AniketTec/github-issues huggingface dataset:
Title: Easily create loading script for benchmark comprising multiple huggingface datasets
State: closed

Hi,

I would like to create a loading script for a benchmark comprising multiple huggingface datasets.
The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a single interface to all the underlying datasets. 
I thought about downloading the files with the load_dataset function and then providing the link to the cached file. But this seems a bit inelegant to me. What approach would you propose to do this?

Please let me know if you have any questions.

Cheers,
Joel

Comments:
Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467

Ok, I see. Thank you
Title: Shard parquet in `download_and_prepare`
State: closed

Following https://github.com/huggingface/datasets/pull/4724 (needs to be merged first)

It's good practice to shard parquet files to enable parallelism with spark/dask/etc.

I added the `max_shard_size` parameter to `download_and_prepare` (default to 500MB for parquet, and None for arrow).

```python
from datasets import *

output_dir = "./output_dir"  # also supports "s3://..."
builder = load_dataset_builder("squad")
builder.download_and_prepare(output_dir, file_format="parquet", max_shard_size="5MB")
```

### Implementation details

The examples are written to a parquet file until `ParquetWriter._num_bytes > max_shard_size`. When this happens, a new writer is instantiated to start writing the next shard. At the end, all the shards are renamed to include the total number of shards in their names: `{builder.name}-{split}-{shard_id:05d}-of-{num_shards:05d}.parquet`

I also added the `MAX_SHARD_SIZE` config variable (default to 500MB)

TODO:
- [x] docstrings
- [x] docs
- [x] tests

cc @severo 

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

This is ready for review cc @mariosasko :) please let me know what you think !
Title: Dataset Viewer issue for yanekyuk/wikikey
State: closed
Labels: description: Related to the dataset viewer on huggingface.co
name: dataset-viewer

### Link

_No response_

### Description

_No response_

### Owner

_No response_

Comments:
The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main

Maybe the viewer can display a better message for empty datasets

OK. Closing as it's not an error. We will work on making the error message a lot clearer.
Title: Allow `list_datasets` to include private datasets
State: closed
Labels: description: New feature or request
name: enhancement

I am working with a large collection of private datasets, it would be convenient for me to be able to list them.

I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:

```
list_datasets(use_auth_token="my_token")
```

would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually.

Comments:
Thanks for opening this issue :)

If it can help, I think you can already use `huggingface_hub` to achieve this:
```python
>>> from huggingface_hub import HfApi
>>> [ds_info.id for ds_info in HfApi().list_datasets(use_auth_token=token) if ds_info.private]
['bigscience/xxxx', 'bigscience-catalogue-data/xxxxxxx', ... ]
```

---------

Though the latest versions of `huggingface_hub` that contain this feature are not available on python 3.6, so maybe we should first drop support for python 3.6 (see #4460) to update `list_datasets` in `datasets` as well (or we would have to copy/paste some `huggingface_hub` code)

Great, thanks @lhoestq the workaround works! I think it would be intuitive to have the support directly in `datasets` but it makes sense to wait given that the workaround exists :)

i also think that going forward we should replace more and more implementations inside datasets with the corresponding ones from `huggingface_hub` (same as we're doing in `transformers`)

`datasets.list_datasets` is now deprecated in favor of `huggingface_hub.list_datasets` (returns private datasets when `token` is present), so I'm closing this issue.
Title: Remove instructions to generate dummy data from our docs
State: closed
Labels: description: Improvements or additions to documentation
name: documentation

In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata

However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
  - they do not pass any CI test requiring dummy data
  - there are no instructions on how they can test their dataset locally using the dummy data
  - the generation of the dummy data assumes our GitHub directory structure:
    - the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue:
      - #4742 

CC: @stevhliu 

Comments:
Note that for me personally, conceptually all the dummy data (even for "canonical" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones)

I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.

But for now, we could at least start by removing the indication to generate dummy data from our docs.
Title: Dummy data nowhere to be found
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
To finalize my dataset, I wanted to create dummy data as per the guide and I ran 

```shell
 datasets-cli dummy_data datasets/hebban-reviews --auto_generate
```

where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs and shows a message at the end that it succeeded, I cannot find the dummy data anywhere. Where is it?

