Business Context

LexiSearch is upgrading its document ranking stack for enterprise knowledge search. The team wants an NLP engineer to explain the Transformer architecture clearly and justify why it replaced older sequence models such as RNNs and LSTMs in modern language systems.

Data

You are given a corpus of 2.5 million English documents and 180,000 query-document relevance labels. Query length ranges from 2-20 tokens, while documents range from 30-512 tokens after truncation. Relevance labels are imbalanced: 68% not relevant, 22% partially relevant, and 10% highly relevant. Text includes product names, abbreviations, punctuation-heavy logs, and repeated boilerplate.

Success Criteria

A strong answer should accurately describe self-attention, positional encoding, multi-head attention, feed-forward layers, residual connections, and encoder-decoder structure. It should also connect the architecture to practical benefits: parallelization, long-range dependency modeling, and transfer learning performance.

Constraints

• Explanation must be understandable to engineers who have used BERT APIs but not built Transformers from scratch
• Include a realistic preprocessing and fine-tuning pipeline in Python
• Assume training must fit on a single A10 GPU with 24GB VRAM
• Inference target is under 150 ms for query-document scoring

Requirements

1. Describe the Transformer architecture and the role of each major component.
2. Explain why Transformers outperformed RNNs/LSTMs on sequence modeling tasks.
3. Show how you would preprocess query-document text for a Transformer ranker.
4. Provide Python code using Hugging Face Transformers for tokenization, fine-tuning, and evaluation.
5. Discuss trade-offs versus recurrent and CNN-based sequence models.