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I_kwDODunzps5PlEVM

Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.

closed

NONE

Hi, when i try to run prepare_dataset function in [fine tuning ASR tutorial 4](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) , i got this error. I got this error Terminating: fork() called from a process already using GNU OpenMP, this is unsafe. There is no other logs available, so i have no clue what is the cause of it. ``` def prepare_dataset(batch): audio = batch["path"] # batched output is "un-batched" batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] batch["input_length"] = len(batch["input_values"]) with processor.as_target_processor(): batch["labels"] = processor(batch["text"]).input_ids return batch data = data.map(prepare_dataset, remove_columns=data.column_names["train"], num_proc=4) ``` Specify the actual results or traceback. There is no traceback except `Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-43-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps48-xc6

Add missing language tags to resources

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Add missing language tags to resources, required by existing datasets on GitHub.

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PR_kwDODunzps48-W7a

Add add cc-by-sa-2.5 license tag

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CONTRIBUTOR

- [ ] add it to moon-landing - [ ] add it to hub-docs

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I_kwDODunzps5Pi_SD

Outdated Link for mkqa Dataset

open

NONE

## Describe the bug The URL used to download the mkqa dataset is outdated. It seems the URL to download the dataset is currently https://github.com/apple/ml-mkqa/blob/main/dataset/mkqa.jsonl.gz instead of https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz (master branch has been renamed to main). ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mkqa") ``` ## Expected results downloads the dataset ## Actual results ```python Downloading builder script: 4.79k/? [00:00<00:00, 201kB/s] Downloading metadata: 13.2k/? [00:00<00:00, 504kB/s] Downloading and preparing dataset mkqa/mkqa (download: 11.35 MiB, generated: 34.29 MiB, post-processed: Unknown size, total: 45.65 MiB) to /home/lhr/.cache/huggingface/datasets/mkqa/mkqa/1.0.0/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d... Downloading data files: 0% 0/1 [00:00<?, ?it/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Input In [3], in <cell line: 3>() 1 from datasets import load_dataset ----> 3 dataset = load_dataset("mkqa") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/load.py:1746, 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) 1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1745 # Download and prepare data -> 1746 builder_instance.download_and_prepare( 1747 download_config=download_config, 1748 download_mode=download_mode, 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, 1751 use_auth_token=use_auth_token, 1752 ) 1754 # Build dataset for splits 1755 keep_in_memory = ( 1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1757 ) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info 708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos) 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/mkqa/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d/mkqa.py:130, in Mkqa._split_generators(self, dl_manager) 128 # download and extract URLs 129 urls_to_download = _URLS --> 130 downloaded_files = dl_manager.download_and_extract(urls_to_download) 132 return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})] File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls) 415 def download_and_extract(self, url_or_urls): 416 """Download and extract given url_or_urls. 417 418 Is roughly equivalent to: (...) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:309, in DownloadManager.download(self, url_or_urls) 306 download_func = partial(self._download, download_config=download_config) 308 start_time = datetime.now() --> 309 downloaded_path_or_paths = map_nested( 310 download_func, 311 url_or_urls, 312 map_tuple=True, 313 num_proc=download_config.num_proc, 314 disable_tqdm=not is_progress_bar_enabled(), 315 desc="Downloading data files", 316 ) 317 duration = datetime.now() - start_time 318 logger.info(f"Downloading took {duration.total_seconds() // 60} min") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:393, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: --> 393 mapped = [ 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:394, in <listcomp>(.0) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: 393 mapped = [ --> 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:330, in _single_map_nested(args) 328 # Singleton first to spare some computation 329 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 330 return function(data_struct) 332 # Reduce logging to keep things readable in multiprocessing with tqdm 333 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:335, in DownloadManager._download(self, url_or_filename, download_config) 332 if is_relative_path(url_or_filename): 333 # append the relative path to the base_path 334 url_or_filename = url_or_path_join(self._base_path, url_or_filename) --> 335 return cached_path(url_or_filename, download_config=download_config) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:185, in cached_path(url_or_filename, download_config, **download_kwargs) 181 url_or_filename = str(url_or_filename) 183 if is_remote_url(url_or_filename): 184 # URL, so get it from the cache (downloading if necessary) --> 185 output_path = get_from_cache( 186 url_or_filename, 187 cache_dir=cache_dir, 188 force_download=download_config.force_download, 189 proxies=download_config.proxies, 190 resume_download=download_config.resume_download, 191 user_agent=download_config.user_agent, 192 local_files_only=download_config.local_files_only, 193 use_etag=download_config.use_etag, 194 max_retries=download_config.max_retries, 195 use_auth_token=download_config.use_auth_token, 196 ignore_url_params=download_config.ignore_url_params, 197 download_desc=download_config.download_desc, 198 ) 199 elif os.path.exists(url_or_filename): 200 # File, and it exists. 201 output_path = url_or_filename File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:530, in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc) 525 raise FileNotFoundError( 526 f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been" 527 " disabled. To enable file online look-ups, set 'local_files_only' to False." 528 ) 529 elif response is not None and response.status_code == 404: --> 530 raise FileNotFoundError(f"Couldn't find file at {url}") 531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 532 if head_error is not None: FileNotFoundError: Couldn't find file at https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

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https://github.com/huggingface/datasets/pull/4816

PR_kwDODunzps487kpq

Update version of opus_paracrawl dataset

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This PR updates OPUS ParaCrawl from 7.1 to 9 version. Fix #4815.

