Datasets:

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MDExOlB1bGxSZXF1ZXN0NTUzNzgxMDY0

Add MNIST dataset

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CONTRIBUTOR

This PR adds the MNIST dataset to the library.

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I_kwDODunzps5F_f8g

Cannot preview cnn_dailymail dataset

closed

NONE

## Dataset viewer issue for '*cnn_dailymail*' **Link:** https://huggingface.co/datasets/cnn_dailymail *short description of the issue* Am I the one who added this dataset ? Yes-No

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MDExOlB1bGxSZXF1ZXN0NTM4OTc5MjIz

Added Wiki Summary Dataset

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Wiki Summary: Dataset extracted from Persian Wikipedia into the form of articles and highlights. Link: https://github.com/m3hrdadfi/wiki-summary

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PR_kwDODunzps4uYmkr

Add ETHICS dataset

closed

NONE

This PR adds the ETHICS dataset, including all 5 sub-datasets. From https://arxiv.org/abs/2008.02275

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PR_kwDODunzps4y-syt

Rm sphinx doc

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CONTRIBUTOR

Checklist - [x] Update circle ci yaml - [x] Delete sphinx static & python files in docs dir - [x] Update readme in docs dir - [ ] Update docs config in setup.py

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I_kwDODunzps5iQY6Q

Wikipedia download_and_prepare for GCS

open

NONE

### Describe the bug I am unable to download the wikipedia dataset onto GCS. When I run the script provided the memory firstly gets eaten up, then it crashes. I tried running this on a VM with 128GB RAM and all I got was a two empty files: _data_builder.lock_, _data.incomplete/beam-temp-wikipedia-train-1ab2039acf3611ed87a9893475de0093_ I have troubleshot this for two straight days now, but I am just unable to get the dataset into storage. ### Steps to reproduce the bug Run this and insert a path: ``` import datasets builder = datasets.load_dataset_builder( "wikipedia", language="en", date="20230320", beam_runner="DirectRunner") builder.download_and_prepare({path}, file_format="parquet") ``` This is where the problem of it eating RAM occurs. I have also tried several versions of this, based on the docs: ``` import gcsfs import datasets storage_options = {"project": "tdt4310", "token": "cloud"} fs = gcsfs.GCSFileSystem(**storage_options) output_dir = "gcs://wikipediadata/" builder = datasets.load_dataset_builder( "wikipedia", date="20230320", language="en", beam_runner="DirectRunner") builder.download_and_prepare( output_dir, storage_options=storage_options, file_format="parquet") ``` The error message that is received here is: > ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://wikipediadata/wikipedia-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite'] I have ran `pip install apache-beam[gcp]` ### Expected behavior The wikipedia data loaded into GCS Everything worked when testing with a smaller demo dataset found somewhere in the docs ### Environment info Newest published version of datasets. Python 3.9. Also tested with Python 3.7. 128GB RAM Google Cloud VM instance.

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MDExOlB1bGxSZXF1ZXN0NTM0NjQxOTE5

Add Open Subtitles Dataset

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I_kwDODunzps5F3TBY

Add ImageNet Sketch

closed

CONTRIBUTOR

## Adding a Dataset - **Name:** ImageNet Sketch - **Description:** ImageNet-Sketch is a dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale. - **Paper:** [Learning Robust Global Representations by Penalizing Local Predictive Power](https://arxiv.org/abs/1905.13549) - **Data:** https://github.com/HaohanWang/ImageNet-Sketch - **Motivation:** Allows for evaluating the robustness of vision models. 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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MDExOlB1bGxSZXF1ZXN0NDc1NzMzNzMz

[BUGFIX] Fix Race Dataset Checksum bug

closed

CONTRIBUTOR

In #537 I noticed that there was a bug in checksum checking when I have tried to download the race dataset. The reason for this is that the current preprocessing was just considering the `high school` data and it was ignoring the `middle` one. This PR just fixes it :) Moreover, I have added some descriptions.

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PR_kwDODunzps41adPA

Create a metric card for Competition MATH

closed

NONE

Proposing metric card for Competition MATH

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PR_kwDODunzps4yBUrZ

Prioritize `module.builder_kwargs` over defaults in `TestCommand`

closed

MEMBER

This fixes a bug in the `TestCommand` where multiple kwargs for `name` were passed if it was set in both default and `module.builder_kwargs`. Example error: ```Python Traceback (most recent call last): File "create_metadata.py", line 96, in <module> main(**vars(args)) File "create_metadata.py", line 86, in main metadata_command.run() File "/opt/conda/lib/python3.7/site-packages/datasets/commands/test.py", line 144, in run for j, builder in enumerate(get_builders()): File "/opt/conda/lib/python3.7/site-packages/datasets/commands/test.py", line 141, in get_builders name=name, cache_dir=self._cache_dir, data_dir=self._data_dir, **module.builder_kwargs TypeError: type object got multiple values for keyword argument 'name' ``` Let me know what you think.

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I_kwDODunzps5WhxDX

Protect the main branch from inadvertent direct pushes

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We have decided to implement a protection mechanism in this repository, so that nobody (not even administrators) can inadvertently push accidentally directly to the main branch. See context here: - d7c942228b8dcf4de64b00a3053dce59b335f618 To do: - [x] Protect main branch - Settings > Branches > Branch protection rules > main > Edit - [x] Check: Do not allow bypassing the above settings - The above settings will apply to administrators and custom roles with the "bypass branch protections" permission. - [x] Additionally, uncheck: Require approvals [under "Require a pull request before merging", which was already checked] - Before, we could exceptionally merge a non-approved PR, using Administrator bypass - Now that Administrator bypass is no longer possible, we would always need an approval to be able to merge; and pull request authors cannot approve their own pull requests. This could be an inconvenient in some exceptional circumstances when an urgent fix is needed - Nevertheless, although it is no longer enforced, it is strongly recommended to merge PRs only if they have at least one approval - [ ] #5250 - So that direct pushes to main branch are no longer necessary

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MDExOlB1bGxSZXF1ZXN0NTMyMzg5MDQ4

add urdu fake news dataset

closed

CONTRIBUTOR

Added Urdu fake news dataset. The dataset can be found <a href="https://github.com/MaazAmjad/Datasets-for-Urdu-news">here</a>.

