Sulak2020/github-issues-multirepo-datasets-Sulakshana huggingface dataset:
Title: CI Build PR Documentation is broken: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'
State: closed

Our CI Build PR Documentation is broken. See: https://github.com/huggingface/datasets/actions/runs/6799554060/job/18486828777?pr=6390
```
ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'
```
Title: Cannot import datasets on google colab (python 3.10.12)
State: closed

### Describe the bug

I'm trying A full colab demo notebook of zero-shot-distillation from https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation but i got this type of error when importing  datasets on my google colab (python version is 3.10.12)

![image](https://github.com/huggingface/datasets/assets/15389235/6f7758a2-681d-4436-87d0-5e557838e368)

I found the same problem that have been solved in [#3326 ] but it seem still error on the google colab. I can't try on my local using jupyter notebook because of my laptop resource doesn't fulfill the requirements. 

Please can anyone help me solve this problem. Thank you 😅 

### Steps to reproduce the bug

Error:

```
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
[<ipython-input-8-b6e092f83978>](https://localhost:8080/#) in <cell line: 1>()
----> 1 from datasets import load_dataset
      2 
      3 # Print all the available datasets
      4 from huggingface_hub import list_datasets
      5 print([dataset.id for dataset in list_datasets()])

6 frames
[/usr/lib/python3.10/functools.py](https://localhost:8080/#) in update_wrapper(wrapper, wrapped, assigned, updated)
     59     # Issue #17482: set __wrapped__ last so we don't inadvertently copy it
     60     # from the wrapped function when updating __dict__
---> 61     wrapper.__wrapped__ = wrapped
     62     # Return the wrapper so this can be used as a decorator via partial()
     63     return wrapper

AttributeError: readonly attribute
```

### Expected behavior

Run success on Google Colab (free)

### Environment info

Windows 11 x64, Google Colab free

Comments: You are most likely using an outdated version of `datasets` in the notebook, which can be verified with the `!datasets-cli env` command. You can run `!pip install -U datasets` to update the installation.okay, it works! thank you so much! 😄
Title: Safely load datasets by disabling execution of dataset loading script
State: closed
Labels: name: enhancement
description: New feature or request

### Feature request

Is there a way to disable execution of dataset loading script using `load_dataset`? This is a security vulnerability that could lead to arbitrary code execution. 

Any suggested workarounds are welcome as well. 

### Motivation

This is a security vulnerability that could lead to arbitrary code execution. 

### Your contribution

n/a

Comments: great idea IMO

this could be a `trust_remote_code=True` flag like in transformers. We could also default to loading the Parquet conversion rather than executing code (for dataset repos that have both)@julien-c that would be great!We added the `trust_remote_code` argument to `load_dataset()` in `datasets` 2.16:
- in the future users will have to pass trust_remote_code=True to use datasets with a script
- for now we just show a warning when a dataset script is used
- we fallback on the Hugging Face Parquet exports when possible (to keep compatibility with old datasets with scripts)

So feel free to use `trust_remote_code=False` in the meantime to disable loading from dataset loading scripts :)Passing `trust_remote_code=True` explicitly is now mandatory to load a dataset with a script since https://github.com/huggingface/datasets/pull/6954
Title: TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array
State: open

### Describe the bug

Hi, I am preprocessing a large custom dataset with numpy arrays. I am running into this TypeError during writing in a dataset.map() function. I've tried decreasing writer batch size, but this error persists. This error does not occur for smaller datasets. 



Thank you!



### Steps to reproduce the bug

Traceback (most recent call last):
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/multiprocess/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1354, in _write_generator_to_queue
    for i, result in enumerate(func(**kwargs)):
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3493, in _map_single
    writer.write_batch(batch)
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 555, in write_batch
    arrays.append(pa.array(typed_sequence))
  File "pyarrow/array.pxi", line 243, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 184, in __arrow_array__
    out = numpy_to_pyarrow_listarray(data)
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/features/features.py", line 1394, in numpy_to_pyarrow_listarray
    values = pa.ListArray.from_arrays(offsets, values)
  File "pyarrow/array.pxi", line 2004, in pyarrow.lib.ListArray.from_arrays
TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array

### Expected behavior

Type should not be a ChunkedArray

### Environment info

datasets v2.14.5
arrow v1.2.3
pyarrow v12.0.1

Comments: Seconding encountering this issue.
Title: Raise a different exception for inexisting dataset vs files without known extension
State: closed

See https://github.com/huggingface/datasets-server/issues/2082#issuecomment-1805716557

