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I_kwDODunzps5BCp3Y

Discuss whether support canonical datasets w/o dataset_infos.json and/or dummy data

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MEMBER

I open this PR to have a public discussion about this topic and make a decision. As previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)? On the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However: - the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though) - we are migrating canonical datasets to the Hub Do we really need to continue testing them in out CI? Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data). Feel free to ping other people for the discussion. CC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw

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https://github.com/huggingface/datasets/pull/3506

PR_kwDODunzps4wZpot

Allows DatasetDict.filter to have batching option

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- Related to: #3244 - Fixes: #3503 We extends `.filter( ... batched: bool)` support to DatasetDict.

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https://github.com/huggingface/datasets/issues/3505

I_kwDODunzps5BCaPk

cast_column function not working with map function in streaming mode for Audio features

open

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## Describe the bug I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only. I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset, Audio from transformers import Wav2Vec2Processor def encode(batch, processor): print("Audio: ",batch['audio']) batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values return batch def print_ds(ds): iterator = iter(ds) for d in iterator: print("Data: ",d) break processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path) dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'}, data_dir="data", streaming=True, split="train") print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000)) print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.map(lambda x: encode(x,processor)) print("Features: ",dataset.features) print_ds(dataset) ``` ## Expected results map function not printing Audio type features be used with processor function and getting error in processor call due to this. ## Actual results # after load_dataset call Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'} # after cast_column call Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ..., 1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}} # after map call Features: None Audio: data/0116_003.wav Traceback (most recent call last): File "demo2.py", line 36, in <module> print_ds(dataset) File "demo2.py", line 11, in print_ds for d in iterator: File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__ yield key, self.function(example) File "demo2.py", line 32, in <lambda> dataset = dataset.map(lambda x: batch_encode(x,processor)) File "demo2.py", line 6, in batch_encode batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values TypeError: string indices must be integers ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-4.14.243 with-debian-bullseye-sid - Python version: 3.7.9 - PyArrow version: 6.0.1

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I_kwDODunzps5BAn12

Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst

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NONE

## Describe the bug I am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https://huggingface.co/course/chapter5/4?fw=pt). https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset # This takes a few minutes to run, so go grab a tea or coffee while you wait :) data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" pubmed_dataset = load_dataset("json", data_files=data_files, split="train") pubmed_dataset ``` I also tried with `wget` as follows. ``` wget https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst ``` ## Expected results I expect to be able to download this file. ## Actual results Traceback ``` --------------------------------------------------------------------------- timeout Traceback (most recent call last) /usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self) 158 try: --> 159 conn = connection.create_connection( 160 (self._dns_host, self.port), self.timeout, **extra_kw /usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 /usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock timeout: timed out During handling of the above exception, another exception occurred: ConnectTimeoutError Traceback (most recent call last) /usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 664 # Make the request on the httplib connection object. --> 665 httplib_response = self._make_request( 666 conn, /usr/lib/python3/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 375 try: --> 376 self._validate_conn(conn) 377 except (SocketTimeout, BaseSSLError) as e: /usr/lib/python3/dist-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 995 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 996 conn.connect() 997 /usr/lib/python3/dist-packages/urllib3/connection.py in connect(self) 313 # Add certificate verification --> 314 conn = self._new_conn() 315 hostname = self.host /usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self) 163 except SocketTimeout: --> 164 raise ConnectTimeoutError( 165 self, ConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)') During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) /usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 438 if not chunked: --> 439 resp = conn.urlopen( 440 method=request.method, /usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 718 --> 719 retries = retries.increment( 720 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] /usr/lib/python3/dist-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 435 if new_retry.is_exhausted(): --> 436 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 437 MaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')) During handling of the above exception, another exception occurred: ConnectTimeout Traceback (most recent call last) /tmp/ipykernel_15104/606583593.py in <module> 3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :) 4 data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" ----> 5 pubmed_dataset = load_dataset("json", data_files=data_files, split="train") 6 pubmed_dataset ~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1655 1656 # Create a dataset builder -> 1657 builder_instance = load_dataset_builder( 1658 path=path, 1659 name=name, ~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1492 download_config = download_config.copy() if download_config else DownloadConfig() 1493 download_config.use_auth_token = use_auth_token -> 1494 dataset_module = dataset_module_factory( 1495 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1496 ) ~/.local/lib/python3.8/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1116 # Try packaged 1117 if path in _PACKAGED_DATASETS_MODULES: -> 1118 return PackagedDatasetModuleFactory( 1119 path, data_files=data_files, download_config=download_config, download_mode=download_mode 1120 ).get_module() ~/.local/lib/python3.8/site-packages/datasets/load.py in get_module(self) 773 else get_patterns_locally(str(Path().resolve())) 774 ) --> 775 data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) 776 module_path, hash = _PACKAGED_DATASETS_MODULES[self.name] 777 builder_kwargs = {"hash": hash, "data_files": data_files} ~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token) 576 for key, patterns_for_key in patterns.items(): 577 out[key] = ( --> 578 DataFilesList.from_local_or_remote( 579 patterns_for_key, 580 base_path=base_path, ~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token) 545 base_path = base_path if base_path is not None else str(Path().resolve()) 546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) --> 547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token) 548 return cls(data_files, origin_metadata) 549 ~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token) 492 data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None 493 ) -> Tuple[str]: --> 494 return thread_map( 495 partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token), 496 data_files, ~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs) 92 """ 93 from concurrent.futures import ThreadPoolExecutor ---> 94 return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs) 95 96 ~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs) 74 map_args.update(chunksize=chunksize) 75 with PoolExecutor(**pool_kwargs) as ex: ---> 76 return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs)) 77 78 ~/.local/lib/python3.8/site-packages/tqdm/notebook.py in __iter__(self) 252 def __iter__(self): 253 try: --> 254 for obj in super(tqdm_notebook, self).__iter__(): 255 # return super(tqdm...) will not catch exception 256 yield obj ~/.local/lib/python3.8/site-packages/tqdm/std.py in __iter__(self) 1171 # (note: keep this check outside the loop for performance) 1172 if self.disable: -> 1173 for obj in iterable: 1174 yield obj 1175 return /usr/lib/python3.8/concurrent/futures/_base.py in result_iterator() 617 # Careful not to keep a reference to the popped future 618 if timeout is None: --> 619 yield fs.pop().result() 620 else: 621 yield fs.pop().result(end_time - time.monotonic()) /usr/lib/python3.8/concurrent/futures/_base.py in result(self, timeout) 442 raise CancelledError() 443 elif self._state == FINISHED: --> 444 return self.__get_result() 445 else: 446 raise TimeoutError() /usr/lib/python3.8/concurrent/futures/_base.py in __get_result(self) 387 if self._exception: 388 try: --> 389 raise self._exception 390 finally: 391 # Break a reference cycle with the exception in self._exception /usr/lib/python3.8/concurrent/futures/thread.py in run(self) 55 56 try: ---> 57 result = self.fn(*self.args, **self.kwargs) 58 except BaseException as exc: 59 self.future.set_exception(exc) ~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token) 483 if isinstance(data_file, Url): 484 data_file = str(data_file) --> 485 return (request_etag(data_file, use_auth_token=use_auth_token),) 486 else: 487 data_file = str(data_file.resolve()) ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in request_etag(url, use_auth_token) 489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]: 490 headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token) --> 491 response = http_head(url, headers=headers, max_retries=3) 492 response.raise_for_status() 493 etag = response.headers.get("ETag") if response.ok else None ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries) 474 headers = copy.deepcopy(headers) or {} 475 headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent")) --> 476 response = _request_with_retry( 477 method="HEAD", 478 url=url, ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params) 407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err: 408 if tries > max_retries: --> 409 raise err 410 else: 411 logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]") ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params) 403 tries += 1 404 try: --> 405 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params) 406 success = True 407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err: /usr/lib/python3/dist-packages/requests/api.py in request(method, url, **kwargs) 58 # cases, and look like a memory leak in others. 59 with sessions.Session() as session: ---> 60 return session.request(method=method, url=url, **kwargs) 61 62 /usr/lib/python3/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 531 } 532 send_kwargs.update(settings) --> 533 resp = self.send(prep, **send_kwargs) 534 535 return resp /usr/lib/python3/dist-packages/requests/sessions.py in send(self, request, **kwargs) 644 645 # Send the request --> 646 r = adapter.send(request, **kwargs) 647 648 # Total elapsed time of the request (approximately) /usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 502 # TODO: Remove this in 3.0.0: see #2811 503 if not isinstance(e.reason, NewConnectionError): --> 504 raise ConnectTimeout(e, request=request) 505 506 if isinstance(e.reason, ResponseError): ConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')) ``` ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 6.0.1

