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Sentence Embeddings & Contrastive Loss

Master sentence embedding training with contrastive learning (InfoNCE), optimize retrieval with bi-encoder vs. cross-encoder architectures, and use modern advances like Matryoshka representations.

What you'll master

InfoNCE contrastive loss derivation and implementation

Temperature parameter effect on training dynamics

Hard negative mining strategies (BM25, cross-encoder, iterative)

Bi-encoder vs cross-encoder speed-accuracy tradeoff

Late interaction models (ColBERT MaxSim)

Matryoshka Representation Learning for flexible dimensionality

Instruction-tuned embeddings for task adaptation

MTEB evaluation across 8 task categories

Two-stage retrieval: bi-encoder + cross-encoder reranking

Medium30 min readIncludes code examples, architecture diagrams, and expert-level follow-up questions.

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