Datasets:
Tasks:
Text Classification
Languages:
Turkish
Multilinguality:
monolingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
Tags:
License:
| import os | |
| import numpy as np | |
| import pandas as pd | |
| import pickle | |
| import math | |
| import random | |
| """ | |
| Dataset url: https://github.com/koc-lab/law-turk/tree/main/data/constitutional/deep | |
| Paper url: https://www.sciencedirect.com/science/article/abs/pii/S0306457321001692 | |
| """ | |
| def prepare_data(): | |
| label_dict = {' İhlal' : 0, ' İhlal Olmadığı' : 1} | |
| ### Load data | |
| with open('old_dataset/constitutional_tokenized.law' ,'rb') as pickle_file: | |
| tokenized = pickle.load(pickle_file) | |
| with open('old_dataset/constitutional_labels.law' ,'rb') as pickle_file: | |
| labels = pickle.load(pickle_file) | |
| tokenized_lower = [] | |
| for text in tokenized: | |
| temp_text = "" | |
| for word in text: | |
| temp_text += word.lower() + " " | |
| tokenized_lower.append(temp_text) | |
| ### Split the data | |
| train_ratio = 0.70 | |
| val_ratio = 0.15 | |
| list_indices = [] | |
| for i, lbl in enumerate(labels): | |
| if lbl in label_dict: | |
| list_indices.append(i) | |
| random.Random(13).shuffle(list_indices) | |
| new_length = len(list_indices) | |
| train_idx = math.floor(new_length * train_ratio) | |
| val_idx = math.floor(new_length * (train_ratio + val_ratio)) | |
| train_indices = list_indices[0:train_idx] | |
| val_indices = list_indices[train_idx : val_idx] | |
| test_indices = list_indices[val_idx:] | |
| train_list = [] | |
| val_list = [] | |
| test_list = [] | |
| for ind in train_indices: | |
| train_list.append(tokenized_lower[ind]) | |
| for ind in val_indices: | |
| val_list.append(tokenized_lower[ind]) | |
| for ind in test_indices: | |
| test_list.append(tokenized_lower[ind]) | |
| train_labels = [] | |
| val_labels = [] | |
| test_labels = [] | |
| count = 0 | |
| for ind in train_indices: | |
| if labels[ind] == " İhlal": | |
| train_labels.append("Violation") | |
| else: | |
| train_labels.append("No violation") | |
| for ind in val_indices: | |
| if labels[ind] == " İhlal": | |
| val_labels.append("Violation") | |
| else: | |
| val_labels.append("No violation") | |
| for ind in test_indices: | |
| if labels[ind] == " İhlal": | |
| test_labels.append("Violation") | |
| else: | |
| test_labels.append("No violation") | |
| print(count) | |
| train_split = np.concatenate((np.expand_dims(train_list, axis=1), np.expand_dims(train_labels, axis=1)), axis=1) | |
| val_split = np.concatenate((np.expand_dims(val_list, axis=1), np.expand_dims(val_labels, axis=1)), axis=1) | |
| test_split = np.concatenate((np.expand_dims(test_list, axis=1), np.expand_dims(test_labels, axis=1)), axis=1) | |
| return pd.DataFrame(train_split, columns=["Text", "Label"]), pd.DataFrame(val_split, columns=["Text", "Label"]), pd.DataFrame(test_split, columns=["Text", "Label"]) | |
| train_split, val_split, test_split = prepare_data() | |
| train_split.to_json(os.path.join("train.jsonl"), lines=True, orient="records", force_ascii=False) | |
| val_split.to_json(os.path.join("validation.jsonl"), lines=True, orient="records", force_ascii=False) | |
| test_split.to_json(os.path.join("test.jsonl"), lines=True, orient="records", force_ascii=False) |