rachel521/github-issues-simple huggingface dataset:
SacreBLEU update
Opened by devrimcavusoglu (closed)

With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.

    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'

this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`

## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0


Comments: Hi @devrimcavusoglu, 
I tried your code with latest version of `datasets`and `sacrebleu==1.5.1` and it's running fine after changing one small thing:
```
sacrebleu = datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = [["It is a guide to action that ensures that the military will forever heed Party commands"]]  # double brackets here should do the work
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
output: {'score': 41.180376356915765, 'counts': [11, 8, 6, 4], 'totals': [18, 17, 16, 15], 'precisions': [61.111111111111114, 47.05882352941177, 37.5, 26.666666666666668], 'bp': 1.0, 'sys_len': 18, 'ref_len': 16}
```

@bhavitvyamalik hmm. I forgot double brackets, but still didn't work when used it with double brackets. It may be an isseu with platform (using win-10 currently), or versions. What is your platform and your version info for datasets, python, and sacrebleu ?

You can check that here, I've reproduced your code in [Google colab](https://colab.research.google.com/drive/1X90fHRgMLKczOVgVk7NDEw_ciZFDjaCM?usp=sharing). Looks like there was some issue in `sacrebleu` which was fixed later from what I've found [here](https://github.com/pytorch/fairseq/issues/2049#issuecomment-622367967). Upgrading `sacrebleu` to latest version should work.

It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https://colab.research.google.com/drive/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing

I'm reopening this Issue and making a Pull Request to fix it.

> It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https://colab.research.google.com/drive/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing
> 
> I'm reopening this Issue and making a Pull Request to fix it.

How did you solve him
User access requests with manual review do not notify the dataset owner
Opened by leondz (closed)

### Describe the bug

When a user access requests are enabled, and new requests are set to Manual Review, the dataset owner should be notified of the pending requests. However, instead, currently nothing happens, and so the dataset request can go unanswered for quite some time until the owner happens to check that particular dataset's Settings pane.

### Steps to reproduce the bug

1. Enable a dataset's user access requests
2. Set to Manual Review
3. Ask another HF user to request access to the dataset
4. Dataset owner is not notified

### Expected behavior

The dataset owner should receive some kind of notification, perhaps in their HF site inbox, or by email, when a dataset access request is made and manual review is enabled.

### Environment info

n/a

Comments: cc @SBrandeis

I think this has been addressed.

Please open a new issue if you are still not getting notified.
Implementation of iteration over values of a column in an IterableDataset object
Opened by TopCoder2K (closed)

Refers to [this issue](https://github.com/huggingface/datasets/issues/7381).

Comments: A couple of questions:
1. I've noticed two strange things: 1) "Around 80% of the final dataset is made of the `en_dataset`" in https://huggingface.co/docs/datasets/stream, 2) "Click on "Pull request" to send your to the project maintainers" in https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md Are `en_dataset` and "your [???]" typos? If so, I can fix them in this PR.
2. Should I update https://huggingface.co/docs/datasets/stream or https://huggingface.co/docs/datasets/access#iterabledataset to include the new feature?

Great ! and chained indexing was easy indeed, thanks :)

regarding your questions:

> I've noticed two strange things: 1) "Around 80% of the final dataset is made of the en_dataset" in https://huggingface.co/docs/datasets/stream, 2) "Click on "Pull request" to send your to the project maintainers" in https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md Are en_dataset and "your [???]" typos? If so, I can fix them in this PR.

Oh good catch, both should be fixed indeed. Feel free to open a new PR for those docs fixes

> Should I update https://huggingface.co/docs/datasets/stream or https://huggingface.co/docs/datasets/access#iterabledataset to include the new feature?

Yep good idea, I think in both places, since /stream is supposed to be exhaustive, and /access already mentions accessing a specific column for `Dataset`

@lhoestq, thank you for the answers!

> Yep good idea, I think in both places, since /stream is supposed to be exhaustive, and /access already mentions accessing a specific column for Dataset

👍, I'll try to add something.

By the way, do you have any ideas about why the CI pipelines have failed? Essentially, I've already encountered these problems [here](https://github.com/huggingface/datasets/issues/7381#issuecomment-2863421974).
I think `check_code_quality` has failed due to the usage of `pre-commit`. The problem seems to be the old version of the ruff hook. I've tried `v0.11.8` (the one that was installed with `pip install -e ".[quality]"`) and `pre-commit` seems to work like `make style` now. However, I don't have any ideas about `pyav` since I don't know what it is...

