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
| { | |
| "modes": [ | |
| "fp16", | |
| "q8", | |
| "int8", | |
| "uint8", | |
| "q4", | |
| "q4f16", | |
| "bnb4" | |
| ], | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "block_size": null, | |
| "is_symmetric": true, | |
| "accuracy_level": null, | |
| "quant_type": 1, | |
| "op_block_list": null | |
| } |