Sentence Similarity
sentence-transformers
ONNX
Safetensors
bert
feature-extraction
Generated from Trainer
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Mozilla/smart-tab-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Mozilla/smart-tab-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Mozilla/smart-tab-embedding") sentences = [ "Oracle Cloud - Infrastructure and Platform Services for Enterprises", "PulseAudio - Ubuntu Wiki", "Documentation page not found - Read the Docs", "Dwarf Fortress beginner tips - Video Games on Sports Illustrated" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Download onnx/model.onnx from Mozilla/smart-tab-embedding: direct link, hf CLI and curl.
- Browser
- Download file 90.4 MB
-
https://huggingface.co/Mozilla/smart-tab-embedding/resolve/main/onnx/model.onnx
- Command line
-
hf download hf://Mozilla/smart-tab-embedding/onnx/model.onnx
-
curl -L -o model.onnx https://huggingface.co/Mozilla/smart-tab-embedding/resolve/main/onnx/model.onnx
90.4 MB
- Xet hash:
- 50643363e68fac1f43db8e28e44344ac873a853ce189605c94e25e9e5d019a7e
- Size of remote file:
- 90.4 MB
- SHA256:
- 0a2964c169dd7b5c7505025e67622937568a5af7d640cab9f96cfbfbd1da180f
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