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QCRI
/
SentSecBert_10k_AllDataSplit

Sentence Similarity
sentence-transformers
PyTorch
bert
feature-extraction
mitre_ttps
security
adversarial-threat-annotation
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use QCRI/SentSecBert_10k_AllDataSplit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use QCRI/SentSecBert_10k_AllDataSplit with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("QCRI/SentSecBert_10k_AllDataSplit")
    
    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
SentSecBert_10k_AllDataSplit / eval
4.88 kB
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  • 1 contributor
History: 1 commit
lekssays's picture
lekssays
Upload folder using huggingface_hub
014188d verified almost 2 years ago
  • similarity_evaluation_results.csv
    4.88 kB
    Upload folder using huggingface_hub almost 2 years ago