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I_kwDODunzps6My3NC

Convert polars DataFrame back to datasets

open

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### 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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I_kwDODunzps6MxBgd

cannot split dataset when using load_dataset

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### 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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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? - [ ] Fix use of `array`: https://github.com/huggingface/datasets/pull/6976 - [ ] Remove [NumPy version limit](https://github.com/huggingface/datasets/pull/6975).

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I_kwDODunzps6MrWsD

How can I load partial parquet files only?

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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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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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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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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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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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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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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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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

open

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

open

NONE

### 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

open

NONE

### 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

open

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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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

### Feature request Add an option when sharding a dataset to have all shards the same size. Will be good to provide both an option of duplication, and by truncation. ### Motivation Currently the behavior of sharding is "If n % i == l, then the first l shards will have length (n // i) + 1, and the remaining shards will have length (n // i).". However, when using FSDP we want the shards to have the same size. This requires the user to manually handle this situation, but it will be nice if we had an option to shard the dataset into equally sized shards. ### Your contribution For now just a PR. I can also add code that does what is needed, but probably not efficient. Shard to equal size by duplication: ``` remainder = len(dataset) % num_shards num_missing_examples = num_shards - remainder duplicated = dataset.select(list(range(num_missing_examples))) dataset = concatenate_datasets([dataset, duplicated]) shard = dataset.shard(num_shards, shard_idx) ``` Or by truncation: ``` shard = dataset.shard(num_shards, shard_idx) num_examples_per_shard = len(dataset) // num_shards shard = shard.select(list(range(num_examples_per_shard))) ```

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I_kwDODunzps6Kw136

ExpectedMoreSplits error when using data_dir

closed

MEMBER

As reported by @regisss, an `ExpectedMoreSplits` error is raised when passing `data_dir`: ```python from datasets import load_dataset dataset = load_dataset( "lvwerra/stack-exchange-paired", split="train", cache_dir=None, data_dir="data/rl", ) ``` ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2609, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1140, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/usr/local/lib/python3.10/dist-packages/datasets/utils/info_utils.py", line 92, in verify_splits raise ExpectedMoreSplits(str(set(expected_splits) - set(recorded_splits))) datasets.utils.info_utils.ExpectedMoreSplits: {'test'} ```

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I_kwDODunzps6KtnJD

JSON loader implicitly coerces floats to integers

open

MEMBER

The JSON loader implicitly coerces floats to integers. The column values `[0.0, 1.0, 2.0]` are coerced to `[0, 1, 2]`. See CI error in dataset-viewer: https://github.com/huggingface/dataset-viewer/actions/runs/9290164936/job/25576926446 ``` =================================== FAILURES =================================== ___________________________ test_statistics_endpoint ___________________________ normal_user_public_json_dataset = 'DVUser/tmp-dataset-17170199043860' def test_statistics_endpoint(normal_user_public_json_dataset: str) -> None: dataset = normal_user_public_json_dataset config, split = get_default_config_split() statistics_response = poll_until_ready_and_assert( relative_url=f"/statistics?dataset={dataset}&config={config}&split={split}", check_x_revision=True, dataset=dataset, ) content = statistics_response.json() assert len(content) == 3 assert sorted(content) == ["num_examples", "partial", "statistics"], statistics_response statistics = content["statistics"] num_examples = content["num_examples"] partial = content["partial"] assert isinstance(statistics, list), statistics assert len(statistics) == 6 assert num_examples == 4 assert partial is False string_label_column = statistics[0] assert "column_name" in string_label_column assert "column_statistics" in string_label_column assert "column_type" in string_label_column assert string_label_column["column_name"] == "col_1" assert string_label_column["column_type"] == "string_label" # 4 unique values -> label assert isinstance(string_label_column["column_statistics"], dict) assert string_label_column["column_statistics"] == { "nan_count": 0, "nan_proportion": 0.0, "no_label_count": 0, "no_label_proportion": 0.0, "n_unique": 4, "frequencies": { "There goes another one.": 1, "Vader turns round and round in circles as his ship spins into space.": 1, "We count thirty Rebel ships, Lord Vader.": 1, "The wingman spots the pirateship coming at him and warns the Dark Lord": 1, }, } int_column = statistics[1] assert "column_name" in int_column assert "column_statistics" in int_column assert "column_type" in int_column assert int_column["column_name"] == "col_2" assert int_column["column_type"] == "int" assert isinstance(int_column["column_statistics"], dict) assert int_column["column_statistics"] == { "histogram": {"bin_edges": [0, 1, 2, 3, 3], "hist": [1, 1, 1, 1]}, "max": 3, "mean": 1.5, "median": 1.5, "min": 0, "nan_count": 0, "nan_proportion": 0.0, "std": 1.29099, } float_column = statistics[2] assert "column_name" in float_column assert "column_statistics" in float_column assert "column_type" in float_column assert float_column["column_name"] == "col_3" > assert float_column["column_type"] == "float" E AssertionError: assert 'int' == 'float' E - float E + int tests/test_14_statistics.py:72: AssertionError =========================== short test summary info ============================ FAILED tests/test_14_statistics.py::test_statistics_endpoint - AssertionError: assert 'int' == 'float' - float + int ``` This bug was introduced after: - #6914 We have reported the issue to pandas: - https://github.com/pandas-dev/pandas/issues/58866

