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MDExOlB1bGxSZXF1ZXN0NjU1MTM3NDUy

Add KLUE dataset

closed

CONTRIBUTOR

Add `KLUE (Korean Language Understanding Evaluation)` dataset released recently from [paper](https://arxiv.org/abs/2105.09680), [github](https://github.com/KLUE-benchmark/KLUE) and [webpage](https://klue-benchmark.com/tasks). Please let me know if there's anything missing in the code or README. Thanks!

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https://github.com/huggingface/datasets/issues/2415

MDU6SXNzdWU5MDM5MjMwOTc=

Cached dataset not loaded

closed

CONTRIBUTOR

## Describe the bug I have a large dataset (common_voice, english) where I use several map and filter functions. Sometimes my cached datasets after specific functions are not loaded. I always use the same arguments, same functions, no seed… ## Steps to reproduce the bug ```python def filter_by_duration(batch): return ( batch["duration"] <= 10 and batch["duration"] >= 1 and len(batch["target_text"]) > 5 ) def prepare_dataset(batch): batch["input_values"] = processor( batch["speech"], sampling_rate=batch["sampling_rate"][0] ).input_values with processor.as_target_processor(): batch["labels"] = processor(batch["target_text"]).input_ids return batch train_dataset = train_dataset.filter( filter_by_duration, remove_columns=["duration"], num_proc=data_args.preprocessing_num_workers, ) # PROBLEM HERE -> below function is reexecuted and cache is not loaded train_dataset = train_dataset.map( prepare_dataset, remove_columns=train_dataset.column_names, batch_size=training_args.per_device_train_batch_size, batched=True, num_proc=data_args.preprocessing_num_workers, ) # Later in script set_caching_enabled(False) # apply map on trained model to eval/test sets ``` ## Expected results The cached dataset should always be reloaded. ## Actual results The function is reexecuted. I have access to cached files `cache-xxxxx.arrow`. Is there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)? ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29 - Python version: 3.8.5 - PyTorch version (GPU?): 1.8.1+cu102 (True) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: Yes - Using distributed or parallel set-up in script?: No

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https://github.com/huggingface/datasets/pull/2414

MDExOlB1bGxSZXF1ZXN0NjU1MDg5OTIw

Update README.md

closed

CONTRIBUTOR

Provides description of data instances and dataset features

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https://github.com/huggingface/datasets/issues/2413

MDU6SXNzdWU5MDM3Nzc1NTc=

AttributeError: 'DatasetInfo' object has no attribute 'task_templates'

closed

CONTRIBUTOR

## Describe the bug Hello, I'm trying to add dataset and contribute, but test keep fail with below cli. ` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>` ## Steps to reproduce the bug It seems like a bug when I see an error with the existing dataset, not the dataset I'm trying to add. ` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<any_dataset>` ## Expected results All test passed ## Actual results ``` # check that dataset is not empty self.parent.assertListEqual(sorted(dataset_builder.info.splits.keys()), sorted(dataset)) for split in dataset_builder.info.splits.keys(): # check that loaded datset is not empty self.parent.assertTrue(len(dataset[split]) > 0) # check that we can cast features for each task template > task_templates = dataset_builder.info.task_templates E AttributeError: 'DatasetInfo' object has no attribute 'task_templates' tests/test_dataset_common.py:175: AttributeError ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Darwin-20.4.0-x86_64-i386-64bit - Python version: 3.7.7 - PyTorch version (GPU?): 1.7.0 (False) - Tensorflow version (GPU?): 2.3.0 (False) - Using GPU in script?: No - Using distributed or parallel set-up in script?: No

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MDU6SXNzdWU5MDM3NjkxNTE=

Docstring mistake: dataset vs. metric

closed

CONTRIBUTOR

This: https://github.com/huggingface/datasets/blob/d95b95f8cf3cb0cff5f77a675139b584dcfcf719/src/datasets/load.py#L582 Should better be something like: `a metric identifier on HuggingFace AWS bucket (list all available metrics and ids with ``datasets.list_metrics()``)` I can provide a PR l8er...

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https://github.com/huggingface/datasets/pull/2411

MDExOlB1bGxSZXF1ZXN0NjU0OTAzNjg2

Add DOI badge to README

closed

MEMBER

Once published the latest release, the DOI badge has been automatically generated by Zenodo.

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https://github.com/huggingface/datasets/pull/2410

MDExOlB1bGxSZXF1ZXN0NjU0ODUwMjY4

fix #2391 add original answers in kilt-TriviaQA

closed

CONTRIBUTOR

cc @yjernite is it ok like this?

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https://github.com/huggingface/datasets/pull/2409

MDExOlB1bGxSZXF1ZXN0NjU0Njk3NjA0

Add HF_ prefix to env var MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES

closed

MEMBER

As mentioned in https://github.com/huggingface/datasets/pull/2399 the env var should be prefixed by HF_

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https://github.com/huggingface/datasets/pull/2408

MDExOlB1bGxSZXF1ZXN0NjU0NjgxMzE4

Fix head_qa keys

closed

MEMBER

There were duplicate in the keys, as mentioned in #2382

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MDU6SXNzdWU5MDMxMTE3NTU=

.map() function got an unexpected keyword argument 'cache_file_name'

closed

NONE

## Describe the bug I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that ".map() function got an unexpected keyword argument 'cache_file_name'". I believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function. Here is the code I use ## Steps to reproduce the bug ```datasets = load_from_disk(dataset_path=my_path) [...] def tokenize_function(examples): return tokenizer(examples[text_column_name]) logger.info("Mapping dataset to tokenized dataset.") tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=True, cache_file_name="my_tokenized_file" ) ``` ## Actual results tokenized_datasets = datasets.map( TypeError: map() got an unexpected keyword argument 'cache_file_name' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:1.6.2 - Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10 - Python version:3.8.5 - PyArrow version:3.0.0

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MDU6SXNzdWU5MDI2NDM4NDQ=

Add guide on using task templates to documentation

closed

MEMBER

Once we have a stable API on the text classification and question answering task templates, add a guide on how to use them in the documentation.

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https://github.com/huggingface/datasets/pull/2405

MDExOlB1bGxSZXF1ZXN0NjUyNzA2OTk1

Add dataset tags

closed

CONTRIBUTOR

The dataset tags were provided by Peter Clark following the guide.

