Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:12001
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use kttt294/english-sbert-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kttt294/english-sbert-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kttt294/english-sbert-finetuned") sentences = [ "The country's president and his deputies urged security forces to intensify their efforts to secure the country.", "Since January he has been on loan with his first club, Fluminense .", "Plan is to eventually simulate an entire Neanderthal sentence .", "Parked car bomb in same area of Baghdad kills at least 9 people on Tuesday ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K