rachel521/github-issues-simple huggingface dataset:
Preserve dtype for numpy/torch/tf/jax arrays
Opened by bhavitvyamalik (closed)

Fixes #625. This lets the user preserve the dtype of numpy array to pyarrow array which was getting lost due to conversion of numpy array -> list -> pyarrow array. 

Comments: Hi @lhoestq, 
It turns out that pyarrow `ListArray` are not recognized as list-like when we get output from `numpy_to_pyarrow_listarray`. This might cause tests to fail. If possible can we convert that `ListArray` output to list inorder for tests to pass? Under the hood it'll maintain the dtype as that of numpy array passed during input only

Brought down the failing tests from 7 to 4. Let me know if that part looks good. Failing tests are looking quite similar. In `test_map_torch` https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1039 and `test_map_tf`https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1056 
they're expecting `float64`. Shouldn't that be `float32` now?

It's normal: pytorch and tensorflow use `float32` by default, unlike numpy which uses `float64`.

I think that we should always keep the precision of the original tensor (torch/tf/numpy).
It means that as it is in this PR it's fine (the precision is conserved when doing the torch/tf -> numpy conversion).

This is a breaking change but in my opinion the fact that we had Value("float64") for torch.float32 tensors was an issue already.

Let me know what you think. Cc @albertvillanova if you have an opinion on this

If we agree on doing this breaking change, we can just change the test. 

Hi @lhoestq, 
Merged master into this branch. Only changing the test is left for now (mentioned below) after which all tests should pass.

> Brought down the failing tests from 7 to 4. Let me know if that part looks good. Failing tests are looking quite similar. In `test_map_torch`
> 
> https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1039
> 
> and `test_map_tf`
> https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1056
> 
> 
> they're expecting `float64`. Shouldn't that be `float32` now?



> they're expecting float64. Shouldn't that be float32 now?

Yes feel free to update those tests :)

It would be nice to have the same test for JAX as well

Added same test for for JAX too. Also, I saw that I missed changing `test_cast_to_python_objects_jax` like I did for TF and PyTorch. Finished that as well
Update data URL in pubmed dataset
Opened by albertvillanova (closed)

Fix #3655.

Comments: - I updated the previous dummy data: I just had to rename the file and its directory
  - the dummy data zip contains only a single file: `pubmed22n0001.xml.gz`

Then I discover it fails: https://app.circleci.com/pipelines/github/huggingface/datasets/9800/workflows/173a4433-8feb-4fc6-ab9e-59762084e3e1/jobs/60437
```
No such file or directory: '.../dummy_data/pubmed22n0002.xml.gz'
```
- it needs dummy data for all the 1114 files: 
  `_URLs = [f"ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed22n{i:04d}.xml.gz" for i in range(1, 1115)]`
- this confirms me that it never passed the test: these dummy data files were not present before my PR
- therefore, is it really useful the data test if we just ignore it when it does not pass?

In relation with JSON metadata, I was generating the file for `pubmed` (see above) in a GCP instance: after running during ~12h without finishing, I decided to stop the process.

Hi ! Yes I remembered we hardcoded an exception for this one:
https://github.com/huggingface/datasets/blob/36db39c75179a0a491c69a4491f7ae7e4615e66f/src/datasets/utils/mock_download_manager.py#L174-L176

The exception was used to only require one dummy data file, feel free to update it if you want
How to load data from lakefs
Opened by d710055071 (closed)

My dataset is stored on the company's lakefs server. How can I write code to load the dataset? It would be great if I could provide code examples or provide some references


Comments: You can create a `pandas` DataFrame following [this](https://lakefs.io/data-version-control/dvc-using-python/) tutorial, and then convert this DataFrame to a `Dataset` with `datasets.Dataset.from_pandas`. For larger datasets (to memory map them), you can use `Dataset.from_generator` with a generator function that reads lakeFS files with `s3fs`.

