Datasets:

Short-Answer-Feedback
/

saf_legal_domain_german

	import os
	import string
	import math
	import random
	import xml.etree.ElementTree as et
	import jsonlines
	import uuid
	import pandas as pd

	# set random seed for shuffling
	random.seed(1)

	# column names of the reference answers file
	FILE_NUMBER_COL = 'file_number'
	REFERENCE_ANSWER_COL = 'reference_answer'

	# column names of the files with the data
	QUESTION_COL = 'Frage'
	ANSWER_COL = 'Antwort'
	SCORE_COL = 'Score'
	ERROR_CLASS_COL = 'Fehlerklasse'
	FEEDBACK_COL = 'Feedback'

	# labels for verification_feedback
	CORRECT_LABEL = 'Correct'
	PARTIALLY_CORRECT_LABEL = 'Partially correct'
	INCORRECT_LABEL = 'Incorrect'

	def convert_xlsx_to_jsonl(
	path_to_dataset,
	path_to_reference_answers_file,
	dir,
	filename,
	train_split=None):
	"""
	Utility function used for conversion of .xlsx files from the dataset into JSON lines

	Params:
	path_to_dataset (string): path to the folder containing the dataset (in .xlsx format)
	path_to_reference_answers_file (string): path to the folder containing the reference answers (in .xlsx format)
	dir (string): name of the directory where the JSON lines file will be stored
	filename (string): name of the JSON lines file that will store the dataset
	train_split (float or None): if not None, defines which percentage of the dataset to use for the train and validation splits

	Returns:
	None: the file is saved JSON lines format in the specified location
	"""
	def return_verification_feedback(score):
	if math.isclose(score, 1.0):
	return CORRECT_LABEL
	elif math.isclose(score, 0.0):
	return INCORRECT_LABEL
	else:
	return PARTIALLY_CORRECT_LABEL

	data = []

	# get reference answers from file
	reference_answers_df = pd.read_excel(path_to_reference_answers_file)
	# the keys of the dictionary are the number of the files padded with zeroes
	# so that it has two digits, and the values are the reference answers themselves
	reference_answers = {
	f'{row[FILE_NUMBER_COL]:02}': row[REFERENCE_ANSWER_COL].strip()
	for _, row in reference_answers_df.iterrows()}

	# loop through all files in directory
	for f in os.listdir(path_to_dataset):
	if f.endswith('.xlsx'):
	# read file
	file_df = pd.read_excel(os.path.join(path_to_dataset, f))
	# get question
	question = file_df[QUESTION_COL].iat[0].strip()
	# get reference answer based on file name
	ref_answer = reference_answers[f.split('.')[0]]

	# loop through all rows and store the appropriate fields in a list
	for _, row in file_df.iterrows():
	response = row[ANSWER_COL].strip()
	score = float(row[SCORE_COL])
	feedback = str(row[FEEDBACK_COL]).strip()
	verification_feedback = return_verification_feedback(score)
	error_class = row[ERROR_CLASS_COL].strip()

	# create dictionary with the appropriate fields
	data.append({
	'id': uuid.uuid4().hex, # generate unique id in HEX format
	'question': question,
	'reference_answer': ref_answer,
	'provided_answer': response,
	'answer_feedback': feedback,
	'verification_feedback': verification_feedback,
	'error_class': error_class,
	'score': score
	})

	if not os.path.exists(dir):
	print('Creating directory where JSON file will be stored\n')
	os.makedirs(dir)

	if train_split is None:
	with jsonlines.open(f'{os.path.join(dir, filename)}.jsonl', 'w') as writer:
	writer.write_all(data)
	else:
	# shuffle data and divide it into train and validation splits
	random.shuffle(data)
	train_data = data[: int(train_split * (len(data) - 1))]
	val_data = data[int(train_split * (len(data) - 1)) :]

	# write JSON lines file with train data
	with jsonlines.open(f'{os.path.join(dir, filename)}-train.jsonl', 'w') as writer:
	writer.write_all(train_data)

	# write JSON lines file with validation data
	with jsonlines.open(f'{os.path.join(dir, filename)}-validation.jsonl', 'w') as writer:
	writer.write_all(val_data)

	if __name__ == '__main__':
	# convert legal domain dataset (german) to JSON lines
	convert_xlsx_to_jsonl(
	'data/training', 'data/reference_answers.xlsx',
	'data/json', 'saf-legal-domain-german',
	train_split=0.8)

	convert_xlsx_to_jsonl(
	'data/unseen_answers', 'data/reference_answers.xlsx',
	'data/json', 'saf-legal-domain-german-unseen-answers')

	convert_xlsx_to_jsonl(
	'data/unseen_questions', 'data/reference_answers.xlsx',
	'data/json', 'saf-legal-domain-german-unseen-questions')