Feature Extraction
sentence-transformers
PyTorch
English
Vietnamese
xlm-roberta
embedding
dense-retrieval
contrastive-learning
cve
cybersecurity
qdrant
secAI
text-embeddings-inference
Instructions to use DuyTa/sec-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DuyTa/sec-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DuyTa/sec-embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4702ee9935e9f7269c6ea843b54e61fb68515ac2b383365d9fdc2018ebaa34d9
- Size of remote file:
- 17.1 MB
- SHA256:
- 21106b6d7dab2952c1d496fb21d5dc9db75c28ed361a05f5020bbba27810dd08
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