AniketTec/github-issues huggingface dataset:
Title: add prachathai67k
State: closed

`prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com
The prachathai-67k dataset was scraped from the news site Prachathai.
We filtered out those articles with less than 500 characters of body text, mostly images and cartoons.
It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018.
The dataset was originally scraped by @lukkiddd and cleaned by @cstorm125.
You can also see preliminary exploration at https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb

Comments:
Title: Prachathai67k
State: closed

Add `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com

The `prachathai-67k` dataset was scraped from the news site [Prachathai](prachathai.com). We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by [@lukkiddd](https://github.com/lukkiddd) and cleaned by [@cstorm125](https://github.com/cstorm125). Download the dataset [here](https://www.dropbox.com/s/fsxepdka4l2pr45/prachathai-67k.zip?dl=1). You can also see preliminary exploration in [exploration.ipynb](https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb).

This dataset is a part of [pyThaiNLP](https://github.com/PyThaiNLP/) Thai text [classification-benchmarks](https://github.com/PyThaiNLP/classification-benchmarks). For the benchmark, we selected the following tags with substantial volume that resemble **classifying types of articles**:

* `การเมือง` - politics
* `สิทธิมนุษยชน` - human_rights
* `คุณภาพชีวิต` - quality_of_life
* `ต่างประเทศ` - international
* `สังคม` - social
* `สิ่งแวดล้อม` - environment
* `เศรษฐกิจ` - economics
* `วัฒนธรรม` - culture
* `แรงงาน` - labor
* `ความมั่นคง` - national_security
* `ไอซีที` - ict
* `การศึกษา` - education

Comments:
Title: The FLUE Benchmark
State: closed

This PR adds the [FLUE](https://github.com/getalp/Flaubert/tree/master/flue) benchmark which is a set of different datasets to evaluate models for French content.

Two datasets are missing, the French Treebank that we can use only for research purpose and we are not allowed to distribute, and the Word Sense disambiguation for Nouns that will be added later.

Comments:
Title: D
State: closed

## Adding a Dataset
- **Name:** *name of the dataset*
- **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:** *what are some good reasons to have this dataset*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Comments:
Title: Local machine/cluster Beam Datasets example/tutorial
State: closed

Hi,

I'm wondering if https://huggingface.co/docs/datasets/beam_dataset.html has an non-GCP or non-Dataflow version example/tutorial? I tried to migrate it to run on DirectRunner and SparkRunner, however, there were way too many runtime errors that I had to fix during the process, and even so I wasn't able to get either runner correctly producing the desired output.

Thanks!
Shang

Comments:
Title: Add NumerSense
State: closed

Adds the NumerSense dataset
- Webpage/leaderboard: https://inklab.usc.edu/NumerSense/
- Paper: https://arxiv.org/abs/2005.00683
- Description: NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes. Basically, it's a benchmark to see whether your MLM can figure out the right number in a fill-in-the-blank task based on commonsense knowledge (a bird has **two** legs)

Comments:
Title: [WIP] complex_webqa - Error zipfile.BadZipFile: Bad CRC-32
State: closed
Labels: description: Contribution to a dataset script
name: dataset contribution

Have a string `zipfile.BadZipFile: Bad CRC-32 for file 'web_snippets_train.json'` error when downloading the largest file from dropbox: `https://www.dropbox.com/sh/7pkwkrfnwqhsnpo/AABVENv_Q9rFtnM61liyzO0La/web_snippets_train.json.zip?dl=1`

Didn't managed to see how to solve that.

Putting aside for now.


Comments:
Title: Lambada
State: closed

Added LAMBADA dataset.

A couple of points of attention (mostly because I am not sure)
- The training data are compressed in a .tar file inside the main tar.gz file. I had to manually un-tar the training file to access the examples.
- The dev and test splits don't have the `category` field so I put `None` by default.

Happy to make changes if it doesn't respect the guidelines!

