Sentence Similarity
sentence-transformers
ONNX
Safetensors
feature-extraction
Generated from Trainer
dataset_size:62698210
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
Instructions to use sentence-transformers/static-similarity-mrl-multilingual-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/static-similarity-mrl-multilingual-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/static-similarity-mrl-multilingual-v1") sentences = [ "A man is jumping unto his filthy bed.", "A man is ouside near the beach.", "The bed is dirty.", "The man is on the moon." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8b9ad007da6cf9567b1c1675e837d6b92cc45eaac25ee5e8ce3a9a3f009afa6
- Size of remote file:
- 217 MB
- SHA256:
- 808eb814d651b2839391bd0c66502f8e60b591ece028a9ece9b739b5918dbc28
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.