## Expected results

To see the dummy data in the datasets' folder or in the folder where I ran the command.

## Actual results

I see the following message but I cannot find the dummy data anywhere.

```
Dummy data generation done and dummy data test succeeded for config 'filtered''.
Automatic dummy data generation succeeded for all configs of '.\datasets\hebban-reviews\'
```

## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3



Comments:
Hi @BramVanroy, thanks for reporting.

First of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for datasets on the Hub.
- We should definitely update our docs.

Second, the dummy data is generated locally:
- in your case, the dummy data will be generated inside the directory:  `./datasets/hebban-reviews/dummy`
- please note the preceding `./datasets` directory: the reason for this is that the command to generate the dummy data was specifically created for our `datasets` library, and therefore assumes our directory structure: commands are run from the root directory of our GitHub repo, and datasets scripts are under `./datasets` 


 

I have opened an Issue to update the instructions on dummy data generation:
- #4744

Dummy data generation is deprecated now, so I think we can close this issue.
Title: Fix multiprocessing in map_nested 
State: closed

As previously discussed:

Before, multiprocessing was not used in `map_nested` if `num_proc` was greater than or equal to `len(iterable)`.
- Multiprocessing was not used e.g. when passing `num_proc=20` but having 19 files to download
- As by default, `DownloadManager` sets `num_proc=16`, before multiprocessing was only used when `len(iterable)>16` by default

Now, if `num_proc` is greater than or equal to ``len(iterable)``, `num_proc` is set to ``len(iterable)`` and multiprocessing is used.
- We pass the variable `parallel_min_length=16`, so that multiprocessing is only used if at least 16 files to be downloaded
- ~As by default, `DownloadManager` sets `num_proc=16`, now multiprocessing is used when `len(iterable)>1` by default~

See discussion below.

~After having had to fix some tests (87602ac), I am wondering:~
- ~do we want to have multiprocessing by default?~
  - ~please note that `DownloadManager.download` sets `num_proc=16` by default~
- ~or would it be better to ask the user to set it explicitly if they want multiprocessing (and default to `num_proc=1`)?~

Fix #4636.

CC: @nateraw 

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

@lhoestq as a workaround to preserve previous behavior, the parameter `multiprocessing_min_length=16` is passed from `download` to `map_nested`, so that multiprocessing is only used if at least 16 files to be downloaded.

Note that there is a small breaking change (I think previously it was unintended behavior, so that I have fixed it):
- Before (with default `num_proc=16`) if there were 16 files to be downloaded, multiprocessing was not used
- Now (with default `num_proc=16`) if there are 16 files to be downloaded, multiprocessing is used

Thanks for the workaround !
Title: Deprecate metrics
State: closed

Deprecate metrics:
- deprecate public functions: `load_metric`, `list_metrics` and `inspect_metric`: docstring and warning
- test deprecation warnings are issues
- deprecate metrics in all docs
- remove mentions to metrics in docs and README
- deprecate internal functions/classes

Maybe we should also stop testing metrics?

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

I mark this as Draft because the deprecated version number needs being updated after the latest release.

Perhaps now is the time to also update the `inspect_metric` from `evaluate` with the changes introduced in https://github.com/huggingface/datasets/pull/4433 (cc @lvwerra) 

What do you think of including what changes users have to do to switch to `evaluate` in the warning message ?
(basically replace `datasets.load_metric` by `evaluate.load`)

I think it can help users migrate to `evaluate` and silence the warnings
Title: Use CI unit/integration tests
State: closed

This PR:
- Implements separate unit/integration tests
- A fail in integration tests does not cancel the rest of the jobs
  - We should implement more robust integration tests: work in progress in a subsequent PR
- For the moment, test involving network requests are marked as integration: to be evolved

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

I think this PR can be merged. Willing to see it in action.

CC: @lhoestq
Title: Download error on scene_parse_150
State: closed
Labels: description: Something isn't working
name: bug

```
from datasets import load_dataset
dataset = load_dataset("scene_parse_150", "scene_parsing")

FileNotFoundError: Couldn't find file at http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
```


Comments:
Hi! The server with the data seems to be down. I've reported this issue (https://github.com/CSAILVision/sceneparsing/issues/34) in the dataset repo. 

