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PR_kwDODunzps5z27Fs

Re-enable raising error from huggingface-hub FutureWarning in CI

closed

MEMBER

Re-enable raising error from huggingface-hub FutureWarning in tests, once that the fix in transformers - https://github.com/huggingface/transformers/pull/31007 was just released yesterday in transformers-4.42.0: https://github.com/huggingface/transformers/releases/tag/v4.42.0 Fix #7010.

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I_kwDODunzps6N2IXI

Re-enable raising error from huggingface-hub FutureWarning in CI

closed

MEMBER

Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR: - #6876 Note that this can only be done once transformers releases the fix: - https://github.com/huggingface/transformers/pull/31007

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PR_kwDODunzps5z2Xe6

Support ruff 0.5.0 in CI

closed

MEMBER

Support ruff 0.5.0 in CI and revert: - #7007 Fix #7008.

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I_kwDODunzps6N1a3l

Support ruff 0.5.0 in CI

closed

MEMBER

Support ruff 0.5.0 in CI. Also revert: - #7007

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PR_kwDODunzps5z2Q68

Fix CI by temporarily pinning ruff < 0.5.0

closed

MEMBER

As a hotfix for CI, temporarily pin ruff upper version < 0.5.0. Fix #7006. Revert once root cause is fixed.

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I_kwDODunzps6N1Yhn

CI is broken after ruff-0.5.0: E721

closed

MEMBER

After ruff-0.5.0 release (https://github.com/astral-sh/ruff/releases/tag/0.5.0), our CI is broken due to E721 rule. See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983 > src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks

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I_kwDODunzps6Nw-Ad

EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files

closed

NONE

### Describe the bug while trying to load custom dataset from jsonl file, I get the error: "metadata.jsonl doesn't contain any data files" ### Steps to reproduce the bug This is my [metadata_v2.jsonl](https://github.com/user-attachments/files/16016011/metadata_v2.json) file. I have this file in the folder with all images mentioned in that json(l) file. Through below mentioned command I am trying to load_dataset so that I can upload it as mentioned here on the [official website](https://huggingface.co/docs/datasets/en/image_dataset#upload-dataset-to-the-hub). ```` from datasets import load_dataset dataset = load_dataset("imagefolder", data_dir="path/to/jsonl/metadata.jsonl") ```` error: ```` EmptyDatasetError Traceback (most recent call last) Cell In[18], line 3 1 from datasets import load_dataset ----> 3 dataset = load_dataset("imagefolder", 4 data_dir="path/to/jsonl/file/metadata.jsonl") 5 dataset[0]["objects"] File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2594, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2589 verification_mode = VerificationMode( 2590 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS 2591 ) 2593 # Create a dataset builder -> 2594 builder_instance = load_dataset_builder( 2595 path=path, 2596 name=name, 2597 data_dir=data_dir, 2598 data_files=data_files, 2599 cache_dir=cache_dir, 2600 features=features, 2601 download_config=download_config, 2602 download_mode=download_mode, 2603 revision=revision, 2604 token=token, 2605 storage_options=storage_options, 2606 trust_remote_code=trust_remote_code, 2607 _require_default_config_name=name is None, 2608 **config_kwargs, 2609 ) 2611 # Return iterable dataset in case of streaming 2612 if streaming: File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2266, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs) 2264 download_config = download_config.copy() if download_config else DownloadConfig() 2265 download_config.storage_options.update(storage_options) -> 2266 dataset_module = dataset_module_factory( 2267 path, 2268 revision=revision, 2269 download_config=download_config, 2270 download_mode=download_mode, 2271 data_dir=data_dir, 2272 data_files=data_files, 2273 cache_dir=cache_dir, 2274 trust_remote_code=trust_remote_code, 2275 _require_default_config_name=_require_default_config_name, 2276 _require_custom_configs=bool(config_kwargs), 2277 ) 2278 # Get dataset builder class from the processing script 2279 builder_kwargs = dataset_module.builder_kwargs File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1805, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs) 1782 # We have several ways to get a dataset builder: 1783 # 1784 # - if path is the name of a packaged dataset module (...) 1796 1797 # Try packaged 1798 if path in _PACKAGED_DATASETS_MODULES: 1799 return PackagedDatasetModuleFactory( 1800 path, 1801 data_dir=data_dir, 1802 data_files=data_files, 1803 download_config=download_config, 1804 download_mode=download_mode, -> 1805 ).get_module() 1806 # Try locally 1807 elif path.endswith(filename): File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1140, in PackagedDatasetModuleFactory.get_module(self) 1135 def get_module(self) -> DatasetModule: 1136 base_path = Path(self.data_dir or "").expanduser().resolve().as_posix() 1137 patterns = ( 1138 sanitize_patterns(self.data_files) 1139 if self.data_files is not None -> 1140 else get_data_patterns(base_path, download_config=self.download_config) 1141 ) 1142 data_files = DataFilesDict.from_patterns( 1143 patterns, 1144 download_config=self.download_config, 1145 base_path=base_path, 1146 ) 1147 supports_metadata = self.name in _MODULE_SUPPORTS_METADATA File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/data_files.py:503, in get_data_patterns(base_path, download_config) 501 return _get_data_files_patterns(resolver) 502 except FileNotFoundError: --> 503 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None EmptyDatasetError: The directory at path/to/jsonl/file/metadata.jsonl doesn't contain any data files` ``` ### Expected behavior It should be able load the whole file in a format of "dataset" inside the dataset variable. But it gives error "The directory at "path/to/jsonl/metadata.jsonl" doesn't contain any data files." ### Environment info I am using conda environment.

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PR_kwDODunzps5zrIYR

Fix WebDatasets KeyError for user-defined Features when a field is missing in an example

closed

CONTRIBUTOR

Fixes: https://github.com/huggingface/datasets/issues/6900 Not sure if this needs any addition stuff before merging

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PR_kwDODunzps5zhRAK

minor fix for bfloat16

closed

MEMBER

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PR_kwDODunzps5zhBld

Fix dump of bfloat16 torch tensor

closed

MEMBER

close https://github.com/huggingface/datasets/issues/7000

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I_kwDODunzps6NcA0_

Datasetbuilder Local Download FileNotFoundError

open

NONE

### Describe the bug So I was trying to download a dataset and save it as parquet and I follow the [tutorial](https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage) of Huggingface. However, during the excution I face a FileNotFoundError. I debug the code and it seems there is a bug there: So first it creates a .incomplete folder and before moving its contents the following code deletes the directory [Code](https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/builder.py#L984) hence as a result I face with: ``` FileNotFoundError: [Errno 2] No such file or directory: '~/data/Parquet/.incomplete '``` ### Steps to reproduce the bug ``` from datasets import load_dataset_builder from pathlib import Path parquet_dir = "~/data/Parquet/" Path(parquet_dir).mkdir(parents=True, exist_ok=True) builder = load_dataset_builder( "rotten_tomatoes", ) builder.download_and_prepare(parquet_dir, file_format="parquet") ``` ### Expected behavior Downloads the files and saves as parquet ### Environment info Ubuntu, Python 3.10 ``` datasets 2.19.1 ```

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I_kwDODunzps6Nb2Qh

IterableDataset: Unsupported ScalarType BFloat16

closed

NONE

### Describe the bug `IterableDataset.from_generator` crashes when using BFloat16: ``` File "/usr/local/lib/python3.11/site-packages/datasets/utils/_dill.py", line 169, in _save_torchTensor args = (obj.detach().cpu().numpy(),) ^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: Got unsupported ScalarType BFloat16 ``` ### Steps to reproduce the bug ```python import torch from datasets import IterableDataset def demo(x): yield {"x": x} x = torch.tensor([1.], dtype=torch.bfloat16) dataset = IterableDataset.from_generator( demo, gen_kwargs=dict(x=x), ) example = next(iter(dataset)) print(example) ``` ### Expected behavior Code sample should print: ```python {'x': tensor([1.], dtype=torch.bfloat16)} ``` ### Environment info ``` datasets==2.20.0 torch==2.2.2 ```

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PR_kwDODunzps5zd-ak

Remove tasks

open

MEMBER

Remove tasks, as part of the 3.0 release.

