this model is derrived from
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/stsb-roberta-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
You need to have ibm-fms installed:
https://github.com/foundation-model-stack/foundation-model-stack/issues/169
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from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaelfeil/stsb-roberta-base-v2", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]