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I_kwDODunzps6KpcWt

save_to_disk() freezes when saving on s3 bucket with multiprocessing

open

NONE

### Describe the bug I'm trying to save a `Dataset` using the `save_to_disk()` function with: - `num_proc > 1` - `dataset_path` being a s3 bucket path e.g. "s3://{bucket_name}/{dataset_folder}/" The hf progress bar shows up but the saving does not seem to start. When using one processor only (`num_proc=1`), everything works fine. When saving the dataset on local disk (as opposed to s3 bucket) with `num_proc > 1`, everything works fine. Thank you for your help! :) ### Steps to reproduce the bug I tried without any storage options: ``` from datasets import load_dataset sandbox_ds = load_dataset("openai_humaneval") sandbox_ds["test"].save_to_disk( "s3://bucket-name/test_multiprocessing_saving/", num_proc=4, ) ``` and with the specific s3fs storage options: ``` from datasets import load_dataset from s3fs import S3FileSystem def get_s3fs(): return S3FileSystem() sandbox_ds = load_dataset("openai_humaneval") sandbox_ds["test"].save_to_disk( "s3://bucket-name/test_multiprocessing_saving/", num_proc=4, storage_options=get_s3fs().storage_options, # also tried: storage_options=S3FileSystem().storage_options ) ``` I'm guessing I might use `storage_options` parameter wrongly, but I didn't find anything online that made it work. **NB**: Behavior is the same when trying to save the whole `DatasetDict`. ### Expected behavior Progress bar fills in and saving is carried out. ### Environment info `datasets==2.18.0`

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I_kwDODunzps6KngX2

Support for pathlib.Path in datasets 2.19.0

open

NONE

### Describe the bug After the recent update of `datasets`, Dataset.save_to_disk does not accept a pathlib.Path anymore. It was supported in 2.18.0 and previous versions. Is this intentional? Was it supported before only because of a Python dusk-typing miracle? ### Steps to reproduce the bug ``` from datasets import Dataset import pathlib path = pathlib.Path("./my_out_path") Dataset.from_dict( {"text": ["hello world"], "label": [777], "split": ["train"]} .save_to_disk(path) ``` This results in an error when using datasets 2.19: ``` Traceback (most recent call last): File "<stdin>", line 3, in <module> File "/Users/jb/scratch/venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 1515, in save_to_disk fs, _ = url_to_fs(dataset_path, **(storage_options or {})) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/jb/scratch/venv/lib/python3.11/site-packages/fsspec/core.py", line 383, in url_to_fs chain = _un_chain(url, kwargs) ^^^^^^^^^^^^^^^^^^^^^^ File "/Users/jb/scratch/venv/lib/python3.11/site-packages/fsspec/core.py", line 323, in _un_chain if "::" in path ^^^^^^^^^^^^ TypeError: argument of type 'PosixPath' is not iterable ``` Converting to str works, however. ``` Dataset.from_dict( {"text": ["hello world"], "label": [777], "split": ["train"]} ).save_to_disk(str(path)) ``` ### Expected behavior My dataset gets saved to disk without an error. ### Environment info aiohttp==3.9.5 aiosignal==1.3.1 attrs==23.2.0 certifi==2024.2.2 charset-normalizer==3.3.2 datasets==2.19.0 dill==0.3.8 filelock==3.14.0 frozenlist==1.4.1 fsspec==2024.3.1 huggingface-hub==0.23.2 idna==3.7 multidict==6.0.5 multiprocess==0.70.16 numpy==1.26.4 packaging==24.0 pandas==2.2.2 pyarrow==16.1.0 pyarrow-hotfix==0.6 python-dateutil==2.9.0.post0 pytz==2024.1 PyYAML==6.0.1 requests==2.32.3 six==1.16.0 tqdm==4.66.4 typing_extensions==4.12.0 tzdata==2024.1 urllib3==2.2.1 xxhash==3.4.1 yarl==1.9.4