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PR_kwDODunzps5zddYG

Fix tests using hf-internal-testing/librispeech_asr_dummy

closed

MEMBER

Fix tests using hf-internal-testing/librispeech_asr_dummy once that dataset has been converted to Parquet. Fix #6997.

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I_kwDODunzps6NYVSv

CI is broken for tests using hf-internal-testing/librispeech_asr_dummy

closed

MEMBER

CI is broken: https://github.com/huggingface/datasets/actions/runs/9657882317/job/26637998686?pr=6996 ``` FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other'] Right contains one more item: 'other' Full diff: [ 'clean', - 'other', ] FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None ``` Note that repository was recently converted to Parquet: https://huggingface.co/datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a

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PR_kwDODunzps5zdAg0

Remove deprecated code

open

MEMBER

Remove deprecated code, as part of the 3.0 release. First merge: - [ ] #6983 - [x] #6987 - [ ] #6999

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I_kwDODunzps6NTjeD

ImportError when importing datasets.load_dataset

closed

NONE

### Describe the bug I encountered an ImportError while trying to import `load_dataset` from the `datasets` module in Hugging Face. The error message indicates a problem with importing 'CommitInfo' from 'huggingface_hub'. ### Steps to reproduce the bug 1. pip install git+https://github.com/huggingface/datasets 2. from datasets import load_dataset ### Expected behavior ImportError Traceback (most recent call last) Cell In[7], [line 1](vscode-notebook-cell:?execution_count=7&line=1) ----> [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset [3](vscode-notebook-cell:?execution_count=7&line=3) train_set = load_dataset("mispeech/speechocean762", split="train") [4](vscode-notebook-cell:?execution_count=7&line=4) test_set = load_dataset("mispeech/speechocean762", split="test") File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py:[1](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:1)7 1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors. [2](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:2) # [3](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:3) # Licensed under the Apache License, Version 2.0 (the "License"); (...) [12](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:12) # See the License for the specific language governing permissions and [13](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:13) # limitations under the License. [15](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:15) __version__ = "2.20.1.dev0" ---> [17](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:17) from .arrow_dataset import Dataset [18](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:18) from .arrow_reader import ReadInstruction [19](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:19) from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py:63 [61](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:61) import pyarrow.compute as pc [62](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:62) from fsspec.core import url_to_fs ---> [63](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:63) from huggingface_hub import ( [64](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:64) CommitInfo, [65](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:65) CommitOperationAdd, ... [70](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:70) ) [71](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:71) from huggingface_hub.hf_api import RepoFile [72](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:72) from multiprocess import Pool ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (d:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py) Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?580889ab-0f61-4f37-9214-eaa2b3807f85) or open in a [text editor](command:workbench.action.openLargeOutput?580889ab-0f61-4f37-9214-eaa2b3807f85). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)... ### Environment info Leo@DESKTOP-9NHUAMI MSYS /d/Anaconda3/envs/CS224S/Lib/site-packages/huggingface_hub $ datasets-cli env Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "D:\Anaconda3\envs\CS224S\Scripts\datasets-cli.exe\__main__.py", line 4, in <module> File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py", line 17, in <module> from .arrow_dataset import Dataset File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py", line 63, in <module> from huggingface_hub import ( ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py) (CS224S)

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PR_kwDODunzps5zYYXr

Fix incorrect rank value in data splitting

closed

CONTRIBUTOR

Fix #6990.

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PR_kwDODunzps5zYN7P

less script docs

closed

MEMBER

+ mark as legacy in some parts of the docs since we'll not build new features for script datasets

