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PR_kwDODunzps47hobQ

Add MANtIS dataset

closed

CONTRIBUTOR

This PR adds MANtIS dataset. Arxiv: [https://arxiv.org/abs/1912.04639](https://arxiv.org/abs/1912.04639) Github: [https://github.com/Guzpenha/MANtIS](https://github.com/Guzpenha/MANtIS) README and dataset tags are WIP.

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PR_kwDODunzps47go-F

update `samsum` script

closed

CONTRIBUTOR

update `samsum` script after #4672 was merged (citation is also updated)

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I_kwDODunzps5N4UgQ

Unable to cast a column with `Image()` by using the `cast_column()` feature

closed

NONE

## Describe the bug A clear and concise description of what the bug is. When I create a dataset, then add a column to the created dataset through the `dataset.add_column` feature and then try to cast a column of the dataset (this column contains image paths) with `Image()` by using the `cast_column()` feature, I get the following error - ``` TypeError: Couldn't cast array of type string to {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ``` When I try and cast the same column, but without doing the `add_column` in the previous step, it works as expected. ## Steps to reproduce the bug ```python from datasets import Dataset, Image data_dict = { "img_path": ["https://picsum.photos/200/300"] } dataset = Dataset.from_dict(data_dict) #NOTE Comment out this line and use cast_column and it works properly dataset = dataset.add_column("yeet", [1]) #NOTE This line fails to execute properly if `add_column` is called before dataset = dataset.cast_column("img_path", Image()) # #NOTE This is my current workaround. This seems to work fine with/without `add_column`. While # # running this, make sure to comment out the `cast_column` line # new_features = dataset.features.copy() # new_features["img_path"] = Image() # dataset = dataset.cast(new_features) print(dataset) print(dataset.features) print(dataset[0]) ``` ## Expected results A clear and concise description of the expected results. Able to successfully use `cast_column` to cast a column containing img_paths to now be Image() features after modifying the dataset using `add_column` in a previous step ## Actual results Specify the actual results or traceback. ``` Traceback (most recent call last): File "/home/surya/Desktop/hf_bug_test.py", line 14, in <module> dataset = dataset.cast_column("img_path", Image()) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1580, in cast_column dataset._data = dataset._data.cast(dataset.features.arrow_schema) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1487, in cast new_tables.append(subtable.cast(subschema, *args, **kwargs)) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 834, in cast return InMemoryTable(table_cast(self.table, *args, **kwargs)) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1897, in table_cast return cast_table_to_schema(table, schema) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1846, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type string to {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Ubuntu 20.04.3 LTS - Python version: 3.9.7 - PyArrow version: 7.0.0

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I_kwDODunzps5N3eyY

Dataset Viewer issue for rajistics/indian_food_images

closed

NONE

### Link https://huggingface.co/datasets/rajistics/indian_food_images/viewer/rajistics--indian_food_images/train ### Description I have a train/test split in my dataset <img width="410" alt="Screen Shot 2022-07-15 at 11 44 42 AM" src="https://user-images.githubusercontent.com/6808012/179293215-7b419ec3-3527-46f2-8dad-adbc5568cfa0.png"> t The dataset viewer works for the test split (images of indian food), but does not show my train split. My guess is maybe there is some corrupt image file that is guessing this. But I have no idea. The original dataset was pulled from here: https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification?resource=download-directory ### Owner Yes

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PR_kwDODunzps47fG6w

Refactor base extractors

closed

MEMBER

This PR: - Refactors base extractors as subclasses of `BaseExtractor`: - this is an abstract class defining the interface with: - `is_extractable`: abstract class method - `extract`: abstract static method - Implements abstract `MagicNumberBaseExtractor` (as subclass of `BaseExtractor`): - this has a default implementation of `is_extractable` - this improves performance (reducing the number of file reads) by allowing passing already read `magic_number` - Refactors `Extractor`: - reads magic number from file only once This PR deprecates: ```python is_extractable, extractor = self.extractor.is_extractable(input_path, return_extractor=True) self.extractor.extract(input_path, output_path, extractor=extractor) ``` and uses more Pythonic instead: ```python extractor_format = self.extractor.infer_extractor_format(input_path) self.extractor.extract(input_path, output_path, extractor_format) ```

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PR_kwDODunzps47eyw5

Test extractors for all compression formats

closed

MEMBER

This PR: - Adds all compression formats to `test_extractor` - Tests each base extractor for all compression formats Note that all compression formats are tested except "rar".

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PR_kwDODunzps47eW6C

Skip test_extractor only for zstd param if zstandard not installed

closed

MEMBER

Currently, if `zstandard` is not installed, `test_extractor` is skipped for all compression format parameters. This PR fixes `test_extractor` so that if `zstandard` is not installed, `test_extractor` is skipped only for the `zstd` compression parameter, that is, it is not skipped for all the other compression parameters (`gzip`, `xz`,...).

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PR_kwDODunzps47eF_E

Trigger CI also on push to main

closed

MEMBER

Currently, new CI (on GitHub Actions) is only triggered on pull requests branches when the base branch is main. This PR also triggers the CI when a PR is merged to main branch.

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PR_kwDODunzps47dju8

Fix mock fsspec

closed

MEMBER

This PR: - Removes an unused method from `DummyTestFS` - Refactors `mock_fsspec` to make it simpler

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I_kwDODunzps5N0S7e

How to assign new values to Dataset?

closed

NONE

 Hi, if I want to change some values of the dataset, or add new columns to it, how can I do it? For example, I want to change all the labels of the SST2 dataset to `0`: ```python from datasets import load_dataset data = load_dataset('glue','sst2') data['train']['label'] = [0]*len(data) ``` I will get the error: ``` TypeError: 'Dataset' object does not support item assignment ```

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PR_kwDODunzps47cLkm

Update create dataset card docs

closed

MEMBER

This PR proposes removing the [online dataset card creator](https://huggingface.co/datasets/card-creator/) in favor of simply copy/pasting a template and using the [Datasets Tagger app](https://huggingface.co/spaces/huggingface/datasets-tagging) to generate the tags. The Tagger app provides more guidance by showing all possible values a user can select in the dropdown menus, whereas the online dataset card creator doesn't, which can make it difficult to know what tag values to input. Let me know what you think! :)