The URL seems to work now, and therefore the script as well.
Title: Dataset Viewer issue for deepklarity/huggingface-spaces-dataset
State: closed
Labels: description: Related to the dataset viewer on huggingface.co
name: dataset-viewer

### Link

https://huggingface.co/datasets/deepklarity/huggingface-spaces-dataset/viewer/deepklarity--huggingface-spaces-dataset/train

### Description

Hi Team, 
I'm getting the following error on a uploaded dataset. I'm getting the same status for a couple of hours now. The dataset size is `<1MB` and the format is csv, so I'm not sure if it's supposed to take this much time or not. 
```
Status code:   400
Exception:     Status400Error
Message:       The split is being processed. Retry later.
```

Is there any explicit step to be taken to get the viewer to work? 

### Owner

Yes

Comments:
Thanks for reporting. You're right, workers were under-provisioned due to a manual error, and the job queue was full. It's fixed now.
Title: Package rouge-score cannot be imported
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
After the today release of `rouge_score-0.0.7` it seems no longer importable. Our CI fails: https://github.com/huggingface/datasets/runs/7463218591?check_suite_focus=true
```
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_configs_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_bigbench
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_rouge
```
with errors:
```
>   from rouge_score import rouge_scorer
E   ModuleNotFoundError: No module named 'rouge_score'
```
```
E           ImportError: To be able to use rouge, you need to install the following dependency: rouge_score.
E           Please install it using 'pip install rouge_score' for instance'
```


Comments:
We have added a comment on an existing issue opened in their repo: https://github.com/google-research/google-research/issues/1212#issuecomment-1192267130
- https://github.com/google-research/google-research/issues/1212
Title: rouge metric
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
A clear and concise description of what the bug is.
Loading  Rouge metric gives error after latest rouge-score==0.0.7 release.
Downgrading rougemetric==0.0.4 works fine.

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```

## Expected results
A clear and concise description of the expected results.
from rouge_score import rouge_scorer, scoring 
should run

## Actual results
Specify the actual results or traceback.
File "/root/.cache/huggingface/modules/datasets_modules/metrics/rouge/0ffdb60f436bdb8884d5e4d608d53dbe108e82dac4f494a66f80ef3f647c104f/rouge.py", line 21, in <module>
    from rouge_score import rouge_scorer, scoring
ImportError: cannot import name 'rouge_scorer' from 'rouge_score' (unknown location)

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version:3.9
- PyArrow version:


Comments:
Fixed by:
- #4735
Title: Document better that loading a dataset passing its name does not use the local script
State: closed
Labels: description: Improvements or additions to documentation
name: documentation

As reported by @TrentBrick here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191858596, it could be more clear that loading a dataset by passing its name does not use the (modified) local script of it.

What he did:
- he installed `datasets` from source
- he modified locally `datasets/the_pile/the_pile.py` loading script
- he tried to load it but using `load_dataset("the_pile")` instead of `load_dataset("datasets/the_pile")`
  - as explained here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191040245:
    - the former does not use the local script, but instead it downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.

He suggests adding a more clear explanation about this. He suggests adding it maybe in [Installation > source](https://huggingface.co/docs/datasets/installation))

CC: @stevhliu 

Comments:
Thanks for the feedback!

I think since this issue is closely related to loading, I can add a clearer explanation under [Load > local loading script](https://huggingface.co/docs/datasets/main/en/loading#local-loading-script).

That makes sense but I think having a line about it under https://huggingface.co/docs/datasets/installation#source the "source" header here would be useful. My mental model of `pip install -e .` does not include the fact that the source files aren't actually being used. 

Thanks for sharing your perspective. I think the `load_dataset` function is the only one that pulls from GitHub, and since this use-case is very specific, I don't think we need to include such a broad clarification in the Installation section.

Feel free to check out the linked PR and let me know if it needs any additional explanation 😊
Title: Loading imagenet-1k validation split takes much more RAM than expected
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug

Loading into memory the validation split of imagenet-1k takes much more RAM than expected. Assuming ImageNet-1k is 150 GB, split is 50000 validation images and 1,281,167 train images, I would expect only about 6 GB loaded in RAM.


