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PR_kwDODunzps4teNPC

Add SLR83 to OpenSLR

closed

CONTRIBUTOR

The PR resolves #3119, adding SLR83 (UK and Ireland dialects) to the previously created OpenSLR dataset.

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PR_kwDODunzps4td-5w

More efficient nested features encoding

closed

CONTRIBUTOR

Nested encoding of features wastes a lot of time on operations which are effectively doing nothing when lists are used. For example, if in the input we have a list of integers, `encoded_nested_example` will iterate over it and apply `encoded_nested_example` on every element even though it just return the int as is. A similar issue is handled at an earlier stage when casting pytorch/tensorflow/pandas objects to python lists/numpy arrays: https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L149-L156 https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L212-L228 In this pull request I suggest to use the same approach in `encoded_nested_example`. In my setup there was a major speedup with this change: loading the data was at least x4 faster.

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I_kwDODunzps49f-o3

Segmentation fault when loading datasets from file

closed

MEMBER

## Describe the bug Custom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features/ ## Steps to reproduce the bug Download an example file: ``` wget https://gist.githubusercontent.com/TevenLeScao/11e2184394b3fa47d693de2550942c6b/raw/4232704d08fbfcaf93e5b51def9e5051507651ad/tiny_kelm.jsonl ``` Then in Python: ``` import datasets tiny_kelm = datasets.load_dataset("json", data_files="tiny_kelm.jsonl", chunksize=100000) ``` ## Expected results a `tiny_kelm` functional dataset ## Actual results ☠️ `Segmentation fault (core dumped)` ☠️ ## Environment info - `datasets` version: 1.14.0 - Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 5.0.0

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I_kwDODunzps49f9P1

OSError with a custom dataset loading script

closed

NONE

## Describe the bug I am getting an OS error when trying to load the newly uploaded dataset classla/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag. ## Steps to reproduce the bug ```python dataset = datasets.load_dataset('classla/janes_tag', split='validation') ``` ## Expected results Dataset correctly loaded. ## Actual results Traceback (most recent call last): File "C:/mypath/test.py", line 91, in <module> load_and_print('janes_tag') File "C:/mypath/test.py", line 32, in load_and_print dataset = datasets.load_dataset('classla/{}'.format(ds_name), split='validation') File "C:\mypath\venv\lib\site-packages\datasets\load.py", line 1632, in load_dataset use_auth_token=use_auth_token, File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 608, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 704, in _download_and_prepare ) from None OSError: Cannot find data file. Original error: [Errno 2] No such file or directory: 'C:\\mypath\\.cache\\huggingface\\datasets\\downloads\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\data\\train_all.conllup' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.14.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.5 - PyArrow version: 3.0.0

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PR_kwDODunzps4tc_6q

Use huggingface_hub.HfApi to list datasets/metrics

closed

CONTRIBUTOR

Delete `datasets.inspect.HfApi` and use `huggingface_hub.HfApi` instead. WIP until https://github.com/huggingface/huggingface_hub/pull/429 is merged, then wait for the new release of `huggingface_hub`, update the `huggingface_hub` version in `setup.py` and merge this PR. cc: @lhoestq

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PR_kwDODunzps4tcril

Correctly update metadata to preserve features when concatenating datasets with axis=1

closed

CONTRIBUTOR

This PR correctly updates metadata to preserve higher-level feature types (e.g. `ClassLabel`) in `datasets.concatenate_datasets` when `axis=1`. Previously, we would delete the feature metadata in `datasets.concatenate_datasets` if `axis=1` and restore the feature types from the arrow table schema in `Dataset.__init__`. However, this approach only works for simple feature types (e.g. `Value`). Fixes #3111

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I_kwDODunzps49eNEs

Add OpenSLR 83 - Crowdsourced high-quality UK and Ireland English Dialect speech

closed

CONTRIBUTOR

## Adding a Dataset - **Name:** *openslr** - **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.* - **Paper:** *https://www.openslr.org/resources/83/about.html* - **Data:** *Eleven separate data files can be found via https://www.openslr.org/resources/83/* - **Motivation:** *Increase english ASR data with UK and Irish dialects* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). The *openslr* dataset already exists, this will add additional subset, *SLR83*.

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https://github.com/huggingface/datasets/pull/3118

PR_kwDODunzps4tb0LY

Fix CI error at each release commit

closed

MEMBER

Fix test_load_dataset_canonical at release commit. Fix #3117.

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I_kwDODunzps49eIMz

CI error at each release commit

closed

MEMBER

After 1.12.0, there is a recurrent CI error at each release commit: https://app.circleci.com/pipelines/github/huggingface/datasets/8289/workflows/665d954d-e409-4602-8202-e678594d2946/jobs/51110 ``` ____________________ LoadTest.test_load_dataset_canonical _____________________ [gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe self = <tests.test_load.LoadTest testMethod=test_load_dataset_canonical> def test_load_dataset_canonical(self): scripts_version = os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION) with self.assertRaises(FileNotFoundError) as context: datasets.load_dataset("_dummy") self.assertIn( f"https://raw.githubusercontent.com/huggingface/datasets/{scripts_version}/datasets/_dummy/_dummy.py", > str(context.exception), ) E AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/1.14.0/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at C:\\Users\\circleci\\datasets\\_dummy\\_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py" tests\test_load.py:358: AssertionError ```

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https://github.com/huggingface/datasets/pull/3116

PR_kwDODunzps4tbr6g

Update doc links to point to new docs

closed

CONTRIBUTOR

This PR: * updates the README links and the ADD_NEW_DATASET template to point to the new docs (the new docs don't have a section with the list of all the possible features, so I added that info to the `Features` docstring, which is then referenced in the ADD_NEW_DATASET template) * fixes some broken links in the `.rst` files (fixed with the `make linkcheck` tool)

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PR_kwDODunzps4tZ-Vr

Fill in dataset card for NCBI disease dataset

closed

CONTRIBUTOR

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I_kwDODunzps49byEK

load_from_disk in DatasetsDict/Dataset not working with PyArrowHDFS wrapper implementing fsspec.spec.AbstractFileSystem

closed

CONTRIBUTOR

## Describe the bug Passing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter. ## Steps to reproduce the bug The documentation for the `fs` parameter states: ``` fs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``): Instance of the remote filesystem used to download the files from. ``` `PyArrowHDFS` from [fsspec](https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/implementations/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error. ```python from fsspec.implementations.hdfs import PyArrowHDFS ... transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/" fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket) dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True) ``` ## Expected results Previous to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing: ```python transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/" fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket) my_datasets.save_to_disk(transformed_corpus_path, fs=fs) ``` As I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS: ```sh $ hadoop fs -ls "/user/my_user/clickbait/transformed_ds/" Found 4 items -rw------- 3 my_user users 43 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/dataset_dict.json drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/test drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/train drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/validation ``` I would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above. ## Actual results However, when trying to recover the saved datasets, I get this error: ``` ... File "/home/fperez/dev/neuromancer/neuromancer/corpus.py", line 186, in load_transformed_corpus_from_disk dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True) File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/dataset_dict.py", line 748, in load_from_disk dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory) File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1048, in load_from_disk fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True) File "pyarrow/_hdfsio.pyx", line 438, in pyarrow._hdfsio.HadoopFileSystem.download TypeError: download() got an unexpected keyword argument 'recursive' ``` Examining the [signature of the download method in pyarrow 5.0.0](https://github.com/apache/arrow/blob/54d2bd89c99df72fa091b025452f85dd5d88e3cf/python/pyarrow/_hdfsio.pyx#L438) we can see that there's no download parameter: ```python def download(self, path, stream, buffer_size=None): with self.open(path, 'rb') as f: f.download(stream, buffer_size=buffer_size) ``` ## Environment info - `datasets` version: 1.13.3 - Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps49br0b

Loading Data from HDF files

open

NONE

**Is your feature request related to a problem? Please describe.** More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset. **Describe the solution you'd like** I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc). **Describe alternatives you've considered** Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible. **Additional context** HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.

