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"""No Language Left Behind (NLLB)""" |
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import datasets |
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import csv |
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import json |
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_CITATION = ( |
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"@article{team2022NoLL," |
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"title={No Language Left Behind: Scaling Human-Centered Machine Translation}," |
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"author={Nllb team and Marta Ruiz Costa-juss{\`a} and James Cross and Onur cCelebi and Maha Elbayad and Kenneth Heafield and Kevin Heffernan and Elahe Kalbassi and Janice Lam and Daniel Licht and Jean Maillard and Anna Sun and Skyler Wang and Guillaume Wenzek and Alison Youngblood and Bapi Akula and Lo{\"i}c Barrault and Gabriel Mejia Gonzalez and Prangthip Hansanti and John Hoffman and Semarley Jarrett and Kaushik Ram Sadagopan and Dirk Rowe and Shannon L. Spruit and C. Tran and Pierre Andrews and Necip Fazil Ayan and Shruti Bhosale and Sergey Edunov and Angela Fan and Cynthia Gao and Vedanuj Goswami and Francisco Guzm'an and Philipp Koehn and Alexandre Mourachko and Christophe Ropers and Safiyyah Saleem and Holger Schwenk and Jeff Wang}," |
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"journal={ArXiv}," |
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"year={2022}," |
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"volume={abs/2207.04672}" |
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"}" |
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) |
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_DESCRIPTION = "" |
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_HOMEPAGE = "" |
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_LICENSE = "https://opendatacommons.org/licenses/by/1-0/" |
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from .nllb_lang_pairs import LANG_PAIRS as _LANGUAGE_PAIRS |
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_URL_BASE = "https://storage.googleapis.com/allennlp-data-bucket/nllb/" |
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_URLs = { |
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f"{src_lg}-{trg_lg}": f"{_URL_BASE}{src_lg}-{trg_lg}.gz" |
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for src_lg, trg_lg in _LANGUAGE_PAIRS |
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} |
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class NLLBTaskConfig(datasets.BuilderConfig): |
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"""BuilderConfig for No Language Left Behind Dataset.""" |
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def __init__(self, src_lg, tgt_lg, **kwargs): |
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super(NLLBTaskConfig, self).__init__(**kwargs) |
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self.src_lg = src_lg |
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self.tgt_lg = tgt_lg |
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class NLLB(datasets.GeneratorBasedBuilder): |
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"""No Language Left Behind Dataset.""" |
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BUILDER_CONFIGS = [ |
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NLLBTaskConfig( |
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name=f"{src_lg}-{tgt_lg}", |
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version=datasets.Version("1.0.0"), |
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description=f"No Language Left Behind (NLLB): {src_lg} - {tgt_lg}", |
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src_lg=src_lg, |
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tgt_lg=tgt_lg, |
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) |
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for (src_lg, tgt_lg) in _LANGUAGE_PAIRS |
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] |
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BUILDER_CONFIG_CLASS = NLLBTaskConfig |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"translation": datasets.Translation( |
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languages=(self.config.src_lg, self.config.tgt_lg) |
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), |
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"laser_score": datasets.Value("float32"), |
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"source_sentence_lid": datasets.Value("float32"), |
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"target_sentence_lid": datasets.Value("float32"), |
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"source_sentence_source": datasets.Value("string"), |
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"source_sentence_url": datasets.Value("string"), |
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"target_sentence_source": datasets.Value("string"), |
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"target_sentence_url": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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pair = f"{self.config.src_lg}-{self.config.tgt_lg}" |
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url = _URLs[pair] |
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data_file = dl_manager.download_and_extract( |
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url |
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) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": data_file, |
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"source_lg": self.config.src_lg, |
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"target_lg": self.config.tgt_lg, |
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}, |
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) |
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] |
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def _generate_examples(self, filepath, source_lg, target_lg): |
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with open(filepath, encoding="utf-8") as f: |
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for id_, example in enumerate(f): |
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try: |
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datarow = example.split("\t") |
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row = {} |
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row["translation"] = { |
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source_lg: datarow[0], |
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target_lg: datarow[1], |
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} |
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row["laser_score"] = float(datarow[2]) |
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row["source_sentence_lid"] = float(datarow[3]) |
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row["target_sentence_lid"] = float(datarow[4]) |
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row["source_sentence_source"] = datarow[5] |
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row["source_sentence_url"] = datarow[6] |
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row["target_sentence_source"] = datarow[7] |
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row["target_sentence_url"] = datarow[8] |
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row = { |
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k: None if not v else v for k, v in row.items() |
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} |
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except: |
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print(datarow) |
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raise |
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yield id_, row |
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