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SentenceTransformer based on microsoft/mpnet-base

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Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'This form of necrosis, also termed necroptosis, requires the activity of receptor-interacting protein kinase 1 (RIP1) and its related kinase, RIP3 ',
    'TNF-mediated programmed necrosis typically involves the receptor-interacting serine-threonine kinases 1 and 3 (RIP1 and RIP3), as evidenced in human, mouse, and zebrafish cell lines, as well as in a murine sepsis model',
    'This large-scale study showed that IDH1/IDH2 mutations were mutually exclusive with inactivating TET2 mutations, suggesting that the two types of mutations had similar effects and were thus functionally redundant.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
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