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I_kwDODunzps49behb

OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB

open

NONE

## Describe the bug Despite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error : > OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB Note that I always run `batch_size=writer_batch_size` : ## Steps to reproduce the bug ```python datasets.map(lambda example : {"column_name" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc="blablabla") ``` ## Introspecting CUDA memory during bug Placed within `function(arguments)` the following statement to introspect memory usage, merely a little over 1/4 of 2Gb `print(torch.cuda.memory_summary(device=device, abbreviated=False))` > |===========================================================================| | PyTorch CUDA memory summary, device ID 0 | |---------------------------------------------------------------------------| | CUDA OOMs: 0 | cudaMalloc retries: 0 | |===========================================================================| | Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed | |---------------------------------------------------------------------------| | Allocated memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB | | from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB | | from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB | |---------------------------------------------------------------------------| | Active memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB | | from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB | | from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB | |---------------------------------------------------------------------------| | GPU reserved memory | 598016 KB | 598016 KB | 598016 KB | 0 B | | from large pool | 595968 KB | 595968 KB | 595968 KB | 0 B | | from small pool | 2048 KB | 2048 KB | 2048 KB | 0 B | |---------------------------------------------------------------------------| | Non-releasable memory | 36117 KB | 52292 KB | 274275 KB | 238158 KB | | from large pool | 34816 KB | 51537 KB | 261713 KB | 226897 KB | | from small pool | 1301 KB | 2045 KB | 12562 KB | 11261 KB | |---------------------------------------------------------------------------| | Allocations | 198 | 224 | 478 | 280 | | from large pool | 74 | 75 | 75 | 1 | | from small pool | 124 | 150 | 403 | 279 | |---------------------------------------------------------------------------| | Active allocs | 198 | 224 | 478 | 280 | | from large pool | 74 | 75 | 75 | 1 | | from small pool | 124 | 150 | 403 | 279 | |---------------------------------------------------------------------------| | GPU reserved segments | 21 | 21 | 21 | 0 | | from large pool | 20 | 20 | 20 | 0 | | from small pool | 1 | 1 | 1 | 0 | |---------------------------------------------------------------------------| | Non-releasable allocs | 18 | 23 | 166 | 148 | | from large pool | 17 | 18 | 19 | 2 | | from small pool | 1 | 6 | 147 | 146 | |===========================================================================| ## Expected results Efficiently process the datasets and write it down to disk. ## Actual results -------------------------------------------------------------------------- OverflowError Traceback (most recent call last) ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2390 else: -> 2391 writer.write(example) 2392 else: ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write(self, example, key, writer_batch_size) 367 --> 368 self.write_examples_on_file() 369 ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self) 316 if not isinstance(pa_array[0], pa.lib.FloatScalar): --> 317 raise OverflowError( 318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format( OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB During handling of the above exception, another exception occurred: OverflowError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_16268/2456940807.py in <module> 3 #tracker = OfflineEmissionsTracker(country_iso_code="FRA", project_name='xxx'+time_stamp,output_dir='./codecarbon') 4 #tracker.start() ----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True, 6 clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False, 7 max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) # ~\xxx\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc) 481 for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False): 482 if "xxx_" in column: --> 483 dataset = dataset.map(lambda example : 484 {"embeddings_"+str(column).replace("translated_",""):function(input_ids=example[column], 485 token_type_ids=example[column.replace("input_ids","token_type_ids")], ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2034 2035 if num_proc is None or num_proc == 1: -> 2036 return self._map_single( 2037 function=function, 2038 with_indices=with_indices, ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs) 501 self: "Dataset" = kwargs.pop("self") 502 # apply actual function --> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 505 for dataset in datasets: ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs) 468 } 469 # apply actual function --> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 472 # re-apply format to the output ~\anaconda3\envs\xxx\lib\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms + update in-place history of transforms ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2425 if update_data: 2426 if writer is not None: -> 2427 writer.finalize() 2428 if tmp_file is not None: 2429 tmp_file.close() ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in finalize(self, close_stream) 440 # Re-intializing to empty list for next batch 441 self.hkey_record = [] --> 442 self.write_examples_on_file() 443 if self.pa_writer is None: 444 if self._schema is not None: ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self) 315 # This check fails with FloatArrays with nans, which is not what we want, so account for that: 316 if not isinstance(pa_array[0], pa.lib.FloatScalar): --> 317 raise OverflowError( 318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format( 319 type(pa_array) OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.13.3 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.8.11 - PyArrow version: 3.0.0 ##Next steps Testing on Linux. @albertvillanova

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I_kwDODunzps49bbFH

concatenate_datasets removes ClassLabel typing.

closed

CONTRIBUTOR

## Describe the bug When concatenating two datasets, we lose typing of ClassLabel columns. I can work on this if this is a legitimate bug, ## Steps to reproduce the bug ```python import datasets from datasets import Dataset, ClassLabel, Value, concatenate_datasets DS_LEN = 100 my_dataset = Dataset.from_dict( { "sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)], "label": [i % 2 for i in range(DS_LEN)] } ) my_predictions = Dataset.from_dict( { "pred": [(i + 1) % 2 for i in range(DS_LEN)] } ) my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])})) print("Original") print(my_dataset) print(my_dataset.features) concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1) print("Concatenated") print(concat_ds) print(concat_ds.features) ``` ## Expected results The features of `concat_ds` should contain ClassLabel. ## Actual results On master, I get: ``` {'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)} ``` ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.11 - PyArrow version: 4.0.1

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PR_kwDODunzps4tZakS

Stream TAR-based dataset using iter_archive

closed

MEMBER

I converted all the dataset based on TAR archive to use iter_archive instead, so that they can be streamable. It means that around 80 datasets become streamable :)

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PR_kwDODunzps4tZXmC

Update BibTeX entry

closed

MEMBER

Update BibTeX entry.

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PR_kwDODunzps4tY8ID

Add Google BLEU (aka GLEU) metric

closed

CONTRIBUTOR

This PR adds the NLTK implementation of Google BLEU metric. This is also a part of an effort to resolve an unfortunate naming collision between GLEU for machine translation and GLEU for grammatical error correction. I used [this page](https://huggingface.co/docs/datasets/add_metric.html) for reference. Please, point me to the right direction if I missed anything.

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PR_kwDODunzps4tYyhF

Add paper BibTeX citation

closed

MEMBER

Add paper BibTeX citation to README file.

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PR_kwDODunzps4tYA6i

Fix URLs in blog_authorship_corpus dataset

closed

MEMBER

After contacting the authors of the paper "Effects of Age and Gender on Blogging", they confirmed: - the old URLs are no longer valid - there are alternative host URLs Fix #3091.

