# 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 os import re from typing import Dict, List, Tuple import datasets from .bigbiohub import BigBioConfig, Tasks, text_features _LOCAL = False _LANGUAGES = ["English"] _PUBMED = False _CITATION = """\ @inproceedings{, author = {Dannenfelser, Ruth and Zhong, Jeffrey and Zhang, Ran and Yao, Vicky}, title = {Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts}, publisher = {Advances in Neural Information Processing Systems}, volume = {36}, year = {2024}, url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/23e3d86c9a19d0caf2ec997e73dfcfbd-Paper-Datasets_and_Benchmarks.pdf}, } """ _DATASETNAME = "flambe" _DISPLAYNAME = "Flambe" _DESCRIPTION = """\ FlaMBe is a dataset aimed at procedural knowledge extraction from biomedical texts, particularly focusing on single cell research methodologies described in academic papers. It includes annotations from 55 full-text articles and 1,195 abstracts, covering nearly 710,000 tokens, and is distinguished by its comprehensive named entity recognition (NER) and disambiguation (NED) for tissue/cell types, software tools, and computational methods. This dataset, to our knowledge, is the largest of its kind for tissue/cell types, links entities to identifiers in relevant knowledge bases and annotates nearly 400 workflow relations between tool-context pairs. """ _HOMEPAGE = "https://github.com/ylaboratory/flambe" _LICENSE = "CC_BY_4p0" _URLS = { _DATASETNAME: "https://zenodo.org/records/10050681/files/data.zip?download", "ned": { "tissue_test": "https://zenodo.org/records/11218662/files/tissue_ned_test.csv?download", "tissue_train": "https://zenodo.org/records/11218662/files/tissue_ned_train.csv?download", "tissue_val": "https://zenodo.org/records/11218662/files/tissue_ned_val.csv?download", "tool_test": "https://zenodo.org/records/11218662/files/tool_ned_test.csv?download", "tool_train": "https://zenodo.org/records/11218662/files/tool_ned_train.csv?download", "tool_val": "https://zenodo.org/records/11218662/files/tool_ned_val.csv?download", }, } _SUPPORTED_TASKS = [ Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION, ] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class FlambeDataset(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="flambe_ner_fulltext_tools_source", version=SOURCE_VERSION, description="NER dataset for tools from full papers", schema="source", subset_id="flambe_ner_fulltext_tools_source", ), BigBioConfig( name="flambe_ner_fulltext_tissues_source", version=SOURCE_VERSION, description="NER dataset for tissues from full papers", schema="source", subset_id="flambe_ner_fulltext_tissues_source", ), BigBioConfig( name="flambe_ner_abstract_tissues_source", version=SOURCE_VERSION, description="NER dataset for tissues from abstracts", schema="source", subset_id="flambe_ner_abstract_tissues_source", ), BigBioConfig( name="flambe_ned_tissues", version=SOURCE_VERSION, description="NED dataset for tissues from full papers", schema="source_ned_tissue", subset_id="flambe_ned_tissues", ), BigBioConfig( name="flambe_ned_tools", version=SOURCE_VERSION, description="NED dataset for tools from full papers", schema="source_ned_tool", subset_id="flambe_ned_tools", ), BigBioConfig( name="flambe_fulltext_tools_bigbio_text", version=BIGBIO_VERSION, description="Flambe Tissues BigBio schema", schema="bigbio_text", subset_id="flambe_tool_bigbio", ), BigBioConfig( name="flambe_fulltext_tissues_bigbio_text", version=BIGBIO_VERSION, description="Flambe Tool BigBio schema", schema="bigbio_text", subset_id="flambe_tissue_bigbio", ), BigBioConfig( name="flambe_abstract_tissues_bigbio_text", version=BIGBIO_VERSION, description="Flambe Tool BigBio schema", schema="bigbio_text", subset_id="flambe_tissue_bigbio", ), ] DEFAULT_CONFIG_NAME = "flambe_ner_fulltext_tools_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "tags": datasets.Sequence(datasets.Value("string")), } ) elif self.config.schema == "source_ned_tissue": features = datasets.Features( { "orginal_text": datasets.Value("string"), "mapped_NCIT": datasets.Value("string"), "NCIT_name": datasets.Value("string"), } ) elif self.config.schema == "source_ned_tool": features = datasets.Features( { "orginal_text": datasets.Value("string"), "standardized_name": datasets.Value("string"), "url": datasets.Value("string"), } ) elif self.config.schema == "bigbio_text": features = text_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" # TODO: KEEP if your dataset is PUBLIC; remove if not # TODO: KEEP if your dataset is PUBLIC; remove