Text Embeddings
Text embeddings are numerical vector representations of text that capture semantic meaning and relationships between words, sentences, or documents. They are generated using machine learning models, such as word2vec, BERT, or OpenAI's embeddings, to convert text into dense vectors in a high-dimensional space. These embeddings enable computers to process and compare text based on meaning rather than just surface-level features.
Developers should learn text embeddings when building natural language processing (NLP) applications, such as semantic search, recommendation systems, or text classification, as they provide a way to quantify and compare textual similarity. They are essential for tasks like clustering documents, detecting duplicates, or powering chatbots, where understanding context and meaning is critical. In AI-driven projects, embeddings improve performance by enabling models to handle nuanced language patterns.