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I_kwDODunzps49Vs1b

download_mode=`force_redownload` does not work on removed datasets

open

CONTRIBUTOR

## Describe the bug If a cached dataset is removed from the library, I don't see how to delete it programmatically. I thought that using `force_redownload` would try to refresh the cache, then raise an exception, but it reuses the cache instead. ## Steps to reproduce the bug _requires to already have `wit` in the cache_: see https://github.com/huggingface/datasets/pull/2981 ```python import datasets as ds dataset = ds.load_dataset("wit", split="train", download_mode='force_redownload') ``` ## Expected results It should raise an exception, since the dataset does not exist anymore. ## Actual results It uses the cached result ``` Using the latest cached version of the module from /home/slesage/.cache/huggingface/modules/datasets_modules/datasets/wit/107afbffd48e058b19101bddc47fbee25fa68eb6d50a733e262875f1285a5171 (last modified on Wed Sep 29 08:21:10 2021) since it couldn't be found locally at wit, or remotely on the Hugging Face Hub. ``` ## Environment info - `datasets` version: 1.13.4.dev0 - Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 4.0.1

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I_kwDODunzps49VoVc

Missing Zenodo 1.13.3 release

closed

MEMBER

After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305 TODO: - [x] Contact Zenodo support - [x] Check it is fixed

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PR_kwDODunzps4tUzJQ

Fix project description in PyPI

closed

MEMBER

Fix project description appearing in PyPI, so that it contains the content of the README.md file (like transformers). Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/ Fix #3102.

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I_kwDODunzps49VlE2

Unsuitable project description in PyPI

closed

MEMBER

Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/

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PR_kwDODunzps4tUelE

Update SUPERB to use Audio features

closed

MEMBER

This is the same dataset refresh as the other Audio ones: https://github.com/huggingface/datasets/pull/3081 cc @patrickvonplaten

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PR_kwDODunzps4tTwpn

Replace FSTimeoutError with parent TimeoutError

closed

MEMBER

PR #3050 introduced a dependency on `fsspec.FSTiemoutError`. Note that this error only exists from `fsspec` version `2021.06.0` (June 2021). To fix #3097, there are 2 alternatives: - Either pinning `fsspec` to versions newer or equal to `2021.06.0` - Or replacing `fsspec.FSTimeoutError` wth its parent `asyncio.TimeoutError`, which exists from Python 3.8.0 (Sep 2018). This PR implements the second approach. Fix #3097.

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I_kwDODunzps49SzGe

AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'

closed

NONE

## Describe the bug When using `pip install datasets` or use `conda install -c huggingface -c conda-forge datasets` cannot install datasets ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("sst", "default") ``` ## Actual results --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-5-fbe7981e6e21> in <module> 1 import torch 2 import transformers ----> 3 from datasets import load_dataset 4 5 dataset = load_dataset("sst", "default") ~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/__init__.py in <module> 35 from .arrow_reader import ArrowReader, ReadInstruction 36 from .arrow_writer import ArrowWriter ---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder 38 from .combine import interleave_datasets 39 from .dataset_dict import DatasetDict, IterableDatasetDict ~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/builder.py in <module> 42 ) 43 from .arrow_writer import ArrowWriter, BeamWriter ---> 44 from .data_files import DataFilesDict, _sanitize_patterns 45 from .dataset_dict import DatasetDict, IterableDatasetDict 46 from .fingerprint import Hasher ~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/data_files.py in <module> 118 119 def _exec_patterns_in_dataset_repository( --> 120 dataset_info: huggingface_hub.hf_api.DatasetInfo, 121 patterns: List[str], 122 allowed_extensions: Optional[list] = None, AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.13.3 - Platform: macOS-11.3.1-arm64-arm-64bit - Python version: 3.8.10 - PyArrow version: 5.0.0

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PR_kwDODunzps4tSRSZ

Push to hub capabilities for `Dataset` and `DatasetDict`

closed

MEMBER

This PR implements a `push_to_hub` method on `Dataset` and `DatasetDict`. This does not currently work in `IterableDatasetDict` nor `IterableDataset` as those are simple dicts and I would like your opinion on how you would like to implement this before going ahead and doing it. This implementation needs to be used with the following `huggingface_hub` branch in order to work correctly: https://github.com/huggingface/huggingface_hub/pull/415 ### Implementation The `push_to_hub` API is entirely based on HTTP requests rather than a git-based workflow: - This allows pushing changes without firstly cloning the repository, which reduces the time in half for the `push_to_hub` method. - Collaboration, as well as the system of branches/merges/rebases is IMO less straightforward than for models and spaces. In the situation where such collaboration is needed, I would *heavily* advocate for the `Repository` helper of the `huggingface_hub` to be used instead of the `push_to_hub` method which will always be, by design, limiting in that regard (even if based on a git-workflow instead of HTTP requests) In order to overcome the limit of 5GB files set by the HTTP requests, dataset sharding is used. ### Testing The test suite implemented here makes use of the moon-staging instead of the production setup. As several repositories are created and deleted, it is better to use the staging. It does not require setting an environment variable or any kind of special attention but introduces a new decorator `with_staging_testing` which patches global variables to use the staging endpoint instead of the production endpoint. ### Examples The tests cover a lot of examples and behavior.

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I_kwDODunzps49Qjub

`ModuleNotFoundError: No module named 'fsspec.exceptions'`

closed

MEMBER

## Describe the bug I keep runnig into a fsspec ModuleNotFound error ## Steps to reproduce the bug ```python >>> from datasets import get_dataset_infos 2021-10-15 15:25:37.863206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2021-10-15 15:25:37.863252: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/__init__.py", line 37, in <module> from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 56, in <module> from .utils.streaming_download_manager import StreamingDownloadManager File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 11, in <module> from fsspec.exceptions import FSTimeoutError ModuleNotFoundError: No module named 'fsspec.exceptions' ``` Yet, I do have `fsspec`: ```bash hf@victor-scale:~/dev/promptsource$ pip show fsspec Name: fsspec Version: 2021.5.0 Summary: File-system specification Home-page: http://github.com/intake/filesystem_spec Author: None Author-email: None License: BSD Location: /home/hf/dev/promptsource/.venv/lib/python3.7/site-packages Requires: Required-by: datasets ``` With the same version of fsspec and `datasets==1.9.0`, I don't see this problem.... ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> I can't even run `datasets-cli env` actually.., but here's my env: - `datasets` version: 1.13.3 - Platform: Ubuntu 18.04 - Python version: 3.7.10 - PyArrow version: 3.0.0

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PR_kwDODunzps4tQblQ

Fix Audio feature mp3 resampling

closed

MEMBER

Issue #3095 is related to mp3 resampling, not to `cast_column`. This PR fixes Audio feature mp3 resampling. Fix #3095.

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I_kwDODunzps49PbDa

`cast_column` makes audio decoding fail

closed

MEMBER

## Describe the bug After changing the sampling rate automatic decoding fails. ## Steps to reproduce the bug ```python from datasets import load_dataset import datasets ds = load_dataset("common_voice", "ab", split="train") ds = ds.cast_column("audio", datasets.features.Audio(sampling_rate=16_000)) print(ds[0]["audio"]) # <- this fails currently ``` yields: ``` TypeError: forward() takes 2 positional arguments but 4 were given ``` ## Expected results no failure ## 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. --> Copy-and-paste the text below in your GitHub issue. - `datasets` version: 1.13.2 (master) - Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 5.0.0

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I_kwDODunzps49O8p5

Support loading a dataset from SQLite files

open

MEMBER

As requested by @julien-c, we could eventually support loading a dataset from SQLite files, like it is the case for JSON/CSV files.

