metadata

pipeline_tag: sentence-similarity
language: en
license: apache-2.0
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers

hku-nlp/instructor-base

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.)

The model is easy to use with sentence-transformer library.

Installation

git clone https://github.com/HKUNLP/instructor-embedding
cd sentence-transformers
pip install -e .

Compute your customized embeddings

Then you can use the model like this to calculate domain-specific and task-aware embeddings:

from sentence_transformers import SentenceTransformer
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title; Input:"
model = SentenceTransformer('hku-nlp/instructor-base')
embeddings = model.encode([[instruction,sentence,0]])
print(embeddings)

Calculate Sentence similarities

You can further use the model to compute similarities between two groups of sentences, with customized embeddings.

from sklearn.metrics.pairwise import cosine_similarity
sentences_a = [['Represent the Science sentence; Input: ','Parton energy loss in QCD matter',0], 
               ['Represent the Financial statement; Input: ','The Federal Reserve on Wednesday raised its benchmark interest rate.',0]
sentences_b = [['Represent the Science sentence; Input: ','The Chiral Phase Transition in Dissipative Dynamics', 0],
               ['Represent the Financial statement; Input: ','The funds rose less than 0.5 per cent on Friday',0]
embeddings_a = model.encode(sentences_a)
embeddings_b = model.encode(sentences_b)
similarities = cosine_similarity(embeddings_a,embeddings_b)
print(similarities)