AI is happening. The future is updating.SecureBERT2.0-base
--- license: apache-2.0 language: - en tags: - fill-mask - transformers - safetensors - modernbert - cybersecurity - CiscoAI - code - CTI datasets: - custom library_name: transformers pipeline_tag: fill-mask model-index: - name: SecureBERT2.0-base results: [] base_model: - answerdotai/ModernBERT-base --- SecureBERT 2.0 Base is a domain-specific transformer model optimized for cybersecurity tasks. It extends the ModernBERT architecture with cybersecurity-focused pretraining to produce contextualized embeddings for both technical text and code. SecureBERT 2.0 supports tasks like masked language modeling, semantic search, named entity recognition, vulnerability detection, and code analysis. SecureBERT 2.0 Base is designed for **deep contextual understanding of cybersecurity language and code**. It leverages domain-specific pretraining on a large, heterogeneous corpus covering threat reports, blogs, documentation, and codebases, making it effective for reasoning across natural language and programming syntax.
--- license: apache-2.0 language: - en tags: - fill-mask - transformers - safetensors - modernbert - cybersecurity - CiscoAI - code - CTI datasets: - custom library_name: transformers pipeline_tag: fill-mask model-index: - name: SecureBERT2.0-base results: [] base_model: - answerdotai/ModernBERT-base --- SecureBERT 2.0 Base is a domain-specific transformer model optimized for cybersecurity tasks. It extends the ModernBERT architecture with cybersecurity-focused pretraining to produce contextualized embeddings for both technical text and code. SecureBERT 2.0 supports tasks like masked language modeling, semantic search, named entity recognition, vulnerability detection, and code analysis. SecureBERT 2.0 Base is designed for **deep contextual understanding of cybersecurity language and code**. It leverages domain-specific pretraining on a large, heterogeneous corpus covering threat reports, blogs, documentation, and codebases, making it effective for reasoning across natural language and programming syntax.