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  - feature-extraction
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- # Lychee Embed
 
 
 
 
 
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- [Github](https://github.com/vec-ai/lychee-embed) | [Paper](https://openreview.net/pdf?id=NC6G1KCxlt)
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  `Lychee-embed` is the latest generalist text embedding model based on the `Qwen2.5` model. It is suitable for text retrieval (semantic correlation), text similarity and other downstream tasks, and supports multiple languages of `Qwen2.5`.
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  `Lychee-embed` is jointly developed by the NLP Team of Harbin Institute of Technology, Shenzhen and is built based on an innovative multi-stage training framework (warm-up, task-learning, model merging, annealing).
 
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+ <a href="https://github.com/vec-ai/lychee-embed">
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+ <img src="https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white">
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+ </a>
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+ <a href="https://openreview.net/pdf?id=NC6G1KCxlt">
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+ <img src="https://img.shields.io/badge/Paper-Openreview-red">
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+ </a>
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+ # Lychee Embed
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  `Lychee-embed` is the latest generalist text embedding model based on the `Qwen2.5` model. It is suitable for text retrieval (semantic correlation), text similarity and other downstream tasks, and supports multiple languages of `Qwen2.5`.
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  `Lychee-embed` is jointly developed by the NLP Team of Harbin Institute of Technology, Shenzhen and is built based on an innovative multi-stage training framework (warm-up, task-learning, model merging, annealing).