Chapter 7

NLP Implementation

The gap between a working NLP model in a notebook and a working NLP system in production is filled with implementation details and applied technique. This chapter covers the practical mechanics — padding and masks for variable-length sequences, text augmentation, imbalanced data, data-splitting pitfalls, and transfer learning strategies — then moves to the applied NLP tasks that put these skills to work: text similarity, summarization (extractive vs. abstractive), and topic modeling.

• 1. Handling Variable-Length Sequences→
• 2. Text Data Augmentation→
• 3. Handling Class Imbalance→
• 4. Data Splitting Best Practices→
• 5. Transfer Learning and Fine-Tuning Strategies→
• 6. NLP Applications:Text Similarity→
• 7. NLP Applications: Text Summarization→
• 8. NLP Applications:Topic Modeling→