# 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. """OPUS-100""" import os import datasets _CITATION = """\ @misc{zhang2020improving, title={Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation}, author={Biao Zhang and Philip Williams and Ivan Titov and Rico Sennrich}, year={2020}, eprint={2004.11867}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _DESCRIPTION = """\ OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English).OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k. """ _URL = { "supervised": "https://object.pouta.csc.fi/OPUS-100/v1.0/opus-100-corpus-{}-v1.0.tar.gz", "zero-shot": "https://object.pouta.csc.fi/OPUS-100/v1.0/opus-100-corpus-zeroshot-v1.0.tar.gz", } _SupervisedLanguagePairs = [ "af-en", "am-en", "an-en", "ar-en", "as-en", "az-en", "be-en", "bg-en", "bn-en", "br-en", "bs-en", "ca-en", "cs-en", "cy-en", "da-en", "de-en", "dz-en", "el-en", "en-eo", "en-es", "en-et", "en-eu", "en-fa", "en-fi", "en-fr", "en-fy", "en-ga", "en-gd", "en-gl", "en-gu", "en-ha", "en-he", "en-hi", "en-hr", "en-hu", "en-hy", "en-id", "en-ig", "en-is", "en-it", "en-ja", "en-ka", "en-kk", "en-km", "en-ko", "en-kn", "en-ku", "en-ky", "en-li", "en-lt", "en-lv", "en-mg", "en-mk", "en-ml", "en-mn", "en-mr", "en-ms", "en-mt", "en-my", "en-nb", "en-ne", "en-nl", "en-nn", "en-no", "en-oc", "en-or", "en-pa", "en-pl", "en-ps", "en-pt", "en-ro", "en-ru", "en-rw", "en-se", "en-sh", "en-si", "en-sk", "en-sl", "en-sq", "en-sr", "en-sv", "en-ta", "en-te", "en-tg", "en-th", "en-tk", "en-tr", "en-tt", "en-ug", "en-uk", "en-ur", "en-uz", "en-vi", "en-wa", "en-xh", "en-yi", "en-yo", "en-zh", "en-zu", ] _0shotLanguagePairs = [ "ar-de", "ar-fr", "ar-nl", "ar-ru", "ar-zh", "de-fr", "de-nl", "de-ru", "de-zh", "fr-nl", "fr-ru", "fr-zh", "nl-ru", "nl-zh", "ru-zh", ] class Opus100Config(datasets.BuilderConfig): """BuilderConfig for Opus100""" def __init__(self, language_pair, **kwargs): super().__init__(**kwargs) """ Args: language_pair: language pair, you want to load **kwargs: keyword arguments forwarded to super. """ self.language_pair = language_pair class Opus100(datasets.GeneratorBasedBuilder): """OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side.""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIG_CLASS = Opus100Config BUILDER_CONFIGS = [ Opus100Config(name=pair, description=_DESCRIPTION, language_pair=pair) for pair in _SupervisedLanguagePairs + _0shotLanguagePairs ] def _info(self): src_tag, tgt_tag = self.config.language_pair.split("-") return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features({"translation": datasets.features.Translation(languages=(src_tag, tgt_tag))}), supervised_keys=(src_tag, tgt_tag), homepage="http://opus.nlpl.eu/opus-100.php", citation=_CITATION, ) def _split_generators(self, dl_manager): lang_pair = self.config.language_pair src_tag, tgt_tag = lang_pair.split("-") domain = "supervised" if lang_pair in _0shotLanguagePairs: domain = "zero-shot" if domain == "supervised": dl_dir = dl_manager.download_and_extract(_URL["supervised"].format(lang_pair)) elif domain == "zero-shot": dl_dir = dl_manager.download_and_extract(_URL["zero-shot"]) data_dir = os.path.join(dl_dir, os.path.join("opus-100-corpus", "v1.0", domain, lang_pair)) output = [] test = datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, f"opus.{lang_pair}-test.{src_tag}"), "labelpath": os.path.join(data_dir, f"opus.{lang_pair}-test.{tgt_tag}"), }, ) if f"opus.{lang_pair}-test.{src_tag}" in os.listdir(data_dir): output.append(test) if domain == "supervised": train = datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, f"opus.{lang_pair}-train.{src_tag}"), "labelpath": os.path.join(data_dir, f"opus.{lang_pair}-train.{tgt_tag}"), }, ) if f"opus.{lang_pair}-train.{src_tag}" in os.listdir(data_dir): output.append(train) valid = datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, f"opus.{lang_pair}-dev.{src_tag}"), "labelpath": os.path.join(data_dir, f"opus.{lang_pair}-dev.{tgt_tag}"), }, ) if f"opus.{lang_pair}-dev.{src_tag}" in os.listdir(data_dir): output.append(valid) return output def _generate_examples(self, filepath, labelpath): """Yields examples.""" src_tag, tgt_tag = self.config.language_pair.split("-") with open(filepath, encoding="utf-8") as f1, open(labelpath, encoding="utf-8") as f2: src = f1.read().split("\n")[:-1] tgt = f2.read().split("\n")[:-1] for idx, (s, t) in enumerate(zip(src, tgt)): yield idx, {"translation": {src_tag: s, tgt_tag: t}}