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I_kwDODunzps5A_0sf

Batched in filter throws error

open

NONE

I hope this is really a bug, I could not find it among the open issues ## Describe the bug using `batched=False` in DataSet.filter throws error ```python TypeError: filter() got an unexpected keyword argument 'batched' ``` but in the docs it is lister as an argument. ## Steps to reproduce the bug ```python task = "mnli" max_length = 128 tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/") dataset = load_dataset("glue", task) task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mnli-mm": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } ##### tokenization_parameters sentence1_key, sentence2_key = task_to_keys[task] def preprocess_function(examples, max_length): if sentence2_key is None: return tokenizer( examples[sentence1_key], truncation=True, max_length=max_length ) return tokenizer( examples[sentence1_key], examples[sentence2_key], truncation=False, padding="max_length", max_length=max_length, ) encoded_dataset = dataset.map( lambda x: preprocess_function(x, max_length=max_length), batched=False ) encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1, 1.17.0 - Platform: ubuntu - Python version: 3.8.12

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https://github.com/huggingface/datasets/pull/3502

PR_kwDODunzps4wXSLi

Add QuALITY

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CONTRIBUTOR

Fixes #3441.

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https://github.com/huggingface/datasets/pull/3501

PR_kwDODunzps4wXM8H

Update pib dataset card

closed

MEMBER

Related to #3496

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https://github.com/huggingface/datasets/pull/3500

PR_kwDODunzps4wXLTB

Docs: Add VCTK dataset description

open

CONTRIBUTOR

This PR is a very minor followup to #1837, with only docs changes (single comment string).

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https://github.com/huggingface/datasets/issues/3499

I_kwDODunzps5A-hqK

Adjusting chunk size for streaming datasets

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** I want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing. **Describe the solution you'd like** I would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.

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https://github.com/huggingface/datasets/pull/3498

PR_kwDODunzps4wWL5U

update `pretty_name` for all datasets

open

CONTRIBUTOR

I made a script some time back to fetch `pretty_names` from `papers_with_code` dataset along with some other rules. Updating them in the `README` of `datasets`. Took only first 200 datasets in consideration and after some eye balling most of them were looking good to me!

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I_kwDODunzps5A-Nhk

Changing sampling rate in audio dataset and subsequently mapping with `num_proc > 1` leads to weird bug

open

MEMBER

Running: ```python from datasets import load_dataset, DatasetDict import datasets from transformers import AutoFeatureExtractor raw_datasets = DatasetDict() raw_datasets["train"] = load_dataset("common_voice", "ab", split="train") feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base") raw_datasets = raw_datasets.cast_column( "audio", datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) num_workers = 16 def prepare_dataset(batch): sample = batch["audio"] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) return batch raw_datasets.map( prepare_dataset, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=16, desc="preprocess datasets", ) ``` gives ```bash File "/home/patrick/experiments/run_bug.py", line 25, in <module> raw_datasets.map( File "/home/patrick/python_bin/datasets/dataset_dict.py", line 492, in map { File "/home/patrick/python_bin/datasets/dataset_dict.py", line 493, in <dictcomp> k: dataset.map( File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2139, in map shards = [ File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2140, in <listcomp> self.shard(num_shards=num_proc, index=rank, contiguous=True, keep_in_memory=keep_in_memory) File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 3164, in shard return self.select( File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 485, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/patrick/python_bin/datasets/fingerprint.py", line 411, in wrapper out = func(self, *args, **kwargs) File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2756, in select return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint) File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2667, in _new_dataset_with_indices return Dataset( File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 659, in __init__ raise ValueError( ValueError: External features info don't match the dataset: Got {'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)} with type struct<client_id: string, path: string, audio: string, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string> but expected something like {'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)} with type struct<client_id: string, path: string, audio: struct<path: string, bytes: binary>, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string> ``` Versions: ```python - `datasets` version: 1.16.2.dev0 - Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33 - Python version: 3.9.7 - PyArrow version: 6.0.1 ``` and `transformers`: ``` - `transformers` version: 4.16.0.dev0 - Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33 - Python version: 3.9.7 ```