if not urls = _URLS[_DATASETNAME] data_dir = dl_manager.download_and_extract(urls) path = { "flambe_ner_fulltext_tools_source": { "train": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tools_train.iob"), "test": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tools_test.iob"), "dev": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tools_validation.iob"), }, "flambe_ner_fulltext_tissues_source": { "train": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tissues_train.iob"), "test": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tissues_test.iob"), "dev": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tissues_validation.iob"), }, "flambe_ner_abstract_tissues_source": { "train": os.path.join(data_dir, "data/tags/abstract_iob/abstract_tissues_train.iob"), "test": os.path.join(data_dir, "data/tags/abstract_iob/abstract_tissues_test.iob"), "dev": os.path.join(data_dir, "data/tags/abstract_iob/abstract_tissues_validation.iob"), }, "flambe_ned_tissues": { "train": dl_manager.download_and_extract(_URLS["ned"]["tissue_train"]), "test": dl_manager.download_and_extract(_URLS["ned"]["tissue_test"]), "dev": dl_manager.download_and_extract(_URLS["ned"]["tissue_val"]), }, "flambe_ned_tools": { "train": dl_manager.download_and_extract(_URLS["ned"]["tool_train"]), "test": dl_manager.download_and_extract(_URLS["ned"]["tool_test"]), "dev": dl_manager.download_and_extract(_URLS["ned"]["tool_val"]), }, "flambe_fulltext_tools_bigbio_text": { "train": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tools_train.iob"), "test": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tools_test.iob"), "dev": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tools_validation.iob"), }, "flambe_fulltext_tissues_bigbio_text": { "train": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tissues_train.iob"), "test": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tissues_test.iob"), "dev": os.path.join(data_dir, "data/tags/fulltext_iob/fulltext_tissues_validation.iob"), }, "flambe_abstract_tissues_bigbio_text": { "train": os.path.join(data_dir, "data/tags/abstract_iob/abstract_tissues_train.iob"), "test": os.path.join(data_dir, "data/tags/abstract_iob/abstract_tissues_test.iob"), "dev": os.path.join(data_dir, "data/tags/abstract_iob/abstract_tissues_validation.iob"), }, } return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": path[self.config.name]["train"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": path[self.config.name]["test"], "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": path[self.config.name]["dev"], "split": "dev", }, ), ] def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" if self.config.schema == "source": with open(filepath, "r") as f: id_value = None tokens = [] tags = [] key = 0 for line in f: line = line.strip() if line: parts = line.split() if parts[1] == "begin": if id_value is not None: yield key, {"id": id_value, "tokens": tokens, "tags": tags} key += 1 tokens = [] tags = [] id_value = parts[0] elif parts[1] == "end": yield key, {"id": id_value, "tokens": tokens, "tags": tags} key += 1 id_value = None tokens = [] tags = [] else: tokens.append(parts[0]) tags.append(parts[1]) if id_value is not None: yield key, {"id": id_value, "tokens": tokens, "tags": tags} key += 1 elif self.config.schema == "bigbio_text": with open(filepath, "r") as f: id_value = None tokens = [] tags = [] key = 0 for line in f: line = line.strip() if line: parts = line.split() if parts[1] == "begin": if id_value is not None: yield key, { "id": key, "document_id": id_value, "text": " ".join(tokens), "labels": tags, } key += 1 tokens = [] tags = [] id_value = parts[0] elif parts[1] == "end": yield key, { "id": key, "document_id": id_value, "text": " ".join(tokens), "labels": tags, } key += 1 id_value = None tokens = [] tags = [] else: tokens.append(parts[0]) tags.append(parts[1]) if id_value is not None: yield key, { "id": key, "document_id": id_value, "text": " ".join(tokens), "labels": tags, } key += 1 elif self.config.schema == "source_ned_tissue": key = 0 for line in open(filepath): csv_row = line.strip("\n").split(",") if csv_row is not None: yield key, {"orginal_text": csv_row[0], "mapped_NCIT": csv_row[1], "NCIT_name": csv_row[2]} key += 1 elif self.config.schema == "source_ned_tool": key = 0 for line in open(filepath): csv_row = line.strip("\n").split(",") if csv_row is not None: yield key, {"orginal_text": csv_row[0], "standardized_name": csv_row[1], "url": csv_row[2]} key += 1 if __name__ == "__main__": datasets.load_dataset(__file__)