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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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MDExOlB1bGxSZXF1ZXN0NjU4MDUwMjAx

Add copyright info to MLSUM dataset

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MDU6SXNzdWU3MTk1NTg4NzY=

Better error message when one forgets to call `add_batch` before `compute`

closed

CONTRIBUTOR

When using metrics, if for some reason a user forgets to call `add_batch` to a metric before `compute` (with no arguments), the error message is a bit cryptic and could probably be made clearer. ## Reproducer ```python import datasets import torch from datasets import Metric class GatherMetric(Metric): def _info(self): return datasets.MetricInfo( description="description", citation="citation", inputs_description="kwargs", features=datasets.Features({ 'predictions': datasets.Value('int64'), 'references': datasets.Value('int64'), }), codebase_urls=[], reference_urls=[], format='numpy' ) def _compute(self, predictions, references): return {"predictions": predictions, "labels": references} metric = GatherMetric(cache_dir="test-metric") inputs = torch.randint(0, 2, (1024,)) targets = torch.randint(0, 2, (1024,)) batch_size = 8 for i in range(0, 1024, batch_size): pass # User forgets to call `add_batch` result = metric.compute() ``` ## Stack trace: ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-13-267729d187fa> in <module> 3 pass 4 # metric.add_batch(predictions=inputs[i:i+batch_size], references=targets[i:i+batch_size]) ----> 5 result = metric.compute() ~/git/datasets/src/datasets/metric.py in compute(self, *args, **kwargs) 380 if predictions is not None: 381 self.add_batch(predictions=predictions, references=references) --> 382 self._finalize() 383 384 self.cache_file_name = None ~/git/datasets/src/datasets/metric.py in _finalize(self) 343 elif self.process_id == 0: 344 # Let's acquire a lock on each node files to be sure they are finished writing --> 345 file_paths, filelocks = self._get_all_cache_files() 346 347 # Read the predictions and references ~/git/datasets/src/datasets/metric.py in _get_all_cache_files(self) 280 filelocks = [] 281 for process_id, file_path in enumerate(file_paths): --> 282 filelock = FileLock(file_path + ".lock") 283 try: 284 filelock.acquire(timeout=self.timeout) TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' ```

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PR_kwDODunzps43-4fC

Remove dots in config names

closed

MEMBER

20+ datasets have dots in their config names. However it causes issues with the YAML tags of the dataset cards since we can't have dots in YAML keys. This is related to https://github.com/huggingface/datasets/pull/2362 (internal https://github.com/huggingface/moon-landing/issues/946). Also removing the dots in the config names would allow us to merge https://github.com/huggingface/datasets/pull/4302 which removes a hack that replaces all the dots by underscores in the YAML tags. I also added a test in the CI that checks that all the YAML tags to make sure that: - they can be parsed using a YAML parser - they contain only valid YAML tags like `languages` or `task_ids` - they contain valid config names (no invalid characters `<>:/\|?*.`)

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MDExOlB1bGxSZXF1ZXN0NTMxOTQ0MDc0

Add ChrEn

closed

MEMBER

Adding the Cherokee English machine translation dataset of https://github.com/ZhangShiyue/ChrEn

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MDExOlB1bGxSZXF1ZXN0NTM1MTc1NzMx

adding UNPC

closed

MEMBER

Adding United Nations Parallel Corpus http://opus.nlpl.eu/UNPC.php

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MDExOlB1bGxSZXF1ZXN0NDc3MzAzOTM2

Adding the KILT knowledge source and tasks

closed

MEMBER

This adds Wikipedia pre-processed for KILT, as well as the task data. Only the question IDs are provided for TriviaQA, but they can easily be mapped back with: ``` import nlp kilt_wikipedia = nlp.load_dataset('kilt_wikipedia') kilt_tasks = nlp.load_dataset('kilt_tasks') triviaqa = nlp.load_dataset('trivia_qa', 'unfiltered.nocontext') triviaqa_map = {} for k in ['train', 'validation', 'test']: triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(triviaqa[k]['question_id'])]) kilt_tasks[k + '_triviaqa'] = kilt_tasks[k + '_triviaqa'].filter(lambda x: x['id'] in triviaqa_map) kilt_tasks[k + '_triviaqa'].map(lambda x: {'input': triviaqa[split][triviaqa_map[x['id']]]['question']}) ``` It would be great to have the dataset by Monday, which is when the paper should land on Arxiv and @fabiopetroni is planning on tweeting about the paper and `facebookresearch` repository for the datasett

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MDExOlB1bGxSZXF1ZXN0NDk3OTUwOTA2

Fixes #712 Error in the Overview.ipynb notebook

closed

CONTRIBUTOR

Fixes #712 Error in the Overview.ipynb notebook by adding `with_details=True` parameter to `list_datasets` function in Cell 3 of **overview** notebook

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PR_kwDODunzps4xy3De

Update link in wiki_bio dataset

closed

CONTRIBUTOR

Fixes #3580 and makes the wiki_bio dataset work again. I changed the link and some documentation, and all the tests pass. Thanks @lhoestq for uploading the dataset to the HuggingFace data bucket. @lhoestq -- all the tests pass, but I'm still not able to import the dataset, as the old Google Drive link is cached somewhere: ```python >>> from datasets import load_dataset load_dataset("wiki_bio>>> load_dataset("wiki_bio") Using custom data configuration default Downloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to /home/jxm3/.cache/huggingface/datasets/wiki_bio/default/1.1.0/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9... Traceback (most recent call last): ... File "/home/jxm3/random/datasets/src/datasets/utils/file_utils.py", line 612, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil ``` what do I have to do to invalidate the cache and actually import the dataset? It's clearly set up correctly, since the data is downloaded and processed by the tests. As an aside, this caching-loading-scripts behavior makes for a really bad developer experience. I just wasted an hour trying to figure out where the caching was happening and how to disable it, and I don't know. All I wanted to do was update the link and submit a pull request! I recommend that you all either change this behavior (i.e. updating the link to a dataset should "just work") or document it, since I couldn't find any information about this in the contributing.md or readme or anywhere else! Thanks!