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PR_kwDODunzps4wV1_w

Update version of pib dataset and make it streamable

closed

MEMBER

This PR: - Updates version of pib dataset: from 0.0.0 to 1.3.0 - Makes the dataset streamable Fix #3491. CC: @severo

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I_kwDODunzps5A99SQ

Add VoxLingua107

open

CONTRIBUTOR

## Adding a Dataset - **Name:** VoxLingua107 - **Description:** VoxLingua107 is a speech dataset for training spoken language identification models. - **Paper:** https://arxiv.org/abs/2011.12998 - **Data:** http://bark.phon.ioc.ee/voxlingua107/ - **Motivation:** 107 languages, totaling 6628 hours for the train split. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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PR_kwDODunzps4wV0vB

Clone full repo to detect new tags when mirroring datasets on the Hub

closed

MEMBER

The new releases of `datasets` were not detected because the shallow clone in the CI wasn't getting the git tags. By cloning the full repository we can properly detect a new release, and tag all the dataset repositories accordingly cc @SBrandeis

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PR_kwDODunzps4wVxfr

Fix VCTK encoding

closed

MEMBER

utf-8 encoding was missing in the VCTK dataset builder added in #3351

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PR_kwDODunzps4wVufr

Add `gzip` for `to_json`

open

CONTRIBUTOR

(Partially) closes #3480. I have added `gzip` compression for `to_json`. I realised we can run into this compression problem with `to_csv` as well. `IOHandler` can be used for `to_csv` too. Please let me know if any changes are required.

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I_kwDODunzps5A9tRC

Update version of pib dataset

closed

MEMBER

On the Hub we have v0, while there exists v1.3. Related to bigscience-workshop/data_tooling#130

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I_kwDODunzps5A8_aF

Does datasets support load text from HDFS?

open

NONE

The raw text data is stored on HDFS due to the dataset's size is too large to store on my develop machine, so I wander does datasets support read data from hdfs?

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PR_kwDODunzps4wT97d

Avoid unnecessary list creations

open

CONTRIBUTOR

Like in `join([... for s in ...])`. Also changed other things that I saw: * Use a `with` statement for many `open` that missed them, so the files don't remain open. * Remove unused variables. * Many HTTP links converted into HTTPS (verified). * Remove unnecessary "r" mode arg in `open` (double-checked it was actually the default in each case). * Remove Python 2 style of using `super`. * Run `pyupgrade $(find . -name "*.py" -type f) --py36-plus` (which already does some of the previous points). * Run `dos2unix $(find . -name "*.py" -type f)` (CRLF to LF line endings). * Fix typos.

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I_kwDODunzps5A7hh1

URL query parameters are set as path in the compression hop for fsspec

open

MEMBER

## Describe the bug There is an ssue with `StreamingDownloadManager._extract`. I don't know how the test `test_streaming_gg_drive_gzipped` passes: For ```python TEST_GG_DRIVE_GZIPPED_URL = "https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz" urlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL) ``` gives `urlpath`: ```python 'gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz' ``` The gzip path makes no sense: `gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz` ## Steps to reproduce the bug ```python from datasets.utils.streaming_download_manager import StreamingDownloadManager dl_manager = StreamingDownloadManager() urlpath = dl_manager.extract("https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz") print(urlpath) ``` ## Expected results The query parameters should not be set as part of the path.

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PR_kwDODunzps4wTVeN

Update ADD_NEW_DATASET.md

closed

CONTRIBUTOR

fixed make style prompt for Windows Terminal

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PR_kwDODunzps4wTNd1

Fix weird spacing in ManualDownloadError message

closed

CONTRIBUTOR

`textwrap.dedent` works based on the spaces at the beginning. Before this change, there wasn't any space.

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I_kwDODunzps5A6T39

skip columns which cannot set to specific format when set_format

closed

NONE

**Is your feature request related to a problem? Please describe.** When using `dataset.set_format("torch")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns. **Describe the solution you'd like** skip columns which cannot set to specific format when set_format instead of raise an error.

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I_kwDODunzps5A53RC

make shape verification to use ArrayXD instead of nested lists for map

open

NONE

As describe in https://github.com/huggingface/datasets/issues/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added.

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PR_kwDODunzps4wSAW4

Remove unused phony rule from Makefile

open

CONTRIBUTOR

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PR_kwDODunzps4wQqE1

Fix duplicate keys in NewsQA

open

CONTRIBUTOR

* Fix duplicate keys in NewsQA when loading from CSV files. * Fix s/narqa/newsqa/ in the download manually error message. * Make the download manually error message show nicely when printed. Otherwise, is hard to read due to spacing issues. * Fix the format of the license text. * Reformat the code to make it simpler.

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PR_kwDODunzps4wQoJu

Fix overriding of filesystem info

closed

MEMBER

Previously, `BaseCompressedFileFileSystem.info` was overridden and transformed from function to dict. This generated a bug for filesystem methods that use `self.info()`, like e.g. `fs.isfile()`. This PR: - Adds tests for `fs.isfile` (that use `fs.info`). - Fixes custom `BaseCompressedFileFileSystem.info` by removing its overriding.

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I_kwDODunzps5A3aNm

the compression format requested when saving a dataset in json format is not respected

open

NONE

## Describe the bug In the documentation of the `to_json` method, it is stated in the parameters that > **to_json_kwargs – Parameters to pass to pandas’s pandas.DataFrame.to_json. however when we pass for example `compression="gzip"`, the saved file is not compressed. Would you also have expected compression to be applied? :relaxed: ## Steps to reproduce the bug ```python my_dict = {"a": [1, 2, 3], "b": [1, 2, 3]} ``` ### Result with datasets ```python from datasets import Dataset dataset = Dataset.from_dict(my_dict) dataset.to_json("dic_with_datasets.jsonl.gz", compression="gzip") !cat dic_with_datasets.jsonl.gz ``` output ``` {"a":1,"b":1} {"a":2,"b":2} {"a":3,"b":3} ``` Note: I would expected to see binary data here ### Result with pandas ```python import pandas as pd df = pd.DataFrame(my_dict) df.to_json("dic_with_pandas.jsonl.gz", lines=True, orient="records", compression="gzip") !cat dic_with_pandas.jsonl.gz ``` output ``` 4��a�dic_with_pandas.jsonl��VJT�2�QJ��\� ��g��yƵ���������)��� ``` Note: It looks like binary data ## Expected results I would have expected that the saved result with datasets would also be a binary file ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1 - Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.11 - PyArrow version: 5.0.0

