Sentence Similarity
sentence-transformers
PyTorch
bert
feature-extraction
mitre_ttps
security
adversarial-threat-annotation
text-embeddings-inference
Instructions to use QCRI/SentSecBert_10k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use QCRI/SentSecBert_10k with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("QCRI/SentSecBert_10k") 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
- Xet hash:
- 957f750358f8c60d149eb797366bfac15455ddce8868aba0f725503b123f3f4a
- Size of remote file:
- 334 MB
- SHA256:
- e80a78608b1bb1beb2ce90bf8ce47c5c3afc94d200ff561210f591709404510e
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