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I_kwDODunzps5NwuIM

IndexError when loading ImageFolder

closed

NONE

## Describe the bug Loading an image dataset with `imagefolder` throws `IndexError: list index out of range` when the given folder contains a non-image file (like a csv). ## Steps to reproduce the bug Put a csv file in a folder with images and load it: ```python import datasets datasets.load_dataset("imagefolder", data_dir=path/to/folder) ``` ## Expected results I would expect a better error message, like `Unsupported file` or even the dataset loader just ignoring every file that is not an image in that case. ## Actual results Here is the whole traceback: ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.11.0-051100-generic-x86_64-with-glibc2.27 - Python version: 3.9.9 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5NwZ7y

Dataset Viewer issue for codeparrot/xlcost-text-to-code

closed

NONE

### Link https://huggingface.co/datasets/codeparrot/xlcost-text-to-code ### Description Error ``` Server Error Status code: 400 Exception: TypeError Message: 'NoneType' object is not iterable ``` Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully. Thanks! ### Owner Yes

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PR_kwDODunzps47XX67

Added method to remove excess nesting in a DatasetDict

closed

NONE

Added the ability for a DatasetDict to remove additional nested layers within its features to avoid conflicts when collating. It is meant to accompany [this PR](https://github.com/huggingface/transformers/pull/18119) to resolve the same issue [#15505](https://github.com/huggingface/transformers/issues/15505). @stas00 @lhoestq

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I_kwDODunzps5NnuMI

Random 400 Client Error when pushing dataset

closed

NONE

## Describe the bug When pushing a dataset, the client errors randomly with `Bad Request for url:...`. At the next call, a new parquet file is created for each shard. The client may fail at any random shard. ## Steps to reproduce the bug ```python dataset.push_to_hub("ORG/DATASET", private=True, branch="main") ``` ## Expected results Push all the dataset to the Hub with no duplicates. If it fails, it should retry or fail, but continue from the last failed shard. ## Actual results ``` --------------------------------------------------------------------------- HTTPError Traceback (most recent call last) testing.ipynb Cell 29 in <cell line: 1>() ----> [1](testing.ipynb?line=0) dataset.push_to_hub("ORG/DATASET", private=True, branch="main") File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4297, in Dataset.push_to_hub(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files) 4291 warnings.warn( 4292 "'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0.", 4293 FutureWarning, 4294 ) 4295 max_shard_size = shard_size -> 4297 repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub( 4298 repo_id=repo_id, 4299 split=split, 4300 private=private, 4301 token=token, 4302 branch=branch, 4303 max_shard_size=max_shard_size, 4304 embed_external_files=embed_external_files, 4305 ) 4306 organization, dataset_name = repo_id.split("/") 4307 info_to_dump = self.info.copy() File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4195, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files) 4193 shard.to_parquet(buffer) 4194 uploaded_size += buffer.tell() -> 4195 _retry( 4196 api.upload_file, 4197 func_kwargs=dict( 4198 path_or_fileobj=buffer.getvalue(), 4199 path_in_repo=shard_path_in_repo, 4200 repo_id=repo_id, 4201 token=token, 4202 repo_type="dataset", 4203 revision=branch, 4204 identical_ok=False, 4205 ), 4206 exceptions=HTTPError, 4207 status_codes=[504], 4208 base_wait_time=2.0, 4209 max_retries=5, 4210 max_wait_time=20.0, 4211 ) 4212 shards_path_in_repo.append(shard_path_in_repo) 4214 # Cleanup to remove unused files File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:284, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 282 except exceptions as err: 283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): --> 284 raise err 285 else: 286 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:281, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 279 while True: 280 try: --> 281 return func(*func_args, **func_kwargs) 282 except exceptions as err: 283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1967, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, identical_ok, commit_message, commit_description, create_pr) 1957 commit_message = ( 1958 commit_message 1959 if commit_message is not None 1960 else f"Upload {path_in_repo} with huggingface_hub" 1961 ) 1962 operation = CommitOperationAdd( 1963 path_or_fileobj=path_or_fileobj, 1964 path_in_repo=path_in_repo, 1965 ) -> 1967 pr_url = self.create_commit( 1968 repo_id=repo_id, 1969 repo_type=repo_type, 1970 operations=[operation], 1971 commit_message=commit_message, 1972 commit_description=commit_description, 1973 token=token, 1974 revision=revision, 1975 create_pr=create_pr, 1976 ) 1977 if pr_url is not None: 1978 re_match = re.match(REGEX_DISCUSSION_URL, pr_url) File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1844, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads) 1836 commit_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/commit/{revision}" 1838 commit_resp = requests.post( 1839 url=commit_url, 1840 headers={"Authorization": f"Bearer {token}"}, 1841 json=commit_payload, 1842 params={"create_pr": 1} if create_pr else None, 1843 ) -> 1844 _raise_for_status(commit_resp) 1845 return commit_resp.json().get("pullRequestUrl", None) File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:84, in _raise_for_status(request) 76 if request.status_code == 401: 77 # The repo was not found and the user is not Authenticated 78 raise RepositoryNotFoundError( 79 f"401 Client Error: Repository Not Found for url: {request.url}. If the" 80 " repo is private, make sure you are authenticated. (Request ID:" 81 f" {request_id})" 82 ) ---> 84 _raise_with_request_id(request) File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:95, in _raise_with_request_id(request) 92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str): 93 e.args = (e.args[0] + f" (Request ID: {request_id})",) + e.args[1:] ---> 95 raise e File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:90, in _raise_with_request_id(request) 88 request_id = request.headers.get("X-Request-Id") 89 try: ---> 90 request.raise_for_status() 91 except Exception as e: 92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str): File ~/.local/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self) 1016 http_error_msg = ( 1017 f"{self.status_code} Server Error: {reason} for url: {self.url}" 1018 ) 1020 if http_error_msg: -> 1021 raise HTTPError(http_error_msg, response=self) HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/ORG/DATASET/commit/main (Request ID: a_F0IQAHJdxGKVRYyu1cF) ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.31 - Python version: 3.9.4 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5Nngas