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I_kwDODunzps6KeZ1C

ValueError: Couldn't infer the same data file format for all splits. Got {'train': ('json', {}), 'validation': (None, {})}

open

NONE

### Describe the bug When I run the code en = load_dataset("allenai/c4", "en", streaming=True), I encounter an error: raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}") ValueError: Couldn't infer the same data file format for all splits. Got {'train': ('json', {}), 'validation': (None, {})}. However, running dataset = load_dataset('allenai/c4', streaming=True, data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation') works fine. What is the issue here? ### Steps to reproduce the bug run code： import os os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' from datasets import load_dataset en = load_dataset("allenai/c4", "en", streaming=True) ### Expected behavior Successfully loaded the dataset. ### Environment info - `datasets` version: 2.18.0 - Platform: Linux-6.5.0-28-generic-x86_64-with-glibc2.17 - Python version: 3.8.19 - `huggingface_hub` version: 0.22.2 - PyArrow version: 15.0.2 - Pandas version: 2.0.3 - `fsspec` version: 2024.2.0

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I_kwDODunzps6Kddzt

Avoid downloading the whole dataset when only README.me has been touched on hub.

open

NONE

### Feature request `datasets.load_dataset()` triggers a new download of the **whole dataset** when the README.md file has been touched on huggingface hub, even if data files / parquet files are the exact same. I think the current behaviour of the load_dataset function is triggered whenever a change of the hash of latest commit on huggingface hub, but is there a clever way to only download again the dataset **if and only if** data is modified ? ### Motivation The current behaviour is a waste of network bandwidth / disk space / research time. ### Your contribution I don't have time to submit a PR, but I hope a simple solution will emerge from this issue !

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I_kwDODunzps6KUH5H

Caching map result of DatasetDict.

open

NONE

Hi! I'm currenty using the map function to tokenize a somewhat large dataset, so I need to use the cache to save ~25 mins. Changing num_proc incduces the recomputation of the map, I'm not sure why and if this is excepted behavior? here it says, that cached files are loaded sequentially: https://github.com/huggingface/datasets/blob/bb2664cf540d5ce4b066365e7c8b26e7f1ca4743/src/datasets/arrow_dataset.py#L3005-L3006 it seems like I can pass in a fingerprint, and load it directly: https://github.com/huggingface/datasets/blob/bb2664cf540d5ce4b066365e7c8b26e7f1ca4743/src/datasets/arrow_dataset.py#L3108-L3125 **Environment Setup:** - Python 3.11.9 - datasets 2.19.1 conda-forge - Linux 6.1.83-1.el9.elrepo.x86_64 **MRE** ```python fixed raw_datasets fixed tokenize_function tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=9, remove_columns=['text'], load_from_cache_file= True, desc="Running tokenizer on dataset line_by_line", ) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=5, remove_columns=['text'], load_from_cache_file= True, desc="Running tokenizer on dataset line_by_line", ) ```

