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https://github.com/huggingface/datasets/pull/7293

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Updated inconsistent output in documentation examples for `ClassLabel`

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fix #7129 @stevhliu

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I_kwDODunzps6ezT3n

DataFilesNotFoundError for datasets `OpenMol/PubChemSFT`

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### Describe the bug Cannot load the dataset https://huggingface.co/datasets/OpenMol/PubChemSFT ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset('OpenMol/PubChemSFT') ``` ### Expected behavior ``` --------------------------------------------------------------------------- DataFilesNotFoundError Traceback (most recent call last) Cell In[7], [line 2](vscode-notebook-cell:?execution_count=7&line=2) [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset ----> [2](vscode-notebook-cell:?execution_count=7&line=2) dataset = load_dataset('OpenMol/PubChemSFT') File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2587, 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) [2582](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2582) verification_mode = VerificationMode( [2583](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2583) (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS [2584](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2584) ) [2586](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2586) # Create a dataset builder -> [2587](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2587) builder_instance = load_dataset_builder( [2588](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2588) path=path, [2589](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2589) name=name, [2590](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2590) data_dir=data_dir, [2591](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2591) data_files=data_files, [2592](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2592) cache_dir=cache_dir, [2593](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2593) features=features, [2594](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2594) download_config=download_config, [2595](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2595) download_mode=download_mode, [2596](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2596) revision=revision, [2597](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2597) token=token, [2598](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2598) storage_options=storage_options, [2599](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2599) trust_remote_code=trust_remote_code, [2600](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2600) _require_default_config_name=name is None, [2601](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2601) **config_kwargs, [2602](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2602) ) [2604](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2604) # Return iterable dataset in case of streaming [2605](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2605) if streaming: File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2259, 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) [2257](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2257) download_config = download_config.copy() if download_config else DownloadConfig() [2258](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2258) download_config.storage_options.update(storage_options) -> [2259](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2259) dataset_module = dataset_module_factory( [2260](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2260) path, [2261](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2261) revision=revision, [2262](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2262) download_config=download_config, [2263](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2263) download_mode=download_mode, [2264](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2264) data_dir=data_dir, [2265](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2265) data_files=data_files, [2266](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2266) cache_dir=cache_dir, [2267](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2267) trust_remote_code=trust_remote_code, [2268](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2268) _require_default_config_name=_require_default_config_name, [2269](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2269) _require_custom_configs=bool(config_kwargs), [2270](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2270) ) [2271](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2271) # Get dataset builder class from the processing script [2272](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2272) builder_kwargs = dataset_module.builder_kwargs File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1904, 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) [1902](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1902) raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None [1903](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1903) if isinstance(e1, (DataFilesNotFoundError, DatasetNotFoundError, EmptyDatasetError)): -> [1904](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1904) raise e1 from None [1905](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1905) if isinstance(e1, FileNotFoundError): [1906](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1906) raise FileNotFoundError( [1907](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1907) f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " [1908](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1908) f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" [1909](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1909) ) from None File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1885, 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) [1876](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1876) return HubDatasetModuleFactoryWithScript( [1877](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1877) path, [1878](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1878) revision=revision, (...) [1882](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1882) trust_remote_code=trust_remote_code, [1883](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1883) ).get_module() [1884](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1884) else: -> [1885](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1885) return HubDatasetModuleFactoryWithoutScript( [1886](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1886) path, [1887](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1887) revision=revision, [1888](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1888) data_dir=data_dir, [1889](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1889) data_files=data_files, [1890](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1890) download_config=download_config, [1891](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1891) download_mode=download_mode, [1892](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1892) ).get_module() [1893](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1893) except Exception as e1: [1894](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1894) # All the attempts failed, before raising the error we should check if the module is already cached [1895](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1895) try: File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1270, in HubDatasetModuleFactoryWithoutScript.get_module(self) [1263](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1263) patterns = get_data_patterns(base_path, download_config=self.download_config) [1264](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1264) data_files = DataFilesDict.from_patterns( [1265](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1265) patterns, [1266](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1266) base_path=base_path, [1267](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1267) allowed_extensions=ALL_ALLOWED_EXTENSIONS, [1268](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1268) download_config=self.download_config, [1269](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1269) ) -> [1270](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1270) module_name, default_builder_kwargs = infer_module_for_data_files( [1271](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1271) data_files=data_files, [1272](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1272) path=self.name, [1273](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1273) download_config=self.download_config, [1274](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1274) ) [1275](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1275) data_files = data_files.filter_extensions(_MODULE_TO_EXTENSIONS[module_name]) [1276](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1276) # Collect metadata files if the module supports them File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:597, in infer_module_for_data_files(data_files, path, download_config) [595](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:595) raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}") [596](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:596) if not module_name: --> [597](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:597) raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else "")) [598](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:598) return module_name, default_builder_kwargs DataFilesNotFoundError: No (supported) data files found in OpenMol/PubChemSFT ``` ### Environment info ``` - `datasets` version: 3.1.0 - Platform: Linux-5.15.0-125-generic-x86_64-with-glibc2.31 - Python version: 3.9.18 - `huggingface_hub` version: 0.25.2 - PyArrow version: 18.0.0 - Pandas version: 2.0.3 - `fsspec` version: 2023.9.2 ```

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I_kwDODunzps6erqEj

Why return_tensors='pt' doesn't work？

open

NONE

### Describe the bug I tried to add input_ids to dataset with map(), and I used the return_tensors='pt', but why I got the callback with the type of List？  ### Steps to reproduce the bug  ### Expected behavior Sorry for this silly question, I'm noob on using this tool. But I think it should return a tensor value as I have used the protocol？ When I tokenize only one sentence using tokenized_input=tokenizer(input, return_tensors='pt' )，it does return in tensor type. Why doesn't it work in map()？ ### Environment info transformers>=4.41.2,<=4.45.0 datasets>=2.16.0,<=2.21.0 accelerate>=0.30.1,<=0.34.2 peft>=0.11.1,<=0.12.0 trl>=0.8.6,<=0.9.6 gradio>=4.0.0 pandas>=2.0.0 scipy einops sentencepiece tiktoken protobuf uvicorn pydantic fastapi sse-starlette matplotlib>=3.7.0 fire packaging pyyaml numpy<2.0.0

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I_kwDODunzps6eaBNQ

`Dataset.save_to_disk` hangs when using num_proc > 1

open

NONE

### Describe the bug Hi, I'm encountered a small issue when saving datasets that led to the saving taking up to multiple hours. Specifically, [`Dataset.save_to_disk`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.save_to_disk) is a lot slower when using `num_proc>1` than when using `num_proc=1` The documentation mentions that "Multiprocessing is disabled by default.", but there is no explanation on how to enable it. ### Steps to reproduce the bug ``` import numpy as np from datasets import Dataset n_samples = int(4e6) n_tokens_sample = 100 data_dict = { 'tokens' : np.random.randint(0, 100, (n_samples, n_tokens_sample)), } dataset = Dataset.from_dict(data_dict) dataset.save_to_disk('test_dataset', num_proc=1) dataset.save_to_disk('test_dataset', num_proc=4) dataset.save_to_disk('test_dataset', num_proc=8) ``` This results in: ``` >>> dataset.save_to_disk('test_dataset', num_proc=1) Saving the dataset (7/7 shards): 100%|██████████████| 4000000/4000000 [00:17<00:00, 228075.15 examples/s] >>> dataset.save_to_disk('test_dataset', num_proc=4) Saving the dataset (7/7 shards): 100%|██████████████| 4000000/4000000 [01:49<00:00, 36583.75 examples/s] >>> dataset.save_to_disk('test_dataset', num_proc=8) Saving the dataset (8/8 shards): 100%|██████████████| 4000000/4000000 [02:11<00:00, 30518.43 examples/s] ``` With larger datasets it can take hours, but I didn't benchmark that for this bug report. ### Expected behavior I would expect using `num_proc>1` to be faster instead of slower than `num_proc=1`. ### Environment info - `datasets` version: 3.1.0 - Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.26.2 - PyArrow version: 18.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1

