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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

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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

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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

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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

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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

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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

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### 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

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- [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

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### 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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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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https://github.com/huggingface/datasets/pull/7265

PR_kwDODunzps6AYofJ

Disallow video push_to_hub

closed

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PR_kwDODunzps6AYfwL

fix docs relative links

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https://github.com/huggingface/datasets/pull/7263

PR_kwDODunzps6ARg7m

Small addition to video docs

closed

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PR_kwDODunzps6AOWI8

Allow video with disabeld decoding without decord

closed

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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https://github.com/huggingface/datasets/pull/7259

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

closed

MEMBER

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

closed

MEMBER

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

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PR_kwDODunzps6AG25R

fix decord import

closed

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

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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

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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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https://github.com/huggingface/datasets/pull/7227

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", "[email protected]" ) 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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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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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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I_kwDODunzps6Z8IoR

TypeError: Couldn't cast array of type string to null in long json

open

NONE

### Describe the bug In general, changing the type from string to null is allowed within a dataset — there are even examples of this in the documentation. However, if the dataset is large and unevenly distributed, this allowance stops working. The schema gets locked in after reading a chunk. Consequently, if all values in the first chunk of a field are, for example, null, the field will be locked as type null, and if a string appears in that field in the second chunk, it will trigger this error: <details> <summary>Traceback </summary> ``` TypeError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1868 try: -> 1869 writer.write_table(table) 1870 except CastError as cast_error: 14 frames [/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size) 579 pa_table = pa_table.combine_chunks() --> 580 pa_table = table_cast(pa_table, self._schema) 581 if self.embed_local_files: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema) 2291 if table.schema != schema: -> 2292 return cast_table_to_schema(table, schema) 2293 elif table.schema.metadata != schema.metadata: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema) 2244 ) -> 2245 arrays = [ 2246 cast_array_to_feature( [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in <listcomp>(.0) 2245 arrays = [ -> 2246 cast_array_to_feature( 2247 table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type), [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in wrapper(array, *args, **kwargs) 1794 if isinstance(array, pa.ChunkedArray): -> 1795 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1796 else: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in <listcomp>(.0) 1794 if isinstance(array, pa.ChunkedArray): -> 1795 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1796 else: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_array_to_feature(array, feature, allow_primitive_to_str, allow_decimal_to_str) 2101 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 2102 return array_cast( 2103 array, [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in wrapper(array, *args, **kwargs) 1796 else: -> 1797 return func(array, *args, **kwargs) 1798 [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str) 1947 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type): -> 1948 raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}") 1949 return array.cast(pa_type) TypeError: Couldn't cast array of type string to null The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [<ipython-input-353-e02f83980611>](https://localhost:8080/#) in <cell line: 1>() ----> 1 dd = load_dataset("json", data_files=["TEST.json"]) [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) 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) 2094 2095 # Download and prepare data -> 2096 builder_instance.download_and_prepare( 2097 download_config=download_config, 2098 download_mode=download_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 922 if num_proc is not None: 923 prepare_split_kwargs["num_proc"] = num_proc --> 924 self._download_and_prepare( 925 dl_manager=dl_manager, 926 verification_mode=verification_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 997 try: 998 # Prepare split will record examples associated to the split --> 999 self._prepare_split(split_generator, **prepare_split_kwargs) 1000 except OSError as e: 1001 raise OSError( [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1738 job_id = 0 1739 with pbar: -> 1740 for job_id, done, content in self._prepare_split_single( 1741 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1742 ): [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1894 if isinstance(e, DatasetGenerationError): 1895 raise -> 1896 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1897 1898 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` </details> ### Steps to reproduce the bug ```python import json from datasets import load_dataset with open("TEST.json", "w") as f: row = {"ballast": "qwerty" * 1000, "b": None} row_str = json.dumps(row) + "\n" line_size = len(row_str) chunk_size = 10 << 20 lines_in_chunk = chunk_size // line_size + 1 print(f"Writing {lines_in_chunk} lines") for i in range(lines_in_chunk): f.write(row_str) null_row = {"ballast": "Gotcha", "b": "Not Null"} f.write(json.dumps(null_row) + "\n") load_dataset("json", data_files=["TEST.json"]) ``` ### Expected behavior Concatenation of the chunks without errors ### Environment info - `datasets` version: 3.0.1 - Platform: Linux-6.1.85+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.24.7 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1

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https://github.com/huggingface/datasets/pull/7221

PR_kwDODunzps5-YNI8

add CustomFeature base class to support user-defined features with encoding/decoding logic

open

CONTRIBUTOR

intended as fix for #7220 if this kind of extensibility is something that datasets is willing to support! ```python from datasets.features.features import CustomFeature class ListOfStrs(CustomFeature): requires_encoding = True def _encode_example(self, value): if isinstance(value, str): return [str] else: return value feats = Features(strlist=ListOfStrs()) feats.encode_example({"strlist": "a"})["strlist"] == feats["strlist"].encode_example("a") ```

