CTI-BERT
CTI-BERT is a pre-trained language model for the cybersecurity domain.
The model was trained on a large corpus of security-related text data, comprising approximately 1.2 billion tokens sourced from
a diverse range of sources, including security news articles, vulnerability descriptions, books, academic publications, and security-related Wikipedia pages.
For additional technical details and the model's performance metrics, please refer to
this paper.
Model description
This model has a vocabulary of 50,000 tokens and the sequence length of 256.
Both the tokenizer and the BERT model were trained from scratch using the
run_mlm script
with the Masked language modeling (MLM) objective.
Intended uses & limitations
You can use the model for masked language modeling or token embedding generation, but the model is aimed at being fine-tuned on a downstream task, such as
sequence classification, text classification or question answering.
The model has shown improved performance for various cybersecurity text classification. However, it is not designed to be used as the main model for general-domain text.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 200000
Framework versions
- Transformers 4.18.0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