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I_kwDODunzps6d1ZIz

Dataset viewer displays wrong statists

closed

NONE

### Describe the bug In [my dataset](https://huggingface.co/datasets/speedcell4/opus-unigram2), there is a column called `lang2`, and there are 94 different classes in total, but the viewer says there are 83 values only. This issue only arises in the `train` split. The total number of values is also 94 in the `test` and `dev` columns, viewer tells the correct number of them. <img width="177" alt="image" src="https://github.com/user-attachments/assets/78d76ef2-fe0e-4fa3-85e0-fb2552813d1c"> ### Steps to reproduce the bug ```python3 from datasets import load_dataset ds = load_dataset('speedcell4/opus-unigram2').unique('lang2') for key, lang2 in ds.items(): print(key, len(lang2)) ``` This script returns the following and tells that the `train` split has 94 values in the `lang2` column. ``` train 94 dev 94 test 94 zero 5 ``` ### Expected behavior 94 in the reviewer. ### Environment info Collecting environment information... PyTorch version: 2.4.1+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: CentOS Linux release 8.2.2004 (Core) (x86_64) GCC version: (GCC) 8.3.1 20191121 (Red Hat 8.3.1-5) Clang version: Could not collect CMake version: version 3.11.4 Libc version: glibc-2.28 Python version: 3.9.20 (main, Oct 3 2024, 07:27:41) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.28 Is CUDA available: True CUDA runtime version: 12.2.140 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB GPU 1: NVIDIA A100-SXM4-40GB GPU 2: NVIDIA A100-SXM4-40GB GPU 3: NVIDIA A100-SXM4-40GB GPU 4: NVIDIA A100-SXM4-40GB GPU 5: NVIDIA A100-SXM4-40GB GPU 6: NVIDIA A100-SXM4-40GB GPU 7: NVIDIA A100-SXM4-40GB Nvidia driver version: 525.85.05 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 64 On-line CPU(s) list: 0-63 Thread(s) per core: 1 Core(s) per socket: 32 Socket(s): 2 NUMA node(s): 4 Vendor ID: AuthenticAMD CPU family: 23 Model: 49 Model name: AMD EPYC 7542 32-Core Processor Stepping: 0 CPU MHz: 3389.114 BogoMIPS: 5789.40 Virtualization: AMD-V L1d cache: 32K L1i cache: 32K L2 cache: 512K L3 cache: 16384K NUMA node0 CPU(s): 0-15 NUMA node1 CPU(s): 16-31 NUMA node2 CPU(s): 32-47 NUMA node3 CPU(s): 48-63 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] torch==2.4.1+cu121 [pip3] torchaudio==2.4.1+cu121 [pip3] torchdevice==0.1.1 [pip3] torchglyph==0.3.2 [pip3] torchmetrics==1.5.0 [pip3] torchrua==0.5.1 [pip3] torchvision==0.19.1+cu121 [pip3] triton==3.0.0 [pip3] datasets==3.0.1 [conda] numpy 1.26.4 pypi_0 pypi [conda] torch 2.4.1+cu121 pypi_0 pypi [conda] torchaudio 2.4.1+cu121 pypi_0 pypi [conda] torchdevice 0.1.1 pypi_0 pypi [conda] torchglyph 0.3.2 pypi_0 pypi [conda] torchmetrics 1.5.0 pypi_0 pypi [conda] torchrua 0.5.1 pypi_0 pypi [conda] torchvision 0.19.1+cu121 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi

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PR_kwDODunzps6BbIpz

Release v3.1.1

closed

CONTRIBUTOR

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I_kwDODunzps6dxWE5

Support for identifier-based automated split construction

open

CONTRIBUTOR

### Feature request As far as I understand, automated construction of splits for hub datasets is currently based on either file names or directory structure ([as described here](https://huggingface.co/docs/datasets/en/repository_structure)) It would seem to be pretty useful to also allow splits to be based on identifiers of individual examples This could be configured like {"split_name": {"column_name": [column values in split]}} (This in turn requires unique 'index' columns, which could be explicitly supported or just assumed to be defined appropriately by the user). I guess a potential downside would be that shards would end up spanning different splits - is this something that can be handled somehow? Would this only affect streaming from hub? ### Motivation The main motivation would be that all data files could be stored in a single directory, and multiple sets of splits could be generated from the same data. This is often useful for large datasets with multiple distinct sets of splits. This could all be configured via the README.md yaml configs ### Your contribution May be able to contribute if it seems like a good idea

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I_kwDODunzps6drNcH

Concurrent loading in `load_from_disk` - `num_proc` as a param

closed

NONE

### Feature request https://github.com/huggingface/datasets/pull/6464 mentions a `num_proc` param while loading dataset from disk, but can't find that in the documentation and code anywhere ### Motivation Make loading large datasets from disk faster ### Your contribution Happy to contribute if given pointers

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PR_kwDODunzps6BV3Gu

Release v3.1.0

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PR_kwDODunzps6BVUSh

support for custom feature encoding/decoding

open

CONTRIBUTOR

Fix for https://github.com/huggingface/datasets/issues/7220 as suggested in discussion, in preference to #7221 (only concern would be on effect on type checking with custom feature types that aren't covered by FeatureType?)

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PR_kwDODunzps6BQUgH

Allow for variation in metadata file names as per issue #7123

open

NONE

Allow metadata files to have an identifying preface. Specifically, it will recognize files with `-metadata.csv` or `_metadata.csv` as metadata files for the purposes of the dataset viewer functionality. Resolves #7123.

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I_kwDODunzps6det9j

Faulty datasets.exceptions.ExpectedMoreSplitsError

open

CONTRIBUTOR

### Describe the bug Trying to download only the 'validation' split of my dataset; instead hit the error `datasets.exceptions.ExpectedMoreSplitsError`. Appears to be the same undesired behavior as reported in [#6939](https://github.com/huggingface/datasets/issues/6939), but with `data_files`, not `data_dir`. Here is the Traceback: ``` Traceback (most recent call last): File "/home/user/app/app.py", line 12, in <module> ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val*'}, split='validation', trust_remote_code=True) File "/usr/local/lib/python3.10/site-packages/datasets/load.py", line 2154, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/site-packages/datasets/builder.py", line 924, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/site-packages/datasets/builder.py", line 1018, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/usr/local/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 68, in verify_splits raise ExpectedMoreSplitsError(str(set(expected_splits) - set(recorded_splits))) datasets.exceptions.ExpectedMoreSplitsError: {'train', 'test'} ``` Note: I am using the `data_files` argument only because I am trying to specify that I only want the 'validation' split, and the whole dataset will be downloaded even when the `split='validation'` argument is specified, unless you also specify `data_files`, as described here: https://discuss.huggingface.co/t/how-can-i-download-a-specific-split-of-a-dataset/79027 ### Steps to reproduce the bug 1. Create a Space with the default blank 'gradio' SDK https://huggingface.co/new-space 2. Create a file 'app.py' that loads a dataset to only extract a 'validation' split: `ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val*'}, split='validation', trust_remote_code=True)` ### Expected behavior Downloading validation split. ### Environment info Default environment for creating a new Space. Relevant to this bug, that is: ``` FROM docker.io/library/python:3.10@sha256:fd0fa50d997eb56ce560c6e5ca6a1f5cf8fdff87572a16ac07fb1f5ca01eb608 --> RUN pip install --no-cache-dir pip==22.3.1 && pip install --no-cache-dir datasets "huggingface-hub>=0.19" "hf-transfer>=0.1.4" "protobuf<4" "click<8.1" ```

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I_kwDODunzps6dYHzj

File not found error

open

NONE

### Describe the bug I get a FileNotFoundError: <img width="944" alt="image" src="https://github.com/user-attachments/assets/1336bc08-06f6-4682-a3c0-071ff65efa87"> ### Steps to reproduce the bug See screenshot. ### Expected behavior I want to load one audiofile from the dataset. ### Environment info MacOs Intel 14.6.1 (23G93) Python 3.10.9 Numpy 1.23 Datasets latest version

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I_kwDODunzps6dWtp1

Add filename in error message when ReadError or similar occur

open

NONE

Please update error messages to include relevant information for debugging when loading datasets with `load_dataset()` that may have a few corrupted files. Whenever downloading a full dataset, some files might be corrupted (either at the source or from downloading corruption). However the errors often only let me know it was a tar file if `tarfile.ReadError` appears on the traceback, and I imagine similarly for other file types. This makes it really hard to debug which file is corrupted, and when dealing with very large datasets, it shouldn't be necessary to force download everything again.

