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Conan Embedding V1 Q4 K M GGUF
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Conan Embedding V1 Q4 K M GGUF
Developed by lagoon999
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.
Downloads 30
Release Time : 10/29/2024
Model Overview
This model focuses on generating embedding representations for Chinese text, supporting multiple tasks such as semantic similarity calculation, text classification, clustering, retrieval, and re-ranking, and has demonstrated outstanding performance in multiple Chinese benchmark tests.
Model Features
Multi-task Support
Supports various Chinese NLP tasks, including semantic similarity calculation, text classification, clustering, retrieval, and re-ranking.
High Performance
Demonstrates excellent performance in multiple Chinese benchmark tests, particularly excelling in medical-related tasks.
Chinese Optimization
Specifically optimized for Chinese text, better capturing Chinese semantic features.
Model Capabilities
Text embedding generation
Semantic similarity calculation
Text classification
Text clustering
Information retrieval
Search result re-ranking
Use Cases
Medical Field
Medical Q&A Retrieval
Used in retrieval systems for medical-related questions, helping users quickly find relevant medical information.
Achieved a map@100 of 42.495 in the CMedQA retrieval task.
Medical Document Re-ranking
Re-ranks medical document retrieval results by relevance to improve user experience.
Achieved an mrr of 93.358 in the CMedQAv1 re-ranking task.
E-commerce
Product Review Classification
Performs sentiment and topic classification on product reviews in e-commerce platforms.
Achieved an accuracy of 90.318% in the JDReview classification task.
Product Retrieval
Enhances the relevance of product searches in e-commerce platforms.
Achieved an ndcg@10 of 70.991 in the EcomRetrieval task.
General NLP
Semantic Similarity Calculation
Calculates the semantic similarity between two Chinese texts.
Achieved a cos_sim_spearman of 81.244 in the STSB task.
Text Clustering
Performs unsupervised clustering analysis on Chinese text.
Achieved a v_measure of 60.635 in the CLSClusteringP2P task.
Transformers
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