Introduction to Embeddings for Semantic Search

Embeddings are numerical vectors that represent the meaning of words or phrases in a high-dimensional space. They are widely used in natural language processing (NLP) for tasks like semantic search, where the goal is to find documents or sentences that have a similar meaning to a given query.

In this article, we will cover the basics of embeddings and how they can be used for semantic search. We'll also provide a concrete example using Python's transformers library and discuss common pitfalls to avoid.

Understanding Embeddings

Embeddings are created using pre-trained models such as Word2Vec, GloVe, or more advanced models like BERT, ELMo, or Sentence Transformers. These models learn to map words into a vector space where semantic similarity is preserved.…