# 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. """ NEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website. English articles are distributed through https://www.nejm.org/ and Chinese articles are distributed through http://nejmqianyan.cn/. The corpus contains all article pairs (around 2000 pairs) since 2011. The script loads dataset in bigbio schema (using schemas/text-to-text) AND/OR source (default) schema """ import os # useful for paths from typing import Dict, Iterable, List import datasets from .bigbiohub import text2text_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks logger = datasets.logging.get_logger(__name__) _LANGUAGES = ['English', 'Chinese'] _PUBMED = False _LOCAL = False _CITATION = """\ @article{liu2021paramed, author = {Liu, Boxiang and Huang, Liang}, title = {ParaMed: a parallel corpus for English–Chinese translation in the biomedical domain}, journal = {BMC Medical Informatics and Decision Making}, volume = {21}, year = {2021}, url = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01621-8}, doi = {10.1186/s12911-021-01621-8} } """ _DATASETNAME = "paramed" _DISPLAYNAME = "ParaMed" _DESCRIPTION = """\ NEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website. English articles are distributed through https://www.nejm.org/ and Chinese articles are distributed through http://nejmqianyan.cn/. The corpus contains all article pairs (around 2000 pairs) since 2011. """ _HOMEPAGE = "https://github.com/boxiangliu/ParaMed" _LICENSE = 'Creative Commons Attribution 4.0 International' _URLs = { "source": "https://github.com/boxiangliu/ParaMed/blob/master/data/nejm-open-access.tar.gz?raw=true", "bigbio_t2t": "https://github.com/boxiangliu/ParaMed/blob/master/data/nejm-open-access.tar.gz?raw=true", } _SUPPORTED_TASKS = [Tasks.TRANSLATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" _DATA_DIR = "./processed_data/open_access/open_access" class ParamedDataset(datasets.GeneratorBasedBuilder): """Write a short docstring documenting what this dataset is""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="paramed_source", version=SOURCE_VERSION, description="Paramed source schema", schema="source", subset_id="paramed", ), BigBioConfig( name="paramed_bigbio_t2t", version=BIGBIO_VERSION, description="Paramed BigBio schema", schema="bigbio_t2t", subset_id="paramed", ), ] DEFAULT_CONFIG_NAME = "paramed_source" def _info(self): if self.config.schema == "source": features = datasets.Features( { "document_id": datasets.Value("string"), "text_1": datasets.Value("string"), "text_2": datasets.Value("string"), "text_1_name": datasets.Value("string"), "text_2_name": datasets.Value("string"), } ) elif self.config.schema == "bigbio_t2t": features = text2text_features return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=str(_LICENSE), citation=_CITATION, ) def _split_generators( self, dl_manager: datasets.DownloadManager ) -> List[datasets.SplitGenerator]: my_urls = _URLs[self.config.schema] data_dir = os.path.join(dl_manager.download_and_extract(my_urls), _DATA_DIR) print(data_dir) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir, "zh_file": os.path.join(data_dir, "nejm.train.zh"), "en_file": os.path.join(data_dir, "nejm.train.en"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": data_dir, "zh_file": os.path.join(data_dir, "nejm.dev.zh"), "en_file": os.path.join(data_dir, "nejm.dev.en"), "split": "dev", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir, "zh_file": os.path.join(data_dir, "nejm.test.zh"), "en_file": os.path.join(data_dir, "nejm.test.en"), "split": "test", }, ), ] def _generate_examples(self, filepath, zh_file, en_file, split): logger.info("generating examples from = %s", filepath) zh_file = open(zh_file, "r") en_file = open(en_file, "r") zh_file.seek(0) en_file.seek(0) zh_lines = zh_file.readlines() en_lines = en_file.readlines() assert len(en_lines) == len(zh_lines), "Line mismatch" if self.config.schema == "source": for key, (zh_line, en_line) in enumerate(zip(zh_lines, en_lines)): yield key, { "document_id": str(key), "text_1": zh_line, "text_2": en_line, "text_1_name": "zh", "text_2_name": "en", } zh_file.close() en_file.close() elif self.config.schema == "bigbio_t2t": uid = 0 for key, (zh_line, en_line) in enumerate(zip(zh_lines, en_lines)): uid += 1 yield key, { "id": str(uid), "document_id": str(key), "text_1": zh_line, "text_2": en_line, "text_1_name": "zh", "text_2_name": "en", } zh_file.close() en_file.close()