# Chinese Semantic Similarity

This model is a sentence embedding model obtained through distillation training from multiple excellent Chinese vector models, focusing on Chinese text similarity calculation tasks.

This model is converted from thenlper/gte-large-zh to GGUF format via llama.cpp, primarily used for Chinese text embedding and sentence similarity calculation.

A fine-tuned sentence transformer model based on sentence-transformers/all-MiniLM-L6-v2 for calculating semantic similarity between sentences and paragraphs.

This model has been evaluated on multiple tasks in the Chinese Text Embedding Benchmark (MTEB), including text similarity, classification, clustering, and retrieval tasks.

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

Dmeta-embedding is a model for sentence similarity calculation and feature extraction, supporting various text-related 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.

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.

Piccolo is a Chinese base model specializing in various natural language processing tasks such as semantic text similarity (STS), classification, clustering, and retrieval.

A sentence representation model for semantic retrieval compressed via homomorphic projection distillation, with only 23 million parameters and a model size of 87MB

A financial domain question matching model optimized based on bert-base-chinese, specifically designed for banking question matching scenarios