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PR_kwDODunzps5amAQ1

Update create_dataset.mdx

closed

CONTRIBUTOR

modified , as AudioFolder and ImageFolder not in Dataset Library. ``` from datasets import AudioFolder ``` and ```from datasets import ImageFolder``` to ```from datasets import load_dataset``` ``` cannot import name 'AudioFolder' from 'datasets' (/home/eswardivi/miniconda3/envs/Hugformers/lib/python3.10/site-packages/datasets/__init__.py) ```

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MDExOlB1bGxSZXF1ZXN0NjI2MjgzNTYy

Fixed typo seperate->separate

closed

CONTRIBUTOR

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I_kwDODunzps5XNG4I

Missing documentation build for versions 2.7.1 and 2.6.2

closed

MEMBER

After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered). There was a fix by: - #5291 However, both documentations were built from main branch, instead of their corresponding version branch. We are rebuilding them.

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PR_kwDODunzps4u-xdv

Add push_to_hub docs

closed

MEMBER

Since #3098 it's now possible to upload a dataset on the Hub directly from python using the `push_to_hub` method. I just added a section in the "Upload a dataset to the Hub" tutorial. I kept the section quite simple but let me know if it sounds good to you @LysandreJik @stevhliu :)

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MDExOlB1bGxSZXF1ZXN0NjU0ODUwMjY4

fix #2391 add original answers in kilt-TriviaQA

closed

CONTRIBUTOR

cc @yjernite is it ok like this?

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PR_kwDODunzps4_FhTm

docs: ✏️ add links to the Datasets API

closed

CONTRIBUTOR

I added some links to the Datasets API in the docs. See https://github.com/huggingface/datasets-server/pull/566 for a companion PR in the datasets-server. The idea is to improve the discovery of the API through the docs. I'm a bit shy about pasting a lot of links to the API in the docs, so it's minimal for now. I'm interested in ideas to integrate the API better in these docs without being too much. cc @lhoestq @julien-c @albertvillanova @stevhliu.

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I_kwDODunzps5pHyQc

"Couldn't cast array of type" in complex datasets

closed

NONE

### Describe the bug When doing a map of a dataset with complex types, sometimes `datasets` is unable to interpret the valid schema of a returned datasets.map() function. This often comes from conflicting types, like when both empty lists and filled lists are competing for the same field value. This is prone to happen in batch mapping, when the mapper returns a sequence of null/empty values and other batches are non-null. A workaround is to manually cast the new batch to a pyarrow table (like implemented in this [workaround](https://github.com/piercefreeman/lassen/pull/3)) but it feels like this ideally should be solved at the core library level. Note that the reproduction case only throws this error if the first datapoint has the empty list. If it is processed later, datasets already detects its representation as list-type and therefore allows the empty list to be provided. ### Steps to reproduce the bug A trivial reproduction case: ```python from typing import Iterator, Any import pandas as pd from datasets import Dataset def batch_to_examples(batch: dict[str, list[Any]]) -> Iterator[dict[str, Any]]: for i in range(next(iter(lengths))): yield {feature: values[i] for feature, values in batch.items()} def examples_to_batch(examples) -> dict[str, list[Any]]: batch = {} for example in examples: for feature, value in example.items(): if feature not in batch: batch[feature] = [] batch[feature].append(value) return batch def batch_process(examples, explicit_schema: bool): new_examples = [] for example in batch_to_examples(examples): new_examples.append(dict(texts=example["raw_text"].split())) return examples_to_batch(new_examples) df = pd.DataFrame( [ {"raw_text": ""}, {"raw_text": "This is a test"}, {"raw_text": "This is another test"}, ] ) dataset = Dataset.from_pandas(df) # datasets won't be able to typehint a dataset that starts with an empty example. with pytest.raises(TypeError, match="Couldn't cast array of type"): dataset = dataset.map( batch_process, batched=True, batch_size=1, num_proc=1, remove_columns=dataset.column_names, ) ``` This results in crashes like: ```bash File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 2109, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1998, in array_cast raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") TypeError: Couldn't cast array of type string to null ``` ### Expected behavior The code should successfully map and create a new dataset without error. ### Environment info Mac OSX, Linux

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MDU6SXNzdWU5ODc2NzY0MjA=

TypeError: 'NoneType' object is not callable

closed

NONE

## Describe the bug TypeError: 'NoneType' object is not callable ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric dataset = datasets.load_dataset("glue", 'cola') ``` ## 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: 1.11.0 - Platform: - Python version: 3.7 - PyArrow version:

