Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
dense
Generated from Trainer
dataset_size:90000
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/mpnet-base-gooaq-cmnrl-1024bs-lmk-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/mpnet-base-gooaq-cmnrl-1024bs-lmk-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/mpnet-base-gooaq-cmnrl-1024bs-lmk-v3") sentences = [ "what is the difference between trojan virus and worm?", "Worms spread from computer to computer, but unlike a virus, it has the capability to travel without any help from a person. ... A Trojan horse is not a virus. It is a destructive program that looks as a genuine application. Unlike viruses, Trojan horses do not replicate themselves but they can be just as destructive.", "You're usually no longer infectious 24 hours after starting a course of antibiotics, but this time period can sometimes vary. For example, the antibiotics may take longer to work if your body takes longer to absorb them, or if you're taking other medicine that interacts with the antibiotics.", "Eating salt raises the amount of sodium in your bloodstream and wrecks the delicate balance, reducing the ability of your kidneys to remove the water. The result is a higher blood pressure due to the extra fluid and extra strain on the delicate blood vessels leading to the kidneys." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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