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I_kwDODunzps6NIyS-

Dataset with streaming doesn't work with proxy

open

NONE

### Describe the bug I'm currently trying to stream data using dataset since the dataset is too big but it hangs indefinitely without loading the first batch. I use AIMOS which is a supercomputer that uses proxy to connect to the internet. I assume it has to do with the network configurations. I've already set up both HTTP_PROXY and HTTPS_PROXY. streaming = False works fine. ### Steps to reproduce the bug use load_dataset with streaming = True in AIMOS ### Expected behavior does not hang indefinitely and loads batches to start training run ### Environment info _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge _pytorch_select 2.0 cuda_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 abseil-cpp 20220623.0 h9888cd1_6 conda-forge absl-py 1.0.0 py311h399429b_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 aiofiles 23.2.1 pyhd8ed1ab_0 conda-forge aiohttp 3.8.6 py311hf118e41_0 aiosignal 1.2.0 pyhd3eb1b0_0 archspec 0.2.3 pyhd8ed1ab_0 conda-forge arrow-cpp 11.0.0 ha3edaa6_5_cpu conda-forge async-timeout 4.0.2 py311h6ffa863_0 attrs 23.1.0 py311h6ffa863_0 av 10.0.0 py311he6153ed_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 aws-c-auth 0.6.24 hb81f6d7_5 conda-forge aws-c-cal 0.5.20 h3c2b4d9_6 conda-forge aws-c-common 0.8.11 h4194056_0 conda-forge aws-c-compression 0.2.16 ha19333d_3 conda-forge aws-c-event-stream 0.2.18 h12a9399_6 conda-forge aws-c-http 0.7.4 ha2cde00_2 conda-forge aws-c-io 0.13.17 h9189062_2 conda-forge aws-c-mqtt 0.8.6 h40d1a04_6 conda-forge aws-c-s3 0.2.4 hbdbe4f0_3 conda-forge aws-c-sdkutils 0.1.7 ha19333d_3 conda-forge aws-checksums 0.1.14 ha19333d_3 conda-forge aws-crt-cpp 0.19.7 hd018011_7 conda-forge aws-sdk-cpp 1.10.57 hb9575ba_4 conda-forge blas 1.0 openblas blinker 1.8.2 pyhd8ed1ab_0 conda-forge boltons 23.0.0 py311h6ffa863_0 boost-cpp 1.82.0 h25e6d66_2 bottleneck 1.3.5 py311h34f6284_0 brotli 1.0.9 hf118e41_7 brotli-bin 1.0.9 hf118e41_7 brotli-python 1.0.9 py311h4a02239_7 bzip2 1.0.8 h7b6447c_0 c-ares 1.19.1 hf118e41_0 ca-certificates 2024.6.2 h0f6029e_0 conda-forge cachetools 5.3.3 pyhd8ed1ab_0 conda-forge certifi 2024.6.2 pyhd8ed1ab_0 conda-forge cffi 1.15.1 py311hf118e41_3 charset-normalizer 2.0.4 pyhd3eb1b0_0 click 8.1.7 unix_pyh707e725_0 conda-forge conda 24.5.0 py311h1af927a_0 conda-forge conda-content-trust 0.2.0 py311h6ffa863_0 conda-libmamba-solver 23.11.1 py311h6ffa863_0 conda-package-handling 2.2.0 py311h6ffa863_0 conda-package-streaming 0.9.0 py311h6ffa863_0 contourpy 1.0.5 py311h25e6d66_0 cryptography 41.0.3 py311hb0e80e7_0 cudatoolkit 11.8.0 hedcfb66_13 conda-forge cudnn 8.9.2_11.8 h9ceb136_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 cycler 0.11.0 pyhd3eb1b0_0 datasets 2.12.0 py311h6ffa863_0 dill 0.3.6 py311h6ffa863_0 distro 1.9.0 pyhd8ed1ab_0 conda-forge ffmpeg 4.2.2 opence_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 filelock 3.9.0 py311h6ffa863_0 fmt 9.1.0 h25e6d66_0 fonttools 4.25.0 pyhd3eb1b0_0 freetype 2.12.1 hd23a775_0 frozendict 2.4.4 py311hb02d432_0 conda-forge frozenlist 1.4.0 py311hf118e41_0 fsspec 2023.9.2 py311h6ffa863_0 gflags 2.2.2 he6710b0_0 giflib 5.2.1 hf118e41_3 glog 0.6.0 hbe088e0_0 conda-forge gmp 6.3.0 h46f38da_0 conda-forge gmpy2 2.1.5 py311h2758da7_1 conda-forge google-auth 2.30.0 pyhff2d567_0 conda-forge google-auth-oauthlib 0.5.3 pyhd8ed1ab_0 conda-forge grpc-cpp 1.51.1 h8ba971d_1 conda-forge grpcio 1.54.3 py311h414e0d3_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 huggingface_hub 0.17.3 py311h6ffa863_0 icu 73.1 h4a02239_0 idna 3.4 py311h6ffa863_0 importlib-metadata 6.0.0 py311h6ffa863_0 jinja2 3.1.4 pyhd8ed1ab_0 conda-forge jpeg 9e hf118e41_1 jsonpatch 1.32 pyhd3eb1b0_0 jsonpointer 2.1 pyhd3eb1b0_0 kiwisolver 1.4.4 py311h4a02239_0 krb5 1.20.1 hc019ccd_1 lame 3.100 hb283c62_1003 conda-forge lcms2 2.12 h2045e0b_0 ld_impl_linux-ppc64le 2.38 hec883e6_1 lerc 3.0 h29c3540_0 leveldb 1.23 h24532b4_1 conda-forge libabseil 20220623.0 cxx17_h9235812_6 conda-forge libarchive 3.6.2 hd8ab008_2 libarrow 11.0.0 h837770b_5_cpu conda-forge libboost 1.82.0 haf51a6a_2 libbrotlicommon 1.0.9 hf118e41_7 libbrotlidec 1.0.9 hf118e41_7 libbrotlienc 1.0.9 hf118e41_7 libcrc32c 1.1.2 h3b9df90_0 conda-forge libcurl 8.4.0 h4d62439_0 libdeflate 1.17 hf118e41_1 libedit 3.1.20221030 hf118e41_0 libev 4.33 h140841e_1 libevent 2.1.10 h19c23f1_4 conda-forge libexpat 2.6.2 h46f38da_0 conda-forge libffi 3.4.4 h4a02239_0 libgcc-ng 13.2.0 h31e42bb_10 conda-forge libgfortran-ng 11.2.0 hb3889a9_1 libgfortran5 11.2.0 h1234567_1 libgomp 13.2.0 h31e42bb_10 conda-forge libgoogle-cloud 2.7.0 h11140b6_1 conda-forge libgrpc 1.51.1 h4d29a31_1 conda-forge libmamba 1.5.3 h7c6fafd_0 libmambapy 1.5.3 py311h828bf7b_0 libnghttp2 1.57.0 h44e5816_0 libnsl 2.0.1 ha17a0cc_0 conda-forge libopenblas 0.3.23 hc5a31fb_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 libopus 1.3.1 h4e0d66e_1 conda-forge libpng 1.6.39 hf118e41_0 libprotobuf 3.21.12 h1776448_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 libsolv 0.7.24 h0f529ac_0 libsqlite 3.45.3 hd4bbf49_0 conda-forge libssh2 1.10.0 h50fa78f_2 libstdcxx-ng 13.2.0 h262982c_10 conda-forge libthrift 0.18.0 h82f1162_0 conda-forge libtiff 4.5.1 h4a02239_0 libutf8proc 2.8.0 hb283c62_0 conda-forge libuuid 2.38.1 h4194056_0 conda-forge libvpx 1.13.1 h46f38da_0 conda-forge libwebp 1.3.2 h0f96ee2_0 libwebp-base 1.3.2 hf118e41_0 libxcrypt 4.4.36 ha17a0cc_1 conda-forge libxml2 2.10.4 h18e3229_1 libzlib 1.2.13 h1f2b957_6 conda-forge llvm-openmp 14.0.6 hc028133_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 lmdb 0.9.31 ha17a0cc_1 conda-forge lz4-c 1.9.4 h4a02239_0 markdown 3.4.4 pyhd8ed1ab_0 conda-forge markupsafe 2.1.5 py311h32d8acf_0 conda-forge matplotlib 3.8.0 py311h6ffa863_0 matplotlib-base 3.8.0 py311h52e1fcc_0 menuinst 2.1.1 py311h1af927a_0 conda-forge mpc 1.3.1 heaf1863_0 conda-forge mpfr 4.2.1 haad2271_1 conda-forge mpmath 1.3.0 pyhd8ed1ab_0 conda-forge multidict 6.0.2 py311hf118e41_0 multiprocess 0.70.14 py311h6ffa863_0 munkres 1.1.4 py_0 mypy_extensions 1.0.0 pyha770c72_0 conda-forge nccl 2.18.3 cuda11.8_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 ncurses 6.4 h4a02239_0 nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge networkx 2.8.8 pyhd8ed1ab_0 conda-forge nomkl 3.0 0 https://ftp.osuosl.org/pub/open-ce/1.10.0 numactl 2.0.16 hba61f60_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 numexpr 2.8.7 py311hc46fc55_0 numpy 1.24.3 py311h148a09e_0 numpy-base 1.24.3 py311h06b82f6_0 oauthlib 3.2.2 pyhd8ed1ab_0 conda-forge openjpeg 2.4.0 hfe35807_0 openssl 3.3.1 h1f2b957_0 conda-forge orc 1.8.2 h341c9a4_2 conda-forge packaging 23.1 py311h6ffa863_0 pandas 2.1.1 py311h52e1fcc_0 pcre2 10.42 h280155c_0 pillow 10.0.1 py311he33076b_0 pip 23.3 py311h6ffa863_0 platformdirs 4.2.2 pyhd8ed1ab_0 conda-forge pluggy 1.0.0 py311h6ffa863_1 pooch 1.8.2 pyhd8ed1ab_0 conda-forge protobuf 4.21.12 py311ha7baec7_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 psutil 5.9.8 py311hd26027c_0 conda-forge pyarrow 11.0.0 py311h04a18d5_1 pyasn1 0.6.0 pyhd8ed1ab_0 conda-forge pyasn1-modules 0.4.0 pyhd8ed1ab_0 conda-forge pybind11-abi 4 hd3eb1b0_1 pycosat 0.6.6 py311hf118e41_0 pycparser 2.21 pyhd3eb1b0_0 pyjwt 2.8.0 pyhd8ed1ab_1 conda-forge pyopenssl 23.2.0 py311h6ffa863_0 pyparsing 3.0.9 py311h6ffa863_0 pyre-extensions 0.0.30 pyhd8ed1ab_0 conda-forge pysocks 1.7.1 py311h6ffa863_0 python 3.11.8 h3332dee_0_cpython conda-forge python-dateutil 2.8.2 pyhd3eb1b0_0 python-tzdata 2023.3 pyhd3eb1b0_0 python-xxhash 2.0.2 py311hf118e41_1 python_abi 3.11 4_cp311 conda-forge pytorch 2.0.1 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 pytorch-base 2.0.1 cuda11.8_py311_pb4.21.12_4 https://ftp.osuosl.org/pub/open-ce/1.10.0 pytz 2023.3.post1 py311h6ffa863_0 pyu2f 0.1.5 pyhd8ed1ab_0 conda-forge pyyaml 6.0.1 py311hf118e41_0 re2 2023.02.01 h883269e_0 conda-forge readline 8.2 hf118e41_0 regex 2023.10.3 py311hf118e41_0 reproc 14.2.4 h29c3540_1 reproc-cpp 14.2.4 h29c3540_1 requests 2.31.0 py311h6ffa863_0 requests-oauthlib 2.0.0 pyhd8ed1ab_0 conda-forge responses 0.13.3 pyhd3eb1b0_0 rsa 4.9 pyhd8ed1ab_0 conda-forge ruamel.yaml 0.17.21 py311hf118e41_0 s2n 1.3.37 h5e47323_0 conda-forge safetensors 0.4.0 py311hda16d9e_0 scipy 1.11.1 py311hd69e9bb_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 sentencepiece 0.1.97 h1e74c73_py311_pb4.21.12_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 setuptools 68.0.0 py311h6ffa863_0 six 1.16.0 pyhd3eb1b0_1 snappy 1.1.9 h29c3540_0 sqlite 3.41.2 hf118e41_0 sympy 1.12.1 pypyh2585a3b_103 conda-forge tabulate 0.8.10 pyhd8ed1ab_0 conda-forge tensorboard 2.13.0 pyhab0730d_pb4.21.12_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 tensorboard-data-server 0.7.0 pyh6f84499_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 tensorboard-plugin-wit 1.6.0 pyh9f0ad1d_0 conda-forge tk 8.6.13 hd4bbf49_0 conda-forge tokenizers 0.13.3 py311h3d4f45a_0 torchdata 0.6.0 py311_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 torchsnapshot 0.1.0 pyhd8ed1ab_0 conda-forge torchtext-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 torchtnt 0.2.4 pyhd8ed1ab_0 conda-forge torchvision-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 tornado 6.3.3 py311hf118e41_0 tqdm 4.65.0 py311h7837921_0 transformers 4.32.1 py311h6ffa863_0 truststore 0.8.0 py311h6ffa863_0 typing-extensions 4.7.1 py311h6ffa863_0 typing_extensions 4.7.1 py311h6ffa863_0 typing_inspect 0.9.0 pyhd8ed1ab_0 conda-forge tzdata 2023c h04d1e81_0 urllib3 1.26.18 py311h6ffa863_0 utf8proc 2.6.1 h140841e_0 werkzeug 2.3.8 pyhd8ed1ab_0 conda-forge wheel 0.41.2 py311h6ffa863_0 xxhash 0.8.0 h140841e_3 xz 5.4.2 hf118e41_0 yaml 0.2.5 h7b6447c_0 yaml-cpp 0.8.0 h4a02239_0 yarl 1.8.1 py311hf118e41_0 zipp 3.11.0 py311h6ffa863_0 zlib 1.2.13 h1f2b957_6 conda-forge zstandard 0.19.0 py311hf118e41_0 zstd 1.5.5 h57e4825_0

