noamaanMulla-03/datasets-issues huggingface dataset:
Adding chemistry dataset/models in huggingface

### Feature request

Huggingface is really amazing platform for open science.

In addition to computer vision, video and NLP, would it be of interest to add chemistry/materials science dataset/models in Huggingface? Or, if its already done, can you provide some pointers.

We have been working on a comprehensive benchmark on this topic: [JARVIS-Leaderboard](https://pages.nist.gov/jarvis_leaderboard/) and I am wondering if we could contribute/integrate this project as a part of huggingface. 

### Motivation

Similar to the main stream AI field, there is need of large scale benchmarks/models/infrastructure for chemistry/materials data.

### Your contribution

We can start adding datasets as our [benchmarks](https://github.com/usnistgov/jarvis_leaderboard/tree/main/jarvis_leaderboard/benchmarks) should be easily convertible to the dataset format.

Labels: name: enhancement

Hi! 

This would be a nice addition to the Hub! You can find the existing chemistry datasets/models on the Hub (using the `chemistry` tag) [here](https://huggingface.co/search/full-text?q=chemistry&type=model&type=dataset).

Feel free to ping us here on the Hub if you need help adding the datasets.
User access requests with manual review do not notify the dataset owner

### 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


cc @SBrandeis

I think this has been addressed.

Please open a new issue if you are still not getting notified.
wiki_dpr details for Open Domain Question Answering tasks

Hey guys!

Thanks for creating the wiki_dpr dataset!

I am currently trying to combine wiki_dpr and my own datasets. but I don't know how to make the embedding value the same way as wiki_dpr.

As an experiment, I embeds the text of id="7" of wiki_dpr, but this result was very different from wiki_dpr.


Hi ! I don't remember exactly how it was done, but maybe you have to embed `f"{title}<sep>{text}"` ?

Using a HF tokenizer it corresponds to doing
```python
tokenized = tokenizer(titles, texts)
```
[dataset request] Add Common Voice 12.0

### Feature request

Please add the common voice 12_0 datasets. Apart from English, a significant amount of audio-data has been added to the other minor-language datasets.

### Motivation

The dataset link:
https://commonvoice.mozilla.org/en/datasets


Labels: name: enhancement

@polinaeterna any tentative date on when the Common Voice 12.0 dataset will be added ?

This dataset is now hosted on the Hub here: https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0
label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator

### Describe the bug

When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default column name is `label` if binary or `label_ids` if multi-class problem.

It is required to rename the column accordingly to the expected name : `label` or `label_ids`

### Steps to reproduce the bug

```python
from datasets import TextClassification, AutoTokenized, DataCollatorWithPadding
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0'))
print(ds_prepared)

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
ds_tokenized = ds_prepared.map(lambda x: tokenizer(x['text'], truncation=True), batched=True)
print(ds_tokenized)

data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")

tf_data = model.prepare_tf_dataset(ds_tokenized, shuffle=True, batch_size=16, collate_fn=data_collator)

print(tf_data)
```

### Expected behavior

Without renaming the the column, the  target column is not in the final tf_data since it is not in the column name expected by the data_collator.

To correct this, we have to rename the column:

```python
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0')).rename_column('labels', 'label')
```

### Environment info

- `datasets` version: 2.8.0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.1
- Pandas version: 1.5.2

- `transformers` version: 4.26.0.dev0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.11.1
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): 2.11.0 (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>


Hi! Thanks for pointing out this inconsistency. Changing the default value at this point is probably not worth it, considering we've started discussing the state of the task API internally - we will most likely deprecate the current one and replace it with a more robust solution that relies on the `train_eval_index` field stored in the YAML section of the dataset cards.

The task templates API has been deprecated (will be removed in version 3.0), so I'm closing this issue.
Datasets: classification_report() got an unexpected keyword argument 'suffix'

https://github.com/huggingface/datasets/blob/main/metrics/seqeval/seqeval.py


> import datasets
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
seqeval = datasets.load_metric("seqeval")
results = seqeval.compute(predictions=predictions, references=references)
print(list(results.keys()))
print(results["overall_f1"])
print(results["PER"]["f1"])

It raises the error:

> TypeError: classification_report() got an unexpected keyword argument 'suffix'

For context, versions on my pip list -v

> datasets                 1.12.1  
seqeval                  1.2.2


This seems similar to https://github.com/huggingface/datasets/issues/2512 Can you try to update seqeval ? 

@JonathanAlis also note that the metrics are deprecated in our `datasets` library.

Please, use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate
Multi-processed `Dataset.map` slows down a lot when `import torch`

### Describe the bug

When using `Dataset.map` with `num_proc > 1`, the speed slows down much if I add `import torch` to the start of the script even though I don't use it.

I'm not sure if it's `torch` only or if any other package that is "large" will also cause the same result.
BTW, `import lightning` also slows it down.

Below are the progress bars of `Dataset.map`, the only difference between them is with or without `import torch`, but the speed varies by 6-7 times.