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I_kwDODunzps5PhGtf

Outdated loading script for OPUS ParaCrawl dataset

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## Describe the bug Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.

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I_kwDODunzps5PeWbG

Support CSV as metadata file format in AudioFolder/ImageFolder

open

CONTRIBUTOR

Requested here: https://discuss.huggingface.co/t/how-to-structure-an-image-dataset-repo-using-the-image-folder-approach/21004. CSV is also used in AutoTrain for specifying metadata in image datasets.

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PR_kwDODunzps48446r

Fix loading example in opus dataset cards

closed

MEMBER

This PR: - fixes the examples to load the datasets, with the corrected dataset name, in their dataset cards for: - opus_dgt - opus_paracrawl - opus_wikipedia - fixes their dataset cards with the missing required information: title, data instances/fields/splits - enumerates the supported languages - adds a missing citation reference for opus_wikipedia Related to: - #4806

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PR_kwDODunzps484Fzq

Fix bug in function validate_type for Python >= 3.9

open

MEMBER

Fix `validate_type` function, so that it uses `get_origin` instead. This makes the function forward compatible. This fixes #4811 because: ```python In [4]: typing.Optional[str] Out[4]: typing.Optional[str] In [5]: get_origin(typing.Optional[str]) Out[5]: typing.Union ``` Fix #4811.

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I_kwDODunzps5PdKDd

Bug in function validate_type for Python >= 3.9

open

MEMBER

## Describe the bug The function `validate_type` assumes that the type `typing.Optional[str]` is automatically transformed to `typing.Union[str, NoneType]`. ```python In [4]: typing.Optional[str] Out[4]: typing.Union[str, NoneType] ``` However, this is not the case for Python 3.9: ```python In [3]: typing.Optional[str] Out[3]: typing.Optional[str] ```

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I_kwDODunzps5PlEVM

Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.

closed

NONE

Hi, when i try to run prepare_dataset function in [fine tuning ASR tutorial 4](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) , i got this error. I got this error Terminating: fork() called from a process already using GNU OpenMP, this is unsafe. There is no other logs available, so i have no clue what is the cause of it. ``` def prepare_dataset(batch): audio = batch["path"] # batched output is "un-batched" batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] batch["input_length"] = len(batch["input_values"]) with processor.as_target_processor(): batch["labels"] = processor(batch["text"]).input_ids return batch data = data.map(prepare_dataset, remove_columns=data.column_names["train"], num_proc=4) ``` Specify the actual results or traceback. There is no traceback except `Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-43-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps48-xc6

Add missing language tags to resources

closed

MEMBER

Add missing language tags to resources, required by existing datasets on GitHub.

