This is a
sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model was built using
recobo/agriculture-bert-uncased, which is a BERT model trained on 6.5 million passages from the agricultural domain. Hence, this model is expected to perform well on sentence similarity tasks specifically for agricultural text data.
Using this model becomes easy when you have
sentence-transformers installed:
1from sentence_transformers import SentenceTransformer
2sentences = ["A man is eating food.", "A man is eating a piece of bread"]
3
4model = SentenceTransformer('recobo/agri-sentence-transformer')
5embeddings = model.encode(sentences)
6print(embeddings)