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PR_kwDODunzps5zPoQs

Unblock NumPy 2.0

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Fixes https://github.com/huggingface/datasets/issues/6980

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I_kwDODunzps6NEGCx

Problematic rank after calling `split_dataset_by_node` twice

closed

CONTRIBUTOR

### Describe the bug I'm trying to split `IterableDataset` by `split_dataset_by_node`. But when doing split on a already split dataset, the resulting `rank` is greater than `world_size`. ### Steps to reproduce the bug Here is the minimal code for reproduction: ```py >>> from datasets import load_dataset >>> from datasets.distributed import split_dataset_by_node >>> dataset = load_dataset('fla-hub/slimpajama-test', split='train', streaming=True) >>> dataset = split_dataset_by_node(dataset, 1, 32) >>> dataset._distributed DistributedConfig(rank=1, world_size=32) >>> dataset = split_dataset_by_node(dataset, 1, 15) >>> dataset._distributed DistributedConfig(rank=481, world_size=480) ``` As you can see, the second rank 481 > 480, which is problematic. ### Expected behavior I think this error comes from this line @lhoestq https://github.com/huggingface/datasets/blob/a6ccf944e42c1a84de81bf326accab9999b86c90/src/datasets/iterable_dataset.py#L2943-L2944 We may need to obtain the rank first. Then the above code gives ```py >>> dataset._distributed DistributedConfig(rank=16, world_size=480) ``` ### Environment info datasets==2.20.0

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I_kwDODunzps6M_4bh

cache in nfs error

open

NONE

### Describe the bug - When reading dataset, a cache will be generated to the ~/. cache/huggingface/datasets directory - When using .map and .filter operations, runtime cache will be generated to the /tmp/hf_datasets-* directory - The default is to use the path of tempfile.tempdir - If I modify this path to the NFS disk, an error will be reported, but the program will continue to run - https://github.com/huggingface/datasets/blob/main/src/datasets/config.py#L257 ``` Traceback (most recent call last): File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap self.run() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server server.serve_forever() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever sys.exit(0) SystemExit: 0 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers finalizer() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__ res = self._callback(*self._args, **self._kwargs) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir rmtree(tempdir) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree _rmtree_safe_fd(fd, path, onerror) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd onerror(os.unlink, fullname, sys.exc_info()) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd os.unlink(entry.name, dir_fd=topfd) OSError: [Errno 16] Device or resource busy: '.nfs000000038330a012000030b4' Traceback (most recent call last): File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap self.run() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server server.serve_forever() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever sys.exit(0) SystemExit: 0 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers finalizer() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__ res = self._callback(*self._args, **self._kwargs) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir rmtree(tempdir) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree _rmtree_safe_fd(fd, path, onerror) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd onerror(os.unlink, fullname, sys.exc_info()) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd os.unlink(entry.name, dir_fd=topfd) OSError: [Errno 16] Device or resource busy: '.nfs0000000400064d4a000030e5' ``` ### Steps to reproduce the bug ``` import os import time import tempfile from datasets import load_dataset def add_column(sample): # print(type(sample)) # time.sleep(0.1) sample['__ds__stats__'] = {'data': 123} return sample def filt_column(sample): # print(type(sample)) if len(sample['content']) > 10: return True else: return False if __name__ == '__main__': input_dir = '/mnt/temp/CN/small' # some json dataset dataset = load_dataset('json', data_dir=input_dir) temp_dir = '/media/release/release/temp/temp' # a nfs folder os.makedirs(temp_dir, exist_ok=True) # change huggingface-datasets runtime cache in nfs（default in /tmp） tempfile.tempdir = temp_dir aa = dataset.map(add_column, num_proc=64) aa = aa.filter(filt_column, num_proc=64) print(aa) ``` ### Expected behavior no error occur ### Environment info datasets==2.18.0 ubuntu 20.04