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I_kwDODunzps5GjEtl

Add support for IIIF in datasets

open

MEMBER

This is a feature request for support for IIIF in `datasets`. Apologies for the long issue. I have also used a different format to the usual feature request since I think that makes more sense but happy to use the standard template if preferred. ## What is [IIIF](https://iiif.io/)? IIIF (International Image Interoperability Framework) > is a set of open standards for delivering high-quality, attributed digital objects online at scale. It’s also an international community developing and implementing the IIIF APIs. IIIF is backed by a consortium of leading cultural institutions. The tl;dr is that IIIF provides various specifications for implementing useful functionality for: - Institutions to make available images for various use cases - Users to have a consistent way of interacting/requesting these images - For developers to have a common standard for developing tools for working with IIIF images that will work across all institutions that implement a particular IIIF standard (for example the image viewer for the BNF can also work for the Library of Congress if they both use IIIF). Some institutions that various levels of support IIF include: The British Library, Internet Archive, Library of Congress, Wikidata. There are also many smaller institutions that have IIIF support. An incomplete list can be found here: https://iiif.io/guides/finding_resources/ ## IIIF APIs IIIF consists of a number of APIs which could be integrated with datasets. I think the most obvious candidate for inclusion would be the [Image API](https://iiif.io/api/image/3.0/) ### IIIF Image API The Image API https://iiif.io/api/image/3.0/ is likely the most suitable first candidate for integration with datasets. The Image API offers a consistent protocol for requesting images via a URL: ```{scheme}://{server}{/prefix}/{identifier}/{region}/{size}/{rotation}/{quality}.{format}``` A concrete example of this: ```https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg``` As you can see the scheme offers a number of options that can be specified in the URL, for example, size. Using the example URL we return:  We can change the size to request a size of 250 by 250, this is done by changing the size from `full` to `250,250` i.e. switching the URL to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg`  We can also request the image with max width 250, max height 250 whilst maintaining the aspect ratio using `!w,h`. i.e. change the url to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg`  A full overview of the options for size can be found here: https://iiif.io/api/image/3.0/#42-size ## Why would/could this be useful for datasets? There are a few reasons why support for the IIIF Image API could be useful. Broadly the ability to have more control over how an image is returned from a server is useful for many ML workflows: - images can be requested in the right size, this prevents having to download/stream large images when the actual desired size is much smaller - can select a subset of an image: it is possible to select a sub-region of an image, this could be useful for example when you already have a bounding box for a subset of an image and then want to use this subset of an image for another task. For example, https://github.com/Living-with-machines/nnanno uses IIIF to request parts of a newspaper image that have been detected as 'photograph', 'illustration' etc for downstream use. - options for quality, rotation, the format can all be encoded in the URL request. These may become particularly useful when pre-training models on large image datasets where the cost of downloading images with 1600 pixel width when you actually want 240 has a larger impact. ## What could this look like in datasets? I think there are various ways in which support for IIIF could potentially be included in `datasets`. These suggestions aren't fully fleshed out but hopefully, give a sense of possible approaches that match existing `datasets` methods in their approach. ### Use through datasets scripts Loading images via URL is already supported. There are a few possible 'extras' that could be included when using IIIF. One option is to leverage the IIIF protocol in datasets scripts, i.e. the dataset script can expose the IIIF options via the dataset script: ```python ds = load_dataset("iiif_dataset", image_size="250,250", fmt="jpg") ``` This is already possible. The approach to parsing the IIIF URLs would be left to the person creating the dataset script. ### Support through dataset scripts (with some datasets support) This is similar to the above but `datasets` would offer some way of saying this is a iiif URL and then expose the options associated with IIIF images automatically. i.e. if you did something like: ```python features = {"label": ClassLabel(names=['dog','cat']), "url": datasets.IIIFURL()} ``` inside your loading script, you would automatically have exposed `size`, `fmt` etc. options when loading the dataset. ### Other possible integrations Some other possible pseudocode ways that a user could interact with IIIF URLs: The ability to cast to an `IIIFImage` feature type: ``` ds.cast_column('url', IIIFImage, download=False) ``` The ability to specify some options associated with IIIF urls. ``` ds = ds.set_iiif_options(column='url', size="250,250") ``` I think all of these would rely on having an `IIIFImage` feature type - this would be a little bit of a Frankenstein between a `string` and `datasets.Image`. I think most of the actual image behaviour would be exactly the same as `datasets.Image`, the difference would be that the underlying URL could be modified in various ways. ## prerequisite requirements There are a few pre-requisites that I can anticipate. This doesn't cover a full implementation of IIIF support which would have different requirements depending on the approach taken to implementing IIIF. Some of these features would be useful independently of adding IIIF support: ### support for handling failed images loaded via a URL (or a specific IIIFImage feature). Working with images via web requests will inevitably return the odd failed request. If these images are then requests and don't return it would be useful to have a `None` returned instead of an error. For example, when using `push_to_hub` `datasets` will try and include the image but currently fails with bad URLs. ```python from datasets import Dataset import datasets urls = ['https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg']*3 urls.append("badurl.com/image.jpg") data = {"url":urls} ds = Dataset.from_dict(data) ds = ds.cast_column('url', datasets.Image()) ds[3]['url'] ``` returns a `FileNotFoundError`, for streaming large datasets of images using their URLs it could be useful to have `None` returned instead. This has implications for the actual training loop i.e. you now need to somehow skip those examples because of this it might not be desirable to support this. ### Caching support Since IIIF requests images via a URL it would be great to have a way of not requesting the images multiple times. This is tracked in https://github.com/huggingface/datasets/issues/3142 and I think this would also be very desirable to have here particularly as one of the primary use cases of IIIF may be to do unsupervised pre-training on large datasets of IIIF URLs. ### Support for Parsing IIIF URLs This gets closer to the actual implementation. Here the requirement would be some way for `datasets` to parse a URL that the users specify is an IIIF URL. An example of a Python library that does this: https://github.com/Princeton-CDH/piffle. I also have a rough version that uses `dataclasses` which I can share. ## Why it might not be worthwhile/suitable for datasets There are some reasons that this might not be worth implementing: - currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models. - It may end up being better to leave this to the user. It would for example be possible for someone to write map functions to change an IIIF URL to the correct size etc. Adding direct support for IIIF in datasets may potentially not be worth the trouble. - The impact of different approaches to doing image scaling can impact the downstream model's performance, see: https://twitter.com/wightmanr/status/1479528581466243073?s=20. Since different IIIF image servers may implement different approaches to resizing images this could have a downstream impact on model performance. think this is something that could be flagged to the end-user in the documentation. This probably also falls into general "gotchas" that probably aren't the `datasets` libraries' role to protect users from. Some of the requirements outlined above would be useful for images anyway. These could be implemented prior to a final decision about whether IIIF support could/should be added to datasets. ## Suggested next steps: I realise this is a long and slightly open-ended issue. I am happy to clarify/answer questions on IIIF and possible integrations. If the prerequisite requirements seem worth exploring/are better explored in their own issues let me know and I can open new issues for those.

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MDU6SXNzdWU2MzkxNTY2MjU=

PG-19

closed

CONTRIBUTOR

Hi, and thanks for all your open-sourced work, as always! I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling.

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I_kwDODunzps52nn-B

Cannot import datasets on google colab (python 3.10.12)

closed

NONE

### Describe the bug I'm trying A full colab demo notebook of zero-shot-distillation from https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation but i got this type of error when importing datasets on my google colab (python version is 3.10.12)  I found the same problem that have been solved in [#3326 ] but it seem still error on the google colab. I can't try on my local using jupyter notebook because of my laptop resource doesn't fulfill the requirements. Please can anyone help me solve this problem. Thank you 😅 ### Steps to reproduce the bug Error: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-8-b6e092f83978>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import load_dataset 2 3 # Print all the available datasets 4 from huggingface_hub import list_datasets 5 print([dataset.id for dataset in list_datasets()]) 6 frames [/usr/lib/python3.10/functools.py](https://localhost:8080/#) in update_wrapper(wrapper, wrapped, assigned, updated) 59 # Issue #17482: set __wrapped__ last so we don't inadvertently copy it 60 # from the wrapped function when updating __dict__ ---> 61 wrapper.__wrapped__ = wrapped 62 # Return the wrapper so this can be used as a decorator via partial() 63 return wrapper AttributeError: readonly attribute ``` ### Expected behavior Run success on Google Colab (free) ### Environment info Windows 11 x64, Google Colab free

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PR_kwDODunzps4uynjc

fix old_val typo in f-string

closed

CONTRIBUTOR

This PR is to correct a typo in #3277 that @Carlosbogo revieled in a comment. Related closed issue : #3257 Sorry about that 😅.

