text_plain_output_1.png
import pandas as pd

folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)

with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
    lines = f.readlines()
    file_names = []
    for i in lines:
        file_names.append(i[:i.find('\n')])

for i in range(len(file_names)):
    df_train.loc[len(df_train.index)] = [1, 'img/1', 1, file_names[i]]

data = df_train
print('Distribution of training dataset after augmentation\n', data.label.value_counts())