import os import json import random import datasets import numpy as np import pandas as pd _CITATION = """\ ddd """ _DESCRIPTION = """\ This article presents MORFITT, the first multi-label corpus in French annotated in specialties in the medical field. MORFITT is composed of 3~624 abstracts of scientific articles from PubMed, annotated in 12 specialties for a total of 5,116 annotations. We detail the corpus, the experiments and the preliminary results obtained using a classifier based on the pre-trained language model CamemBERT. These preliminary results demonstrate the difficulty of the task, with a weighted average F1-score of 61.78%. """ _HOMEPAGE = "ddd" _URL = "https://huggingface.co/datasets/Dr-BERT/MORFITT/resolve/main/data.zip" _LICENSE = "unknown" _SPECIALITIES = ['microbiology', 'etiology', 'virology', 'physiology', 'immunology', 'parasitology', 'genetics', 'chemistry', 'veterinary', 'surgery', 'pharmacology', 'psychology'] class MORFITT(datasets.GeneratorBasedBuilder): DEFAULT_CONFIG_NAME = "source" BUILDER_CONFIGS = [ datasets.BuilderConfig(name="source", version="1.0.0", description="The MORFITT corpora"), ] def _info(self): features = datasets.Features( { "id": datasets.Value("string"), "abstract": datasets.Value("string"), "specialities": datasets.Sequence( datasets.features.ClassLabel(names=_SPECIALITIES), ), "specialities_one_hot": datasets.Sequence( datasets.Value("float"), ), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=str(_LICENSE), citation=_CITATION, ) def _split_generators(self, dl_manager): data_dir = dl_manager.download_and_extract(_URL).rstrip("/") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "tsv_file": data_dir + "/train.tsv", "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "tsv_file": data_dir + "/dev.tsv", "split": "validation", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "tsv_file": data_dir + "/test.tsv", "split": "test", }, ), ] def _generate_examples(self, tsv_file, split): # Load TSV file df = pd.read_csv(tsv_file, sep="\t") for index, e in df.iterrows(): specialities = e["specialities"].split("|") # Empty one hot vector one_hot = [0.0 for i in _SPECIALITIES] # Fill up the one hot vector for s in specialities: one_hot[_SPECIALITIES.index(s)] = 1.0 yield e["identifier"], { "id": e["identifier"], "abstract": e["abstract"], "specialities": specialities, "specialities_one_hot": one_hot, }