Dataset.map gets stuck on _cast_to_python_objects

closed

NONE

## Describe the bug `Dataset.map`, when fed a Huggingface Tokenizer as its map func, can sometimes spend huge amounts of time doing casts. A minimal example follows. Not all usages suffer from this. For example, I profiled the preprocessor at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb , and it did _not_ have this problem. However, I'm at a loss to figure out how it avoids it, as the example below is simple and minimal and still has this problem. This casting, where it occurs, causes the `Dataset.map` to run approximately 7x slower than it runs for code which does not cause this casting. This may be related to https://github.com/huggingface/datasets/issues/1046 . However, the tokenizer is _not_ set to return Tensors. ## Steps to reproduce the bug A minimal, self-contained example to reproduce is below: ```python import transformers from transformers import AutoTokenizer from datasets import load_dataset import torch import cProfile pretrained = 'distilbert-base-uncased' tokenizer = AutoTokenizer.from_pretrained(pretrained) squad = load_dataset('squad') squad_train = squad['train'] squad_tiny = squad_train.select(range(5000)) assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast) def tokenize(ds): tokens = tokenizer(text=ds['question'], text_pair=ds['context'], add_special_tokens=True, padding='max_length', truncation='only_second', max_length=160, stride=32, return_overflowing_tokens=True, return_offsets_mapping=True, ) return tokens cmd = 'squad_tiny.map(tokenize, batched=True, remove_columns=squad_tiny.column_names)' cProfile.run(cmd, sort='tottime') ``` ## Actual results The code works, but takes 10-25 sec per batch (about 7x slower than non-casting code), with the following profile. Note that `_cast_to_python_objects` is the culprit. ``` 63524075 function calls (58206482 primitive calls) in 121.836 seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 5274034/40 68.751 0.000 111.060 2.776 features.py:262(_cast_to_python_objects) 42223832 24.077 0.000 33.310 0.000 {built-in method builtins.isinstance} 16338/20 5.121 0.000 111.053 5.553 features.py:361(<listcomp>) 5274135 4.747 0.000 4.749 0.000 {built-in method _abc._abc_instancecheck} 80/40 4.731 0.059 116.292 2.907 {pyarrow.lib.array} 5274135 4.485 0.000 9.234 0.000 abc.py:96(__instancecheck__) 2661564/2645196 2.959 0.000 4.298 0.000 features.py:1081(_check_non_null_non_empty_recursive) 5 2.786 0.557 2.786 0.557 {method 'encode_batch' of 'tokenizers.Tokenizer' objects} 2668052 0.930 0.000 0.930 0.000 {built-in method builtins.len} 5000 0.930 0.000 0.938 0.000 tokenization_utils_fast.py:187(_convert_encoding) 5 0.750 0.150 0.808 0.162 {method 'to_pydict' of 'pyarrow.lib.Table' objects} 1 0.444 0.444 121.749 121.749 arrow_dataset.py:2501(_map_single) 40 0.375 0.009 116.291 2.907 arrow_writer.py:151(__arrow_array__) 10 0.066 0.007 0.066 0.007 {method 'write_batch' of 'pyarrow.lib._CRecordBatchWriter' objects} 1 0.060 0.060 121.835 121.835 fingerprint.py:409(wrapper) 11387/5715 0.049 0.000 0.175 0.000 {built-in method builtins.getattr} 36 0.049 0.001 0.049 0.001 {pyarrow._compute.call_function} 15000 0.040 0.000 0.040 0.000 _collections_abc.py:719(__iter__) 3 0.023 0.008 0.023 0.008 {built-in method _imp.create_dynamic} 77 0.020 0.000 0.020 0.000 {built-in method builtins.dir} 37 0.019 0.001 0.019 0.001 socket.py:543(send) 15 0.017 0.001 0.017 0.001 tokenization_utils_fast.py:460(<listcomp>) 432/421 0.015 0.000 0.024 0.000 traitlets.py:1388(_notify_observers) 5000 0.015 0.000 0.018 0.000 _collections_abc.py:672(keys) 51 0.014 0.000 0.042 0.001 traitlets.py:276(getmembers) 5 0.014 0.003 3.775 0.755 tokenization_utils_fast.py:392(_batch_encode_plus) 3/1 0.014 0.005 0.035 0.035 {built-in method _imp.exec_dynamic} 5 0.012 0.002 0.950 0.190 tokenization_utils_fast.py:438(<listcomp>) 31626 0.012 0.000 0.012 0.000 {method 'append' of 'list' objects} 1532/1001 0.011 0.000 0.189 0.000 traitlets.py:643(get) 5 0.009 0.002 3.796 0.759 arrow_dataset.py:2631(apply_function_on_filtered_inputs) 51 0.009 0.000 0.062 0.001 traitlets.py:1766(traits) 5 0.008 0.002 3.784 0.757 tokenization_utils_base.py:2632(batch_encode_plus) 368 0.007 0.000 0.044 0.000 traitlets.py:1715(_get_trait_default_generator) 26 0.007 0.000 0.022 0.001 traitlets.py:1186(setup_instance) 51 0.006 0.000 0.010 0.000 traitlets.py:1781(<listcomp>) 80/32 0.006 0.000 0.052 0.002 table.py:1758(cast_array_to_feature) 684 0.006 0.000 0.007 0.000 {method 'items' of 'dict' objects} 4344/1794 0.006 0.000 0.192 0.000 traitlets.py:675(__get__) ... ``` ## Environment info I observed this on both Google colab and my local workstation: ### Google colab - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 ### Local - `datasets` version: 2.3.2 - Platform: Windows-7-6.1.7601-SP1 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5NkC78

Issue loading datasets -- pyarrow.lib has no attribute

closed

NONE

## Describe the bug I am trying to load sentiment analysis datasets from huggingface, but any dataset I try to use via load_dataset, I get the same error: `AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'` ## Steps to reproduce the bug ```python dataset = load_dataset("glue", "cola") ``` ## Expected results Download datasets without issue. ## Actual results `AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 8.0.0 - Pandas version: 1.1.0

