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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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+ ```