Fine-tuning domain-adaptive Sentence-BERT embedding models for semantic search, retrieval, product search, support ticket matching, and job/CV matching.
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Jun 22, 2026
A semantic search and representation learning project focused on adapting embedding models to domain-specific retrieval tasks.
Improves matching and retrieval quality by fine-tuning embeddings for specific domains and similarity tasks.
Retrieval
End-to-end local retrieval pipeline with BM25, FAISS dense search, hybrid ranking, a custom scikit-learn feature reranker, evaluation metrics, CLI tooling, and Streamlit demo.
Improves document retrieval quality by combining sparse search, dense search, and learned reranking.
NLP
Advanced tokenizer engineering project implementing BPE, byte-level BPE, WordPiece, and SentencePiece-style unigram tokenization, with benchmarking against Hugging Face tokenizers and API/demo tooling.
Explains and implements the core tokenization algorithms behind modern language models.