Sulak2020/github-issues-multirepo-datasets-Sulakshana huggingface dataset:
Title: DataFilesNotFoundError for datasets LM1B
State: closed

### Describe the bug

Cannot load the dataset https://huggingface.co/datasets/billion-word-benchmark/lm1b

### Steps to reproduce the bug

`dataset = datasets.load_dataset('lm1b', split=split)`

### Expected behavior

`Traceback (most recent call last):
  File "/home/hml/projects/DeepLearning/Generative_model/Diffusion-BERT/word_freq.py", line 13, in <module>
    train_data = DiffusionLoader(tokenizer=tokenizer).my_load(task_name='lm1b', splits=['train'])[0]
  File "/home/hml/projects/DeepLearning/Generative_model/Diffusion-BERT/dataloader.py", line 20, in my_load
    return [self._load(task_name, name) for name in splits]
  File "/home/hml/projects/DeepLearning/Generative_model/Diffusion-BERT/dataloader.py", line 20, in <listcomp>
    return [self._load(task_name, name) for name in splits]
  File "/home/hml/projects/DeepLearning/Generative_model/Diffusion-BERT/dataloader.py", line 13, in _load
    dataset = datasets.load_dataset('lm1b', split=split)
  File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 2594, in load_dataset
    builder_instance = load_dataset_builder(
  File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 2266, in load_dataset_builder
    dataset_module = dataset_module_factory(
  File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 1827, in dataset_module_factory
    ).get_module()
  File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 1040, in get_module
    module_name, default_builder_kwargs = infer_module_for_data_files(
  File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 598, in infer_module_for_data_files
    raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in lm1b`

### Environment info

datasets: 2.20.0

Comments: Hi ! Can you try with a more recent version of `datasets` ? Also you might need to pass trust_remote_code=True since it's a script based dataset
Title: Let server decide default repo visibility
State: closed

Until now, all repos were public by default when created without passing the `private` argument. This meant that passing `private=False` or `private=None` was strictly the same. This is not the case anymore. Enterprise Hub offers organizations to set a default visibility setting for new repos. This is useful for organizations forbidding public repos for security matters. This PR mostly updates docstrings + default values so that `private=None` is always passed when users don't set it manually.

This PR doesn't create any breaking change. The real update has been done server-side when introducing the new Enterprise Hub feature. Related to https://github.com/huggingface/huggingface_hub/pull/2679.

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7302). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.No need for a specific version of huggingface_hub to avoid a breaking change no (it's a server-side change)
Title: fix: update elasticsearch version
State: closed

This should fix the `test_py311 (windows latest, deps-latest` errors.

```
=========================== short test summary info ===========================
ERROR tests/test_search.py - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead.
ERROR tests/test_search.py - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead.
===== 2822 passed, 54 skipped, 10 warnings, 2 errors in 373.36s (0:06:13) =====
Error: Process completed with exit code 1.
```

The elasticsearch version used is `elasticsearch==7.9.1`, which is 4 years old and uses the removed `numpy.float_`.

elasticsearch fixed this in [https://github.com/elastic/elasticsearch-py/pull/2551](https://github.com/elastic/elasticsearch-py/pull/2551) and released in 8.15.0 (August 2024) and 7.17.12 (September 2024).


Comments: May I request a review @lhoestqThe docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7300). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Efficient Image Augmentation in Hugging Face Datasets
State: open

### Describe the bug

 I'm using the Hugging Face datasets library to load images in batch and would like to apply a torchvision transform to solve the inconsistent image sizes in the dataset and apply some on the fly image augmentation. I can just think about using the collate_fn, but seems quite inefficient. 
 
 I'm new to the Hugging Face datasets library, I didn't find nothing in the documentation or the issues here on github.

Is there an existing way to add image transformations directly to the dataset loading pipeline? 