@mariosasko  hello,
This can achieve and https://huggingface.co/datasets Does the same effect apply to the dataset? For example, downloading while using

There is a blogspot from lakes on this topic: https://lakefs.io/blog/data-version-control-hugging-face-datasets/
Fix conda release
Opened by lhoestq (closed)

There were a few issues with conda releases (they've been failing for a while now).
To fix this I had to:
- add the --single-version-externally-managed tag to the build stage (suggestion from [here](https://stackoverflow.com/a/64825075))
- set the python version of the conda build stage to 3.8 since 3.9 isn't supported
- sync the evrsion requirement of `huggingface_hub`

With these changes I'm working on uploading all missing versions until 1.6.2 to conda

EDIT: I managed to build and upload all missing versions until 1.6.2 to conda :)
Add PubTables-1M
Opened by NielsRogge (open)

### Name

PubTables-1M

### Paper

https://openaccess.thecvf.com/content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html

### Data

https://github.com/microsoft/table-transformer

### Motivation

Table Transformer is now available in 🤗 Transformer, and it was trained on PubTables-1M. It's a large dataset for table extraction and structure recognition in unstructured documents.

Comments: cc @albertvillanova the author would like to add this dataset to the hub: https://github.com/microsoft/table-transformer/issues/68#issuecomment-1319114621. Could you help him out?
Fix ConnectionError in Scielo dataset
Opened by mariosasko (closed)

This PR:
* allows 403 status code in HEAD requests to S3 buckets to fix the connection error in the Scielo dataset (instead of `url`, uses `response.url` to check the URL of the final endpoint)
* makes the Scielo dataset streamable

Fixes #3255. 

Comments: The CI error is unrelated to the change.
[Docs] map() example uses undefined `tokenizer` — causes NameError
Opened by Sanjaykumar030 (open)

## Description

The current documentation example for `datasets.Dataset.map()` demonstrates batched processing but uses a `tokenizer` object without defining or importing it. This causes an error every time it's copied.

Here is the problematic line:

```python
# process a batch of examples
>>> ds = ds.map(lambda example: tokenizer(example["text"]), batched=True)
```
This assumes the user has already set up a tokenizer, which contradicts the goal of having self-contained, copy-paste-friendly examples. 

## Problem
Users who copy and run the example as-is will encounter:

```python
NameError: name 'tokenizer' is not defined
```

This breaks the flow for users and violates HuggingFace's documentation principle that examples should "work as expected" when copied directly. 

## Proposal
Update the example to include the required tokenizer setup using the Transformers library, like so:

```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
ds_tokenized = ds.map(lambda example: tokenizer(example["text"]), batched=True)
```
This will help new users understand the workflow and apply the method correctly.

## Note
This PR complements ongoing improvements like #7700, which clarifies multiprocessing in .map(). My change focuses on undefined tokenizer — causes NameError






Comments: I've submitted PR #7704 which adds documentation to clarify the behavior of `map()` when returning `None`.
Add ffmpeg4 installation instructions in warnings
Opened by polinaeterna (closed)

Adds instructions on how to install `ffmpeg=4` on Linux (relevant for Colab users).

Looks pretty ugly because I didn't find a way to check `ffmpeg` version from python (without `subprocess.call()`; `ctypes.util.find_library` doesn't work`), so the warning is raised on each decoding. Any suggestions on how to make it look nice are welcome!

This is how it looks on Colab:
![image](https://user-images.githubusercontent.com/16348744/198052412-d48018d1-4416-4aa5-9114-f7f9b4af031f.png)


Comments: _The documentation is not available anymore as the PR was closed or merged._

To make it warn only once, feel free to use a global counter in python - and if the warning has already been done, you don't do it again

> Added the same formatting for the error message :)

nice!! thank you! 

> Oh and regarding the warning counter, you can do it in another PR maybe ?

Yes, more warnings is better then no warnings.... I'll merge when the CI passes
Adding CLIMATE-FEVER dataset
Opened by tdiggelm (closed)

This PR request the addition of the CLIMATE-FEVER dataset:
A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present.