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I_kwDODunzps6KPZnI

Export Parquet Tablet Audio-Set is null bytes in Arrow

open

NONE

### Describe the bug Exporting the processed audio inside the table with the dataset.to_parquet function, the object pyarrow {bytes: null, path: "Some/Path"} At the same time, the same dataset uploaded to the hub has bit arrays   ### Steps to reproduce the bug 1.Get dataset from audio and cast it 2.Export and push dataset 3.It’s scary to be indignant at the difference in the uploaded dataset and the fact that it was saved locally ```py from datasets import Dataset, Audio df = Dataset.from_csv("./datasets.csv") df = df.cast_column("audio", Audio(16000)) df.to_parquet("./datasets.parquet") df.push_to_hub(repo_id="************", token="**********************") ``` You can use "try replicate case" for this [replicate_packet.zip](https://github.com/huggingface/datasets/files/15457114/replicate_packet.zip) ### Expected behavior Two parquet tables identical in content. It is obvious? ### Environment info Python 3.11+ (I try did it in 3.12 and got same result )

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I_kwDODunzps6KBYqx

Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple>

open

NONE

### Describe the bug I wrote a notebook to load an existing dataset, process it, and upload as a private dataset using `dataset.push_to_hub(...)` at the end. The push to hub is failing with: ``` ValueError: Invalid metadata in README.md. - Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python[/tuple](http://192.168.1.128:8888/tuple)> (50:11) 47 | - 4 48 | - 4 49 | - 8 50 | - !!binary | ----------------^ 51 | TwAAAA== 52 | '1': !!python[/object/apply](http://192.168.1.128:8888/object/apply):nump ... ``` My dataset has a `train` and `validation` dataset. These are the features: ``` {'c1': Value(dtype='string', id=None), 'c2': Value(dtype='string', id=None), 'c3': [{'value': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}], 'c4': Value(dtype='string', id=None), 'c5': Value(dtype='string', id=None), 'c6': Value(dtype='string', id=None), 'c7': Value(dtype='string', id=None), 'c8': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'c9': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'c10': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'labels': Sequence(feature=ClassLabel(names=['O', 'B-ABC', 'I-ABC', ...], id=None), length=-1, id=None), 'c12': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} ``` This used to work until I decided to cast the `labels` feature to a `Sequence(ClassLabel(...))` type with: ``` ds['train'] = ds['train'].cast_column("labels", Sequence(ClassLabel(names=list(labels)))) ds['validation'] = ds['validation'].cast_column("labels", Sequence(ClassLabel(names=list(labels)))) ``` ### Steps to reproduce the bug 1. Start with any token classification dataset. 2. Add a `labels` column with data such as `[0,0,0,12,13,13,13,0,0]`. 3. Cast the label column from `Sequence` to `Sequence(ClassLabel))` with: ``` labels = ['O', 'B-TEST', 'I-TEST'] ds = ds.cast_column("labels", Sequence(ClassLabel(names=labels))) ``` 4. Push to hub with `ds.push_to_hub("me/awesome-stuff-dataset")` ### Expected behavior I expected `push_to_hub` to successfully push my dataset to the hub without error. ### Environment info Python 3.11.9 datasets==2.19.1 transformers==4.41.1 PyYAML==6.0.1

