ahmedfarazsyk/datasets-github-issues huggingface dataset:
404 not found error on loading WIKIANN dataset
Author: ronbutan
State: closed
Labels: name: bug

## Describe the bug
Unable to retreive wikiann English dataset

## Steps to reproduce the bug
```python
from datasets import list_datasets, load_dataset, list_metrics, load_metric
WIKIANN = load_dataset("wikiann","en")
```

## Expected results
Colab notebook should display successful download status

## Actual results
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0


Comments: Hi @ronbutan, thanks for reporting.

You are right: we have recently found that the link to the original PAN-X dataset (also called WikiANN), hosted at Dropbox, is no longer working.

We have opened an issue in the GitHub repository of the original dataset (afshinrahimi/mmner#4) and we have also contacted the author by email to ask if they are planning to fix this issue. See the details here: https://github.com/huggingface/datasets/issues/2691#issuecomment-885463027

I close this issue because it is the same as in #2691. Feel free to subscribe to that other issue to be informed about any updates.
Bad message when config name is missing
Author: lhoestq
State: closed

When loading a dataset that have several configurations, we expect to see an error message if the user doesn't specify a config name.

However in `datasets` 1.10.0 and 1.10.1 it doesn't show the right message:

```python
import datasets

datasets.load_dataset("glue")
```
raises
```python
AttributeError: 'BuilderConfig' object has no attribute 'text_features'
```
instead of
```python
ValueError: Config name is missing.
Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']
Example of usage:
        `load_dataset('glue', 'cola')`
```
from datasets import Dataset is failing 
Author: kswamy15
State: closed
Labels: name: bug

## Describe the bug
A clear and concise description of what the bug is.

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import Dataset
```

## Expected results
A clear and concise description of the expected results.

## Actual results
Specify the actual results or traceback.
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
     25 import posixpath
     26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
     28 
     29 from .. import __version__, config, utils

ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'

---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: latest version as of 07/21/2021
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:


Comments: Hi @kswamy15, thanks for reporting.

We are fixing this critical issue and making an urgent patch release of the `datasets` library today.

In the meantime, you can circumvent this issue by updating the `tqdm` library: `!pip install -U tqdm`
cannot combine splits merging and streaming?
Author: eyaler
State: open
Labels: name: bug

this does not work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`
with error:
`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`

these work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`
`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`
`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`

i could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits

Comments: Hi ! That's missing indeed. We'll try to implement this for the next version :)

I guess we just need to implement #2564 first, and then we should be able to add support for splits combinations

is there an update on this? ran into the same issue on 2.17.1.

On a similar note, the keyword `split="all"` also does not work as intended when `streaming=True`. 

No update so far, especially since we haven't implemented an efficient way to query `split=train[50%:]` for example. The addition of two splits should be easy though, since we have `concatenate_datasets()`

Can you concatenate_datasets that are being streamed now? I was led to believe concatenation works on non streaming datasets only.

Yes `concatenate_datasets` works for datasets loaded in streaming mode as well
xtreme / pan-x cannot be downloaded
Author: severo
State: closed
Labels: name: bug

## Describe the bug

Dataset xtreme / pan-x cannot be loaded

Seems related to https://github.com/huggingface/datasets/pull/2326

## Steps to reproduce the bug

