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README.md
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# hku-nlp/instructor-base
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This is a general embedding model: It maps sentences & paragraphs to a 768 dimensional dense vector space.
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The model was trained on diverse tasks.
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It takes customized instructions and text inputs, and generates task-specific embeddings for general purposes, e.g., information retrieval, classification, clustering, etc.
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```
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git clone https://github.com/HKUNLP/instructor-embedding
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cd sentence-transformers
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pip instal -e .
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
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instruction = "Represent the Science title; Input:"
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model = SentenceTransformer('instructor-large')
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embeddings = model.encode([[instruction,sentence,0]])
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print(embeddings)
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```
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