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This modelcard aims to be a base model for cybersecurity Tasks.

Model Details

Model Description

  • Developed by: Markus Bayer, Philipp Kuehn, Ramin Shanehsaz, and Christian Reuter
  • Model type: BERT-base
  • Language(s) (NLP): English
  • Finetuned from model: bert-base-uncased.

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Uses

Direct Use

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Downstream Use [optional]

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure [optional]

Preprocessing

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Speeds, Sizes, Times

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

@misc{https://doi.org/10.48550/arxiv.2212.02974, doi = {10.48550/ARXIV.2212.02974},

url = {https://arxiv.org/abs/2212.02974},

author = {Bayer, Markus and Kuehn, Philipp and Shanehsaz, Ramin and Reuter, Christian},

keywords = {Cryptography and Security (cs.CR), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},

title = {CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain},

publisher = {arXiv},

year = {2022},

copyright = {arXiv.org perpetual, non-exclusive license} }

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