Victor

Comments:
Title: Add the Multilingual Amazon Reviews Corpus
State: closed

- **Name:** Multilingual Amazon Reviews Corpus* (`amazon_reviews_multi`)
- **Description:** A collection of Amazon reviews in English, Japanese, German, French, Spanish and Chinese.
- **Paper:** https://arxiv.org/abs/2010.02573

### Checkbox

- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.

Comments:
Title: Add DART
State: closed

- **Name:** *DART*
- **Description:** *DART is a large dataset for open-domain structured data record to text generation.*
- **Paper:** *https://arxiv.org/abs/2007.02871*
- **Data:** *https://github.com/Yale-LILY/dart#leaderboard*

### Checkbox

- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.

Comments:
Title: wrong length with datasets 
State: closed

Hi
I have a MRPC dataset which I convert it to seq2seq format, then this is of this format:

`Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10)
`

I feed it to a dataloader:
```
dataloader = DataLoader(
            train_dataset,
            batch_size=self.args.train_batch_size,
            sampler=train_sampler,
            collate_fn=self.data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
        )
```

now if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks 


Comments:
Title: Add conll2002
State: closed

Adding the Conll2002 dataset for NER.

More info here : https://www.clips.uantwerpen.be/conll2002/ner/

### Checkbox

- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.


Comments:
Title: Shall we change the hashing to encoding to reduce potential replicated cache files?
State: open
Labels: description: New feature or request
name: enhancement

description: Generic discussion on the library
name: generic discussion

Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.

If you have interest in this, I'd love to help :).

Comments:
Title: Add list_github_datasets api for retrieving dataset name list in github repo
State: closed

Thank you for your great effort on unifying data processing for NLP!

This pr is trying to add a new api `list_github_datasets` in the `inspect` module. The reason for it is that the current `list_datasets` api need to access https://huggingface.co/api/datasets to get a large json. However, this connection can be really slow... (I was visiting from China) and from my own experience, most of the time `requests.get` failed to download the whole json after a long wait and will trigger fault in `r.json()`.
I also noticed that the current implementation will first try to download from github, which makes me be able to smoothly run `load_dataset('squad')` in the example.
Therefore, I think it would be better if we can have an api to get the list of datasets that are available on github, and it will also improve newcomers' experience (it is a little frustrating if one cannot successfully run the first function in the README example.) before we have faster source for huggingface.co.

As for the implementation, I've added a `dataset_infos.json` file under the `datasets` folder, and it has the following structure:
```json
    {
        "id": "aeslc",
        "folder": "datasets/aeslc",
        "dataset_infos": "datasets/aeslc/dataset_infos.json"
    },
    ...
    {
        "id": "json",
        "folder": "datasets/json"
    },
   ...
```
The script I used to get this file is:
```python
import json
import os

DATASETS_BASE_DIR = "/root/datasets" 
DATASET_INFOS_JSON = "dataset_infos.json"

datasets = []
for item in os.listdir(os.path.join(DATASETS_BASE_DIR, "datasets")):
    if os.path.isdir(os.path.join(DATASETS_BASE_DIR, "datasets", item)):
        datasets.append(item)

datasets.sort()

total_ds_info = []
for ds in datasets:
    ds_dir = os.path.join("datasets", ds)
    ds_info_dir = os.path.join(ds_dir, DATASET_INFOS_JSON)
    if os.path.isfile(os.path.join(DATASETS_BASE_DIR, ds_info_dir)):
        total_ds_info.append({"id": ds,
                              "folder": ds_dir,
                              "dataset_infos": ds_info_dir})
    else:
        total_ds_info.append({"id": ds,
                              "folder": ds_dir})

with open(DATASET_INFOS_JSON, "w") as f:
    json.dump(total_ds_info, f)
```
The new `dataset_infos.json` was saved as a formated json so that it will be easy to add new dataset.

When calling `list_github_datasets`, the user will get the list of dataset names in this github repo and if `with_details` is set to be `True`, they can get the url of specific dataset info.

Thank you for your time on reviewing this pr :).

