Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:333
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use srikarvar/multilingual-e5-small-cogcache-contrastive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use srikarvar/multilingual-e5-small-cogcache-contrastive with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("srikarvar/multilingual-e5-small-cogcache-contrastive") sentences = [ "What is the capital of Canada?", "Main ingredient in guacamole", "Prime Minister of the United Kingdom", "What is the capital of Australia?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6803d80dfd27b26211a967d9e4a230d7caffa8564ff4adb8f96610825c9b6c92
- Size of remote file:
- 17.1 MB
- SHA256:
- ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.