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.

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:

from sentence_transformers import SentenceTransformer
sentences = ["A man is eating food.", "A man is eating a piece of bread"]

model = SentenceTransformer('recobo/agri-sentence-transformer')
embeddings = model.encode(sentences)
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This model can be loaded on the Inference API on-demand.