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:
- 09ebf850310d7d0391878656b7fec9788b48bdac12a43fcdcdc46dc6a608ae26
- Size of remote file:
- 90.9 MB
- SHA256:
- 9cf6e2c69c36de75ad67d37b89b1ad3ff7f7b29b2c226327f9bc58b840b67f9b
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