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--- |
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metadata: |
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name: Canstralian |
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tags: |
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- cybersecurity |
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- penetration-testing |
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- red-team |
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- ai |
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- offensive-security |
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- threat-detection |
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- code-generation |
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license: MIT |
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model_index: |
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model_name: RedTeamAI |
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model_description: > |
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AI-powered model designed for penetration testing and security automation, |
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focused on detecting and analyzing known cybersecurity exploits. |
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model_type: text-classification |
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language: English |
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framework: PyTorch |
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pipeline_tag: text-classification |
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sdk: transformers |
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results: |
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task: text-classification |
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dataset: PenTest-2024 (custom) |
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metrics: |
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accuracy: 92.5 |
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precision: 89.3 |
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recall: 91.8 |
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f1_score: 90.5 |
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source: Internal Benchmark |
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license: mit |
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language: |
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- en |
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tags: |
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- ai |
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- offensive-security |
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- code-generation |
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datasets: |
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- 0dAI/PentestingCommandLogic |
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- royweiss1/GPT_Keylogger_Dataset |
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- allenai/real-toxicity-prompts |
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- HuggingFaceH4/instruction-dataset |
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- togethercomputer/RedPajama-Data-1T |
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- Canstralian/CyberExploitDB |
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metrics: |
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- code_eval |
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base_model: |
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- replit/replit-code-v1_5-3b |
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- microsoft/OmniParser |
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library_name: transformers |
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--- |
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# Model Card for Model ID |
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). |
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## Model Details |
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### Model Description |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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## Training Details |
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### Results |
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#### Summary |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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