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MDExOlB1bGxSZXF1ZXN0NjY5MDc4OTcw

add tensorflow-macos support

closed

CONTRIBUTOR

ref - https://github.com/huggingface/datasets/issues/2068

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MDU6SXNzdWU2MzcwMjgwMTU=

[Feature request] Support for external modality for language datasets

closed

CONTRIBUTOR

# Background In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https://arxiv.org/abs/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https://github.com/huggingface/nlp/pull/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. # Language + Vision ## Use case Typically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https://cs.stanford.edu/people/dorarad/gqa/download.html#seconddown) dataset. Currently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https://arxiv.org/abs/1611.08481), [Shekhar et.al, 2019](https://www.aclweb.org/anthology/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https://arxiv.org/abs/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https://arxiv.org/abs/1908.03557), is to use FastRCNN features. For all these types of features, people use one of the following formats: 1. [HD5F](https://pypi.org/project/h5py/) 2. [NumPy](https://numpy.org/doc/stable/reference/generated/numpy.savez.html) 3. [LMDB](https://lmdb.readthedocs.io/en/release/) ## Implementation considerations I was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following: 1. Download dataset 2. Download images associated with the dataset 3. Write a script that generates the visual features for every image and store them in a specific file 4. Create a DataLoader that maps the visual features to the corresponding language example In my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https://github.com/pytorch/pytorch/issues/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it. For ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. Looking forward to hearing your thoughts about it!

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I_kwDODunzps5Oo28N

Easily create loading script for benchmark comprising multiple huggingface datasets

closed

CONTRIBUTOR

Hi, I would like to create a loading script for a benchmark comprising multiple huggingface datasets. The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a single interface to all the underlying datasets. I thought about downloading the files with the load_dataset function and then providing the link to the cached file. But this seems a bit inelegant to me. What approach would you propose to do this? Please let me know if you have any questions. Cheers, Joel

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MDExOlB1bGxSZXF1ZXN0Njc2MDI5MDY5

remove wi_locness dataset due to licensing issues

closed

CONTRIBUTOR

It was brought to my attention that this dataset's license is not only missing, but also prohibits redistribution. I contacted the original author to apologize for this oversight and asked if we could still use it, but unfortunately we can't and the author kindly asked to take down this dataset.

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MDExOlB1bGxSZXF1ZXN0NDI0NTQ1NjAy

have 'add' and 'add_batch' for metrics

closed

MEMBER

This should fix #116 Previously the `.add` method of metrics expected a batch of examples. Now `.add` expects one prediction/reference and `.add_batch` expects a batch. I think it is more coherent with the way the ArrowWriter works.

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MDU6SXNzdWU5NTY4OTUxOTk=

Add Microsoft Building Footprints dataset

open

MEMBER

## Adding a Dataset - **Name:** Microsoft Building Footprints - **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge. - **Paper:** *link to the dataset paper if available* - **Data:** https://www.microsoft.com/en-us/maps/building-footprints - **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Reported by: @sashavor

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I_kwDODunzps4-vK1E

FET DATA

closed

NONE

## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* 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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MDU6SXNzdWU5OTAwOTMzMTY=

NotADirectoryError: [WinError 267] During load_from_disk

open

NONE

## Describe the bug Trying to load saved dataset or dataset directory from Amazon S3 on a Windows machine fails. Performing the same operation succeeds on non-windows environment (AWS Sagemaker). ## Steps to reproduce the bug ```python # Followed https://huggingface.co/docs/datasets/filesystems.html#loading-a-processed-dataset-from-s3 from datasets import load_from_disk from datasets.filesystems import S3FileSystem s3_file = "output of save_to_disk" s3_filesystem = S3FileSystem() load_from_disk(s3_file, fs=s3_filesystem) ``` ## Expected results load_from_disk succeeds without error ## Actual results Seems like it succeeds in pulling the file into a windows temp directory, as it exists in my system, but fails to process it. ``` Exception ignored in: <finalize object at 0x26409231ce0; dead> Traceback (most recent call last): File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\weakref.py", line 566, in __call__ return info.func(*info.args, **(info.kwargs or {})) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 817, in _cleanup cls._rmtree(name) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 813, in _rmtree _shutil.rmtree(name, onerror=onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 740, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) [Previous line repeated 2 more times] File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 618, in _rmtree_unsafe onerror(os.unlink, fullname, sys.exc_info()) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 805, in onerror cls._rmtree(path) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 813, in _rmtree _shutil.rmtree(name, onerror=onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 740, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 599, in _rmtree_unsafe onerror(os.scandir, path, sys.exc_info()) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 596, in _rmtree_unsafe with os.scandir(path) as scandir_it: NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\grassycup\\AppData\\Local\\Temp\\tmp45f_qbma\\tests3bucket\\output\\test_output\\train\\dataset.arrow' Exception ignored in: <finalize object at 0x264091c7880; dead> Traceback (most recent call last): File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\weakref.py", line 566, in __call__ return info.func(*info.args, **(info.kwargs or {})) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 817, in _cleanup cls._rmtree(name) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 813, in _rmtree _shutil.rmtree(name, onerror=onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 740, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 613, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) [Previous line repeated 2 more times] File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 618, in _rmtree_unsafe onerror(os.unlink, fullname, sys.exc_info()) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 805, in onerror cls._rmtree(path) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\tempfile.py", line 813, in _rmtree _shutil.rmtree(name, onerror=onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 740, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 599, in _rmtree_unsafe onerror(os.scandir, path, sys.exc_info()) File "C:\Users\grassycup\Anaconda3\envs\hello.world\lib\shutil.py", line 596, in _rmtree_unsafe with os.scandir(path) as scandir_it: NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\grassycup\\AppData\\Local\\Temp\\tmp45f_qbma\\tests3bucket\\output\\test_output\\train\\dataset.arrow' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.11.0 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.8.11 - PyArrow version: 3.0.0

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PR_kwDODunzps5DB-1y

Change release procedure to use only pull requests

closed

MEMBER

This PR changes the release procedure so that: - it only make changes to main branch via pull requests - it is no longer necessary to directly commit/push to main branch Close #5251.

