Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:48393
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use richie-ghost/sentence-transformers-all-mpnet-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use richie-ghost/sentence-transformers-all-mpnet-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("richie-ghost/sentence-transformers-all-mpnet-base-v2") sentences = [ "Tennis champ Rafael Nadal lunges to return a ball.", "The tennis champ has decided to quit playing tennis.", "A woman stands alone at a restaurant.", "A blond woman running" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K