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PR_kwDODunzps5zDpXX

[`feat`] Move dataset card creation to method for easier overriding

open

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Hello! ## Pull Request overview * Move dataset card creation to method for easier overriding ## Details It's common for me to fully automatically download, reformat, and upload a dataset (e.g. see https://huggingface.co/datasets?other=sentence-transformers), but one aspect that I cannot easily automate is the dataset card generation. This is because during `push_to_hub`, the dataset card is created in 3 lines of code in a much larger method. To automatically generate a dataset card, I need to either: 1. Subclass `Dataset`/`DatasetDict`, copy the entire `push_to_hub` method to override the ~3 lines used to generate the dataset card. This is not viable as the method is likely to change over time. 2. Use `push_to_hub` normally, then separately download the pushed (but empty) dataset card, update it, and reupload the modified dataset. This works fine, but prevents me from being able to return a `Dataset` to my users which will automatically use a nice dataset card. So, in this PR I'm proposing to move the dataset generation into another method so that it can be overridden more easily. For example, imagine the following use case: ````python import json from typing import Any, Dict, Optional from datasets import Dataset, load_dataset from datasets.info import DatasetInfosDict, DatasetInfo from datasets.utils.metadata import MetadataConfigs from huggingface_hub import DatasetCardData, DatasetCard TEMPLATE = r"""--- {dataset_card_data} --- # Dataset Card for {source_dataset_name} with mined hard negatives This dataset is a collection of {column_one}-{column_two}-negative triplets from the {source_dataset_name} dataset. See [{source_dataset_name}](https://huggingface.co/datasets/{source_dataset_id}) for additional information. This dataset can be used directly with Sentence Transformers to train embedding models. ## Mining Parameters The negative samples have been mined using the following parameters: - `range_min`: {range_min}, i.e. we skip the {range_min} most similar samples - `range_max`: {range_max}, i.e. we only look at the top {range_max} most similar samples - `margin`: {margin}, i.e. we require negative similarity + margin < positive similarity, so negative samples can't be more similar than the known true answer - `sampling_strategy`: {sampling_strategy}, i.e. whether to randomly sample from the candidate negatives or take the "top" negatives - `num_negatives`: {num_negatives}, i.e. we mine {num_negatives} negatives per question-answer pair ## Dataset Format - Columns: {column_one}, {column_two}, negative - Column types: str, str, str - Example: ```python {example} ``` """ class HNMDataset(Dataset): @classmethod def from_dict(cls, *args, mining_kwargs: Dict[str, Any], **kwargs) -> "HNMDataset": dataset = super().from_dict(*args, **kwargs) dataset.mining_kwargs = mining_kwargs return dataset def _create_dataset_card( self, dataset_card_data: DatasetCardData, dataset_card: Optional[DatasetCard], config_name: str, info_to_dump: DatasetInfo, metadata_config_to_dump: MetadataConfigs, ) -> DatasetCard: if dataset_card: return dataset_card DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data) MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data) dataset_card_data.tags = ["sentence-transformers"] dataset_name = self.mining_kwargs["source_dataset"].info.dataset_name # Very messy, just as an example: dataset_id = list(self.mining_kwargs["source_dataset"].info.download_checksums.keys())[0].removeprefix("hf://datasets/").split("@")[0] content = TEMPLATE.format(**{ "dataset_card_data": str(dataset_card_data), "source_dataset_name": dataset_name, "source_dataset_id": dataset_id, "range_min": self.mining_kwargs["range_min"], "range_max": self.mining_kwargs["range_max"], "margin": self.mining_kwargs["margin"], "sampling_strategy": self.mining_kwargs["sampling_strategy"], "num_negatives": self.mining_kwargs["num_negatives"], "column_one": self.column_names[0], "column_two": self.column_names[1], "example": json.dumps(self[0], indent=4), }) return DatasetCard(content) source_dataset = load_dataset("sentence-transformers/gooaq", split="train[:100]") dataset = HNMDataset.from_dict({ "query": source_dataset["question"], "answer": source_dataset["answer"], # "negative": ... <- In my case, this column would be 'mined' automatically with these parameters }, mining_kwargs={ "range_min": 10, "range_max": 20, "max_score": 0.9, "margin": 0.1, "sampling_strategy": "random", "num_negatives": 3, "source_dataset": source_dataset, }) dataset.push_to_hub("tomaarsen/mining_demo", private=True) ```` In this script, I've created a subclass which stores some additional information about how the dataset was generated. It's a bit hacky (e.g. setting a `mining_kwargs` parameter in `from_dict` that wasn't created in `__init__`, but that's just a consequence of how the `from_...` methods don't accept kwargs), but it allows me to create a "hard negatives mining" function that returns a dataset which people can use locally like normal, but if they choose to upload it, then it'll automatically include some information, e.g.: https://huggingface.co/datasets/tomaarsen/mining_demo This allows others to actually find this dataset (e.g. via the `sentence-transformers` tag) and get an idea of the quality, source, etc. by looking at the model card. ## Note I'm not fixed on this solution whatsoever: I am also completely fine with other solutions, e.g. a `dataset.set_dataset_card_creator` method that allows me to provide a function without even having to subclass anything. I'm open to all ideas :) cc @albertvillanova @lhoestq cc @LysandreJik - Tom Aarsen

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PR_kwDODunzps5zCRH6

Remove beam

closed

MEMBER

Remove beam, as part of the 3.0 release.

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PR_kwDODunzps5y-Zi0

Add large_list type support in string_to_arrow

open

NONE

add large_list type support in string_to_arrow() and _arrow_to_datasets_dtype() in features.py Fix #6984

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I_kwDODunzps6Mzwgk

AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'

closed

NONE

### Describe the bug I have been struggling with this for two days, any help would be appreciated. Python 3.10 ``` from setfit import SetFitModel from huggingface_hub import login access_token_read = "cccxxxccc" # Authenticate with the Hugging Face Hub login(token=access_token_read) # Load the models from the Hugging Face Hub trainer_relv = SetFitModel.from_pretrained("snowdere/trainer_relevance") trainer_trust = SetFitModel.from_pretrained("snowdere/trainer_trust") trainer_sent = SetFitModel.from_pretrained("snowdere/trainer_sent") trainer_topic = SetFitModel.from_pretrained("snowdere/trainer_topic") ``` ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[6], line 1 ----> 1 from setfit import SetFitModel 2 from huggingface_hub import login 4 access_token_read = "ccsddsds" File /opt/conda/lib/python3.10/site-packages/setfit/__init__.py:7 4 import os 5 import warnings ----> 7 from .data import get_templated_dataset, sample_dataset 8 from .model_card import SetFitModelCardData 9 from .modeling import SetFitHead, SetFitModel File /opt/conda/lib/python3.10/site-packages/setfit/data.py:5 3 import pandas as pd 4 import torch ----> 5 from datasets import Dataset, DatasetDict, load_dataset 6 from torch.utils.data import Dataset as TorchDataset 8 from . import logging File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18 1 # ruff: noqa 2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors. 3 # (...) 13 # See the License for the specific language governing permissions and 14 # limitations under the License. 16 __version__ = "2.19.0" ---> 18 from .arrow_dataset import Dataset 19 from .arrow_reader import ReadInstruction 20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:76 73 from tqdm.contrib.concurrent import thread_map 75 from . import config ---> 76 from .arrow_reader import ArrowReader 77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 78 from .data_files import sanitize_patterns File /opt/conda/lib/python3.10/site-packages/datasets/arrow_reader.py:29 26 from typing import TYPE_CHECKING, List, Optional, Union 28 import pyarrow as pa ---> 29 import pyarrow.parquet as pq 30 from tqdm.contrib.concurrent import thread_map 32 from .download.download_config import DownloadConfig File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20 1 # Licensed to the Apache Software Foundation (ASF) under one 2 # or more contributor license agreements. See the NOTICE file 3 # distributed with this work for additional information (...) 17 18 # flake8: noqa ---> 20 from .core import * File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33 30 import pyarrow as pa 32 try: ---> 33 import pyarrow._parquet as _parquet 34 except ImportError as exc: 35 raise ImportError( 36 "The pyarrow installation is not built with support " 37 f"for the Parquet file format ({str(exc)})" 38 ) from None File /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet() AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType' ``` setfit: 1.0.3 transformers: 4.41.2 lingua-language-detector: 2.0.2 polars: 0.20.31 lightning: None google-cloud-bigquery: 3.24.0 shapely: 2.0.4 pyarrow: 16.0.0 ### Steps to reproduce the bug I have tried all version combinations for Dataset and Pyarrow, the all have the same error since a few days ago. This is accross multiple scripts I have. ### Expected behavior Just ron normally. ### Environment info 3.10

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I_kwDODunzps6My3NC

Convert polars DataFrame back to datasets

open

NONE

### Feature request This returns error. ```python from datasets import Dataset dsdf = Dataset.from_dict({"x": [[1, 2], [3, 4, 5]], "y": ["a", "b"]}) Dataset.from_polars(dsdf.to_polars()) ``` ValueError: Arrow type large_list<item: int64> does not have a datasets dtype equivalent. ### Motivation When datasets contain Sequence data type, it will be converted to Arrow type large_list. However, the reverse (from large_list to Sequence) does not work. ### Your contribution No

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PR_kwDODunzps5y7tK7

Remove metrics

closed

MEMBER

Remove all metrics, as part of the 3.0 release. Note they are deprecated since 2.5.0 version.

