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PR_kwDODunzps434izs

Fix example in opus_ubuntu, Add license info

closed

CONTRIBUTOR

This PR * fixes a typo in the example for the`opus_ubuntu` dataset where it's mistakenly referred to as `ubuntu` * adds the declared license info for this corpus' origin * adds an example instance * updates the data origin type

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PR_kwDODunzps434Pb6

Fix Version equality

closed

MEMBER

I think `Version` equality should align with other similar cases in Python, like: ```python In [1]: "a" == 5, "a" == None Out[1]: (False, False) In [2]: "a" != 5, "a" != None Out[2]: (True, True) ``` With this PR, we will get: ```python In [3]: Version("1.0.0") == 5, Version("1.0.0") == None Out[3]: (False, False) In [4]: Version("1.0.0") != 5, Version("1.0.0") != None Out[4]: (True, True) ``` Note I found this issue when `doc-builder` tried to compare: ```python if param.default != inspect._empty ``` where `param.default` is an instance of `Version`.

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I_kwDODunzps5JvWAs

Missing dataset tags and sections in some dataset cards

open

CONTRIBUTOR

Summary of CircleCI errors for different dataset metadata: - **BoolQ**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - **Conllpp**: expected some content in section `Citation Information` but it is empty. - **GLUE**: 'annotations_creators', 'language_creators', 'source_datasets' :['unknown'] are not registered tags - **ConLL2003**: field 'task_ids': ['part-of-speech-tagging'] are not registered tags for 'task_ids' - **Hate_speech18:** Expected some content in section `Data Instances` but it is empty, Expected some content in section `Data Splits` but it is empty - **Jjigsaw_toxicity_pred**: `Citation Information` but it is empty. - **LIAR** : `Data Instances`,`Data Fields`, `Data Splits`, `Citation Information` are empty. - **MSRA NER** : Dataset Summary`, `Data Instances`, `Data Fields`, `Data Splits`, `Citation Information` are empty. - **sem_eval_2010_task_8**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - **sms_spam**: `Data Instances` and`Data Splits` are empty. - **Quora** : Expected some content in section `Citation Information` but it is empty, missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - **sentiment140**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'

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PR_kwDODunzps4333b9

Fix warning in push_to_hub

closed

MEMBER

Fix warning: ``` FutureWarning: 'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0. ```

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PR_kwDODunzps433OsB

Fix dataset builder default version

closed

MEMBER

Currently, when using a custom config (subclass of `BuilderConfig`), default version set at the builder level is ignored: we must set default version in the custom config class. However, when loading a dataset with `config_kwargs` (for a configuration not present in `BUILDER_CONFIGS`), the default version set in the custom config is ignored and "0.0.0" is used instead: ```python ds = load_dataset("wikipedia", language="co", date="20220501", beam_runner="DirectRunner") ``` generates the following config: ```python WikipediaConfig(name='20220501.co', version=0.0.0, data_dir=None, data_files=None, description='Wikipedia dataset for co, parsed from 20220501 dump.') ``` with version "0.0.0" instead of "2.0.0". See as a counter-example, when the config is present in `BUILDER_CONFIGS`: ```python ds = load_dataset("wikipedia", "20220301.fr", beam_runner="DirectRunner") ``` generates the following config: ```python WikipediaConfig(name='20220301.fr', version=2.0.0, data_dir=None, data_files=None, description='Wikipedia dataset for fr, parsed from 20220301 dump.') ``` with correct version "2.0.0", as set in the custom config class. The reason for this is that `DatasetBuilder` has a default VERSION ("0.0.0") that overwrites the default version set at the custom config class. This PR: - Removes the default VERSION at `DatasetBuilder` (set to None, so that the class attribute exists but it does not override the custom config default version). - Note that the `BuilderConfig` class already sets a default version = "0.0.0"; no need to pass this from the builder.

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PR_kwDODunzps433EgP

Fix warning in upload_file

closed

MEMBER

Fix warning: ``` FutureWarning: Pass path_or_fileobj='...' as keyword args. From version 0.7 passing these as positional arguments will result in an error ```

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I_kwDODunzps5Jsgif

Problems with WMT dataset

closed

NONE

## Describe the bug I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore. ## Steps to reproduce the bug ```shell >>> import datasets >>> a = datasets.translate.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'translate' >>> a = datasets.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'wmt' ``` ## Expected results To load WMT15 with given data-sources. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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PR_kwDODunzps43016x

Don't strip proceeding hyphen

closed

CONTRIBUTOR

Closes #4320.