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I_kwDODunzps5Ni9eb

load_datasets on csv returns everything as a string

closed

NONE

## Describe the bug If you use: `conll_dataset.to_csv("ner_conll.csv")` It will create a csv file with all of your data as expected, however when you load it with: `conll_dataset = load_dataset("csv", data_files="ner_conll.csv")` everything is read in as a string. For example if I look at everything in 'ner_tags' I get back `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']` instead of what I originally saved which was `[[3, 0, 7, 0, 0, 0, 7, 0, 0], [1, 2], [5, 0]]` I think maybe there is something funky going on with the csv delimiter ## Steps to reproduce the bug ```python # Sample code to reproduce the bug #load original conll dataset orig_conll = load_dataset("conll2003") #save original conll as a csv orig_conll.to_csv("ner_conll.csv") #reload conll data as a csv new_conll = load_dataset("csv", data_files="ner_conll.csv")` ``` ## Expected results A clear and concise description of the expected results. I would expect the data be returned as the data type I saved it as. I.e. if I save a list of ints [[3, 0, 7, 0, 0, 0, 7, 0, 0]], I shouldnt get back a string ['[3 0 7 0 0 0 7 0 0]'] I also get back a string when I pass a list of strings ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'] ## Actual results A list of strings `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']` A string "['EU' 'rejects' 'German' 'call' 'to' 'boycott' 'British' 'lamb' '.']" ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 8.0.0

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PR_kwDODunzps47NEfV

Support extract 7-zip compressed data files

closed

MEMBER

Fix partially #3541, fix #4670.

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I_kwDODunzps5NglB1

Dataset Viewer issue for wmt16

closed

MEMBER

### Link https://huggingface.co/datasets/wmt16 ### Description [Reported](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/12#62cb83f14c7f35284e796f9c) by a user of AutoTrain Evaluate. AFAIK this dataset was working 1-2 weeks ago, and I'm not sure how to interpret this error. ``` Status code: 400 Exception: NotImplementedError Message: This is a abstract method ``` Thanks! ### Owner No

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I_kwDODunzps5NfC92

Can't extract files from `.7z` zipfile using `download_and_extract`

closed

CONTRIBUTOR

## Describe the bug I'm adding a new dataset which is a `.7z` zip file in Google drive and contains 3 json files inside. I'm able to download the data files using `download_and_extract` but after downloading it throws this error: ``` >>> dataset = load_dataset("./datasets/mantis/") Using custom data configuration default Downloading and preparing dataset mantis/default to /Users/bhavitvyamalik/.cache/huggingface/datasets/mantis/default/1.1.0/611affa804ec53e2055a335cc1b8b213bb5a0b5142d919967729d5ee23c6bab4... Downloading data: 100%|█████████████████████████████████████████████████████████| 77.2M/77.2M [00:23<00:00, 3.28MB/s] /Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6 Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/load.py", line 1745, in load_dataset use_auth_token=use_auth_token, File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 595, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 690, in _download_and_prepare ) from None OSError: Cannot find data file. Original error: [Errno 20] Not a directory: '/Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6/merged_train.json' ``` just before generating the splits. I checked `fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6` file and it's `7z` zip file (similar to downloaded Google drive file) which means it didn't get unzip. Do I need to unzip it separately and then pass the paths for train,dev,test files in `SplitGenerator`? ## Environment info - `datasets` version: 1.18.4.dev0 - Platform: Darwin-19.6.0-x86_64-i386-64bit - Python version: 3.7.8 - PyArrow version: 5.0.0

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I_kwDODunzps5NehtD

loading oscar-corpus/OSCAR-2201 raises an error

closed

NONE

## Describe the bug load_dataset('oscar-2201', 'af') raises an error: Traceback (most recent call last): File "/usr/lib/python3.8/code.py", line 90, in runcode exec(code, self.locals) File "<input>", line 1, in <module> File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset builder_instance = load_dataset_builder( File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder dataset_module = dataset_module_factory( File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py I've tried other permutations such as : oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-2201', 'af') oscar_22 = load_dataset('oscar-corpus/OSCAR-2201') with the same unfortunate result. ## Steps to reproduce the bug oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-2201', 'af') oscar_22 = load_dataset('oscar-corpus/OSCAR-2201') # Sample code to reproduce the bug ``` ## Expected results loaded data ## Actual results Traceback (most recent call last): File "/usr/lib/python3.8/code.py", line 90, in runcode exec(code, self.locals) File "<input>", line 1, in <module> File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset builder_instance = load_dataset_builder( File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder dataset_module = dataset_module_factory( File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.13.0-37-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5NeGVV

Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed

closed

NONE

### Link https://huggingface.co/hungnm/multilingual-amazon-review-sentiment ### Description _No response_ ### Owner Yes

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I_kwDODunzps5NeGSX

Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed

closed

NONE

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

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I_kwDODunzps5NeFcO

Issues with concatenating datasets

closed

NONE

## Describe the bug It is impossible to concatenate datasets if a feature is sequence of dict in one dataset and a dict of sequence in another. But based on the document, it should be automatically converted. > A [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence) with a internal dictionary feature will be automatically converted into a dictionary of lists. This behavior is implemented to have a compatilbity layer with the TensorFlow Datasets library but may be un-wanted in some cases. If you don’t want this behavior, you can use a python list instead of the [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence). ## Steps to reproduce the bug ```python from datasets import concatenate_datasets, load_dataset squad = load_dataset("squad_v2") squad["train"].to_json("output.jsonl", lines=True) temp = load_dataset("json", data_files={"train": "output.jsonl"}) concatenate_datasets([temp["train"], squad["train"]]) ``` ## Expected results No error executing that code ## Actual results ``` ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)} or Value("null"). ``` ## Environment info - `datasets` version: 2.3.2 - Platform: macOS-12.4-arm64-arm-64bit - Python version: 3.8.11 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5NdyAe

Unable to create dataset having Python dataset script only

closed

CONTRIBUTOR

## Describe the bug Hi there, I'm trying to add the following dataset to Huggingface datasets: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/blob/ I'm trying to do so using the CLI commands but seems that this command generates the wrong `dataset_info.json` file (you can find it in the repo already): ``` datasets-cli test Heriot-WattUniversity/dialog-babi/dialog_babi.py --save_infos --all-configs ``` while it errors when I remove the python script: ``` datasets-cli test Heriot-WattUniversity/dialog-babi/ --save_infos --all-configs ``` The error message is the following: ``` FileNotFoundError: Unable to resolve any data file that matches '['**']' at /Users/as2180/workspace/Heriot-WattUniversity/dialog-babi with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` ## Environment info - `datasets` version: 2.3.2 - Platform: macOS-12.4-arm64-arm-64bit - Python version: 3.9.9 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps47IvfG

Add stanford dog dataset

closed

CONTRIBUTOR

This PR is for adding dataset, related to issue #4504. We are adding Stanford dog breed dataset. It is a multi class image classification dataset. Details can be found here - http://vision.stanford.edu/aditya86/ImageNetDogs/ Tests on dummy data is failing currently, which I am looking into.

