Summary
In this enlightening chapter, we embarked on a comprehensive exploration of DL and its remarkable application to text classification tasks through language models. We began with an overview of DL, revealing its profound ability to learn complex patterns from vast amounts of data and its indisputable role in advancing state-of-the-art NLP systems.
We then delved into the transformative world of transformer models, which have revolutionized NLP by providing an effective alternative to traditional RNNs and CNNs for processing sequence data. By unpacking the attention mechanism—a key feature in transformers—we highlighted its capacity to focus on different parts of the input sequence, hence facilitating a better understanding of context.
Our journey continued with an in-depth exploration of the BERT model. We detailed its architecture, emphasizing its pioneering use of bidirectional training to generate contextually rich word embeddings, and we highlighted its...