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I_kwDODunzps5JrS2a

When using `dataset.map()` if passed `Features` types do not match what is returned from the mapped function, execution does not except in an obvious way

open

NONE

## Describe the bug Recently I was trying to using `.map()` to preprocess a dataset. I defined the expected Features and passed them into `.map()` like `dataset.map(preprocess_data, features=features)`. My expected `Features` keys matched what came out of `preprocess_data`, but the types i had defined for them did not match the types that came back. Because of this, i ended up in tracebacks deep inside arrow_dataset.py and arrow_writer.py with exceptions that [did not make clear what the problem was](https://github.com/huggingface/datasets/issues/4349). In short i ended up with overflows and the OS killing processes when Arrow was attempting to write. It wasn't until I dug into `def write_batch` and the loop that loops over cols that I figured out what was going on. It seems like `.map()` could set a boolean that it's checked that for at least 1 instance from the dataset, the returned data's types match the types provided by the `features` param and error out with a clear exception if they don't. This would make the cause of the issue much more understandable and save people time. This could be construed as a feature but it feels more like a bug to me. ## Steps to reproduce the bug I don't have explicit code to repro the bug, but ill show an example Code prior to the fix: ```python def preprocess(examples): # returns an encoded data dict with keys that match the features, but the types do not match ... def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['audit_type'].unique().tolist() features = Features({ 'image': Array3D(dtype="uint8", shape=(3, 224, 224))), 'input_ids': Sequence(feature=Value(dtype='int64'))), 'attention_mask': Sequence(Value(dtype='int64'))), 'token_type_ids': Sequence(Value(dtype='int64'))), 'bbox': Array2D(dtype="int64", shape=(512, 4))), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names) ``` The Features set that fixed it: ```python features = Features({ 'image': Sequence(Array3D(dtype="uint8", shape=(3, 224, 224))), 'input_ids': Sequence(Sequence(feature=Value(dtype='int64'))), 'attention_mask': Sequence(Sequence(Value(dtype='int64'))), 'token_type_ids': Sequence(Sequence(Value(dtype='int64'))), 'bbox': Sequence(Array2D(dtype="int64", shape=(512, 4))), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) ``` The difference between my original code (which was based on documentation) and the working code is the addition of the `Sequence(...)` to 4/5 features as I am working with paginated data and the doc examples are not. ## Expected results Dataset.map() attempts to validate the data types for each Feature on the first iteration and errors out if they are not validated. ## Actual results Specify the actual results or traceback. Based on the value of `writer_batch_size`, execution errors out when Arrow attempts to write because the types do not match, though its error messages dont make this obvious Example errors: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> datasets version: 2.1.0 Platform: macOS-12.2.1-arm64-arm-64bit Python version: 3.9.12 PyArrow version: 6.0.1 Pandas version: 1.4.2

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I_kwDODunzps5JqxqB

Add optional progress bar for .save_to_disk(..) and .load_from_disk(..) when working with remote filesystems

closed

NONE

**Is your feature request related to a problem? Please describe.** When working with large datasets stored on remote filesystems(such as s3), the process of uploading a dataset could take really long time. For instance: I was uploading a re-processed version of wmt17 en-ru to my s3 bucket and it took like 35 minutes(and that's given that I have a fiber optic connection). The only output during that process was a progress bar for flattening indices and then ~35 minutes of complete silence. **Describe the solution you'd like** I want to be able to enable a progress bar when calling .save_to_disk(..) and .load_from_disk(..), it would track either amount of bytes sent/received or amount of records written/loaded, and will give some ETA. Basically just tqdm. **Describe alternatives you've considered** - Save dataset to tmp folder at the disk and then upload it using custom wrapper over botocore, which will work with progress bar, like [this](https://alexwlchan.net/2021/04/s3-progress-bars/).

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PR_kwDODunzps43zKIV

Add a new metric: CTC_Consistency

closed

NONE

Add CTC_Consistency metric Do I also need to modify the `test_metric_common.py` file to make it run on test?

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I_kwDODunzps5Jo9lN

Dataset.map()'s fails at any value of parameter writer_batch_size

closed

NONE

## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2

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I_kwDODunzps5JozYQ

`inspect` functions can't fetch dataset script from the Hub

closed

MEMBER

The `inspect_dataset` and `inspect_metric` functions are unable to retrieve a dataset or metric script from the Hub and store it locally at the specified `local_path`: ```py >>> from datasets import inspect_dataset >>> inspect_dataset('rotten_tomatoes', local_path='path/to/my/local/folder') FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory. ```

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PR_kwDODunzps43yihq

Support remote cache_dir

closed

MEMBER

This PR implements complete support for remote `cache_dir`. Before, the support was just partial. This is useful to create datasets using Apache Beam (parallel data processing) builder with `cache_dir` in a remote bucket, e.g., for Wikipedia dataset.

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I_kwDODunzps5JnaC2

GH Action to build documentation never ends

closed

MEMBER

## Describe the bug See: https://github.com/huggingface/datasets/runs/6418035586?check_suite_focus=true I finally forced the cancel of the workflow.

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PR_kwDODunzps43xrky

Fix never ending GH Action to build documentation

closed

MEMBER

There was an unclosed code block introduced by: - #4313 https://github.com/huggingface/datasets/pull/4313/files#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R538 This causes the "Make documentation" step in the "Build documentation" workflow to never finish. - I think this issue should also be addressed in the `doc-builder` lib. Fix #4346.

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PR_kwDODunzps43xFEn

Fix docstring in DatasetDict::shuffle

closed

CONTRIBUTOR

I think due to #1626, the docstring contained this error ever since `seed` was added.