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PR_kwDODunzps48-W7a

Add add cc-by-sa-2.5 license tag

open

CONTRIBUTOR

- [ ] add it to moon-landing - [ ] add it to hub-docs

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I_kwDODunzps5Pi_SD

Outdated Link for mkqa Dataset

open

NONE

## Describe the bug The URL used to download the mkqa dataset is outdated. It seems the URL to download the dataset is currently https://github.com/apple/ml-mkqa/blob/main/dataset/mkqa.jsonl.gz instead of https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz (master branch has been renamed to main). ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mkqa") ``` ## Expected results downloads the dataset ## Actual results ```python Downloading builder script: 4.79k/? [00:00<00:00, 201kB/s] Downloading metadata: 13.2k/? [00:00<00:00, 504kB/s] Downloading and preparing dataset mkqa/mkqa (download: 11.35 MiB, generated: 34.29 MiB, post-processed: Unknown size, total: 45.65 MiB) to /home/lhr/.cache/huggingface/datasets/mkqa/mkqa/1.0.0/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d... Downloading data files: 0% 0/1 [00:00<?, ?it/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Input In [3], in <cell line: 3>() 1 from datasets import load_dataset ----> 3 dataset = load_dataset("mkqa") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/load.py:1746, 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) 1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1745 # Download and prepare data -> 1746 builder_instance.download_and_prepare( 1747 download_config=download_config, 1748 download_mode=download_mode, 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, 1751 use_auth_token=use_auth_token, 1752 ) 1754 # Build dataset for splits 1755 keep_in_memory = ( 1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1757 ) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info 708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos) 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/mkqa/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d/mkqa.py:130, in Mkqa._split_generators(self, dl_manager) 128 # download and extract URLs 129 urls_to_download = _URLS --> 130 downloaded_files = dl_manager.download_and_extract(urls_to_download) 132 return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})] File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls) 415 def download_and_extract(self, url_or_urls): 416 """Download and extract given url_or_urls. 417 418 Is roughly equivalent to: (...) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:309, in DownloadManager.download(self, url_or_urls) 306 download_func = partial(self._download, download_config=download_config) 308 start_time = datetime.now() --> 309 downloaded_path_or_paths = map_nested( 310 download_func, 311 url_or_urls, 312 map_tuple=True, 313 num_proc=download_config.num_proc, 314 disable_tqdm=not is_progress_bar_enabled(), 315 desc="Downloading data files", 316 ) 317 duration = datetime.now() - start_time 318 logger.info(f"Downloading took {duration.total_seconds() // 60} min") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:393, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: --> 393 mapped = [ 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:394, in <listcomp>(.0) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: 393 mapped = [ --> 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:330, in _single_map_nested(args) 328 # Singleton first to spare some computation 329 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 330 return function(data_struct) 332 # Reduce logging to keep things readable in multiprocessing with tqdm 333 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:335, in DownloadManager._download(self, url_or_filename, download_config) 332 if is_relative_path(url_or_filename): 333 # append the relative path to the base_path 334 url_or_filename = url_or_path_join(self._base_path, url_or_filename) --> 335 return cached_path(url_or_filename, download_config=download_config) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:185, in cached_path(url_or_filename, download_config, **download_kwargs) 181 url_or_filename = str(url_or_filename) 183 if is_remote_url(url_or_filename): 184 # URL, so get it from the cache (downloading if necessary) --> 185 output_path = get_from_cache( 186 url_or_filename, 187 cache_dir=cache_dir, 188 force_download=download_config.force_download, 189 proxies=download_config.proxies, 190 resume_download=download_config.resume_download, 191 user_agent=download_config.user_agent, 192 local_files_only=download_config.local_files_only, 193 use_etag=download_config.use_etag, 194 max_retries=download_config.max_retries, 195 use_auth_token=download_config.use_auth_token, 196 ignore_url_params=download_config.ignore_url_params, 197 download_desc=download_config.download_desc, 198 ) 199 elif os.path.exists(url_or_filename): 200 # File, and it exists. 201 output_path = url_or_filename File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:530, in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc) 525 raise FileNotFoundError( 526 f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been" 527 " disabled. To enable file online look-ups, set 'local_files_only' to False." 528 ) 529 elif response is not None and response.status_code == 404: --> 530 raise FileNotFoundError(f"Couldn't find file at {url}") 531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 532 if head_error is not None: FileNotFoundError: Couldn't find file at https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps487kpq

Update version of opus_paracrawl dataset

open

MEMBER

This PR updates OPUS ParaCrawl from 7.1 to 9 version. Fix #4815.

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I_kwDODunzps5PhGtf

Outdated loading script for OPUS ParaCrawl dataset

open

MEMBER

## Describe the bug Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.

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I_kwDODunzps5PeWbG

Support CSV as metadata file format in AudioFolder/ImageFolder

open

CONTRIBUTOR

Requested here: https://discuss.huggingface.co/t/how-to-structure-an-image-dataset-repo-using-the-image-folder-approach/21004. CSV is also used in AutoTrain for specifying metadata in image datasets.

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PR_kwDODunzps48446r

Fix loading example in opus dataset cards

closed

MEMBER

This PR: - fixes the examples to load the datasets, with the corrected dataset name, in their dataset cards for: - opus_dgt - opus_paracrawl - opus_wikipedia - fixes their dataset cards with the missing required information: title, data instances/fields/splits - enumerates the supported languages - adds a missing citation reference for opus_wikipedia Related to: - #4806

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PR_kwDODunzps484Fzq

Fix bug in function validate_type for Python >= 3.9

open

MEMBER

Fix `validate_type` function, so that it uses `get_origin` instead. This makes the function forward compatible. This fixes #4811 because: ```python In [4]: typing.Optional[str] Out[4]: typing.Optional[str] In [5]: get_origin(typing.Optional[str]) Out[5]: typing.Union ``` Fix #4811.

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I_kwDODunzps5PdKDd

Bug in function validate_type for Python >= 3.9

open

MEMBER

## Describe the bug The function `validate_type` assumes that the type `typing.Optional[str]` is automatically transformed to `typing.Union[str, NoneType]`. ```python In [4]: typing.Optional[str] Out[4]: typing.Union[str, NoneType] ``` However, this is not the case for Python 3.9: ```python In [3]: typing.Optional[str] Out[3]: typing.Optional[str] ```

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PR_kwDODunzps484C9l

hellaswag: add non-empty description to fix metadata issue

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PR_kwDODunzps483Y4h

Complete the mlqa dataset card

closed

CONTRIBUTOR

I fixed the issue #4808 Details of PR: - Added languages included in the dataset. - Added task id and task category. - Updated the citation information. Fix #4808.

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I_kwDODunzps5PcYcZ

Add more information to the dataset card of mlqa dataset

closed

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PR_kwDODunzps483MSH

document fix in opus_gnome dataset

closed

CONTRIBUTOR

I fixed a issue #4805. I changed `"gnome"` to `"opus_gnome"` in[ README.md](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary).

