# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """macocu_parallel""" import os import csv import datasets _CITATION = """\ @inproceedings{banon2022macocu, title={MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages}, author={Ban{\'o}n, Marta and Espla-Gomis, Miquel and Forcada, Mikel L and Garc{\'\i}a-Romero, Cristian and Kuzman, Taja and Ljube{\v{s}}i{\'c}, Nikola and van Noord, Rik and Sempere, Leopoldo Pla and Ram{\'\i}rez-S{\'a}nchez, Gema and Rupnik, Peter and others}, booktitle={23rd Annual Conference of the European Association for Machine Translation, EAMT 2022}, pages={303--304}, year={2022}, organization={European Association for Machine Translation} } """ _DESCRIPTION = """\ The MaCoCu parallel dataset is an English-centric collection of 11 parallel corpora including the following languages: Albanian, Bulgarian, Bosnian, Croatian, Icelandic, Macedonian, Maltese, Montenegrin, Serbian, Slovenian, and Turkish. These corpora have been automatically crawled from national and generic top-level domains (for example, ".hr" for croatian, or ".is" for icelandic); then, a parallel curation pipeline has been applied to produce the final data (see https://github.com/bitextor/bitextor). """ _LanguagePairs = [ "en-is" ] #_LanguagePairs = [ "en-bg", "en-is", "en-sq", "en-mt", "en-mk", "en-sl", "en-tr" ] #_LanguagePairs = [ "en-bs", "en-bg", "en-is", "en-hr", "en-sq", "en-mt", "en-mk", "en-cnr", "en-sr", "en-sl", "en-tr" ] _LICENSE = "cc0" _HOMEPAGE = "https://macocu.eu" class macocuConfig(datasets.BuilderConfig): """BuilderConfig for macocu_parallel""" def __init__(self, language_pair, **kwargs): super().__init__(**kwargs) """ Args: language_pair: language pair to be loaded **kwargs: keyword arguments forwarded to super. """ self.language_pair = language_pair class MaCoCu_parallel(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIG_CLASS = macocuConfig BUILDER_CONFIGS = [ macocuConfig(name=pair, description=_DESCRIPTION, language_pair=pair ) for pair in _LanguagePairs ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features({ "src_url": datasets.Value("string"), "trg_url": datasets.Value("string"), "src_text": datasets.Value("string"), "trg_text": datasets.Value("string"), "bleualign_score": datasets.Value("string"), "src_deferred_hash": datasets.Value("string"), "trg_deferred_hash": datasets.Value("string"), "src_paragraph_id": datasets.Value("string"), "trg_paragraph_id": datasets.Value("string"), "src_doc_title": datasets.Value("string"), "trg_doc_title": datasets.Value("string"), "src_crawl_date": datasets.Value("string"), "trg_crawl_date": datasets.Value("string"), "src_file_type": datasets.Value("string"), "trg_file_type": datasets.Value("string"), "src_boilerplate": datasets.Value("string"), "trg_boilerplate": datasets.Value("string"), "src_heading_html_tag": datasets.Value("string"), "trg_heading_html_tag": datasets.Value("string"), "bifixer_hash": datasets.Value("string"), "bifixer_score": datasets.Value("string"), "bicleaner_ai_score": datasets.Value("string"), "biroamer_entities_detected": datasets.Value("string"), "dsi": datasets.Value("string"), "translation_direction": datasets.Value("string"), "en_document_level_variant": datasets.Value("string"), "domain_en": datasets.Value("string"), "en_domain_level_variant": datasets.Value("string") }), homepage=_HOMEPAGE, citation=_CITATION, license=_LICENSE ) def _split_generators(self, dl_manager): lang_pair = self.config.language_pair path = os.path.join("data", f"{lang_pair}.tsv") data_file = dl_manager.download_and_extract({"data_file": path}) return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=data_file)] def _generate_examples(self, data_file): """Yields examples.""" with open(data_file, encoding="utf-8") as f: reader = csv.reader(f, delimiter="\t", quotechar='"') for id_, row in enumerate(reader): if id_ == 0: continue yield id_, { "src_url": row[0], "trg_url": row[1], "src_text": row[2], "trg_text": row[3], "bleualign_score": row[4], "src_deferred_hash": row[5], "trg_deferred_hash": row[6], "src_paragraph_id": row[7], "trg_paragraph_id": row[8], "src_doc_title": row[9], "trg_doc_title": row[10], "src_crawl_date": row[11], "trg_crawl_date": row[12], "src_file_type": row[13], "trg_file_type": row[14], "src_boilerplate": row[15], "trg_boilerplate": row[16], "src_heading_html_tag": row[17], "trg_heading_html_tag": row[18], "bifixer_hash": row[19], "bifixer_score": row[20], "bicleaner_ai_score": row[21], "biroamer_entities_detected": row[22], "dsi": row[23], "translation_direction": row[24], "en_document_level_variant": row[25], "domain_en": row[26], "en_domain_level_variant": row[27] }