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I_kwDODunzps6KAQVy

NonMatchingSplitsSizesError when using data_dir

closed

NONE

### Describe the bug Loading a dataset from with a data_dir argument generates a NonMatchingSplitsSizesError if there are multiple directories in the dataset. This appears to happen because the expected split is calculated based on the data in all the directories whereas the recorded split is calculated based on the data in the directory specified using the data_dir argument. This is recent behavior. Until the past few weeks loading using the data_dir argument worked without any issue. ### Steps to reproduce the bug Simple test dataset available here: https://huggingface.co/datasets/srehaag/hf-bug-temp The dataset contains two directories "data1" and "data2", each with a file called "train.parquet" with a 2 x 5 table. from datasets import load_dataset dataset = load_dataset("srehaag/hf-bug-temp", data_dir = "data1") Generates: --------------------------------------------------------------------------- NonMatchingSplitsSizesError Traceback (most recent call last) Cell In[3], <a href='vscode-notebook-cell:?execution_count=3&line=2'>line 2</a> <a href='vscode-notebook-cell:?execution_count=3&line=1'>1</a> from datasets import load_dataset ----> <a href='vscode-notebook-cell:?execution_count=3&line=2'>2</a> dataset = load_dataset("srehaag/hf-bug-temp", data_dir = "data1") File ~/.python/current/lib/python3.10/site-packages/datasets/load.py:2609, 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) <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2606'>2606</a> return builder_instance.as_streaming_dataset(split=split) <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2608'>2608</a> # Download and prepare data -> <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2609'>2609</a> builder_instance.download_and_prepare( <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2610'>2610</a> download_config=download_config, <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2611'>2611</a> download_mode=download_mode, <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2612'>2612</a> verification_mode=verification_mode, <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2613'>2613</a> num_proc=num_proc, <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2614'>2614</a> storage_options=storage_options, <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2615'>2615</a> ) <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2617'>2617</a> # Build dataset for splits <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2618'>2618</a> keep_in_memory = ( <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2619'>2619</a> keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2620'>2620</a> ) File ~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1025'>1025</a> if num_proc is not None: <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1026'>1026</a> prepare_split_kwargs["num_proc"] = num_proc -> <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1027'>1027</a> self._download_and_prepare( <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1028'>1028</a> dl_manager=dl_manager, <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1029'>1029</a> verification_mode=verification_mode, <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1030'>1030</a> **prepare_split_kwargs, <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1031'>1031</a> **download_and_prepare_kwargs, <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1032'>1032</a> ) <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1033'>1033</a> # Sync info <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1034'>1034</a> self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1140, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1137'>1137</a> dl_manager.manage_extracted_files() <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1139'>1139</a> if verification_mode == VerificationMode.BASIC_CHECKS or verification_mode == VerificationMode.ALL_CHECKS: -> <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1140'>1140</a> verify_splits(self.info.splits, split_dict) <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1142'>1142</a> # Update the info object with the splits. <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1143'>1143</a> self.info.splits = split_dict File ~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:101, in verify_splits(expected_splits, recorded_splits) <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:95'>95</a> bad_splits = [ <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:96'>96</a> {"expected": expected_splits[name], "recorded": recorded_splits[name]} <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:97'>97</a> for name in expected_splits <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:98'>98</a> if expected_splits[name].num_examples != recorded_splits[name].num_examples <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:99'>99</a> ] <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:100'>100</a> if len(bad_splits) > 0: --> <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:101'>101</a> raise NonMatchingSplitsSizesError(str(bad_splits)) <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:102'>102</a> logger.info("All the splits matched successfully.") NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=212, num_examples=10, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=106, num_examples=5, shard_lengths=None, dataset_name='hf-bug-temp')}] __________ By contrast, this loads the data from both data1/train.parquet and data2/train.parquet without any error message: from datasets import load_dataset dataset = load_dataset("srehaag/hf-bug-temp") ### Expected behavior Should load the 5 x 2 table from data1/train.parquet without error message. ### Environment info Used Codespaces to simplify environment (see details below), but bug is present across various configurations. - `datasets` version: 2.19.1 - Platform: Linux-6.5.0-1021-azure-x86_64-with-glibc2.31 - Python version: 3.10.13 - `huggingface_hub` version: 0.23.1 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.3.1