More information can be found at:
- Homepage: <http://climatefever.ai>
- Paper: <https://arxiv.org/abs/2012.00614>



Comments: I `git rebase`ed my branch to `upstream/master` as suggested in point 7 of <https://huggingface.co/docs/datasets/share_dataset.html> and subsequently used `git pull` to be able to push to my remote branch. However, I think this messed up the history.

Please let me know if I should create a clean new PR with my changes.

Update: I also fixed the dataset name in the Dataset Card.

Dear @SBrandeis , @lhoestq . I am not sure how to fix the PR with respect to the additional files that are currently included in the commits. Could you provide me with an example? Otherwise I would be happy to close/re-open another PR. Please let me know if anything is missing for the review.

Hi @tdiggelm, thanks for the contribution! This dataset is really awesome.
I believe creating a new branch from master and opening a new PR with your changes is the simplest option since no review has been done yet. Feel free to ping us when it's done.

> Hi @tdiggelm, thanks for the contribution! This dataset is really awesome.
> I believe creating a new branch from master and opening a new PR with your changes is the simplest option since no review has been done yet. Feel free to ping us when it's done.

Thank you very much for your quick reply! Will do ASAP and ping you when done.

closing in favor of #1623
Fix streaming zip files from canonical datasets
Opened by albertvillanova (closed)

Previous PR #2798 fixed streaming remote zip files when passing the parameter `data_files`.

However, that broke streaming zip files used in canonical `datasets` scripts, which normally have a subsequent `join()` (patched with `xjoin()`) after the `StreamingDownloadManager.download_and_extract()` is called.

This PR fixes this issue and allows streaming zip files both from:
- canonical datasets scripts and
- data files.
Added the Ascent KB
Opened by phongnt570 (closed)

Added the Ascent Commonsense KB of 8.9M assertions.

- Paper: [Advanced Semantics for Commonsense Knowledge Extraction (WWW'21)](https://arxiv.org/abs/2011.00905)
- Website: https://ascent.mpi-inf.mpg.de/

(I am the author of the dataset)

Comments: Thanks for approving it!
Add KorNLU dataset
Opened by sumanthd17 (closed)

Added Korean NLU datasets. The link to the dataset can be found [here](https://github.com/kakaobrain/KorNLUDatasets) and the paper can be found [here](https://arxiv.org/abs/2004.03289)

**Note**: The MNLI tsv file is broken, so this code currently excludes the file. Please suggest other alternative if any @lhoestq 

- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data

Comments: Nice thank you !
Can you regenerate the dataset_infos.json file ? Since we changed the features we must update it

Then I think we'll be good to merge :)
Support WebDataset containing file basenames with dots
Opened by albertvillanova (closed)

Support WebDataset containing file basenames with dots.

Fix #6880.

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6888). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

I think webdataset splits the file name and extension using the first dot no ?

https://github.com/webdataset/webdataset/blob/945b251a872ec0d337be8f9ea17a9c5b0d017ff3/webdataset/tariterators.py#L226

links to this function that splits on first dot

```python

def base_plus_ext(path):
    """Split off all file extensions.

    Returns base, allext.

    Args:
        path: path with extensions

    Returns:
        path with all extensions removed
    """
    match = re.match(r"^((?:.*/|)[^.]+)[.]([^/]*)$", path)
    if not match:
        return None, None
    return match.group(1), match.group(2)
```

So maybe the original issue is actually due to one of the files containing a dot in its file name that is not for the extension

```python
>>> base_plus_ext("15_Cohen_1-s2.0-S0929664620300449-gr3_lrg-b.png")
('15_Cohen_1-s2', '0-S0929664620300449-gr3_lrg-b.png')
```

Thanks for your review, @lhoestq.

I was not aware that `webdataset` requires filenames without dots in their basenames.

