readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  language:
  - en
  - fr
  license: apache-2.0
  ---
  
  ## `semanlink_all_mpnet_base_v2`
  
  This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
  
  `semanlink_all_mpnet_base_v2` has been fine-tuned on the knowledge graph [Semanlink](http://www.semanlink.net/sl/home?lang=fr) via the library [MKB](https://github.com/raphaelsty/mkb) on the link-prediction task. The model is dedicated to the representation of both technical and generic terminology in machine learning, NLP, news.
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have sentence-transformers installed:
  
  ```
  pip install -U sentence-transformers
  ```
  
  Then you can use the model like this:
  
  ```python
  from sentence_transformers import SentenceTransformer
  sentences = ["Machine Learning", "Geoffrey Hinton"]
  
  model = SentenceTransformer('raphaelsty/semanlink_all_mpnet_base_v2')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```