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MDExOlB1bGxSZXF1ZXN0NjUyNjYzOTcz

Paperswithcode dataset mapping

closed

MEMBER

This is a continuation of https://github.com/huggingface/huggingface_hub/pull/43, encoded directly inside dataset cards. As discussed: - `paperswithcode_id: null` when the dataset doesn't exist on paperswithcode's side. - I've added this new key at the end of the yaml instead of ordering all keys alphabetically as pyyaml's default. No strong opinion on that one though

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MDExOlB1bGxSZXF1ZXN0NjUxNjcxMTMw

Free datasets with cache file in temp dir on exit

closed

CONTRIBUTOR

This PR properly cleans up the memory-mapped tables that reference the cache files inside the temp dir. Since the built-in `_finalizer` of `TemporaryDirectory` can't be modified, this PR defines its own `TemporaryDirectory` class that accepts a custom clean-up function. Fixes #2402

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MDU6SXNzdWU5MDAwMjUzMjk=

PermissionError on Windows when using temp dir for caching

closed

CONTRIBUTOR

Currently, the following code raises a PermissionError on master if working on Windows: ```python # run as a script or call exit() in REPL to initiate the temp dir cleanup from datasets import * d = load_dataset("sst", split="train", keep_in_memory=False) set_caching_enabled(False) d.map(lambda ex: ex) ``` Error stack trace: ``` Traceback (most recent call last): File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 624, in _exitfunc f() File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 548, in __call__ return info.func(*info.args, **(info.kwargs or {})) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\tempfile.py", line 799, in _cleanup _shutil.rmtree(name) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 500, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 395, in _rmtree_unsafe onerror(os.unlink, fullname, sys.exc_info()) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 393, in _rmtree_unsafe os.unlink(fullname) PermissionError: [WinError 5] Access is denied: 'C:\\Users\\Mario\\AppData\\Local\\Temp\\tmp20epyhmq\\cache-87a87ffb5a956e68.arrow' ```

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MDU6SXNzdWU4OTk5MTA1MjE=

load_dataset('natural_questions') fails with "ValueError: External features info don't match the dataset"

closed

NONE

## Describe the bug load_dataset('natural_questions') throws ValueError ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset('natural_questions', split='validation[:10]') ``` ## Expected results Call to load_dataset returns data. ## Actual results ``` Using custom data configuration default Reusing dataset natural_questions (/mnt/d/huggingface/datasets/natural_questions/default/0.0.2/19bc04755018a3ad02ee74f7045cde4ba9b4162cb64450a87030ab786b123b76) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-2-d55ab8a8cc1c> in <module> ----> 1 datasets = load_dataset('natural_questions', split='validation[:10]', cache_dir='/mnt/d/huggingface/datasets') ~/miniconda3/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, script_version, use_auth_token, **config_kwargs) 756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 757 ) --> 758 ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) 759 if save_infos: 760 builder_instance._save_infos() ~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in as_dataset(self, split, run_post_process, ignore_verifications, in_memory) 735 736 # Create a dataset for each of the given splits --> 737 datasets = utils.map_nested( 738 partial( 739 self._build_single_dataset, ~/miniconda3/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) 193 # Singleton 194 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 195 return function(data_struct) 196 197 disable_tqdm = bool(logger.getEffectiveLevel() > INFO) ~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _build_single_dataset(self, split, run_post_process, ignore_verifications, in_memory) 762 763 # Build base dataset --> 764 ds = self._as_dataset( 765 split=split, 766 in_memory=in_memory, ~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _as_dataset(self, split, in_memory) 838 in_memory=in_memory, 839 ) --> 840 return Dataset(**dataset_kwargs) 841 842 def _post_process(self, dataset: Dataset, resources_paths: Dict[str, str]) -> Optional[Dataset]: ~/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint) 271 assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object" 272 if self.info.features.type != inferred_features.type: --> 273 raise ValueError( 274 "External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format( 275 self.info.features, self.info.features.type, inferred_features, inferred_features.type ValueError: External features info don't match the dataset: Got {'id': Value(dtype='string', id=None), 'document': {'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'html': Value(dtype='string', id=None), 'tokens': Sequence(feature={'token': Value(dtype='string', id=None), 'is_html': Value(dtype='bool', id=None)}, length=-1, id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': Sequence(feature={'id': Value(dtype='string', id=None), 'long_answer': {'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None)}, 'short_answers': Sequence(feature={'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}, length=-1, id=None), 'yes_no_answer': ClassLabel(num_classes=2, names=['NO', 'YES'], names_file=None, id=None)}, length=-1, id=None)} with type struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<start_token: int64, end_token: int64, start_byte: int64, end_byte: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<title: string, url: string, html: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>>, id: string, question: struct<text: string, tokens: list<item: string>>> but expected something like {'id': Value(dtype='string', id=None), 'document': {'html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'tokens': {'is_html': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None), 'token': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'url': Value(dtype='string', id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': {'id': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'long_answer': [{'end_byte': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'start_token': Value(dtype='int64', id=None)}], 'short_answers': [{'end_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'end_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}], 'yes_no_answer': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}} with type struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<end_byte: int64, end_token: int64, start_byte: int64, start_token: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<html: string, title: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>, url: string>, id: string, question: struct<text: string, tokens: list<item: string>>> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10 - Python version: 3.8.3 - PyTorch version (GPU?): 1.6.0 (False) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: No - Using distributed or parallel set-up in script?: No

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MDU6SXNzdWU4OTk4NjcyMTI=

Concatenate several datasets with removed columns is not working.

closed

MEMBER

## Describe the bug You can't concatenate datasets when you removed columns before. ## Steps to reproduce the bug ```python from datasets import load_dataset, concatenate_datasets wikiann= load_dataset("wikiann","en") wikiann["train"] = wikiann["train"].remove_columns(["langs","spans"]) wikiann["test"] = wikiann["test"].remove_columns(["langs","spans"]) assert wikiann["train"].features.type == wikiann["test"].features.type concate = concatenate_datasets([wikiann["train"],wikiann["test"]]) ``` ## Expected results Merged dataset ## Actual results ```python ValueError: External features info don't match the dataset: Got {'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=ClassLabel(num_classes=7, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], names_file=None, id=None), length=-1, id=None), 'langs': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'spans': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} with type struct<langs: list<item: string>, ner_tags: list<item: int64>, spans: list<item: string>, tokens: list<item: string>> but expected something like {'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} with type struct<ner_tags: list<item: int64>, tokens: list<item: string>> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: ~1.6.2~ 1.5.0 - Platform: macos - Python version: 3.8.5 - PyArrow version: 3.0.0

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MDExOlB1bGxSZXF1ZXN0NjUxNDk0OTc2

Add env variable for MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES

closed

MEMBER

Add env variable for `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES`. This will allow to turn off default behavior: loading in memory (and not caching) small datasets. Fix #2387.

