# 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. from pathlib import Path from typing import List 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 _DATASETNAME = "bionlp_st_2013_ge" _DISPLAYNAME = "BioNLP 2013 GE" _SOURCE_VIEW_NAME = "source" _UNIFIED_VIEW_NAME = "bigbio" _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @inproceedings{kim-etal-2013-genia, title = "The {G}enia Event Extraction Shared Task, 2013 Edition - Overview", author = "Kim, Jin-Dong and Wang, Yue and Yasunori, Yamamoto", booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop", month = aug, year = "2013", address = "Sofia, Bulgaria", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W13-2002", pages = "8--15", } """ _DESCRIPTION = """\ The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE """ _HOMEPAGE = "https://github.com/openbiocorpora/bionlp-st-2013-ge" _LICENSE = 'GENIA Project License for Annotated Corpora' _URLs = { "source": "https://github.com/openbiocorpora/bionlp-st-2013-ge/archive/refs/heads/master.zip", "bigbio_kb": "https://github.com/openbiocorpora/bionlp-st-2013-ge/archive/refs/heads/master.zip", } _SUPPORTED_TASKS = [ Tasks.EVENT_EXTRACTION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION, Tasks.COREFERENCE_RESOLUTION, ] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class bionlp_st_2013_ge(datasets.GeneratorBasedBuilder): """The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="bionlp_st_2013_ge_source", version=SOURCE_VERSION, description="bionlp_st_2013_ge source schema", schema="source", subset_id="bionlp_st_2013_ge", ), BigBioConfig( name="bionlp_st_2013_ge_bigbio_kb", version=BIGBIO_VERSION, description="bionlp_st_2013_ge BigBio schema", schema="bigbio_kb", subset_id="bionlp_st_2013_ge", ), ] DEFAULT_CONFIG_NAME = "bionlp_st_2013_ge_source" def _info(self): """ - `features` defines the schema of the parsed data set. The schema depends on the chosen `config`: If it is `_SOURCE_VIEW_NAME` the schema is the schema of the original data. If `config` is `_UNIFIED_VIEW_NAME`, then the schema is the canonical KB-task schema defined in `biomedical/schemas/kb.py`. """ if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "document_id": datasets.Value("string"), "text": datasets.Value("string"), "text_bound_annotations": [ # T line in brat, e.g. type or event trigger { "offsets": datasets.Sequence([datasets.Value("int32")]), "text": datasets.Sequence(datasets.Value("string")), "type": datasets.Value("string"), "id": datasets.Value("string"), } ], "events": [ # E line in brat { "trigger": datasets.Value( "string" ), # refers to the text_bound_annotation of the trigger, "id": datasets.Value("string"), "type": datasets.Value("string"), "arguments": datasets.Sequence( { "role": datasets.Value("string"), "ref_id": datasets.Value("string"), } ), } ], "relations": [ # R line in brat { "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"), } ], "equivalences": [ # Equiv line in brat { "id": datasets.Value("string"), "ref_ids": datasets.Sequence(datasets.Value("string")), } ], "attributes": [ # M or A lines in brat { "id": datasets.Value("string"), "type": datasets.Value("string"), "ref_id": datasets.Value("string"), "value": datasets.Value("string"), } ], "normalizations": [ # N lines in brat { "id": datasets.Value("string"), "type": datasets.Value("string"), "ref_id": datasets.Value("string"), "resource_name": datasets.Value( "string" ), # Name of the resource, e.g. "Wikipedia" "cuid": datasets.Value( "string" ), # ID in the resource, e.g. 534366 "text": datasets.Value( "string" ), # Human readable description/name of the entity, e.g. "Barack Obama" } ], }, ) 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: datasets.DownloadManager ) -> List[datasets.SplitGenerator]: my_urls = _URLs[self.config.schema] data_dir = Path(dl_manager.download_and_extract(my_urls)) data_files = { "train": data_dir / f"bionlp-st-2013-ge-master" / "original-data" / "train", "dev": data_dir / f"bionlp-st-2013-ge-master" / "original-data" / "devel", "test": data_dir / f"bionlp-st-2013-ge-master" / "original-data" / "test", } return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"data_files": data_files["train"]}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"data_files": data_files["dev"]}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"data_files": data_files["test"]}, ), ] def _generate_examples(self, data_files: Path): if self.config.schema == "source": txt_files = list(data_files.glob("*txt")) for guid, txt_file in enumerate(txt_files): example = parse_brat_file(txt_file) example["id"] = str(guid) yield guid, example elif self.config.schema == "bigbio_kb": txt_files = list(data_files.glob("*txt")) for guid, txt_file in enumerate(txt_files): example = brat_parse_to_bigbio_kb( parse_brat_file(txt_file) ) example["id"] = str(guid) yield guid, example else: raise ValueError(f"Invalid config: {self.config.name}")