Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:604740
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/NewMiniLM-V26Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/NewMiniLM-V26Data-256ConstantBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/NewMiniLM-V26Data-256ConstantBATCH-SemanticEngine") sentences = [ "casa chandelier", "new eleganza - 6-999-x", "casa chandelier", "chandlier" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 162081291b0e20e47ebc9cb225e6f2088b28daf4d01ace788eff4bf1bd1232e1
- Size of remote file:
- 1.06 kB
- SHA256:
- df6caf2fd89c2c4b41807a7ee2314f12f5a4bb17cae0b4169d57e3a77df30eea
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