You can deploy this model and start earning money today!

IEITYuan/
Yuan-embedding-2.0-en

Name: Yuan-embedding-2.0-en
Availability: InStock

sentence-transformers

Views

No views yet

Yuan-embedding-2.0-en

Yuan-embedding-2.0-en is an embedding model specifically designed for English text retrieval tasks. Built on the foundation of Qwen/Qwen3-Embedding-0.6B, we have further optimized it for both Retrieval and Reranking tasks. The key work is as follows:

Data Augmentation
- Hard negative sampling: Dual evaluation is conducted using a Rerank model and LLM to filter out high-quality positive and negative samples
- LLM-synthesized data: The Yuan2-M32 is employed to perform LLM-based rewriting on the query data within the training dataset
Loss function design
- Multi-Task loss
- Matryoshka Representation Learning
- The Retrieval tasks adopt InfoNCE with in-batch-negative
- The Reranking tasks adopt InfoNCE with in-batch-negative

Yuan-embedding-2.0-en 是专门为英文文本检索任务设计的嵌入模型。我们在Qwen/Qwen3-Embedding-0.6B的基础上，针对Retrieval任务与Reranking任务进行了进一步优化。主要工作如下：

数据增强
- Hard negative sampling：利用Rerank模型与LLM进行双重评估，筛选出高质量正负样本
- LLM合成数据：利用Yuan2-M32针对训练数据query进行LLM重写
损失函数设计
- Multi-Task loss
- Matryoshka Representation Learning
- Retrieval任务使用InfoNCE with in-batch-negative
- 针对Reranking任务使用InfoNCE with in-batch-negative

Usage

pip install -U sentence-transformers==3.4.1

使用示例：

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("IEITYuan/Yuan-embedding-2.0-en")
sentences_1 = ["data-1", "data-2"]
sentences_2 = ["data-3", "data-4"]
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)