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I_kwDODunzps5A3R2w

Dataset preview is not available (I think for all Hugging Face datasets)

closed

NONE

## Dataset viewer issue for '*french_book_reviews*' **Link:** https://huggingface.co/datasets/Abirate/french_book_reviews **short description of the issue** For my dataset, the dataset preview is no longer functional (it used to work: The dataset had been added the day before and it was fine...) And, after looking over the datasets, I discovered that this issue affects all Hugging Face datasets (as of yesterday, December 23, 2021, around 10 p.m. (CET)). **Am I the one who added this dataset** : Yes **Note**: here a screenshot showing the issue  **And here for glue dataset :** 

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PR_kwDODunzps4wPMWq

Extend support for streaming datasets that use os.walk

closed

MEMBER

This PR extends the support in streaming mode for datasets that use `os.walk`, by patching that function. This PR adds support for streaming mode to datasets: 1. autshumato 1. code_x_glue_cd_code_to_text 1. code_x_glue_tc_nl_code_search_adv 1. nchlt CC: @severo

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PR_kwDODunzps4wPKPX

Use `iter_files` instead of `str(Path(...)` in image dataset

closed

CONTRIBUTOR

Use `iter_files` in the `beans` and the `cats_vs_dogs` dataset scripts as suggested by @albertvillanova. Additional changes: * Fix `iter_files` in `MockDownloadManager` (see this [CI error](https://app.circleci.com/pipelines/github/huggingface/datasets/9247/workflows/2657ff8a-b531-4fd9-a9fc-6541a72e8d83/jobs/57028)) * Add support for `os.path.isdir` and `os.path.isfile` in streaming (`os.path.isfile` is needed in `StreamingDownloadManager`'s `iter_files` to make `cats_vs_dogs` streamable) TODO: - [ ] add tests for `xisdir` and `xisfile`

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PR_kwDODunzps4wOZ8a

Extend support for streaming datasets that use ET.parse

closed

MEMBER

This PR extends the support in streaming mode for datasets that use `ET.parse`, by patching the function. This PR adds support for streaming mode to datasets: 1. ami 1. assin 1. assin2 1. counter 1. enriched_web_nlg 1. europarl_bilingual 1. hyperpartisan_news_detection 1. polsum 1. qa4mre 1. quail 1. ted_talks_iwslt 1. udhr 1. web_nlg 1. winograd_wsc CC: @severo

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I_kwDODunzps5Az6zp

The rotten_tomatoes dataset of movie reviews contains some reviews in Spanish

open

NONE

## Describe the bug See title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user. ## Steps to reproduce the bug Go to the [dataset viewer](https://huggingface.co/datasets/viewer/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that. ## Expected results English movie reviews only. ## Actual results Example of a Spanish movie review (4173): > "É uma pena que , mais tarde , o próprio filme abandone o tom de paródia e passe a utilizar os mesmos clichês que havia satirizado "

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PR_kwDODunzps4wMMt0

Decode images when iterating

closed

MEMBER

If I iterate over a vision dataset, the images are not decoded, and the dictionary with the bytes is returned. This PR enables image decoding in `Dataset.__iter__` Close https://github.com/huggingface/datasets/issues/3473

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I_kwDODunzps5AyVoK

Iterating over a vision dataset doesn't decode the images

closed

MEMBER

## Describe the bug If I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned. ## Steps to reproduce the bug ```python from datasets import load_dataset import PIL mnist = load_dataset("mnist", split="train") first_image = mnist[0]["image"] assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # passes first_image = next(iter(mnist))["image"] assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # fails ``` ## Expected results The image should be decoded, as a PIL Image ## Actual results We get a dictionary ``` {'bytes': b'\x89PNG\r\n\x1a\n\x00..., 'path': None} ``` ## Environment info - `datasets` version: 1.17.1.dev0 - Platform: Darwin-20.6.0-x86_64-i386-64bit - Python version: 3.7.2 - PyArrow version: 6.0.0 The bug also exists in 1.17.0 ## Investigation I think the issue is that decoding is disabled in `__iter__`: https://github.com/huggingface/datasets/blob/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6/src/datasets/arrow_dataset.py#L1651-L1661 Do you remember why it was disabled in the first place @albertvillanova ? Also cc @mariosasko @NielsRogge

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PR_kwDODunzps4wMEwA

Fix `str(Path(...))` conversion in streaming on Linux

closed

CONTRIBUTOR

Fix `str(Path(...))` conversion in streaming on Linux. This should fix the streaming of the `beans` and `cats_vs_dogs` datasets.

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PR_kwDODunzps4wLAk6

Fix Tashkeela dataset to yield stripped text

closed

MEMBER

This PR: - Yields stripped text - Fix path for Windows - Adds license - Adds more info in dataset card Close bigscience-workshop/data_tooling#279

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PR_kwDODunzps4wJO8t

Fix rendering of docs

closed

MEMBER

Minor fix in docs. Currently, `ClassLabel` docstring rendering is not right.

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PR_kwDODunzps4wIrOV

Fix METEOR missing NLTK's omw-1.4

closed

MEMBER

NLTK 3.6.6 now requires `omw-1.4` to be downloaded for METEOR to work. This should fix the CI on master

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PR_kwDODunzps4wIozO

Add COCO dataset

open

CONTRIBUTOR

This PR adds the MS COCO dataset. Compared to the [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/object_detection/coco.py) script, this implementation adds 8 additional configs to cover the tasks other than object detection. Some notes: * the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here * I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`) * this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427 TODOs: - [x] dataset card - [ ] dummy data cc @merveenoyan Closes #2526

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PR_kwDODunzps4wIoqd

Push dataset infos.json to Hub

closed

MEMBER

When doing `push_to_hub`, the feature types are lost (see issue https://github.com/huggingface/datasets/issues/3394). This PR fixes this by also pushing a `dataset_infos.json` file to the Hub, that stores the feature types. Other minor changes: - renamed the `___` separator to `--`, since `--` is now disallowed in a name in the back-end. I tested this feature with datasets like conll2003 that has feature types like `ClassLabel` that were previously lost. Close https://github.com/huggingface/datasets/issues/3394 I would like to include this in today's release (though not mandatory), so feel free to comment/suggest changes

