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LamaDiab
/
MiniLM-v2-v29-SemanticEngine

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:799002
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use LamaDiab/MiniLM-v2-v29-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use LamaDiab/MiniLM-v2-v29-SemanticEngine with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("LamaDiab/MiniLM-v2-v29-SemanticEngine")
    
    sentences = [
        "pappardelle beef ragu",
        "chocolate molten",
        "slow-cooked pasta",
        "pasta",
        "pasta"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [5, 5]
  • Notebooks
  • Google Colab
  • Kaggle
MiniLM-v2-v29-SemanticEngine / eval
421 Bytes
Ctrl+K
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  • 1 contributor
History: 3 commits
LamaDiab's picture
LamaDiab
Training in progress, epoch 2
cfdfe43 verified 7 months ago
  • triplet_evaluation_results.csv
    421 Bytes
    Training in progress, epoch 2 7 months ago