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PR_kwDODunzps482yiS

Fix opus_gnome dataset card

closed

CONTRIBUTOR

I fixed a issue #4805. I changed `"gnome"` to `"opus_gnome"` in[ README.md](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary). Fix #4805

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I_kwDODunzps5Pbq3b

Wrong example in opus_gnome dataset card

closed

CONTRIBUTOR

## Describe the bug I found that [the example on opus_gone dataset ](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary) doesn't work. ## Steps to reproduce the bug ```python load_dataset("gnome", lang1="it", lang2="pl") ``` `"gnome"` should be `"opus_gnome"` ## Expected results ```bash 100% 1/1 [00:00<00:00, 42.09it/s] DatasetDict({ train: Dataset({ features: ['id', 'translation'], num_rows: 8368 }) }) ``` ## Actual results ```bash Couldn't find 'gnome' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/gnome/gnome.py ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.27 - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5PblNW

streaming dataset with concatenating splits raises an error

open

NONE

## Describe the bug streaming dataset with concatenating splits raises an error ## Steps to reproduce the bug ```python from datasets import load_dataset # no error repo = "nateraw/ade20k-tiny" dataset = load_dataset(repo, split="train+validation") ``` ```python from datasets import load_dataset # error repo = "nateraw/ade20k-tiny" dataset = load_dataset(repo, split="train+validation", streaming=True) ``` ```sh --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>() 3 # error 4 repo = "nateraw/ade20k-tiny" ----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True) 1 frames [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path) 1030 splits_generator = splits_generators[split] 1031 else: -> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}") 1033 1034 # Create a dataset for each of the given splits ValueError: Bad split: train+validation. Available splits: ['validation', 'train'] ``` [Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing) ## Expected results load successfully or throws an error saying it is not supported. ## Actual results above ## Environment info - `datasets` version: 2.4.0 - Platform: Windows-10-10.0.22000-SP0 (windows11 x64) - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5PZevK

Support `pipeline` argument in inspect.py functions

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** The `wikipedia` dataset requires a `pipeline` argument to build the list of splits: https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py#L937 But this is currently not supported in `get_dataset_config_info`: https://github.com/huggingface/datasets/blob/main/src/datasets/inspect.py#L373-L375 which is called by other functions, e.g. `get_dataset_split_names`. **Additional context** The dataset viewer is not working out-of-the-box on `wikipedia` for this reason: https://huggingface.co/datasets/wikipedia/viewer <img width="637" alt="Capture d’écran 2022-08-08 à 12 01 16" src="https://user-images.githubusercontent.com/1676121/183461838-5330783b-0269-4ba7-a999-314cde2023d8.png">

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I_kwDODunzps5PX8YT

`with_format` behavior is inconsistent on different datasets

open

CONTRIBUTOR

## Describe the bug I found a case where `with_format` does not transform the dataset to the requested format. ## Steps to reproduce the bug Run: ```python from transformers import AutoTokenizer, AutoFeatureExtractor from datasets import load_dataset raw = load_dataset("glue", "sst2", split="train") raw = raw.select(range(100)) tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled") def preprocess_func(examples): return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True) data = raw.map(preprocess_func, batched=True) print(type(data[0]["input_ids"])) data = data.with_format("torch", columns=["input_ids"]) print(type(data[0]["input_ids"])) ``` printing as expected: ```python <class 'list'> <class 'torch.Tensor'> ``` Then run: ```python raw = load_dataset("beans", split="train") raw = raw.select(range(100)) preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans") def preprocess_func(examples): imgs = [img.convert("RGB") for img in examples["image"]] return preprocessor(imgs) data = raw.map(preprocess_func, batched=True) print(type(data[0]["pixel_values"])) data = data.with_format("torch", columns=["pixel_values"]) print(type(data[0]["pixel_values"])) ``` Printing, unexpectedly ```python <class 'list'> <class 'list'> ``` ## Expected results `with_format` should transform into the requested format; it's not the case. ## Actual results `type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979 - Platform: Linux - Python version: 3.9.12 - PyArrow version: 7.0.0

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PR_kwDODunzps48yTYu

Fix fine classes in trec dataset

open

MEMBER

This PR: - replaces the fine labels, so that there are 50 instead of 47 - once more labels are added, all they (fine and coarse) have been re-ordered, so that they align with the order in: https://cogcomp.seas.upenn.edu/Data/QA/QC/definition.html - the feature names have been fixed: `fine_label` instead of `label-fine` - to sneak-case /underscores instead of hyphens) - words have been reordered Fix #4790.

