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LamaDiab
/
MiniLM-V6Data-SemanticEngine

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:291522
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use LamaDiab/MiniLM-V6Data-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use LamaDiab/MiniLM-V6Data-SemanticEngine with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("LamaDiab/MiniLM-V6Data-SemanticEngine")
    
    sentences = [
        "cream 21 baby oil with almond oil",
        "hi, barbie! bundle",
        "nourishing baby oil",
        "material: wooden. size: 15 x 30 cm."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
MiniLM-V6Data-SemanticEngine / 1_Pooling
Ctrl+K
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  • 1 contributor
History: 1 commit
LamaDiab's picture
LamaDiab
Updating model weights
c138d35 verified 6 months ago
  • config.json
    312 Bytes
    Updating model weights 6 months ago