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metarank
/
ce-esci-MiniLM-L6-v2

Sentence Similarity
sentence-transformers
PyTorch
ONNX
bert
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use metarank/ce-esci-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use metarank/ce-esci-MiniLM-L6-v2 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("metarank/ce-esci-MiniLM-L6-v2")
    
    sentences = [
        "That is a happy person",
        "That is a happy dog",
        "That is a very happy person",
        "Today is a sunny day"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
ce-esci-MiniLM-L6-v2
183 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
shuttie's picture
shuttie
use proper model output dim
63d7144 over 3 years ago
  • .gitattributes
    171 Bytes
    initial commit over 3 years ago
  • .gitignore
    10 Bytes
    initial commit over 3 years ago
  • README.md
    2.27 kB
    initial commit over 3 years ago
  • config.json
    852 Bytes
    initial commit over 3 years ago
  • finetune.py
    3.38 kB
    initial commit over 3 years ago
  • onnx_convert.py
    797 Bytes
    use proper model output dim over 3 years ago
  • pytorch_model.bin
    90.9 MB
    xet
    initial commit over 3 years ago
  • pytorch_model.onnx
    91 MB
    xet
    use proper model output dim over 3 years ago
  • requirements.txt
    79 Bytes
    initial commit over 3 years ago
  • special_tokens_map.json
    125 Bytes
    initial commit over 3 years ago
  • tokenizer.json
    712 kB
    initial commit over 3 years ago
  • tokenizer_config.json
    515 Bytes
    initial commit over 3 years ago
  • vocab.txt
    232 kB
    xet
    initial commit over 3 years ago