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PR_kwDODunzps6A8pTJ

Feature proposal: Stacking, potentially heterogeneous, datasets

open

NONE

### Introduction Hello there, I noticed that there are two ways to combine multiple datasets: Either through `datasets.concatenate_datasets` or `datasets.interleave_datasets`. However, to my knowledge (please correct me if I am wrong) both approaches require the datasets that are combined to have the same features. I think it would be a great idea to add support for combining multiple datasets that might not follow the same schema (i.e. have different features), for example an image and text dataset. That is why I propose a third function of the `datasets.combine` module called `stack_datasets`, which can be used to combine a list of datasets with (potentially) different features. This would look as follows: ```python >>> from datasets import stack_datasets >>> image_dataset = ... >>> next(iter(image_dataset)) {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=555x416 at 0x313E79CD0> } >>> text_dataset = ... >>> next(iter(text_dataset)) {'text': "This is a test."} >>> stacked = stack_datasets(datasets={'i_ds': image_dataset, 't_ds': text_dataset}, stopping_strategy='all_exhausted') >>> next(iter(stacked)) { 'i_ds': {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=555x416 at 0x313E79CD0> } 't_ds': {'text': "This is a test."} } ``` <br /> ### Motivation I motivate this by: **A**: The fact that Pytorch offers a similar functionality under `torch.utils.data.StackDataset` ([link](https://pytorch.org/docs/stable/data.html#torch.utils.data.StackDataset)). **B**: In settings where one would like to e.g. train a Vision-Language model using an image-text dataset, an image dataset, and a text dataset, this functionality would offer a clean and intuitive solution to create multimodal datasets. I am aware that the aforementioned is also feasible without my proposed function, but I believe this offers a nice approach that aligns with existing functionality and is directly provided within the `datasets` package. ### API `stack_datasets` has two arguments: `datasets` and `stopping_strategy `. <br /> `datasets` is a dictionary of either type `Dict[str, Dataset]` or `Dict[str, IterableDatasets]`, a mixture is not allowed. It contains the names of the datasets (the keys) and the datasets themselves (the values) that should be stacked. Each item returned is a dictionary with one key-value pair for each dataset. The keys are the names of the datasets as provided in the argument `datasets`, and the values are the respective examples from the datasets. <br /> `stopping_strategy` is the same as for `interleave_datasets`. If it is `first_exhausted` we stop if the smallest dataset runs out of examples, if it is `all_exhausted` we stop if all datasets ran out of examples at least once. For `all_exhausted` that means that we may visit examples from datasets multiple times. ### Docs I saw that there are multiple documentations and guides on the HuggingFace website that introduce `concatenate_datasets` and `interleave_datasets`, for example [here](https://huggingface.co/docs/datasets/process#concatenate). If this request is merged I would be willing to add the new functionality at the appropriate points in the documentation (if desired). ### Tests I also added some tests to ensure correctness. Some tests I wrote in [tests/test_iterable_dataset.py](https://github.com/TimCares/datasets/blob/fadc1159debf2a65d44e40cbf7758f2bd2cc8b08/tests/test_iterable_dataset.py#L2169) run for both `Dataset` and `IterableDataset` even though tests for `Dataset` technically do not belong in this script, but I found that this was a nice way to cover more cases with mostly the same code. ### Additional information I tried to write the code in a way so that it is similar to that of `concatenate_datasets` and `interleave_datasets`. I’m open to feedback and willing to make adjustments based on your suggestions, so feel free to give me your take. :)

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PR_kwDODunzps6A1ORG

Let soundfile directly read local audio files

open

NONE

- [x] Fixes #7276

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PR_kwDODunzps6AyB7O

Add link to video dataset

closed

CONTRIBUTOR

This PR updates https://huggingface.co/docs/datasets/loading to also link to the new video loading docs. cc @mfarre

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I_kwDODunzps6c3993

Accessing audio dataset value throws Format not recognised error

open

NONE

### Describe the bug Accessing audio dataset value throws `Format not recognised error` ### Steps to reproduce the bug **code:** ```py from datasets import load_dataset dataset = load_dataset("fawazahmed0/bug-audio") for data in dataset["train"]: print(data) ``` **output:** ```bash (mypy) C:\Users\Nawaz-Server\Documents\ml>python myest.py [C:\vcpkg\buildtrees\mpg123\src\0d8db63f9b-3db975bc05.clean\src\libmpg123\layer3.c:INT123_do_layer3():1801] error: dequantization failed! {'audio': {'path': 'C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037135.mp3', 'array': array([ 0.00000000e+00, -2.86519935e-22, -2.56504911e-21, ..., -1.94239747e-02, -2.42924765e-02, -2.99104657e-02]), 'sampling_rate': 22050}, 'reciter': 'Ghamadi', 'transcription': 'الا عجوز ا في الغبرين', 'line': 3923, 'chapter': 37, 'verse': 135, 'text': 'إِلَّا عَجُوزࣰ ا فِي ٱلۡغَٰبِرِينَ'} Traceback (most recent call last): File "C:\Users\Nawaz-Server\Documents\ml\myest.py", line 5, in <module> for data in dataset["train"]: ~~~~~~~^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\arrow_dataset.py", line 2372, in __iter__ formatted_output = format_table( ^^^^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 639, in format_table return formatter(pa_table, query_type=query_type) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 403, in __call__ return self.format_row(pa_table) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 444, in format_row row = self.python_features_decoder.decode_row(row) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 222, in decode_row return self.features.decode_example(row) if self.features else row ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\features.py", line 2042, in decode_example column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\features.py", line 1403, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\audio.py", line 184, in decode_example array, sampling_rate = sf.read(f) ^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 285, in read with SoundFile(file, 'r', samplerate, channels, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 658, in __init__ self._file = self._open(file, mode_int, closefd) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 1216, in _open raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name)) soundfile.LibsndfileError: Error opening <_io.BufferedReader name='C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037136.mp3'>: Format not recognised. ``` ### Expected behavior Everything should work fine, as loading the problematic audio file directly with soundfile package works fine **code:** ``` import soundfile as sf print(sf.read('C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037136.mp3')) ``` **output:** ```bash (mypy) C:\Users\Nawaz-Server\Documents\ml>python myest.py [C:\vcpkg\buildtrees\mpg123\src\0d8db63f9b-3db975bc05.clean\src\libmpg123\layer3.c:INT123_do_layer3():1801] error: dequantization failed! (array([ 0.00000000e+00, -8.43723821e-22, -2.45370628e-22, ..., -7.71464454e-03, -6.90496899e-03, -8.63333419e-03]), 22050) ``` ### Environment info - `datasets` version: 3.0.2 - Platform: Windows-11-10.0.22621-SP0 - Python version: 3.12.7 - `huggingface_hub` version: 0.26.2 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.10.0 - soundfile: 0.12.1

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I_kwDODunzps6c3MJ1

load_dataset

open

NONE

### Describe the bug I am performing two operations I see on a hugging face tutorial (Fine-tune a language model), and I am defining every aspect inside the mapped functions, also some imports of the library because it doesnt identify anything not defined outside that function where the dataset elements are being mapped: https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb#scrollTo=iaAJy5Hu3l_B `- lm_datasets = tokenized_datasets.map( group_texts, batched=True, batch_size=batch_size, num_proc=4, ) - tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"]) def tokenize_function(examples): model_checkpoint = 'gpt2' from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True) return tokenizer(examples["text"])` ### Steps to reproduce the bug Currently handle all the imports inside the function ### Expected behavior The code must work es expected in the notebook, but currently this is not happening. https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb#scrollTo=iaAJy5Hu3l_B ### Environment info print(transformers.__version__) 4.46.1

