Sentence Similarity
sentence-transformers
PyTorch
bert
feature-extraction
mitre_ttps
security
adversarial-threat-annotation
text-embeddings-inference
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
- Xet hash:
- 03e694541a08e23ccb9e669b70ccae2869c9d3a47137923d4efcb61bce107ac6
- Size of remote file:
- 70.8 MB
- SHA256:
- 1e34c47c63ebb63e7c36a489488747244db894fd1cf8d1cafbd3ef73af88f0be
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