Comments:
Title: Grindr meeting app web.Grindr
State: closed

## Adding a Dataset
- **Name:** *name of the dataset*
- **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:** *what are some good reasons to have this dataset*

Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

Comments:
Title: Add FiNER dataset
State: closed

Hi,

this PR adds "A Finnish News Corpus for Named Entity Recognition" as new `finer` dataset.

The dataset is described in [this paper](https://arxiv.org/abs/1908.04212). The data is publicly available in [this GitHub](https://github.com/mpsilfve/finer-data).

Notice: they provide two testsets. The additional test dataset taken from Wikipedia is named as "test_wikipedia" split.

Comments:
Title: Disallow backslash in urls
State: closed

Following #903 @albertvillanova noticed that there are sometimes bad usage of `os.path.join` in datasets scripts to create URLS. However this should be avoided since it doesn't work on windows.

I'm suggesting a test to make sure we that all the urls don't have backslashes in them in the datasets scripts.
The tests works by adding a callback feature to the MockDownloadManager used to test the dataset scripts. In a download callback I just make sure that the url is valid.

Comments:
Title: Addition of Nl2Bash Dataset
State: closed

## Overview
The NL2Bash data contains over 10,000 instances of linux shell commands and their corresponding natural language descriptions provided by experts, from the Tellina system. The dataset features 100+ commonly used shell utilities.
## Footnotes
The following dataset marks the first ML on source code related Dataset in datasets module. It'll be really useful as a lot of the research direction involves Transformer Based Model.
Thanks.
### Reference Links

> Paper Link = https://arxiv.org/pdf/1802.08979.pdf 
> Github Link = https://github.com/TellinaTool/nl2bash







Comments:
Title: datasets.load_dataset() custom chaching directory bug
State: closed

Hello,
I'm having issue with loading a dataset with a custom `cache_dir`. Despite specifying the output dir, it is still downloaded to 
 `~/.cache`.

## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3

## The code I'm running:
```python
import datasets
from pathlib import Path

validation_dataset = datasets.load_dataset("natural_questions", split="validation[:5%]", cache_dir=Path("./data"))  
```

## The output:

* The dataset is downloaded to my home directory's `.cache` 
* A new empty directory named "`natural_questions` is created in the specified directory `.data`
* `tree data` in the shell outputs:
```
data
└── natural_questions
    └── default
        └── 0.0.2
3 directories, 0 files
```

The output:
```
Downloading: 8.61kB [00:00, 5.11MB/s]                                                                                                                                                                              
Downloading: 13.6kB [00:00, 7.89MB/s]                                                                                                                                                                              
Using custom data configuration default                                                                                                                                                                            
Downloading and preparing dataset natural_questions/default (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size, total: 134.92 GiB) to ./data/natural_questions/default/0.0.2/867dbbaf9137c1b8
3ecb19f5eb80559e1002ea26e702c6b919cfa81a17a8c531...                                                                                                                                                                
Downloading: 100%|██████████████████████████████████████████████████| 13.6k/13.6k [00:00<00:00, 1.51MB/s]                                                                                                          
Downloading:   7%|███▎                                            | 6.70G/97.4G [03:46<1:37:05, 15.6MB/s]
```

## Expected behaviour:
The dataset "Natural Questions" should be downloaded to the directory "./data"


Comments:
Title: Dataset viewer issues
State: closed
Labels: name: nlp-viewer

I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues/bugs though:

- the URL is still under `nlp`, perhaps an alias for `datasets` can be made
- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user

```bash
IndexError: list index out of range
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
    exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 316, in <module>
    st.table(style)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 122, in wrapped_method
    return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 367, in _enqueue_new_element_delta
    rv = marshall_element(msg.delta.new_element)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element
    return method(dg, element, *args, **kwargs)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 2944, in table
    data_frame_proto.marshall_data_frame(data, element.table)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 54, in marshall_data_frame
    _marshall_styles(proto_df.style, df, styler)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 73, in _marshall_styles
    translated_style = styler._translate()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/pandas/io/formats/style.py", line 351, in _translate
    * (len(clabels[0]) - len(hidden_columns))
```

- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https://huggingface.co/nlp/viewer/?dataset=wmt19&config=cs-en). This problem goes away when you enable "List view", because then some syntax highlighteris used, and the special characters are coded correctly.