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I_kwDODunzps5UhJPL

Inconsistent documentation on map remove_columns

closed

NONE

### Describe the bug The page [process](https://huggingface.co/docs/datasets/process) says this about the parameter `remove_columns` of the function `map`: When you remove a column, it is only removed after the example has been provided to the mapped function. So it seems that the `remove_columns` parameter removes after the mapped functions. However, another page, [the documentation of the function map](https://huggingface.co/docs/datasets/v2.6.1/en/package_reference/main_classes#datasets.Dataset.map.remove_columns) says: Columns will be removed before updating the examples with the output of `function`, i.e. if `function` is adding columns with names in remove_columns, these columns will be kept. So one page says "after the mapped function" and another says "before the mapped function." Is there something wrong? ### Steps to reproduce the bug Not about code. ### Expected behavior consistent about the descriptions of the behavior of the parameter `remove_columns` in the function `map`. ### Environment info datasets V2.6.0

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PR_kwDODunzps4spE9m

Remove unused parameter in xdirname

closed

MEMBER

Minor fix to remove unused args `*p` in `xdirname`.

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PR_kwDODunzps49e1CP

amend docstring for dunder

open

NONE

display dunder method in docsting with underlines an not bold markdown.

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MDExOlB1bGxSZXF1ZXN0NTM1MDkwMjM0

Re-adding narrativeqa dataset

closed

CONTRIBUTOR

An update of #309.

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I_kwDODunzps5Ure7H

Problems after upgrading to 2.6.1

open

NONE

### Describe the bug Loading a dataset_dict from disk with `load_from_disk` is now creating a `KeyError "length"` that was not occurring in v2.5.2. Context: - Each individual dataset in the dict is created with `Dataset.from_pandas` - The dataset_dict is create from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds}) - The pandas dataframe, besides text columns, has a column with a dictionary inside and potentially different keys in each row. Correctly the `Dataset.from_pandas` function adds `key: None` to all dictionaries in each row so that the schema can be correctly inferred. ### Steps to reproduce the bug Steps to reproduce: - Upgrade to datasets==2.6.1 - Create a dataset from pandas dataframe with `Dataset.from_pandas` - Create a dataset_dict from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds}) - Save to disk with the `save` function ### Expected behavior Same as in v2.5.2, that is load from disk without errors ### Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.4.209-129.367.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.5.1

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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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MDExOlB1bGxSZXF1ZXN0NTMyNzAwNTY5

Add Urdu Sentiment Corpus (USC).

closed

CONTRIBUTOR

Added Urdu Sentiment Corpus. More details about the dataset over <a href="https://github.com/MuhammadYaseenKhan/Urdu-Sentiment-Corpus">here</a>.

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MDU6SXNzdWU3NjA3MDcxMTM=

Can't map dataset (loaded from csv)

closed

NONE

Hello! I am trying to load single csv file with two columns: ('label': str, 'text' str), where is label is str of two possible classes. Below steps are similar with [this notebook](https://colab.research.google.com/drive/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing), where bert model and tokenizer are used to classify lmdb loaded dataset. Only one difference it is the dataset loaded from .csv file. Here is how I load it: ```python data_path = 'data.csv' data = pd.read_csv(data_path) # process class name to indices classes = ['neg', 'pos'] class_to_idx = { cl: i for i, cl in enumerate(classes) } # now data is like {'label': int, 'text' str} data['label'] = data['label'].apply(lambda x: class_to_idx[x]) # load dataset and map it with defined `tokenize` function features = Features({ target: ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None), feature: Value(dtype='string', id=None), }) dataset = Dataset.from_pandas(data, features=features) dataset.map(tokenize, batched=True, batch_size=len(dataset)) ``` It ruins on the last line with following error: ``` --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-112-32b6275ce418> in <module>() 9 }) 10 dataset = Dataset.from_pandas(data, features=features) ---> 11 dataset.map(tokenizer, batched=True, batch_size=len(dataset)) 2 frames /usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, 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) 1237 test_inputs = self[:2] if batched else self[0] 1238 test_indices = [0, 1] if batched else 0 -> 1239 update_data = does_function_return_dict(test_inputs, test_indices) 1240 logger.info("Testing finished, running the mapping function on the dataset") 1241 /usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py in does_function_return_dict(inputs, indices) 1208 fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns] 1209 processed_inputs = ( -> 1210 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) 1211 ) 1212 does_return_dict = isinstance(processed_inputs, Mapping) /usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs) 2281 ) 2282 ), ( -> 2283 "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) " 2284 "or `List[List[str]]` (batch of pretokenized examples)." 2285 ) AssertionError: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples). ``` which I think is not expected. I also tried the same steps using `Dataset.from_csv` which resulted in the same error. For reproducing this, I used [this dataset from kaggle](https://www.kaggle.com/team-ai/spam-text-message-classification)

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MDExOlB1bGxSZXF1ZXN0NTMxMzE4MTQw

Add Gutenberg time references dataset

closed

CONTRIBUTOR

This PR adds the gutenberg_time dataset: https://arxiv.org/abs/2011.04124

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MDExOlB1bGxSZXF1ZXN0NTM5MTAwNjAy

Lama

closed

CONTRIBUTOR

This the LAMA dataset for probing facts and common sense from language models. See https://github.com/facebookresearch/LAMA for more details.

