# 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. import itertools import os from pydoc import doc from typing import Dict, Iterator, List, Tuple import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @article{wei2013tmvar, title={tmVar: a text mining approach for extracting sequence variants in biomedical literature}, author={Wei, Chih-Hsuan and Harris, Bethany R and Kao, Hung-Yu and Lu, Zhiyong}, journal={Bioinformatics}, volume={29}, number={11}, pages={1433--1439}, year={2013}, publisher={Oxford University Press} } """ _DATASETNAME = "tmvar_v1" _DISPLAYNAME = "tmVar v1" _DESCRIPTION = """This dataset contains 500 PubMed articles manually annotated with mutation mentions of various kinds. It can be used for NER tasks only. The dataset is split into train(334) and test(166) splits""" _HOMEPAGE = "https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/" _LICENSE = 'License information unavailable' _URLS = { _DATASETNAME: "https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/download/tmVar/tmVarCorpus.zip", } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" logger = datasets.utils.logging.get_logger(__name__) class TmvarV1Dataset(datasets.GeneratorBasedBuilder): """ The tmVar dataset contains 500 PubMed articles manually annotated with mutation mentions of various kinds. It can be used for biomedical NER tasks """ DEFAULT_CONFIG_NAME = "tmvar_v1_source" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [] BUILDER_CONFIGS.append( BigBioConfig( name=f"{_DATASETNAME}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}", ) ) BUILDER_CONFIGS.append( BigBioConfig( name=f"{_DATASETNAME}_bigbio_kb", version=BIGBIO_VERSION, description=f"{_DATASETNAME} BigBio schema", schema="bigbio_kb", subset_id=f"{_DATASETNAME}", ) ) def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "pmid": datasets.Value("string"), "passages": [ { "type": datasets.Value("string"), "text": datasets.Value("string"), "offsets": [datasets.Value("int32")], } ], "entities": [ { "text": datasets.Value("string"), "offsets": [datasets.Value("int32")], "concept_id": datasets.Value("string"), "semantic_type_id": 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]: """Returns SplitGenerators.""" url = _URLS[_DATASETNAME] data_dir = dl_manager.download_and_extract(url) train_filepath = os.path.join(data_dir, "tmVarCorpus", "train.PubTator.txt") test_filepath = os.path.join(data_dir, "tmVarCorpus", "test.PubTator.txt") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": train_filepath, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": test_filepath, }, ), ] def _generate_examples(self, filepath) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" if self.config.schema == "source": with open(filepath, "r", encoding="utf8") as fstream: for raw_document in self.generate_raw_docs(fstream): document = self.parse_raw_doc(raw_document) yield document["pmid"], document elif self.config.schema == "bigbio_kb": with open(filepath, "r", encoding="utf8") as fstream: uid = itertools.count(0) for raw_document in self.generate_raw_docs(fstream): document = self.parse_raw_doc(raw_document) pmid = document.pop("pmid") document["id"] = next(uid) document["document_id"] = pmid entities_ = [] for entity in document["entities"]: entities_.append( { "id": next(uid), "type": entity["semantic_type_id"], "text": [entity["text"]], "normalized": [], "offsets": [entity["offsets"]], } ) for passage in document["passages"]: passage["id"] = next(uid) document["entities"] = entities_ document["relations"] = [] document["events"] = [] document["coreferences"] = [] yield document["document_id"], document def generate_raw_docs(self, fstream): """ Given a filestream, this function yields documents from it """ raw_document = [] for line in fstream: if line.strip(): raw_document.append(line.strip()) elif raw_document: yield raw_document raw_document = [] if raw_document: yield raw_document def parse_raw_doc(self, raw_doc): pmid, _, title = raw_doc[0].split("|") pmid = int(pmid) _, _, abstract = raw_doc[1].split("|") if self.config.schema == "source": passages = [ {"type": "title", "text": title, "offsets": [0, len(title)]}, { "type": "abstract", "text": abstract, "offsets": [len(title) + 1, len(title) + len(abstract) + 1], }, ] elif self.config.schema == "bigbio_kb": passages = [ {"type": "title", "text": [title], "offsets": [[0, len(title)]]}, { "type": "abstract", "text": [abstract], "offsets": [[len(title) + 1, len(title) + len(abstract) + 1]], }, ] entities = [] for line in raw_doc[2:]: mentions = line.split("\t") ( pmid_, start_idx, end_idx, mention, semantic_type_id, entity_id, ) = mentions entity = { "offsets": [int(start_idx), int(end_idx)], "text": mention, "semantic_type_id": semantic_type_id, "concept_id": entity_id, } entities.append(entity) return {"pmid": pmid, "passages": passages, "entities": entities}