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import csv |
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import os |
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import textwrap |
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import numpy as np |
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import datasets |
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import pandas as pd |
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_CITATION = """ |
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@article{sileo2023generating, |
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title={Generating multiple-choice questions for medical question answering with distractors and cue-masking}, |
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author={Sileo, Damien and Uma, Kanimozhi and Moens, Marie-Francine}, |
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journal={arXiv preprint arXiv:2303.07069}, |
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year={2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Anonymous submission |
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""" |
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URL = 'https://sileod.s3.eu-west-3.amazonaws.com/wikimedqa/' |
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class WikiMedQAConfig(datasets.BuilderConfig): |
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"""BuilderConfig for WikiMedQA.""" |
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def __init__( |
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self, |
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data_dir, |
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label_classes=None, |
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process_label=lambda x: x, |
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**kwargs, |
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): |
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super(WikiMedQAConfig, self).__init__(version=datasets.Version("1.0.5", ""), **kwargs) |
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self.text_features = {k:k for k in ['text']+[f'option_{i}' for i in range(8)]} |
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self.label_column = 'label' |
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self.label_classes = list('01234567') |
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self.data_url = URL |
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self.url=URL |
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self.data_dir=data_dir |
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self.citation = _CITATION |
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self.process_label = process_label |
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class WikiMedQA(datasets.GeneratorBasedBuilder): |
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"""Evaluation of word estimative of probability understanding""" |
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BUILDER_CONFIGS = [ |
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WikiMedQAConfig( |
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name="medwiki", |
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data_dir="medwiki"), |
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WikiMedQAConfig( |
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name="wikem", |
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data_dir="wikem"), |
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WikiMedQAConfig( |
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name="wikidoc", |
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data_dir="wikidoc"), |
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] |
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def _info(self): |
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features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()} |
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features["label"] = datasets.features.ClassLabel(names=self.config.label_classes) |
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features["idx"] = datasets.Value("int32") |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features(features), |
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homepage=self.config.url, |
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citation=self.config.citation + "\n" + _CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dirs=[] |
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for split in ['train','validation','test']: |
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url=f'{URL}{self.config.data_dir}.csv' |
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print(url) |
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data_dirs+=[dl_manager.download(url)] |
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print(data_dirs) |
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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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"data_file": data_dirs[0], |
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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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"data_file": data_dirs[1], |
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"split": "dev", |
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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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"data_file": data_dirs[2], |
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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, data_file, split): |
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df = pd.read_csv(data_file) |
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df=df[['text','options','label']] |
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train, dev, test = np.split(df.sample(frac=1, random_state=42), |
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[int(.9*len(df)), int(.95*len(df))]) |
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df=eval(split) |
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df['options']=df['options'].map(eval) |
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for i in range(8): |
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df[f'option_{i}']=df.options.map(lambda x:x[i]) |
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del df['options'] |
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df['idx']=df.index |
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for idx, example in df.iterrows(): |
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yield idx, dict(example) |
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