DistilBERT Fine-tuned on Superhero Texts

Model Summary

• Task: Binary text classification
• Classes: DC vs Marvel
• Base Model: distilbert-base-uncased
• Training Setup: 3 epochs, batch size 16, learning rate 2e-5
• Evaluation Metrics: Accuracy, precision, recall, F1 score

Dataset

• Source: rlogh/superhero-texts
• Split: 70% train, 15% val, 15% test
• Augmented split (~1.1k examples)

Preprocessing

• Tokenization with DistilBERT tokenizer
• Max sequence length: 256
• Labels encoded: DC = 0, Marvel = 1

Results

• Accuracy and F1 reported on test set
• Confusion matrix included in notebook

Error Analysis

Intended Use

• Educational purpose only
• Demonstration of fine-tuning transformers on text classification
• Not suitable for production deployment

License

Hardware/Compute

• Trained on 1 GPU (Colab environment)
• Training time: ~10 minutes