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I_kwDODunzps6MxBgd

cannot split dataset when using load_dataset

open

NONE

### Describe the bug when I use load_dataset methods to load mozilla-foundation/common_voice_7_0, it can successfully download and extracted the dataset but It cannot generating the arrow document, This bug happened in my server, my laptop, so as #6906 , but it won't happen in the google colab. I work for it for days, even I load the datasets from local path, it can Generating train split and validation split but bug happen again in test split. ### Steps to reproduce the bug from datasets import load_dataset, load_metric, Audio common_voice_train = load_dataset("mozilla-foundation/common_voice_7_0", "ja", split="train", token=selftoken, trust_remote_code=True) ### Expected behavior ``` { "name": "ValueError", "message": "Instruction \"train\" corresponds to no data!", "stack": "--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[2], line 3 1 from datasets import load_dataset, load_metric, Audio ----> 3 common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\",token='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')#,trust_remote_code=True)#,streaming=True) 4 common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"test\",token='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')#,trust_remote_code=True)#,streaming=True) File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\load.py:2626, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2622 # Build dataset for splits 2623 keep_in_memory = ( 2624 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2625 ) -> 2626 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2627 # Rename and cast features to match task schema 2628 if task is not None: 2629 # To avoid issuing the same warning twice File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1266, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1263 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1265 # Create a dataset for each of the given splits -> 1266 datasets = map_nested( 1267 partial( 1268 self._build_single_dataset, 1269 run_post_process=run_post_process, 1270 verification_mode=verification_mode, 1271 in_memory=in_memory, 1272 ), 1273 split, 1274 map_tuple=True, 1275 disable_tqdm=True, 1276 ) 1277 if isinstance(datasets, dict): 1278 datasets = DatasetDict(datasets) File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\utils\\py_utils.py:484, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc) 482 if batched: 483 data_struct = [data_struct] --> 484 mapped = function(data_struct) 485 if batched: 486 mapped = mapped[0] File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1296, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory) 1293 split = Split(split) 1295 # Build base dataset -> 1296 ds = self._as_dataset( 1297 split=split, 1298 in_memory=in_memory, 1299 ) 1300 if run_post_process: 1301 for resource_file_name in self._post_processing_resources(split).values(): File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1370, in DatasetBuilder._as_dataset(self, split, in_memory) 1368 if self._check_legacy_cache(): 1369 dataset_name = self.name -> 1370 dataset_kwargs = ArrowReader(cache_dir, self.info).read( 1371 name=dataset_name, 1372 instructions=split, 1373 split_infos=self.info.splits.values(), 1374 in_memory=in_memory, 1375 ) 1376 fingerprint = self._get_dataset_fingerprint(split) 1377 return Dataset(fingerprint=fingerprint, **dataset_kwargs) File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\arrow_reader.py:256, in BaseReader.read(self, name, instructions, split_infos, in_memory) 254 msg = f'Instruction \"{instructions}\" corresponds to no data!' 255 #msg = f'Instruction \"{self._path}\",\"{name}\",\"{instructions}\",\"{split_infos}\" corresponds to no data!' --> 256 raise ValueError(msg) 257 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ValueError: Instruction \"train\" corresponds to no data!" } ``` ### Environment info Environment: python 3.9 windows 11 pro VScode+jupyter

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PR_kwDODunzps5y6tnN

Update docs on trust_remote_code defaults to False

closed

MEMBER

Update docs on trust_remote_code defaults to False. The docs needed to be updated due to this PR: - #6954

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I_kwDODunzps6MuKBq

Support NumPy 2.0

open

NONE

### Feature request Support NumPy 2.0. ### Motivation NumPy introduces the Array API, which bridges the gap between machine learning libraries. Many clients of HuggingFace are eager to start using the Array API. Besides that, NumPy 2 provides a cleaner interface than NumPy 1. ### Tasks NumPy 2.0 was released for testing so that libraries could ensure compatibility [since mid-March](https://github.com/numpy/numpy/issues/24300#issuecomment-1986815755). What needs to be done for HuggingFace to support Numpy 2? - [x] Fix use of `array`: https://github.com/huggingface/datasets/pull/6976 - [ ] Remove [NumPy version limit](https://github.com/huggingface/datasets/pull/6975): https://github.com/huggingface/datasets/pull/6991

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I_kwDODunzps6MrWsD

How can I load partial parquet files only?

closed

NONE

I have a HUGE dataset about 14TB, I unable to download all parquet all. I just take about 100 from it. dataset = load_dataset("xx/", data_files="data/train-001*-of-00314.parquet") How can I just using 000 - 100 from a 00314 from all partially? I search whole net didn't found a solution, **this is stupid if they didn't support it, and I swear I wont using stupid parquet any more**

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PR_kwDODunzps5yz0h6

Fix regression for pandas < 2.0.0 in JSON loader

closed

MEMBER

A regression was introduced for pandas < 2.0.0 in PR: - #6914 As described in pandas docs, the `dtype_backend` parameter was first added in pandas 2.0.0: https://pandas.pydata.org/docs/reference/api/pandas.read_json.html This PR fixes the regression by passing (or not) the `dtype_backend` parameter depending on pandas version. Maybe, in a future 3.0 `datasets` release, we could just require pandas > 2.0. Reported by: - #6977

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I_kwDODunzps6Mn_xF

load json file error with v2.20.0

closed

NONE

### Describe the bug ``` load_dataset(path="json", data_files="./test.json") ``` ``` Generating train split: 0 examples [00:00, ? examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 132, in _generate_tables pa_table = paj.read_json( File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1997, in _prepare_split_single for _, table in generator: File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 155, in _generate_tables df = pd.read_json(f, dtype_backend="pyarrow") File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper return func(*args, **kwargs) TypeError: read_json() got an unexpected keyword argument 'dtype_backend' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/app/t1.py", line 11, in <module> load_dataset(path=data_path, data_files="./t2.json") File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2616, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1029, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1124, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1884, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 2040, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset ``` ``` import pandas as pd with open("./test.json", "r") as f: df = pd.read_json(f, dtype_backend="pyarrow") ``` ``` Traceback (most recent call last): File "/app/t3.py", line 3, in <module> df = pd.read_json(f, dtype_backend="pyarrow") File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper return func(*args, **kwargs) TypeError: read_json() got an unexpected keyword argument 'dtype_backend' ``` ### Steps to reproduce the bug . ### Expected behavior . ### Environment info ``` datasets 2.20.0 pandas 1.5.3 ```

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PR_kwDODunzps5yrmNP

Ensure compatibility with numpy 2.0.0

closed

CONTRIBUTOR

Following the conversion guide, copy=False is no longer required and will result in an error: https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword. The following fix should resolve the issue. error found during testing on the MTEB repository e.g. [here](https://github.com/embeddings-benchmark/mteb/pull/938)

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PR_kwDODunzps5yrPct

Set temporary numpy upper version < 2.0.0 to fix CI

closed

MEMBER

Set temporary numpy upper version < 2.0.0 to fix CI. See: https://github.com/huggingface/datasets/actions/runs/9546031216/job/26308072017 ``` A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.0 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. ```

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I_kwDODunzps6MZley

IndexError during training with Squad dataset and T5-small model

open

NONE

### Describe the bug I am encountering an IndexError while training a T5-small model on the Squad dataset using the transformers and datasets libraries. The error occurs even with a minimal reproducible example, suggesting a potential bug or incompatibility. ### Steps to reproduce the bug 1.Install the required libraries: !pip install transformers datasets 2.Run the following code: !pip install transformers datasets import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer, DataCollatorWithPadding # Load a small, publicly available dataset from datasets import load_dataset dataset = load_dataset("squad", split="train[:100]") # Use a small subset for testing # Load a pre-trained model and tokenizer model_name = "t5-small" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) # Define a basic data collator data_collator = DataCollatorWithPadding(tokenizer=tokenizer) # Define training arguments training_args = TrainingArguments( output_dir="./results", per_device_train_batch_size=2, num_train_epochs=1, ) # Create a trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset, data_collator=data_collator, ) # Train the model trainer.train() ### Expected behavior --------------------------------------------------------------------------- IndexError Traceback (most recent call last) [<ipython-input-23-f13a4b23c001>](https://localhost:8080/#) in <cell line: 34>() 32 33 # Train the model ---> 34 trainer.train() 10 frames [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size) 427 if isinstance(key, int): 428 if (key < 0 and key + size < 0) or (key >= size): --> 429 raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") 430 return 431 elif isinstance(key, slice): IndexError: Invalid key: 42 is out of bounds for size 0 ### Environment info transformers version:4.41.2 datasets version:1.18.4 Python version:3.10.12

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PR_kwDODunzps5yfa_e

Fix webdataset pickling

closed

MEMBER

...by making tracked iterables picklable. This is important to make streaming datasets compatible with multiprocessing e.g. for parallel data loading

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PR_kwDODunzps5yZoc3

packaging: Remove useless dependencies

closed

CONTRIBUTOR

Revert changes in #6396 and #6404. CVE-2023-47248 has been fixed since PyArrow v14.0.1. Meanwhile Python requirements requires `pyarrow>=15.0.0`.

