Semantic Similarity and Sentence Embeddings
Sentence-BERT, cosine similarity for semantic search, embedding clustering.
Sentences, Not Just Words
Word embeddings represent words, but we often need to compare whole sentences or documents. Averaging word vectors loses nuance; we want a single vector that captures full meaning.
What Are Sentence Embeddings
Sentence embeddings map an entire sentence to one dense vector so that sentences with similar meaning have nearby vectors, enabling semantic search and clustering.
All lessons in this course
- Word2Vec: Skip-gram and CBOW
- GloVe and FastText Embeddings
- Text Classification with BERT
- Semantic Similarity and Sentence Embeddings