advanced~6h
Embedding Model Fine-Tuning
Building and fine-tuning custom embedding models for domain-specific retrieval: contrastive learning with triplet loss, the SBERT architecture, MultipleNegativesRankingLoss for efficient training, hard negative mining, and Matryoshka Representation Learning for multi-size embeddings.
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Exercises
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Flashcards
▶📚 Prerequisites(2)
🎓 Learning objectives
- •Explain why general-purpose embedding models underperform on domain-specific retrieval tasks
- •Implement contrastive learning with triplet loss (anchor, positive, negative) to train an embedding model
- •Use MultipleNegativesRankingLoss for efficient fine-tuning without explicit negative mining
- •Apply hard negative mining to select the most informative training negatives
- •Describe Matryoshka Representation Learning and when dimension truncation is useful
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📂 Subtopics
Related concepts
contrastive learningtriplet lossSBERTsiamese networkMultipleNegativesRankingLosshard negative miningMatryoshka embeddingsRecall@KNDCGRAG retrievalFAISSsentence-transformersMTEB benchmark
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