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PR_kwDODunzps5yYF37

Set dev version

closed

MEMBER

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PR_kwDODunzps5yX_nC

Release: 2.20.0

closed

MEMBER

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PR_kwDODunzps5yX7Qr

Use `HF_HUB_OFFLINE` instead of `HF_DATASETS_OFFLINE`

closed

CONTRIBUTOR

To use `datasets` offline, one can use the `HF_DATASETS_OFFLINE` environment variable. This PR makes `HF_HUB_OFFLINE` the recommended environment variable for offline training. Goal is to be more consistent with the rest of HF ecosystem and have a single config value to set. The changes are backward-compatible meaning that: - `HF_DATASETS_OFFLINE` environment is still taken into account, though not documented - `datasets.config.HF_DATASETS_OFFLINE` still exists, though it is not used anymore (in favor of `datasets.config.HF_HUB_OFFLINE`) **Note:** it might break things in downstream libraries if they were monkeypatching `datasets.config.HF_DATASETS_OFFLINE` in their CI tests (for instance). Not much of a problem IMO.

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I_kwDODunzps6MBSEe

Method to load Laion400m

open

NONE

### Feature request Large datasets like Laion400m are provided as embeddings. The provided methods in load_dataset are not straightforward for loading embedding files, i.e. img_emb_XX.npy ; XX = 0 to 99 ### Motivation The trial and experimentation is the key pivot of HF. It would be great if HF can load embeddings files s,ealessly. ### Your contribution I cam write the loader with some help.

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PR_kwDODunzps5yPwL4

Remove underlines between badges

closed

CONTRIBUTOR

## Before: <img width="935" alt="image" src="https://github.com/huggingface/datasets/assets/35881688/93666e72-059b-4180-9e1d-ff176a3d9dac"> ## After: <img width="956" alt="image" src="https://github.com/huggingface/datasets/assets/35881688/75df7c3e-f473-44f0-a872-eeecf6a85fe2">

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PR_kwDODunzps5yOyNG

Improve skip take shuffling and distributed

closed

MEMBER

set the right behavior of skip/take depending on whether it's called after or before shuffle/split_by_node

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PR_kwDODunzps5yCNGa

Fix resuming arrow format

closed

MEMBER

following https://github.com/huggingface/datasets/pull/6658

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PR_kwDODunzps5x_yu-

[Streaming] retry on requests errors

closed

MEMBER

reported in https://discuss.huggingface.co/t/speeding-up-streaming-of-large-datasets-fineweb/90714/6 when training using a streaming a dataloader cc @Wauplin it looks like the retries from `hfh` are not always enough. In this PR I let `datasets` do additional retries (that users can configure in `datasets.config`) since I couldn't find an easy way to increase the max_retries for `hfh` users in general.

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PR_kwDODunzps5x8yHt

fix(ci): remove unnecessary permissions

closed

MEMBER

### What does this PR do? Remove unnecessary permissions granted to the actions workflow. Sorry for the mishap.

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I_kwDODunzps6LmG0S

Manual downloads should count as downloads

open

NONE

### Feature request I would like to request that manual downloads of data files from Hugging Face dataset repositories count as downloads of a dataset. According to the documentation for the Hugging Face Hub, that is currently not the case: https://huggingface.co/docs/hub/en/datasets-download-stats ### Motivation This would ensure that downloads are accurately reported to end users. ### Your contribution N/A

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PR_kwDODunzps5x0R3T

feat(ci): add trufflehog secrets detection

closed

MEMBER

### What does this PR do? Adding a GH action to scan for leaked secrets on each commit.

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PR_kwDODunzps5xyVt6

Better error handling in `dataset_module_factory`

closed

CONTRIBUTOR

cc @cakiki who reported it on [slack](https://huggingface.slack.com/archives/C039P47V1L5/p1717754405578539) (private link) This PR updates how errors are handled in `dataset_module_factory` when the `dataset_info` cannot be accessed: 1. Use multiple `except ... as e` instead of using `isinstance(e, ...)` 2. Always raise `DatasetNotFoundError` with `from e` so that the initial error is explicitly logged in the stacktrace. 3. Differentiate `RepoNotFoundError` / `GatedRepoError` / `RevisionNotFoundError` cases

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I_kwDODunzps6LUw8f

My Private Dataset doesn't exist on the Hub or cannot be accessed

open

NONE

### Describe the bug ``` File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 1852, in dataset_module_factory raise DatasetNotFoundError(msg + f" at revision '{revision}'" if revision else msg) datasets.exceptions.DatasetNotFoundError: Dataset 'xxx' doesn't exist on the Hub or cannot be accessed >>> dataset = load_dataset("xxxx", token=True) 404 error 404 Client Error. (Request ID: Root=xxxx) Repository Not Found for url: https://huggingface.co/api/datasets/xxx/xxx. Please make sure you specified the correct `repo_id` and `repo_type`. If you are trying to access a private or gated repo, make sure you are authenticated. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 2593, in load_dataset builder_instance = load_dataset_builder( File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 2265, in load_dataset_builder dataset_module = dataset_module_factory( File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 1910, in dataset_module_factory raise e1 from None File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 1852, in dataset_module_factory raise DatasetNotFoundError(msg + f" at revision '{revision}'" if revision else msg) datasets.exceptions.DatasetNotFoundError: Dataset 'xxx' doesn't exist on the Hub or cannot be accessed ``` ### Steps to reproduce the bug 123 ### Expected behavior 123 ### Environment info 123

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PR_kwDODunzps5xiTwJ

Fix typos in docs

closed

MEMBER

Fix typos in docs introduced by: - #6956 Typos: - `comparisions` => `comparisons` - two consecutive sentences both ending in colon - split one sentence into two Sorry, I did not have time to review that PR. CC: @lhoestq

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PR_kwDODunzps5xcwXz

update docs on N-dim arrays

closed

MEMBER

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PR_kwDODunzps5xcSYm

Fix small typo

closed

CONTRIBUTOR

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PR_kwDODunzps5xbWtU

Remove default `trust_remote_code=True`

closed

MEMBER

TODO: - [x] fix tests

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I_kwDODunzps6LFFdo

Remove canonical datasets from docs

open

MEMBER

Remove canonical datasets from docs, now that we no longer have canonical datasets.

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PR_kwDODunzps5xaosH

Move info_utils errors to exceptions module

closed

MEMBER

Move `info_utils` errors to `exceptions` module. Additionally rename some of them, deprecate the former ones, and make the deprecation backward compatible (by making the new errors inherit from the former ones).

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I_kwDODunzps6LEkfC

load_dataset() should load all subsets, if no specific subset is specified

open

NONE

### Feature request Currently load_dataset() is forcing users to specify a subset. Example `from datasets import load_dataset dataset = load_dataset("m-a-p/COIG-CQIA")` ```--------------------------------------------------------------------------- ValueError Traceback (most recent call last) [<ipython-input-10-c0cb49385da6>](https://localhost:8080/#) in <cell line: 2>() 1 from datasets import load_dataset ----> 2 dataset = load_dataset("m-a-p/COIG-CQIA") 3 frames [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _create_builder_config(self, config_name, custom_features, **config_kwargs) 582 if not config_kwargs: 583 example_of_usage = f"load_dataset('{self.dataset_name}', '{self.BUILDER_CONFIGS[0].name}')" --> 584 raise ValueError( 585 "Config name is missing." 586 f"\nPlease pick one among the available configs: {list(self.builder_configs.keys())}" ValueError: Config name is missing. Please pick one among the available configs: ['chinese_traditional', 'coig_pc', 'exam', 'finance', 'douban', 'human_value', 'logi_qa', 'ruozhiba', 'segmentfault', 'wiki', 'wikihow', 'xhs', 'zhihu'] Example of usage: `load_dataset('coig-cqia', 'chinese_traditional')` ``` This means a dataset cannot contain all the subsets at the same time. I guess one workaround is to manually specify the subset files like in [here](https://huggingface.co/datasets/m-a-p/COIG-CQIA/discussions/1#658698b44bb41498f75c5622), which is clumsy. ### Motivation Ideally, if not subset is specified, the API should just try to load all subsets. This makes it much easier to handle datasets w/ subsets. ### Your contribution Not sure since I'm not familiar w/ the lib src.

