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 via the library MKB on the link-prediction task. The model is dedicated to the representation of both technical and generic terminology in machine learning, NLP, news.
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer sentences = ["Machine Learning", "Geoffrey Hinton"] model = SentenceTransformer('raphaelsty/semanlink_all_mpnet_base_v2') embeddings = model.encode(sentences) print(embeddings)
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