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LamaDiab
/
MiniLM-SemanticEngine

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:169967
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

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

  • Libraries
  • sentence-transformers

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

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("LamaDiab/MiniLM-SemanticEngine")
    
    sentences = [
        "blue dianne",
        "soap",
        "maximize the freshness of your food for 12 hours with the blue dianne thermal bag. its triple compartments, spacious storage, heat resistance, and 100% leakproof design will keep it fresh. this bpa-free and pvc-free bag is also 100% non-toxic and comes with a 3-month guarantee. ideal for everyday food storage.",
        "trolley backpack coral high colors 17 l 3 zippers 23977"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
MiniLM-SemanticEngine / eval
Ctrl+K
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  • 1 contributor
History: 5 commits
LamaDiab's picture
LamaDiab
Training in progress, epoch 5
9a04d28 verified 6 months ago
  • triplet_evaluation_results.csv
    1.17 kB
    Training in progress, epoch 5 6 months ago