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