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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: security-bert256-50k |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# CTI-BERT |
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CTI-BERT is a pre-trained language model for the cybersecurity domain. |
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The model was trained on a large corpus of security-related text data, comprising approximately 1.2 billion tokens sourced from |
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a diverse range of sources, including security news articles, vulnerability descriptions, books, academic publications, and security-related Wikipedia pages. |
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For additional technical details and the model's performance metrics, please refer to [this paper](https://aclanthology.org/2023.emnlp-industry.12.pdf). |
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## Model description |
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This model has a vocabulary of 50,000 tokens and the sequence length of 256. |
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Both the tokenizer and the BERT model were trained from scratch using the [run_mlm script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) |
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with the Masked language modeling (MLM) objective. |
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## Intended uses & limitations |
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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 |
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sequence classification, text classification or question answering. |
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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. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 2048 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 10000 |
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- training_steps: 200000 |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.12.1+cu102 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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