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JayThinkDiff
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CRE-0.5

Sentence Similarity
sentence-transformers
Safetensors
Transformers
ONNX
bert
feature-extraction
text-embeddings-inference
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Update README.md

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@@ -54,7 +54,6 @@ passage_embedding = model.encode([
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  print("查询结果:", util.cos_sim(query_embedding, passage_embedding))
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  ```
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  ### 📊 预期结果对比 (Expected Output Comparison)
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-
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  | 模型名称 (Model) | 相似度 1 (与简历 1) | 相似度 2 (与简历 2) |
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  | :--- | :---: | :---: |
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  | **CRE-0.5** | 0.6854 | **0.6886** |
 
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  print("查询结果:", util.cos_sim(query_embedding, passage_embedding))
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  ```
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  ### 📊 预期结果对比 (Expected Output Comparison)
 
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  | 模型名称 (Model) | 相似度 1 (与简历 1) | 相似度 2 (与简历 2) |
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  | :--- | :---: | :---: |
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  | **CRE-0.5** | 0.6854 | **0.6886** |