| from __future__ import annotations |
|
|
| from pathlib import Path |
| import numpy as np |
|
|
| import datasets |
|
|
|
|
| _HF_AFFIX = { |
| "ara": "arabic", |
| "cmn": "mandarin", |
| "eng": "", |
| "deu": "german", |
| "fra": "french", |
| "hin": "hindi", |
| "ita": "italian", |
| "nld": "dutch", |
| "pol": "polish", |
| "por": "portuguese", |
| "spa": "spanish", |
| } |
|
|
| _HF_AFFIX_REV = {v:k for k,v in _HF_AFFIX.items()} |
|
|
| _REVISION_DICT = { |
| "ara": "65eb7455a05cb77b3ae0c69d444569a8eee54628", |
| "cmn": "617d3e9fccd186277297cc305f6588af7384b008", |
| "eng": "9d2ac89df04254e5c427bcc8d61b6d6c83a1f59b", |
| "deu": "5229a5cc475f36c08d03ca52f0ccb005705e60d2", |
| "fra": "5d3085f2129139abc10d2b58becd4d4f2978e5d5", |
| "hin": "e9e68e1a4db04726b9278192377049d0f9693012", |
| "ita": "21e3d5c827cb60619a89988b24979850a7af85a5", |
| "nld": "d622427417d37a8d74e110e6289bc29af4ba4056", |
| "pol": "28d7098e2e5a211c4810d0a4d8deccc5889e55b6", |
| "por": "323bdf67e0fbd3d7f8086fad0971b5bd5a62524b", |
| "spa": "a7ea759535bb9fad6361cca151cf94a46e88edf3", |
| } |
|
|
| def _transform(dataset): |
| target_cols = ["test_case", "label_gold"] |
| new_cols = ['text', 'is_hateful'] |
| rename_dict = dict(zip(target_cols, ["text", "is_hateful"])) |
| dataset = dataset.rename_columns(rename_dict) |
| keep_cols = new_cols + ["functionality"] |
| remove_cols = [col for col in dataset["test"].column_names if col not in keep_cols] |
| dataset = dataset.remove_columns(remove_cols) |
| return dataset |
|
|
|
|
| def make_dataset(): |
| """ |
| Load dataset from HuggingFace hub |
| """ |
| ds = {} |
| for lang in _HF_AFFIX.values(): |
| lcode = _HF_AFFIX_REV[lang] |
| path = f'Paul/hatecheck-{lang}'.rstrip('-') |
| dataset = datasets.load_dataset( |
| path=path, revision=_REVISION_DICT[lcode] |
| ) |
| dataset = _transform(dataset) |
| out_path = Path('..') / lcode / 'test.jsonl' |
| dataset['test'].to_json(out_path) |
| ds[lcode] = dataset |
| return ds |
| |
|
|
| if __name__ == '__main__': |
| dataset = make_dataset() |
| AVG_CHAR = 0 |
| for lang in _HF_AFFIX: |
| AVG_CHAR += np.mean([len(x['text']) for x in dataset[lang]['test']]) |
| print(f'avg char: {AVG_CHAR / len(_HF_AFFIX)}') |
|
|
|
|