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I_kwDODunzps6LDtiW

`Dataset.with_format` behaves inconsistently with documentation

closed

NONE

### Describe the bug The actual behavior of the interface `Dataset.with_format` is inconsistent with the documentation. https://huggingface.co/docs/datasets/use_with_pytorch#n-dimensional-arrays https://huggingface.co/docs/datasets/v2.19.0/en/use_with_tensorflow#n-dimensional-arrays > If your dataset consists of N-dimensional arrays, you will see that by default they are considered as nested lists. > In particular, a PyTorch formatted dataset outputs nested lists instead of a single tensor. > A TensorFlow formatted dataset outputs a RaggedTensor instead of a single tensor. But I get a single tensor by default, which is inconsistent with the description. Actually the current behavior seems more reasonable to me. Therefore, the document needs to be modified. ### Steps to reproduce the bug ```python >>> from datasets import Dataset >>> data = [[[1, 2],[3, 4]],[[5, 6],[7, 8]]] >>> ds = Dataset.from_dict({"data": data}) >>> ds = ds.with_format("torch") >>> ds[0] {'data': tensor([[1, 2], [3, 4]])} >>> ds = ds.with_format("tf") >>> ds[0] {'data': <tf.Tensor: shape=(2, 2), dtype=int64, numpy= array([[1, 2], [3, 4]])>} ``` ### Expected behavior ```python >>> from datasets import Dataset >>> data = [[[1, 2],[3, 4]],[[5, 6],[7, 8]]] >>> ds = Dataset.from_dict({"data": data}) >>> ds = ds.with_format("torch") >>> ds[0] {'data': [tensor([1, 2]), tensor([3, 4])]} >>> ds = ds.with_format("tf") >>> ds[0] {'data': <tf.RaggedTensor [[1, 2], [3, 4]]>} ``` ### Environment info datasets==2.19.1 torch==2.1.0 tensorflow==2.13.1

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I_kwDODunzps6LBKG9

load_dataset error

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### Describe the bug Why does the program get stuck when I use load_dataset method, and it still gets stuck after loading for several hours? In fact, my json file is only 21m, and I can load it in one go using open('', 'r'). ### Steps to reproduce the bug 1. pip install datasets==2.19.2 2. from datasets import Dataset, DatasetDict, NamedSplit, Split, load_dataset 3. data = load_dataset('json', data_files='train.json') ### Expected behavior It is able to load my json correctly ### Environment info datasets==2.19.2

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I_kwDODunzps6K-87c

to_tf_dataset: Visible devices cannot be modified after being initialized

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### Describe the bug When trying to use to_tf_dataset with a custom data_loader collate_fn when I use parallelism I am met with the following error as many times as number of workers there were in ``num_workers``. File "/opt/miniconda/envs/env/lib/python3.11/site-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/opt/miniconda/envs/env/lib/python3.11/site-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/opt/miniconda/envs/env/lib/python3.11/site-packages/datasets/utils/tf_utils.py", line 438, in worker_loop tf.config.set_visible_devices([], "GPU") # Make sure workers don't try to allocate GPU memory ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/miniconda/envs/env/lib/python3.11/site-packages/tensorflow/python/framework/config.py", line 566, in set_visible_devices context.context().set_visible_devices(devices, device_type) File "/opt/miniconda/envs/env/lib/python3.11/site-packages/tensorflow/python/eager/context.py", line 1737, in set_visible_devices raise RuntimeError( RuntimeError: Visible devices cannot be modified after being initialized ### Steps to reproduce the bug 1. Download a dataset using HuggingFace load_dataset 2. Define a function that transforms the data in some way to be used in the collate_fn argument 3. Provide a ``batch_size`` and ``num_workers`` value in the ``to_tf_dataset`` function 4. Either retrieve directly or use tfds benchmark to test the dataset ``` python from datasets import load_datasets import tensorflow_datasets as tfds from keras_cv.layers import Resizing def data_loader(examples): x = Resizing(examples[0]['image'], 256, 256, crop_to_aspect_ratio=True) return {X[0]: x} ds = load_datasets("logasja/FDF", split="test") ds = ds.to_tf_dataset(collate_fn=data_loader, batch_size=16, num_workers=2) tfds.benchmark(ds) ``` ### Expected behavior Use multiple processes to apply transformations from the collate_fn to the tf dataset on the CPU. ### Environment info - `datasets` version: 2.19.1 - Platform: Linux-6.5.0-1023-oracle-x86_64-with-glibc2.35 - Python version: 3.11.8 - `huggingface_hub` version: 0.22.2 - PyArrow version: 15.0.2 - Pandas version: 2.2.1 - `fsspec` version: 2024.2.0

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I_kwDODunzps6K8fpH

FileNotFoundError：error when loading C4 dataset

closed

NONE

### Describe the bug can't load c4 datasets When I replace the datasets package to 2.12.2 I get raise datasets.utils.info_utils.ExpectedMoreSplits: {'train'} How can I fix this？ ### Steps to reproduce the bug 1.from datasets import load_dataset 2.dataset = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation') 3. raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at local_path/c4_val/allenai/c4/c4.py or any data file in the same directory. Couldn't find 'allenai/c4' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/allenai/c4@1588ec454efa1a09f29cd18ddd04fe05fc8653a2/en/c4-validation.00003-of-00008.json.gz' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.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', '.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', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] ### Expected behavior The data was successfully imported ### Environment info python version 3.9 datasets version 2.19.2

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PR_kwDODunzps5xQNao

Re-enable import sorting disabled by flake8:noqa directive when using ruff linter

closed

MEMBER

Re-enable import sorting that was wrongly disabled by `flake8: noqa` directive after switching to `ruff` linter in datasets-2.10.0 PR: - #5519 Note that after the linter switch, we wrongly replaced `flake8: noqa` with `ruff: noqa` in datasets-2.17.0 PR: - #6619 That replacement was wrong because we kept the `isort: skip` directives although they were indeed disabled by `flake8: noqa` first and by `ruff: noqa` afterwards. See for example `__init__.py` file after the linter switch: - We kept the `flake8: noqa` directive https://github.com/huggingface/datasets/blob/06ae3f678651bfbb3ca7dd3274ee2f38e0e0237e/src/datasets/__init__.py#L1 - Whereas we also kept the `isort: skip` directives (that were disabled) https://github.com/huggingface/datasets/blob/06ae3f678651bfbb3ca7dd3274ee2f38e0e0237e/src/datasets/__init__.py#L82-L84 Fix #6942.

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PR_kwDODunzps5xQCCx

Update yanked version of minimum requests requirement

closed

MEMBER

Update yanked version of minimum requests requirement. Version 2.32.1 was yanked: https://pypi.org/project/requests/2.32.1/

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PR_kwDODunzps5xP-KD

Set dev version

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PR_kwDODunzps5xP3jp

Release 2.19.2

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I_kwDODunzps6K2k0O

Import sorting is disabled by flake8 noqa directive after switching to ruff linter

closed

MEMBER

When we switched to `ruff` linter in PR: - #5519 import sorting was disabled in all files containing the `# flake8: noqa` directive - https://github.com/astral-sh/ruff/issues/11679 We should re-enable import sorting on those files.

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I_kwDODunzps6K0Kd1

Supporting FFCV: Fast Forward Computer Vision

open

NONE

### Feature request Supporting FFCV, https://github.com/libffcv/ffcv ### Motivation According to the benchmark, FFCV seems to be fastest image loading method. ### Your contribution no

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I_kwDODunzps6KzDGH

Enable Sharding to Equal Sized Shards

open

NONE