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I_kwDODunzps49Osas

Error loading json dataset with multiple splits if keys in nested dicts have a different order

closed

NONE

## Describe the bug Loading a json dataset with multiple splits that have nested dicts with keys in different order results in the error below. If the keys in the nested dicts always have the same order or even if you just load a single split in which the nested dicts don't have the same order, everything works fine. ## Steps to reproduce the bug Create two json files: train.json ``` {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} ``` test.json ``` {"a": {"b": 1, "c": 2}} {"a": {"b": 3, "c": 4}} ``` ```python from datasets import load_dataset # Loading the files individually works (even though the keys in train.json don't have the same order) load_dataset('json', data_files={"test": "test.json"}) load_dataset('json', data_files={"train": "train.json"}) # Loading both splits fails load_dataset('json', data_files={"train": "train.json", "test": "test.json"}) ``` ## Expected results Loading both splits should not give an error whether the nested dicts are have the same order or not. ## Actual results ``` >>> load_dataset('json', data_files={"train": "train.json", "test": "test.json"}) Using custom data configuration default-f1bc76fd07398c4c Downloading and preparing dataset json/default to /home/dthulke/.cache/huggingface/datasets/json/default-f1bc76fd07398c4c/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426... 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 8839.42it/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 477.82it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/load.py", line 1632, in load_dataset use_auth_token=use_auth_token, File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 608, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 1159, in _prepare_split writer.write_table(table) File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/arrow_writer.py", line 428, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1596, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 592, in pyarrow.lib._sanitize_arrays File "pyarrow/array.pxi", line 329, in pyarrow.lib.asarray File "pyarrow/table.pxi", line 277, in pyarrow.lib.ChunkedArray.cast File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/pyarrow/compute.py", line 297, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 527, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 337, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct ``` ## Environment info - `datasets` version: 1.13.2 - Platform: Linux-4.15.0-147-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyArrow version: 5.0.0

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PR_kwDODunzps4tPj6e

Fix JNLBA dataset

closed

CONTRIBUTOR

As mentioned in #3089, I've added more tags and also updated the link for dataset which was earlier using a Google Drive link. I'm having problem with generating dummy data as `datasets-cli dummy_data ./datasets/jnlpba --auto_generate --match_text_files "*.iob2"` is giving `datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! ` error. I'll try to add dummy data manually.

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I_kwDODunzps49Op1K

`blog_authorship_corpus` is broken

closed

NONE

## Describe the bug The dataset `blog_authorship_corpus` is broken. By bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty. I suspect it is because the data download url is broken (http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip). ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("blog_authorship_corpus", split="train", download_mode='force_redownload') ``` ## Expected results No error. ## Actual results ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) /tmp/ipykernel_5237/1729238701.py in <module> 2 ds = load_dataset( 3 "blog_authorship_corpus", split="train", ----> 4 download_mode='force_redownload' 5 ) /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs) 1115 ignore_verifications=ignore_verifications, 1116 try_from_hf_gcs=try_from_hf_gcs, -> 1117 use_auth_token=use_auth_token, 1118 ) 1119 /opt/conda/lib/python3.7/site-packages/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) 635 if not downloaded_from_gcs: 636 self._download_and_prepare( --> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 638 ) 639 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 707 if verify_infos: 708 verify_checksums( --> 709 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" 710 ) 711 /opt/conda/lib/python3.7/site-packages/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: ['http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip'] ``` ## Environment info - `datasets` version: 1.13.2 - Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11 - Python version: 3.7.10 - PyArrow version: 5.0.0

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PR_kwDODunzps4tPEtH

Update BibTeX entry

closed

MEMBER

Update BibTeX entry.

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I_kwDODunzps49Nl6w

JNLPBA Dataset

closed

NONE

## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` ## Expected results The dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L81-L83) are: O, B, and I. The correct entities from the original data file are: ['O', 'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-cell_line', 'I-cell_line', 'B-cell_type', 'I-cell_type', 'B-protein', 'I-protein'] ## Actual results The dataset loader script needs to include the following NER names: ['O', 'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-cell_line', 'I-cell_line', 'B-cell_type', 'I-cell_type', 'B-protein', 'I-protein'] And the [data](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:

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PR_kwDODunzps4tOhRx

Use template column_mapping to transmit_format instead of template features

closed

CONTRIBUTOR

Use `template.column_mapping` to check for modified columns since `template.features` represent a generic template/column mapping. Fix #3087 TODO: - [x] Add a test

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I_kwDODunzps49M201

Removing label column in a text classification dataset yields to errors

closed

MEMBER

## Describe the bug This looks like #3059 but it's not linked to the cache this time. Removing the `label` column from a text classification dataset and then performing any processing will result in an error. To reproduce: ```py from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("imdb") raw_datasets = raw_datasets.remove_columns("label") model_checkpoint = "distilbert-base-cased" tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) context_length = 128 def tokenize_pad_and_truncate(texts): return tokenizer(texts["text"], truncation=True, padding="max_length", max_length=context_length) tokenized_datasets = raw_datasets.map(tokenize_pad_and_truncate, batched=True) ``` Traceback: ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-1-ba61bb32f786> in <module> 12 return tokenizer(texts["text"], truncation=True, padding="max_length", max_length=context_length) 13 ---> 14 tokenized_datasets = raw_datasets.map(tokenize_pad_and_truncate, batched=True) ~/git/datasets/src/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc) 500 desc=desc, 501 ) --> 502 for k, dataset in self.items() 503 } 504 ) ~/git/datasets/src/datasets/dataset_dict.py in <dictcomp>(.0) 500 desc=desc, 501 ) --> 502 for k, dataset in self.items() 503 } 504 ) ~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2051 new_fingerprint=new_fingerprint, 2052 disable_tqdm=disable_tqdm, -> 2053 desc=desc, 2054 ) 2055 else: ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 501 self: "Dataset" = kwargs.pop("self") 502 # apply actual function --> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 505 for dataset in datasets: ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 468 } 469 # apply actual function --> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 472 # re-apply format to the output ~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms + update in-place history of transforms ~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2243 if os.path.exists(cache_file_name) and load_from_cache_file: 2244 logger.warning("Loading cached processed dataset at %s", cache_file_name) -> 2245 info = self.info.copy() 2246 info.features = features 2247 info.task_templates = None ~/git/datasets/src/datasets/info.py in copy(self) 278 279 def copy(self) -> "DatasetInfo": --> 280 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) 281 282 ~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes) ~/git/datasets/src/datasets/info.py in __post_init__(self) 177 for idx, template in enumerate(self.task_templates): 178 if isinstance(template, TextClassification): --> 179 labels = self.features[template.label_column].names 180 self.task_templates[idx] = TextClassification( 181 text_column=template.text_column, label_column=template.label_column, labels=labels KeyError: 'label' ```

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PR_kwDODunzps4tNIvp

Remove _resampler from Audio fields

closed

MEMBER

The `_resampler` Audio attribute was implemented to optimize audio resampling, but it should not be cached. This PR removes `_resampler` from Audio fields, so that it is not returned by `fields()` or `asdict()`. Fix #3083.

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PR_kwDODunzps4tNFza

Fixes to `to_tf_dataset`

closed

MEMBER

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I_kwDODunzps49LhBA

VisibleDeprecationWarning when using `set_format("numpy")`

closed

MEMBER

Code to reproduce: ``` from datasets import load_dataset dataset = load_dataset("glue", "mnli") from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('distilbert-base-cased') def tokenize_function(dataset): return tokenizer(dataset['premise']) tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset['train'].features) tokenized_datasets.set_format("numpy") tokenized_datasets['train'][5:8] ``` Outputs: ``` python3.9/site-packages/datasets/formatting/formatting.py:167: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray return np.array(array, copy=False, **self.np_array_kwargs) ```

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I_kwDODunzps49LZOG

Datasets with Audio feature raise error when loaded from cache due to _resampler parameter

closed

MEMBER

## Describe the bug As reported by @patrickvonplaten, when loaded from the cache, datasets containing the Audio feature raise TypeError. ## Steps to reproduce the bug ```python from datasets import load_dataset # load first time works ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") # load from cache breaks ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") ``` ## Actual results ``` TypeError: __init__() got an unexpected keyword argument '_resampler' ```

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PR_kwDODunzps4tM2BV

Fix error related to huggingface_hub timeout parameter

closed

MEMBER

The `huggingface_hub` package added the parameter `timeout` from version 0.0.19. This PR bumps this minimal version. Fix #3080.

