# Multi-task Text Embedding

Speed Embedding 7B Instruct is a large-scale language model based on the Transformer architecture, focusing on text embedding and classification tasks, and has demonstrated outstanding performance in multiple benchmarks.

This is a merged model composed of multiple foundation models including bert-large-uncased, WhereIsAI/UAE-Large-V1, BAAI/bge-large-en-v1.5, mixedbread-ai/mxbai-embed-large-v1, and avsolatorio/GIST-large-Embedding-v0. It is primarily used for text classification, retrieval, and clustering tasks.

A 7B-parameter sentence embedding model based on the Qwen1.5 architecture, focusing on sentence similarity calculation and multi-task evaluation

The ACGE Text Embedding Model is designed for generating text embeddings and supports various natural language processing tasks.

xiaobu-embedding is a multi-task embedding model that supports various Chinese natural language processing tasks, including text similarity calculation, classification, clustering, and retrieval.

The GTE Small Chinese Model is a sentence transformer model specialized in Chinese text processing, suitable for various natural language processing tasks.

gte-base-zh is a general-purpose text embedding model optimized for Chinese, supporting various natural language processing tasks including semantic similarity calculation, text classification, information retrieval, etc.

The GTE Large Chinese Model is a multilingual text embedding model focused on semantic representation and similarity calculation for Chinese text.

stella-base-en-v2 is an English text embedding model based on sentence-transformers, focusing on sentence similarity and feature extraction tasks.

GTE-small is a compact general-purpose text embedding model suitable for various natural language processing tasks, including sentence similarity calculation, text classification, and retrieval.

GTE-Base is a general-purpose text embedding model focused on sentence similarity and text retrieval tasks, performing well on multiple benchmarks.

INSTRUCTOR is a text embedding model based on the T5 architecture, focusing on sentence similarity calculation and information retrieval tasks. It excels in various NLP tasks, including text classification, clustering, and semantic similarity evaluation.

A text embedding model based on the T5 architecture, focusing on sentence similarity calculation and text retrieval tasks, with excellent performance in multiple benchmark tests.

SGPT-1.3B is a sentence transformer model optimized with weighted mean and bitfit techniques, primarily used for sentence similarity and feature extraction tasks.