I've updated /stream and /access, please check the style and clarity. By the way, I would like to add `IterableDataset.skip` near `IterableDataset.take` to mimic [slicing](https://huggingface.co/docs/datasets/access/#slicing). What do you think?

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7564). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Added metadata and correct splits for swda.
Opened by gmihaila (closed)

Switchboard Dialog Act Corpus

I made some changes following  @bhavitvyamalik recommendation in #1678:

* Contains all metadata.
* Used official implementation from the [/swda](https://github.com/cgpotts/swda) repo.
* Add official train and test splits used in [Stolcke et al. (2000)](https://web.stanford.edu/~jurafsky/ws97) and validation split used in [Probabilistic-RNN-DA-Classifier](https://github.com/NathanDuran/Probabilistic-RNN-DA-Classifier).

Comments: I will push updates tomorrow.

@lhoestq thank you for your comments! I went ahead and fixed the code 😃. Please let me know if I missed anything.
Add Dengue dataset in Filipino
Opened by anaerobeth (closed)

This PR adds the Dengue Dataset, a benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets.

Link to the paper: https://ieeexplore.ieee.org/document/8459963

Link to the dataset/repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
Add WMT20 MLQE 3 shared tasks
Opened by VictorSanh (closed)

3 tasks for the WMT 20 MLQE shared tasks -> 3 different datasets

(I re-created #1137 because it was too messy).

Note that in L199 `task3.py`, I used `logging.warning` to print some missing data in the train set.

Comments: Thanks for the comments Quentin!
I integrated them

It should be ok now!
Sorry I wasn't attentive enough.
(tests are currently failing, I understand it's from other datasets)

merging since the CI is fixed on master
with_format() function returns bytes instead of PIL images even when image column is not part of "columns"
Opened by leot13 (closed)

### Describe the bug

When using the with_format() function on a dataset containing images, even if the image column is not part of the columns provided in the function, its type will be changed to bytes.
Here is a minimal reproduction of the bug:
https://colab.research.google.com/drive/1hyaOspgyhB41oiR1-tXE3k_gJCdJUQCf?usp=sharing

### Steps to reproduce the bug

1. Load the image dataset
2. apply with_format(columns=["text"])
3. Check the type of images in the "image" column before and after applying with_format

### Expected behavior

The type should stay the same, but it does not

### Environment info

datasets==2.14.6


Comments: Thanks for reporting! I've opened a PR with a fix.
How to use Distill-BERT with different datasets?
Opened by sauravtii (closed)

### Describe the bug

- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyTorch version (GPU?): 1.12.0+cu102 (True)
- Tensorflow version (GPU?): 2.10.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>


### Steps to reproduce the bug

I recently read [this](https://huggingface.co/docs/transformers/quicktour#train-with-tensorflow:~:text=The%20most%20important%20thing%20to%20remember%20is%20you%20need%20to%20instantiate%20a%20tokenizer%20with%20the%20same%20model%20name%20to%20ensure%20you%E2%80%99re%20using%20the%20same%20tokenization%20rules%20a%20model%20was%20pretrained%20with.) and was wondering how to use distill-BERT (which is pre-trained with imdb dataset) with a different dataset (for eg. [this](https://huggingface.co/datasets/yhavinga/imdb_dutch) dataset)?

### Expected behavior

Distill-BERT should work with different datasets.

### Environment info

- `datasets` version: 1.12.1
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 11.0.0

Comments: Closing this one in favor of the same issue opened in the `transformers` repo.
Messages are being printed to the `stdout`
Opened by mahnerak (closed)

In this code segment, we can see some messages are being printed to the `stdout`.
https://github.com/huggingface/datasets/blob/7e60bb509b595e8edc60a87f32b2bacfc065d607/src/datasets/builder.py#L545-L554
According to the comment, it is done intentionally, but I don't really understand why don't we log it with a higher level or print it directly to the `stderr`.
In my opinion, this kind of messages should never printed to the stdout. At least some configuration/flag should make it possible to provide in order to explicitly prevent the package to contaminate the stdout.


Comments: This is expected to show this message to the user via stdout.
This way the users see it directly and can cancel the downloading if they want to.
Could you elaborate why it would be better to have it in stderr instead of stdout ?