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I_kwDODunzps6Z5r6O

Custom features not compatible with special encoding/decoding logic

open

CONTRIBUTOR

### Describe the bug It is possible to register custom features using datasets.features.features.register_feature (https://github.com/huggingface/datasets/pull/6727) However such features are not compatible with Features.encode_example/decode_example if they require special encoding / decoding logic because encode_nested_example / decode_nested_example checks whether the feature is in a fixed list of encodable types: https://github.com/huggingface/datasets/blob/16a121d7821a7691815a966270f577e2c503473f/src/datasets/features/features.py#L1349 This prevents the extensibility of features to complex cases ### Steps to reproduce the bug ```python class ListOfStrs: def encode_example(self, value): if isinstance(value, str): return [str] else: return value feats = Features(strlist=ListOfStrs()) assert feats.encode_example({"strlist": "a"})["strlist"] = feats["strlist"].encode_example("a")} ``` ### Expected behavior Registered feature types should be encoded based on some property of the feature (e.g. requires_encoding)? ### Environment info 3.0.2

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PR_kwDODunzps5-XAQF

bump fsspec

closed

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I_kwDODunzps6Z2GK6

ds.map(f, num_proc=10) is slower than df.apply

open

NONE

### Describe the bug pandas columns: song_id, song_name ds = Dataset.from_pandas(df) def has_cover(song_name): if song_name is None or pd.isna(song_name): return False return 'cover' in song_name.lower() df['has_cover'] = df.song_name.progress_apply(has_cover) ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10) time cost: 1. df.apply: 100%|██████████| 12500592/12500592 [00:13<00:00, 959825.47it/s] 2. ds.map: Map (num_proc=10):  31%  3899028/12500592 [00:28<00:38, 222532.89 examples/s] ### Steps to reproduce the bug pandas columns: song_id, song_name ds = Dataset.from_pandas(df) def has_cover(song_name): if song_name is None or pd.isna(song_name): return False return 'cover' in song_name.lower() df['has_cover'] = df.song_name.progress_apply(has_cover) ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10) ### Expected behavior ds.map is ~num_proc faster than df.apply ### Environment info pandas: 2.2.2 datasets: 2.19.1

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I_kwDODunzps6Zxs4b

Iterable dataset map with explicit features causes slowdown for Sequence features

open

CONTRIBUTOR

### Describe the bug When performing map, it's nice to be able to pass the new feature type, and indeed required by interleave and concatenate datasets. However, this can cause a major slowdown for certain types of array features due to the features being re-encoded. This is separate to the slowdown reported in #7206 ### Steps to reproduce the bug ``` from datasets import Dataset, Features, Array3D, Sequence, Value import numpy as np import time features=Features(**{"array0": Sequence(feature=Value("float32"), length=-1), "array1": Sequence(feature=Value("float32"), length=-1)}) dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,), dtype=np.float32) for x in [5000,10000]*25] for i in range(2)}, features=features) ``` ``` ds = dataset.to_iterable_dataset() ds = ds.with_format("numpy").map(lambda x: x) t0 = time.time() for ex in ds: pass t1 = time.time() ``` ~1.5 s on main ``` ds = dataset.to_iterable_dataset() ds = ds.with_format("numpy").map(lambda x: x, features=features) t0 = time.time() for ex in ds: pass t1 = time.time() ``` ~ 3 s on main ### Expected behavior I'm not 100% sure whether passing new feature types to formatted outputs of map should be supported or not, but assuming it should, then there should be a cost-free way to specify the new feature type - knowing feature type is required by interleave_datasets and concatenate_datasets for example ### Environment info 3.0.2

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I_kwDODunzps6ZtIGh

Formatted map + with_format(None) changes array dtype for iterable datasets

open

CONTRIBUTOR

### Describe the bug When applying with_format -> map -> with_format(None), array dtypes seem to change, even if features are passed ### Steps to reproduce the bug ```python features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32")}) dataset = Dataset.from_dict({f"array0": [np.zeros((100,10,10), dtype=np.float32)]*25}, features=features) ds = dataset.to_iterable_dataset().with_format("numpy").map(lambda x: x, features=features) ex_0 = next(iter(ds)) ds = dataset.to_iterable_dataset().with_format("numpy").map(lambda x: x, features=features).with_format(None) ex_1 = next(iter(ds)) assert ex_1["array0"].dtype == ex_0["array0"].dtype, f"{ex_1['array0'].dtype} {ex_0['array0'].dtype}" ``` ### Expected behavior Dtypes should be preserved. ### Environment info 3.0.2

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I_kwDODunzps6Zs3dt

Add with_rank to Dataset.from_generator

open