- without `import torch` ![image](https://github.com/huggingface/datasets/assets/47121592/0233055a-ced4-424a-9f0f-32a2afd802c2)
- with `import torch` ![image](https://github.com/huggingface/datasets/assets/47121592/463eafb7-b81e-4eb9-91ca-fd7fe20f3d59)


### Steps to reproduce the bug

Below is the code I used, but I don't think the dataset and the mapping function have much to do with the phenomenon.

```python3
from datasets import load_from_disk, disable_caching
from transformers import AutoTokenizer
# import torch
# import lightning

def rearrange_datapoints(
    batch,
    tokenizer,
    sequence_length,
):
    datapoints = []
    
    input_ids = []
    for x in batch['input_ids']:
        input_ids += x
        while len(input_ids) >= sequence_length:
            datapoint = input_ids[:sequence_length]
            datapoints.append(datapoint)
            input_ids[:sequence_length] = []

    if input_ids:
        paddings = [-1] * (sequence_length - len(input_ids))
        datapoint = paddings + input_ids if tokenizer.padding_side == 'left' else input_ids + paddings
        datapoints.append(datapoint)
    
    batch['input_ids'] = datapoints
    return batch


if __name__ == '__main__':
    disable_caching()

    tokenizer = AutoTokenizer.from_pretrained('...', use_fast=False)
    dataset = load_from_disk('...')
    
    dataset = dataset.map(
        rearrange_datapoints,
        fn_kwargs=dict(
            tokenizer=tokenizer,
            sequence_length=2048,
        ),
        batched=True,
        num_proc=8,
    )

```

### Expected behavior

The multi-processed `Dataset.map` function speed between with and without `import torch` should be the same.

### Environment info

- `datasets` version: 2.13.1
- Platform: Linux-3.10.0-1127.el7.x86_64-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1

Labels: name: duplicate

A duplicate of https://github.com/huggingface/datasets/issues/5929
Misalignment between card tag validation and docs

## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: Union[str, List[str]]`

They should be aligned.

CC: @julien-c 


Labels: name: bug

(Note that the doc is aligned with the hub validation rules, and the "ground truth" is the hub validation rules given that they apply to all datasets, not just the canonical ones)

Instead of our own implementation, we now use `huggingface_hub`'s `DatasetCardData`, which has the correct type hint, so I think we can close this issue.
Failing to upload dataset to the hub

### Describe the bug

Trying to upload a dataset of hundreds of thousands of audio samples (the total volume is not very large, 60 gb) to the hub with push_to_hub, it doesn't work.
From time to time one piece of the data (parquet) gets pushed and then I get RemoteDisconnected even though my internet is stable.
Please help.
I'm trying to upload the dataset for almost a week.
Thanks

### Steps to reproduce the bug

not relevant 

### Expected behavior

Be able to upload thedataset

### Environment info

python: 3.9


Hi ! Feel free to re-run your code later, it will resume automatically where you left

Tried many times in the last 2 weeks, problem remains.

Alternatively you can save your dataset in parquet files locally and upload them to the hub manually

```python
from tqdm import tqdm
num_shards = 60
for index in tqdm(range(num_shards)):
    ds.shard(num_shards=num_shards, index=index, contiguous=True).to_parquet(f"{index:05d}.parquet")
````
I tried to load a custom dataset using the following statement: dataset = load_dataset('json', data_files=data_files). The dataset contains 50 million text-image pairs, but an error occurred.

### Describe the bug

File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
Downloading and preparing dataset json/default to /home/kas/diffusers/examples/dreambooth/cache_data/datasets/json/default-acf423d8c6ef99d0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data files: 100%|██████████| 1/1 [00:00<00:00, 84.35it/s]
Extracting data files: 0%| | 0/1 [00:00<?, ?it/s] for _, table in generator:
File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 114, in _generate_tables
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
File "pyarrow/_json.pyx", line 258, in pyarrow._json.read_json
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 27.72it/s]
Generating train split: 0 examples [00:00, ? examples/s] File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 125, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2390448764

### Steps to reproduce the bug

1、data_files = ["1.json", "2.json", "3.json"]
2、dataset = load_dataset('json', data_files=data_files)

### Expected behavior

Read the dataset normally.



### Environment info

- `datasets` version: 2.12.0
- Platform: Linux-4.15.0-29-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 1.3.5


Thanks for reporting, @cjt222.

What is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead. 

> Thanks for reporting, @cjt222.
> 
> What is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead.

Thanks! I have encountered similar problems. I modify the json format from list to line and works!
Downloaded datasets do not cache at $HF_HOME

### Describe the bug

In the huggingface course (https://huggingface.co/course/chapter3/2?fw=pt) it said that if we set HF_HOME, downloaded datasets would be cached at specified address but it does not. downloaded models from checkpoint names are downloaded and cached at HF_HOME but this is not the case for datasets, they are still cached at ~/.cache/huggingface/datasets.

### Steps to reproduce the bug

Run the following code
```
from datasets import load_dataset
raw_datasets = load_dataset("glue", "mrpc")
raw_datasets
```

it downloads and store dataset at ~/.cache/huggingface/datasets

### Expected behavior

to cache dataset at HF_HOME.

