| from pathlib import Path |
| from typing import List |
|
|
| import datasets |
|
|
| from seacrowd.utils import schemas |
| from seacrowd.utils.configs import SEACrowdConfig |
| from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME, |
| DEFAULT_SOURCE_VIEW_NAME, Licenses, |
| Tasks) |
|
|
| _DATASETNAME = "wikimatrix" |
| _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME |
| _UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME |
|
|
| |
| _LANGUAGES = ["ilo", "min", "jav", "sun", "ceb", "ind", "tgl", "vie"] |
| _LOCAL = False |
| _CITATION = """\ |
| @inproceedings{schwenk-etal-2021-wikimatrix, |
| title = "{W}iki{M}atrix: Mining 135{M} Parallel Sentences in 1620 Language Pairs from {W}ikipedia", |
| author = "Schwenk, Holger and |
| Chaudhary, Vishrav and |
| Sun, Shuo and |
| Gong, Hongyu and |
| Guzm{\'a}n, Francisco", |
| editor = "Merlo, Paola and |
| Tiedemann, Jorg and |
| Tsarfaty, Reut", |
| booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", |
| month = apr, |
| year = "2021", |
| address = "Online", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2021.eacl-main.115", |
| doi = "10.18653/v1/2021.eacl-main.115", |
| pages = "1351--1361", |
| abstract = "We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content |
| of Wikipedia articles in 96 languages, including several dialects or low-resource languages. We do not limit the extraction process to |
| alignments with English, but we systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences |
| for 16720 different language pairs, out of which only 34M are aligned with English. This corpus is freely available. To get an indication |
| on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate |
| them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting |
| to train MT systems between distant languages without the need to pivot through English.", |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| WikiMatrix is automatically extracted parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects |
| or low-resource languages. 8 languages among them are spoken in Southeast Asia region. In total, there are 135M parallel sentences from 1620 |
| different language pairs. |
| """ |
|
|
| _HOMEPAGE = "https://github.com/facebookresearch/LASER/tree/main/tasks/WikiMatrix" |
|
|
| _LICENSE = Licenses.CC_BY_SA_4_0.value |
|
|
| _URLs = "https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.{lang1}-{lang2}.tsv.gz" |
|
|
| _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
|
|
| _SOURCE_VERSION = "1.0.0" |
| _SEACROWD_VERSION = "2024.06.20" |
|
|
| config = { |
| "jv": ["en", "es", "fr", "id", "it", "pt"], |
| "ceb": ["bg", "ar", "ca", "cs", "de", "en", "es", "fi", "fr", "hu", "it", "ja", "nl", "no", "pl", "pt", "ro", "ru", "sv", "uk"], |
| "id": [ |
| "jv", |
| "is", |
| "it", |
| "ja", |
| "ko", |
| "lt", |
| "mk", |
| "ml", |
| "mr", |
| "ne", |
| "nl", |
| "no", |
| "pl", |
| "pt", |
| "ro", |
| "ru", |
| "sh", |
| "si", |
| "sk", |
| "sl", |
| "sq", |
| "sr", |
| "sv", |
| "sw", |
| "ta", |
| "te", |
| "tl", |
| "tr", |
| "tt", |
| "uk", |
| "vi", |
| "zh", |
| "ar", |
| "az", |
| "ba", |
| "bg", |
| "bn", |
| "bs", |
| "ca", |
| "cs", |
| "da", |
| "de", |
| "el", |
| "en", |
| "eo", |
| "es", |
| "et", |
| "eu", |
| "fa", |
| "fi", |
| "fr", |
| "gl", |
| "he", |
| "hi", |
| "hr", |
| "hu", |
| ], |
