model/train_classifier.py

# model/train_classifier.py

from datasets import load_dataset, DatasetDict
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch
import numpy as np
from sklearn.metrics import accuracy_score

# Load the dataset
dataset = load_dataset("csv", data_files="data/cleaned_injection_prompts.csv")

# Convert string labels to integers (0 for safe, 1 for injection)
def encode_labels(example):
    example["label"] = int(example["label"])
    return example

dataset = dataset.map(encode_labels)

# Split into train and test
dataset = dataset["train"].train_test_split(test_size=0.1)
tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")

# Tokenize inputs
def tokenize(example):
    return tokenizer(example["text"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize, batched=True)

# Load model
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)

# Metrics
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    return {"accuracy": accuracy_score(labels, predictions)}

# Training arguments
args = TrainingArguments(
    output_dir="./model/injection_classifier",
    evaluation_strategy="epoch",
    logging_strategy="epoch",
    save_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=4,
    weight_decay=0.01,
    save_total_limit=1,
    load_best_model_at_end=True,
    metric_for_best_model="accuracy"
)

# Trainer
trainer = Trainer(
    model=model,
    args=args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["test"],
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)

# Train the model
trainer.train()

# Save the model
trainer.save_model("model/injection_classifier")
tokenizer.save_pretrained("model/injection_classifier")