# High-precision Semantic Similarity

A 1.5B-parameter general text embedding model based on the Qwen2 architecture, supporting both Chinese and English, focusing on sentence similarity computation and text retrieval tasks.

Multilingual E5 large instruction model supporting text embedding and classification tasks for over 100 languages

Conan-embedding-v1 is a Chinese text embedding model developed by the Tencent BAC team, focusing on semantic representation and similarity calculation for Chinese text.

Ruri is a general-purpose text embedding model optimized for Japanese, based on the Sentence Transformers architecture, specifically designed for sentence similarity computation and feature extraction tasks.

Conan-embedding-v1 is a Chinese text embedding model developed by the Tencent BAC team, implemented based on the sentence-transformers library, suitable for various Chinese natural language processing tasks.

Ruri-Large is a high-performance embedding model specialized in Japanese text similarity calculation, based on transformer architecture with support for long text processing (maximum length 8192).

A T5 architecture-based sentence embedding model focused on semantic similarity and information retrieval tasks for English text.

GTE-Large is a powerful sentence transformer model focused on sentence similarity and text embedding tasks, excelling in multiple benchmark tests.

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