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MDU6SXNzdWU4OTk1MTE4Mzc=

News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs

closed

NONE

I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi. ``` train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]') val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]') # filtering out examples that are not ar-en translations but ar-hi val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True) ``` * I'm fairly new to using datasets so I might be doing something wrong

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MDExOlB1bGxSZXF1ZXN0NjUxMTMxMTY0

Fix number of classes in indic_glue sna.bn dataset

closed

MEMBER

As read in the [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.445.pdf), Table 11.

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MDExOlB1bGxSZXF1ZXN0NjUwNTk3NjI0

`pretty_name` for dataset in YAML tags

closed

CONTRIBUTOR

I'm updating `pretty_name` for datasets in YAML tags as discussed with @lhoestq. Here are the first 10, please let me know if they're looking good. If dataset has 1 config, I've added `pretty_name` as `config_name: full_name_of_dataset` as config names were `plain_text`, `default`, `squad` etc (not so important in this case) whereas when dataset has >1 configs, I've added `config_name: full_name_of_dataset+config_name` so as to let user know about the `config` here.

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MDExOlB1bGxSZXF1ZXN0NjUwMDYxOTE3

Update text classification template labels in DatasetInfo __post_init__

closed

MEMBER

This PR implements the idea discussed in #2389 to update the `labels` of the `TextClassification` template in the `DatasetInfo.__post_init__`. The main reason for doing so is so avoid duplicating the label definitions in both `DatasetInfo.features` and `DatasetInfo.task_templates`. To avoid storing state in `DatasetInfo.__post_init__`, the current implementation flushes `DatasetInfo.task_templates` before the features are cast in `Dataset.prepare_for_task` (thanks to @mariosasko for this idea!). Here is an example of the current workflow: ```python ds1 = load_dataset("./datasets/emotion/") # cast features and flush templates ds2 = ds1.prepare_for_task("text-classification") assert ds2.info.task_templates is None ``` Note that if users want to pass a `TextClassification` template to `prepare_for_task`, we require them to set `TextClassification.labels` to match the dataset's features corresponding to `label_column`: ```python ds1 = load_dataset("./datasets/emotion/") # TextClassification.labels is None by default => invalid template task = TextClassification(text_column="text", label_column="label") # Raises ValueError ds1.prepare_for_task(task) # Specifying the labels => valid template task = TextClassification(text_column="text", label_column="label", labels=['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']) ds1.prepare_for_task(task) ``` This PR also adds: * New tests + fixed some old tests that weren't testing `assertRaises` properly * A decorator to share docstrings across common functions. This allows us to document `DatasetDict.prepare_for_task` and `Dataset.prepare_for_task` in one place. * Fixes to avoid side-effects from in-place replacements of `DatasetInfo.task_templates` in `DatasetInfo.__post_init__`. Thanks to @lhoestq for figuring this out! * Removal of `FeaturesWithLazyClassLabel` since we now create a new instance of `TextClassification` in `DatasetInfo.__post_init__` and avoid the side-effects first pointed out by @mariosasko ### PR Description from original WIP Hi @yjernite and @lhoestq, here's a first stab at the suggestion discussed in #2389 to update the `labels` of the `TextClassification` template in the `DatasetInfo.__post_init__`. One problem I've spotted is that my current implementation introduces state into the `__post_init__`: * When we call `load_dataset`, `DatasetInfo.features` are the "raw" features without any casting so we can access the column names by the `label_column` specified in `TextClassification` * When we call `Dataset.prepare_for_task` we run into a problem because the `DatasetInfo.features` are first cast into the new schema which triggers a `KeyError` when we update the infos [here](https://github.com/huggingface/datasets/blob/8b2a78520828e0cc13c14a31f413a5395ef25110/src/datasets/arrow_dataset.py#L1959). Here's an explicit example of what I mean with the stack trace appended below: ```python from datasets import load_dataset # this works ds = load_dataset("emotion") # we can verify the task template is correctly set ds["train"].info.task_templates # returns [TextClassification(labels=('sadness', 'joy', 'love', 'anger', 'fear', 'surprise'), text_column='text', label_column='label')] # but this fails because the _post_init__ is looking for the original column names ds.prepare_for_task("text-classification") ``` ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-4-54a43019b319> in <module> ----> 1 ds.prepare_for_task("text-classification") ~/git/datasets/src/datasets/dataset_dict.py in prepare_for_task(self, task) 807 """ 808 self._check_values_type() --> 809 return DatasetDict({k: dataset.prepare_for_task(task=task) for k, dataset in self.items()}) ~/git/datasets/src/datasets/dataset_dict.py in <dictcomp>(.0) 807 """ 808 self._check_values_type() --> 809 return DatasetDict({k: dataset.prepare_for_task(task=task) for k, dataset in self.items()}) ~/git/datasets/src/datasets/arrow_dataset.py in prepare_for_task(self, task) 1421 dataset = self.remove_columns(columns_to_drop) 1422 dataset = dataset.rename_columns(column_mapping) -> 1423 dataset = dataset.cast(features=template.features) 1424 return dataset 1425 ~/git/datasets/src/datasets/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc) 970 format = self.format 971 dataset = self.with_format("arrow") --> 972 dataset = dataset.map( 973 lambda t: t.cast(schema), 974 batched=True, ~/git/datasets/src/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) 1583 1584 if num_proc is None or num_proc == 1: -> 1585 return self._map_single( 1586 function=function, 1587 with_indices=with_indices, ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 173 } 174 # apply actual function --> 175 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 176 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 177 # re-apply format to the output ~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs) 338 # Call actual function 339 --> 340 out = func(self, *args, **kwargs) 341 342 # Update fingerprint of in-place transforms + update in-place history of transforms ~/git/datasets/src/datasets/arrow_dataset.py in _map_single(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, new_fingerprint, rank, offset) 1959 if update_data: 1960 # Create new Dataset from buffer or file -> 1961 info = self.info.copy() 1962 info.features = writer._features 1963 if buf_writer is None: ~/git/datasets/src/datasets/info.py in copy(self) 274 275 def copy(self) -> "DatasetInfo": --> 276 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) 277 278 ~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes) ~/git/datasets/src/datasets/info.py in __post_init__(self) 174 # The reason is that Dataset.prepare_for_task calls Dataset.cast which converts the 175 # DatasetInfo.features to the new schema and thus template.label_column is no longer a valid key --> 176 object.__setattr__(template, "labels", tuple(self.features[template.label_column].names)) 177 template.label_schema["labels"] = ClassLabel(names=template.labels) 178 self.task_templates[idx] = template KeyError: 'label' ``` What do you think? I did this a bit quickly, so maybe I'm overlooking something obvious :) One thing would be to only update the labels of the task template on load, but this seems a bit hacky IMO