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PR_kwDODunzps47H19n

Add text decorators

closed

MEMBER

This PR adds some decoration to text about different modalities to make it more obvious separate guides exist for audio, vision, and text. The goal is to make it easier for users to discover these guides!  TODO: - [x] Open PR to support new Tailwind classes

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PR_kwDODunzps47GTEc

Fix: conll2003 - fix empty example

closed

MEMBER

As reported in https://huggingface.co/datasets/conll2003/discussions/2#62c45a14f93fc97e8260532f, there was an extra empty example at the end of the dataset

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PR_kwDODunzps47AYDq

Fix _resolve_single_pattern_locally on Windows with multiple drives

closed

MEMBER

Currently, when `_resolve_single_pattern_locally` is called from a different drive than the one in `pattern`, it raises an exception: ``` _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\io\parquet.py:35: in __init__ **kwargs, C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\builder.py:287: in __init__ sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:761: in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:723: in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:321: in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:239: in _resolve_single_pattern_locally for filepath in glob_iter C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:242: in <listcomp> os.path.relpath(filepath, base_path), os.path.relpath(pattern, base_path) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ path = 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pytest-of-runneradmin\\pytest-0\\popen-gw0\\data6\\dataset.parquet' start = '/' ... E ValueError: path is on mount 'C:', start on mount 'D:' ``` This PR makes sure that `base_path` is in the same drive as `pattern`.

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PR_kwDODunzps47AQo9

Transfer CI to GitHub Actions

closed

MEMBER

This PR transfers CI from CircleCI to GitHub Actions. The implementation in GitHub Actions tries to be as faithful as possible to the implementation in CircleCI and get the same output results (exceptions below). **IMPORTANT NOTE**: The fast-fail policy (described below) is not finally implemented, so that: - we can continue merging PRs with CI in red because of some random error returned by the Hub - it is not annoying for maintainers to have to relaunch failed CI jobs See comments here: https://github.com/huggingface/datasets/pull/4659#discussion_r918802348 Differences in the implementation in GitHub Actions compared to the CircleCI one: - This PR introduces some *fail-fast* mechanisms to significantly reduce the total time CI is running, both because of environmental impact and because CI in GitHub Actions billing depends on the minutes per month running time (see [About billing for GitHub Actions](https://docs.github.com/en/billing/managing-billing-for-github-actions/about-billing-for-github-actions)): - All tests *depend* on `check_code_quality` job: only if `check_code_quality` passes, then the other test jobs are launched - The tests are implemented with a matrix strategy (cross-product: OS and PyArrow versions) and fail-fast: if any of the 4 processes fails, the others are cancelled - OS dependencies for Linux (see table below) | OS dependencies | Passed tests | Skipped tests | | --- | ---: | ---: | | libsndfile1-dev | 4786 | 3119 | | libsndfile1 | 4786 | 3119 | | libsndfile1, sox | 4788 | 3117 | - This PR replaces `libsndfile1-dev` with `libsndfile1`: the same number of passing tests but less packages installed - This PR adds `sox`: required by MP3 tests (2 more tests are passed: 4788 instead of 4786) - For tests using PyArrow 6, this PR uses 6.0.1 instead of 6.0.0 TO DO: - [ ] Remove old CircleCI CI: kept for the moment to compare stability and performance Close #4658. ## Comparison between CircleCI and GitHub Actions | | | CircleCI | GitHub Actions | | --- | --- | ---: | ---: | | Ubuntu, pyarrow-latest |||| || Passed tests | 4786 | 4788 | || Duration | 11m 0s | 10m 10s | | Windows, pyarrow-latest |||| || Passed tests | 4783 | 4783 | || Duration | 29m 59s | 22m 56s |

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I_kwDODunzps5NTquu

Transfer CI tests to GitHub Actions

closed

MEMBER

Let's try CI tests using GitHub Actions to see if they are more stable than on CircleCI.

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I_kwDODunzps5NSrrd

Add SQuAD2.0 Dataset

closed

NONE

## Adding a Dataset - **Name:** *SQuAD2.0* - **Description:** *Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.* - **Paper:** *https://aclanthology.org/P18-2124.pdf* - **Data:** *https://rajpurkar.github.io/SQuAD-explorer/* - **Motivation:** *Dataset for training and evaluating models of conversational response*

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I_kwDODunzps5NSq-q

Add Amazon-QA Dataset

closed

NONE

## Adding a Dataset - **Name:** *Amazon-QA* - **Description:** *The dataset is .jsonl format, where each line in the file is a json string that corresponds to a question, existing answers to the question and the extracted review snippets (relevant to the question).* - **Paper:** *https://github.com/amazonqa/amazonqa/tree/master/paper* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon-qa.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*

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I_kwDODunzps5NSmQA

Simple Wikipedia

closed

NONE

## Adding a Dataset - **Name:** *Simple Wikipedia* - **Description:** *Two different versions of the data set now exist. Both were generated by aligning Simple English Wikipedia and English Wikipedia. A complete description of the extraction process can be found in "Simple English Wikipedia: A New Simplification Task", William Coster and David Kauchak (2011).* - **Paper:** *https://aclanthology.org/P11-2117/* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/SimpleWiki.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*

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I_kwDODunzps5NSlFX

Add Quora Question Triplets Dataset

closed

NONE

## Adding a Dataset - **Name:** *Quora Question Triplets* - **Description:** *This dataset consists of over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair.* - **Paper:** - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates_triplets.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*

