Sentence Similarity
sentence-transformers
Safetensors
Transformers
ONNX
bert
feature-extraction
text-embeddings-inference
Instructions to use JayThinkDiff/CRE-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JayThinkDiff/CRE-0.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JayThinkDiff/CRE-0.5") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use JayThinkDiff/CRE-0.5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JayThinkDiff/CRE-0.5") model = AutoModel.from_pretrained("JayThinkDiff/CRE-0.5") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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print("查询结果:", util.cos_sim(query_embedding, passage_embedding))
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"""
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Expected Output:
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CRE-0.5: tensor([[0.6854, 0.6886]])
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bge-large-zh-v1.5: tensor([[0.7563, 0.7551]])
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"""
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```
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<small>
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<strong>注意事项:</strong>
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])
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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** |
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| **bge-large-zh-v1.5** | **0.7563** | 0.7551 |
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<small>
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<strong>注意事项:</strong>
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