```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```

## Expected results

Load the dataset

## Actual results

```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```

## Environment info

- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1


Comments: Hi @severo, thanks for reporting.

However I have not been able to reproduce this issue. Could you please confirm if the problem persists for you?

Maybe Dropbox (where the data source is hosted) was temporarily unavailable when you tried.

Hmmm, the file (https://www.dropbox.com/s/dl/12h3qqog6q4bjve/panx_dataset.tar) really seems to be unavailable... I tried from various connexions and machines and got the same 404 error. Maybe the dataset has been loaded from the cache in your case?

Yes @severo, weird... I could access the file when I answered to you, but now I cannot longer access it either... Maybe it was from the cache as you point out.

Anyway, I have opened an issue in the GitHub repository responsible for the original dataset: https://github.com/afshinrahimi/mmner/issues/4
I have also contacted the maintainer by email.

I'll keep you informed with their answer.

Reply from the author/maintainer: 
> Will fix the issue and let you know during the weekend.

The author told that apparently Dropbox has changed their policy and no longer allow downloading the file without having signed in first. The author asked Hugging Face to host their dataset.
cannot save the dataset to disk after rename_column
Author: PaulLerner
State: closed
Labels: name: bug

## Describe the bug
If you use `rename_column` and do no other modification, you will be unable to save the dataset using `save_to_disk`

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
In [1]: from datasets import Dataset, load_from_disk
In [5]: dataset=Dataset.from_dict({'foo': [0]})
In [7]: dataset.save_to_disk('foo')
In [8]: dataset=load_from_disk('foo')
In [10]: dataset=dataset.rename_column('foo', 'bar')
In [11]: dataset.save_to_disk('foo')
---------------------------------------------------------------------------
PermissionError                           Traceback (most recent call last)
<ipython-input-11-a3bc0d4fc339> in <module>
----> 1 dataset.save_to_disk('foo')

/mnt/beegfs/projects/meerqat/anaconda3/envs/meerqat/lib/python3.7/site-packages/datasets/arrow_dataset.py in save_to_disk(self, dataset_path
, fs)
    597             if Path(dataset_path, config.DATASET_ARROW_FILENAME) in cache_files_paths:
    598                 raise PermissionError(
--> 599                     f"Tried to overwrite {Path(dataset_path, config.DATASET_ARROW_FILENAME)} but a dataset can't overwrite itself."
    600                 )
    601             if Path(dataset_path, config.DATASET_INDICES_FILENAME) in cache_files_paths:

PermissionError: Tried to overwrite foo/dataset.arrow but a dataset can't overwrite itself.
```

N. B. I created the dataset from dict to enable easy reproduction but the same happens if you load an existing dataset (e.g. starting from `In [8]`)

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-3.10.0-1160.11.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core
- Python version: 3.7.10
- PyArrow version: 3.0.0



Comments: Hi ! That's because you are trying to overwrite a file that is already open and being used.
Indeed `foo/dataset.arrow` is open and used by your `dataset` object.

When you do `rename_column`, the resulting dataset reads the data from the same arrow file.
In other cases like when using `map` on the other hand, the resulting dataset reads the data from another arrow file that is the result of the map transform.

Therefore overwriting a dataset after `rename_column` is not possible, but it is possible after `map`, since `rename_column` doesn't switch to using another arrow file (the actual data stay the same).

Ok, thanks for clearing it up :)

so what would be the right way to read a dataset, then change something and then overwrite it with the new version?

You have to write to a new directory, then delete the former directory, and finally rename the new directory.

This worked for me; I know it's hacky but it works:

```
ds.add_column...
ds = Dataset.from_dict(ds.to_dict())
ds.save_to_disk...
```
hebrew language codes he and iw should be treated as aliases
Author: eyaler
State: closed
Labels: name: bug

https://huggingface.co/datasets/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability. 

Comments: Hi @eyaler, thanks for reporting.

While you are true with respect the Hebrew language tag ("iw" is deprecated and "he" is the preferred value), in the "mc4" dataset (which is a derived dataset) we have kept the language tags present in the original dataset: [Google C4](https://www.tensorflow.org/datasets/catalog/c4).

For discoverability on the website I updated the YAML tags at the top of the mC4 dataset card https://github.com/huggingface/datasets/commit/38288087b1b02f97586e0346e8f28f4960f1fd37

Once the website is updated, mC4 will be listed in https://huggingface.co/datasets?filter=languages:he
Cache directories changed due to recent changes in how config kwargs are handled
Author: lhoestq
State: closed

Since #2659 I can see weird cache directory names with hashes in the config id, even though no additional config kwargs are passed. For example:

```python
from datasets import load_dataset_builder

c4_builder = load_dataset_builder("c4", "en")
print(c4_builder.cache_dir)
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en-174d3b7155eb68db/0.0.0/...

# instead of 
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en/0.0.0/...
```
This issue could be annoying since it would simply ignore old cache directories for users, and regenerate datasets

cc @stas00 this is what you experienced a few days ago
5 duplicate datasets
Author: severo
State: closed
Labels: name: bug