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PR_kwDODunzps48yIss

support LargeListArray in pyarrow

open

CONTRIBUTOR

```python import numpy as np import datasets a = np.zeros((5000000, 768)) res = datasets.Dataset.from_dict({"embedding": a}) ''' File "/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/arrow_writer.py", line 178, in __arrow_array__ out = numpy_to_pyarrow_listarray(data) File "/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/features/features.py", line 1173, in numpy_to_pyarrow_listarray offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32()) File "pyarrow/array.pxi", line 312, in pyarrow.lib.array File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Integer value 2147483904 not in range: -2147483648 to 2147483647 ''' ``` Loading a large numpy array currently raises the error above as the type of offsets is `int32`. And pyarrow has supported [LargeListArray](https://arrow.apache.org/docs/python/generated/pyarrow.LargeListArray.html) for this case.

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I_kwDODunzps5PU8R-

video dataset loader/parser

closed

CONTRIBUTOR

you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)? could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem.

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PR_kwDODunzps48wVEG

Shard generator

open

NONE

Hi everyone! I was using Hugging Face datasets to process some very large datasets and found that it would be quite handy to have a feature that will allow to "split" these large datasets into chunks with equal size. Even better - be able to run through these chunks one by one in simple and convenient way. So I decided to add the method called shard_generator() to the main Dataset class. It works similar to shard method but it returns a generator of datasets with equal size (defined by shard_size attribute). Example: ```python >>> from datasets import load_dataset >>> ds = load_dataset("rotten_tomatoes", split="validation") >>> ds Dataset({ features: ['text', 'label'], num_rows: 1066 }) >>> next(ds.shard_generator(300)) Dataset({ features: ['text', 'label'], num_rows: 300 }) ``` I hope it can be helpful to someone. Thanks!

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PR_kwDODunzps48uL-t

Torgo dataset creation

closed

NONE

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I_kwDODunzps5PRHpC

ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset

open

CONTRIBUTOR

## Describe the bug When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature. ## Steps to reproduce the bug ```python from datasets import load_dataset from PIL import Image dataset = load_dataset("hf-internal-testing/example-documents") # load any random Pillow image image = Image.open("/content/cord_example.png").convert("RGB") new_image = {'image': image} dataset['test'] = dataset['test'].add_item(new_image) ``` ## Expected results The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`: ``` import datasets feature = datasets.Image(decode=False) new_image = {'image': feature.encode_example(image)} dataset['test'] = dataset['test'].add_item(new_image) ``` ## Actual results ``` ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type ```

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I_kwDODunzps5PPvPk

MBPP splits

open

NONE

(@albertvillanova) The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split: > In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**. If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected. The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples: > We specify a train and test split to use for evaluation. Specifically: > > * Task IDs 11-510 are used for evaluation. > * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples. I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974. Regarding the "sanitized" split the paper states the following: > For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning. The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split.

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I_kwDODunzps5PMLwJ

Add DocVQA

open

CONTRIBUTOR

## Adding a Dataset - **Name:** DocVQA - **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this information. - **Paper:** https://arxiv.org/abs/2007.00398 - **Data:** https://www.docvqa.org/datasets/docvqa - **Motivation:** Models like LayoutLM and Donut in the Transformers library are fine-tuned on DocVQA. Would be very handy to directly load this dataset from the hub. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).

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I_kwDODunzps5PMGK4

Dataset Viewer issue for Team-PIXEL/rendered-wikipedia-english

closed

NONE

### Link https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train ### Description The dataset can be loaded fine but the viewer shows this error: ``` Server Error Status code: 400 Exception: Status400Error Message: The dataset does not exist. ``` I'm guessing this is because I recently renamed the dataset. Based on related issues (e.g. https://github.com/huggingface/datasets/issues/4759) , is there something server-side that needs to be refreshed? ### Owner Yes

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I_kwDODunzps5PMARY

Issue with fine classes in trec dataset

open

MEMBER

## Describe the bug According to their paper, the TREC dataset contains 2 kinds of classes: - 6 coarse classes: TREC-6 - 50 fine classes: TREC-50 However, our implementation only has 47 (instead of 50) fine classes. The reason for this is that we only considered the last segment of the label, which is repeated for several coarse classes: - We have one `desc` fine label instead of 2: - `DESC:desc` - `HUM:desc` - We have one `other` fine label instead of 3: - `ENTY:other` - `LOC:other` - `NUM:other` From their paper: > We define a two-layered taxonomy, which represents a natural semantic classification for typical answers in the TREC task. The hierarchy contains 6 coarse classes and 50 fine classes, > Each coarse class contains a non-overlapping set of fine classes.

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PR_kwDODunzps48o3Kk

Update doc upload_dataset.mdx

open

CONTRIBUTOR

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PR_kwDODunzps48oUNx

Fix NonMatchingChecksumError in mbpp dataset

closed

MEMBER

Fix issue reported on the Hub: https://huggingface.co/datasets/mbpp/discussions/1 Fix #4787.

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I_kwDODunzps5PK2TH

NonMatchingChecksumError in mbpp dataset

closed

MEMBER

