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--- 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.

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The **SecureBERT 2.0 Bi-Encoder** is a cybersecurity-domain sentence-similarity and document-embedding model fine-tuned from SecureBERT 2.0. It independently encodes queries and documents into a shared vector space for **semantic search**, **information retrieval**, and **cybersecurity knowledge retrieval**. The Bi-Encoder encodes queries and documents **independently** into a joint vector space. This architecture enables scalable **approximate nearest-neighbor search** for candidate retrieval and semantic ranking. The model was optimized to maximize semantic similarity between relevant cybersecurity text pairs using contrastive learning.

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The **SecureBERT 2.0 Cross-Encoder** is a cybersecurity domain-specific model fine-tuned from SecureBERT 2.0. It computes **pairwise similarity scores** between two texts, enabling use in **text reranking, semantic search, and cybersecurity intelligence retrieval** tasks. Can be integrated into: - Cyber threat intelligence search engines - SOC automation pipelines - Cybersecurity knowledge graph enrichment - Threat hunting and incident response systems The model reflects the distribution of cybersecurity-related data used during fine-tuning. Potential risks include: - Overrepresentation of specific malware, technologies, or threat actors - Bias toward technical English sources - Reduced performance on non-English or mixed technical/natural text

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