From Zero to Semantic Search Embedding Model
Source: https://blog.metarank.ai/from-zero-to-semantic-search-embedding-model-592e16d94b61 Author: Metarank team
Summary
Practical walkthrough of building a production Semantic Search embedding model from scratch — covering model selection, fine-tuning, evaluation, and deployment. One of the few end-to-end guides covering the full lifecycle.
Key Stages
1. Start with a Pre-trained Model
Don’t train from scratch — use SBERT, E5, or similar pre-trained bi-encoder as the base. Selection criteria: latency budget, embedding dimension, training data overlap with your domain.
2. Domain Fine-tuning
Fine-tune on domain-specific (query, document) pairs using contrastive loss or MSE distillation. Even small amounts of in-domain training data yield large improvements over the pre-trained baseline.
3. Evaluation Before Deployment
Build an offline evaluation set with judgment labels before fine-tuning. Measure NDCG and MRR at fixed k to confirm fine-tuning improved the right things.
4. Production Considerations
- Embedding dimensionality vs. Dense Vector Retrieval index size/latency
- Batch embedding for indexing; single-query embedding for retrieval
- ANN index choice (HNSW for accuracy, IVF for memory efficiency)