# 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. """ The DDI corpus has been manually annotated with drugs and pharmacokinetics and pharmacodynamics interactions. It contains 1025 documents from two different sources: DrugBank database and MedLine. """ import os from pathlib import Path from typing import Dict, List, Tuple import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks from .bigbiohub import parse_brat_file from .bigbiohub import brat_parse_to_bigbio_kb _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @article{HERREROZAZO2013914, title = { The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions }, author = { María Herrero-Zazo and Isabel Segura-Bedmar and Paloma Martínez and Thierry Declerck }, year = 2013, journal = {Journal of Biomedical Informatics}, volume = 46, number = 5, pages = {914--920}, doi = {https://doi.org/10.1016/j.jbi.2013.07.011}, issn = {1532-0464}, url = {https://www.sciencedirect.com/science/article/pii/S1532046413001123}, keywords = {Biomedical corpora, Drug interaction, Information extraction} } """ _DATASETNAME = "ddi_corpus" _DISPLAYNAME = "DDI Corpus" _DESCRIPTION = """\ The DDI corpus has been manually annotated with drugs and pharmacokinetics and \ pharmacodynamics interactions. It contains 1025 documents from two different \ sources: DrugBank database and MedLine. """ _HOMEPAGE = "https://github.com/isegura/DDICorpus" _LICENSE = 'Creative Commons Attribution Non Commercial 4.0 International' _URLS = { _DATASETNAME: "https://github.com/isegura/DDICorpus/raw/master/DDICorpus-2013(BRAT).zip", } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class DDICorpusDataset(datasets.GeneratorBasedBuilder): """DDI Corpus""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="ddi_corpus_source", version=SOURCE_VERSION, description="DDI Corpus source schema", schema="source", subset_id="ddi_corpus", ), BigBioConfig( name="ddi_corpus_bigbio_kb", version=BIGBIO_VERSION, description="DDI Corpus BigBio schema", schema="bigbio_kb", subset_id="ddi_corpus", ), ] DEFAULT_CONFIG_NAME = "ddi_corpus_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "document_id": datasets.Value("string"), "text": datasets.Value("string"), "entities": [ { "offsets": datasets.Sequence(datasets.Value("int32")), "text": datasets.Value("string"), "type": datasets.Value("string"), "id": datasets.Value("string"), } ], "relations": [ { "id": datasets.Value("string"), "head": { "ref_id": datasets.Value("string"), "role": datasets.Value("string"), }, "tail": { "ref_id": datasets.Value("string"), "role": datasets.Value("string"), }, "type": datasets.Value("string"), } ], } ) elif self.config.schema == "bigbio_kb": features = kb_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=str(_LICENSE), citation=_CITATION, ) def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: urls = _URLS[_DATASETNAME] data_dir = dl_manager.download_and_extract(urls) standoff_dir = os.path.join(data_dir, "DDICorpusBrat") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(standoff_dir, "Train"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": os.path.join(standoff_dir, "Test"), "split": "test", }, ), ] def _generate_examples(self, filepath: str, split: str) -> Tuple[int, Dict]: if self.config.schema == "source": for subdir, _, files in os.walk(filepath): for file in files: # Ignore configuration files and annotation files - we just consider the brat text files if not file.endswith(".txt"): continue brat_example = parse_brat_file(Path(subdir) / file) source_example = self._to_source_example(brat_example) yield source_example["document_id"], source_example elif self.config.schema == "bigbio_kb": for subdir, _, files in os.walk(filepath): for file in files: # Ignore configuration files and annotation files - we just consider the brat text files if not file.endswith(".txt"): continue # Read brat annotations for the given text file and convert example to the BigBio-KB format brat_example = parse_brat_file(Path(subdir) / file) kb_example = brat_parse_to_bigbio_kb(brat_example) kb_example["id"] = kb_example["document_id"] yield kb_example["id"], kb_example @staticmethod def _to_source_example(brat_example: Dict) -> Dict: source_example = { "document_id": brat_example["document_id"], "text": brat_example["text"], "relations": brat_example["relations"], } source_example["entities"] = [] for entity_annotation in brat_example["text_bound_annotations"]: entity_ann = entity_annotation.copy() source_example["entities"].append( { # These are lists in the parsed output, so just take the first element to # match the source schema. "offsets": entity_annotation["offsets"][0], "text": entity_ann["text"][0], "type": entity_ann["type"], "id": entity_ann["id"], } ) return source_example