# Semantic similarity computation

MiniCOIL is a sparse contextualized word-by-word embedding model specifically designed for efficient semantic similarity computation

This is a multilingual sentence embedding model based on sentence-transformers, capable of mapping text to a 1024-dimensional vector space, suitable for semantic search and clustering tasks.

BERT-based dual-encoder model, specifically designed for zero-shot text classification tasks, trained on the UTCD dataset

A model that maps abstract sentence descriptions to matching sentences, trained on Wikipedia using a dual encoder architecture.

This is a multilingual sentence embedding model based on sentence-transformers, supporting English, German, and Italian, capable of mapping text into a 768-dimensional vector space.

This model is used to generate sentence embeddings for Sanskrit and Tibetan, suitable for semantic similarity tasks.