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
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## Citation
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If you find our work helpful, please cite this patent:
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---
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*Stay hungry, Stay foolish. 推进招聘场景的 AI 变革。*
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## Citation
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If you find our work helpful, please cite this patent:
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```bibtex
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@misc{jiao2025recruitment,
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title={一种基于局部特征投影的招聘场景语义匹配方法及其系统},
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author={焦英浩 and 王广智 and 刘之},
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year={2025},
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note={中国专利申请号: 2025108424429, 实审中}
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}
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---
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*Stay hungry, Stay foolish. 推进招聘场景的 AI 变革。*
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