## Describe the bug As reported on the Hub [Fix Checksum Mismatch](https://huggingface.co/datasets/mbpp/discussions/1), there is a `NonMatchingChecksumError` when loading mbpp dataset ## Steps to reproduce the bug ```python ds = load_dataset("mbpp", "full") ``` ## Expected results Loading of the dataset without any exception raised. ## Actual results ``` NonMatchingChecksumError Traceback (most recent call last) <ipython-input-1-a3fbdd3ed82e> in <module> ----> 1 ds = load_dataset("mbpp", "full") .../huggingface/datasets/src/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) 1791 1792 # Download and prepare data -> 1793 builder_instance.download_and_prepare( 1794 download_config=download_config, 1795 download_mode=download_mode, .../huggingface/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) .../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1225 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1228 1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: .../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: --> 775 verify_checksums( 776 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" 777 ) .../huggingface/datasets/src/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://raw.githubusercontent.com/google-research/google-research/master/mbpp/mbpp.jsonl'] ```

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I_kwDODunzps5PHZ0c

.save_to_disk('path', fs=s3) TypeError

closed

NONE

The following code: ```python import datasets train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"]) s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key) train_dataset.save_to_disk("s3://datasets/", fs=s3) ``` produces following traceback: ```shell File "C:\Users\Hong Knop\AppData\Local\Programs\Python\Python310\lib\site-packages\botocore\auth.py", line 374, in scope return '/'.join(scope) ``` I invoke print(scope) in <auth.py> (line 373) and find this: ```python [('4VA08VLL3VTKQJKCAI8M',), '20220803', 'us-east-1', 's3', 'aws4_request'] ```

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PR_kwDODunzps48k8y4

Require torchaudio<0.12.0 in docs

closed

MEMBER

This PR adds to docs the requirement of torchaudio<0.12.0 to avoid RuntimeError. Subsequent to PR: - #4777

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I_kwDODunzps5PDy-Q

Add Multiface dataset

open

MEMBER

## Adding a Dataset - **Name:** Multiface dataset - **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps - **Data:** https://github.com/facebookresearch/multiface The whole dataset is 65TB though, so I'm not sure Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).

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PR_kwDODunzps48iHey

[WIP] Docs for creating a loading script for image datasets

open

MEMBER

This PR is a first draft of creating a loading script for image datasets. Feel free to let me know if there are any specificities I'm missing for this. 🙂 To do: - [x] Document how to create different configurations.

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I_kwDODunzps5PDOz2

pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648

open

NONE

## Describe the bug Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets. ## Steps to reproduce the bug ```python dataset_name = "the_pile" ds = load_dataset(dataset_name, split="train") ds = ds.map(preprocess, num_proc=num_workers) uniques = set(ds.unique("hash")) ``` Gists for minimum reproducible example: https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2 ## Expected results Chunking and writing out a deduplicated dataset. ## Actual results ``` return dataset._data.column(column).unique().to_pylist() File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 ```

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PR_kwDODunzps48hOie

Fix label renaming and add a battery of tests

open

MEMBER

This PR makes some changes to label renaming in `to_tf_dataset()`, both to fix some issues when users input something we weren't expecting, and also to make it easier to deprecate label renaming in future, if/when we want to move this special-casing logic to a function in `transformers`. The main changes are: - Label renaming now only happens when the `auto_rename_labels` argument is set. For backward compatibility, this defaults to `True` for now. - If the user requests "label" but the data collator renames that column to "labels", the label renaming logic will now handle that case correctly. - Added a battery of tests to make this more reliable in future. - Adds an optimization to loading in `to_tf_dataset()` for unshuffled datasets (uses slicing instead of a list of indices) Fixes #4772

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PR_kwDODunzps48g9oA

Remove apache_beam import from module level in natural_questions dataset

closed

MEMBER

Instead of importing `apache_beam` at the module level, import it in the method `_build_pcollection`. Fix #4779.

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I_kwDODunzps5PCRyp

Loading natural_questions requires apache_beam even with existing preprocessed data

closed

MEMBER

## Describe the bug When loading "natural_questions", the package "apache_beam" is required: ``` ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam. Please install it using 'pip install apache_beam' for instance' ``` This requirement is unnecessary, once there exists preprocessed data and the script just needs to download it. ## Steps to reproduce the bug ```python load_dataset("natural_questions", "dev", split="validation", revision="main") ``` ## Expected results No ImportError raised. ## Actual results ``` ImportError Traceback (most recent call last) [<ipython-input-3-c938e7c05d02>](https://localhost:8080/#) in <module>() ----> 1 from datasets import load_dataset; ds = load_dataset("natural_questions", "dev", split="validation", revision="main") [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) 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) 1732 revision=revision, 1733 use_auth_token=use_auth_token, -> 1734 **config_kwargs, 1735 ) 1736 [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1504 download_mode=download_mode, 1505 data_dir=data_dir, -> 1506 data_files=data_files, 1507 ) 1508 [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1245 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" 1246 ) from None -> 1247 raise e1 from None 1248 else: 1249 raise FileNotFoundError( [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1180 download_config=download_config, 1181 download_mode=download_mode, -> 1182 dynamic_modules_path=dynamic_modules_path, 1183 ).get_module() 1184 elif path.count("/") == 1: # community dataset on the Hub [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 490 base_path=hf_github_url(path=self.name, name="", revision=revision), 491 imports=imports, --> 492 download_config=self.download_config, 493 ) 494 additional_files = [(config.DATASETDICT_INFOS_FILENAME, dataset_infos_path)] if dataset_infos_path else [] [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in _download_additional_modules(name, base_path, imports, download_config) 214 _them_str = "them" if len(needs_to_be_installed) > 1 else "it" 215 raise ImportError( --> 216 f"To be able to use {name}, you need to install the following {_depencencies_str}: " 217 f"{', '.join(needs_to_be_installed)}.\nPlease install {_them_str} using 'pip install " 218 f"{' '.join(needs_to_be_installed.values())}' for instance'" ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam. Please install it using 'pip install apache_beam' for instance' ``` ## Environment info Colab notebook.

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PR_kwDODunzps48dRPh