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PR_kwDODunzps4tM1Gy

[Audio datasets] Adapting all audio datasets

closed

MEMBER

This PR adds the new `Audio(...)` features - see: https://github.com/huggingface/datasets/pull/2324 to the most important audio datasets: - Librispeech - Timit - Common Voice - AMI - ... (others I'm forgetting now) The PR is curently blocked because the following leads to a problem: ```python from datasets import load_dataset # load first time works ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") # load from cache breaks ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") ``` As soon as it's unblocked, I'll adapt the other audio datasets as well.

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I_kwDODunzps49LVNS

Error related to timeout keyword argument

closed

MEMBER

## Describe the bug As reported by @patrickvonplaten, a TypeError is raised when trying to load a dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") ``` ## Actual results ``` TypeError: dataset_info() got an unexpected keyword argument 'timeout' ```

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PR_kwDODunzps4tMFPG

Fix loading a metric with internal import

closed

MEMBER

After refactoring the module factory (#2986), a bug was introduced when loading metrics with internal imports. This PR adds a new test case and fixes this bug. Fix #3076. CC: @sgugger @merveenoyan

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I_kwDODunzps49KT_M

Error when loading a metric

closed

MEMBER

## Describe the bug As reported by @sgugger, after last release, exception is thrown when loading a metric. ## Steps to reproduce the bug ```python from datasets import load_metric metric = load_metric("squad_v2") ``` ## Actual results ``` FileNotFoundError Traceback (most recent call last) <ipython-input-1-e612a8cab787> in <module> 1 from datasets import load_metric ----> 2 metric = load_metric("squad_v2") d:\projects\huggingface\datasets\src\datasets\load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, script_version, **metric_init_kwargs) 1336 ) 1337 revision = script_version -> 1338 metric_module = metric_module_factory( 1339 path, revision=revision, download_config=download_config, download_mode=download_mode 1340 ).module_path d:\projects\huggingface\datasets\src\datasets\load.py in metric_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, **download_kwargs) 1237 if not isinstance(e1, FileNotFoundError): 1238 raise e1 from None -> 1239 raise FileNotFoundError( 1240 f"Couldn't find a metric script at {relative_to_absolute_path(combined_path)}. " 1241 f"Metric '{path}' doesn't exist on the Hugging Face Hub either." FileNotFoundError: Couldn't find a metric script at D:\projects\huggingface\datasets\squad_v2\squad_v2.py. Metric 'squad_v2' doesn't exist on the Hugging Face Hub either. ```

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PR_kwDODunzps4tL75E

Updates LexGLUE and MultiEURLEX README.md files

closed

CONTRIBUTOR

Updates LexGLUE and MultiEURLEX README.md files - Fix leaderboard in LexGLUE. - Fix an error in the CaseHOLD data example. - Turn MultiEURLEX dataset statistics table into HTML to nicely render in HF website.

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PR_kwDODunzps4tLbe-

add XCSR dataset

closed

CONTRIBUTOR

Hi, I wanted to add the [XCSR ](https://inklab.usc.edu//XCSR/xcsr_datasets) dataset to huggingface! :) I followed the instructions of adding new dataset to huggingface and have all the required files ready now! It would be super helpful if you can take a look and review them. Thanks in advance for your time and help. Look forward to hearing from you and can't wait to add XCSR to huggingface :D

