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import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, Trainer
# Read the dataset
df = pd.read_csv('Training_Essay_Data.csv') # Make sure the file name is correct
# Splitting the dataset
train_df, eval_df = train_test_split(df, test_size=0.1)
# Tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Tokenize function
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
# Tokenize the dataset
train_encodings = tokenize_function(train_df)
eval_encodings = tokenize_function(eval_df)
# Essay dataset class
class EssayDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
item['labels'] = torch.tensor(int(self.labels[idx]))
return item
def __len__(self):
return len(self.labels)
# Dataset preparation
train_dataset = EssayDataset(train_encodings, train_df['label'].tolist())
eval_dataset = EssayDataset(eval_encodings, eval_df['label'].tolist())
# Model
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
# Training arguments
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01,
logging_dir='./logs',
evaluation_strategy="epoch"
)
# Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset
)
# Train the model
trainer.train()
# Save the model
model.save_pretrained("./saved_model")
# Load the model for prediction
model = BertForSequenceClassification.from_pretrained("./saved_model")
# Predicting
def predict(text):
inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=-1)
return "AI-generated" if predictions.item() == 1 else "Human-written"
# Get user input and predict
user_input = input("Enter the text you want to classify: ")
print("Classified as:", predict(user_input))
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