File size: 3,845 Bytes
1897ad8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
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') |