We have the same error for:
- https://huggingface.co/datasets/severo/a_dataset_that_does_not_exist: a dataset that does not exist
- https://huggingface.co/datasets/severo/test_files_without_extension: a dataset with files without a known extension

```
>>> import datasets
>>> datasets.get_dataset_config_names('severo/a_dataset_that_does_not_exist')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
    dataset_module = dataset_module_factory(
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory
    raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/a_dataset_that_does_not_exist/a_dataset_that_does_not_exist.py or any data file in the same directory. Couldn't find 'severo/a_dataset_that_does_not_exist' on the Hugging Face Hub either: FileNotFoundError: Dataset 'severo/a_dataset_that_does_not_exist' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.
>>> datasets.get_dataset_config_names('severo/test_files_without_extension')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
    dataset_module = dataset_module_factory(
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory
    raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/test_files_without_extension/test_files_without_extension.py or any data file in the same directory. Couldn't find 'severo/test_files_without_extension' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in severo/test_files_without_extension.
```

To differentiate, we must parse the error message (only the end is different). We should have a different exception for these two errors.
Title: Add ability to set lock type
State: closed
Labels: name: enhancement
description: New feature or request

### Feature request

Allow setting file lock type, maybe from an environment variable

Currently, it only depends on whether fnctl is available:

https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/utils/filelock.py#L463-L470C16

### Motivation

In my environment, flock isn't supported on a network attached drive

### Your contribution

I'll be happy to submit a pr.

Comments: We've replaced our filelock implementation with the `filelock` package, so their repo is the right place to request this feature.

In the meantime, the following should work: 
```python
import filelock
filelock.FileLock = filelock.SoftFileLock

import datasets
...
```
Title: Webdataset dataset builder
State: closed

Allow `load_dataset` to support the Webdataset format.

It allows users to download/stream data from local files or from the Hugging Face Hub.

Moreover it will enable the Dataset Viewer for Webdataset datasets on HF.

## Implementation details

- I added a new Webdataset builder
- dataset with TAR files are now read using the Webdataset builder
- Basic decoding from `webdataset` is used by default, except unsafe ones like pickle
- HF authentication support is done with `xopen`

## TODOS

- [x] tests
- [x] docs

Comments: _The documentation is not available anymore as the PR was closed or merged._I added an error message if the first examples don't appear to be in webdataset format
```
"The TAR archives of the dataset should be in Webdataset format, "
"but the files in the archive don't share the same prefix or the same types."
```@mariosasko could you review this ? I think it's fine to have webdataset as an optional dependency for now, then depending on usage and user feedbacks see if it makes sense to have our own implementation or notI just removed the dependency on `webdataset` @mariosasko :)took your comments into account, lmk if you see anything else
Title: How to create 3d medical imgae dataset?
State: open
Labels: name: enhancement
description: New feature or request

### Feature request

I am newer to huggingface, after i look up `datasets` docs, I can't find how to create the dataset contains 3d medical image (ends with '.mhd', '.dcm', '.nii')

### Motivation

help us to upload 3d medical dataset to huggingface!

### Your contribution

I'll submit a PR if I find a way to add this feature
Title: How to load existing downloaded dataset ?
State: closed
Labels: name: enhancement
description: New feature or request

Hi @mariosasko @lhoestq  @katielink 

Thanks for your contribution and hard work.

### Feature request

First, I download a dataset as normal by:
```
from datasets import load_dataset

dataset = load_dataset('username/data_name', cache_dir='data')
```

The dataset format in `data` directory will be:
```
-data
  |-data_name
    |-test-00000-of-00001-bf4c733542e35fcb.parquet
    |-train-00000-of-00001-2a1df75c6bce91ab.parquet
```


Then I use SCP to clone this dataset into another machine, and then try:
```
from datasets import load_dataset

dataset = load_dataset('data/data_name')  # load from local path
```

This leads to re-generating training and validation split for each time, and the disk quota will be duplicated occupation.

How can I just load the dataset without generating and saving these splits again?

### Motivation

I do not want to download the same dataset in two machines, scp is much faster and better than HuggingFace API. I hope we can directly load the downloaded datasets (.parquest)

### Your contribution

Please refer to the feature

Comments: Feel free to use `dataset.save_to_disk(...)`, then scp the directory containing the saved dataset and reload it on your other machine using `dataset = load_from_disk(...)`
Title: Formatting overhead
State: closed

### Describe the bug

Hi! I very recently noticed that my training time is dominated by batch formatting. Using Lightning's profilers, I located the bottleneck within `datasets.formatting.formatting` and then narrowed it down with `line-profiler`. It turns out that almost all of the overhead is due to creating new instances of `self.python_arrow_extractor`. I admit I'm confused why that could be the case - as far as I can tell there's no complex `__init__` logic to execute.