### Steps to reproduce the bug

from datasets import load_dataset
from torch.utils.data import DataLoader

```python
def collate_fn(batch):
    images = [item['image'] for item in batch]
    texts = [item['text'] for item in batch]
    return {
        'images': images,
        'texts': texts
    }

dataset = load_dataset("Yuki20/pokemon_caption", split="train")
dataloader = DataLoader(dataset, batch_size=4, collate_fn=collate_fn)

# Output shows varying image sizes:
# [(1280, 1280), (431, 431), (789, 789), (769, 769)]
```

### Expected behavior

I'm looking for a way to resize images on-the-fly when loading the dataset, similar to PyTorch's Dataset.__getitem__ functionality. This would be more efficient than handling resizing in the collate_fn.


### Environment info

- `datasets` version: 3.1.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.11.10
- `huggingface_hub` version: 0.26.2
- PyArrow version: 18.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
Title: wrong return type for `IterableDataset.shard()`
State: closed

### Describe the bug

`IterableDataset.shard()` has the wrong typing for its return as `"Dataset"`. It should be `"IterableDataset"`. Makes my IDE unhappy.

### Steps to reproduce the bug

look at [the source code](https://github.com/huggingface/datasets/blob/main/src/datasets/iterable_dataset.py#L2668)?

### Expected behavior

Correct return type as `"IterableDataset"`

### Environment info

datasets==3.1.0

Comments: Oops my bad ! thanks for reporting
Title: Updated inconsistent output in documentation examples for `ClassLabel`
State: closed

fix #7129 

@stevhliu

Comments: Updated! 😄 The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7293). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.@lhoestq, can you help with this failing test please? 🙏
Title: Why return_tensors='pt' doesn't work？
State: open

### Describe the bug

I tried to add input_ids to dataset with map(), and I used the return_tensors='pt', but why I got the callback with the type of List？
![image](https://github.com/user-attachments/assets/ab046e20-2174-4e91-9cd6-4a296a43e83c)


### Steps to reproduce the bug

![image](https://github.com/user-attachments/assets/5d504d4c-22c7-4742-99a1-9cab78739b17)

### Expected behavior

Sorry for this silly question, I'm noob on using this tool. But I think it should return a tensor value as I have used the protocol？
When I tokenize only one sentence using tokenized_input=tokenizer(input, return_tensors='pt' )，it does return in tensor type. Why doesn't it work in map()？

### Environment info

transformers>=4.41.2,<=4.45.0
datasets>=2.16.0,<=2.21.0
accelerate>=0.30.1,<=0.34.2
peft>=0.11.1,<=0.12.0
trl>=0.8.6,<=0.9.6
gradio>=4.0.0
pandas>=2.0.0
scipy
einops
sentencepiece
tiktoken
protobuf
uvicorn
pydantic
fastapi
sse-starlette
matplotlib>=3.7.0
fire
packaging
pyyaml
numpy<2.0.0


Comments: Hi ! `datasets` uses Arrow as storage backend which is agnostic to deep learning frameworks like torch. If you want to get torch tensors back, you need to do `dataset = dataset.with_format("torch")`> Hi ! `datasets` uses Arrow as storage backend which is agnostic to deep learning frameworks like torch. If you want to get torch tensors back, you need to do `dataset = dataset.with_format("torch")`

It does work! Thanks for your suggestion!
Title: Support for identifier-based automated split construction
State: open
Labels: name: enhancement
description: New feature or request

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

Comments: Hi ! You can already configure the README.md to have multiple sets of splits, e.g.

```yaml
configs:
- config_name: my_first_set_of_split
  data_files:
  - split: train
    path: *.csv
- config_name: my_second_set_of_split
  data_files:
  - split: train
    path: train-*.csv
  - split: test
    path: test-*.csv
```Hi - I had something slightly different in mind:

Currently the yaml splits specified like this only allow specifying which filenames to pass to each split.
But what if I have a situation where I know which individual *training examples* I want to put in each split.

I could build split-specific files, however for large datasets with overlapping (e.g. multiple sets of) splits this could result in significant duplication of data.

I can see that this could actually be very much intended (i.e. to discourage overlapping splits), but wondered whether some support for handling splits based on individual identifiers is something that could be considered. This is not supported right now :/ Though you can load the data in two steps like this
```python
from datasets import load_dataset

full_dataset = load_dataset("username/dataset", split="train")
my_first_set_indices = load_dataset("username/dataset", "my_first_set_of_split", split="train")

my_first_set = full_dataset.select(my_first_set_indices["indices"])
```

you can create such a dataset by adapting this code for example
```python
# upload the full dataset
full_dataset.push_to_hub("username/dataset")
# then upload the indices for each set
DatasetDict({
    "train": Dataset.from_dict({"indices": [0, 1, 2, 3]}),
    "test": Dataset.from_dict({"indices": [4, 5]}),
}).push_to_hub("username/dataset", "my_first_set_of_split")
Title: Concurrent loading in `load_from_disk` - `num_proc` as a param 
State: closed
Labels: name: enhancement
description: New feature or request

### 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
Title: support for custom feature encoding/decoding
State: closed

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?)

Comments: @lhoestq The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7284). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Faulty datasets.exceptions.ExpectedMoreSplitsError
State: open

### 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" 
```
Title: File not found error
State: open

### 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

Comments: Link to the dataset: https://huggingface.co/datasets/MichielBontenbal/UrbanSounds
Title: Add filename in error message when ReadError or similar occur
State: open

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.

Comments: Hi Elisa, please share the error traceback here, and if you manage to find the location in the `datasets` code where the error occurs, feel free to open a PR to add the necessary logging / improve the error message.> please share the error traceback

I don't have access to it but it should be during [this exception](https://github.com/huggingface/datasets/blob/2049c00921c59cdeb835137a1c49639cf175af07/src/datasets/builder.py#L1643) which happens during the loading of a dataset. If one of the downloaded files is corrupted, the for loop will not yield correctly, and the error will come from, say, in the case of tar files, [this iterable](https://github.com/huggingface/datasets/blob/2049c00921c59cdeb835137a1c49639cf175af07/src/datasets/utils/file_utils.py#L1293) which has no explicit error handling that leaves clues as to which file has failed.

I only know the case for tar files, but I consider this issue could be happening across different file types too.I think having a better error handling for this tar iterable would be useful already, maybe a simple try/except in `_iter_from_urlpath` that checks for `tarfile.ReadError` and raises an error with the `urlpath` mentioned in the error ?I think not just from higher calls like the `_iter_from_urlpath` but directly wherever a file is attempted to be opened would be the best case, as the traceback would simply lead to that.so maybe there should be better error messages in each dataset builder definition ? e.g. in https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/webdataset/webdataset.py for webdataset TAR archives
Title: Add link to video dataset
State: closed

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

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7277). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: load_dataset
State: open

### 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
Title: Raise error for incorrect JSON serialization
State: closed

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

Comments: PTAL @lhoestq @albertvillanova The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7273). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: load_from_disk
State: open

### 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

Comments: Hello, It's an interesting issue here. I have the same problem, I have a local dataset and I want to push the dataset to the hub but huggingface does a copy of it.

```py
from datasets import load_dataset

dataset = load_dataset("webdataset", data_files="/media/works/data/*.tar") # copy here
dataset.push_to_hub("WaveGenAI/audios2")
```

Edit: I can use HfApi for my use case
Is there any update on this issue? I found the same behavior too.
My datasets version is `2.13.2`Updating to the newest version of datasets lib resolved the issue.
Title: Source installation fails on Macintosh with python 3.10
State: open

### 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 

Comments: I encountered the same problem on M1, a workaround I did was to simply comment out the dependency:

```python
...
    "zstandard",
    "polars[timezone]>=0.20.0",
    # "decord==0.6.0",
]
```

This worked for me as the adjustments I did to the code do not use the dependency, but I do not know if the same holds for you.

I also do not think it is a good idea to rely on a dependency (I mean decord) that has not been maintained for 2 years, but I saw that even eva-decord hasn't been maintained since last year.

Did you get it to work with eva-decord?
Title: The dataset viewer should be available soon. Please retry later.
State: closed

### 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

Comments: Waiting is all you need. 10 hours later, it works.
Title: Allow video with disabeld decoding without decord
State: closed

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

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7262). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.