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PR_kwDODunzps4wII3w

Add CRASS dataset

open

CONTRIBUTOR

Added crass dataset

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I_kwDODunzps5AseVw

Unable to load 'cnn_dailymail' dataset

open

NONE

## Describe the bug I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True) ``` ## Expected results Expecting to load 'cnn_dailymail' dataset. ## Actual results `NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0

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I_kwDODunzps5AseA5

struct.error: 'i' format requires -2147483648 <= number <= 2147483647

open

NONE

## Describe the bug A clear and concise description of what the bug is. using latest datasets=datasets-1.16.1-py3-none-any.whl process my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:  then I get this error:  I have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: linux docker - Python version: 3.6

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PR_kwDODunzps4wGB4P

Update swahili_news dataset

closed

MEMBER

Update dataset with latest verion data files. Fix #3462. Close bigscience-workshop/data_tooling#107

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I_kwDODunzps5ArIs9

Update swahili_news dataset

closed

MEMBER

Please note also: the HuggingFace version at https://huggingface.co/datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203. ## Adding a Dataset - **Name:** swahili_news Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Related to: - bigscience-workshop/data_tooling#107

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PR_kwDODunzps4wFzDP

Fix links in metrics description

closed

MEMBER

Remove Markdown syntax for links in metrics description, as it is not properly rendered. Related to #3437.

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PR_kwDODunzps4wFyCf

Don't encode lists as strings when using `Value("string")`

open

MEMBER

Following https://github.com/huggingface/datasets/pull/3456#event-5792250497 it looks like `datasets` can silently convert lists to strings using `str()`, instead of raising an error. This PR fixes this and should fix the issue with WER showing low values if the input format is not right.

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I_kwDODunzps5Aq1LI

dataset.filter overwriting previously set dataset._indices values, resulting in the wrong elements being selected.

