > <ipython-input-3-dd10a0c5212f> in <module> > ----> 1 metric = datasets.load_metric('rouge') > > /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs) > 613 download_config=download_config, > 614 download_mode=download_mode, > --> 615 dataset=False, > 616 ) > 617 metric_cls = import_main_class(module_path, dataset=False) > > /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs) > 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version) > 329 try: > --> 330 local_path = cached_path(file_path, download_config=download_config) > 331 except FileNotFoundError: > 332 if script_version is not None: > > /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) > 296 use_etag=download_config.use_etag, > 297 max_retries=download_config.max_retries, > --> 298 use_auth_token=download_config.use_auth_token, > 299 ) > 300 elif os.path.exists(url_or_filename): > > /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) > 603 raise FileNotFoundError("Couldn't find file at {}".format(url)) > 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") > --> 605 raise ConnectionError("Couldn't reach {}".format(url)) > 606 > 607 # Try a second time > > ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py Is there any remedy to solve the connection issue ?

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PR_kwDODunzps4tftyZ

Support Audio feature in streaming mode

closed

MEMBER

Fix #3132.

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I_kwDODunzps49ishW

Support Audio feature in streaming mode

closed

MEMBER

Currently, Audio feature is only supported for non-streaming datasets. Due to the large size of many speech datasets, we should also support Audio feature in streaming mode.

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I_kwDODunzps49h8xp

Add ADE20k