Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:985575
loss:CosineSimilarityTripleEncoderLoss
loss:ContrastiveLoss
text-embeddings-inference
Instructions to use abhiraj1/eval_triple_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use abhiraj1/eval_triple_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("abhiraj1/eval_triple_encoder") sentences = [ "A small white and tan dog licking up peanut butter.", "Someone is making dinner in the kitchen.", "Someone put peanut butter on the dog's nose because that's always good for a laugh.", "Two dogs are eating food from a bowl in a kitchen" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K