Datasets:
Tasks:
Text2Text Generation
Languages:
German
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
other
Annotations Creators:
expert-generated
Source Datasets:
original
License:
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') |