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PR_kwDODunzps6ArEt-

[MINOR:TYPO] Fix typo in exception text

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https://github.com/huggingface/datasets/pull/7273

PR_kwDODunzps6An6n8

Raise error for incorrect JSON serialization

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CONTRIBUTOR

Raise error when `lines = False` and `batch_size < Dataset.num_rows` in `Dataset.to_json()`. Issue: #7037 Related PRs: #7039 #7181

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PR_kwDODunzps6AirL2

fix conda release worlflow

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PR_kwDODunzps6AiZaj

Set dev version

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PR_kwDODunzps6AiTJm

Release: 3.1.0

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I_kwDODunzps6ckunz

Memory leak when streaming

open

NONE

### Describe the bug I try to use a dataset with streaming=True, the issue I have is that the RAM usage becomes higher and higher until it is no longer sustainable. I understand that huggingface store data in ram during the streaming, and more worker in dataloader there are, more a lot of shard will be stored in ram, but the issue I have is that the ram usage is not constant. So after each new shard loaded, the ram usage will be higher and higher. ### Steps to reproduce the bug You can run this code and see you ram usage, after each shard of 255 examples, your ram usage will be extended. ```py from datasets import load_dataset from torch.utils.data import DataLoader dataset = load_dataset("WaveGenAI/dataset", streaming=True) dataloader = DataLoader(dataset["train"], num_workers=3) for i, data in enumerate(dataloader): print(i, end="\r") ``` ### Expected behavior The Ram usage should be always the same (just 3 shards loaded in the ram). ### Environment info - `datasets` version: 3.0.1 - Platform: Linux-6.10.5-arch1-1-x86_64-with-glibc2.40 - Python version: 3.12.4 - `huggingface_hub` version: 0.26.0 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1

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I_kwDODunzps6cj7jv

load_from_disk

open

NONE

### Describe the bug I have data saved with save_to_disk. The data is big (700Gb). When I try loading it, the only option is load_from_disk, and this function copies the data to a tmp directory, causing me to run out of disk space. Is there an alternative solution to that? ### Steps to reproduce the bug when trying to load data using load_From_disk after being saved using save_to_disk ### Expected behavior run out of disk space ### Environment info lateest version

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I_kwDODunzps6cjQ6t

Source installation fails on Macintosh with python 3.10

open

NONE

### Describe the bug Hi, Decord is a dev dependency not maintained since couple years. It does not have an ARM package available rendering it uninstallable on non-intel based macs Suggestion is to move to eva-decord (https://github.com/georgia-tech-db/eva-decord) which doesnt have this problem. Happy to raise a PR ### Steps to reproduce the bug Source installation as mentioned in contributinog.md ### Expected behavior Installation without decord failing to be installed. ### Environment info python=3.10, M3 Mac

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I_kwDODunzps6ccTnn

The dataset viewer should be available soon. Please retry later.

closed

NONE

### Describe the bug After waiting for 2 hours, it still presents ``The dataset viewer should be available soon. Please retry later.'' ### Steps to reproduce the bug dataset link: https://huggingface.co/datasets/BryanW/HI_EDIT ### Expected behavior Present the dataset viewer. ### Environment info NA

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PR_kwDODunzps6AYofJ

Disallow video push_to_hub

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PR_kwDODunzps6AYfwL

fix docs relative links

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PR_kwDODunzps6ARg7m

Small addition to video docs

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PR_kwDODunzps6AOWI8

Allow video with disabeld decoding without decord

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MEMBER

for the viewer, this way it can use Video(decode=False) and doesn't need decord (which causes segfaults)

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I_kwDODunzps6cMdJ4

Cannot load the cache when mapping the dataset

open

NONE

### Describe the bug I'm training the flux controlnet. The train_dataset.map() takes long time to finish. However, when I killed one training process and want to restart a new training with the same dataset. I can't reuse the mapped result even I defined the cache dir for the dataset. with accelerator.main_process_first(): from datasets.fingerprint import Hasher # fingerprint used by the cache for the other processes to load the result # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401 new_fingerprint = Hasher.hash(args) train_dataset = train_dataset.map( compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=10, ) ### Steps to reproduce the bug train flux controlnet and start again ### Expected behavior will not map again ### Environment info latest diffusers

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I_kwDODunzps6cKj7N

cache can't cleaned or disabled

open

NONE

### Describe the bug I tried following ways, the cache can't be disabled. I got 2T data, but I also got more than 2T cache file. I got pressure on storage. I need to diable cache or cleaned immediately after processed. Following ways are all not working, please give some help! ```python from datasets import disable_caching from transformers import AutoTokenizer disable_caching() tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path) def tokenization_fn(examples): column_name = 'text' if 'text' in examples else 'data' tokenized_inputs = tokenizer( examples[column_name], return_special_tokens_mask=True, truncation=False, max_length=tokenizer.model_max_length ) return tokenized_inputs data = load_dataset('json', data_files=save_local_path, split='train', cache_dir=None) data.cleanup_cache_files() updated_dataset = data.map(tokenization_fn, load_from_cache_file=False) updated_dataset .cleanup_cache_files() ``` ### Expected behavior no cache file generated ### Environment info Ubuntu 20.04.6 LTS datasets 3.0.2

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PR_kwDODunzps6AIEY-

Don't embed videos

closed

MEMBER

don't include video bytes when running download_and_prepare(format="parquet") this also affects push_to_hub which will just upload the local paths of the videos though

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PR_kwDODunzps6AHlK1

Always set non-null writer batch size

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bug introduced in #7230, it was preventing the Viewer limit writes to work

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PR_kwDODunzps6AHEfy

fix ci for pyarrow 18

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PR_kwDODunzps6AG_qk

Retry all requests timeouts

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as reported in https://github.com/huggingface/datasets/issues/6843

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PR_kwDODunzps6AG25R

fix decord import

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MEMBER

delay the import until Video() is instantiated + also import duckdb first (otherwise importing duckdb later causes a segfault)

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I_kwDODunzps6b76mU

mismatch for datatypes when providing `Features` with `Array2D` and user specified `dtype` and using with_format("numpy")

open

NONE

### Describe the bug If the user provides a `Features` type value to `datasets.Dataset` with members having `Array2D` with a value for `dtype`, it is not respected during `with_format("numpy")` which should return a `np.array` with `dtype` that the user provided for `Array2D`. It seems for floats, it will be set to `float32` and for ints it will be set to `int64` ### Steps to reproduce the bug ```python import numpy as np import datasets from datasets import Dataset, Features, Array2D print(f"datasets version: {datasets.__version__}") data_info = { "arr_float" : "float64", "arr_int" : "int32" } sample = {key : [np.zeros([4, 5], dtype=dtype)] for key, dtype in data_info.items()} features = {key : Array2D(shape=(None, 5), dtype=dtype) for key, dtype in data_info.items()} features = Features(features) dataset = Dataset.from_dict(sample, features=features) ds = dataset.with_format("numpy") for key in features: print(f"{key} feature dtype: ", ds.features[key].dtype) print(f"{key} dtype:", ds[key].dtype) ``` Output: ```bash datasets version: 3.0.2 arr_float feature dtype: float64 arr_float dtype: float32 arr_int feature dtype: int32 arr_int dtype: int64 ``` ### Expected behavior It should return a `np.array` with `dtype` that the user provided for the corresponding member in the `Features` type value ### Environment info - `datasets` version: 3.0.2 - Platform: Linux-6.11.5-arch1-1-x86_64-with-glibc2.40 - Python version: 3.12.7 - `huggingface_hub` version: 0.26.1 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6b6uO6

Unable to upload a large dataset zip either from command line or UI

open

NONE

### Describe the bug Unable to upload a large dataset zip from command line or UI. UI simply says error. I am trying to a upload a tar.gz file of 17GB. <img width="550" alt="image" src="https://github.com/user-attachments/assets/f9d29024-06c8-49c4-a109-0492cff79d34"> <img width="755" alt="image" src="https://github.com/user-attachments/assets/a8d4acda-7f02-4279-9c2d-b2e0282b4faa"> ### Steps to reproduce the bug Upload a large file ### Expected behavior The file should upload without any issue. ### Environment info None