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I_kwDODunzps5Jmogo

Metrics documentation is not accessible in the datasets doc UI

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Search for a metric name like "seqeval" yields no results on https://huggingface.co/docs/datasets/master/en/index . One needs to go look in `datasets/metrics/README.md` to find the doc. Even in the `README.md`, it can be hard to understand what the metric expects as an input, for example for `squad` there is a [key `id`](https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L42) documented only in the function doc but not in the `README.md`, and one needs to go look into the code to understand what the metric expects. **Describe the solution you'd like** Have the documentation for metrics appear as well in the doc UI, e.g. this https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L21-L63 I know there are plans to migrate metrics to the evaluate library, but just pointing this out.

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PR_kwDODunzps43woHm

Fix failing CI on Windows for sari and wiki_split metrics

closed

MEMBER

This PR adds `sacremoses` as explicit tests dependency (required by sari and wiki_split metrics). Before, this library was installed as a third-party dependency, but this is no longer the case for Windows. Fix #4341.

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I_kwDODunzps5JmKH3

Failing CI on Windows for sari and wiki_split metrics

closed

MEMBER

## Describe the bug Our CI is failing from yesterday on Windows for metrics: sari and wiki_split ``` FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_sari - ... FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_wiki_split ``` See: https://app.circleci.com/pipelines/github/huggingface/datasets/11928/workflows/79daa5e7-65c9-4e85-829b-00d2bfbd076a/jobs/71594

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PR_kwDODunzps43wY1U

Fix irc_disentangle dataset script

closed

NONE

updated extracted dataset's repo's latest commit hash (included in tarball's name), and updated the related data_infos.json

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PR_kwDODunzps43v0WT

Dataset loader for the MSLR2022 shared task

closed

CONTRIBUTOR

This PR adds a dataset loader for the [MSLR2022 Shared Task](https://github.com/allenai/mslr-shared-task). Both the MS^2 and Cochrane datasets can be loaded with this dataloader: ```python from datasets import load_dataset ms2 = load_dataset("mslr2022", "ms2") cochrane = load_dataset("mslr2022", "cochrane") ``` Usage looks like: ```python >>> ms2 = load_dataset("mslr2022", "ms2", split="validation") >>> ms2.keys() dict_keys(['review_id', 'pmid', 'title', 'abstract', 'target', 'background', 'reviews_info']) >>> ms2[0].target 'Conclusions SC therapy is effective for PAH in pre clinical studies .\nThese results may help to st and ardise pre clinical animal studies and provide a theoretical basis for clinical trial design in the future .' ``` I have tested this works with the following command: ```bash datasets-cli test datasets/mslr2022 --save_infos --all_configs ``` However I have having a little trouble generating the dummy data ```bash datasets-cli dummy_data datasets/mslr2022 --auto_generate ``` errors out with the following stack trace: ``` Couldn't generate dummy file 'datasets/mslr2022/dummy/ms2/1.0.0/dummy_data/mslr_data.tar.gz/mslr_data/ms2/convert_to_cochrane.py'. Ignore that if this file is not useful for dummy data. Traceback (most recent call last): File "/Users/johngiorgi/.pyenv/versions/datasets/bin/datasets-cli", line 11, in <module> load_entry_point('datasets', 'console_scripts', 'datasets-cli')() File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/commands/datasets_cli.py", line 39, in main service.run() File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/commands/dummy_data.py", line 319, in run keep_uncompressed=self._keep_uncompressed, File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/commands/dummy_data.py", line 361, in _autogenerate_dummy_data dataset_builder._prepare_split(split_generator, check_duplicate_keys=False) File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/builder.py", line 1146, in _prepare_split desc=f"Generating {split_info.name} split", File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/Users/johngiorgi/.cache/huggingface/modules/datasets_modules/datasets/mslr2022/b4becd2f52cf18255d4934d7154c2a1127fb393371b87b3c1fc2c8b35a777cea/mslr2022.py", line 149, in _generate_examples reviews_info_df = pd.read_csv(reviews_info_filepath, index_col=0) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper return func(*args, **kwargs) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 586, in read_csv return _read(filepath_or_buffer, kwds) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 488, in _read return parser.read(nrows) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 1047, in read index, columns, col_dict = self._engine.read(nrows) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 224, in read chunks = self._reader.read_low_memory(nrows) File "pandas/_libs/parsers.pyx", line 801, in pandas._libs.parsers.TextReader.read_low_memory File "pandas/_libs/parsers.pyx", line 857, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 843, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 1925, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: EOF inside string starting at row 2 ``` I think this may have to do with unusual line terminators in the original data. When I open it in VSCode, it complains: ``` The file 'dev-inputs.csv' contains one or more unusual line terminator characters, like Line Separator (LS) or Paragraph Separator (PS). It is recommended to remove them from the file. This can be configured via `editor.unusualLineTerminators`. ``` Tagging the organizers of the shared task in case they want to sanity check this or add any info to the model card :) @lucylw @jayded

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PR_kwDODunzps43vwsm

Eval metadata Batch 4: Tweet Eval, Tweets Hate Speech Detection, VCTK, Weibo NER, Wisesight Sentiment, XSum, Yahoo Answers Topics, Yelp Polarity, Yelp Review Full

closed

CONTRIBUTOR

Adding evaluation metadata for: - Tweet Eval - Tweets Hate Speech Detection - VCTK - Weibo NER - Wisesight Sentiment - XSum - Yahoo Answers Topics - Yelp Polarity - Yelp Review Full

