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