Update local loading script docs

open

MEMBER

This PR clarifies the local loading script section to include how to load a dataset after you've modified the local loading script (closes #4732).

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PR_kwDODunzps48cByL

Require torchaudio<0.12.0 to avoid RuntimeError

closed

MEMBER

Related to: - https://github.com/huggingface/transformers/issues/18379 Fix partially #4776.

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I_kwDODunzps5O8iwk

RuntimeError when using torchaudio 0.12.0 to load MP3 audio file

open

MEMBER

Current version of `torchaudio` (0.12.0) raises a RuntimeError when trying to use `sox_io` backend but non-Python dependency `sox` is not installed: https://github.com/pytorch/audio/blob/2e1388401c434011e9f044b40bc8374f2ddfc414/torchaudio/backend/sox_io_backend.py#L21-L29 ```python def _fail_load( filepath: str, frame_offset: int = 0, num_frames: int = -1, normalize: bool = True, channels_first: bool = True, format: Optional[str] = None, ) -> Tuple[torch.Tensor, int]: raise RuntimeError("Failed to load audio from {}".format(filepath)) ``` Maybe we should raise a more actionable error message so that the user knows how to fix it. UPDATE: - this is an incompatibility of latest torchaudio (0.12.0) and the sox backend TODO: - [x] as a temporary solution, we should recommend installing torchaudio<0.12.0 - #4777 - #4785 - [ ] however, a stable solution must be found for torchaudio>=0.12.0 Related to: - https://github.com/huggingface/transformers/issues/18379

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I_kwDODunzps5O7Lgm

Streaming not supported in Theivaprakasham/wildreceipt

closed

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### Link _No response_ ### Description _No response_ ### Owner _No response_

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I_kwDODunzps5O4Rzk

Training hangs at the end of epoch, with set_transform/with_transform+multiple workers

open

NONE

## Describe the bug I use load_dataset() (I tried with [wiki](https://huggingface.co/datasets/wikipedia) and my own json data) and use set_transform/with_transform for preprocessing. But it hangs at the end of the 1st epoch if dataloader_num_workers>=1. No problem with single worker. ## Steps to reproduce the bug ```python train_dataset = datasets.load_dataset("wikipedia", "20220301.en", split='train', cache_dir=model_args.cache_dir, streaming=False) train_dataset.set_transform(psg_parse_fn) train_dataloader = DataLoader( train_dataset, batch_size=args.train_batch_size, sampler=DistributedSampler(train_dataset), collate_fn=data_collator, drop_last=args.dataloader_drop_last, num_workers=args.dataloader_num_workers, ) ``` ## Expected results ## Actual results It simply hangs. The ending step is num_example/batch_size (one epoch). ## Environment info - `datasets` version: 2.4.1.dev0 - Platform: Linux-5.4.170+-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.1

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PR_kwDODunzps48WNV3

Document loading from relative path

open

MEMBER

This PR describes loading a dataset from the Hub by specifying a relative path in `data_dir` or `data_files` in `load_dataset` (see #4757).

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I_kwDODunzps5O1rID

AssertionError when using label_cols in to_tf_dataset

open

NONE

## Describe the bug An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`. The assertion is in this line: https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475 ## Steps to reproduce the bug ```python from datasets import load_dataset from transformers import DefaultDataCollator dataset = load_dataset('glue', 'mrpc', split='train') tf_dataset = dataset.to_tf_dataset( columns=["sentence1", "sentence2", "idx"], label_cols=["label"], batch_size=16, collate_fn=DefaultDataCollator(return_tensors="tf"), ) ``` ## Expected results No assertion error. ## Actual results ``` AssertionError: in user code: File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels * assert set(features.keys()).union(labels.keys()) == set(input_batch.keys()) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps48VjWx

Remove dummy data generation docs

closed

MEMBER

This PR removes instructions to generate dummy data since that is no longer necessary for datasets that are uploaded to the Hub instead of our GitHub repo. Close #4744

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PR_kwDODunzps48UEBT

fix typo

closed

CONTRIBUTOR

By defaul -> By default

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I_kwDODunzps5OzflS

Fail to process SQuADv1.1 datasets with max_seq_length=128, doc_stride=96.

open

NONE

## Describe the bug datasets fail to process SQuADv1.1 with max_seq_length=128, doc_stride=96 when calling datasets["train"].train_dataset.map(). ## Steps to reproduce the bug I used huggingface[ TF2 question-answering examples](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering). And my scripts are as follows: ``` python run_qa.py \ --model_name_or_path $BERT_DIR \ --dataset_name $SQUAD_DIR \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 128 \ --doc_stride 96 \ --output_dir $OUTPUT \ --save_steps 10000 \ --overwrite_cache \ --overwrite_output_dir \ ``` ## Expected results Normally process SQuADv1.1 datasets with max_seq_length=128, doc_stride=96. ## Actual results ``` INFO:__main__:Padding all batches to max length because argument was set or we're on TPU. WARNING:datasets.fingerprint:Parameter 'function'=<function main.<locals>.prepare_train_features at 0x7f15bc2d07a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. 