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This is a
sentence-transformers
model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This is a model used in our work "Semantic Ranking for Automated Adversarial Technique Annotation in Security Text". The code is available at:
https://github.com/qcri/Text2TTP
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('SentSecBert')
embeddings = model.encode(sentences)
print(embeddings)
Citation
@article{kumarasinghe2024semantic,
title={Semantic Ranking for Automated Adversarial Technique Annotation in Security Text},
author={Kumarasinghe, Udesh and Lekssays, Ahmed and Sencar, Husrev Taha and Boughorbel, Sabri and Elvitigala, Charitha and Nakov, Preslav},
journal={arXiv preprint arXiv:2403.17068},
year={2024}
}
Runs of QCRI SentSecBert_10k_AllDataSplit on huggingface.co
7
Total runs
0
24-hour runs
0
3-day runs
2
7-day runs
6
30-day runs
More Information About SentSecBert_10k_AllDataSplit huggingface.co Model
SentSecBert_10k_AllDataSplit huggingface.co
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SentSecBert_10k_AllDataSplit huggingface.co is an online trial and call api platform, which integrates SentSecBert_10k_AllDataSplit's modeling effects, including api services, and provides a free online trial of SentSecBert_10k_AllDataSplit, you can try SentSecBert_10k_AllDataSplit online for free by clicking the link below.
QCRI SentSecBert_10k_AllDataSplit online free url in huggingface.co:
SentSecBert_10k_AllDataSplit is an open source model from GitHub that offers a free installation service, and any user can find SentSecBert_10k_AllDataSplit on GitHub to install. At the same time, huggingface.co provides the effect of SentSecBert_10k_AllDataSplit install, users can directly use SentSecBert_10k_AllDataSplit installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
SentSecBert_10k_AllDataSplit install url in huggingface.co: