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import os |
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import datasets |
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_DESCRIPTION = """\ |
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Corpus used for training AfriBERTa models |
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""" |
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_CITATION = """\ |
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@inproceedings{ogueji-etal-2021-small, |
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title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages", |
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author = "Ogueji, Kelechi and |
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Zhu, Yuxin and |
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Lin, Jimmy", |
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booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning", |
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month = nov, |
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year = "2021", |
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address = "Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.mrl-1.11", |
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pages = "116--126", |
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} |
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""" |
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_HOMEPAGE_URL = "https://github.com/keleog/afriberta" |
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_VERSION = "1.0.0" |
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_LANGUAGES = [ |
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"afaanoromoo", |
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"amharic", |
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"gahuza", |
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"hausa", |
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"igbo", |
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"pidgin", |
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"somali", |
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"swahili", |
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"tigrinya", |
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"yoruba"] |
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_DATASET_URLS = { |
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language: { |
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"train": f"https://huggingface.co/datasets/castorini/afriberta-corpus/resolve/main/{language}/train.zip", |
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"test": f"https://huggingface.co/datasets/castorini/afriberta-corpus/resolve/main/{language}/eval.zip", |
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} for language in _LANGUAGES |
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} |
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class AfribertaCorpus(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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version=datasets.Version(_VERSION), |
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name=language, |
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description=f"AfriBERTa corpus for {language}." |
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) for language in _LANGUAGES |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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}, |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE_URL, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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language = self.config.name |
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downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[language]) |
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splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"file_path": os.path.join(downloaded_files["train"], "train.txt"), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"file_path": os.path.join(downloaded_files["test"], "eval.txt"), |
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}, |
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), |
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] |
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return splits |
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def _generate_examples(self, file_path): |
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with open(file_path, encoding="utf-8") as f: |
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for sentence_counter, line in enumerate(f): |
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result = ( |
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sentence_counter, |
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{ |
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"id": str(sentence_counter), |
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"text": line, |
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}, |
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) |
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yield result |
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