closed

NONE

## Describe the bug When using dataset.select to select a subset of a dataset, dataset._indices are set to indicate which elements are now considered in the dataset. The same thing happens when you shuffle the dataset; dataset._indices are set to indicate what the new order of the data is. However, if you then use a dataset.filter, that filter interacts with those dataset._indices values in a non-intuitive manner. https://huggingface.co/docs/datasets/_modules/datasets/arrow_dataset.html#Dataset.filter Effectively, it looks like the original set of _indices were discared and overwritten by the set created during the filter operation. I think this is actually an issue with how the map function handles dataset._indices. Ideally it should use the _indices it gets passed, and then return an updated _indices which reflect the map transformation applied to the starting _indices. ## Steps to reproduce the bug ```python dataset = load_dataset('imdb', split='train', keep_in_memory=True) dataset = dataset.shuffle(keep_in_memory=True) dataset = dataset.select(range(0, 10), keep_in_memory=True) print("initial 10 elements") print(dataset['label']) # -> [1, 1, 0, 1, 0, 0, 0, 1, 0, 0] dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True) print("filtered 10 elements looking for label 0") print(dataset['label']) # -> [1, 1, 1, 1, 1, 1] ``` ## Actual results ``` $ python indices_bug.py initial 10 elements [1, 1, 0, 1, 0, 0, 0, 1, 0, 0] filtered 10 elements looking for label 0 [1, 1, 1, 1, 1, 1] ``` This code block first shuffles the dataset (to get a mix of label 0 and label 1). Then it selects just the first 10 elements (the number of elements does not matter, 10 is just easy to visualize). The important part is that you select some subset of the dataset. Finally, a filter is applied to pull out just the elements with `label == 0`. The bug is that you cannot combine any dataset operation which sets the dataset._indices with filter. In this case I have 2, shuffle and subset. If you just use a single dataset._indices operation (in this case shuffle) the bug still shows up. The shuffle sets the dataset._indices and then filter uses those indices in the map, then overwrites dataset._indices with the filter results. ```python dataset = load_dataset('imdb', split='train', keep_in_memory=True) dataset = dataset.shuffle(keep_in_memory=True) dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True) dataset = dataset.select(range(0, 10), keep_in_memory=True) print(dataset['label']) # -> [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ``` ## Expected results In an ideal world, the dataset filter would respect any dataset._indices values which had previously been set. If you use dataset.filter with the base dataset (where dataset._indices has not been set) then the filter command works as expected. ## Environment info Here are the commands required to rebuild the conda environment from scratch. ``` # create a virtual environment conda create -n dataset_indices python=3.8 -y # activate the virtual environment conda activate dataset_indices # install huggingface datasets conda install datasets ``` <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 3.0.0 ### Full Conda Environment ``` $ conda env export name: dasaset_indices channels: - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=4.5=1_gnu - abseil-cpp=20210324.2=h2531618_0 - aiohttp=3.8.1=py38h7f8727e_0 - aiosignal=1.2.0=pyhd3eb1b0_0 - arrow-cpp=3.0.0=py38h6b21186_4 - attrs=21.2.0=pyhd3eb1b0_0 - aws-c-common=0.4.57=he6710b0_1 - aws-c-event-stream=0.1.6=h2531618_5 - aws-checksums=0.1.9=he6710b0_0 - aws-sdk-cpp=1.8.185=hce553d0_0 - bcj-cffi=0.5.1=py38h295c915_0 - blas=1.0=mkl - boost-cpp=1.73.0=h27cfd23_11 - bottleneck=1.3.2=py38heb32a55_1 - brotli=1.0.9=he6710b0_2 - brotli-python=1.0.9=py38heb0550a_2 - brotlicffi=1.0.9.2=py38h295c915_0 - brotlipy=0.7.0=py38h27cfd23_1003 - bzip2=1.0.8=h7b6447c_0 - c-ares=1.17.1=h27cfd23_0 - ca-certificates=2021.10.26=h06a4308_2 - certifi=2021.10.8=py38h06a4308_0 - cffi=1.14.6=py38h400218f_0 - conllu=4.4.1=pyhd3eb1b0_0 - cryptography=36.0.0=py38h9ce1e76_0 - dataclasses=0.8=pyh6d0b6a4_7 - dill=0.3.4=pyhd3eb1b0_0 - double-conversion=3.1.5=he6710b0_1 - et_xmlfile=1.1.0=py38h06a4308_0 - filelock=3.4.0=pyhd3eb1b0_0 - frozenlist=1.2.0=py38h7f8727e_0 - gflags=2.2.2=he6710b0_0 - glog=0.5.0=h2531618_0 - gmp=6.2.1=h2531618_2 - grpc-cpp=1.39.0=hae934f6_5 - huggingface_hub=0.0.17=pyhd3eb1b0_0 - icu=58.2=he6710b0_3 - idna=3.3=pyhd3eb1b0_0 - importlib-metadata=4.8.2=py38h06a4308_0 - importlib_metadata=4.8.2=hd3eb1b0_0 - intel-openmp=2021.4.0=h06a4308_3561 - krb5=1.19.2=hac12032_0 - ld_impl_linux-64=2.35.1=h7274673_9 - libboost=1.73.0=h3ff78a5_11 - libcurl=7.80.0=h0b77cf5_0 - libedit=3.1.20210910=h7f8727e_0 - libev=4.33=h7f8727e_1 - libevent=2.1.8=h1ba5d50_1 - libffi=3.3=he6710b0_2 - libgcc-ng=9.3.0=h5101ec6_17 - libgomp=9.3.0=h5101ec6_17 - libnghttp2=1.46.0=hce63b2e_0 - libprotobuf=3.17.2=h4ff587b_1 - libssh2=1.9.0=h1ba5d50_1 - libstdcxx-ng=9.3.0=hd4cf53a_17 - libthrift=0.14.2=hcc01f38_0 - libxml2=2.9.12=h03d6c58_0 - libxslt=1.1.34=hc22bd24_0 - lxml=4.6.3=py38h9120a33_0 - lz4-c=1.9.3=h295c915_1 - mkl=2021.4.0=h06a4308_640 - mkl-service=2.4.0=py38h7f8727e_0 - mkl_fft=1.3.1=py38hd3c417c_0 - mkl_random=1.2.2=py38h51133e4_0 - multiprocess=0.70.12.2=py38h7f8727e_0 - multivolumefile=0.2.3=pyhd3eb1b0_0 - ncurses=6.3=h7f8727e_2 - numexpr=2.7.3=py38h22e1b3c_1 - numpy=1.21.2=py38h20f2e39_0 - numpy-base=1.21.2=py38h79a1101_0 - openpyxl=3.0.9=pyhd3eb1b0_0 - openssl=1.1.1l=h7f8727e_0 - orc=1.6.9=ha97a36c_3 - packaging=21.3=pyhd3eb1b0_0 - pip=21.2.4=py38h06a4308_0 - py7zr=0.16.1=pyhd3eb1b0_1 - pycparser=2.21=pyhd3eb1b0_0 - pycryptodomex=3.10.1=py38h27cfd23_1 - pyopenssl=21.0.0=pyhd3eb1b0_1 - pyparsing=3.0.4=pyhd3eb1b0_0 - pyppmd=0.16.1=py38h295c915_0 - pysocks=1.7.1=py38h06a4308_0 - python=3.8.12=h12debd9_0 - python-dateutil=2.8.2=pyhd3eb1b0_0 - python-xxhash=2.0.2=py38h7f8727e_0 - pyzstd=0.14.4=py38h7f8727e_3 - re2=2020.11.01=h2531618_1 - readline=8.1=h27cfd23_0 - requests=2.26.0=pyhd3eb1b0_0 - setuptools=58.0.4=py38h06a4308_0 - six=1.16.0=pyhd3eb1b0_0 - snappy=1.1.8=he6710b0_0 - sqlite=3.36.0=hc218d9a_0 - texttable=1.6.4=pyhd3eb1b0_0 - tk=8.6.11=h1ccaba5_0 - typing_extensions=3.10.0.2=pyh06a4308_0 - uriparser=0.9.3=he6710b0_1 - utf8proc=2.6.1=h27cfd23_0 - wheel=0.37.0=pyhd3eb1b0_1 - xxhash=0.8.0=h7f8727e_3 - xz=5.2.5=h7b6447c_0 - zipp=3.6.0=pyhd3eb1b0_0 - zlib=1.2.11=h7f8727e_4 - zstd=1.4.9=haebb681_0 - pip: - async-timeout==4.0.2 - charset-normalizer==2.0.9 - datasets==1.16.1 - fsspec==2021.11.1 - huggingface-hub==0.2.1 - multidict==5.2.0 - pandas==1.3.5 - pyarrow==6.0.1 - pytz==2021.3 - pyyaml==6.0 - tqdm==4.62.3 - typing-extensions==4.0.1 - urllib3==1.26.7 - yarl==1.7.2 ```

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PR_kwDODunzps4wFiRb

Fix duplicated tag in wikicorpus dataset card

closed

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I_kwDODunzps5Aqa6p

Add CMU Graphics Lab Motion Capture dataset

open

NONE

## Adding a Dataset - **Name:** CMU Graphics Lab Motion Capture database - **Description:** The database contains free motions which you can download and use. - **Data:** http://mocap.cs.cmu.edu/ - **Motivation:** Nice motion capture dataset Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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PR_kwDODunzps4wEwXz

[WER] Better error message for wer

closed

MEMBER

Currently we have the following problem when using the WER. When the input format to the WER metric is wrong, instead of throwing an error message a word-error-rate is computed which is incorrect. E.g. when doing the following: ```python from datasets import load_metric wer = load_metric("wer") target_str = ["hello this is nice", "hello the weather is bloomy"] pred_str = [["hello it's nice"], ["hello it's the weather"]] print("Wrong:", wer.compute(predictions=pred_str, references=target_str)) print("Correct", wer.compute(predictions=[x[0] for x in pred_str], references=target_str)) ``` We get: ``` Wrong: 1.0 Correct 0.5555555555555556 ``` meaning that we get a word-error rate for incorrectly passed input formats. We should raise an error here instead so that people don't spent hours fixing a model while it's their incorrect evaluation metric is the problem for a low WER.

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I_kwDODunzps5Apa1i

Easier information editing

open

NONE

**Is your feature request related to a problem? Please describe.** It requires a lot of effort to improve a datasheet. **Describe the solution you'd like** UI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.) **Describe alternatives you've considered** The current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.

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PR_kwDODunzps4wENam

Fix iter_archive generator

closed

MEMBER

This PR: - Adds tests to DownloadManager and StreamingDownloadManager `iter_archive` for both path and file inputs - Fixes bugs in `iter_archive` introduced in: - #3443 Fix #3453.

