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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CRE(CareerInternational Recruitment Embedding)是一个Embedding模型,擅长编码招聘的工作技能等领域信息的语义。
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```python
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model = SentenceTransformer("JayThinkDiff/CRE-0.5")
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query_embedding = model.encode("嵌入式")
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CRE(CareerInternational Recruitment Embedding)是一个Embedding模型,擅长编码招聘的工作技能等领域信息的语义。
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<small>
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```python
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model = SentenceTransformer("JayThinkDiff/CRE-0.5")
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query_embedding = model.encode("嵌入式")
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