# coding=utf-8 # Copyright 2022 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. """ Parallel corpus of full-text articles in Portuguese, English and Spanish from SciELO. """ from typing import IO, Any, Generator, List, Optional, Tuple import datasets from .bigbiohub import text2text_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English', 'Spanish', 'Portuguese'] _PUBMED = False _LOCAL = False _CITATION = """\ @inproceedings{soares2018large, title = {A Large Parallel Corpus of Full-Text Scientific Articles}, author = {Soares, Felipe and Moreira, Viviane and Becker, Karin}, year = 2018, booktitle = { Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018) } } """ _DATASETNAME = "scielo" _DISPLAYNAME = "SciELO" _DESCRIPTION = """\ A parallel corpus of full-text scientific articles collected from Scielo \ database in the following languages: English, Portuguese and Spanish. The corpus \ is sentence aligned for all language pairs, as well as trilingual aligned for a \ small subset of sentences. Alignment was carried out using the Hunalign \ algorithm. """ _HOMEPAGE = "https://sites.google.com/view/felipe-soares/datasets#h.p_92uSCyAjWSRB" _LICENSE = 'Creative Commons Attribution 4.0 International' _URLS = { "en_es": "https://ndownloader.figstatic.com/files/14019287", "en_pt": "https://ndownloader.figstatic.com/files/14019308", "en_pt_es": "https://ndownloader.figstatic.com/files/14019293", } _SUPPORTED_TASKS = [Tasks.TRANSLATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class ScieloDataset(datasets.GeneratorBasedBuilder): """Parallel corpus of full-text articles in Portuguese, English and Spanish from SciELO.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) # NOTE: bigbio_t2t schema doesn't allow only for more than two texts in text-to-text schema. # en-pt-es translation is not implemented using the bigbio schema BUILDER_CONFIGS = [ BigBioConfig( name="scielo_en_es_source", version=SOURCE_VERSION, description="English-Spanish", schema="source", subset_id="scielo_en_es", ), BigBioConfig( name="scielo_en_pt_source", version=SOURCE_VERSION, description="English-Portuguese", schema="source", subset_id="scielo_en_pt", ), BigBioConfig( name="scielo_en_pt_es_source", version=SOURCE_VERSION, description="English-Portuguese-Spanish", schema="source", subset_id="scielo_en_pt_es", ), BigBioConfig( name="scielo_en_es_bigbio_t2t", version=BIGBIO_VERSION, description="scielo BigBio schema English-Spanish", schema="bigbio_t2t", subset_id="scielo_en_es", ), BigBioConfig( name="scielo_en_pt_bigbio_t2t", version=BIGBIO_VERSION, description="scielo BigBio schema English-Portuguese", schema="bigbio_t2t", subset_id="scielo_en_pt", ), ] DEFAULT_CONFIG_NAME = "scielo_source_en_es" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": lang_list: List[str] = self.config.subset_id.split("_")[1:] features = datasets.Features( {"translation": datasets.features.Translation(languages=lang_list)} ) elif self.config.schema == "bigbio_t2t": features = text2text_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=str(_LICENSE), citation=_CITATION, ) def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" lang_list: List[str] = self.config.subset_id.split("_")[1:] languages = "_".join(lang_list) archive = dl_manager.download(_URLS[languages]) fname = languages if languages == "en_pt_es": return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "source_file": f"{fname}.en", "target_file": f"{fname}.pt", "target_file_2": f"{fname}.es", "files": dl_manager.iter_archive(archive), "languages": languages, "split": "train", }, ), ] else: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "source_file": f"{fname}.{lang_list[0]}", "target_file": f"{fname}.{lang_list[1]}", "files": dl_manager.iter_archive(archive), "languages": languages, "split": "train", }, ), ] def _generate_examples( self, languages: str, split: str, source_file: str, target_file: str, files: Generator[Tuple[str, IO[bytes]], Any, None], target_file_2: Optional[str] = None, ) -> Tuple[int, dict]: if self.config.schema == "source": for path, f in files: if path == source_file: source_sentences = f.read().decode("utf-8").split("\n") elif path == target_file: target_sentences = f.read().decode("utf-8").split("\n") elif languages == "en_pt_es" and path == target_file_2: target_sentences_2 = f.read().decode("utf-8").split("\n") if languages == "en_pt_es": source, target, target_2 = tuple(languages.split("_")) for idx, (l1, l2, l3) in enumerate( zip(source_sentences, target_sentences, target_sentences_2) ): result = {"translation": {source: l1, target: l2, target_2: l3}} yield idx, result else: source, target = tuple(languages.split("_")) for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)): result = {"translation": {source: l1, target: l2}} yield idx, result elif self.config.schema == "bigbio_t2t": for path, f in files: if path == source_file: source_sentences = f.read().decode("utf-8").split("\n") elif path == target_file: target_sentences = f.read().decode("utf-8").split("\n") uid = 0 source, target = tuple(languages.split("_")) for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)): uid += 1 yield idx, { "id": str(uid), "document_id": str(idx), "text_1": l1, "text_2": l2, "text_1_name": source, "text_2_name": target, }