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I_kwDODunzps5NSh1y

Add Altlex dataset

closed

NONE

## Adding a Dataset - **Name:** *Altlex* - **Description:** *Git repository for software associated with the 2016 ACL paper "Identifying Causal Relations Using Parallel Wikipedia Articles.”* - **Paper:** *https://aclanthology.org/P16-1135.pdf* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/altlex.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*

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I_kwDODunzps5NSgia

Add Sentence Compression Dataset

closed

NONE

## Adding a Dataset - **Name:** *Sentence Compression* - **Description:** *Large corpus of uncompressed and compressed sentences from news articles.* - **Paper:** *https://www.aclweb.org/anthology/D13-1155/* - **Data:** *https://github.com/google-research-datasets/sentence-compression/tree/master/data* - **Motivation:** *Dataset for training and evaluating models of conversational response*

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I_kwDODunzps5NSekG

Add Flickr 30k Dataset

closed

NONE

## Adding a Dataset - **Name:** *Flickr 30k* - **Description:** *To produce the denotation graph, we have created an image caption corpus consisting of 158,915 crowd-sourced captions describing 31,783 images. This is an extension of our previous Flickr 8k Dataset. The new images and captions focus on people involved in everyday activities and events.* - **Paper:** *https://transacl.org/ojs/index.php/tacl/article/view/229/33* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*

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I_kwDODunzps5NSauw

Add PAQ dataset

closed

NONE

## Adding a Dataset - **Name:** *PAQ* - **Description:** *This repository contains code and models to support the research paper PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them* - **Paper:** *https://arxiv.org/abs/2102.07033* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/PAQ_pairs.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*

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I_kwDODunzps5NSXOH

Add WikiAnswers dataset

closed

NONE

## Adding a Dataset - **Name:** *WikiAnswers* - **Description:** *The WikiAnswers corpus contains clusters of questions tagged by WikiAnswers users as paraphrases. Each cluster optionally contains an answer provided by WikiAnswers users.* - **Paper:** *https://dl.acm.org/doi/10.1145/2623330.2623677* - **Data:** *https://github.com/afader/oqa#wikianswers-corpus* - **Motivation:** *Dataset for training and evaluating models of conversational response*

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PR_kwDODunzps468oZ6

Set HF_SCRIPTS_VERSION to main

closed

MEMBER

After renaming "master" to "main", the CI fails with ``` AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/main/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at /home/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" ``` This is because in the CI we were still using `HF_SCRIPTS_VERSION=master`. I changed it to "main"

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PR_kwDODunzps468mQb

[Minor fix] Typo correction

closed

CONTRIBUTOR

recieve -> receive

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PR_kwDODunzps468Cqk

Rename master to main

closed

MEMBER

This PR renames mentions of "master" by "main" in the code base for several cases: - set the default dataset script version to "main" if the local installation of `datasets` is a dev installation - update URLs to this github repository to use "main" - update the DVC benchmark - update the github workflows - update docstrings - update tests to compare the changes in dataset cards against "main"

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I_kwDODunzps5NO4vz

Streaming issue for ccdv/pubmed-summarization

closed

MEMBER

### Link https://huggingface.co/datasets/ccdv/pubmed-summarization ### Description This was reported by a [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/7). It seems like streaming doesn't work due to the way the dataset loading script is defined? ``` Status code: 400 Exception: FileNotFoundError Message: https://huggingface.co/datasets/ccdv/pubmed-summarization/resolve/main/train.zip/train.txt ``` ### Owner No

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I_kwDODunzps5NOcti

Dataset Viewer issue for kmfoda/booksum

closed

MEMBER

### Link https://huggingface.co/datasets/kmfoda/booksum ### Description A [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/9) discovered this dataset cannot be streamed due to: ``` Status code: 400 Exception: ClientResponseError Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/kmfoda/booksum/resolve/47953f583d6967f086cb16a2f4d2346e9834024d/test.csv') ``` I'm not sure why it says "Unauthorized" since it's just a bunch of CSV files in a repo ### Owner No

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PR_kwDODunzps4656H9

The speechocean762 dataset

closed

NONE

[speechocean762](https://www.openslr.org/101/) is a non-native English corpus for pronunciation scoring tasks. It is free for both commercial and non-commercial use. I believe it will be easier to use if it could be available on Hugging Face.

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I_kwDODunzps5NKTt8

Add info in docs about behavior of download_config.num_proc

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** I went to override `download_config.num_proc` and was confused about what was happening under the hood. It would be nice to have the behavior documented a bit better so folks know what's happening when they use it. **Describe the solution you'd like** - Add note about how the default number of workers is 16. Related code: https://github.com/huggingface/datasets/blob/7bcac0a6a0fc367cc068f184fa132b8de8dfa11d/src/datasets/download/download_manager.py#L299-L302 - Add note that if the number of workers is higher than the number of files to download, it won't use multiprocessing. **Describe alternatives you've considered** maybe it would also be nice to set `num_proc` = `num_files` when `num_proc` > `num_files`. **Additional context** ...

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I_kwDODunzps5NKCKb

Dataset Viewer issue for vadis/sv-ident

closed

NONE

### Link https://huggingface.co/datasets/vadis/sv-ident/viewer/default/validation ### Description Error message when loading validation split in the viewer: ``` Status code: 400 Exception: Status400Error Message: The split cache is empty. ``` ### Owner _No response_

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I_kwDODunzps5NJw6D

Can't load the Hausa audio dataset

closed

NONE

common_voice_train = load_dataset("common_voice", "ha", split="train+validation")

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PR_kwDODunzps462_qX

[data_files] Only match separated split names

closed

MEMBER

As reported in https://github.com/huggingface/datasets/issues/4477, the current pattern matching to infer which file goes into which split is too permissive. For example a file "contest.py" would be considered part of a test split (it contains "test") and "seqeval.py" as well (it contains "eval"). In this PR I made the pattern matching more robust by only matching split names **between separators**. The supported separators are dots, dashes, spaces and underscores. I updated the docs accordingly. One detail about the tests: I had to update one test because it was using `PurePath.match` as a reference for globbing, but it doesn't support the `[..]` glob pattern. Therefore I added a `mock_fs` context manager that can be used to easily define a dummy filesystem with certain files in it and run pattern matching tests. Its code comes mostly from test_streaming_download_manager.py Close https://github.com/huggingface/datasets/issues/4477

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PR_kwDODunzps460Vy0

Update WinoBias README

closed

CONTRIBUTOR

I'm adding some information about Winobias that I got from the paper :smile: I think this makes it a bit clearer!

