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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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_CITATION = """@misc{delfino2017passing, |
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title={Passing the Brazilian OAB Exam: data preparation and some experiments}, |
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author={Pedro Delfino and Bruno Cuconato and Edward Hermann Haeusler and Alexandre Rademaker}, |
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year={2017}, |
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eprint={1712.05128}, |
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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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This repository contains the bar exams from the Ordem dos Advogados do Brasil (OAB) in Brazil from 2010 to 2018. |
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In Brazil, all legal professionals must demonstrate their knowledge of the law and its application by passing the OAB exams, the national bar exams. The OAB exams therefore provide an excellent benchmark for the performance of legal information systems since passing the exam would arguably signal that the system has acquired capacity of legal reasoning comparable to that of a human lawyer. |
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""" |
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_HOMEPAGE="https://github.com/legal-nlp/oab-exams" |
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BASE_URL = "https://raw.githubusercontent.com/legal-nlp/oab-exams/master/official/raw/" |
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FILES = [ |
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'2010-01.txt', |
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'2010-02.txt', |
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'2011-03.txt', |
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'2011-04.txt', |
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'2011-05.txt', |
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'2012-06.txt', |
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'2012-06a.txt', |
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'2012-07.txt', |
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'2012-08.txt', |
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'2012-09.txt', |
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'2013-10.txt', |
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'2013-11.txt', |
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'2013-12.txt', |
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'2014-13.txt', |
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'2014-14.txt', |
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'2014-15.txt', |
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'2015-16.txt', |
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'2015-17.txt', |
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'2015-18.txt', |
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'2016-19.txt', |
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'2016-20.txt', |
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'2016-20a.txt', |
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'2016-21.txt', |
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'2017-22.txt', |
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'2017-23.txt', |
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'2017-24.txt', |
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'2018-25.txt' |
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] |
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def join_lines(lines): |
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texts = [] |
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for line in lines: |
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if line.strip() == "" and len(texts) > 0 and texts[-1] != "\n": |
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texts.append("\n") |
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else: |
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if len(texts) > 0 and texts[-1] != "\n": |
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texts.append(" ") |
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texts.append(line.strip()) |
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return "".join(texts).strip() |
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class OABExams(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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"question_type": 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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links = [BASE_URL + file for file in FILES] |
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downloaded_files = dl_manager.download(links) |
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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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"filepaths": downloaded_files, |
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"filenames": FILES |
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} |
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) |
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] |
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def _generate_examples(self, filepaths, filenames): |
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for filepath, filename in zip(filepaths, filenames): |
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exam_id = filename.replace(".txt", "") |
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exam_year = int(filename.split("-")[0]) |
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questions_temp = [] |
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with open(filepath, encoding="utf-8") as f: |
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lines = f.readlines() |
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for i, line in enumerate(lines): |
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if re.match(r"Questão \d{1,2}(\sNULL)?", line.strip()): |
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nullified = 'NULL' in line |
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question_number = int(line.strip().split(" ")[1]) |
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question_id = exam_id + "_" + str(question_number) |
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questions_temp.append( |
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{ |
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"question_id": question_id, |
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"question_number": question_number, |
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"exam_id": exam_id, |
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"exam_year": exam_year, |
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"lines": [line], |
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"nullified": nullified |
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} |
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) |
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else: |
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questions_temp[-1]["lines"].append(line) |
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for question_temp in questions_temp: |
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question_lines = question_temp["lines"] |
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area_index = 2 |
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if question_lines[1].startswith("AREA"): |
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area_index = 1 |
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area_line = question_lines[area_index].strip() |
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question_type = None if area_line == "AREA" else area_line.split(" ")[1] |
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index_options = None |
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for i, line in enumerate(question_lines): |
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if line.strip() == "OPTIONS": |
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index_options = i |
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break |
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if index_options is None: |
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print(question_temp) |
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question = join_lines(question_lines[3:index_options]) |
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choices = { |
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"text": [], |
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"label": [] |
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} |
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answerKey = None |
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temp_question_text = None |
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for i, line in enumerate(question_lines[index_options+2:]): |
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if "CORRECT)" in line: |
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answerKey = line[0] |
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if line[0] in ["A", "B", "C", "D", "E"] and (line[1:3] == ") " or line[1:11] == ":CORRECT) "): |
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if temp_question_text is not None: |
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choices["text"].append(join_lines(temp_question_text)) |
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temp_question_text = [line[line.find(')')+2:]] |
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choices["label"].append(line[0]) |
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else: |
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if temp_question_text is not None: |
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temp_question_text.append(line) |
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if temp_question_text is not None: |
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choices["text"].append(join_lines(temp_question_text)) |
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temp_question_text = None |
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if question_temp["nullified"]: |
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continue |
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yield question_temp['question_id'], { |
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"id": question_temp['question_id'], |
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"question_number": question_temp['question_number'], |
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"exam_id": question_temp['exam_id'], |
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"exam_year": question_temp['exam_year'], |
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"question_type": question_type, |
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"nullified": question_temp['nullified'], |
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"question": question, |
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"choices": choices, |
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"answerKey": answerKey |
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} |