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PR_kwDODunzps43vuzF

Eval metadata batch 3: Reddit, Rotten Tomatoes, SemEval 2010, Sentiment 140, SMS Spam, Snips, SQuAD, SQuAD v2, Timit ASR

closed

CONTRIBUTOR

Adding evaluation metadata for: - Reddit - Rotten Tomatoes - SemEval 2010 - Sentiment 140 - SMS Spam - Snips - SQuAD - SQuAD v2 - Timit ASR

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PR_kwDODunzps43vpqG

Eval metadata batch 2 : Health Fact, Jigsaw Toxicity, LIAR, LJ Speech, MSRA NER, Multi News, NCBI Disease, Poem Sentiment

closed

CONTRIBUTOR

Adding evaluation metadata for : - Health Fact - Jigsaw Toxicity - LIAR - LJ Speech - MSRA NER - Multi News - NCBI Diseas - Poem Sentiment

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PR_kwDODunzps43usJP

Eval metadata batch 1: BillSum, CoNLL2003, CoNLLPP, CUAD, Emotion, GigaWord, GLUE, Hate Speech 18, Hate Speech

closed

CONTRIBUTOR

Adding evaluation metadata for: - BillSum - CoNLL2003 - CoNLLPP - CUAD - Emotion - GigaWord - GLUE - Hate Speech 18 - Hate Speech Offensive

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PR_kwDODunzps43uguB

Adding eval metadata for billsum

closed

CONTRIBUTOR

Adding eval metadata for billsum

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PR_kwDODunzps43uSuj

Adding eval metadata for Banking 77

closed

CONTRIBUTOR

Adding eval metadata for Banking 77

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PR_kwDODunzps43uO8S

Adding eval metadata for arabic speech corpus

closed

CONTRIBUTOR

Adding eval metadata for arabic speech corpus

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PR_kwDODunzps43uN2R

Adding eval metadata to Amazon Polarity

closed

CONTRIBUTOR

Adding eval metadata to Amazon Polarity

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PR_kwDODunzps43uIwm

Adding eval metadata to Allociné dataset

closed

CONTRIBUTOR

Adding eval metadata to Allociné dataset

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PR_kwDODunzps43uIcF

Adding eval metadata for AG News

closed

CONTRIBUTOR

Adding eval metadata for AG News

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PR_kwDODunzps43trrd

Fix and clean Apache Beam functionality

closed

MEMBER

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I_kwDODunzps5JiueU

`wikipedia` pre-processed datasets

closed

NONE

## Describe the bug [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("wikipedia", "20220301.en") ``` ## Expected results To load the dataset ## Actual results Takes a very long time to load (after downloading) After `Downloading data files: 100%`. It takes hours and gets killed. Tried `wikipedia.simple` and it got processed after ~30mins.

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PR_kwDODunzps43tjWy

Fix type hint and documentation for `new_fingerprint`

closed

CONTRIBUTOR

Currently, there are no type hints nor `Optional` for the argument `new_fingerprint` in several methods of `datasets.arrow_dataset.Dataset`. There was some documentation missing as well. Note that pylance is happy with the type hints, but pyright does not detect that `new_fingerprint` is set within the decorator. The modifications in this PR are fine since here https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/src/datasets/fingerprint.py#L446-L454 for the non-inplace case we make sure to auto-generate a new fingerprint (as indicated in the doc).

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I_kwDODunzps5Jinrf

Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance

closed

CONTRIBUTOR

### Link https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train ### Description The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time. * https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train * https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped! ### Owner Yes

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I_kwDODunzps5JigCG

Support >1 PWC dataset per dataset card

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Some datasets cover more than one dataset on PapersWithCode. For example, the OffensEval 2020 challenge involved five languages, and there's one dataset to cover all five datasets, [`strombergnlp/offenseval_2020`](https://huggingface.co/datasets/strombergnlp/offenseval_2020). However, the yaml `paperswithcode_id:` dataset card entry only supports one value; when multiple are added, the PWC link disappears from the dataset page. Because the link from a PapersWithCode dataset to a Hugging Face Hub entry can't be entered manually and seems to be scraped, this means end users don't have a way of getting a dataset reader link to appear on all the PWC datasets supported by one HF Hub Dataset reader. It's not super unusual to have papers introduce multiple parallel variants of a dataset and would be handy to reflect this, so e.g. dataset maintainers can DRY, and so dataset users can keep what they're doing simple. **Describe the solution you'd like** I'd like `paperswithcode_id:` to support lists and be able to connect with multiple PWC datasets. **Describe alternatives you've considered** De-normalising the datasets on HF Hub to create multiple readers for each variation on a task, i.e. instead of a single `offenseval_2020`, having `offenseval_2020_ar`, `offenseval_2020_da`, `offenseval_2020_gr`, ... **Additional context** Hope that's enough **Priority** Low

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I_kwDODunzps5Jh8Zw

Audio can not find value["bytes"]

closed

CONTRIBUTOR

## Describe the bug I wrote down _generate_examples like:  but where is the bytes?  ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1

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PR_kwDODunzps43s1wy

Added stratify option to train_test_split function.

closed

CONTRIBUTOR

This PR adds `stratify` option to `train_test_split` method. I took reference from scikit-learn's `StratifiedShuffleSplit` class for implementing stratified split and integrated the changes as were suggested by @lhoestq. It fixes #3452. @lhoestq Please review and let me know, if any changes are required.