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I_kwDODunzps5ApGZH

ValueError while iter_archive

closed

MEMBER

## Describe the bug After the merge of: - #3443 the method `iter_archive` throws a ValueError: ``` ValueError: read of closed file ``` ## Steps to reproduce the bug ```python for path, file in dl_manager.iter_archive(archive_path): pass ```

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I_kwDODunzps5AmYYq

why the stratify option is omitted from test_train_split function?

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why the stratify option is omitted from test_train_split function? is there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset.

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PR_kwDODunzps4wA5LP

[Staging] Update dataset repos automatically on the Hub

closed

MEMBER

Let's have a script that updates the dataset repositories on staging for now. This way we can make sure it works fine before going in prod. Related to https://github.com/huggingface/datasets/issues/3341 The script runs on each commit on `master`. It checks the datasets that were changed, and it pushes the changes to the corresponding repositories on the Hub. If there's a new dataset, then a new repository is created. If the commit is a new release of `datasets`, it also pushes the tag to all the repositories.

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I_kwDODunzps5AlCMu

Unexpected behavior doing Split + Filter

open

NONE

## Describe the bug I observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter') ## Steps to reproduce the bug ``` from datasets import Dataset import pandas as pd dic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']} df = pd.DataFrame.from_dict(dic) dataset = Dataset.from_pandas(df) split_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42) train_dataset = split_dataset["train"] eval_dataset = split_dataset["test"] eval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0) print( eval_dataset['x']) print(eval_dataset_2['x']) ``` One observes that elements in eval_dataset2 are actually coming from the training dataset... ## Expected results The expected results would be that the filtered eval dataset would only contain elements from the original eval dataset. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Windows 10 - Python version: 3.7 - PyArrow version: 5.0.0

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I_kwDODunzps5AkvXa

Add `__add__()`, `__iadd__()` and similar to `Dataset` class

open

NONE

**Is your feature request related to a problem? Please describe.** No. **Describe the solution you'd like** I would like to be able to concatenate datasets as follows: ```python >>> dataset["train"] += dataset["validation"] ``` ... instead of using `concatenate_datasets()`: ```python >>> raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], raw_datasets["validation"]]) >>> del raw_datasets["validation"] ``` **Describe alternatives you've considered** Well, I have considered `concatenate_datasets()` 😀 **Additional context** N.a.

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I_kwDODunzps5AkMto

JSONDecodeError with HuggingFace dataset viewer

open

NONE

## Dataset viewer issue for 'pubmed_neg' **Link:** https://huggingface.co/datasets/IGESML/pubmed_neg I am getting the error: Status code: 400 Exception: JSONDecodeError Message: Expecting property name enclosed in double quotes: line 61 column 2 (char 1202) I have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue. Am I the one who added this dataset ? Yes

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I_kwDODunzps5Ahj8O

HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading

open

NONE

## Describe the bug According to https://huggingface.co/docs/datasets/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to "run in full offline mode". It didn't work for me. At the very beginning, datasets still tried to download "custom data configuration" for JSON, despite I have run the program once and cached all data into the same --cache_dir. "Downloading" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers "FileNotFound" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here. ## Steps to reproduce the bug ``` export HF_DATASETS_OFFLINE=1 python run_clm.py --model_name_or_path=models/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache/trainpy.v2 ``` ## Expected results datasets should stop all "downloading" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, "default-471372bed4b51b53", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time. ## Actual results The logging shows datasets are still downloading into "datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426". ``` 12/16/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53 12/16/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426) Downloading and preparing dataset json/default to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426... 100%|██████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 17623.13it/s] 12/16/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min 12/16/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min 100%|███████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1206.99it/s] 12/16/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums. 12/16/2021 10:26:00 - INFO - datasets.builder - Generating split train 12/16/2021 10:26:01 - INFO - datasets.builder - Generating split validation 12/16/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes. Dataset json downloaded and prepared to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data. 100%|█████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 53.54it/s] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1 - Platform: Linux - Python version: 3.8.10 - PyArrow version: 6.0.1

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PR_kwDODunzps4v9dFM

Remove redundant local path information in audio/image datasets

open

CONTRIBUTOR

Remove the redundant path information in the audio/image dataset as discussed in https://github.com/huggingface/datasets/pull/3430#issuecomment-994734828 TODOs: * [ ] merge https://github.com/huggingface/datasets/pull/3430 * [ ] merge https://github.com/huggingface/datasets/pull/3364 * [ ] re-generate the info files of the updated audio datasets cc: @patrickvonplaten @anton-l @nateraw (I expect this to break the audio/vision examples in Transformers; after this change you'll be able to access underlying paths as follows `dset = dset.cast_column("audio", Audio(..., decode=False)); path = dset[0]["audio"]`)

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I_kwDODunzps5Ag6uY

question

open

NONE

## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No

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I_kwDODunzps5Afzbx

Align the Dataset and IterableDataset processing API

open

MEMBER

## Intro Currently the two classes have two distinct API for processing: ### The `.map()` method Both have those parameters in common: function, batched, batch_size - IterableDataset is missing those parameters: with_indices, with_rank, input_columns, drop_last_batch, remove_columns, features, disable_nullable, fn_kwargs, num_proc - Dataset also has additional parameters that are exclusive, due to caching: keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint - There is also an important difference in terms of behavior: **Dataset.map adds new columns** (with dict.update) BUT **IterableDataset discards previous columns** (it overwrites the dict) IMO the two methods should have the same behavior. This would be an important breaking change though. - Dataset.map is eager while IterableDataset.map is lazy ### The `.shuffle()` method - Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling. - IterableDataset is missing the parameter generator - Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint ### The `.with_format()` method - IterableDataset only supports "torch" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs ### Other methods - Both have the same `remove_columns` method - IterableDataset is missing: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard - Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform - And others don't really make sense for an iterable dataset: select, sort, add_column, add_item - Dataset is missing skip and take, that IterableDataset implements. ## Questions I think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly. 1. What should be aligned and what shouldn't between those two APIs ? IMO the minimum is to align the main processing methods. It would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters. It would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard 2. What are the breaking changes for IterableDataset ? The main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them. 3. Shall we also do some changes for regular datasets ? I agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are: - keep the eager Dataset.map with caching - keep the with_transform method for lazy processing - keep Dataset.select (it could also be added to IterableDataset even though it's not recommended) We could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that. For information, TFDS does lazy map by default, and has an additional `.cache()` method. ## Opinions ? I'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other. cc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger

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PR_kwDODunzps4v8QDX

Extend iter_archive to support file object input

closed

MEMBER

This PR adds support to passing a file object to `[Streaming]DownloadManager.iter_archive`. With this feature, we can iterate over a tar file inside another tar file.