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MDU6SXNzdWU4OTgxMjgwOTk=

Missing original answers in kilt-TriviaQA

closed

CONTRIBUTOR

I previously opened an issue at https://github.com/facebookresearch/KILT/issues/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets ## Describe the bug The `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative answer which are accepted for the question. However it'd be nice to know the original answer to the question (the only fields in `output` are `'answer', 'meta', 'provenance'`) ## How to fix It can be fixed by retrieving the original answer from the original TriviaQA (e.g. `trivia_qa['train'][0]['answer']['value']`), perhaps at the same place as here where one retrieves the questions https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md#loading-the-kilt-knowledge-source-and-task-data cc @yjernite who previously answered to an issue about KILT and TriviaQA :)

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MDExOlB1bGxSZXF1ZXN0NjQ5ODQ0NjQ2

Add check for task templates on dataset load

closed

MEMBER

This PR adds a check that the features of a dataset match the schema of each compatible task template.

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MDExOlB1bGxSZXF1ZXN0NjQ5Nzc3MDMz

Insert task templates for text classification

closed

MEMBER

This PR inserts text-classification templates for datasets with the following properties: * Only one config * At most two features of `(Value, ClassLabel)` type Note that this misses datasets like `sentiment140` which only has `Value` type features - these will be handled in a separate PR

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MDU6SXNzdWU4OTc3Njc0NzA=

Incorrect URLs for some datasets

closed

MEMBER

## Describe the bug It seems that the URLs for the following datasets are invalid: - [ ] `bn_hate_speech` has been renamed: https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset/commit/c67ecfc4184911e12814f6b36901f9828df8a63a - [ ] `covid_tweets_japanese` has been renamed: http://www.db.info.gifu-u.ac.jp/covid-19-twitter-dataset/ As a result we can no longer load these datasets using `load_dataset`. The simple fix is to rename the URL in the dataset script - will do this asap. ## Steps to reproduce the bug ```python from datasets import load_dataset # pick one of the datasets from the list above ds = load_dataset("bn_hate_speech") ``` ## Expected results Dataset loads without error. ## Actual results ``` Downloading: 3.36kB [00:00, 1.07MB/s] Downloading: 2.03kB [00:00, 678kB/s] Using custom data configuration default Downloading and preparing dataset bn_hate_speech/default (download: 951.48 KiB, generated: 949.84 KiB, post-processed: Unknown size, total: 1.86 MiB) to /Users/lewtun/.cache/huggingface/datasets/bn_hate_speech/default/0.0.0/a2dc726e511a2177523301bcad196af05d4d8a2cff30d2769ba8aacc1f5fdb5c... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/load.py", line 744, in load_dataset builder_instance.download_and_prepare( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/builder.py", line 574, in download_and_prepare self._download_and_prepare( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/builder.py", line 630, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/lewtun/.cache/huggingface/modules/datasets_modules/datasets/bn_hate_speech/a2dc726e511a2177523301bcad196af05d4d8a2cff30d2769ba8aacc1f5fdb5c/bn_hate_speech.py", line 76, in _split_generators train_path = dl_manager.download_and_extract(_URL) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 287, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 195, in download downloaded_path_or_paths = map_nested( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 195, in map_nested return function(data_struct) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 218, in _download return cached_path(url_or_filename, download_config=download_config) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 281, in cached_path output_path = get_from_cache( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/rezacsedu/Bengali-Hate-Speech-Dataset/main/Bengali_%20Hate_Speech_Dataset_Subset.csv ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2.dev0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.8 - PyArrow version: 3.0.0

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MDU6SXNzdWU4OTc1NjY2NjY=

datasets 1.6 ignores cache

closed

MEMBER

Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612 Quoting @VictorSanh: > > I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335): > > > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-97cf4c813e6469c6.arrow'}]}` > > while the same command with the latest version of datasets (actually starting at `1.6.0`) gives: > > `{'train': [], 'validation': []}` > I also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used. to reproduce: ``` USE_TF=0 python examples/pytorch/language-modeling/run_clm.py \ --model_name_or_path gpt2 \ --dataset_name "stas/openwebtext-10k" \ --output_dir output_dir \ --overwrite_output_dir \ --do_train \ --do_eval \ --max_train_samples 1000 \ --max_eval_samples 200 \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size 4 \ --num_train_epochs 1 \ --warmup_steps 8 \ --block_size 64 \ --fp16 \ --report_to none ``` the first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run. @lhoestq

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MDU6SXNzdWU4OTc1NjAwNDk=

Accessing Arrow dataset cache_files

closed

NONE

## Describe the bug In datasets 1.5.0 the following code snippet would have printed the cache_files: ``` train_data = load_dataset('conll2003', split='train', cache_dir='data') print(train_data.cache_files[0]['filename']) ``` However, in the newest release (1.6.1), it prints an empty list. I also tried loading the dataset with `keep_in_memory=True` argument but still `cache_files` is empty. Was wondering if this is a bug or I need to pass additional arguments so I can access the cache_files.