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PR_kwDODunzps43ryW7

Adding dataset enwik8

closed

CONTRIBUTOR

Because I regularly work with enwik8, I would like to contribute the dataset loader 🤗

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I_kwDODunzps5JgUYg

Multi-news dataset loader attempts to strip wrong character from beginning of summaries

closed

CONTRIBUTOR

## Describe the bug The `multi_news.py` data loader has [a line which attempts to strip `"- "` from the beginning of summaries](https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/datasets/multi_news/multi_news.py#L97). The actual character in the multi-news dataset, however, is `"– "`, which is different, e.g. `"– " != "- "`. I would have just opened a PR to fix the mistake, but I am wondering what the motivation for stripping this character is? AFAICT most approaches just leave it in, e.g. the current SOTA on this dataset, [PRIMERA](https://huggingface.co/allenai/PRIMERA-multinews) (you can see its in the generated summaries of the model in their [example notebook](https://github.com/allenai/PRIMER/blob/main/Evaluation_Example.ipynb)). ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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PR_kwDODunzps43q0UY

Adding eval metadata for ade v2

closed

CONTRIBUTOR

Adding metadata to allow evaluation

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PR_kwDODunzps43qkkQ

Don't check f.loc in _get_extraction_protocol_with_magic_number

closed

MEMBER

`f.loc` doesn't always exist for file-like objects in python. I removed it since it was not necessary anyway (we always seek the file to 0 after reading the magic number) Fix https://github.com/huggingface/datasets/issues/4310

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PR_kwDODunzps43qBzh

Fix cnn_dailymail (dm stories were ignored)

closed

MEMBER

https://github.com/huggingface/datasets/pull/4188 introduced a bug in `datasets` 2.2.0: DailyMail stories are ignored when generating the dataset. I fixed that, and removed the google drive link (it has annoying quota limitations issues) We can do a patch release after this is merged

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PR_kwDODunzps43p1Za

Support passing config_kwargs to CLI run_beam

closed

MEMBER

This PR supports passing `config_kwargs` to CLI run_beam, so that for example for "wikipedia" dataset, we can pass: ``` --date 20220501 --language ca ```

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PR_kwDODunzps43pZ6p

Fix CLI run_beam namespace

closed

MEMBER

Currently, it raises TypeError: ``` TypeError: __init__() got an unexpected keyword argument 'namespace' ```

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PR_kwDODunzps43oqXD

Catch pull error when mirroring

closed

MEMBER

Catch pull errors when mirroring so that the script continues to update the other datasets. The error will still be printed at the end of the job. In this case the job also fails, and asks to manually update the datasets that failed.

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PR_kwDODunzps43m4qB

Add API code examples for Builder classes

closed

MEMBER

This PR adds API code examples for the Builder classes.

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PR_kwDODunzps43mlug

added TR-News dataset

closed

NONE

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PR_kwDODunzps43ln8-

[Imagefolder] Docs + Don't infer labels from file names when there are metadata + Error messages when metadata and images aren't linked correctly

closed

MEMBER

I updated the `docs/source/image_process.mdx` documentation and added an example for image captioning and object detection using `ImageFolder`. While doing so I also improved a few aspects: - we don't need to infer labels from file names when there are metadata - they can just be in the metadata if necessary - raise informative error messages when metadata and images aren't linked correctly: - when an image is missing a metadata file - when a metadata file is missing an image I added some tests for these changes as well cc @mariosasko

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I_kwDODunzps5JZHMH

Loading dataset with streaming: '_io.BufferedReader' object has no attribute 'loc'