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PR_kwDODunzps4v7oBZ

Extend text to support yielding lines, paragraphs or documents

closed

MEMBER

Add `config.row` option to `text` module to allow yielding lines (default, current case), paragraphs or documents. Feel free to comment on the name of the config parameter `row`: - Currently, the docs state datasets are made of rows and columns - Other names I considered: `example`, `item`

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I_kwDODunzps5Ad3nI

Add QuALITY dataset

open

MEMBER

## Adding a Dataset - **Name:** QuALITY - **Description:** A challenging question answering with very long contexts (Twitter [thread](https://twitter.com/sleepinyourhat/status/1471225421794529281?s=20)) - **Paper:** No ArXiv link yet, but draft is [here](https://github.com/nyu-mll/quality/blob/main/quality_preprint.pdf) - **Data:** GitHub repo [here](https://github.com/nyu-mll/quality) - **Motivation:** This dataset would serve as a nice way to benchmark long-range Transformer models like BigBird, Longformer and their descendants. In particular, it would be very interesting to see how the S4 model fares on this given it's impressive performance on the Long Range Arena Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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I_kwDODunzps5AdtBq

datasets keeps reading from cached files, although I disabled it

open

NONE

## Describe the bug Hi, I am trying to avoid dataset library using cached files, I get the following bug when this tried to read the cached files. I tried to do the followings: ``` from datasets import set_caching_enabled set_caching_enabled(False) ``` also force redownlaod: ``` download_mode='force_redownload' ``` but none worked so far, this is on a cluster and on some of the machines this reads from the cached files, I really appreciate any idea on how to fully remove caching @lhoestq many thanks ``` File "run_clm.py", line 496, in <module> main() File "run_clm.py", line 419, in main train_result = trainer.train(resume_from_checkpoint=checkpoint) File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 943, in train self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/transformers/trainer.py", line 1445, in _maybe_log_save_evaluate metrics = self.evaluate(ignore_keys=ignore_keys_for_eval) File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 172, in evaluate output = self.eval_loop( File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 241, in eval_loop metrics = self.compute_pet_metrics(eval_datasets, model, self.extra_info[metric_key_prefix], task=task) File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 268, in compute_pet_metrics centroids = self._compute_per_token_train_centroids(model, task=task) File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 353, in _compute_per_token_train_centroids data = get_label_samples(self.get_per_task_train_dataset(task), label) File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 350, in get_label_samples return dataset.filter(lambda example: int(example['labels']) == label) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper out = func(self, *args, **kwargs) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2519, in filter indices = self.map( File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2036, in map return self._map_single( File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 503, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper out = func(self, *args, **kwargs) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2248, in _map_single return Dataset.from_file(cache_file_name, info=info, split=self.split) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 654, in from_file return cls( File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 593, in __init__ self.info.features = self.info.features.reorder_fields_as(inferred_features) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1092, in reorder_fields_as return Features(recursive_reorder(self, other)) File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1081, in recursive_reorder raise ValueError(f"Keys mismatch: between {source} and {target}" + stack_position) ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'candidates_ids': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'labels': Value(dtype='int64', id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'extra_fields': {}, 'task': Value(dtype='string', id=None)} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: linux - Python version: 3.8.12 - PyArrow version: 6.0.1

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PR_kwDODunzps4v6Hxs

Add `cast_column` to `IterableDataset`

closed

CONTRIBUTOR

Closes #3369. cc: @patrickvonplaten

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PR_kwDODunzps4v52Va

Update supported versions of Python in setup.py

closed

CONTRIBUTOR

Update the list of supported versions of Python in `setup.py` to keep the PyPI project description updated.

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PR_kwDODunzps4v5qzI

Update BLEURT hyperlink

closed

MEMBER

The description of BLEURT on the hf.co website has a strange use of URL hyperlinking. This PR attempts to fix this, although I am not 100% sure Markdown syntax is allowed on the frontend or not. 

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PR_kwDODunzps4v5FE3

Add the OneStopQa dataset

closed

CONTRIBUTOR

Adding OneStopQA, a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme.

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PR_kwDODunzps4v4_-0

Improve Wikipedia Loading Script

open

NONE

* More structured approach to detecting redirects * Remove redundant template filter code (covered by strip_code) * Add language-specific lists of additional media namespace aliases for filtering * Add language-specific lists of category namespace aliases for new link text cleaning step * Remove magic words (parser directions like __TOC__ that occasionally occur in text) Fix #3400 With support from @albertvillanova CC @yjernite

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I_kwDODunzps5AbX3G

Add The People's Speech

open

CONTRIBUTOR

## Adding a Dataset - **Name:** The People's Speech - **Description:** a massive English-language dataset of audio transcriptions of full sentences. - **Paper:** https://openreview.net/pdf?id=R8CwidgJ0yT - **Data:** https://mlcommons.org/en/peoples-speech/ - **Motivation:** With over 30,000 hours of speech, this dataset is the largest and most diverse freely available English speech recognition corpus today. [The article](https://thegradient.pub/new-datasets-to-democratize-speech-recognition-technology-2/) which may be useful when working on the dataset. cc: @anton-l Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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I_kwDODunzps5AbWOE

Add Multilingual Spoken Words dataset

open

MEMBER

## Adding a Dataset - **Name:** Multilingual Spoken Words - **Description:** Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken words in 50 languages for academic research and commercial applications in keyword spotting and spoken term search, licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords, totaling 23.4 million 1-second spoken examples (over 6,000 hours). Read more: https://mlcommons.org/en/news/spoken-words-blog/ - **Paper:** https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/fe131d7f5a6b38b23cc967316c13dae2-Paper-round2.pdf - **Data:** https://mlcommons.org/en/multilingual-spoken-words/ - **Motivation:** Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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PR_kwDODunzps4v1NGS

Correctly indent builder config in dataset script docs