@lhoestq, sorry for the late reply

I completely understand why you decided to output a message that is always shown. The only problem is that the message is printed to the `stdout`. For example, if the user runs `python run_glue.py > log_file`, it will redirect `stdout` to the file named  `log_file`, and the message will not be shown to the user.

Instead, we should print this message to `stderr`.  Even in the case of `python run_glue.py > log_file` only `stdout` is being redirected and so the message is always shown.

We now log these messages to `stderr` (the built-in `logging` module's default behavior).
DataLoader(datasets) become more and more slowly within iterations
Opened by shexuan (closed)

Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly!
```
dataset = load_from_disk(dataset_path)  # around 21,000,000 lines

lineloader = tqdm(DataLoader(dataset, batch_size=1))
for idx, line in enumerate(lineloader):
     # do some thing for each line
```
In the begining, the loading speed is around 2000it/s, but after 1 minutes later, the speed is much slower, just around 800it/s.

And when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it/s.

Could you please help me with this problem?
Thanks a lot!

Comments: Hi ! Thanks for reporting.
Do you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)
It would be nice to know whether it comes from the dataloader or not

> Hi ! Thanks for reporting.
> Do you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)
> It would be nice to know whether it comes from the dataloader or not

I did not iter data from raw dataset, maybe I will test later. Now I iter all files directly from `open(file)`,  around 20000it/s.

> > Hi ! Thanks for reporting.
> > Do you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)
> > It would be nice to know whether it comes from the dataloader or not
> 
> I did not iter data from raw dataset, maybe I will test later. Now I iter all files directly from `open(file)`, around 20000it/s.

Hi ! I meet the same problem, how did you solve it?
No Samsum dataset
Opened by IgorKasianenko (closed)

### Describe the bug

https://huggingface.co/datasets/Samsung/samsum dataset not found error 404

Originated from https://github.com/meta-llama/llama-cookbook/issues/948

### Steps to reproduce the bug

go to website https://huggingface.co/datasets/Samsung/samsum
see the error

also downloading it with python throws 
```
Couldn't find 'Samsung/samsum' on the Hugging Face Hub either: FileNotFoundError: Samsung/samsum@f00baf5a7d4abfec6820415493bcb52c587788e6/samsum.py (repository not found)
```

### Expected behavior

Dataset exists

### Environment info

```
- `datasets` version: 3.2.0
- Platform: macOS-15.4.1-arm64-arm-64bit
- Python version: 3.12.2
- `huggingface_hub` version: 0.26.5
- PyArrow version: 16.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
```

Comments: According to the following https://huggingface.co/posts/seawolf2357/424129432408590, as of now the dataset seems to be inaccessible.

@IgorKasianenko, would https://huggingface.co/datasets/knkarthick/samsum suffice for your purpose?


Thanks @SP1029 for the update!
That will work for now, using it as replacement. Is there a officially recommended way to maintain the CC licensed dataset under the organization account? 
Feel free to close this issue

> Is there an officially recommended way to maintain a CC-licensed dataset under an organizational account?

@IgorKasianenko, apologies, this is not my area of expertise.

> Please feel free to close this issue.

I have limited access and may not be able to do that. Since you opened it, you would be able to close it.

dataset_samsum = load_dataset("knkarthick/samsum")

is working
Add Wikisource dataset
Opened by albertvillanova (closed)

## Adding a Dataset
- **Name:** *wikisource*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** Additional high quality textual data, besides Wikipedia.

Add loading script as "canonical" dataset (as it is the case for ""wikipedia").

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

CC: @geohci, @yjernite 

Comments: See notebook by @geohci: https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikisource%20Processing.ipynb

See: https://huggingface.co/datasets/wikimedia/wikisource
process .opus files (for Multilingual Spoken Words)
Opened by polinaeterna (closed)

Opus files requires `libsndfile>=1.0.30`. Add check for this version and tests.


**outdated:**
Add [Multillingual Spoken Words dataset](https://mlcommons.org/en/multilingual-spoken-words/)

You can specify multiple languages for downloading 😌:
```python
ds = load_dataset("datasets/ml_spoken_words", languages=["ar", "tt"])
```

1. I didn't take into account that each time you pass a set of languages the data for a specific language is downloaded even if it was downloaded before (since these are custom configs like `ar+tt` and `ar+tt+br`. Maybe that wasn't a good idea?
2. The script will have to be slightly changed after merge of https://github.com/huggingface/datasets/pull/3664 
2. Just can't figure out what wrong with dummy files... 😞 Maybe we should get rid of them at some point 😁

Comments: @lhoestq  I still have problems with processing `.opus` files with `soundfile` so I actually cannot fully check that it works but it should... Maybe this should be investigated in case of someone else would also have problems with that.