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I_kwDODunzps49IzjF

Import error installing with ppc64le

closed

NONE

## Describe the bug Installing the datasets library with a computer running with ppc64le seems to cause an issue when importing the datasets library. ``` python Python 3.6.13 | packaged by conda-forge | (default, Sep 23 2021, 07:37:44) [GCC 9.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import datasets Illegal instruction (core dumped) ``` Error when importing `Illegal instruction (core dumped)` ## Steps to reproduce the bug I get this error when installing the library by using conda. I can't install with pip I believe because pyarrow only has the ppc64le library on conda forge ``` conda create --name transformers_py36_v2 python=3.6 conda activate transformers_py36_v2 conda install datasets ``` ## Tracebacks conda create --name transformers_py36_v2 python=3.6 ``` Collecting package metadata (current_repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.9.2 latest version: 4.10.3 Please update conda by running $ conda update -n base -c defaults conda ## Package Plan ## environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2 added / updated specs: - python=3.6 The following NEW packages will be INSTALLED: _libgcc_mutex conda-forge/linux-ppc64le::_libgcc_mutex-0.1-conda_forge _openmp_mutex conda-forge/linux-ppc64le::_openmp_mutex-4.5-1_gnu ca-certificates conda-forge/linux-ppc64le::ca-certificates-2021.10.8-h1084571_0 certifi pkgs/main/linux-ppc64le::certifi-2020.12.5-py36h6ffa863_0 ld_impl_linux-ppc~ conda-forge/linux-ppc64le::ld_impl_linux-ppc64le-2.36.1-ha35d02b_2 libffi conda-forge/linux-ppc64le::libffi-3.4.2-h3b9df90_4 libgcc-ng conda-forge/linux-ppc64le::libgcc-ng-11.2.0-h7698a5e_11 libgomp conda-forge/linux-ppc64le::libgomp-11.2.0-h7698a5e_11 libstdcxx-ng conda-forge/linux-ppc64le::libstdcxx-ng-11.2.0-habdf983_11 libzlib conda-forge/linux-ppc64le::libzlib-1.2.11-h339bb43_1013 ncurses conda-forge/linux-ppc64le::ncurses-6.2-hea85c5d_4 openssl conda-forge/linux-ppc64le::openssl-1.1.1l-h4e0d66e_0 pip conda-forge/noarch::pip-21.3-pyhd8ed1ab_0 python conda-forge/linux-ppc64le::python-3.6.13-h57873ef_2_cpython readline conda-forge/linux-ppc64le::readline-8.1-h5c45dff_0 setuptools pkgs/main/linux-ppc64le::setuptools-58.0.4-py36h6ffa863_0 sqlite conda-forge/linux-ppc64le::sqlite-3.36.0-h4e2196e_2 tk conda-forge/linux-ppc64le::tk-8.6.11-h41c6715_1 wheel conda-forge/noarch::wheel-0.37.0-pyhd8ed1ab_1 xz conda-forge/linux-ppc64le::xz-5.2.5-h6eb9509_1 zlib conda-forge/linux-ppc64le::zlib-1.2.11-h339bb43_1013 Proceed ([y]/n)? y Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate transformers_py36_v2 # # To deactivate an active environment, use # # $ conda deactivate ``` conda activate transformers_py36_v2 conda install datasets ``` Collecting package metadata (current_repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source. Collecting package metadata (repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.9.2 latest version: 4.10.3 Please update conda by running $ conda update -n base -c defaults conda ## Package Plan ## environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2 added / updated specs: - datasets The following NEW packages will be INSTALLED: abseil-cpp conda-forge/linux-ppc64le::abseil-cpp-20210324.2-h3b9df90_0 aiohttp conda-forge/linux-ppc64le::aiohttp-3.7.4.post0-py36hc33305d_0 arrow-cpp conda-forge/linux-ppc64le::arrow-cpp-5.0.0-py36hf9cf308_8_cpu async-timeout conda-forge/noarch::async-timeout-3.0.1-py_1000 attrs conda-forge/noarch::attrs-21.2.0-pyhd8ed1ab_0 aws-c-cal conda-forge/linux-ppc64le::aws-c-cal-0.5.11-hb3fac3d_0 aws-c-common conda-forge/linux-ppc64le::aws-c-common-0.6.2-h4e0d66e_0 aws-c-event-stream conda-forge/linux-ppc64le::aws-c-event-stream-0.2.7-h76da5f2_13 aws-c-io conda-forge/linux-ppc64le::aws-c-io-0.10.5-hf6a6c7c_0 aws-checksums conda-forge/linux-ppc64le::aws-checksums-0.1.11-hfe76d68_7 aws-sdk-cpp conda-forge/linux-ppc64le::aws-sdk-cpp-1.8.186-h90855e8_3 brotlipy conda-forge/linux-ppc64le::brotlipy-0.7.0-py36hc33305d_1001 bzip2 conda-forge/linux-ppc64le::bzip2-1.0.8-h4e0d66e_4 c-ares conda-forge/linux-ppc64le::c-ares-1.17.2-h4e0d66e_0 cffi conda-forge/linux-ppc64le::cffi-1.14.6-py36h021ab3c_1 chardet conda-forge/linux-ppc64le::chardet-4.0.0-py36h270354c_1 colorama conda-forge/noarch::colorama-0.4.4-pyh9f0ad1d_0 cryptography conda-forge/linux-ppc64le::cryptography-3.4.7-py36hc71b123_0 dataclasses conda-forge/noarch::dataclasses-0.8-pyh787bdff_2 datasets conda-forge/noarch::datasets-1.12.1-pyhd8ed1ab_1 dill conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0 filelock conda-forge/noarch::filelock-3.3.0-pyhd8ed1ab_0 fsspec conda-forge/noarch::fsspec-2021.10.0-pyhd8ed1ab_0 gflags conda-forge/linux-ppc64le::gflags-2.2.2-hb209c28_1004 glog conda-forge/linux-ppc64le::glog-0.5.0-h4040248_0 grpc-cpp conda-forge/linux-ppc64le::grpc-cpp-1.40.0-h2bf711c_2 huggingface_hub conda-forge/noarch::huggingface_hub-0.0.19-pyhd8ed1ab_0 idna conda-forge/noarch::idna-2.10-pyh9f0ad1d_0 idna_ssl conda-forge/noarch::idna_ssl-1.0.0-0 importlib-metadata conda-forge/linux-ppc64le::importlib-metadata-4.8.1-py36h270354c_0 importlib_metadata conda-forge/noarch::importlib_metadata-4.8.1-hd8ed1ab_0 krb5 conda-forge/linux-ppc64le::krb5-1.19.2-haf43566_2 libblas conda-forge/linux-ppc64le::libblas-3.9.0-11_linuxppc64le_openblas libbrotlicommon conda-forge/linux-ppc64le::libbrotlicommon-1.0.9-h4e0d66e_5 libbrotlidec conda-forge/linux-ppc64le::libbrotlidec-1.0.9-h4e0d66e_5 libbrotlienc conda-forge/linux-ppc64le::libbrotlienc-1.0.9-h4e0d66e_5 libcblas conda-forge/linux-ppc64le::libcblas-3.9.0-11_linuxppc64le_openblas libcurl conda-forge/linux-ppc64le::libcurl-7.79.1-he415e40_1 libedit conda-forge/linux-ppc64le::libedit-3.1.20191231-h41a240f_2 libev conda-forge/linux-ppc64le::libev-4.33-h6eb9509_1 libevent conda-forge/linux-ppc64le::libevent-2.1.10-h97db324_4 libgfortran-ng conda-forge/linux-ppc64le::libgfortran-ng-11.2.0-hfdc3801_11 libgfortran5 conda-forge/linux-ppc64le::libgfortran5-11.2.0-he58fbb4_11 liblapack conda-forge/linux-ppc64le::liblapack-3.9.0-11_linuxppc64le_openblas libnghttp2 conda-forge/linux-ppc64le::libnghttp2-1.43.0-h42039ad_1 libopenblas conda-forge/linux-ppc64le::libopenblas-0.3.17-pthreads_h486567c_1 libprotobuf conda-forge/linux-ppc64le::libprotobuf-3.18.1-h690f14c_0 libssh2 conda-forge/linux-ppc64le::libssh2-1.10.0-ha5a9321_2 libthrift conda-forge/linux-ppc64le::libthrift-0.15.0-h54f692e_1 libutf8proc conda-forge/linux-ppc64le::libutf8proc-2.6.1-h4e0d66e_0 lz4-c conda-forge/linux-ppc64le::lz4-c-1.9.3-h3b9df90_1 multidict conda-forge/linux-ppc64le::multidict-5.2.0-py36hc33305d_0 multiprocess conda-forge/linux-ppc64le::multiprocess-0.70.12.2-py36hc33305d_0 numpy conda-forge/linux-ppc64le::numpy-1.19.5-py36h86665d4_1 orc conda-forge/linux-ppc64le::orc-1.7.0-hae6b4bd_0 packaging conda-forge/noarch::packaging-21.0-pyhd8ed1ab_0 pandas conda-forge/linux-ppc64le::pandas-1.1.5-py36hab1a6e6_0 parquet-cpp conda-forge/noarch::parquet-cpp-1.5.1-2 pyarrow conda-forge/linux-ppc64le::pyarrow-5.0.0-py36h7a46c7e_8_cpu pycparser conda-forge/noarch::pycparser-2.20-pyh9f0ad1d_2 pyopenssl conda-forge/noarch::pyopenssl-21.0.0-pyhd8ed1ab_0 pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0 pysocks conda-forge/linux-ppc64le::pysocks-1.7.1-py36h270354c_3 python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0 python-xxhash conda-forge/linux-ppc64le::python-xxhash-2.0.2-py36hc33305d_0 python_abi conda-forge/linux-ppc64le::python_abi-3.6-2_cp36m pytz conda-forge/noarch::pytz-2021.3-pyhd8ed1ab_0 pyyaml conda-forge/linux-ppc64le::pyyaml-5.4.1-py36hc33305d_1 re2 conda-forge/linux-ppc64le::re2-2021.09.01-h3b9df90_0 requests conda-forge/noarch::requests-2.25.1-pyhd3deb0d_0 s2n conda-forge/linux-ppc64le::s2n-1.0.10-h97db324_0 six conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 snappy conda-forge/linux-ppc64le::snappy-1.1.8-hb209c28_3 tqdm conda-forge/noarch::tqdm-4.62.3-pyhd8ed1ab_0 typing-extensions conda-forge/noarch::typing-extensions-3.10.0.2-hd8ed1ab_0 typing_extensions conda-forge/noarch::typing_extensions-3.10.0.2-pyha770c72_0 urllib3 conda-forge/noarch::urllib3-1.26.7-pyhd8ed1ab_0 xxhash conda-forge/linux-ppc64le::xxhash-0.8.0-h4e0d66e_3 yaml conda-forge/linux-ppc64le::yaml-0.2.5-h6eb9509_0 yarl conda-forge/linux-ppc64le::yarl-1.6.3-py36hc33305d_2 zipp conda-forge/noarch::zipp-3.6.0-pyhd8ed1ab_0 zstd conda-forge/linux-ppc64le::zstd-1.5.0-h65c4b1a_0 The following packages will be UPDATED: certifi pkgs/main::certifi-2020.12.5-py36h6ff~ --> conda-forge::certifi-2021.5.30-py36h270354c_0 Proceed ([y]/n)? y Preparing transaction: done Verifying transaction: done Executing transaction: done ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Red Hat Enterprise Linux 8.2 (Ootpa) - Python version: 3.6 - PyArrow version: pyarrow - 5.0.0 - py36h7a46c7e_8_cpu - conda-forge Any help would be appreciated! I've been struggling on installing datasets on this machine.

