Why Semantic Search with Sentence Transformers Might Return Poor Results
The most common reasons for irrelevant results are mismatched model capabilities, poor data preprocessing, or suboptimal retrieval settings. Sentence Transformers generate embeddings based on the input text's semantic meaning, but if the model wasn't trained on data similar to your domain (e.g., medical text vs. general web content), it may struggle to capture context-specific nuances. For example, using a general-purpose model like all-MiniLM-L6-v2 for technical documentation retrieval might miss specialized terminology. Additionally, raw text preprocessing issues—such as inconsistent casing, missing stopword removal, or overly long input chunks—can degrade embedding quality by introducing noise.
Key Steps to Improve Retrieval Quality
- Model Selection: Use domain-specific models like
multi-qa-mpnet-base-dot-v1for question-answer retrieval orall-mpnet-base-v2for general-purpose tasks. Test alternatives using the MTEB benchmark to find the best fit. - Data Preprocessing: Normalize text (lowercasing, removing special characters), split long documents into smaller chunks (e.g., 256 tokens), and align chunk boundaries with logical sections (paragraphs or headings). For technical terms, avoid stemming/lemmatization to preserve meaning.
- Indexing and Search Tuning: Use efficient similarity search libraries like FAISS or Annoy with cosine similarity. Experiment with
n_neighborsparameters or hybrid approaches (e.g., combining BM25 keyword scoring with semantic embeddings). For large datasets, ensure your index is built with sufficient training data (FAISS requires representative samples for clustering).
Advanced Optimization Techniques If basic fixes fail, consider these steps:
- Re-ranking: Use a cross-encoder model (e.g.,
cross-encoder/ms-marco-MiniLM-L-6-v2) to rescore the top N results from your initial semantic search. This adds computational cost but significantly improves precision. - Fine-tuning: Train the Sentence Transformer on your data using triplet loss with hard negatives. For example, create triplets of queries, positive documents, and mined hard negatives (incorrect but seemingly relevant documents). Tools like sentence-transformers simplify this process.
- Threshold Filtering: Reject results with low similarity scores (e.g., cosine similarity < 0.3) to eliminate marginal matches. Validate thresholds using a labeled test set.
Example workflow: A developer building a legal document search system might preprocess text into 512-token sections, use nlpaueb/legal-bert-base-uncased for embeddings, index with FAISS, and rerank results with a legal-specific cross-encoder. Regularly evaluate using metrics like recall@k or NDCG to quantify improvements.