How to use Corran/SciTopicNomicEmbed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Corran/SciTopicNomicEmbed", trust_remote_code=True) sentences = [ "Despite the crucial role of phosphorus in global food production, there is a lack of comprehensive analysis on the economic and policy aspects of phosphorus supply and demand, highlighting a significant knowledge gap in the field of natural resource economics.", "The human brain is intrinsically organized into dynamic, anticorrelated functional networks", "The story of phosphorus: Global food security and food for thought", "Identifying a knowledge gap in the field of study" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