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I_kwDODunzps6J90UP

WinError 32 The process cannot access the file during load_dataset

open

NONE

### Describe the bug When I try to load the opus_book from hugging face (following the [guide on the website](https://huggingface.co/docs/transformers/main/en/tasks/translation)) ```python from datasets import load_dataset, Dataset dataset = load_dataset("Helsinki-NLP/opus_books", "en-fr", features=["id", "translation"]) ``` I get an error: `PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:/Users/Me/.cache/huggingface/datasets/Helsinki-NLP___parquet/ca-de-a39f1ef185b9b73b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec.incomplete\\parquet-train-00000-00000-of-NNNNN.arrow' ` <details><summary>Full stacktrace</summary> <p> ```python AttributeError Traceback (most recent call last) File c:\Users\Me\.conda\envs\ia\lib\site-packages\datasets\builder.py:1858, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) [1857](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1857) _time = time.time() -> [1858](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1858) for _, table in generator: [1859](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1859) if max_shard_size is not None and writer._num_bytes > max_shard_size: File c:\Users\Me\.conda\envs\ia\lib\site-packages\datasets\packaged_modules\parquet\parquet.py:59, in Parquet._generate_tables(self, files) [58](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/packaged_modules/parquet/parquet.py:58) def _generate_tables(self, files): ---> [59](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/packaged_modules/parquet/parquet.py:59) schema = self.config.features.arrow_schema if self.config.features is not None else None [60](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/packaged_modules/parquet/parquet.py:60) if self.config.features is not None and self.config.columns is not None: AttributeError: 'list' object has no attribute 'arrow_schema' During handling of the above exception, another exception occurred: AttributeError Traceback (most recent call last) File c:\Users\Me\.conda\envs\ia\lib\site-packages\datasets\builder.py:1882, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) [1881](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1881) num_shards = shard_id + 1 -> [1882](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1882) num_examples, num_bytes = writer.finalize() [1883](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1883) writer.close() File c:\Users\Me\.conda\envs\ia\lib\site-packages\datasets\arrow_writer.py:584, in ArrowWriter.finalize(self, close_stream) [583](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/arrow_writer.py:583) # If schema is known, infer features even if no examples were written --> [584](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/arrow_writer.py:584) if self.pa_writer is None and self.schema: ... --> [627](file:///C:/Users/Me/.conda/envs/ia/lib/shutil.py:627) os.unlink(fullname) [628](file:///C:/Users/Me/.conda/envs/ia/lib/shutil.py:628) except OSError: [629](file:///C:/Users/Me/.conda/envs/ia/lib/shutil.py:629) onerror(os.unlink, fullname, sys.exc_info()) PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:/Users/Me/.cache/huggingface/datasets/Helsinki-NLP___parquet/ca-de-a39f1ef185b9b73b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec.incomplete\\parquet-train-00000-00000-of-NNNNN.arrow' ``` </p> </details> ### Steps to reproduce the bug Steps to reproduce: Just execute these lines ```python from datasets import load_dataset, Dataset dataset = load_dataset("Helsinki-NLP/opus_books", "en-fr", features=["id", "translation"]) ``` ### Expected behavior I expect the dataset to be loaded without any errors. ### Environment info | Package| Version| |--------|--------| | transformers| 4.37.2| | python| 3.9.19| | pytorch| 2.3.0| | datasets|2.12.0 | | arrow | 1.2.3| I am using Conda on Windows 11.