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PR_kwDODunzps460HFM

fix(dataset_wrappers): Fixes access to fsspec.asyn in torch_iterable_dataset.py.

closed

CONTRIBUTOR

Fix #4612. Apparently, newest `fsspec` versions do not allow access to attribute-based modules if they are not imported, such as `fsspec.async`. Thus, @mariosasko suggested to add the missing part to the module import to allow for its access.

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I_kwDODunzps5NGAEw

Rename repo default branch to main

closed

MEMBER

Rename repository default branch to `main` (instead of current `master`). Once renamed, users will have to manually update their local repos: - [ ] Upstream: ``` git branch -m master main git fetch upstream main git branch -u upstream/main main git remote set-head upstream -a ``` - [ ] Origin: Rename fork default branch as well at: https://github.com/USERNAME/lam/settings/branches Then: ``` git fetch origin main git remote set-head origin -a ``` CC: @sgugger

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PR_kwDODunzps46zvFJ

Fix time type `_arrow_to_datasets_dtype` conversion

closed

CONTRIBUTOR

Fix #4620 The issue stems from the fact that `pa.array([time_data]).type` returns `DataType(time64[unit])`, which doesn't expose the `unit` attribute, instead of `Time64Type(time64[unit])`. I believe this is a bug in PyArrow. Luckily, the both types have the same `str()`, so in this PR I call `pa.type_for_alias(str(type))` to convert them both to the `Time64Type(time64[unit])` format. cc @severo

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PR_kwDODunzps46zfNa

fixed duplicate calculation of spearmanr function in metrics wrapper.

closed

CONTRIBUTOR

During _compute, the scipy.stats spearmanr function was called twice, redundantly, once for calculating the score and once for calculating the p-value, under the conditional branch where return_pvalue=True. I adjusted the _compute function to execute the spearmanr function once, store the results tuple in a temporary variable, and then pass the indexed contents to the expected keys of the returned dictionary.

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PR_kwDODunzps46zELz

Unpack `dl_manager.iter_files` to allow parallization

closed

CONTRIBUTOR

Iterate over data files outside `dl_manager.iter_files` to allow parallelization in streaming mode. (The issue reported [here](https://discuss.huggingface.co/t/dataset-only-have-n-shard-1-when-has-multiple-shards-in-repo/19887)) PS: Another option would be to override `FilesIterable.__getitem__` to make it indexable and check for that type in `_shard_kwargs` and `n_shards,` but IMO this solution adds too much unnecessary complexity.

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PR_kwDODunzps46yzOK

Remove all paperswithcode_id: null

closed

MEMBER

On the Hub there is a validation error on the `paperswithcode_id` tag when the value is `null`: <img width="686" alt="image" src="https://user-images.githubusercontent.com/42851186/177151825-93d341c5-25bd-41ab-96c2-c0b516d51c68.png"> We've been using `null` to specify that we checked on pwc but the dataset doesn't exist there. To have the validation working again we can simply remove all the `paperswithcode_id: null`. cc @julien-c

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PR_kwDODunzps46ynmT

Fix ImageFolder with parameters drop_metadata=True and drop_labels=False (when metadata.jsonl is present)

closed

CONTRIBUTOR

Will fix #4621 ImageFolder raises `KeyError: 'label'` with params `drop_metadata=True` and `drop_labels=False` (if there is at least one metadata.jsonl file a data directory). This happens because metadata files are collected inside `analyze()` function regardless of `drop_metadata` value. And then the following condition doesn't pass: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/imagefolder/imagefolder.py#L167 So I suggest to double check it inside `analyze()` not to collect metadata files if they are not needed. (and labels too, to be consistent) --- Also, I added a test to check if labels are inferred correctly from directories names in general (because we didn't have it) :)

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I_kwDODunzps5NEhLw

ImageFolder raises an error with parameters drop_metadata=True and drop_labels=False when metadata.jsonl is present

closed

CONTRIBUTOR

## Describe the bug If you pass `drop_metadata=True` and `drop_labels=False` when a `data_dir` contains at least one `matadata.jsonl` file, you will get a KeyError. This is probably not a very useful case but we shouldn't get an error anyway. Asking users to move metadata files manually outside `data_dir` or pass features manually (when there is a tool that can infer them automatically) don't look like a good idea to me either. ## Steps to reproduce the bug ### Clone an example dataset from the Hub ```bash git clone https://huggingface.co/datasets/nateraw/test-imagefolder-metadata ``` ### Try to load it ```python from datasets import load_dataset ds = load_dataset("test-imagefolder-metadata", drop_metadata=True, drop_labels=False) ``` or even just ```python ds = load_dataset("test-imagefolder-metadata", drop_metadata=True) ``` as `drop_labels=False` is a default value. ## Expected results A DatasetDict object with two features: `"image"` and `"label"`. ## Actual results ``` Traceback (most recent call last): File "/home/polina/workspace/datasets/debug.py", line 18, in <module> ds = load_dataset( File "/home/polina/workspace/datasets/src/datasets/load.py", line 1732, in load_dataset builder_instance.download_and_prepare( File "/home/polina/workspace/datasets/src/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/polina/workspace/datasets/src/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1218, in _prepare_split example = self.info.features.encode_example(record) File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1596, in encode_example return encode_nested_example(self, example) File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in encode_nested_example { File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in <dictcomp> { File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in zip_dict yield key, tuple(d[key] for d in dicts) File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in <genexpr> yield key, tuple(d[key] for d in dicts) KeyError: 'label' ``` ## Environment info `datasets` master branch - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.14.0-1042-oem-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.1

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I_kwDODunzps5NDoe2

Data type is not recognized when using datetime.time

closed

CONTRIBUTOR

## Describe the bug Creating a dataset from a pandas dataframe with `datetime.time` format generates an error. ## Steps to reproduce the bug ```python import pandas as pd from datetime import time from datasets import Dataset df = pd.DataFrame({"feature_name": [time(1, 1, 1)]}) dataset = Dataset.from_pandas(df) ``` ## Expected results The dataset should be created. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 823, in from_pandas return cls(table, info=info, split=split) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 679, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in from_arrow_schema obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in <dictcomp> obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1315, in generate_from_arrow_type return Value(dtype=_arrow_to_datasets_dtype(pa_type)) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 83, in _arrow_to_datasets_dtype return f"time64[{arrow_type.unit}]" AttributeError: 'pyarrow.lib.DataType' object has no attribute 'unit' ``` ## Environment info - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.13.0-1031-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps46tADt

Fix `embed_storage` on features inside lists/sequences

closed

CONTRIBUTOR

Add a dedicated function for embed_storage to always preserve the embedded/casted arrays (and to have more control over `embed_storage` in general). Fix #4591 ~~(Waiting for #4608 to be merged to mark this PR as ready for review - required for fixing `xgetsize` in private repos)~~ Done!

