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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@@ -47,16 +47,16 @@ model = SentenceTransformer("JayThinkDiff/CRE-1.1")
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query_embedding = model.encode("图像算法工程师 职位描述: 1、负责开发或优化基于人体工学标准和数字化技术的工人保护系统")
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passage_embedding = model.encode([
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"图像算法工程师 负责设计和实现多种机器学习算法,涵盖数据预处理、特征工程、模型训练与评估等完整流程,提升
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"算法工程师。工作描述:图像分割、图像融合、目标跟踪、特征点匹配等图像处理方面的研究,有MMpose、EHS项目经历",
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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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Expected Output:
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CRE-0.5: tensor([[0.
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bge-large-zh-v1.5: tensor([[0.
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"""
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```
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query_embedding = model.encode("图像算法工程师 职位描述: 1、负责开发或优化基于人体工学标准和数字化技术的工人保护系统")
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passage_embedding = model.encode([
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"图像算法工程师 负责设计和实现多种机器学习算法,涵盖数据预处理、特征工程、模型训练与评估等完整流程,提升人效。",
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"算法工程师。工作描述:图像分割、图像融合、目标跟踪、人体姿态识别、特征点匹配等图像处理方面的研究,有MMpose、EHS项目经历",
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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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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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