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I_kwDODunzps6JyV6s

```push_to_hub()``` - Prevent Automatic Generation of Splits

closed

NONE

### Describe the bug I currently have a dataset which has not been splited. When pushing the dataset to my hugging face dataset repository, it is split into a testing and training set. How can I prevent the split from happening? ### Steps to reproduce the bug 1. Have a unsplit dataset ```python Dataset({ features: ['input', 'output', 'Attack', '__index_level_0__'], num_rows: 944685 }) ``` 2. Push it to huggingface ```python dataset.push_to_hub(dataset_name) ``` 3. On the hugging face dataset repo, the dataset then appears to be splited:  4. Indeed, when loading the dataset from this repo, the dataset is split in two testing and training set. ```python from datasets import load_dataset, Dataset dataset = load_dataset("Jetlime/NF-CSE-CIC-IDS2018-v2", streaming=True) dataset ``` output: ``` IterableDatasetDict({ train: IterableDataset({ features: ['input', 'output', 'Attack', '__index_level_0__'], n_shards: 2 }) test: IterableDataset({ features: ['input', 'output', 'Attack', '__index_level_0__'], n_shards: 1 }) ``` ### Expected behavior The dataset shall not be splited, as not requested. ### Environment info - `datasets` version: 2.19.1 - Platform: Linux-6.2.0-35-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.23.0 - PyArrow version: 15.0.2 - Pandas version: 2.2.2 - `fsspec` version: 2024.3.1

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I_kwDODunzps6JqcoB

Column order is nondeterministic when loading from JSON

closed

MEMBER

As reported by @meg-huggingface, the order of the JSON object keys is not preserved while loading a dataset from a JSON file with a list of objects. For example, when loading a JSON files with a list of objects, each with the following ordered keys: - [ID, Language, Topic], the resulting dataset may have columns: - [ID, Topic, Language], or - [Topic, Language, ID], or - [Topic, ID, Language],... This issue is caused by the use of a Python set (which does not preserve the order): https://github.com/huggingface/datasets/blob/60d21efbc01e15d0b596ac1072750cbecd91548a/src/datasets/packaged_modules/json/json.py#L168 introduced in - #5772

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I_kwDODunzps6JpiDJ

Add MedImg for streaming

open

NONE

### Feature request Host the MedImg dataset (similar to Imagenet but for biomedical images). ### Motivation There is a clear need for biomedical image foundation models and large scale biomedical datasets that are easily streamable. This would be an excellent tool for the biomedical community. ### Your contribution MedImg can be found [here](https://www.cuilab.cn/medimg/#).

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I_kwDODunzps6JYt6k

Fail to load "stas/c4-en-10k" dataset since 2.16 version

closed

NONE

### Describe the bug When update datasets library to version 2.16+ ( I test it on 2.16, 2.19.0 and 2.19.1), using the following code to load stas/c4-en-10k dataset ```python from datasets import load_dataset, Dataset dataset = load_dataset('stas/c4-en-10k') ``` and then it raise UnicodeDecodeError like ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 2523, in load_dataset builder_instance = load_dataset_builder( File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 2195, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 1846, in dataset_module_factory raise e1 from None File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 1798, in dataset_module_factory can_load_config_from_parquet_export = "DEFAULT_CONFIG_NAME" not in f.read() File "/home/*/conda3/envs/watermark/lib/python3.10/codecs.py", line 322, in decode (result, consumed) = self._buffer_decode(data, self.errors, final) UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte ``` I found that fs.open loads a gzip file and parses it like plain text using utf-8 encoder. ```python fs = HfFileSystem('https://huggingface.co') fs.open("datasets/stas/c4-en-10k/c4-en-10k.py", "rb") data = fs.read() # data is gzip bytes begin with b'\x1f\x8b\x08\x00\x00\tn\x88\x00...' data2 = unzip_gzip_bytes(data) # data2 is what we want: '# coding=utf-8\n# Copyright 2020 The HuggingFace Datasets...' ``` ### Steps to reproduce the bug 1. Install datasets between version 2.16 and 2.19 2. Use `datasets.load_dataset` method to load `stas/c4-en-10k` dataset. ### Expected behavior Load dataset normally. ### Environment info Platform = Linux-5.4.0-159-generic-x86_64-with-glibc2.35 Python = 3.10.14 Datasets = 2.19

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I_kwDODunzps6JUgzZ

Support the deserialization of json lines files comprised of lists

open

NONE