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PR_kwDODunzps4tJNnD

Fix pathlib patches for streaming

closed

MEMBER

Fix issue https://github.com/huggingface/datasets/issues/2866 (for good this time) `counter` now works in both streaming and non-streaming mode. And the `AttributeError: 'str' object has no attribute 'as_posix'` related to the patch of Path.open is fixed as well Note : the patches should only affect the datasets module, not the user's ones ! That's why we should probably use something else than patch.object to patch the Path class' methods. cc @severo @albertvillanova

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I_kwDODunzps49FqI1

Custom plain text dataset, plain json dataset and plain csv dataset are remove from datasets template folder

closed

NONE

## Adding a Dataset - **Name:** text, json, csv - **Description:** I am developing a customized dataset loading script. The problem is mainly about my custom dataset is seperate into many files and I only find a dataset loading template in [https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py](https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py) that can handle my circumstance. I'm afraid these templates are too old to use. Could you re-add these three templates to current master branch?

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PR_kwDODunzps4tIBRp

Fix Windows CI with FileNotFoundError when stting up s3_base fixture

closed

MEMBER

Fix #3069.

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I_kwDODunzps49FX34

CI fails on Windows with FileNotFoundError when stting up s3_base fixture

closed

MEMBER

## Describe the bug After commit 9353fc863d0c99ab0427f83cc5a4f04fcf52f1df, the CI fails on Windows with FileNotFoundError when stting up s3_base fixture. See: https://app.circleci.com/pipelines/github/huggingface/datasets/8151/workflows/5db8d154-badd-4d3d-b202-ca7a318997a2/jobs/50321 Error summary: ``` ERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3 - FileNotF... ERROR tests/test_dataset_dict.py::test_dummy_dataset_serialize_s3 - FileNotFo... ``` Stack trace: ``` ______________ ERROR at setup of test_dummy_dataset_serialize_s3 ______________ [gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe @pytest.fixture() def s3_base(): # writable local S3 system import shlex import subprocess # Mocked AWS Credentials for moto. old_environ = os.environ.copy() os.environ.update(S3_FAKE_ENV_VARS) > proc = subprocess.Popen(shlex.split("moto_server s3 -p %s" % s3_port)) tests\s3_fixtures.py:32: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ C:\tools\miniconda3\lib\subprocess.py:729: in __init__ restore_signals, start_new_session) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <subprocess.Popen object at 0x0000012BB8A4B908> args = 'moto_server s3 -p 5555', executable = None, preexec_fn = None close_fds = True, pass_fds = (), cwd = None, env = None startupinfo = <subprocess.STARTUPINFO object at 0x0000012BB8177630> creationflags = 0, shell = False, p2cread = -1, p2cwrite = -1, c2pread = -1 c2pwrite = -1, errread = -1, errwrite = -1, unused_restore_signals = True unused_start_new_session = False def _execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session): """Execute program (MS Windows version)""" assert not pass_fds, "pass_fds not supported on Windows." if not isinstance(args, str): args = list2cmdline(args) # Process startup details if startupinfo is None: startupinfo = STARTUPINFO() if -1 not in (p2cread, c2pwrite, errwrite): startupinfo.dwFlags |= _winapi.STARTF_USESTDHANDLES startupinfo.hStdInput = p2cread startupinfo.hStdOutput = c2pwrite startupinfo.hStdError = errwrite if shell: startupinfo.dwFlags |= _winapi.STARTF_USESHOWWINDOW startupinfo.wShowWindow = _winapi.SW_HIDE comspec = os.environ.get("COMSPEC", "cmd.exe") args = '{} /c "{}"'.format (comspec, args) # Start the process try: hp, ht, pid, tid = _winapi.CreateProcess(executable, args, # no special security None, None, int(not close_fds), creationflags, env, os.fspath(cwd) if cwd is not None else None, > startupinfo) E FileNotFoundError: [WinError 2] The system cannot find the file specified C:\tools\miniconda3\lib\subprocess.py:1017: FileNotFoundError ```

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PR_kwDODunzps4tHhOC

feat: increase streaming retry config

closed

CONTRIBUTOR

Increase streaming config parameters: * retry interval set to 5 seconds * max retries set to 20 (so 1mn 40s)

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PR_kwDODunzps4tFSCy

add story_cloze

closed

CONTRIBUTOR

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PR_kwDODunzps4tFObl

Add iter_archive

closed

MEMBER

Added the `iter_archive` method for the StreamingDownloadManager. It was already implemented in the regular DownloadManager. Now it can be used to stream from TAR archives as mentioned in https://github.com/huggingface/datasets/issues/2829 I also updated the `food101` dataset as an example. Any image/audio dataset using TAR archives can be updated to use `iter_archive` in order to be streamable :) cc @severo

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PR_kwDODunzps4tFDjk

Fix test command after refac

closed

MEMBER

Fix the `datasets-cli` test command after the `prepare_module` change in #2986

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I_kwDODunzps49B3mr

Make `interleave_datasets` more robust

open

NONE

**Is your feature request related to a problem? Please describe.** Right now there are few hiccups using `interleave_datasets`. Interleaved dataset iterates until the smallest dataset completes it's iterator. In this way larger datasets may not complete full epoch of iteration. It creates new problems in calculation of epoch since there are no way to track which dataset from `interleave_datasets` completes how many epoch. **Describe the solution you'd like** For `interleave_datasets` module, - [ ] Add a boolean argument `--stop-iter` in `interleave_datasets` that enables dataset to either iterate infinite amount of time or not. That means it should not return `StopIterator` exception in case `--stop-iter=False`. - [ ] Internal list variable `iter_cnt` that explains how many times (in steps/epochs) each dataset iterates at a given point. - [ ] Add an argument `--max-iter` (list type) that explain maximum amount of time each of the dataset can iterate. After complete `--max-iter` of one dataset, other dataset should continue sampling and when all the dataset finish their respective `--max-iter`, only then return `StopIterator` Note: I'm new to `datasets` api. May be these features are already there in the datasets. Since multitask training is the latest trends, I believe this feature would make the `datasets` api more popular. @lhoestq

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I_kwDODunzps49ArfJ

Windows CI is unable to test streaming properly because of SSL issues

open

MEMBER

In https://github.com/huggingface/datasets/pull/3041 the windows tests were skipped because of SSL issues with moon-staging.huggingface.co:443 The issue appears only on windows with asyncio. On Linux it works. With requests it works as well. And with the production environment huggingface.co it also works. to reproduce on windows: ```python import fsspec # use any URL to a file in a dataset repo url = "https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes" fsspec.open(url).open() ``` raises ```python FileNotFoundError: https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes ``` because of ```python aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')] ```

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https://github.com/huggingface/datasets/pull/3062

PR_kwDODunzps4tCxfK

Update summary on PyPi beyond NLP

closed

MEMBER

More than just NLP now

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I_kwDODunzps48-1CP

Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?)

open

NONE

**A clear and concise description of what you want to happen.** It would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions. **Describe alternatives you've considered** By the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish? **Additional context** Furthering tqdm integration #2374 and huggingface/transformers#11797 solutioned by huggingface/transformers#12226 provided with tqdm description as `desc=` @sgugger @bhavitvyamalik