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MDU6SXNzdWU3NDAxMDIyMTA=

Wikipedia postprocessing

closed

NONE

Hi, thanks for this library! Running this code: ```py import datasets wikipedia = datasets.load_dataset("wikipedia", "20200501.de") print(wikipedia['train']['text'][0]) ``` I get: ``` mini|Ricardo Flores Magón mini|Mexikanische Revolutionäre, Magón in der Mitte anführend, gegen die Diktatur von Porfirio Diaz, Ausschnitt des Gemälde „Tierra y Libertad“ von Idelfonso Carrara (?) von 1930. Ricardo Flores Magón (* 16. September 1874 in San Antonio Eloxochitlán im mexikanischen Bundesstaat Oaxaca; † 22. November 1922 im Bundesgefängnis Leavenworth im US-amerikanischen Bundesstaat Kansas) war als Journalist, Gewerkschafter und Literat ein führender anarchistischer Theoretiker und Aktivist, der die revolutionäre mexikanische Bewegung radikal beeinflusste. Magón war Gründer der Partido Liberal Mexicano und Mitglied der Industrial Workers of the World. Politische Biografie Journalistisch und politisch kämpfte er und sein Bruder sehr kompromisslos gegen die Diktatur Porfirio Diaz. Philosophisch und politisch orientiert an radikal anarchistischen Idealen und den Erfahrungen seiner indigenen Vorfahren bei der gemeinschaftlichen Bewirtschaftung des Gemeindelandes, machte er die Forderung „Land und Freiheit“ (Tierra y Libertad) populär. Besonders Francisco Villa und Emiliano Zapata griffen die Forderung Land und Freiheit auf. Seine Philosophie hatte großen Einfluss auf die Landarbeiter. 1904 floh er in die USA und gründete 1906 die Partido Liberal Mexicano. Im Exil lernte er u. a. Emma Goldman kennen. Er verbrachte die meiste Zeit seines Lebens in Gefängnissen und im Exil und wurde 1918 in den USA wegen „Behinderung der Kriegsanstrengungen“ zu zwanzig Jahren Gefängnis verurteilt. Zu seinem Tod gibt es drei verschiedene Theorien. Offiziell starb er an Herzversagen. Librado Rivera, der die Leiche mit eigenen Augen gesehen hat, geht davon aus, dass Magón von einem Mitgefangenen erdrosselt wurde. Die staatstreue Gewerkschaftszeitung CROM veröffentlichte 1923 einen Beitrag, nachdem Magón von einem Gefängniswärter erschlagen wurde. mini|Die Brüder Ricardo (links) und Enrique Flores Magón (rechts) vor dem Los Angeles County Jail, 1917 [...] ``` so some Markup like `mini|` is still left. Should I run another parser on this text before feeding it to an ML model or is this a known imperfection of parsing Wiki markup? Apologies if this has been asked before.

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MDU6SXNzdWU2Nzk0MDA2ODM=

Errors when I use

closed

NONE

I tried the following example code from https://huggingface.co/deepset/roberta-base-squad2 and got errors I am using **transformers 3.0.2** code . from transformers.pipelines import pipeline from transformers.modeling_auto import AutoModelForQuestionAnswering from transformers.tokenization_auto import AutoTokenizer model_name = "deepset/roberta-base-squad2" nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) The errors are : res = nlp(QA_input) File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in __call__ for s, e, score in zip(starts, ends, scores) File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in <listcomp> for s, e, score in zip(starts, ends, scores) KeyError: 0

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PR_kwDODunzps4zDw9_

Fix typo in train split name

closed

MEMBER

In the README guide (and consequently in many datasets) there was a typo in the train split name: ``` | Tain | Valid | Test | ``` This PR: - fixes the typo in the train split name - fixes the column alignment of the split tables in the README guide and in all datasets.

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PR_kwDODunzps43Tnxo

Fix filesystem docstring

closed

MEMBER

This PR untangles the `S3FileSystem` docstring so the [parameters](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#parameters) are properly displayed.

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PR_kwDODunzps4spDlX

Fix Windows paths in LJ Speech dataset

closed

MEMBER

Minor fix in LJ Speech dataset for Windows pathname component separator. Related to #1878.

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PR_kwDODunzps4wiPA9

Update README.md

closed

CONTRIBUTOR

Updating licensing information & personal and sensitive information.

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PR_kwDODunzps4656H9

The speechocean762 dataset

closed

NONE

[speechocean762](https://www.openslr.org/101/) is a non-native English corpus for pronunciation scoring tasks. It is free for both commercial and non-commercial use. I believe it will be easier to use if it could be available on Hugging Face.

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MDExOlB1bGxSZXF1ZXN0NTQ2OTM4MjAy

Fix NER metric example in Overview notebook

closed

CONTRIBUTOR

Fix errors in `NER metric example` section in `Overview.ipynb`. ``` --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-37-ee559b166e25> in <module>() ----> 1 ner_metric = load_metric('seqeval') 2 references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] 3 predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] 4 ner_metric.compute(predictions, references) /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 340 if needs_to_be_installed: 341 raise ImportError( --> 342 f"To be able to use this {module_type}, you need to install the following dependencies" 343 f"{[lib_name for lib_name, lib_path in needs_to_be_installed]} using 'pip install " 344 f"{' '.join([lib_path for lib_name, lib_path in needs_to_be_installed])}' for instance'" ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance' ``` ``` ValueError Traceback (most recent call last) <ipython-input-39-ee559b166e25> in <module>() 2 references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] 3 predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] ----> 4 ner_metric.compute(predictions, references) /usr/local/lib/python3.6/dist-packages/datasets/metric.py in compute(self, *args, **kwargs) 378 """ 379 if args: --> 380 raise ValueError("Please call `compute` using keyword arguments.") 381 382 predictions = kwargs.pop("predictions", None) ValueError: Please call `compute` using keyword arguments. ```

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I_kwDODunzps5NEhLw

ImageFolder raises an error with parameters drop_metadata=True and drop_labels=False when metadata.jsonl is present

closed

CONTRIBUTOR

## Describe the bug If you pass `drop_metadata=True` and `drop_labels=False` when a `data_dir` contains at least one `matadata.jsonl` file, you will get a KeyError. This is probably not a very useful case but we shouldn't get an error anyway. Asking users to move metadata files manually outside `data_dir` or pass features manually (when there is a tool that can infer them automatically) don't look like a good idea to me either. ## Steps to reproduce the bug ### Clone an example dataset from the Hub ```bash git clone https://huggingface.co/datasets/nateraw/test-imagefolder-metadata ``` ### Try to load it ```python from datasets import load_dataset ds = load_dataset("test-imagefolder-metadata", drop_metadata=True, drop_labels=False) ``` or even just ```python ds = load_dataset("test-imagefolder-metadata", drop_metadata=True) ``` as `drop_labels=False` is a default value. ## Expected results A DatasetDict object with two features: `"image"` and `"label"`. ## Actual results ``` Traceback (most recent call last): File "/home/polina/workspace/datasets/debug.py", line 18, in <module> ds = load_dataset( File "/home/polina/workspace/datasets/src/datasets/load.py", line 1732, in load_dataset builder_instance.download_and_prepare( File "/home/polina/workspace/datasets/src/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/polina/workspace/datasets/src/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1218, in _prepare_split example = self.info.features.encode_example(record) File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1596, in encode_example return encode_nested_example(self, example) File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in encode_nested_example { File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in <dictcomp> { File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in zip_dict yield key, tuple(d[key] for d in dicts) File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in <genexpr> yield key, tuple(d[key] for d in dicts) KeyError: 'label' ``` ## Environment info `datasets` master branch - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.14.0-1042-oem-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.1

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MDExOlB1bGxSZXF1ZXN0NTQyNTA4NTM4

second update of id_newspapers_2018

closed

CONTRIBUTOR

The feature "url" is currently set wrongly to data["date"], this PR fix it to data["url"]. I added also an additional POC.