| "tl": ["ar", "bg", "bs", "ca", "cs", "da", "de", "el", "en", "eo", "es", "et", "fi", "fr", "gl", "he", "hr", "hu", "id", "it", "ja", "lt", "mk", "nl", "no", "pl", "pt", "ro", "ru", "sh", "sk", "sl", "sq", "sr", "sv", "tr", "uk", "vi", "zh"], |
| "vi": [ |
| "ar", |
| "az", |
| "bg", |
| "bn", |
| "bs", |
| "ca", |
| "cs", |
| "da", |
| "de", |
| "el", |
| "en", |
| "eo", |
| "es", |
| "et", |
| "eu", |
| "fa", |
| "fi", |
| "fr", |
| "gl", |
| "he", |
| "hi", |
| "hr", |
| "hu", |
| "id", |
| "is", |
| "it", |
| "ja", |
| "ko", |
| "lt", |
| "mk", |
| "ml", |
| "mr", |
| "nl", |
| "no", |
| "pl", |
| "pt", |
| "ro", |
| "ru", |
| "sh", |
| "si", |
| "sk", |
| "sl", |
| "sq", |
| "sr", |
| "sv", |
| "sw", |
| "ta", |
| "te", |
| "tl", |
| "tr", |
| "uk", |
| "zh", |
| ], |
| } |
| _SUBSETS = set() |
| for lang, pairs in config.items(): |
| for pair in pairs: |
| _SUBSETS.add("{}-{}".format(lang, pair) if lang < pair else "{}-{}".format(pair, lang)) |
| _SUBSETS = list(_SUBSETS) |
|
|
|
|
| class WikiMatrixDataset(datasets.GeneratorBasedBuilder): |
| """WikiMatrix is automatically extracted parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects |
| or low-resource languages.""" |
|
|
| BUILDER_CONFIGS = [ |
| SEACrowdConfig( |
| name=f"wikimatrix_{subset.replace('-', '_')}_source", |
| version=datasets.Version(_SOURCE_VERSION), |
| description="WikiMatrix source schema", |
| schema="source", |
| subset_id=f"wikimatrix_{subset.replace('-', '_')}", |
| ) |
| for subset in _SUBSETS |
| ] + [ |
| SEACrowdConfig( |
| name=f"wikimatrix_{subset.replace('-', '_')}_seacrowd_t2t", |
| version=datasets.Version(_SEACROWD_VERSION), |
| description="WikiMatrix Nusantara schema", |
| schema="seacrowd_t2t", |
| subset_id=f"wikimatrix_{subset.replace('-', '_')}", |
| ) |
| for subset in _SUBSETS |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "wikimatrix_en_id_source" |
|
|
| def _info(self): |
| if self.config.schema == "source": |
| features = datasets.Features( |
| { |
| "id": datasets.Value("string"), |
| "text_1": datasets.Value("string"), |
| "text_2": datasets.Value("string"), |
| "text_1_name": datasets.Value("string"), |
| "text_2_name": datasets.Value("string"), |
| } |
| ) |
| elif self.config.schema == "seacrowd_t2t": |
| features = schemas.text2text_features |
|
|
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
| lang1, lang2 = self.config.name.split("_")[1], self.config.name.split("_")[2] |
| filepath = Path(dl_manager.download_and_extract(_URLs.format(lang1=lang1, lang2=lang2))) |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={"filepath": filepath}, |
| ), |
| ] |
|
|
| def _generate_examples(self, filepath: Path): |
| with open(filepath, "r") as f: |
| data = f.readlines() |
|
|
| lang1, lang2 = self.config.name.split("_")[1], self.config.name.split("_")[2] |
| if self.config.schema == "source": |
| for _id, line in enumerate(data): |
| line = line.strip().split("\t") |
| ex = { |
| "id": str(_id), |
| "text_1": line[1], |
| "text_2": line[2], |
| "text_1_name": lang1, |
| "text_2_name": lang2, |
| } |
| yield _id, ex |
|
|
| elif self.config.schema == "seacrowd_t2t": |
| for _id, line in enumerate(data): |
| line = line.strip().split("\t") |
| ex = { |
| "id": str(_id), |
| "text_1": line[1], |
| "text_2": line[2], |
| "text_1_name": lang1, |
| "text_2_name": lang2, |
| } |
| yield _id, ex |
| else: |
| raise ValueError(f"Invalid config: {self.config.name}") |
|
|
