Instructions to use s2w-ai/CyBERTuned-SecurityLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s2w-ai/CyBERTuned-SecurityLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="s2w-ai/CyBERTuned-SecurityLLM")# Load model directly from transformers import AutoTokenizer, RobertaForTokClassAndMaskedLM tokenizer = AutoTokenizer.from_pretrained("s2w-ai/CyBERTuned-SecurityLLM") model = RobertaForTokClassAndMaskedLM.from_pretrained("s2w-ai/CyBERTuned-SecurityLLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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### Training hyperparameters
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The following hyperparameters were used during training:
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# Citation
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If you're using CyBERTuned please cite the following paper:
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Eugene Jang, Jian Cui, Dayeon Yim, Youngjin Jin, Jin-Woo Chung, Seungwon Shin, and Yongjae Lee. 2024. Ignore Me But Don’t Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 29–42, Mexico City, Mexico. Association for Computational Linguistics.
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### Training hyperparameters
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The following hyperparameters were used during training:
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