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PR_kwDODunzps5_41s7

Add IterableDataset.shard()

closed

MEMBER

Will be useful to distribute a dataset across workers (other than pytorch) like spark I also renamed `.n_shards` -> `.num_shards` for consistency and kept the old name for backward compatibility. And a few changes in internal functions for consistency as well (rank, world_size -> num_shards, index) Breaking change: the new default for `contiguous` in `Dataset.shard()` is `True`, but imo not a big deal since I couldn't find any usage of `contiguous=False` internally (we always do contiguous=True for map-style datasets since its more optimized) or in the wild

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PR_kwDODunzps5_zPTt

Missing video docs

closed

MEMBER

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PR_kwDODunzps5_zDPS

Basic XML support (mostly copy pasted from text)

closed

MEMBER

enable the viewer for datasets like https://huggingface.co/datasets/FrancophonIA/e-calm (there will be more and more apparently)

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I_kwDODunzps6bk4Y8

How to debugging

open

NONE

### Describe the bug I wanted to use my own script to handle the processing, and followed the tutorial documentation by rewriting the MyDatasetConfig and MyDatasetBuilder (which contains the _info,_split_generators and _generate_examples methods) classes. Testing with simple data was able to output the results of the processing, but when I wished to do more complex processing, I found that I was unable to debug (even the simple samples were inaccessible). There are no errors reported, and I am able to print the _info,_split_generators and _generate_examples messages, but I am unable to access the breakpoints. ### Steps to reproduce the bug # my_dataset.py import json import datasets class MyDatasetConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(MyDatasetConfig, self).__init__(**kwargs) class MyDataset(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ MyDatasetConfig( name="default", version=VERSION, description="myDATASET" ), ] def _info(self): print("info") # breakpoints return datasets.DatasetInfo( description="myDATASET", features=datasets.Features( { "id": datasets.Value("int32"), "text": datasets.Value("string"), "label": datasets.ClassLabel(names=["negative", "positive"]), } ), supervised_keys=("text", "label"), ) def _split_generators(self, dl_manager): print("generate") # breakpoints data_file = "data.json" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file} ), ] def _generate_examples(self, filepath): print("example") # breakpoints with open(filepath, encoding="utf-8") as f: data = json.load(f) for idx, sample in enumerate(data): yield idx, { "id": sample["id"], "text": sample["text"], "label": sample["label"], } #main.py import os os.environ["TRANSFORMERS_NO_MULTIPROCESSING"] = "1" from datasets import load_dataset dataset = load_dataset("my_dataset.py", split="train", cache_dir=None) print(dataset[:5]) ### Expected behavior Pause at breakpoints while running debugging ### Environment info pycharm

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I_kwDODunzps6bkE_J

ModuleNotFoundError: No module named 'datasets.tasks'

open

NONE

### Describe the bug --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) [<ipython-input-9-13b5f31bd391>](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <cell line: 1>() ----> 1 dataset = load_dataset('knowledgator/events_classification_biotech') 11 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2130 2131 # Create a dataset builder -> 2132 builder_instance = load_dataset_builder( 2133 path=path, 2134 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs) 1886 raise ValueError(error_msg) 1887 -> 1888 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name) 1889 # Instantiate the dataset builder 1890 builder_instance: DatasetBuilder = builder_cls( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in get_dataset_builder_class(dataset_module, dataset_name) 246 dataset_module.importable_file_path 247 ) if dataset_module.importable_file_path else nullcontext(): --> 248 builder_cls = import_main_class(dataset_module.module_path) 249 if dataset_module.builder_configs_parameters.builder_configs: 250 dataset_name = dataset_name or dataset_module.builder_kwargs.get("dataset_name") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_main_class(module_path) 167 def import_main_class(module_path) -> Optional[Type[DatasetBuilder]]: 168 """Import a module at module_path and return its main class: a DatasetBuilder""" --> 169 module = importlib.import_module(module_path) 170 # Find the main class in our imported module 171 module_main_cls = None [/usr/lib/python3.10/importlib/__init__.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_module(name, package) 124 break 125 level += 1 --> 126 return _bootstrap._gcd_import(name[level:], package, level) 127 128 /usr/lib/python3.10/importlib/_bootstrap.py in _gcd_import(name, package, level) /usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load(name, import_) /usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_) /usr/lib/python3.10/importlib/_bootstrap.py in _load_unlocked(spec) /usr/lib/python3.10/importlib/_bootstrap_external.py in exec_module(self, module) /usr/lib/python3.10/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds) [~/.cache/huggingface/modules/datasets_modules/datasets/knowledgator--events_classification_biotech/9c8086d498c3104de3a3c5b6640837e18ccd829dcaca49f1cdffe3eb5c4a6361/events_classification_biotech.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <module> 1 import datasets 2 from datasets import load_dataset ----> 3 from datasets.tasks import TextClassification 4 5 DESCRIPTION = """ ModuleNotFoundError: No module named 'datasets.tasks' --------------------------------------------------------------------------- NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt. To view examples of installing some common dependencies, click the "Open Examples" button below. --------------------------------------------------------------------------- ### Steps to reproduce the bug !pip install datasets from datasets import load_dataset dataset = load_dataset('knowledgator/events_classification_biotech') ### Expected behavior no ModuleNotFoundError ### Environment info google colab

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I_kwDODunzps6bV-oN

Adding column with dict struction when mapping lead to wrong order

open

NONE

### Describe the bug in `map()` function, I want to add a new column with a dict structure. ``` def map_fn(example): example['text'] = {'user': ..., 'assistant': ...} return example ``` However this leads to a wrong order `{'assistant':..., 'user':...}` in the dataset. Thus I can't concatenate two datasets due to the different feature structures. [Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example This seems an issue in low level pyarrow library instead of datasets, however, I think datasets should allow concatenate two datasets actually in the same structure. ### Steps to reproduce the bug [Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example ### Expected behavior two datasets could be concatenated. ### Environment info N/A

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PR_kwDODunzps5_ehPi

Set dev version

closed

MEMBER

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PR_kwDODunzps5_eaiE

Release: 3.0.2

closed

MEMBER

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PR_kwDODunzps5_dqWP

use huggingface_hub offline mode

closed

MEMBER

and better handling of LocalEntryNotfoundError cc @Wauplin follow up to #7234

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I_kwDODunzps6bJGM0

ArrayXD with None as leading dim incompatible with DatasetCardData

open

CONTRIBUTOR

### Describe the bug Creating a dataset with ArrayXD features leads to errors when downloading from hub due to DatasetCardData removing the Nones @lhoestq ### Steps to reproduce the bug ```python import numpy as np from datasets import Array2D, Dataset, Features, load_dataset def examples_generator(): for i in range(4): yield { "array_1d": np.zeros((10,1), dtype="uint16"), "array_2d": np.zeros((10, 1), dtype="uint16"), } features = Features(array_1d=Array2D((None,1), "uint16"), array_2d=Array2D((None, 1), "uint16")) dataset = Dataset.from_generator(examples_generator, features=features) dataset.push_to_hub("alex-hh/test_array_1d2d") ds = load_dataset("alex-hh/test_array_1d2d") ``` Source of error appears to be DatasetCardData.to_dict invoking DatasetCardData._remove_none ```python from huggingface_hub import DatasetCardData from datasets.info import DatasetInfosDict dataset_card_data = DatasetCardData() DatasetInfosDict({"default": dataset.info.copy()}).to_dataset_card_data(dataset_card_data) print(dataset_card_data.to_dict()) # removes Nones in shape ``` ### Expected behavior Should be possible to load datasets saved with shape None in leading dimension ### Environment info 3.0.2 and latest huggingface_hub

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I_kwDODunzps6a91AU

`push_to_hub` overwrite argument

closed

NONE

### Feature request Add an `overwrite` argument to the `push_to_hub` method. ### Motivation I want to overwrite a repo without deleting it on Hugging Face. Is this possible? I couldn't find anything in the documentation or tutorials. ### Your contribution I can create a PR.