I they can have dots for the extension (that becomes the column name) but not in the key used to group files into samples
Specify dataset columns types in typehint
Opened by Samoed (open)

### Feature request

Make dataset optionaly generic to datasets usage with type annotations like it was done in `torch.Dataloader` https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/utils/data/dataloader.py#L131

### Motivation

In MTEB we're using a lot of datasets objects, but they're a bit poor in typehints. E.g. we can specify this for dataloder

```python
from typing import TypedDict
from torch.utils.data import DataLoader

class CorpusInput(TypedDict):
    title: list[str]
    body: list[str]


class QueryInput(TypedDict):
    query: list[str]
    instruction: list[str]

def queries_loader() -> DataLoader[QueryInput]:
    ...

def corpus_loader() -> DataLoader[CorpusInput]:
   ...
```
But for datasets we can only specify columns in type in comments 
```python
from datasets import Dataset

QueryDataset = Dataset
"""Query dataset should have `query` and `instructions` columns as `str` """
```

### Your contribution

I can create draft implementation
Cannot preview 'indonesian-nlp/eli5_id' dataset
Opened by cahya-wirawan (closed)

## Dataset viewer issue for '*indonesian-nlp/eli5_id*'

**Link:** https://huggingface.co/datasets/indonesian-nlp/eli5_id

I can not see the dataset preview.
```
Server Error
Status code:   400
Exception:     Status400Error
Message:       Not found. Maybe the cache is missing, or maybe the dataset does not exist.
```
Am I the one who added this dataset ? Yes


Comments: Hi @cahya-wirawan, thanks for reporting.

Your dataset is working OK in streaming mode:
```python
In [1]: from datasets import load_dataset
   ...: ds = load_dataset("indonesian-nlp/eli5_id", split="train", streaming=True)
   ...: item = next(iter(ds))
   ...: item
Using custom data configuration indonesian-nlp--eli5_id-9fe728a7e760fb7b

Out[1]: 
{'q_id': '1oy5tc',
 'title': 'dalam sepak bola apa gunanya menyia-nyiakan dua permainan pertama dengan terburu-buru - di tengah - bukan permainan terburu-buru biasa saya mendapatkannya',
 'selftext': '',
 'document': '',
 'subreddit': 'explainlikeimfive',
 'answers': {'a_id': ['ccwtgnz', 'ccwtmho', 'ccwt946', 'ccwvj0u'],
  'text': ['Jaga pertahanan tetap jujur, rasakan operan terburu-buru, buka permainan yang lewat. Pelanggaran yang terlalu satu dimensi akan gagal. Dan mereka yang bergegas ke tengah kadang-kadang dapat dibuka lebar-lebar untuk ukuran yard yang besar.',
   'Jika Anda melempar bola sepanjang waktu, maka pertahanan akan beradaptasi untuk selalu menutupi umpan. Dengan melakukan permainan lari sederhana sesekali, Anda memaksa pertahanan untuk tetap dekat dan menjaga dari lari. Terkadang, pelanggaran dapat membuat pertahanan lengah dengan berpura-pura berlari dan membebaskan penerima mereka. Selain itu, Anda tidak perlu mendapatkan yard besar di setiap permainan. Terkadang, paling baik mendapatkan beberapa yard sekaligus. Selama Anda mendapatkan yang pertama, Anda dalam kondisi yang baik.',
   'Dalam kebanyakan kasus, O-Line seharusnya membuat lubang untuk dilalui kembali. Jika Anda menjalankan terlalu banyak permainan ke luar / melempar, pertahanan akan mengejar. Juga, 2 permainan 5 yard memberi Anda satu set down baru.',
   'Saya Anda tidak suka jenis drama itu, tonton CFL. Kami hanya mendapatkan 3 down sehingga Anda tidak bisa menyia-nyiakannya. Lebih banyak lagi yang lewat.'],
  'score': [3, 2, 2, 2]},
 'title_urls': {'url': []},
 'selftext_urls': {'url': []},
 'answers_urls': {'url': []}}
```
Therefore, it should be properly rendered in the previewer. Let me ping @severo to have a look at it.

Thanks @albertvillanova for checking it. Btw, I have another dataset indonesian-nlp/lfqa_id which has the same issue. However, this dataset is still private, is it the reason why the preview doesn't work?

Yes, preview is not supported on private datasets yet. We are working on that though...

