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""" |
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Contains 110 large-scale ground-truth bilingual dictionaries created and released by Meta using an internal translation tool. |
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The dictionaries account for polysemy. The data comprises of a train and test split of 5000 and 1500 unique source words, as well as a larger set of up to 100k pairs. |
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It comprises of Europeans languages in every direction, and SEA languages to and from English. |
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""" |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Licenses, Tasks |
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_CITATION = """\ |
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@inproceedings{lample2018word, |
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title={Word translation without parallel data}, |
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author={Lample, Guillaume and Conneau, Alexis and Ranzato, Marc'Aurelio and Denoyer, Ludovic and J{\'e}gou, Herv{\'e}}, |
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booktitle={International Conference on Learning Representations}, |
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year={2018}} |
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} |
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""" |
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_DATASETNAME = "muse" |
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_DESCRIPTION = """\ |
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Contains 110 large-scale ground-truth bilingual dictionaries created and released by Meta using an internal translation tool. |
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The dictionaries account for polysemy. The data comprises of a train and test split of 5000 and 1500 unique source words, as well as a larger set of up to 100k pairs. |
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It comprises of Europeans languages in every direction, and SEA languages to and from English. |
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""" |
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_HOMEPAGE = "https://github.com/facebookresearch/MUSE#ground-truth-bilingual-dictionaries" |
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_LANGUAGES = ["tgl", "ind", "zlm", "tha", "vie"] |
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_LICENSE = Licenses.CC_BY_NC_ND_4_0.value |
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_LOCAL = False |
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_TRAIN_URL_TEMPLATE = "https://dl.fbaipublicfiles.com/arrival/dictionaries/{src}-{tgt}.0-5000.txt" |
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_TEST_URL_TEMPLATE = "https://dl.fbaipublicfiles.com/arrival/dictionaries/{src}-{tgt}.5000-6500.txt" |
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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configs = { |
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"tgl": ["eng"], |
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"ind": ["eng"], |
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"zlm": ["eng"], |
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"tha": ["eng"], |
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"vie": ["eng"], |
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"eng": ["tha", "vie", "tgl", "zlm", "ind"], |
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} |
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langid_dict = { |
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"eng": "en", |
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"tgl": "tl", |
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"ind": "id", |
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"zlm": "ms", |
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"tha": "th", |
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"vie": "vi", |
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} |
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class MUSEDataset(datasets.GeneratorBasedBuilder): |
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"""Large-scale ground-truth bilingual dictionaries""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = ( |
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[ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=SOURCE_VERSION, |
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description=f"{_DATASETNAME} source schema", |
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schema="source", |
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subset_id=f"{_DATASETNAME}_tgl_eng", |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_t2t", |
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version=SEACROWD_VERSION, |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema="seacrowd_t2t", |
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subset_id=f"{_DATASETNAME}_tgl_eng", |
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), |
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] |
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+ [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{src_lang}_{tgt_lang}_source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=f"{_DATASETNAME} source schema", |
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schema="source", |
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subset_id=f"{_DATASETNAME}_{src_lang}_{tgt_lang}", |
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) |
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for src_lang in configs |
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for tgt_lang in configs[src_lang] |
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] |
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+ [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{src_lang}_{tgt_lang}_seacrowd_t2t", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema="seacrowd_t2t", |
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subset_id=f"{_DATASETNAME}_{src_lang}_{tgt_lang}", |
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) |
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for src_lang in configs |
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for tgt_lang in configs[src_lang] |
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] |
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) |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"src_text": datasets.Value("string"), |
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"tgt_text": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_t2t": |
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features = schemas.text2text_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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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: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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_, src_lang, tgt_lang = self.config.subset_id.split("_") |
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train_url = _TRAIN_URL_TEMPLATE.format(src=langid_dict[src_lang], tgt=langid_dict[tgt_lang]) |
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test_url = _TEST_URL_TEMPLATE.format(src=langid_dict[src_lang], tgt=langid_dict[tgt_lang]) |
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train_file = dl_manager.download_and_extract(train_url) |
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test_file = dl_manager.download_and_extract(test_url) |
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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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"src_lang": src_lang, |
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"tgt_lang": tgt_lang, |
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"filepath": train_file, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"src_lang": src_lang, |
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"tgt_lang": tgt_lang, |
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"filepath": test_file, |
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}, |
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), |
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] |
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def _generate_examples(self, src_lang: str, tgt_lang: str, filepath: Path) -> Tuple[int, Dict]: |
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if self.config.schema == "source": |
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for row_id, line in enumerate(open(filepath)): |
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src_text, tgt_text = line.strip().split("\t") |
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yield row_id, {"id": row_id, "src_text": src_text, "tgt_text": tgt_text} |
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elif self.config.schema == "seacrowd_t2t": |
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for row_id, line in enumerate(open(filepath)): |
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src_text, tgt_text = line.strip().split("\t") |
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yield row_id, { |
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"id": row_id, |
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"text_1": src_text, |
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"text_2": tgt_text, |
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"text_1_name": src_lang, |
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"text_2_name": tgt_lang, |
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} |
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