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PR_kwDODunzps5_KxSL

Feature Request: Add functionality to pass split types like train, test in DatasetDict.map

open

NONE

Hello datasets! We often encounter situations where we need to preprocess data differently depending on split types such as train, valid, and test. However, while DatasetDict.map has features to pass rank or index, there's no functionality to pass split types. Therefore, I propose adding a 'with_splits' parameter to DatasetDict, which would allow passing the split type through fn_kwargs.

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I_kwDODunzps6a4JcJ

incompatibily issue when using load_dataset with datasets==3.0.1

open

NONE

### Describe the bug There is a bug when using load_dataset with dataset version at 3.0.1 . Please see below in the "steps to reproduce the bug". To resolve the bug, I had to downgrade to version 2.21.0 OS: Ubuntu 24 (AWS instance) Python: same bug under 3.12 and 3.10 The error I had was: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/load.py", line 2096, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 924, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 1647, in _download_and_prepare super()._download_and_prepare( File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 977, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_0/cb17afd34f5799f97e8f48398748f83006335b702bd785f9880797838d541b81/common_voice_6_0.py", line 159, in _split_generators archive_path = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_manager.py", line 150, in download download_config = self.download_config.copy() File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_config.py", line 73, in copy return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) TypeError: DownloadConfig.__init__() got an unexpected keyword argument 'ignore_url_params' ### Steps to reproduce the bug 1. install dataset with ```pip install datasets --upgrade``` 2. launch python; from datasets import loaad_dataset 3. run load_dataset("mozilla-foundation/common_voice_6_0") 4. exit python 5. uninstall datasets; then ```pip install datasets==2.21.0``` 6. launch python; from datasets import loaad_dataset 7. run load_dataset("mozilla-foundation/common_voice_6_0") 8. Everything runs great now ### Expected behavior Be able to download a dataset without error ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 3.0.1 - Platform: Linux-6.8.0-1017-aws-x86_64-with-glibc2.39 - Python version: 3.12.4 - `huggingface_hub` version: 0.26.0 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1

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PR_kwDODunzps5_GIvw

[MINOR:TYPO] Update arrow_dataset.py

closed

CONTRIBUTOR

Fix wrong link. csv kwargs docstring link was pointing to pandas json docs.

