prepare.py

import pandas as pd
from collections import Counter
import json
import random


df = pd.read_csv("original_train.csv")

print(df)
"""
for field in ["target", "severe_toxicity", "obscene", "identity_attack", "insult", "threat"]:
    print("\n\n", field)
    num_greater = 0
    for val in df[field]:
        if val >= 0.5:
            num_greater += 1

    print(num_greater, len(df[field]), f"{num_greater/len(df[field])*100:.2f}%")
"""


rows = [{'text': row['comment_text'].strip(),
        'label': "1" if row['target'] >= 0.5 else "0",
        'label_text': "toxic" if row['target'] >= 0.5 else "not toxic",
         } for idx, row in df.iterrows()]

random.seed(42)
random.shuffle(rows)

num_test = 10000
splits = {'test': rows[0:num_test], 'train': rows[num_test:]}

print("Train:", len(splits['train']))
print("Test:", len(splits['test']))

num_labels = Counter()

for row in splits['test']:
    num_labels[row['label']] += 1
print(num_labels)

for split in ['train', 'test']:
    with open(f'{split}.jsonl', 'w') as fOut:
        for row in splits[split]:
            fOut.write(json.dumps(row)+"\n")