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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') |