Thanks for the confirmation 

Fixed. Thanks for your feedback.
Properly raise FileNotFound even if the dataset is private
Opened by lhoestq (closed)

`tests/test_load.py::test_load_streaming_private_dataset` was failing because the hub now returns 401 when getting the HfApi.dataset_info of a dataset without authentication. `load_dataset` was raising ConnectionError, while it should be FileNoteFoundError since it first checks for local files before checking the Hub.

Moreover when use_auth_token is not set (default is False), we should not pass `token=None` to HfApi.dataset_info, or it will use the local token by default - instead it should use no token. It's currently not possible to ask for no token to be used, so as a workaround I simply set token="no-token"

Comments: _The documentation is not available anymore as the PR was closed or merged._
Failure to save with save_to_disk
Opened by M-Salti (closed)

When I try to save a dataset locally using the `save_to_disk` method I get the error:

```bash
FileNotFoundError: [Errno 2] No such file or directory: '/content/squad/train/squad-train.arrow'
```

To replicate:

1. Install `datasets` from master
2. Run this code:

    ```python
    from datasets import load_dataset
    squad = load_dataset("squad")   # or any other dataset
    squad.save_to_disk("squad")     # error here
    ```

The problem is that the method is not creating a directory with the name `dataset_path` for saving the dataset in (i.e. it's not creating the *train* and *validation* directories in this case). After creating the directory the problem resolves.
I'll open a PR soon doing that and linking this issue.


Comments: Hi thanks for reporting and for proposing a fix :)

I just merged a fix, feel free to try it from the master branch !

Closing since this has been fixed by #1923
load_dataset error
Opened by frederichen01 (closed)

### Describe the bug

Why does the program get stuck when I use load_dataset method, and it still gets stuck after loading for several hours? In fact, my json file is only 21m, and I can load it in one go using open('', 'r').

### Steps to reproduce the bug

1. pip install datasets==2.19.2
2. from datasets import Dataset, DatasetDict, NamedSplit, Split, load_dataset
3. data = load_dataset('json', data_files='train.json')

### Expected behavior

It is able to load my json correctly

### Environment info

datasets==2.19.2

Comments: Hi, @lion-ops.

In our Continuous Integration we have many tests on loading JSON files and all of them work properly.

Could you please share your "train.json" file, so that we can try to reproduce the issue you have? 

> Hi, @lion-ops.
> 
> In our Continuous Integration we have many tests on loading JSON files and all of them work properly.
> 
> Could you please share your "train.json" file, so that we can try to reproduce the issue you have?

Thank you for your reply. I can load it normally in another server. Is it possible that the disk of my server is a network disk in the LAN, so it will be downloaded from the LAN and get stuck?
CI is broken for deps-latest
Opened by albertvillanova (closed)

See: https://github.com/huggingface/datasets/actions/runs/11106149906/job/30853879890

```
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_caching_on_disk - AssertionError: Lists differ: [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] != [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]

First differing element 1:
{'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}
{'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}

  [{'filename': '/tmp/tmp6xcyyjs4/dataset0.arrow'},
-  {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}]
?                                        ^^^^^ --------

+  {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]
?                                       ++++++++++ ^^  +
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AssertionError: Lists differ: [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] != [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]

First differing element 0:
{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}
{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}

- [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}]
?                                         ^^ -----------

+ [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]
?                                       +++++++++++  ^^
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_regex - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ignores_line_definition_of_function - AssertionError: '52e56ee04ad92499' != '0a4f75cec280f634'
- 52e56ee04ad92499
+ 0a4f75cec280f634
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ipython_function - AssertionError: 'a6bd2041ca63d6c0' != '517bf36b7eecdef5'
- a6bd2041ca63d6c0
+ 517bf36b7eecdef5
FAILED tests/test_fingerprint.py::HashingTest::test_hash_tiktoken_encoding - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_compiled_module - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_generator - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_tensor - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_doesnt_depend_on_order - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_stable - NameError: name 'log' is not defined
ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NameError: name 'log' is not defined
= 11 failed, 2850 passed, 3 skipped, 23 warnings, 1 error in 191.06s (0:03:11) =
```