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I_kwDODunzps48-MVM

load_dataset('openwebtext') yields "Compressed file ended before the end-of-stream marker was reached"

closed

NONE

## Describe the bug When I try `load_dataset('openwebtext')`, I receive a "EOFError: Compressed file ended before the end-of-stream marker was reached" error. ## Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset('openwebtext') ``` ## Expected results I expect the `dataset` variable to be properly constructed. ## Actual results ``` File "/home/rschaef/CoCoSci-Language-Distillation/distillation_v2/ratchet_learning/tasks/base.py", line 37, in create_dataset dataset_str, File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/load.py", line 1117, in load_dataset use_auth_token=use_auth_token, File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 637, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 704, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/rschaef/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1/openwebtext.py", line 61, in _split_generators dl_dir = dl_manager.download_and_extract(_URL) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 261, in extract partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 197, in map_nested return function(data_struct) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 316, in cached_path output_path, force_extract=download_config.force_extract File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 40, in extract self.extractor.extract(input_path, output_path, extractor=extractor) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 179, in extract return extractor.extract(input_path, output_path) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 53, in extract tar_file.extractall(output_path) File "/usr/lib/python3.6/tarfile.py", line 2010, in extractall numeric_owner=numeric_owner) File "/usr/lib/python3.6/tarfile.py", line 2052, in extract numeric_owner=numeric_owner) File "/usr/lib/python3.6/tarfile.py", line 2122, in _extract_member self.makefile(tarinfo, targetpath) File "/usr/lib/python3.6/tarfile.py", line 2171, in makefile copyfileobj(source, target, tarinfo.size, ReadError, bufsize) File "/usr/lib/python3.6/tarfile.py", line 249, in copyfileobj buf = src.read(bufsize) File "/usr/lib/python3.6/lzma.py", line 200, in read return self._buffer.read(size) File "/usr/lib/python3.6/_compression.py", line 68, in readinto data = self.read(len(byte_view)) File "/usr/lib/python3.6/_compression.py", line 99, in read raise EOFError("Compressed file ended before the " python-BaseException EOFError: Compressed file ended before the end-of-stream marker was reached ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial - Python version: 3.6.10 - PyArrow version: 5.0.0

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PR_kwDODunzps4tA54w

Fix task reloading from cache

closed

MEMBER

When reloading a dataset from the cache when doing `map`, the tasks templates were kept instead of being updated regarding the output of the `map` function. This is an issue because we drop the tasks templates that are not compatible anymore after `map`, for example if a column of the template was removed. This PR fixes this and for convenience introduces a decorator `@transmit_tasks` that takes care of doing this verification, similar to the `@transmit_format` decorator. This should fix issue https://github.com/huggingface/datasets/issues/3047 cc @sgugger

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I_kwDODunzps4889S4

Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader.

closed

NONE

## Describe the bug I have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`. ## Steps to reproduce the bug Run the `run_mlm_no_trainer.py` and the given script on this [link](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3. ## Expected results The data batchs are fetched from the data loader and train. ## Actual results The first time to fetch data batch occurs error. `Traceback (most recent call last): File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors tensor = as_tensor(value) ValueError: too many dimensions 'str' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "src/original_run_mlm_no_trainer.py", line 528, in <module> main() File "src/original_run_mlm_no_trainer.py", line 488, in main for step, batch in enumerate(train_dataloader): File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/accelerate/data_loader.py", line 303, in __iter__ for batch in super().__iter__(): File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__ data = self._next_data() File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 557, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch return self.collate_fn(data) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 41, in __call__ return self.torch_call(features) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 671, in torch_call batch = self.tokenizer.pad(examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2774, in pad return BatchEncoding(batch_outputs, tensor_type=return_tensors) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 210, in __init__ self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 722, in convert_to_tensors "Unable to create tensor, you should probably activate truncation and/or padding " ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. ` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.6 - PyArrow version: 5.0.0

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I_kwDODunzps488j0b

Error in per class precision computation

closed

NONE

## Describe the bug When trying to get the per class precision values by providing `average=None`, following error is thrown `ValueError: can only convert an array of size 1 to a Python scalar` ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric precision_metric = load_metric("precision") predictions = [0, 2, 1, 0, 0, 1] references = [0, 1, 2, 0, 1, 2] results = precision_metric.compute(predictions=predictions, references=references, average=None) ``` ## Expected results ` {'precision': array([0.66666667, 0. , 0. ])}` as per https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py ## Actual results ``` output = self._compute(predictions=predictions, references=references, **kwargs) File "~/.cache/huggingface/modules/datasets_modules/metrics/precision/94709a71c6fe37171ef49d3466fec24dee9a79846c9f176dff66a649e9811690/precision.py", line 110, in _compute sample_weight=sample_weight, ValueError: can only convert an array of size 1 to a Python scalar ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: linux - Python version: 3.6.9 - PyArrow version: 5.0.0

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PR_kwDODunzps4tAB9h

Fix meteor metric for version >= 3.6.4

closed

MEMBER

After `nltk` update, the meteor metric expects pre-tokenized inputs (breaking change). This PR fixes this issue, while maintaining compatibility with older versions.

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I_kwDODunzps4871qG

CI test suite fails after meteor metric update

closed

MEMBER

## Describe the bug CI test suite fails: https://app.circleci.com/pipelines/github/huggingface/datasets/8110/workflows/f059ba43-9154-4632-bebb-82318447ddc9/jobs/50010 Stack trace: ``` ___________________ LocalMetricTest.test_load_metric_meteor ____________________ [gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 self = <tests.test_metric_common.LocalMetricTest testMethod=test_load_metric_meteor> metric_name = 'meteor' def test_load_metric(self, metric_name): doctest.ELLIPSIS_MARKER = "[...]" metric_module = importlib.import_module(datasets.load.prepare_module(os.path.join("metrics", metric_name))[0]) metric = datasets.load.import_main_class(metric_module.__name__, dataset=False) # check parameters parameters = inspect.signature(metric._compute).parameters self.assertTrue("predictions" in parameters) self.assertTrue("references" in parameters) self.assertTrue(all([p.kind != p.VAR_KEYWORD for p in parameters.values()])) # no **kwargs # run doctest with self.patch_intensive_calls(metric_name, metric_module.__name__): with self.use_local_metrics(): > results = doctest.testmod(metric_module, verbose=True, raise_on_error=True) tests/test_metric_common.py:75: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1951: in testmod runner.run(test) ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1839: in run r = DocTestRunner.run(self, test, compileflags, out, False) ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1476: in run return self.__run(test, compileflags, out) ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1382: in __run exception) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <doctest.DebugRunner object at 0x7f4c26bd3da0> out = <built-in method write of _io.TextIOWrapper object at 0x7f51a21852d0> test = <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Mete...ets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)> example = <doctest.Example object at 0x7f4c26bd3eb8> exc_info = (<class 'TypeError'>, TypeError('"hypothesis" expects pre-tokenized hypothesis (Iterable[str]): It is a guide to action which ensures that the military always obeys the commands of the party',), <traceback object at 0x7f4cd01afec8>) def report_unexpected_exception(self, out, test, example, exc_info): > raise UnexpectedException(test, example, exc_info) E doctest.UnexpectedException: <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Meteor from /tmp/pytest-of-circleci/pytest-0/popen-gw1/cache/modules/datasets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)> ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1845: UnexpectedException ```

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https://github.com/huggingface/datasets/pull/3054

PR_kwDODunzps4s_TmE

Update Biosses

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CONTRIBUTOR

Fix variable naming

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