# High-Dimensional Dense Vectors

This is a sentence-transformers model fine-tuned from BAAI/bge-m3, designed to map sentences and paragraphs into a 1024-dimensional dense vector space, supporting tasks such as semantic text similarity and semantic search.

INF-Retriever-v1-1.5B is a dense retrieval model based on large language models developed by INF TECH, optimized and fine-tuned for Chinese-English data retrieval tasks.

This is a sentence transformer model fine-tuned on the stsb dataset based on the ModernBERT-base model, used for generating 768-dimensional dense vector representations of sentences and paragraphs.

A multilingual text semantic search model based on Siamese-BERT architecture, trained on 215 million (question, answer) pairs to generate 384-dimensional normalized embedding vectors