Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:17093
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use armaniii/all-mpnet-base-v2-augmentation-indomain-bm25-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use armaniii/all-mpnet-base-v2-augmentation-indomain-bm25-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("armaniii/all-mpnet-base-v2-augmentation-indomain-bm25-sts") sentences = [ "In the realm of genetics , it is far better to be safe than sorry .", "Marijuana use harms the brain, and legalization will increase mental health problems.", "We are god now !", "Likewise , the proposal that addictive drugs should be legalized , regulated and opened to \" free market dynamics \" is immediately belied by the recognition that the drug market for an addict is no longer a free market – it is clear that they will pay any price when needing their drug ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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