Also, as the data is in a private repo on the hub (before we come to a decision about audio data privacy), the needed checks cannot be done right now.

@lhoestq I check the data redownloading for configs sharing the same languages, you were right: the data is downloaded once for each language. But samples are generated from scratch each time. Is it a supposed behavior?  

> But samples are generated from scratch each time. Is it a supposed behavior?

Yea that's the way it works right now, because we generate one arrow file per configuration. Since changing the languages creates a new configuration, then it generates a new arrow file.
More tags
Opened by lhoestq (closed)

Since the hub v2 is going to be released soon I figured it would be great to add the missing tags at least for some of the datasets of reference listed [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#write-the-loadingprocessing-code)

Comments: Conll has `multilingual` but is only tagged as `en`

good catch, that was a bad copy paste x)
Fix metric compute (original_instructions missing)
Opened by lhoestq (closed)

When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset.
However metrics load data the same way but don't need instructions (we use one single file).

In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object.

This should fix #269 

Comments: Awesome! This is working now:

```python
import nlp 
seqeval = nlp.load_metric("seqeval") 
y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] 
y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]                                                                                                                                                                                    

results = seqeval.compute(y_true, y_pred)
```

I heavily need this fix for an upcoming `nlp` integration PR for Transformers (token classification example)  😅

Haha nice ! We'll ship this fix with the next release that will probably come out on thursday :)
move under the facebook org?
Opened by lewtun (open)

### Link

https://huggingface.co/datasets/gsarti/flores_101

### Description

It seems like streaming isn't supported for this dataset:

```
Server Error
Status code:   400
Exception:     NotImplementedError
Message:       Extraction protocol for TAR archives like 'https://dl.fbaipublicfiles.com/flores101/dataset/flores101_dataset.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```

### Owner

No

Comments: Related to https://github.com/huggingface/datasets/issues/4562#issuecomment-1166911751

I'll assign @albertvillanova 

I'm just wondering why we don't have this dataset under:
- the `facebook` namespace
- or the canonical dataset `flores`: why does this only have 2 languages?

fwiw: the dataset viewer is working. Renaming the issue
Fix loading from HF GCP cache
Opened by lhoestq (closed)

As reported in https://discuss.huggingface.co/t/error-loading-wikipedia-dataset/26599/4 it's not possible to download a cached version of Wikipedia from the HF GCP cache

I fixed it and added an integration test (runs in 10sec)

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

> Do you know why this stopped working?

It comes from the changes in https://github.com/huggingface/datasets/pull/5107/files#diff-355ae5c229f95f86895404b72378ecd6e966c41cbeebb674af6fe6e9611bc126
Update to simplify some datasets conversion
Opened by thomwolf (closed)

This PR updates the encoding of `Values` like `integers`, `boolean` and `float` to use python casting and avoid having to cast in the dataset scripts, as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r420176626

We could also change (not included in this PR yet):
- `supervized_keys` to make them a NamedTuple instead of a dataclass, and
- handle specifically the `Translation` features.
as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r421740236

@patrickvonplaten @mariamabarham tell me if you want these two last changes as well.

Comments: Awesome! 

Also we should convert `tf.io.gfile.exists` into `os.path.exists` , `tf.io.gfile.listdir`into `os.listdir` and `tf.io.gfile.glob`  into `glob.glob` (will need to add `import glob`)

> Also we should convert `tf.io.gfile.exists` into `os.path.exists` , `tf.io.gfile.listdir`into `os.listdir` and `tf.io.gfile.glob` into `glob.glob` (will need to add `import glob`)

We should probably open a new PR about this

I think it might be a good idea to both change the supervised keys to a named tuple and also handle the translation features specifically.

Just noticed that `pyarrow` apparently does not have a `is_boolean` function. Or do I have the wrong `pyarrow` version? 

Ah, it was a typo `pa.types.is_boolean` is the correct name. Will fix in: https://github.com/huggingface/nlp/pull/59