closed

NONE

## Describe the bug Loading a datasets with `load_dataset` and `streaming=True` returns `AttributeError: '_io.BufferedReader' object has no attribute 'loc'`. Notice that loading with `streaming=False` works fine. In the following steps we load parquet files but the same happens with pickle files. The problem seems to come from `fsspec` lib, I put in the environment info also `s3fs` and `fsspec` versions since I'm loading from an s3 bucket. ## Steps to reproduce the bug ```python from datasets import load_dataset # path is the path to parquet files data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"} dataset = load_dataset("parquet", data_files=data_files, streaming=True) ``` ## Expected results A dataset object `datasets.dataset_dict.DatasetDict` ## Actual results ``` AttributeError Traceback (most recent call last) <command-562086> in <module> 11 12 data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"} ---> 13 dataset = load_dataset("parquet", data_files=data_files, streaming=True) /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1679 if streaming: 1680 extend_dataset_builder_for_streaming(builder_instance, use_auth_token=use_auth_token) -> 1681 return builder_instance.as_streaming_dataset( 1682 split=split, 1683 use_auth_token=use_auth_token, /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/builder.py in as_streaming_dataset(self, split, base_path, use_auth_token) 904 ) 905 self._check_manual_download(dl_manager) --> 906 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)} 907 # By default, return all splits 908 if split is None: /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py in _split_generators(self, dl_manager) 30 if not self.config.data_files: 31 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}") ---> 32 data_files = dl_manager.download_and_extract(self.config.data_files) 33 if isinstance(data_files, (str, list, tuple)): 34 files = data_files /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in download_and_extract(self, url_or_urls) 798 799 def download_and_extract(self, url_or_urls): --> 800 return self.extract(self.download(url_or_urls)) 801 802 def iter_archive(self, urlpath_or_buf: Union[str, io.BufferedReader]) -> Iterable[Tuple]: /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in extract(self, path_or_paths) 776 777 def extract(self, path_or_paths): --> 778 urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True) 779 return urlpaths 780 /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 312 num_proc = 1 313 if num_proc <= 1 or len(iterable) <= num_proc: --> 314 mapped = [ 315 _single_map_nested((function, obj, types, None, True, None)) 316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0) 313 if num_proc <= 1 or len(iterable) <= num_proc: 314 mapped = [ --> 315 _single_map_nested((function, obj, types, None, True, None)) 316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 317 ] /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args) 267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} 268 else: --> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] 270 if isinstance(data_struct, list): 271 return mapped /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0) 267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} 268 else: --> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] 270 if isinstance(data_struct, list): 271 return mapped /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args) 249 # Singleton first to spare some computation 250 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 251 return function(data_struct) 252 253 # Reduce logging to keep things readable in multiprocessing with tqdm /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _extract(self, urlpath) 781 def _extract(self, urlpath: str) -> str: 782 urlpath = str(urlpath) --> 783 protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token) 784 if protocol is None: 785 # no extraction /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol(urlpath, use_auth_token) 371 urlpath, kwargs = urlpath, {} 372 with fsspec.open(urlpath, **kwargs) as f: --> 373 return _get_extraction_protocol_with_magic_number(f) 374 375 /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol_with_magic_number(f) 335 def _get_extraction_protocol_with_magic_number(f) -> Optional[str]: 336 """read the magic number from a file-like object and return the compression protocol""" --> 337 prev_loc = f.loc 338 magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH) 339 f.seek(prev_loc) /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/fsspec/implementations/local.py in __getattr__(self, item) 337 338 def __getattr__(self, item): --> 339 return getattr(self.f, item) 340 341 def __enter__(self): AttributeError: '_io.BufferedReader' object has no attribute 'loc' ``` ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.4.0-1071-aws-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 - `fsspec` version: 2021.08.1 - `s3fs` version: 2021.08.1

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PR_kwDODunzps43lKpm

[WIP] Add TEDLIUM dataset

closed

CONTRIBUTOR

Adds the TED-LIUM dataset https://www.tensorflow.org/datasets/catalog/tedlium#tedliumrelease3 TODO: - [x] Port `tedium.py` from TF datasets using `convert_dataset.sh` script - [x] Make `load_dataset` work - [ ] ~~Run `datasets-cli` command to generate `dataset_infos.json`~~ - [ ] ~~Create dummy data for continuous testing~~ - [ ] ~~Dummy data tests~~ - [ ] ~~Real data tests~~ - [ ] Create the metadata JSON - [ ] Close PR and add directly to the Hub under LIUM org

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PR_kwDODunzps43lHdP

Remove unused multiprocessing args from test CLI

closed

MEMBER

Multiprocessing is not used in the test CLI.

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PR_kwDODunzps43k-Wo

Add packaged builder configs to the documentation

closed

MEMBER

Add the packaged builders configurations to the docs reference is useful to show the list of all parameters one can use when loading data in many formats: CSV, JSON, etc.

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I_kwDODunzps5JYam0

`load_dataset` does not work with certain filename.