Comments:
Title: Add template and documentation for dataset card
State: closed

This PR adds a template for dataset cards, as well as a guide to filling out the template and a completed example for the ELI5 dataset, building on the work of @mcmillanmajora 

New pull requests adding datasets should now have a README.md file which serves both to hold the tags we will have to index the datasets and as a data statement.

The template is designed to be pretty extensive. The idea is that the person who uploads the dataset should put in all the basic information (at least the Dataset Description section) and whatever else they feel comfortable adding and leave the `[More Information Needed]` annotation everywhere else as a placeholder.

We will then work with @mcmillanmajora to involve the data authors more directly in filling out the remaining information.

Direct links to:
- [Documentation](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/templates/README_guide.md)
- [Empty template](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/templates/README.md)
- [ELI5 example](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/datasets/eli5/README.md)

Comments:
Title: Better messages regarding split naming
State: closed

I made explicit the error message when a bad split name is used.

Also I wanted to allow the `-` symbol for split names but actually this symbol is used to name the arrow files `{dataset_name}-{dataset_split}.arrow` so we should probably keep it this way, i.e. not allowing the `-` symbol in split names. Moreover in the future we might want to use `{dataset_name}-{dataset_split}-{shard_id}_of_{n_shards}.arrow` and reuse the `-` symbol.

Comments:
Title: Allow several tags sets
State: closed

Hi !

Currently we have three dataset cards : snli, cnn_dailymail and allocine.
For each one of those datasets a set of tag is defined. The set of tags contains fields like `multilinguality`, `task_ids`, `licenses` etc.

For certain datasets like `glue` for example, there exist several configurations: `sst2`, `mnli` etc. Therefore we should define one set of tags per configuration. However the current format used for tags only supports one set of tags per dataset.

In this PR I propose a simple change in the yaml format used for tags to allow for several sets of tags.

Let me know what you think, especially @julien-c let me know if it's good for you since it's going to be parsed by moon-landing

Comments:
Title: Optional per-dataset default config name
State: closed

This PR adds a `DEFAULT_CONFIG_NAME` class attribute to `DatasetBuilder`. This allows a dataset to have a specified default config name when a dataset has more than one config but the user does not specify it. For example, after defining `DEFAULT_CONFIG_NAME = "combined"` in PolyglotNER, a user can now do the following:

```python
ds = load_dataset("polyglot_ner")
```
which is equivalent to,
```python
ds = load_dataset("polyglot_ner", "combined")
```
In effect (for this particular dataset configuration), this means that if the user doesn't specify a language, they are given the combined dataset including all languages.

Since it doesn't always make sense to have a default config, this feature is opt-in. If `DEFAULT_CONFIG_NAME` is not defined and a user does not pass a config for a dataset with multiple configs available, a ValueError is raised like usual.

Let me know what you think about this approach @lhoestq @thomwolf and I'll add some documentation and define a default for some of our existing datasets.

Comments:
Title: Nested lists are zipped unexpectedly
State: closed

I might misunderstand something, but I expect that if I define:
```python
"top": datasets.features.Sequence({
  "middle": datasets.features.Sequence({
    "bottom": datasets.Value("int32")
  })
})
```

And I then create an example:
```python
yield 1, {
  "top": [{
    "middle": [
      {"bottom": 1},
      {"bottom": 2}
    ]
  }]
}
```

I then load my dataset:
```python
train = load_dataset("my dataset")["train"]
```

and expect to be able to access `data[0]["top"][0]["middle"][0]`.

That is not the case. Here is `data[0]` as JSON:

```json
{"top": {"middle": [{"bottom": [1, 2]}]}}
```

Clearly different than the thing I inputted.
```json
{"top": [{"middle": [{"bottom": 1},{"bottom": 2}]}]}
```

Comments: