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