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MDExOlB1bGxSZXF1ZXN0NjQ5MjM1Mjcy

update citations

closed

CONTRIBUTOR

To update citations for [Offenseval_dravidiain](https://huggingface.co/datasets/offenseval_dravidian)

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MDExOlB1bGxSZXF1ZXN0NjQ4OTI4NTQ0

Add args description to DatasetInfo

closed

MEMBER

Closes #2354 I am not sure what `post_processed` and `post_processing_size` correspond to, so have left them empty for now. I also took a guess at some of the other fields like `dataset_size` vs `size_in_bytes`, so might have misunderstood their meaning.

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MDExOlB1bGxSZXF1ZXN0NjQ3OTU4MTQ0

Improve example in rounding docs

closed

CONTRIBUTOR

Improves the example in the rounding subsection of the Split API docs. With this change, it should more clear what's the difference between the `closest` and the `pct1_dropremainder` rounding.

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MDU6SXNzdWU4OTU2MTAyMTY=

DuplicatedKeysError: FAILURE TO GENERATE DATASET ! load_dataset('head_qa', 'en')

closed

NONE

Hello everyone, I try to use head_qa dataset in [https://huggingface.co/datasets/viewer/?dataset=head_qa&config=en](url) ``` !pip install datasets from datasets import load_dataset dataset = load_dataset( 'head_qa', 'en') ``` When I write above load_dataset(.), it throws the following: ``` DuplicatedKeysError Traceback (most recent call last) <ipython-input-6-ea87002d32f0> in <module>() 2 from datasets import load_dataset 3 dataset = load_dataset( ----> 4 'head_qa', 'en') 5 frames /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self) 347 for hash, key in self.hkey_record: 348 if hash in tmp_record: --> 349 raise DuplicatedKeysError(key) 350 else: 351 tmp_record.add(hash) DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 1 Keys should be unique and deterministic in nature ``` How can I fix the error? Thanks

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MDExOlB1bGxSZXF1ZXN0NjQ3NzkyNDcw

add dataset card title

closed

CONTRIBUTOR

few of them were missed by me earlier which I've added now

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MDExOlB1bGxSZXF1ZXN0NjQ3NTk3NTc3

maintain YAML structure reading from README

closed

CONTRIBUTOR

How YAML used be loaded earlier in the string (structure of YAML was affected because of this and YAML for datasets with multiple configs was not being loaded correctly): ``` annotations_creators: labeled_final: - expert-generated labeled_swap: - expert-generated unlabeled_final: - machine-generated language_creators: - machine-generated languages: - en licenses: - other multilinguality: - monolingual size_categories: labeled_final: - 10K<n<100K labeled_swap: - 10K<n<100K unlabeled_final: - 100K<n<1M source_datasets: - original task_categories: - text-classification - text-scoring task_ids: - semantic-similarity-classification - semantic-similarity-scoring - text-scoring-other-paraphrase-identification ``` How YAML is loaded in string now: ``` annotations_creators: labeled_final: - expert-generated labeled_swap: - expert-generated unlabeled_final: - machine-generated language_creators: - machine-generated languages: - en licenses: - other multilinguality: - monolingual size_categories: labeled_final: - 10K<n<100K labeled_swap: - 10K<n<100K unlabeled_final: - 100K<n<1M source_datasets: - original task_categories: - text-classification - text-scoring task_ids: - semantic-similarity-classification - semantic-similarity-scoring - text-scoring-other-paraphrase-identification ```

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MDExOlB1bGxSZXF1ZXN0NjQ3NDk2ODUx

Disallow duplicate keys in yaml tags

closed

MEMBER

Make sure that there's no duplidate keys in yaml tags. I added the check in the yaml tree constructor's method, so that the verification is done at every level in the yaml structure. cc @julien-c

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MDExOlB1bGxSZXF1ZXN0NjQ3MTU1NDE4

Improve task api code quality

closed

CONTRIBUTOR

Improves the code quality of the `TaskTemplate` dataclasses. Changes: * replaces `return NotImplemented` with raise `NotImplementedError` * replaces `sorted` with `len` in the uniqueness check * defines `label2id` and `id2label` in the `TextClassification` template as properties * replaces the `object.__setattr__(self, attr, value)` syntax with (IMO nicer) `self.__dict__[attr] = value`

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MDExOlB1bGxSZXF1ZXN0NjQ2OTg2NTcw

Dataset Streaming

closed

MEMBER

# Dataset Streaming ## API Current API is ```python from datasets import load_dataset # Load an IterableDataset without downloading data snli = load_dataset("snli", streaming=True) # Access examples by streaming data print(next(iter(snli["train"]))) # {'premise': 'A person on a horse jumps over a broken down airplane.', # 'hypothesis': 'A person is training his horse for a competition.', # 'label': 1} ``` I already implemented a few methods: - IterableDataset.map: apply transforms on-the-fly to the examples - IterableDataset.shuffle: shuffle the data _a la_ TFDS, i.e. with a shuffling buffer - IterableDataset.with_format: set the format to `"torch"` to get a `torch.utils.data.IterableDataset` - merge_datasets: merge two iterable datasets by alternating one or the other (you can specify the probabilities) I would love to have your opinion on the API design :) ## Implementation details ### Streaming Data streaming is done using `fsspec` which has nice caching features. To make dataset streaming work I extend the `open` function of dataset scripts to support opening remote files without downloading them entirely. It also works with remote compressed archives (currently only zip is supported): ```python # Get a file-like object by streaming data from a remote file open("https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt") # Get a file-like object by streaming data from a remote compressed archive by using the hop separator "::" open("zip://snli_1.0_train.txt::https://nlp.stanford.edu/projects/snli/snli_1.0.zip") ``` I also extend the `os.path.join` function to support navigation in remote compressed archives, since it has to deal with the `"::"` separator. This separator is used by `fsspec`. Finally I also added a retry mechanism in case the connection fails during data streaming. ### Transforms An IterableDataset wraps an ExamplesIterable instance. There are different subclasses depending on the transforms we want to apply: - ExamplesIterable: the basic one - MappedExamplesIterable: an iterable with a `map` function applied on the fly - BufferShuffledExamplesIterable: an iterable with a shuffling buffer - CyclingMultiSourcesExamplesIterable: alternates between several ExamplesIterable - RandomlyCyclingMultiSourcesExamplesIterable: randomly alternates between several ExamplesIterable ### DatasetBuilder I use the same builders as usual. I just added a new method `_get_examples_iterable_for_split` to get an ExamplesIterable for a given split. Currently only the GeneratorBasedBuilder and the ArrowBasedBuilder implement it. The BeamBasedBuilder doesn't implement it yet. It means that datasets like wikipedia and natural_questions can't be loaded as IterableDataset for now. ## Other details <S>I may have to do some changes in many dataset script to use `download` instead of `download_and_extract` when extraction is not needed. This will avoid errors for streaming.</s> EDIT: Actually I just check for the extension of the file to do extraction only if needed. EDIT2: It's not possible to stream from .tar.gz files without downloading the file completely. For now I raise an error if one want to get a streaming dataset based on .tar.gz files. ## TODO usual stuff: - [x] make streaming dependency "aiohttp" optional: `pip install datasets[streaming]` - [x] tests - [x] docs

