Transfer some canonical datasets under an organization namespace
Labels: name: dataset contribution
description: Contribution to a dataset script

As discussed during our @huggingface/datasets meeting, we are planning to move some "canonical" dataset scripts under their corresponding organization namespace (if this does not exist).

On the contrary, if the dataset already exists under the organization namespace, we are deprecating the canonical one (and eventually delete it).

First, we should test it using a dummy dataset/organization.

TODO:
- [x] Test with a dummy dataset
  - [x] Create dummy canonical dataset: https://huggingface.co/datasets/dummy_canonical_dataset
  - [x] Create dummy organization: https://huggingface.co/dummy-canonical-org
  - [x] Transfer dummy canonical dataset to dummy organization
- [ ] Transfer datasets
  - [x] babi_qa => facebook
  - [x] cord19 => allenai
  - [x] emotion => dair-ai
  - [ ] gem => GEM
  - [x] hendrycks_test => cais/mmlu
  - [x] indonlu => indonlp
  - [ ] multilingual_librispeech => facebook
    - It already exists "facebook/multilingual_librispeech"
  - [ ] oscar => oscar-corpus
  - [x] peer_read => allenai
  - [x] qasper => allenai
  - [x] reddit => webis/tldr-17
  - [x] russian_super_glue => russiannlp
  - [x] rvl_cdip => aharley
  - [x] s2orc => allenai
  - [x] scicite => allenai
  - [x] scifact => allenai
  - [x] scitldr => allenai
  - [x] swiss_judgment_prediction => rcds
  - [x] the_pile => EleutherAI
  - [ ] wmt14, wmt15, wmt16, wmt17, wmt18, wmt19,... => wmt
- [ ] Deprecate (and eventually remove) datasets that cannot be transferred because they already exist
  - [x] banking77 => PolyAI
  - [x] common_voice => mozilla-foundation
  - [x] german_legal_entity_recognition => elenanereiss
- ... 

EDIT: the list above is continuously being updated

Comments:
The transfer of the dummy dataset to the dummy org works as expected:
```python
In [1]: from datasets import load_dataset; ds = load_dataset("dummy_canonical_dataset", download_mode="force_redownload"); ds
Downloading builder script: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.98k/2.98k [00:00<00:00, 2.01MB/s]
Downloading and preparing dataset dummy_canonical_dataset/default (download: 411 bytes, generated: 385 bytes, post-processed: Unknown size, total: 796 bytes) to .../.cache/huggingface/datasets/dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4...
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 411/411 [00:00<00:00, 293kB/s]
Dataset dummy_canonical_dataset downloaded and prepared to .../.cache/huggingface/datasets/dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 304.16it/s]
Out[1]: 
DatasetDict({
    train: Dataset({
        features: ['langs', 'ner_tags', 'tokens'],
        num_rows: 3
    })
})

In [2]: from datasets import load_dataset; ds = load_dataset("dummy-canonical-org/dummy_canonical_dataset"); ds
Downloading builder script: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.98k/2.98k [00:00<00:00, 1.57MB/s]
Downloading and preparing dataset dummy_canonical_dataset/default to .../.cache/huggingface/datasets/dummy-canonical-org___dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4...
Dataset dummy_canonical_dataset downloaded and prepared to .../.cache/huggingface/datasets/dummy-canonical-org___dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 362.48it/s]
Out[2]: 
DatasetDict({
    train: Dataset({
        features: ['langs', 'ner_tags', 'tokens'],
        num_rows: 3
    })
})
```

Cool ! 🚀