![image](https://github.com/huggingface/datasets/assets/320321/5e022e0b-0d21-43d0-8e6f-9e641142e96b)

### Steps to reproduce the bug

1. Set up a dataset `ds` with potentially several (4+) columns (not sure if this is necessary, but it did at one point of the investigation make overhead worse)
2. Process it using a custom transform, `ds = ds.with_transform(transform_func)`
3. Decorate this function https://github.com/huggingface/datasets/blob/main/src/datasets/formatting/formatting.py#L512 with `@profile` from https://pypi.org/project/line-profiler/
4. Profile with `$ kernprof -l script_to_profile.py`

### Expected behavior

Batch formatting should have acceptable overhead.

### Environment info

```
datasets=2.14.6
pyarrow=14.0.0
```

Comments: Ah I think the `line-profiler` log is off-by-one and it is in fact the `extract_batch` method that's taking forever. Will investigate further.I tracked it down to a quirk of my setup. Apologies.
Title: imagenet-1k downloads over and over
State: closed

### Describe the bug

What could be causing this?
```
$ python3
Python 3.8.13 (default, Mar 28 2022, 11:38:47) 
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> load_dataset("imagenet-1k")
Downloading builder script: 100%|██████████| 4.72k/4.72k [00:00<00:00, 7.51MB/s]
Downloading readme: 100%|███████████████████| 85.4k/85.4k [00:00<00:00, 510kB/s]
Downloading extra modules: 100%|████████████| 46.4k/46.4k [00:00<00:00, 300kB/s]
Downloading data: 100%|████████████████████| 29.1G/29.1G [19:36<00:00, 24.8MB/s]
Downloading data: 100%|████████████████████| 29.3G/29.3G [08:38<00:00, 56.5MB/s]
Downloading data: 100%|████████████████████| 29.0G/29.0G [09:26<00:00, 51.2MB/s]
Downloading data: 100%|████████████████████| 29.2G/29.2G [09:38<00:00, 50.6MB/s]
Downloading data: 100%|███████████████████▉| 29.2G/29.2G [09:37<00:00, 44.1MB/s^Downloading data:   0%|                     | 106M/29.1G [00:05<23:49, 20.3MB/s]
```

### Steps to reproduce the bug

See above commands/code

### Expected behavior

imagenet-1k is downloaded

### Environment info

- `datasets` version: 2.14.6
- Platform: Linux-6.2.0-34-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- Huggingface_hub version: 0.15.1
- PyArrow version: 14.0.0
- Pandas version: 1.5.2

Comments: Have you solved this problem?
Title: Add CheXpert dataset for vision
State: open
Labels: [{'id': 1935892871, 'node_id': 'MDU6TGFiZWwxOTM1ODkyODcx', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/enhancement', 'name': 'enhancement', 'color': 'a2eeef', 'default': True, 'description': 'New feature or request'}
 {'id': 2067376369, 'node_id': 'MDU6TGFiZWwyMDY3Mzc2MzY5', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/dataset%20request', 'name': 'dataset request', 'color': 'e99695', 'default': False, 'description': 'Requesting to add a new dataset'}]

### Feature request

### Name 

**CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison**

### Paper

https://arxiv.org/abs/1901.07031

### Data

https://stanfordaimi.azurewebsites.net/datasets/8cbd9ed4-2eb9-4565-affc-111cf4f7ebe2

### Motivation

CheXpert is one of the fundamental models in medical image classification and can serve as a viable pre-training dataset for radiology classification or low-scale ablation / exploratory studies.

This could also serve as a good pre-training dataset for Kaggle competitions.

### Your contribution

Would love to make a PR and pre-process / get this into 🤗

Comments: Hey @SauravMaheshkar ! Just responded to your email.

_For transparency, copying part of my response here:_
I agree, it would be really great to have this and other BenchMD datasets easily accessible on the hub.

I think the main limiting factor is that the ChexPert dataset is currently hosted on the Stanford AIMI Shared Datasets website, with a license that does not permit redistribution IIRC. Thus, I believe we would need to create a [dataset loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script) that would check authentication with the Stanford AIMI site before downloading and extracting the data. 

I've started a HF dataset repo [here](https://huggingface.co/datasets/katielink/CheXpert), in case you want to collaborate on writing up this loading script! I'm also happy to take a stab when I have some more time next week.Hey @katielink I would love to try this out. Please guide me.Hi @katielink , I would also love to be on board and contribute to this loading script/project if it is still being developed. I'm interested because I personally would like to gain access to the CheXpert dataset and am facing some weird issues, so I'd like to sort it out for me, and potentially others. Please keep me updated and guide me on this as well!!!
Title: Add my dataset
State: closed

## medical data

**Description:**
This dataset, named "medical data," is a collection of text data from various sources, carefully curated and cleaned for use in natural language processing (NLP) tasks. It consists of a diverse range of text, including articles, books, and online content, covering topics from science to literature.

**Citation:**
If applicable, please include a citation for this dataset to give credit to the original sources or contributors.

**Key Features:**
- Language: The text is primarily in English, but it may include content in other languages as well.
- Use Cases: This dataset is suitable for text classification, language modeling, sentiment analysis, and other NLP tasks.

**Usage:**
To access this dataset, use the `load_your_dataset` function provided in the `your_dataset.py` script within this repository. You can specify the dataset split you need, such as "train," "test," or "validation," to get the data for your specific task.

**Contributors:**
- [Keyur Chaudhari]

**Contact:**
If you have any questions or need assistance regarding this dataset, please feel free to contact [keyurchaudhari536@gmail.com].

Please note that this dataset is shared under a specific license, which can be found in the [LICENSE](link to your dataset's license) file. Make sure to review and adhere to the terms of the license when using this dataset for your projects.


Comments: Hi! We do not host datasets in this repo. Instead, you should use `dataset.push_to_hub` to upload the dataset to the HF Hub.@mariosasko could you provide me proper guide to push data on HF hub You can find this info here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingface.co/docs/datasets/loading for how to load a local dataset (before pushing it to the Hub).
Title: Fix for continuation behaviour on broken dataset archives due to starving download connections via HTTP-GET
State: open

This PR proposes a (slightly hacky) fix for an Issue that can occur when downloading large dataset parts over unstable connections.
The underlying issue is also being discussed in https://github.com/huggingface/datasets/issues/5594.

Issue Symptoms & Behaviour:
- Download of a large archive file during dataset download via HTTP-GET fails.
- An silent net exception (which I was unable to identify) is thrown within the `tqdm` download progress.
- Due to missing exception catch code, the above process just continues processing, assuming `http_get` completed successfully.
- Pending Archive file gets renamed to remove the `.incomplete` extension, despite not all data has been downloaded. 
- Also, for reasons I did not investigate, there seems to be no real integrity check for the downloaded files; or it does not detect this problem. This is especially problematic, since the downloader script won't retry downloading this archive after CRC-Checking, even if it is being manually restarted / executed again after running into errors on extraction.

Fix proposal: Adding a retry mechanic for HTTP-GET downloads, which adds the following behaviour:
- Download Progress Thread checks for download size validity in case the HTTP connection starves mid download. If the check fails, a RuntimeError is thrown
- Cache Downloader code with retry mechanic monitors for an exception thrown by the download progress thread, and retries download with updated `resume_size`.
- Cache Downloader will not mark incomplete files which have thrown an exception during download, and exceeded retries, as complete.
Title: Caching problem when deleting a dataset 
State: closed

### Describe the bug

Pushing a dataset with n + m features to a repo which was deleted, but contained n features, will fail.

### Steps to reproduce the bug

1. Create a dataset with n features per row
2. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)`
3. Go on the hub, delete the repo at `YOUR_PATH`
4. Update your local dataset to have n + m features per row
5. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` will fail because of a mismatch in features number


### Expected behavior

Step 5 should work or display a message to indicate the cache has not been cleared

### Environment info

- `datasets` version: 2.12.0
- Platform: Linux-5.15.0-88-generic-x86_64-with-glibc2.31
- Python version: 3.10.10
- Huggingface_hub version: 0.16.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0


Comments: Thanks for reporting! Can you also share the error message printed in step 5?I did not store it at the time but I'll try to re-do a mwe next week to get it againI haven't managed to reproduce this issue using a [notebook](https://colab.research.google.com/drive/1m6eduYun7pFTkigrCJAFgw0BghlbvXIL?usp=sharing) that follows the steps to reproduce the bug. So, I'm closing it.

But feel free to re-open it if you have a better reproducer.
Title: CI is broken: TypeError: Couldn't cast array
State: closed

See: https://github.com/huggingface/datasets/actions/runs/6730567226/job/18293518039
```
FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type
fixed_size_list<item: int32>[3]
to
Sequence(feature=Value(dtype='int64', id=None), length=3, id=None)
```