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  # hku-nlp/instructor-xl
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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 install -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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  model = SentenceTransformer('hku-nlp/instructor-xl')
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  embeddings = model.encode([[instruction,sentence,0]])
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  print(embeddings)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
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  ---
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  # hku-nlp/instructor-xl
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+ This is a general embedding model: It maps **any** piece of text (e.g., a title, a sentence, a document, etc.) to a fixed-length vector in test time **without further training**. With instructions, the embeddings are **domain-specific** (e.g., specialized for science, finance, etc.) and **task-aware** (e.g., customized for classification, information retrieval, etc.)
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+
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+ The model is easy to use with `sentence-transformer` library.
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+
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+ ## Installation
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+ ```bash
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  git clone https://github.com/HKUNLP/instructor-embedding
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  cd sentence-transformers
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  pip install -e .
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  ```
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+
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+ ## Compute your customized embeddings
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+ Then you can use the model like this to calculate domain-specific and task-aware embeddings:
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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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  model = SentenceTransformer('hku-nlp/instructor-xl')
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  embeddings = model.encode([[instruction,sentence,0]])
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  print(embeddings)
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+ ```
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+
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+ ## Calculate Sentence similarities
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+ You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
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+ ```python
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+ from sklearn.metrics.pairwise import cosine_similarity
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+ sentences_a = [['Represent the Science sentence; Input: ','Parton energy loss in QCD matter',0],
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+ ['Represent the Financial statement; Input: ','The Federal Reserve on Wednesday raised its benchmark interest rate.',0]
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+ sentences_b = [['Represent the Science sentence; Input: ','The Chiral Phase Transition in Dissipative Dynamics', 0],
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+ ['Represent the Financial statement; Input: ','The funds rose less than 0.5 per cent on Friday',0]
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+ embeddings_a = model.encode(sentences_a)
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+ embeddings_b = model.encode(sentences_b)
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+ similarities = cosine_similarity(embeddings_a,embeddings_b)
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+ print(similarities)
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  ```