## Describe the bug

In 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are:

- https://paperswithcode.com/dataset/multinli -> https://huggingface.co/datasets/multi_nli and https://huggingface.co/datasets/multi_nli_mismatch
  
  <img width="838" alt="Capture d’écran 2021-07-20 à 16 33 58" src="https://user-images.githubusercontent.com/1676121/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png">

- https://paperswithcode.com/dataset/squad -> https://huggingface.co/datasets/squad and https://huggingface.co/datasets/squad_v2
- https://paperswithcode.com/dataset/narrativeqa -> https://huggingface.co/datasets/narrativeqa and https://huggingface.co/datasets/narrativeqa_manual
- https://paperswithcode.com/dataset/hate-speech-and-offensive-language -> https://huggingface.co/datasets/hate_offensive and https://huggingface.co/datasets/hate_speech_offensive
- https://paperswithcode.com/dataset/newsph-nli -> https://huggingface.co/datasets/newsph and https://huggingface.co/datasets/newsph_nli

Possible solutions:
- don't fix (it works)
- for each pair of duplicate datasets, remove one, and create an alias to the other.

## Steps to reproduce the bug

Visit the Paperswithcode links, and look at the "Dataset Loaders" section

## Expected results

There should only be one reference to a Hugging Face dataset loader

## Actual results

Two Hugging Face dataset loaders


Comments: Yes this was documented in the PR that added this hf->paperswithcode mapping (https://github.com/huggingface/datasets/pull/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix

For context on the paperswithcode mapping you can also refer to https://github.com/huggingface/huggingface_hub/pull/43 which contains a lot of background discussion 

Thanks for the antecedents. I close.
Error when downloading C4
Author: Aktsvigun
State: closed
Labels: name: bug

Hi,
I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:
`datasets.load_dataset('c4', 'en')`

Is this a bug or do I have some configurations missing on my server? 
Thanks!


<img width="1014" alt="Снимок экрана 2021-07-20 в 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png">

Comments: Hi Thanks for reporting !
It looks like these files are not correctly reported in the list of expected files to download, let me fix that ;)

Alright this is fixed now. We'll do a new release soon to make the fix available.

In the meantime feel free to simply pass `ignore_verifications=True` to `load_dataset` to skip this error

@lhoestq thank you for such a quick feedback!
Using sharding to parallelize indexing
Author: ggdupont
State: open
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**
Creating an elasticsearch index on large dataset could be quite long and cannot be parallelized on shard (the index creation is colliding)

**Describe the solution you'd like**
When working on dataset shards, if an index already exists, its mapping should be checked and if compatible, the indexing process should continue with the shard data. 

Additionally, at the end of the process, the `_indexes` dict should be send back to the original dataset object (from which the shards have been created) to allow to use the index for later filtering on the whole dataset.

**Describe alternatives you've considered**
Each dataset shard could created independent partial indices. then on the whole dataset level, indices should be all referred in `_indexes` dict and be used in querying through `get_nearest_examples()`. The drawback is that the scores will be computed independently on the partial indices leading to inconsistent values for most scoring based on corpus level statistics (tf/idf, BM25).

**Additional context**
The objectives is to parallelize the index creation to speed-up the process (ie surcharging the ES server which is fine to handle large load) while later enabling search on the whole dataset.
[`to_json`] add multi-proc sharding support
Author: stas00
State: closed
Labels: name: enhancement

As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR.

I implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards?

I think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process.

I'm not sure whether the user should be responsible to concatenate the shards at the end  or `datasets`, either way works for my needs.

The code I was using:

```
from multiprocessing import cpu_count, Process, Queue

[...]

filtered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count())

DATASET_NAME = "oscar"
SHARDS = 10
def process_shard(idx):
    print(f"Sharding {idx}")
    ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True)
    # ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling
    print(f"Saving {DATASET_NAME}-{idx}.jsonl")
    ds_shard.to_json(f"{DATASET_NAME}-{idx}.jsonl", orient="records", lines=True, force_ascii=False)

queue = Queue()
processes = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)]
for p in processes:
    p.start()

for p in processes:
    p.join()
```

Thank you!