closed

NONE

## Describe the bug This is a weird bug that took me some time to find out. I have a JSON dataset that I want to load with `load_dataset` like this: ``` data_files = dict(train="train.json.zip", val="val.json.zip") dataset = load_dataset("json", data_files=data_files, field="data") ``` ## Expected results No error. ## Actual results The val file is loaded as expected, but the train file throws JSON decoding error: ``` ╭──────────────────────────── Traceback (most recent call last) ────────────────────────────╮ │ <ipython-input-74-97947e92c100>:5 in <module> │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/load.py:1687 in │ │ load_dataset │ │ │ │ 1684 │ try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES │ │ 1685 │ │ │ 1686 │ # Download and prepare data │ │ ❱ 1687 │ builder_instance.download_and_prepare( │ │ 1688 │ │ download_config=download_config, │ │ 1689 │ │ download_mode=download_mode, │ │ 1690 │ │ ignore_verifications=ignore_verifications, │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:605 in │ │ download_and_prepare │ │ │ │ 602 │ │ │ │ │ │ except ConnectionError: │ │ 603 │ │ │ │ │ │ │ logger.warning("HF google storage unreachable. Downloa │ │ 604 │ │ │ │ │ if not downloaded_from_gcs: │ │ ❱ 605 │ │ │ │ │ │ self._download_and_prepare( │ │ 606 │ │ │ │ │ │ │ dl_manager=dl_manager, verify_infos=verify_infos, **do │ │ 607 │ │ │ │ │ │ ) │ │ 608 │ │ │ │ │ # Sync info │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:694 in │ │ _download_and_prepare │ │ │ │ 691 │ │ │ │ │ 692 │ │ │ try: │ │ 693 │ │ │ │ # Prepare split will record examples associated to the split │ │ ❱ 694 │ │ │ │ self._prepare_split(split_generator, **prepare_split_kwargs) │ │ 695 │ │ │ except OSError as e: │ │ 696 │ │ │ │ raise OSError( │ │ 697 │ │ │ │ │ "Cannot find data file. " │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:1151 in │ │ _prepare_split │ │ │ │ 1148 │ │ │ │ 1149 │ │ generator = self._generate_tables(**split_generator.gen_kwargs) │ │ 1150 │ │ with ArrowWriter(features=self.info.features, path=fpath) as writer: │ │ ❱ 1151 │ │ │ for key, table in logging.tqdm( │ │ 1152 │ │ │ │ generator, unit=" tables", leave=False, disable=True # not loggin │ │ 1153 │ │ │ ): │ │ 1154 │ │ │ │ writer.write_table(table) │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/notebook.py:257 in │ │ __iter__ │ │ │ │ 254 │ │ │ 255 │ def __iter__(self): │ │ 256 │ │ try: │ │ ❱ 257 │ │ │ for obj in super(tqdm_notebook, self).__iter__(): │ │ 258 │ │ │ │ # return super(tqdm...) will not catch exception │ │ 259 │ │ │ │ yield obj │ │ 260 │ │ # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/std.py:1183 in │ │ __iter__ │ │ │ │ 1180 │ │ # If the bar is disabled, then just walk the iterable │ │ 1181 │ │ # (note: keep this check outside the loop for performance) │ │ 1182 │ │ if self.disable: │ │ ❱ 1183 │ │ │ for obj in iterable: │ │ 1184 │ │ │ │ yield obj │ │ 1185 │ │ │ return │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/packaged_modules/j │ │ son/json.py:90 in _generate_tables │ │ │ │ 87 │ │ │ # If the file is one json object and if we need to look at the list of │ │ 88 │ │ │ if self.config.field is not None: │ │ 89 │ │ │ │ with open(file, encoding="utf-8") as f: │ │ ❱ 90 │ │ │ │ │ dataset = json.load(f) │ │ 91 │ │ │ │ │ │ 92 │ │ │ │ # We keep only the field we are interested in │ │ 93 │ │ │ │ dataset = dataset[self.config.field] │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:293 in load │ │ │ │ 290 │ To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` │ │ 291 │ kwarg; otherwise ``JSONDecoder`` is used. │ │ 292 │ """ │ │ ❱ 293 │ return loads(fp.read(), │ │ 294 │ │ cls=cls, object_hook=object_hook, │ │ 295 │ │ parse_float=parse_float, parse_int=parse_int, │ │ 296 │ │ parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:357 in loads │ │ │ │ 354 │ if (cls is None and object_hook is None and │ │ 355 │ │ │ parse_int is None and parse_float is None and │ │ 356 │ │ │ parse_constant is None and object_pairs_hook is None and not kw): │ │ ❱ 357 │ │ return _default_decoder.decode(s) │ │ 358 │ if cls is None: │ │ 359 │ │ cls = JSONDecoder │ │ 360 │ if object_hook is not None: │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:337 in decode │ │ │ │ 334 │ │ containing a JSON document). │ │ 335 │ │ │ │ 336 │ │ """ │ │ ❱ 337 │ │ obj, end = self.raw_decode(s, idx=_w(s, 0).end()) │ │ 338 │ │ end = _w(s, end).end() │ │ 339 │ │ if end != len(s): │ │ 340 │ │ │ raise JSONDecodeError("Extra data", s, end) │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:353 in raw_decode │ │ │ │ 350 │ │ │ │ 351 │ │ """ │ │ 352 │ │ try: │ │ ❱ 353 │ │ │ obj, end = self.scan_once(s, idx) │ │ 354 │ │ except StopIteration as err: │ │ 355 │ │ │ raise JSONDecodeError("Expecting value", s, err.value) from None │ │ 356 │ │ return obj, end │ ╰───────────────────────────────────────────────────────────────────────────────────────────╯ JSONDecodeError: Unterminated string starting at: line 85 column 20 (char 60051) ``` However, when I rename the `train.json.zip` to other names (like `training.json.zip`, or even to `train.json`), everything works fine; when I unzip the file to `train.json`, it works as well. ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-4.4.0-131-generic-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 ```

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PR_kwDODunzps43kt4P

Fixes FrugalScore

open

CONTRIBUTOR

There are two minor modifications in this PR: 1) `predictions` and `references` are swapped. Basically Frugalscore is commutative, however some tiny differences can occur if we swap the references and the predictions. I decided to swap them just to obtain the exact results as reported in the paper. 2) I switched to dynamic padding that was was used in the training, forcing the padding to `max_length` introduces errors for some reason that I ignore. @lhoestq

