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Short-Answer-Feedback
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saf_micro_job_german

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Text2Text Generation

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short answer feedback

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saf_micro_job_german / conversion.py

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Update conversion.py

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

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

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

	Params:
	path_to_dataset (string): path to the folder containing the dataset (in XML 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 in JSON lines format in the specified location
	"""
	data = []

	# loop through all files in directory
	for f in os.listdir(path_to_dataset):
	if f.endswith('.xml'):
	root = et.parse(os.path.join(path_to_dataset, f)).getroot()
	# get question
	question = root.find('questionText').text.replace('\n', ' ')
	# get reference and student answers
	ref_answers = [x for x in root.find('referenceAnswers')]
	student_answers = [x for x in root.find('studentAnswers')]

	if len(ref_answers) == 1:
	# get reference answer and clear all spaces
	ref_answer = ref_answers[0].text.strip()

	# loop through all student answers and store the appropriate fields in a list
	for answer in student_answers:
	response = answer.find('response').text.strip()
	score = float(answer.find('score').text)
	feedback = answer.find('response_feedback').text.strip()
	verification_feedback = answer.find('verification_feedback').text.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,
	'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 micro job dataset (german) to JSON lines
	convert_xml_to_jsonl(
	'data/training/german',
	'data/json',
	'saf-micro-job-german',
	train_split=0.8)

	convert_xml_to_jsonl(
	'data/unseen_answers/german',
	'data/json',
	'saf-micro-job-german-unseen-answers')

	convert_xml_to_jsonl(
	'data/unseen_questions/german',
	'data/json',
	'saf-micro-job-german-unseen-questions')