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MDExOlB1bGxSZXF1ZXN0NjQ2OTIyMjkw

add `desc` to `tqdm` in `Dataset.map()`

closed

CONTRIBUTOR

Fixes #2330. Please let me know if anything is also required in this

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MDU6SXNzdWU4OTQ0OTk5MDk=

Loading dataset from local path

closed

NONE

I'm trying to load a local dataset with the code below ``` ds = datasets.load_dataset('my_script.py', data_files='corpus.txt', data_dir='/data/dir', cache_dir='.') ``` But internally a BuilderConfig is created, which tries to use getmtime on the data_files string, without using data_dir. Is this a bug or am I not using the load_dataset correctly? https://github.com/huggingface/datasets/blob/bc61954083f74e6460688202e9f77dde2475319c/src/datasets/builder.py#L153

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MDExOlB1bGxSZXF1ZXN0NjQ2ODUxODc2

ConvQuestions benchmark added

closed

CONTRIBUTOR

Hello, I would like to integrate our dataset on conversational QA. The answers are grounded in the KG. The work was published in CIKM 2019 (https://dl.acm.org/doi/10.1145/3357384.3358016). We hope for further research on how to deal with the challenges of factoid conversational QA. Thanks! :)

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MDExOlB1bGxSZXF1ZXN0NjQ2MDkyNDQy

Adding HendrycksTest dataset

closed

CONTRIBUTOR

Adding Hendrycks test from https://arxiv.org/abs/2009.03300. I'm having a bit of trouble with dummy data creation because some lines in the csv files aren't being loaded properly (only the first entry loaded in a row of length 6). The dataset is loading just fine. Hope you can kindly help! Thank you!

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MDExOlB1bGxSZXF1ZXN0NjQ2MDQ5NDM1

correct labels of conll2003

closed

MEMBER

# What does this PR It fixes/extends the `ner_tags` for conll2003 to include all. Paper reference https://arxiv.org/pdf/cs/0306050v1.pdf Model reference https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english/blob/main/config.json

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MDExOlB1bGxSZXF1ZXN0NjQ1OTI5NzM0

Allow "other-X" in licenses

closed

CONTRIBUTOR

This PR allows "other-X" licenses during metadata validation. @lhoestq

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MDExOlB1bGxSZXF1ZXN0NjQ1ODUxNTE0

Remove getchildren from hyperpartisan news detection

closed

CONTRIBUTOR

`Element.getchildren()` is now deprecated in the ElementTree library (I think in python 3.9, so it still passes the automated tests which are using 3.6. But for those of us on bleeding-edge distros it now fails). https://bugs.python.org/issue29209

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MDU6SXNzdWU4OTMxODUyNjY=

Json loader fails if user-specified features don't match the json data fields order

closed

MEMBER

If you do ```python dataset = load_dataset("json", data_files=data_files, features=features) ``` Then depending on the order of the features in the json data field it fails: ```python [...] ~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 94 if self.config.schema: 95 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT ---> 96 pa_table = pa_table.cast(self.config.schema) 97 yield i, pa_table [...] ValueError: Target schema's field names are not matching the table's field names: ['tokens', 'ner_tags'], ['ner_tags', 'tokens'] ``` This is because one must first re-order the columns of the table to match the `self.config.schema` before calling cast. One way to fix the `cast` would be to replace it with: ```python # reorder the arrays if necessary + cast to schema # we can't simply use .cast here because we may need to change the order of the columns pa_table = pa.Table.from_arrays([pa_table[name] for name in schema.names], schema=schema) ```

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MDU6SXNzdWU4OTMxNzk2OTc=

Missing ClassLabel encoding in Json loader

closed

MEMBER

Currently if you want to load a json dataset this way ```python dataset = load_dataset("json", data_files=data_files, features=features) ``` Then if your features has ClassLabel types and if your json data needs class label encoding (i.e. if the labels in the json files are strings and not integers), then it would fail: ```python [...] ~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 94 if self.config.schema: 95 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT ---> 96 pa_table = pa_table.cast(self.config.schema) 97 yield i, pa_table [...] ArrowInvalid: Failed to parse string: 'O' as a scalar of type int64 ``` This is because it just tries to cast the string data to integers, without applying the mapping str->int first The current workaround is to do instead ```python dataset = load_dataset("json", data_files=data_files) dataset = dataset.map(features.encode_example, features=features) ```

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MDExOlB1bGxSZXF1ZXN0NjQ1MTI4MDYx

README updated for SNLI, MNLI

closed

CONTRIBUTOR

Closes #2275. Mentioned about -1 labels in MNLI, SNLI and how they should be removed before training. @lhoestq `check_code_quality` test might fail for MNLI as the license name `other-Open Portion of the American National Corpus` is not a registered tag for 'licenses'

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MDExOlB1bGxSZXF1ZXN0NjQ0ODYzOTQw

Fix web_nlg metadata

closed

MEMBER

Our metadata storage system does not support `.` inside keys. cc @Pierrci

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MDExOlB1bGxSZXF1ZXN0NjQ0NzYzNTU4

Preserve dtype for numpy/torch/tf/jax arrays

closed

CONTRIBUTOR

Fixes #625. This lets the user preserve the dtype of numpy array to pyarrow array which was getting lost due to conversion of numpy array -> list -> pyarrow array.