## Steps to reproduce the bug
```python
from datasets import  load_dataset

dataset = load_dataset("imagenet-1k", split="validation")

print(dataset)

"""prints
Dataset({
    features: ['image', 'label'],
    num_rows: 50000
})
"""

pipe_inputs = dataset["image"]
# and wait :-)
```

## Expected results
Use only < 10 GB RAM when loading the images.

## Actual results
![image](https://user-images.githubusercontent.com/9808326/180249183-62f75ca4-d127-402a-9330-f12825a22b0a.png)

```
Using custom data configuration default
Reusing dataset imagenet-1k (/home/fxmarty/.cache/huggingface/datasets/imagenet-1k/default/1.0.0/a1e9bfc56c3a7350165007d1176b15e9128fcaf9ab972147840529aed3ae52bc)
Killed
```

## Environment info
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- datasets commit: 4e4222f1b6362c2788aec0dd2cd8cede6dd17b80


Comments:
My bad, `482 * 418 * 50000 * 3 / 1000000 = 30221 MB` ( https://stackoverflow.com/a/42979315 ).

Meanwhile `256 * 256 * 50000 * 3 / 1000000 = 9830 MB`. We are loading the non-cropped images and that is why we take so much RAM.
Title: Refactor Hub tests
State: closed

This PR refactors `test_upstream_hub` by removing unittests and using the following pytest Hub fixtures:
- `ci_hub_config`
- `set_ci_hub_access_token`: to replace setUp/tearDown
- `temporary_repo` context manager: to replace `try... finally`
- `cleanup_repo`: to delete repo accidentally created if one of the tests fails

This is a preliminary work done to manage unit/integration tests separately.

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Title: load_dataset gives "403" error when using Financial Phrasebank
State: closed

I tried both codes below to download the financial phrasebank dataset (https://huggingface.co/datasets/financial_phrasebank) with the sentences_allagree subset. However, the code gives a 403 error when executed from multiple machines locally or on the cloud.

```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree',download_mode=DownloadMode.FORCE_REDOWNLOAD)
```

```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree')
```

**Error**
ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)


Comments:
Hi @rohitvincent, thanks for reporting.

Unfortunately I'm not able to reproduce your issue:
```python
In [2]: from datasets import load_dataset, DownloadMode
   ...: load_dataset(path='financial_phrasebank',name='sentences_allagree', download_mode="force_redownload")
Downloading builder script: 6.04kB [00:00, 2.87MB/s]                                                                                                                                                       
Downloading metadata: 13.7kB [00:00, 7.24MB/s]                                                                                                                                                             
Downloading and preparing dataset financial_phrasebank/sentences_allagree (download: 665.91 KiB, generated: 296.26 KiB, post-processed: Unknown size, total: 962.17 KiB) to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141...
Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 682k/682k [00:00<00:00, 7.66MB/s]
Dataset financial_phrasebank downloaded and prepared to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 918.80it/s]
Out[2]: 
DatasetDict({
    train: Dataset({
        features: ['sentence', 'label'],
        num_rows: 2264
    })
})
```

Are you able to access the link? https://www.researchgate.net/profile/Pekka-Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip

Yes was able to download from the link manually. But still, get the same error when I use load_dataset.

Fixed once data files are hosted on the Hub:
- #4598
Title: Dataset Viewer issue for TheNoob3131/mosquito-data
State: closed
Labels: description: Related to the dataset viewer on huggingface.co
name: dataset-viewer

### Link

https://huggingface.co/datasets/TheNoob3131/mosquito-data/viewer/TheNoob3131--mosquito-data/test

### Description

Dataset preview not showing with large files. Says 'split cache is empty' even though there are train and test splits.

### Owner

_No response_

Comments:
The preview is working OK:

![Screenshot from 2022-07-21 09-46-09](https://user-images.githubusercontent.com/8515462/180158929-bd8faad4-6392-4fc1-8d9c-df38aa9f8438.png)
Title: Fix broken link to the Hub
State: closed

The Markdown link fails to render if it is in the same line as the `<span>`. This PR implements @mishig25's fix by using `<a href=" ">` instead.

![Screen Shot 2022-07-20 at 3 53 05 PM](https://user-images.githubusercontent.com/59462357/180096412-7fbb33be-abb0-4e54-a52d-201b3b58e0f9.png)

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