Fast_Sentence_Embeddings

Why we included this project

Fast Sentence Embeddings is a Gensim add-on for anyone who needs sentence vectors across a very large corpus and finds transformer models too slow or GPUs out of reach. It converts a pretrained word-embedding model (Word2Vec, FastText, or GloVe) into sentence-level representations using plain averaging, smooth inverse frequency (SIF), and unsupervised SIF. The Cython core supports RAM-to-disk and disk-to-disk training, so corpora much larger than memory still get processed, and it reports hundreds of thousands of sentences per second on modest hardware. Teams doing document similarity, clustering, or retrieval over big text collections can use it as a fast baseline before moving to heavier neural encoders. A hub of pretrained vectors and a simple way to plug in your own Gensim models keep the setup effort low.