@lhoestq 

Comments: Hi @stas00, 
I want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert  pyarrow table to `json` using multiprocessing. I've a small code snippet for some clarity:
```
result = list(
            pool.imap(self._apply_df, [(offset, batch_size) for offset in range(0, len(self.dataset), batch_size)])
        )
```
`_apply_df` is a function which will return `batch.to_pandas().to_json(path_or_buf=None, orient="records", lines=True)` which is basically json version of the batched pyarrow table. Later on we can concatenate it to form json file? 

I think the only downside here is to write file from `imap` output (output would be a list and we'll need to iterate over it and write in a file) which might add a little overhead cost. What do you think about this?

Followed up in https://github.com/huggingface/datasets/pull/2747
Can't pass `sep=None` to load_dataset("csv", ...) to infer the separator via pandas.read_csv
Author: lhoestq
State: closed

When doing `load_dataset("csv", sep=None)`, the `sep` passed to `pd.read_csv` is still the default `sep=","` instead, which makes it impossible to make the csv loader infer the separator.

Related to https://github.com/huggingface/datasets/pull/2656

cc @SBrandeis
`to_json` reporting enhancements
Author: stas00
State: open
Labels: name: enhancement

While using `to_json` 2 things came to mind that would have made the experience easier on the user:

1. Could we have a `desc` arg for the tqdm use and a fallback to just `to_json` so that it'd be clear to the user what's happening? Surely, one can just print the description before calling json, but I thought perhaps it'd help to have it self-identify like you did for other progress bars recently.

2. It took me a while to make sense of the reported numbers:
```
 22%|██▏       | 1536/7076 [12:30:57<44:09:42, 28.70s/it]
```
So iteration here happens to be 10K samples, and the total is 70M records. But the user does't know that, so the progress bar is perfect, but the numbers it reports are meaningless until one discovers that 1it=10K samples. And one still has to convert these in the head - so it's not quick. Not exactly sure what's the best way to approach this, perhaps it can be part of `desc`? or report M or K, so it'd be built-in if it were to print, e.g.:
```
 22%|██▏       | 15360K/70760K [12:30:57<44:09:42, 28.70s/it]
```
or 
```
 22%|██▏       | 15.36M/70.76M [12:30:57<44:09:42, 28.70s/it]
```
(while of course remaining friendly to small datasets)

I forget if tqdm lets you add a magnitude identifier to the running count.

Thank you!
Allow the selection of multiple columns at once
Author: Dref360
State: closed
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**

Similar to pandas, it would be great if we could select multiple columns at once.


**Describe the solution you'd like**
```python
my_dataset = ...  # Has columns ['idx', 'sentence', 'label']
idx, label = my_dataset[['idx', 'label']]
```

**Describe alternatives you've considered**
we can do `[dataset[col] for col in ('idx', 'label')]`

**Additional context**
This is of course very minor.


Comments: Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense. 
Is there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like one? Please let me know if I'm missing something.

Hi! Sorry for the delay.

In this case, the dataset would be a `datasets.Dataset` and we want to select multiple columns, the `idx` and `label` columns for example.

My issue is that my dataset is too big for memory if I load everything into pandas.

Hello - any update on this? Thank you.

Please note that you could use the method `Dataset.select_columns`: https://huggingface.co/docs/datasets/v2.15.0/en/package_reference/main_classes#datasets.Dataset.select_columns

Thank you!
Give a user feedback if the dataset he loads is streamable or not
Author: philschmid
State: open
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**
I would love to know if a `dataset` is with the current implementation streamable or not. 

**Describe the solution you'd like**
We could show a warning when a dataset is loaded with `load_dataset('...',streaming=True)` when its lot streamable, e.g. if it is an archive. 

**Describe alternatives you've considered**
Add a new metadata tag for "streaming"


Comments: #self-assign

I understand it already raises a `NotImplementedError` exception, eg:

```
>>> dataset = load_dataset("journalists_questions", name="plain_text", split="train", streaming=True)

[...]
NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet
```