0%| | 0/88 [00:00<?, ?ba/s]thread '<unnamed>' panicked at 'assertion failed: stride < max_len', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/encoding.rs:311:9 note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace 0%| | 0/88 [00:00<?, ?ba/s] Traceback (most recent call last): File "run_qa.py", line 743, in <module> main() File "run_qa.py", line 485, in main load_from_cache_file=not data_args.overwrite_cache, File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2394, in map desc=desc, File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 551, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 518, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2768, in _map_single offset=offset, File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2644, in apply_function_on_filtered_inputs processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2336, in decorated result = f(decorated_item, *args, **kwargs) File "run_qa.py", line 410, in prepare_train_features padding=padding, File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2512, in __call__ **kwargs, File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2703, in batch_encode_plus **kwargs, File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 429, in _batch_encode_plus is_pretokenized=is_split_into_words, pyo3_runtime.PanicException: assertion failed: stride < max_len Traceback (most recent call last): File "./data/SQuADv1.1/evaluate-v1.1.py", line 92, in <module> with open(args.prediction_file) as prediction_file: FileNotFoundError: [Errno 2] No such file or directory: './output/bert_base_squadv1.1_tf2/eval_predictions.json' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Ubuntu, pytorch=1.11.0, tensorflow-gpu=2.9.1 - Python version: 2.7 - PyArrow version: 8.0.0

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PR_kwDODunzps48TRUH

Unpin rouge_score test dependency

closed

MEMBER

Once `rouge-score` has made the 0.1.2 release to fix their issue https://github.com/google-research/google-research/issues/1212, we can unpin it. Related to: - #4735

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PR_kwDODunzps48TCpI

Add 2.4.0 version added to docstrings

closed

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I_kwDODunzps5OyTXk

Dataset Viewer issue for openclimatefix/goes-mrms

open

NONE

### Link _No response_ ### Description _No response_ ### Owner _No response_

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PR_kwDODunzps48S2rM

Fix version in map_nested docstring

closed

MEMBER

After latest release, `map_nested` docstring needs being updated with the right version for versionchanged and versionadded.

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PR_kwDODunzps48RMLu

Update CI badge

closed

CONTRIBUTOR

Replace the old CircleCI badge with a new one for GH Actions.

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PR_kwDODunzps48RMKi

More rigorous shape inference in to_tf_dataset

open

MEMBER

`tf.data` needs to know the shape of tensors emitted from a `tf.data.Dataset`. Although `None` dimensions are possible, overusing them can cause problems - Keras uses the dataset tensor spec at compile-time, and so saying that a dimension is `None` when it's actually constant can hurt performance, or even cause training to fail for dimensions that are needed to determine the shape of weight tensors! The compromise I used here was to sample several batches from the underlying dataset and apply the `collate_fn` to them, and then to see which dimensions were "empirically variable". There's an obvious problem here, though - if you sample 10 batches and they all have the same shape on a certain dimension, there's still a small chance that the 11th batch will be different, and Keras will throw an error if a dataset tries to emit a tensor whose shape doesn't match the spec. I encountered this bug in practice once or twice for datasets that were mostly-but-not-totally constant on a given dimension, and I still don't have a perfect solution, but this PR should greatly reduce the risk. It samples many more batches, and also samples very small batches (size 2) - this increases the variability, making it more likely that a few outlier samples will be detected. Ideally, of course, we'd determine the full output shape analytically, but that's surprisingly tricky when the `collate_fn` can be any arbitrary Python code!

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PR_kwDODunzps48RE56

Improve features resolution in streaming

open

MEMBER

`IterableDataset._resolve_features` was returning the features sorted alphabetically by column name, which is not consistent with non-streaming. I changed this and used the order of columns from the data themselves. It was causing some inconsistencies in the dataset viewer as well. I also fixed `interleave_datasets` that was not filling missing columns with None, because it was not using the columns from `IterableDataset._resolve_features` cc @severo

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I_kwDODunzps5Oved7

parallel searching in multi-gpu setting using faiss

open

CONTRIBUTOR

While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works. Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360

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I_kwDODunzps5OuwCP

Issue with offline mode

open

NONE

## Describe the bug I can't retrieve a cached dataset with offline mode enabled ## Steps to reproduce the bug To reproduce my issue, first, you'll need to run a script that will cache the dataset ```python import os os.environ["HF_DATASETS_OFFLINE"] = "0" import datasets datasets.logging.set_verbosity_info() ds_name = "SaulLu/toy_struc_dataset" ds = datasets.load_dataset(ds_name) print(ds) ``` then, you can try to reload it in offline mode: ```python import os os.environ["HF_DATASETS_OFFLINE"] = "1" import datasets datasets.logging.set_verbosity_info() ds_name = "SaulLu/toy_struc_dataset" ds = datasets.load_dataset(ds_name) print(ds) ``` ## Expected results I would have expected the 2nd snippet not to return any errors ## Actual results The 2nd snippet returns: ``` Traceback (most recent call last): File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module> ds = datasets.load_dataset(ds_name) File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset builder_instance = load_dataset_builder( File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3 Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?

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I_kwDODunzps5OuY3E

Dataset Viewer issue for Toygar/turkish-offensive-language-detection

closed

NONE

### Link https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection ### Description Status code: 400 Exception: Status400Error Message: The dataset does not exist. Hi, I provided train.csv, test.csv and valid.csv files. However, viewer says dataset does not exist. Should I need to do anything else? ### Owner Yes

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