### Environment info

python 3.10.6
Kubuntu 22.04
HF_HOME located on a separate partition


Hi ! Can you make sure you set `HF_HOME` before importing `datasets` ?

Then you can print
```python
print(datasets.config.HF_CACHE_HOME)
print(datasets.config.HF_DATASETS_CACHE)
```
Loading column subset from parquet file produces error since version 2.13

### Describe the bug

`load_dataset` allows loading a subset of columns from a parquet file with the `columns` argument. Since version 2.13, this produces the following error:

```
Traceback (most recent call last):                      
  File "/usr/lib/python3.10/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
    for _, table in generator:
  File "/usr/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
    raise ValueError(
ValueError: Tried to load parquet data with columns '['sepal_length']' with mismatching features '{'sepal_length': Value(dtype='float64', id=None), 'sepal_width': Value(dtype='float64', id=None), 'petal_length': Value(dtype='float64', id=None), 'petal_width': Value(dtype='float64', id=None), 'species': Value(dtype='string', id=None)}'
```

This seems to occur because `datasets` is checking whether the columns in the schema exactly match the provided list of columns, instead of whether they are a subset.

### Steps to reproduce the bug

```python
# Prepare some sample data

import pandas as pd

iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
iris.to_parquet('iris.parquet')

# ['sepal_length', 'sepal_width', 'petal_length', 'petal_width',  'species']
print(iris.columns)

# Load data with datasets

from datasets import load_dataset

# Load full parquet file
dataset = load_dataset('parquet', data_files='iris.parquet')

# Load column subset; throws error for datasets>=2.13
dataset = load_dataset('parquet', data_files='iris.parquet', columns=['sepal_length'])
```

### Expected behavior

No error should be thrown and the given column subset should be loaded.

### Environment info

- `datasets` version: 2.13.0
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.9
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
Schrödinger's dataset_dict

### Describe the bug

If you use load_dataset('json', data_files="path/test.json"), it will return DatasetDict({train:...}).

And if you use load_dataset("path"), it will return DatasetDict({test:...}).

Why can't the output behavior be unified?

### Steps to reproduce the bug

as description above.

### Expected behavior

consistent predictable output.

### Environment info

'2.11.0'


Hi ! Passing `data_files="path/test.json"` is equivalent to `data_files={"train": ["path/test.json"]}`, that's why you end up with a train split. If you don't pass `data_files=`, then split names are inferred from the data files names
load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub

### Describe the bug

When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting.

### Steps to reproduce the bug

```
from datasets import load_dataset

imagenet = load_dataset("imagenet-1k", split="train", streaming=True)

FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub
```
tested on a colab notebook.

### Expected behavior

I would expect a specific error indicating that I have to login then accept the dataset licence.

I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable)

### Environment info

google colab cpu-only instance


Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?

The error is now more informative:
```
FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.
```
Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name?

Hi @mariosasko, @lhoestq, or whoever reads this! :)

After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, shouldn't that be renamed to `format_type` before the 3.0.0 is released?

Just wanted to get your input, and if applicable, tackle this issue myself! Thanks 🤗


Hi! Let's not do this - renaming it would be a breaking change, and going through the deprecation cycle is only worth it if it improves user experience.

Hi @mariosasko, ok it makes sense. Anyway, don't you think it's worth it at some point to start a deprecation cycle e.g. `fs` in `load_from_disk`? It doesn't affect user experience but it's for sure a bad practice IMO, but's up to you 😄 Feel free to close this issue otherwise!

I don't think deprecating a param name in this particular instance is worth the hassle, so I'm closing the issue 🙂.

Sure, makes sense @mariosasko thanks!
Name the default config `default`

Currently, if a dataset has no configuration, a default configuration is created from the dataset name.

For example, for a dataset loaded from the hub repository, such as https://huggingface.co/datasets/user/dataset (repo id is `user/dataset`), the default configuration will be `user--dataset`.

It might be easier to handle to set it to `default`, or another reserved word.

Labels: name: enhancement

name: question

Addressed in #5331.
Allow `list_datasets` to include private datasets

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.

Labels: name: enhancement

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.
'sort' method sorts one column only

The 'sort' method changes the order of one column only (the one defined by the argument 'column'), thus creating a mismatch between a sample fields. I would expect it to change the order of the samples as a whole, based on the 'column' order.


Hi ! `ds.sort()` does sort the full dataset, not just one column:
```python
from datasets import *

ds = Dataset.from_dict({"foo": [3, 2, 1], "bar": ["c", "b", "a"]})
print(d.sort("foo").to_pandas()
#    foo bar
# 0    1   a
# 1    2   b
# 2    3   c
```

What made you think it was not the case ? Did you experience a situation where it was only sorting one column ?

Hi! thank you for your quick reply!
I wanted to sort the `cnn_dailymail` dataset by the length of the labels (num of characters). I added a new column to the dataset (`ds.add_column`) with the lengths and then sorted by this new column. Only the new length column was sorted, the reset left in their original order. 

That's unexpected, can you share the code you used to get this ?