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PR_kwDODunzps5-8Z_-

No need for dataset_info

closed

MEMBER

save a useless call to /api/datasets/repo_id

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I_kwDODunzps6am9IJ

数据集数量问题

open

NONE

### Describe the bug 这里我进行大模型微调，当数据集数量为718时，模型可以正常微调，但是当我添加一个在前718个数据集中的数据或者新增一个数据就会报错 ### Steps to reproduce the bug 1. 这里我的数据集可以微调的最后两个数据集是： { "messages": [ { "role": "user", "content": "完成校正装置设计后需要进行哪些工作？" }, { "role": "assistant", "content": "一旦完成校正装置设计后，需要进行系统实际调校工作，或者进行MATLAB仿真以检查系统的时间响应特性。这时，需将系统建模时省略的部分尽可能加入系统，以保证仿真结果的逼真度。如果由于系统各种固有非线性因素影响，或者由于系统噪声和负载效应等因素的影响，使已校正系统不能满足全部性能指标要求，则需要适当调整校正装置的形式或参数，直到已校正系统满足全部性能指标为止。" } ] } { "messages": [ { "role": "user", "content": "为什么闭环带宽要求会影响串联超前校正的效果？" }, { "role": "assistant", "content": "如果待校正系统不稳定，为了满足相角裕度的要求，超前网络需要提供很大的相角超前量。这会导致超前网络的a值必须选得很大，从而使已校正系统的带宽过大。带宽过大会使通过系统的高频噪声电平变高，可能导致系统失控。因此，闭环带宽的要求会影响串联超前校正的效果。" } ] } 2. 当我把倒数第二个数据集再次添加到数据集中时，此时我的最后三个数据集是 { "messages": [ { "role": "user", "content": "完成校正装置设计后需要进行哪些工作？" }, { "role": "assistant", "content": "一旦完成校正装置设计后，需要进行系统实际调校工作，或者进行MATLAB仿真以检查系统的时间响应特性。这时，需将系统建模时省略的部分尽可能加入系统，以保证仿真结果的逼真度。如果由于系统各种固有非线性因素影响，或者由于系统噪声和负载效应等因素的影响，使已校正系统不能满足全部性能指标要求，则需要适当调整校正装置的形式或参数，直到已校正系统满足全部性能指标为止。" } ] } { "messages": [ { "role": "user", "content": "为什么闭环带宽要求会影响串联超前校正的效果？" }, { "role": "assistant", "content": "如果待校正系统不稳定，为了满足相角裕度的要求，超前网络需要提供很大的相角超前量。这会导致超前网络的a值必须选得很大，从而使已校正系统的带宽过大。带宽过大会使通过系统的高频噪声电平变高，可能导致系统失控。因此，闭环带宽的要求会影响串联超前校正的效果。" } ] } { "messages": [ { "role": "user", "content": "完成校正装置设计后需要进行哪些工作？" }, { "role": "assistant", "content": "一旦完成校正装置设计后，需要进行系统实际调校工作，或者进行MATLAB仿真以检查系统的时间响应特性。这时，需将系统建模时省略的部分尽可能加入系统，以保证仿真结果的逼真度。如果由于系统各种固有非线性因素影响，或者由于系统噪声和负载效应等因素的影响，使已校正系统不能满足全部性能指标要求，则需要适当调整校正装置的形式或参数，直到已校正系统满足全部性能指标为止。" } ] } 这时系统会显示bug： root@autodl-container-027f4cad3d-6baf4e64:~/autodl-tmp# python GLM-4/finetune_demo/finetune.py datasets/ ZhipuAI/glm-4-9b-chat GLM-4/finetune_demo/configs/lora.yaml Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:02<00:00, 4.04it/s] The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable. trainable params: 2,785,280 || all params: 9,402,736,640 || trainable%: 0.0296 Generating train split: 0 examples [00:00, ? examples/s]Failed to load JSON from file '/root/autodl-tmp/datasets/train.jsonl' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 718 Generating train split: 0 examples [00:00, ? examples/s] ╭──────────────────────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ /root/miniconda3/lib/python3.10/site-packages/datasets/packaged_modules/json/json.py:153 in _generate_tables │ │ │ │ 150 │ │ │ │ │ │ │ │ with open( │ │ 151 │ │ │ │ │ │ │ │ │ file, encoding=self.config.encoding, errors=self.con │ │ 152 │ │ │ │ │ │ │ │ ) as f: │ │ ❱ 153 │ │ │ │ │ │ │ │ │ df = pd.read_json(f, dtype_backend="pyarrow") │ │ 154 │ │ │ │ │ │ │ except ValueError: │ │ 155 │ │ │ │ │ │ │ │ logger.error(f"Failed to load JSON from file '{file}' wi │ │ 156 │ │ │ │ │ │ │ │ raise e │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:815 in read_json │ │ │ │ 812 │ if chunksize: │ │ 813 │ │ return json_reader │ │ 814 │ else: │ │ ❱ 815 │ │ return json_reader.read() │ │ 816 │ │ 817 │ │ 818 class JsonReader(abc.Iterator, Generic[FrameSeriesStrT]): │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:1025 in read │ │ │ │ 1022 │ │ │ │ │ │ data_lines = data.split("\n") │ │ 1023 │ │ │ │ │ │ obj = self._get_object_parser(self._combine_lines(data_lines)) │ │ 1024 │ │ │ │ else: │ │ ❱ 1025 │ │ │ │ │ obj = self._get_object_parser(self.data) │ │ 1026 │ │ │ │ if self.dtype_backend is not lib.no_default: │ │ 1027 │ │ │ │ │ return obj.convert_dtypes( │ │ 1028 │ │ │ │ │ │ infer_objects=False, dtype_backend=self.dtype_backend │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:1051 in _get_object_parser │ │ │ │ 1048 │ │ } │ │ 1049 │ │ obj = None │ │ 1050 │ │ if typ == "frame": │ │ ❱ 1051 │ │ │ obj = FrameParser(json, **kwargs).parse() │ │ 1052 │ │ │ │ 1053 │ │ if typ == "series" or obj is None: │ │ 1054 │ │ │ if not isinstance(dtype, bool): │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:1187 in parse │ │ │ │ 1184 │ │ │ 1185 │ @final │ │ 1186 │ def parse(self): │ │ ❱ 1187 │ │ self._parse() │ │ 1188 │ │ │ │ 1189 │ │ if self.obj is None: │ │ 1190 │ │ │ return None │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/pandas/io/json/_json.py:1403 in _parse │ │ │ │ 1400 │ │ │ │ 1401 │ │ if orient == "columns": │ │ 1402 │ │ │ self.obj = DataFrame( │ │ ❱ 1403 │ │ │ │ ujson_loads(json, precise_float=self.precise_float), dtype=None │ │ 1404 │ │ │ ) │ │ 1405 │ │ elif orient == "split": │ │ 1406 │ │ │ decoded = { │ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ValueError: Trailing data During handling of the above exception, another exception occurred: ╭──────────────────────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:1997 in _prepare_split_single │ │ │ │ 1994 │ │ │ ) │ │ 1995 │ │ │ try: │ │ 1996 │ │ │ │ _time = time.time() │ │ ❱ 1997 │ │ │ │ for _, table in generator: │ │ 1998 │ │ │ │ │ if max_shard_size is not None and writer._num_bytes > max_shard_size │ │ 1999 │ │ │ │ │ │ num_examples, num_bytes = writer.finalize() │ │ 2000 │ │ │ │ │ │ writer.close() │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/packaged_modules/json/json.py:156 in _generate_tables │ │ │ │ 153 │ │ │ │ │ │ │ │ │ df = pd.read_json(f, dtype_backend="pyarrow") │ │ 154 │ │ │ │ │ │ │ except ValueError: │ │ 155 │ │ │ │ │ │ │ │ logger.error(f"Failed to load JSON from file '{file}' wi │ │ ❱ 156 │ │ │ │ │ │ │ │ raise e │ │ 157 │ │ │ │ │ │ │ if df.columns.tolist() == [0]: │ │ 158 │ │ │ │ │ │ │ │ df.columns = list(self.config.features) if self.config.f │ │ 159 │ │ │ │ │ │ │ try: │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/packaged_modules/json/json.py:130 in _generate_tables │ │ │ │ 127 │ │ │ │ │ │ try: │ │ 128 │ │ │ │ │ │ │ while True: │ │ 129 │ │ │ │ │ │ │ │ try: │ │ ❱ 130 │ │ │ │ │ │ │ │ │ pa_table = paj.read_json( │ │ 131 │ │ │ │ │ │ │ │ │ │ io.BytesIO(batch), read_options=paj.ReadOptions( │ │ 132 │ │ │ │ │ │ │ │ │ ) │ │ 133 │ │ │ │ │ │ │ │ │ break │ │ │ │ in pyarrow._json.read_json:308 │ │ │ │ in pyarrow.lib.pyarrow_internal_check_status:154 │ │ │ │ in pyarrow.lib.check_status:91 │ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ArrowInvalid: JSON parse error: Missing a name for object member. in row 718 The above exception was the direct cause of the following exception: ╭──────────────────────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ /root/autodl-tmp/GLM-4/finetune_demo/finetune.py:406 in main │ │ │ │ 403 ): │ │ 404 │ ft_config = FinetuningConfig.from_file(config_file) │ │ 405 │ tokenizer, model = load_tokenizer_and_model(model_dir, peft_config=ft_config.peft_co │ │ ❱ 406 │ data_manager = DataManager(data_dir, ft_config.data_config) │ │ 407 │ │ │ 408 │ train_dataset = data_manager.get_dataset( │ │ 409 │ │ Split.TRAIN, │ │ │ │ /root/autodl-tmp/GLM-4/finetune_demo/finetune.py:204 in __init__ │ │ │ │ 201 │ def __init__(self, data_dir: str, data_config: DataConfig): │ │ 202 │ │ self._num_proc = data_config.num_proc │ │ 203 │ │ │ │ ❱ 204 │ │ self._dataset_dct = _load_datasets( │ │ 205 │ │ │ data_dir, │ │ 206 │ │ │ data_config.data_format, │ │ 207 │ │ │ data_config.data_files, │ │ │ │ /root/autodl-tmp/GLM-4/finetune_demo/finetune.py:189 in _load_datasets │ │ │ │ 186 │ │ num_proc: Optional[int], │ │ 187 ) -> DatasetDict: │ │ 188 │ if data_format == '.jsonl': │ │ ❱ 189 │ │ dataset_dct = load_dataset( │ │ 190 │ │ │ data_dir, │ │ 191 │ │ │ data_files=data_files, │ │ 192 │ │ │ split=None, │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/load.py:2616 in load_dataset │ │ │ │ 2613 │ │ return builder_instance.as_streaming_dataset(split=split) │ │ 2614 │ │ │ 2615 │ # Download and prepare data │ │ ❱ 2616 │ builder_instance.download_and_prepare( │ │ 2617 │ │ download_config=download_config, │ │ 2618 │ │ download_mode=download_mode, │ │ 2619 │ │ verification_mode=verification_mode, │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:1029 in download_and_prepare │ │ │ │ 1026 │ │ │ │ │ │ │ prepare_split_kwargs["max_shard_size"] = max_shard_size │ │ 1027 │ │ │ │ │ │ if num_proc is not None: │ │ 1028 │ │ │ │ │ │ │ prepare_split_kwargs["num_proc"] = num_proc │ │ ❱ 1029 │ │ │ │ │ │ self._download_and_prepare( │ │ 1030 │ │ │ │ │ │ │ dl_manager=dl_manager, │ │ 1031 │ │ │ │ │ │ │ verification_mode=verification_mode, │ │ 1032 │ │ │ │ │ │ │ **prepare_split_kwargs, │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:1124 in _download_and_prepare │ │ │ │ 1121 │ │ │ │ │ 1122 │ │ │ try: │ │ 1123 │ │ │ │ # Prepare split will record examples associated to the split │ │ ❱ 1124 │ │ │ │ self._prepare_split(split_generator, **prepare_split_kwargs) │ │ 1125 │ │ │ except OSError as e: │ │ 1126 │ │ │ │ raise OSError( │ │ 1127 │ │ │ │ │ "Cannot find data file. " │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:1884 in _prepare_split │ │ │ │ 1881 │ │ │ gen_kwargs = split_generator.gen_kwargs │ │ 1882 │ │ │ job_id = 0 │ │ 1883 │ │ │ with pbar: │ │ ❱ 1884 │ │ │ │ for job_id, done, content in self._prepare_split_single( │ │ 1885 │ │ │ │ │ gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args │ │ 1886 │ │ │ │ ): │ │ 1887 │ │ │ │ │ if done: │ │ │ │ /root/miniconda3/lib/python3.10/site-packages/datasets/builder.py:2040 in _prepare_split_single │ │ │ │ 2037 │ │ │ │ e = e.__context__ │ │ 2038 │ │ │ if isinstance(e, DatasetGenerationError): │ │ 2039 │ │ │ │ raise │ │ ❱ 2040 │ │ │ raise DatasetGenerationError("An error occurred while generating the dataset │ │ 2041 │ │ │ │ 2042 │ │ yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_ │ │ 2043 │ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ DatasetGenerationError: An error occurred while generating the dataset 3.请问是否可以帮我解决 ### Expected behavior 希望问题可以得到解决 ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.20.0 - Platform: Linux-4.19.90-2107.6.0.0192.8.oe1.bclinux.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.8 - `huggingface_hub` version: 0.24.6 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2023.12.2

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PR_kwDODunzps5-62rY

(Super tiny doc update) Mention to_polars

closed

CONTRIBUTOR

polars is also quite popular now, thus this tiny update can tell users polars is supported

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PR_kwDODunzps5-1jP8

Fix typo in image dataset docs

closed

MEMBER

Fix typo in image dataset docs. Typo reported by @datavistics.