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MDExOlB1bGxSZXF1ZXN0NDEzMzQzMjAy

[Load] Separate Module kwargs and builder kwargs.

closed

MEMBER

Kwargs for the `load_module` fn should be passed with `module_xxxx` to `builder_kwargs` of `load` fn. This is a follow-up PR of: https://github.com/huggingface/nlp/pull/41

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I_kwDODunzps55MjQu

[Feature Request] Dataset versioning

open

NONE

**Is your feature request related to a problem? Please describe.** I am working on a project, where I would like to test different preprocessing methods for my ML-data. Thus, I would like to work a lot with revisions and compare them. Currently, I was not able to make it work with the revision keyword because it was not redownloading the data, it was reading in some cached data, until I put `download_mode="force_redownload"`, even though the reversion was different. Of course, I may have done something wrong or missed a setting somewhere! **Describe the solution you'd like** The solution would allow me to easily work with revisions: - create a new dataset (by combining things, different preprocessing, ..) and give it a new revision (v.1.2.3), maybe like this: `dataset_audio.push_to_hub('kenfus/xy', revision='v1.0.2')` - then, get the current revision as follows: ``` dataset = load_dataset( 'kenfus/xy', revision='v1.0.2', ) ``` this downloads the new version and does not load in a different revision and all future map, filter, .. operations are done on this dataset and not loaded from cache produced from a different revision. - if I rerun the run, the caching should be smart enough in every step to not reuse a mapping operation on a different revision. **Describe alternatives you've considered** I created my own caching, putting `download_mode="force_redownload"` and `load_from_cache_file=False,` everywhere. **Additional context** Thanks a lot for your great work! Creating NLP datasets and training a model with them is really easy and straightforward with huggingface. This is the data loading in my script: ``` ## CREATE PATHS prepared_dataset_path = os.path.join( DATA_FOLDER, str(DATA_VERSION), "prepared_dataset" ) os.makedirs(os.path.join(DATA_FOLDER, str(DATA_VERSION)), exist_ok=True) ## LOAD DATASET if os.path.exists(prepared_dataset_path): print("Loading prepared dataset from disk...") dataset_prepared = load_from_disk(prepared_dataset_path) else: print("Loading dataset from HuggingFace Datasets...") dataset = load_dataset( PATH_TO_DATASET, revision=DATA_VERSION, download_mode="force_redownload" ) print("Preparing dataset...") dataset_prepared = dataset.map( prepare_dataset, remove_columns=["audio", "transcription"], num_proc=os.cpu_count(), load_from_cache_file=False, ) dataset_prepared.save_to_disk(prepared_dataset_path) del dataset if CHECK_DATASET: ## CHECK DATASET dataset_prepared = dataset_prepared.map( check_dimensions, num_proc=os.cpu_count(), load_from_cache_file=False ) dataset_filtered = dataset_prepared.filter( lambda example: not example["incorrect_dimension"], load_from_cache_file=False, ) for example in dataset_prepared.filter( lambda example: example["incorrect_dimension"], load_from_cache_file=False ): print(example["path"]) print( f"Number of examples with incorrect dimension: {len(dataset_prepared) - len(dataset_filtered)}" ) print("Number of examples train: ", len(dataset_filtered["train"])) print("Number of examples test: ", len(dataset_filtered["test"])) ```

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I_kwDODunzps4-4twy

ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py

closed

NONE

## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)* *The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.* ``` raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py ``` Am I the one who added this dataset ? No Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.

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MDExOlB1bGxSZXF1ZXN0NTM4MjEyODUy

Fix namedsplit docs

closed

CONTRIBUTOR

Fixes a broken link and `DatasetInfoMixin.split`'s docstring.

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https://api.github.com/repos/huggingface/datasets/issues/2510

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https://github.com/huggingface/datasets/pull/2510

MDExOlB1bGxSZXF1ZXN0NjcyNDY3MzY3

Add align_labels_with_mapping to DatasetDict

closed

MEMBER

https://github.com/huggingface/datasets/pull/2457 added the `Dataset.align_labels_with_mapping` method. In this PR I also added `DatasetDict.align_labels_with_mapping`

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https://api.github.com/repos/huggingface/datasets/issues/368

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https://github.com/huggingface/datasets/issues/368

MDU6SXNzdWU2NTQwODcyNTE=

load_metric can't acquire lock anymore

closed

NONE

I can't load metric (glue) anymore after an error in a previous run. I even removed the whole cache folder `/home/XXX/.cache/huggingface/`, and the issue persisted. What are the steps to fix this? Traceback (most recent call last): File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 101, in __init__ self.filelock.acquire(timeout=1) File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/filelock.py", line 278, in acquire raise Timeout(self._lock_file) filelock.Timeout: The file lock '/home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock' could not be acquired. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "examples_huggingface_nlp.py", line 268, in <module> main() File "examples_huggingface_nlp.py", line 242, in main dataset, metric = get_dataset_metric(glue_task) File "examples_huggingface_nlp.py", line 77, in get_dataset_metric metric = nlp.load_metric('glue', glue_config, experiment_id=1) File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/load.py", line 440, in load_metric **metric_init_kwargs, File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 104, in __init__ "Cannot acquire lock, caching file might be used by another process, " ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id' for this run. I0709 15:54:41.008838 139854118430464 filelock.py:318] Lock 139852058030936 released on /home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock

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https://github.com/huggingface/datasets/pull/231

MDExOlB1bGxSZXF1ZXN0NDI3MTk3MTcz

Add .download to MockDownloadManager

closed

MEMBER

One method from the DownloadManager was missing and some users couldn't run the tests because of that. @yjernite

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https://api.github.com/repos/huggingface/datasets/issues/731

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