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:
- ea0d08aed61ef8e32c4fdab3804ddb60fb8e0922e37a768a48a7eee0e035be2f
- Size of remote file:
- 29.5 MB
- SHA256:
- fcddac2e3086d6ad4880595b150e3b42f2ce4cafe7248434be06f90683ed6ed1
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