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PR_kwDODunzps5-ttUV

Video support

closed

MEMBER

(wip and experimental) adding the `Video` type based on `VideoReader` from `decord` ```python >>>from datasets import load_dataset >>> ds = load_dataset("path/to/videos", split="train").with_format("torch") >>> print(ds[0]["video"]) <decord.video_reader.VideoReader object at 0x337a47910> >>> print(ds[0]["video"][0]) tensor([[[73, 73, 73], [73, 73, 73], [73, 73, 73], ..., [23, 23, 23], [23, 23, 23], [23, 23, 23]]], dtype=torch.uint8) ``` the storage is the same as for audio and images: `{"path": pa.string(), "bytes": pa.binary()}` and I did a small to keep the hf:// URL in the "path" field if possible, this way the viewer can link to fiels on the hub if possible

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PR_kwDODunzps5-rgrx

handle config_name=None in push_to_hub

closed

CONTRIBUTOR

This caught me out - thought it might be better to explicitly handle None?

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I_kwDODunzps6aNzgO

Composite (multi-column) features

open

CONTRIBUTOR

### Feature request Structured data types (graphs etc.) might often be most efficiently stored as multiple columns, which then need to be combined during feature decoding Although it is currently possible to nest features as structs, my impression is that in particular when dealing with e.g. a feature composed of multiple numpy array / ArrayXD's, it would be more efficient to store each ArrayXD as a separate column (though I'm not sure by how much) Perhaps specification / implementation could be supported by something like: ``` features=Features(**{("feature0", "feature1")=Features(feature0=Array2D((None,10), dtype="float32"), feature1=Array2D((None,10), dtype="float32")) ``` ### Motivation Defining efficient composite feature types based on numpy arrays for representing data such as graphs with multiple node and edge attributes is currently challenging. ### Your contribution Possibly able to contribute

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PR_kwDODunzps5-l-OE

fast array extraction

open

CONTRIBUTOR

Implements #7210 using method suggested in https://github.com/huggingface/datasets/pull/7207#issuecomment-2411789307 ```python import numpy as np from datasets import Dataset, Features, Array3D features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")}) dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features) ``` ~0.02 s vs 0.9s on main ```python ds = dataset.to_iterable_dataset() t0 = time.time() for ex in ds: pass t1 = time.time() ``` < 0.01 s vs 1.3 s on main @lhoestq I can see this breaks a bunch of array-related tests but can update the test cases if you would support making this change? I also added an Array1D feature which will always be decoded into a numpy array and likewise improves extraction performance: ```python from datasets import Dataset, Features, Array1D, Sequence, Value array_features=Features(**{"array0": Array1D((None,), dtype="float32"), "array1": Array1D((None,), dtype="float32")}) sequence_features=Features(**{"array0": Sequence(feature=Value("float32"), length=-1), "array1": Sequence(feature=Value("float32"), length=-1)}) array_dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,), dtype=np.float32) for x in [20000,10000]*25] for i in range(2)}, features=array_features) sequence_dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,), dtype=np.float32) for x in [20000,10000]*25] for i in range(2)}, features=sequence_features) ```python t0 = time.time() for ex in array_dataset.to_iterable_dataset(): pass t1 = time.time() ``` < 0.01 s ```python t0 = time.time() for ex in sequence_dataset.to_iterable_dataset(): pass t1 = time.time() ``` ~1.1s And also added support for extracting structs of arrays as dicts of numpy arrays: ```python import numpy as np from datasets import Dataset, Features, Array3D, Sequence features=Features(struct={"array0": Array3D((None,10,10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")}, _list=Sequence(feature=Array3D((None,10,10), dtype="float32"))) dataset = Dataset.from_dict({"struct": [{f"array{i}": np.zeros((x,10,10), dtype=np.float32) for i in range(2)} for x in [2000,1000]*25], "_list": [[np.zeros((x,10,10), dtype=np.float32) for i in range(2)] for x in [2000,1000]*25]}, features=features) ``` ```python t0 = time.time() for ex in dataset.to_iterable_dataset(): pass t1 = time.time() assert isinstance(ex["struct"]["array0"], np.ndarray) and ex["struct"]["array0"].ndim == 3 ``` ~0.02 s and no exception vs ~7s with an exception on main

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I_kwDODunzps6aMUWf

Add R as a How to use from the Polars (R) Library as an option

open

NONE

### Feature request The boiler plate code to access a dataset via the hugging face file system is very useful. Please addd ## Add Polars (R) option The equivailent code works, because the [Polars-R](https://github.com/pola-rs/r-polars) wrapper has hugging faces funcitonaliy as well. ```r library(polars) df <- pl$read_parquet("hf://datasets/SALURBAL/core__admin_cube_public/core__admin_cube_public.parquet") ``` ## Polars (python) option  ## Libraries Currently  ### Motivation There are many data/analysis/research/statistics teams (particularly in academia and pharma) that use R as the default language. R has great integration with most of the newer data techs (arrow, parquet, polars) and having this included could really help in bringing this community into the hugging faces ecosystem. **This is a small/low-hanging-fruit front end change but would make a big impact expanding the community** ### Your contribution I am not sure which repositroy this should be in, but I have experience in R, Python and JS and happy to submit a PR in the appropriate repository.

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I_kwDODunzps6aJrng

Huggingface GIT returns null as Content-Type instead of application/x-git-receive-pack-result

open

NONE

### Describe the bug We push changes to our datasets programmatically. Our git client jGit reports that the hf git server returns null as Content-Type after a push. ### Steps to reproduce the bug A basic kotlin application: ``` val person = PersonIdent( "padmalcom", "padmalcom@sth.com" ) val cp = UsernamePasswordCredentialsProvider( "padmalcom", "mysecrettoken" ) val git = KGit.cloneRepository { setURI("https://huggingface.co/datasets/sth/images") setTimeout(60) setProgressMonitor(TextProgressMonitor()) setCredentialsProvider(cp) } FileOutputStream("./images/images.csv").apply { writeCsv(images) } git.add { addFilepattern("images.csv") } for (i in images) { FileUtils.copyFile( File("./files/${i.id}"), File("./images/${i.id + File(i.fileName).extension }") ) git.add { addFilepattern("${i.id + File(i.fileName).extension }") } } val revCommit = git.commit { author = person message = "Uploading images at " + LocalDateTime.now() .format(DateTimeFormatter.ISO_DATE_TIME) setCredentialsProvider(cp) } val push = git.push { setCredentialsProvider(cp) } ``` ### Expected behavior The git server is expected to return the Content-Type _application/x-git-receive-pack-result_. ### Environment info It is independent from the datasets library.

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https://github.com/huggingface/datasets/pull/7224

PR_kwDODunzps5-bLNR

fallback to default feature casting in case custom features not available during dataset loading

open

CONTRIBUTOR

a fix for #7223 in case datasets is happy to support this kind of extensibility! seems cool / powerful for allowing sharing of datasets with potentially different feature types

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https://github.com/huggingface/datasets/issues/7223

I_kwDODunzps6Z-Pxm

Fallback to arrow defaults when loading dataset with custom features that aren't registered locally

open

CONTRIBUTOR

### Describe the bug Datasets allows users to create and register custom features. However if datasets are then pushed to the hub, this means that anyone calling load_dataset without registering the custom Features in the same way as the dataset creator will get an error message. It would be nice to offer a fallback in this case. ### Steps to reproduce the bug ```python load_dataset("alex-hh/custom-features-example") ``` (Dataset creation process - must be run in separate session so that NewFeature isn't registered in session in which download is attempted:) ```python from dataclasses import dataclass, field import pyarrow as pa from datasets.features.features import register_feature from datasets import Dataset, Features, Value, load_dataset from datasets import Feature @dataclass class NewFeature(Feature): _type: str = field(default="NewFeature", init=False, repr=False) def __call__(self): return pa.int32() def examples_generator(): for i in range(5): yield {"feature": i} ds = Dataset.from_generator(examples_generator, features=Features(feature=NewFeature())) ds.push_to_hub("alex-hh/custom-features-example") register_feature(NewFeature, "NewFeature") ``` ### Expected behavior It would be nice, and offer greater extensibility, if there was some kind of graceful fallback mechanism in place for cases where user-defined features are stored in the dataset but not available locally. ### Environment info 3.0.2

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