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PR_kwDODunzps46ssfw

Ensure ConcatenationTable.cast uses target_schema metadata

closed

CONTRIBUTOR

Currently, `ConcatenationTable.cast` does not use target_schema metadata when casting subtables. This causes an issue when using cast_column and the underlying table is a ConcatenationTable. Code example of where issue arrises: ``` from datasets import Dataset, Image column1 = [0, 1] image_paths = ['/images/image1.jpg', '/images/image2.jpg'] ds = Dataset.from_dict({"column1": column1}) ds = ds.add_column("image", image_paths) ds.cast_column("image", Image()) # Fails here ``` Output ``` ... TypeError: Couldn't cast array of type string to {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ```

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PR_kwDODunzps46skd6

Align/fix license metadata info

closed

MEMBER

fix bad "other-*" licenses and add the corresponding "license_details" when relevant

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I_kwDODunzps5M8tzU

Release 2.3.0 broke custom iterable datasets

closed

NONE

## Describe the bug Trying to iterate examples from custom iterable dataset fails to bug introduced in `torch_iterable_dataset.py` since the release of 2.3.0. ## Steps to reproduce the bug ```python next(iter(custom_iterable_dataset)) ``` ## Expected results `next(iter(custom_iterable_dataset))` should return examples from the dataset ## Actual results ``` /usr/local/lib/python3.7/dist-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py in _set_fsspec_for_multiprocess() 16 See https://github.com/fsspec/gcsfs/issues/379 17 """ ---> 18 fsspec.asyn.iothread[0] = None 19 fsspec.asyn.loop[0] = None 20 AttributeError: module 'fsspec' has no attribute 'asyn' ``` ## Environment info - `datasets` version: 2.3.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 8.0.0 - Pandas version: 1.3.5

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PR_kwDODunzps46rxIX

Preserve member order by MockDownloadManager.iter_archive

closed

MEMBER

Currently, `MockDownloadManager.iter_archive` yields paths to archive members in an order given by `path.rglob("*")`, which migh not be the same order as in the original archive. See issue in: - https://github.com/huggingface/datasets/pull/4579#issuecomment-1172135027 This PR fixes the order of the members yielded by `MockDownloadManager.iter_archive` so that it is the same as in the original archive.

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I_kwDODunzps5M7Q0z

codeparrot/github-code failing to load

closed

NONE

## Describe the bug codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'` ## Steps to reproduce the bug ```python from datasets import load_dataset ``` ## Expected results loaded dataset object ## Actual results ```python [3]: dataset = load_dataset("codeparrot/github-code") No config specified, defaulting to: github-code/all-all Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817... --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Input In [3], in <cell line: 1>() ----> 1 dataset = load_dataset("codeparrot/github-code") File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1678 # Download and prepare data -> 1679 builder_instance.download_and_prepare( 1680 download_config=download_config, 1681 download_mode=download_mode, 1682 ignore_verifications=ignore_verifications, 1683 try_from_hf_gcs=try_from_hf_gcs, 1684 use_auth_token=use_auth_token, 1685 ) 1687 # Build dataset for splits 1688 keep_in_memory = ( 1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1690 ) File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info 708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos) 1220 def _download_and_prepare(self, dl_manager, verify_infos): -> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager) 162 def _split_generators(self, dl_manager): 164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info( 165 _REPO_NAME, 166 timeout=100.0, 167 ) --> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info) 170 data_files = datasets.data_files.DataFilesDict.from_hf_repo( 171 patterns, 172 dataset_info=hfh_dataset_info, 173 ) 175 files = dl_manager.download_and_extract(data_files["train"]) TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path' ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35 - Python version: 3.10.5 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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I_kwDODunzps5M6dIT

librispeech dataset has to download whole subset when specifing the split to use

closed

NONE

## Describe the bug librispeech dataset has to download whole subset when specifing the split to use ## Steps to reproduce the bug see below # Sample code to reproduce the bug ``` !pip install datasets from datasets import load_dataset raw_dataset = load_dataset("librispeech_asr", "clean", split="train.100") ``` ## Expected results The split "train.clean.100" is downloaded. ## Actual results All four splits in "clean" subset is downloaded. ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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PR_kwDODunzps46pm9A

Fix xisfile, xgetsize, xisdir, xlistdir in private repo

closed

MEMBER

`xisfile` is working in a private repository when passing a chained URL to a file inside an archive, e.g. `zip://a.txt::https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`. However it's not working when passing a simple file `https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`. This is because the authentication headers are not passed correctly in this case. This is causing dataset streaming to fail in private parquet repositories, as noted in https://github.com/huggingface/datasets/issues/4605 I fixed `xisfile` and the other functions that behave the same way: xgetsize, xisdir and xlistdir TODO: - [x] tests fix https://github.com/huggingface/datasets/issues/4605

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PR_kwDODunzps46pLnd

Align more metadata with other repo types (models,spaces)

closed

MEMBER

see also associated PR on the `datasets-tagging` Space: https://huggingface.co/spaces/huggingface/datasets-tagging/discussions/2 (to merge after this one is merged)

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I_kwDODunzps5M5Lza

Dataset Viewer issue for boris/gis_filtered

closed

NONE

### Link https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train ### Description When I try to access this from the website I get this error: Status code: 400 Exception: ClientResponseError Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet') If I try to load with code I also get the same issue: ```python dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True) dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True) ``` ### Owner No

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