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MDU6SXNzdWU4OTE5NDYwMTc=

Allow model labels to be passed during task preparation

closed

MEMBER

Models have a config with label2id. And we have the same for datasets with the ClassLabel feature type. At one point either the model or the dataset must sync with the other. It would be great to do that on the dataset side. For example for sentiment classification on amazon reviews with you could have these labels: - "1 star", "2 stars", "3 stars", "4 stars", "5 stars" - "1", "2", "3", "4", "5" Some models may use the first set, while other models use the second set. Here in the `TextClassification` class, the user can only specify one set of labels, while many models could actually be compatible but have different sets of labels. Should we allow users to pass a list of compatible labels sets ? Then in terms of API, users could use `dataset.prepare_for_task("text-classification", labels=model.labels)` or something like that. The label set could also be the same but not in the same order. For NLI for example, some models use `["neutral", "entailment", "contradiction"]` and some others use `["neutral", "contradiction", "entailment"]`, so we should take care of updating the order of the labels in the dataset to match the labels order of the model. Let me know what you think ! This can be done in a future PR _Originally posted by @lhoestq in https://github.com/huggingface/datasets/pull/2255#discussion_r632412792_

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MDExOlB1bGxSZXF1ZXN0NjQ0MTYyOTY2

Roman Urdu Stopwords List

closed

NONE

A list of most frequently used Roman Urdu words with different spellings and usages. This is a very basic effort to collect some basic stopwords for Roman Urdu to help efforts of analyzing text data in roman Urdu which makes up a huge part of daily internet interaction of Roman-Urdu users.

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MDExOlB1bGxSZXF1ZXN0NjQzNTk0NDcz

Adding Microsoft CodeXGlue Datasets

closed

NONE

Hi there, this is a new pull request to get the CodeXGlue datasets into the awesome HF datasets lib. Most of the work has been done in this PR #997 by the awesome @madlag. However, that PR has been stale for a while now and so I spoke with @lhoestq about finishing up the final mile and so he told me to open a new PR with the final changes :smile:. I believe I've met all of the changes still left in the old PR to do, except for the change to the languages. I believe the READMEs should include the different programming languages used rather than just using the tag "code" as when searching for datasets, SE researchers may specifically be looking only for what type of programming language and so being able to quickly filter will be very valuable. Let me know what you think of that or if you still believe it should be the "code" tag @lhoestq.

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MDExOlB1bGxSZXF1ZXN0NjQzNDk5NTIz

normalized TOCs and titles in data cards

closed

MEMBER

I started fixing some of the READMEs that were failing the tests introduced by @gchhablani but then realized that there were some consistent differences between earlier and newer versions of some of the titles (e.g. Data Splits vs Data Splits Sample Size, Supported Tasks vs Supported Tasks and Leaderboards). We also had different versions of the Table of Content This PR normalizes all of them to the newer version

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MDU6SXNzdWU4OTA0Mzk1MjM=

Document DatasetInfo attributes

closed

MEMBER

**Is your feature request related to a problem? Please describe.** As noted in PR #2255, the attributes of `DatasetInfo` are not documented in the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=datasetinfo#datasetinfo). It would be nice to do so :)

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MDExOlB1bGxSZXF1ZXN0NjQzMzM4MDcz

Update README vallidation rules

closed

CONTRIBUTOR

This PR allows unexpected subsections under third-level headings. All except `Contributions`. @lhoestq

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MDExOlB1bGxSZXF1ZXN0NjQyOTI4NTgz

Set to_json default to JSON lines

closed

MEMBER

With this PR, the method `Dataset.to_json`: - is added to the docs - defaults to JSON lines

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MDExOlB1bGxSZXF1ZXN0NjQyNzI5NDIz

simpllify faiss index save

closed

CONTRIBUTOR

Fixes #2350 In some cases, Faiss GPU index objects do not have neither "device" nor "getDevice". Possibly this happens when some part of the index is computed on CPU. In particular, this would happen with the index `OPQ16_128,IVF512,PQ32` (issue #2350). I did check it, but it is likely that `OPQ` or `PQ` transforms cause it. I propose, instead of using the index object to get the device, to infer it form the `FaissIndex.device` field as it is done in `.add_vectors`. Here we assume that `.device` always corresponds to the index placement and it seems reasonable.

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MDU6SXNzdWU4ODk1ODAyNDc=

`FaissIndex.save` throws error on GPU

closed

CONTRIBUTOR

## Describe the bug After training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error. ``` File "index_wikipedia.py", line 119, in <module> data["train"].save_faiss_index("text_emb", index_save_path) File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 470, in save_faiss_index index.save(file) File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 334, in save faiss.write_index(index, str(file)) File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/faiss/swigfaiss_avx2.py", line 5654, in write_index return _swigfaiss.write_index(*args) RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /root/miniconda3/conda-bld/faiss-pkg_1613235005464/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index ``` ## Steps to reproduce the bug Any dataset will do, I just selected a familiar one. ```python import numpy as np import datasets INDEX_STR = "OPQ16_128,IVF512,PQ32" INDEX_SAVE_PATH = "will_not_save.faiss" data = datasets.load_dataset("Fraser/news-category-dataset", split=f"train[:10000]") def encode(item): return {"text_emb": np.random.randn(768).astype(np.float32)} data = data.map(encode) data.add_faiss_index(column="text_emb", string_factory=INDEX_STR, train_size=10_000, device=0) data.save_faiss_index("text_emb", INDEX_SAVE_PATH) ``` ## Expected results Saving the index ## Actual results Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) ... don't know how to serialize this type of index ## Environment info - `datasets` version: 1.6.2 - Platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyTorch version (GPU?): 1.8.1+cu111 (True) - Tensorflow version (GPU?): 2.2.0 (False) - Using GPU in script?: Yes - Using distributed or parallel set-up in script?: No I will be proposing a fix in a couple of minutes

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MDExOlB1bGxSZXF1ZXN0NjQxNzYzNzg3

Update task_ids for Ascent KB

closed

CONTRIBUTOR

This "other-other-knowledge-base" task is better suited for the dataset.