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I_kwDODunzps5JYEmL

Language code search does direct matches

open

CONTRIBUTOR

## Describe the bug Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search. ## Steps to reproduce the bug 1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL)) 2. Look for datasets using the full code 3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq)) Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`. One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :) ## Expected results Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`). ## Actual results The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches. ## Environment info (web app)

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PR_kwDODunzps43j8cH

Fix: Add missing comma

closed

CONTRIBUTOR

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PR_kwDODunzps43jPE5

Remove hacking license tags when mirroring datasets on the Hub

closed

MEMBER

Currently, when mirroring datasets on the Hub, the license tags are hacked: removed of characters "." and "$". On the contrary, this hacking is not applied to community datasets on the Hub. This generates multiple variants of the same tag on the Hub. I guess this hacking is no longer necessary: - it is not applied to community datasets - all canonical datasets are validated by maintainers before being merged: CI + maintainers make sure license tags are the right ones Fix #4298.

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PR_kwDODunzps43idlE

Add ImageNet-Sketch dataset

closed

CONTRIBUTOR

This PR adds the ImageNet-Sketch dataset and resolves #3953 .

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PR_kwDODunzps43iA86

Add API code examples for loading methods

closed

MEMBER

This PR adds API code examples for loading methods, let me know if I've missed any important parameters we should showcase :) I was a bit confused about `inspect_dataset` and `inspect_metric`. The `path` parameter says it will accept a dataset identifier from the Hub. But when I try the identifier `rotten_tomatoes`, it gives me: ```py from datasets import inspect_dataset inspect_dataset('rotten_tomatoes', local_path='/content/rotten_tomatoes') FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory. ``` Does the user need to have an existing copy of `rotten_tomatoes.py` on their local drive (in which case, it seems like the same option as the first option in `path`)?

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PR_kwDODunzps43h5RP

Remove manual download from imagenet-1k

closed

CONTRIBUTOR

Remove the manual download code from `imagenet-1k` to make it a regular dataset.

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I_kwDODunzps5JTHcm

Normalise license names

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata. **Describe the solution you'd like** I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) . **Describe alternatives you've considered** None **Additional context** None **Priority** Low

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I_kwDODunzps5JTEZK

Datasets YAML tagging space is down

closed

CONTRIBUTOR

## Describe the bug The neat hf spaces app for generating YAML tags for dataset `README.md`s is down ## Steps to reproduce the bug 1. Visit https://huggingface.co/spaces/huggingface/datasets-tagging ## Expected results There'll be a HF spaces web app for generating dataset metadata YAML ## Actual results There's an error message; here's the step where it breaks: ``` Step 18/29 : RUN pip install -r requirements.txt ---> Running in e88bfe7e7e0c Defaulting to user installation because normal site-packages is not writeable Collecting git+https://github.com/huggingface/datasets.git@update-task-list (from -r requirements.txt (line 4)) Cloning https://github.com/huggingface/datasets.git (to revision update-task-list) to /tmp/pip-req-build-bm8t0r0k Running command git clone --filter=blob:none --quiet https://github.com/huggingface/datasets.git /tmp/pip-req-build-bm8t0r0k WARNING: Did not find branch or tag 'update-task-list', assuming revision or ref. Running command git checkout -q update-task-list error: pathspec 'update-task-list' did not match any file(s) known to git error: subprocess-exited-with-error × git checkout -q update-task-list did not run successfully. │ exit code: 1 ╰─> See above for output. note: This error originates from a subprocess, and is likely not a problem with pip. error: subprocess-exited-with-error × git checkout -q update-task-list did not run successfully. │ exit code: 1 ╰─> See above for output. ``` ## Environment info - Platform: Linux / Brave

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PR_kwDODunzps43foZ-

Fix URL query parameters in compression hop path when streaming

open

MEMBER

Fix #3488.

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PR_kwDODunzps43fieR

Fix missing lz4 dependency for tests

closed

MEMBER

Currently, `lz4` is not defined as a dependency for tests. Therefore, all tests marked with `@require_lz4` are skipped.

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PR_kwDODunzps43fTXA

Fix CLI run_beam save_infos

closed

MEMBER

Currently, it raises TypeError: ``` TypeError: _download_and_prepare() got an unexpected keyword argument 'save_infos' ```

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PR_kwDODunzps43dRt9

Fix wrong map parameter name in cache docs

closed

CONTRIBUTOR

The `load_from_cache` parameter of `map` should be `load_from_cache_file`.

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PR_kwDODunzps43bhrp

Add API code examples for remaining main classes

closed

MEMBER

This PR adds API code examples for the remaining functions in the Main classes. I wasn't too familiar with some of the functions (`decode_batch`, `decode_column`, `decode_example`, etc.) so please feel free to add an example of usage and I can fill in the rest :)

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I_kwDODunzps5JLmXc

Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message

closed

CONTRIBUTOR

### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes

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PR_kwDODunzps43Zr08

Update paper link in medmcqa dataset card

closed

CONTRIBUTOR

Updating readme in medmcqa dataset.

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PR_kwDODunzps43XLKi

Add missing `faiss` import to fix https://github.com/huggingface/datasets/issues/4287

closed