Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:546
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use srikarvar/multilingual-e5-small-triplet-final-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use srikarvar/multilingual-e5-small-triplet-final-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("srikarvar/multilingual-e5-small-triplet-final-1") sentences = [ "How to cook a turkey?", "How to make a turkey sandwich?", "World's biggest desert by area", "Steps to roast a turkey" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b03b9e079abc849bdd27d0942fa6a77f9e7836db188512be97e4b3d52f415a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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