### Feature request I manage a somewhat large and popular Hugging Face dataset known as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus). I recently updated my corpus to be stored in a json lines file where each line is an array and each element represents a value at a particular column. Previously, my corpus was stored as a json lines file where each line was a dictionary and the keys were the fields. Essentially, a line in my json lines file used to look like this: ```json {"version_id":"","type":"","jurisdiction":"","source":"","citation":"","url":"","when_scraped":"","text":""} ``` And now it looks like this: ```json ["","","","","","","",""] ``` This saves 65 bytes per document and allows me very quickly serialise and deserialise documents via `msgspec`. After making this change, I found that `datasets` was incapable of deserialising my Corpus without a custom loading script, even if I ensured that the `dataset_info` field in my dataset card contained the desired names of my features. I would like to request that functionality be added to support this format which is more memory-efficent and faster than using dictionaries. ### Motivation The [documentation](https://huggingface.co/docs/datasets/en/dataset_script) for creating dataset loading scripts asserts that: > In the next major release, the new safety features of 🤗 Datasets will disable running dataset loading scripts by default, and you will have to pass trust_remote_code=True to load datasets that require running a dataset script. I would rather not require my users to pass `trust_remote_code=True` which means that I will need built-in support for this format. ### Your contribution I would be happy to submit a PR for this if this is something you would incorporate into `datasets` and if I can be pointed to where the code would need to go.

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I_kwDODunzps6JT1qP

irc_disentangle - Issue with splitting data

open

NONE

### Describe the bug I am trying to access your database through python using "datasets.load_dataset("irc_disentangle")" and I am getting this error message: ValueError: Instruction "train" corresponds to no data! ### Steps to reproduce the bug import datasets ds = datasets.load_dataset('irc_disentangle') ds ### Expected behavior The data is supposed to load into ds and be accessable as such: ds['train'][1050], ds['train'][1055] ### Environment info I tired Python 3.12 and 3.10

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I_kwDODunzps6JRn7b

Extraction protocol for arrow files is not defined

open

NONE

### Describe the bug Passing files with `.arrow` extension into data_files argument, at least when `streaming=True` is very slow. ### Steps to reproduce the bug Basically it goes through the `_get_extraction_protocol` method located [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L820) The method then looks at some base known extensions where `arrow` is not defined so it proceeds to determine the compression with the magic number method which is slow when dealing with a lot of files which are stored in s3 and by looking at this predefined list, I don't see `arrow` in there either so in the end it return None: ``` MAGIC_NUMBER_TO_COMPRESSION_PROTOCOL = { bytes.fromhex("504B0304"): "zip", bytes.fromhex("504B0506"): "zip", # empty archive bytes.fromhex("504B0708"): "zip", # spanned archive bytes.fromhex("425A68"): "bz2", bytes.fromhex("1F8B"): "gzip", bytes.fromhex("FD377A585A00"): "xz", bytes.fromhex("04224D18"): "lz4", bytes.fromhex("28B52FFD"): "zstd", } ``` ### Expected behavior My expectation is that `arrow` would be in the known lists so it would return None without going through the magic number method. ### Environment info datasets 2.19.0

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I_kwDODunzps6JHd5V

Add the option of saving in parquet instead of arrow

open

NONE

### Feature request In dataset.save_to_disk('/path/to/save/dataset'), add the option to save in parquet format dataset.save_to_disk('/path/to/save/dataset', format="parquet"), because arrow is not used for Production Big data.... (only parquet) ### Motivation because arrow is not used for Production Big data.... (only parquet) ### Your contribution I can do the testing !

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I_kwDODunzps6JGcUp

HTTPError 403 raised by CLI convert_to_parquet when creating script branch on 3rd party repos

closed

MEMBER

CLI convert_to_parquet cannot create "script" branch on 3rd party repos. It can only create it on repos where the user executing the script has write access. Otherwise, a 403 Forbidden HTTPError is raised: ``` Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py", line 304, in hf_raise_for_status response.raise_for_status() File "/usr/local/lib/python3.10/dist-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/ORG/DATASET/branch/script The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/bin/datasets-cli", line 8, in <module> sys.exit(main()) File "/usr/local/lib/python3.10/dist-packages/datasets/commands/datasets_cli.py", line 41, in main service.run() File "/usr/local/lib/python3.10/dist-packages/datasets/commands/convert_to_parquet.py", line 92, in run create_branch(dataset_id, branch="script", repo_type="dataset", token=token, exist_ok=True) File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py", line 5503, in create_branch hf_raise_for_status(response) File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py", line 367, in hf_raise_for_status raise HfHubHTTPError(message, response=response) from e huggingface_hub.utils._errors.HfHubHTTPError: (Request ID: Root=1-6645ee0d-4db1ed8a1fbe04956be15897;139a6e23-df7d-4f62-b5ba-adb6d8e6e696) 403 Forbidden: Forbidden: cannot write to script. Cannot access content at: https://huggingface.co/api/datasets/ORG/DATASET/branch/script. If you are trying to create or update content,make sure you have a token with the `write` role. ```

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