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import os |
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from pathlib import Path |
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from typing import Dict, List |
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import datasets |
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from nusacrowd.utils import schemas |
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from nusacrowd.utils.configs import NusantaraConfig |
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from nusacrowd.utils.constants import Tasks |
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_CITATION = """\ |
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@article{published_papers/22434604, |
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title = {TUFS Asian Language Parallel Corpus (TALPCo)}, |
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author = {Hiroki Nomoto and Kenji Okano and David Moeljadi and Hideo Sawada}, |
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journal = {言語処理学会 第24回年次大会 発表論文集}, |
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pages = {436--439}, |
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year = {2018} |
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} |
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@article{published_papers/22434603, |
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title = {Interpersonal meaning annotation for Asian language corpora: The case of TUFS Asian Language Parallel Corpus (TALPCo)}, |
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author = {Hiroki Nomoto and Kenji Okano and Sunisa Wittayapanyanon and Junta Nomura}, |
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journal = {言語処理学会 第25回年次大会 発表論文集}, |
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pages = {846--849}, |
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year = {2019} |
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} |
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""" |
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_DATASETNAME = "talpco" |
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_DESCRIPTION = """\ |
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The TUFS Asian Language Parallel Corpus (TALPCo) is an open parallel corpus consisting of Japanese sentences |
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and their translations into Korean, Burmese (Myanmar; the official language of the Republic of the Union of Myanmar), |
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Malay (the national language of Malaysia, Singapore and Brunei), Indonesian, Thai, Vietnamese and English. |
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""" |
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_HOMEPAGE = "https://github.com/matbahasa/TALPCo" |
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_LOCAL = False |
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_LANGUAGES = ["eng", "ind", "jpn", "kor", "myn", "tha", "vie", "zsm"] |
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_LICENSE = "CC-BY 4.0" |
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_URLS = { |
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_DATASETNAME: "https://github.com/matbahasa/TALPCo/archive/refs/heads/master.zip", |
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} |
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
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_SOURCE_VERSION = "1.0.0" |
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_NUSANTARA_VERSION = "1.0.0" |
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def nusantara_config_constructor(lang_source, lang_target, schema, version): |
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"""Construct NusantaraConfig with talpco_{lang_source}_{lang_target}_{schema} as the name format""" |
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if schema != "source" and schema != "nusantara_t2t": |
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raise ValueError(f"Invalid schema: {schema}") |
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if lang_source == "" and lang_target == "": |
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return NusantaraConfig( |
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name="talpco_{schema}".format(schema=schema), |
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version=datasets.Version(version), |
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description="talpco with {schema} schema for all 7 language pairs from / to ind language".format(schema=schema), |
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schema=schema, |
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subset_id="talpco", |
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) |
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else: |
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return NusantaraConfig( |
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name="talpco_{lang_source}_{lang_target}_{schema}".format(lang_source=lang_source, lang_target=lang_target, schema=schema), |
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version=datasets.Version(version), |
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description="talpco with {schema} schema for {lang_source} source language and {lang_target} target language".format(lang_source=lang_source, lang_target=lang_target, schema=schema), |
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schema=schema, |
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subset_id="talpco", |
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) |
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class TALPCo(datasets.GeneratorBasedBuilder): |
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"""TALPCo datasets contains 1372 datasets in 8 languages""" |
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BUILDER_CONFIGS = ( |
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[nusantara_config_constructor(lang1, lang2, "source", _SOURCE_VERSION) for lang1 in _LANGUAGES for lang2 in _LANGUAGES if lang1 != lang2] |
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+ [nusantara_config_constructor(lang1, lang2, "nusantara_t2t", _NUSANTARA_VERSION) for lang1 in _LANGUAGES for lang2 in _LANGUAGES if lang1 != lang2] |
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+ [nusantara_config_constructor("", "", "source", _SOURCE_VERSION), nusantara_config_constructor("", "", "nusantara_t2t", _NUSANTARA_VERSION)] |
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) |
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DEFAULT_CONFIG_NAME = "talpco_jpn_ind_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source" or self.config.schema == "nusantara_t2t": |
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features = schemas.text2text_features |
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else: |
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raise ValueError(f"Invalid config schema: {self.config.schema}") |
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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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urls = _URLS[_DATASETNAME] |
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base_path = Path(dl_manager.download_and_extract(urls)) / "TALPCo-master" |
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data = {} |
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for lang in _LANGUAGES: |
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lang_file_name = "data_" + lang + ".txt" |
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lang_file_path = base_path / lang / lang_file_name |
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if os.path.isfile(lang_file_path): |
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with open(lang_file_path, "r") as file: |
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data[lang] = file.read().strip("\n").split("\n") |
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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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"data": data, |
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"split": "train", |
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}, |
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), |
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] |
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def _generate_examples(self, data: Dict, split: str): |
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if self.config.schema != "source" and self.config.schema != "nusantara_t2t": |
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raise ValueError(f"Invalid config schema: {self.config.schema}") |
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if self.config.name == "talpco_source" or self.config.name == "talpco_nusantara_t2t": |
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lang_target = "ind" |
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for lang_source in _LANGUAGES: |
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if lang_source == lang_target: |
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continue |
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for language_pair_data in self.generate_language_pair_data(lang_source, lang_target, data): |
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yield language_pair_data |
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lang_source = "ind" |
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for lang_target in _LANGUAGES: |
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if lang_source == lang_target: |
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continue |
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for language_pair_data in self.generate_language_pair_data(lang_source, lang_target, data): |
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yield language_pair_data |
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else: |
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_, lang_source, lang_target = self.config.name.replace(f"_{self.config.schema}", "").split("_") |
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for language_pair_data in self.generate_language_pair_data(lang_source, lang_target, data): |
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yield language_pair_data |
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def generate_language_pair_data(self, lang_source, lang_target, data): |
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dict_source = {} |
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for row in data[lang_source]: |
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id, text = row.split("\t") |
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dict_source[id] = text |
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dict_target = {} |
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for row in data[lang_target]: |
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id, text = row.split("\t") |
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dict_target[id] = text |
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all_ids = set([k for k in dict_source.keys()] + [k for k in dict_target.keys()]) |
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dict_merged = {k: [dict_source.get(k), dict_target.get(k)] for k in all_ids} |
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for id in sorted(all_ids): |
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ex = { |
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"id": lang_source + "_" + lang_target + "_" + id, |
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"text_1": dict_merged[id][0], |
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"text_2": dict_merged[id][1], |
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"text_1_name": lang_source, |
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"text_2_name": lang_target, |
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} |
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yield lang_source + "_" + lang_target + "_" + id, ex |
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