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--- |
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pipeline_tag: sentence-similarity |
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language: en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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--- |
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# hku-nlp/instructor-large |
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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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The model is easy to use with `sentence-transformer` library. |
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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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## 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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instruction = "Represent the Science title; Input:" |
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model = SentenceTransformer('hku-nlp/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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## 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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``` |
