Natural Language Processing with Transformers

Abstract

Transformers have become widely used for Natural Language Processing (NLP) due to their attention mechanism and strong performance on many tasks. This paper implements a transformer-based model for text classification. The workflow includes tokenization, fine-tuning, and evaluation using standard metrics such as accuracy and F1-score. The results indicate that transformer models can capture contextual meaning better than traditional methods, producing improved classification outcomes. The paper provides a lightweight experiment setup suitable for testing and prototyping.