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MDExOlB1bGxSZXF1ZXN0NjQxMTMwOTM4

Add tests for dataset cards

closed

CONTRIBUTOR

Adding tests for dataset cards This PR will potentially remove the scripts being used for dataset tags and readme validation. Additionally, this will allow testing dataset readmes by providing the name as follows: ```bash pytest tests/test_dataset_cards.py::test_dataset_tags[fashion_mnist] ``` and ```bash pytest tests/test_dataset_cards.py::test_readme_content[fashion_mnist] ``` or a combined test as: ```bash pytest tests/test_dataset_cards.py::test_dataset_card[fashion_mnist] ``` @lhoestq

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MDU6SXNzdWU4ODc0MDQ4Njg=

Add an API to access the language and pretty name of a dataset

closed

MEMBER

It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts.

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MDExOlB1bGxSZXF1ZXN0NjM5OTAzMjk3

Add Qasper Dataset

closed

CONTRIBUTOR

[Question Answering on Scientific Research Papers](https://allenai.org/project/qasper/home) Doing NLP on NLP papers to do NLP ♻️ I had to add it~ - [x] Add README (just gotta fill out some more ) - [x] Dataloader code - [x] Make dummy dataset - [x] generate dataset infos - [x] Tests

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MDU6SXNzdWU4ODY1ODY4NzI=

[Question] How to move and reuse preprocessed dataset?

closed

NONE

Hi, I am training a gpt-2 from scratch using run_clm.py. I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess), I tried to : copy path_to_cache_dir/datasets to new_cache_dir/datasets set export HF_DATASETS_CACHE="new_cache_dir/" but the program still re-preprocess the whole dataset without loading cache. I also tried to torch.save(lm_datasets, fw), but the saved file is only 14M. What is the proper way to do this?

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MDExOlB1bGxSZXF1ZXN0NjM2NDg0MzM3

Docs - CER above 1

closed

CONTRIBUTOR

CER can actually be greater than 1.

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MDExOlB1bGxSZXF1ZXN0NjM1OTExODI2

Added the Ascent KB

closed

CONTRIBUTOR

Added the Ascent Commonsense KB of 8.9M assertions. - Paper: [Advanced Semantics for Commonsense Knowledge Extraction (WWW'21)](https://arxiv.org/abs/2011.00905) - Website: https://ascent.mpi-inf.mpg.de/ (I am the author of the dataset)

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MDExOlB1bGxSZXF1ZXN0NjM1OTExNzIx

More consistent copy logic

closed

CONTRIBUTOR

Use `info.copy()` instead of `copy.deepcopy(info)`. `Features.copy` now creates a deep copy.

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MDExOlB1bGxSZXF1ZXN0NjM1NjA3NzQx

fixed download link for web_science

closed

CONTRIBUTOR

Fixes #2337. Should work with: `dataset = load_dataset("web_of_science", "WOS11967", ignore_verifications=True)`

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MDU6SXNzdWU4ODE2MTA1Njc=

NonMatchingChecksumError for web_of_science dataset

closed

NONE

NonMatchingChecksumError when trying to download the web_of_science dataset. >NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1'] Setting `ignore_verfications=True` results in OSError. >OSError: Cannot find data file. Original error: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/37ab2c42f50d553c1d0ea432baca3e9e11fedea4aeec63a81e6b7e25dd10d4e7/WOS5736/X.txt' ```python dataset = load_dataset('web_of_science', 'WOS5736') ``` There are 3 data instances and they all don't work. 'WOS5736', 'WOS11967', 'WOS46985' datasets 1.6.2 python 3.7.10 Ubuntu 18.04.5 LTS

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MDExOlB1bGxSZXF1ZXN0NjM0ODk1OTU5

Fix overflow issue in interpolation search

closed

CONTRIBUTOR

Fixes #2335 More info about this error can be found [here](https://stackoverflow.com/questions/53239890/why-do-i-keep-getting-this-error-runtimewarning-overflow-encountered-in-int-sc/53240100).

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MDU6SXNzdWU4ODEyOTE4ODc=

Index error in Dataset.map

closed

CONTRIBUTOR

The following code, if executed on master, raises an IndexError (due to overflow): ```python >>> from datasets import * >>> d = load_dataset("bookcorpus", split="train") Reusing dataset bookcorpus (C:\Users\Mario\.cache\huggingface\datasets\bookcorpus\plain_text\1.0.0\44662c4a114441c35200992bea923b170e6f13f2f0beb7c14e43759cec498700) 2021-05-08 21:23:46.859818: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll >>> d.map(lambda ex: ex) 0%|▎ | 289430/74004228 [00:13<58:41, 20935.33ex/s]c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py:84: RuntimeWarning: overflow encountered in int_scalars k = i + ((j - i) * (x - arr[i]) // (arr[j] - arr[i])) 0%|▎ | 290162/74004228 [00:13<59:11, 20757.23ex/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1498, in map new_fingerprint=new_fingerprint, File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 174, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\fingerprint.py", line 340, in wrapper out = func(self, *args, **kwargs) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1799, in _map_single for i, example in enumerate(pbar): File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\site-packages\tqdm\std.py", line 1133, in __iter__ for obj in iterable: File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1145, in __iter__ format_kwargs=format_kwargs, File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1337, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 368, in query_table pa_subtable = _query_table(table, key) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 79, in _query_table return table.fast_slice(key % table.num_rows, 1) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 128, in fast_slice i = _interpolation_search(self._offsets, offset) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 91, in _interpolation_search raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.") IndexError: Invalid query '290162' for size 74004228. ``` Tested on Windows, can run on Linux if needed. EDIT: It seems like for this to happen, the default NumPy dtype has to be np.int32.

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MDExOlB1bGxSZXF1ZXN0NjMzNTAzNTEw

Updating the DART file checksums in GEM

closed

MEMBER

The DART files were just updated on the source GitHub https://github.com/Yale-LILY/dart/commit/34b3c872da4811523e334f1631e54ca8105dffab

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