eval.py

from transformers import DistilBertForSequenceClassification, Trainer
from train import train_model

def evaluate():
    # In a real pipeline, we'd load the saved model
    trainer, test_dataset = train_model()
    results = trainer.evaluate()
    print(results)
    with open('results.txt', 'w') as f:
        f.write(str(results))

if __name__ == '__main__':
    evaluate()