Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

LamaDiab
/
v3MiniLM-V18Data-256ConstantBATCH-SemanticEngine

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:705905
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use LamaDiab/v3MiniLM-V18Data-256ConstantBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use LamaDiab/v3MiniLM-V18Data-256ConstantBATCH-SemanticEngine with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("LamaDiab/v3MiniLM-V18Data-256ConstantBATCH-SemanticEngine")
    
    sentences = [
        "gerber baby food fruits apples bananas & cereal",
        "world of sweets puzzle",
        "baby food",
        "baby food"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
v3MiniLM-V18Data-256ConstantBATCH-SemanticEngine / checkpoint-2758 /1_Pooling
312 Bytes
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
LamaDiab's picture
LamaDiab
Training in progress, epoch 1, checkpoint
c36b686 verified 6 months ago
  • config.json
    312 Bytes
    Training in progress, epoch 1, checkpoint 6 months ago