Sentence Similarity
sentence-transformers
Safetensors
English
bert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:9233417
loss:ArcFaceInBatchLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-experimental with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-experimental") sentences = [ "Hayley Vaughan portrayed Ripa on the ABC daytime soap opera , `` All My Children `` , between 1990 and 2002 .", "Traxxpad is a music application for Sony 's PlayStation Portable published by Definitive Studios and developed by Eidos Interactive .", "Between 1990 and 2002 , Hayley Vaughan Ripa portrayed in the ABC soap opera `` All My Children `` .", "Between 1990 and 2002 , Ripa Hayley portrayed Vaughan in the ABC soap opera `` All My Children `` ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "5.1.0", | |
| "transformers": "4.56.0", | |
| "pytorch": "2.8.0+cu128" | |
| }, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |