# Multi-task text embedding

LENS-8000 is a transformer-based text embedding model focused on feature extraction and sentence similarity tasks, excelling in multiple classification and retrieval tasks.

A large language model with 7B parameters based on Qwen2 architecture, focusing on sentence similarity calculation and text embedding tasks.

LLM2Vec is a supervised learning model based on Meta-Llama-3, focusing on natural language processing tasks such as sentence similarity, and supporting various application scenarios such as text embedding, information retrieval, and text classification.

Snowflake Arctic Embed XS is a lightweight sentence embedding model focused on sentence similarity and feature extraction tasks.

Snowflake Arctic M Long is a sentence-transformers-based sentence embedding model focused on sentence similarity and feature extraction tasks.

LLM2Vec-Sheared-LLaMA-supervised is a supervised learning model based on the Sheared-LLaMA architecture, focusing on sentence similarity tasks and providing functions such as text embedding, information retrieval, and text classification.

Ember v1 is an embedding model based on sentence-transformers, primarily used for feature extraction and sentence similarity calculation.

INSTRUCTOR is a text embedding model based on the T5 architecture, focusing on sentence similarity calculation and text classification tasks, and supports English language processing.

E5-small is a compact sentence transformer model focused on sentence similarity and text embedding tasks, performing well on multiple classification and retrieval tasks.