text-and-embedding-features

Turn text into numbers for search, matching, deduplication, classification or clustering, and choose between TF-IDF, embeddings and simpler methods. Use whenever someone asks how to find similar tickets, products, documents, CVs or reviews by their text; detect near-duplicate records from names, addresses or descriptions; build TF-IDF vectors, n-grams or stop-word lists; choose word, sentence or document embeddings (word2vec, GloVe, fastText, sentence transformers or a hosted embedding API); decide whether embeddings are worth the cost over TF-IDF; handle tokenisation, subwords, spelling variants or several languages; measure cosine similarity; or store and search vectors for semantic search. Not for choosing how to cluster the resulting vectors into segments, and not for encoding ordinary categorical columns.