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"""OAB Exams dataset""" |
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import datasets |
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import pandas as pd |
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import re |
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from collections import defaultdict |
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import os |
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import json |
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_CITATION = """@misc{almeida2023bluex, |
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title={BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams}, |
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author={Thales Sales Almeida and Thiago Laitz and Giovana K. Bonás and Rodrigo Nogueira}, |
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year={2023}, |
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eprint={2307.05410}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """ |
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Despite Portuguese being the fifth most widely spoken language, there is a lack of freely available resources for evaluating language models in Portuguese. This repository contains a multimodal dataset consisting of the two leading university entrance exams conducted in Brazil: Convest (Unicamp) and Fuvest (USP), spanning from 2018 to 2024. The dataset comprises a total of 1260 questions, of which 724 do not have accompanying images. |
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""" |
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_HOMEPAGE="https://github.com/Portuguese-Benchmark-Datasets/BLUEX" |
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_URL = "portuguese-benchmark-datasets/BLUEX" |
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_URL = "https://raw.githubusercontent.com/Portuguese-Benchmark-Datasets/BLUEX/main/data/bluex_dataset.zip" |
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class BLUEX_without_images(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.1.0") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"question_number": datasets.Value("int32"), |
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"exam_id": datasets.Value("string"), |
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"exam_year": datasets.Value("string"), |
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"university": datasets.Value("string"), |
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"question_type": datasets.Sequence(datasets.Value("string")), |
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"nullified": datasets.Value("bool"), |
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"question": datasets.Value("string"), |
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"choices": datasets.Sequence(feature={ |
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"text": datasets.Value("string"), |
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"label": datasets.Value("string") |
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}), |
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"answerKey": datasets.Value("string"), |
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}), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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filedir = dl_manager.download_and_extract(_URL) |
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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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"filedir": os.path.join(filedir, 'questions') |
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} |
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) |
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] |
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def _generate_examples(self, filedir): |
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for university in os.listdir(filedir): |
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years = sorted(os.listdir(os.path.join(filedir, university))) |
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for year in years: |
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days = [d for d in os.listdir(os.path.join(filedir, university, year)) if os.path.isdir(os.path.join(filedir, university, year, d))] |
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if len(days) == 0: |
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days = [''] |
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days = sorted(days) |
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for day in days: |
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if day == '': |
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path = os.path.join(filedir, university, year) |
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else: |
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path = os.path.join(filedir, university, year, day) |
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exam_id = f"{university}_{year}" if day == '' else f"{university}_{year}_{day.replace('day', '')}" |
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filenames = sorted(os.listdir(path), key=lambda x: int(re.findall(r'\d+', x)[0])) |
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for filename in filenames: |
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if filename.endswith('.json'): |
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with open(os.path.join(path, filename), 'r') as f: |
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example = json.load(f) |
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if example['IU'] or example['alternatives_type'] != 'string' or example['has_associated_images']: |
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continue |
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choices = { |
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"text": [], |
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"label": ["A", "B", "C", "D", "E"] |
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} |
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for alternative in example['alternatives']: |
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choices['text'].append(alternative[3:].strip()) |
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choices['label'] = choices['label'][:len(choices['text'])] |
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doc_id = f"{exam_id}_{example['number']}" |
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yield doc_id, { |
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"id": doc_id, |
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"question_number": example['number'], |
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"exam_id": exam_id, |
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"exam_year": year, |
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"university": university, |
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"question_type": example['subject'], |
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"nullified": None, |
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"question": example['question'], |
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"choices": choices, |
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"answerKey": example['answer'] |
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} |
