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  ---
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  library_name: transformers
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- license: mit
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  base_model: roberta-base
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  tags:
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  - generated_from_trainer
 
 
 
 
 
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  metrics:
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  - accuracy
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  model-index:
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  - name: cwe-parent-vulnerability-classification-roberta-base
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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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- # cwe-parent-vulnerability-classification-roberta-base
 
 
 
 
 
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- This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 1.7510
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  - Accuracy: 0.5455
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  - F1 Macro: 0.3776
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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-
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  ## Training procedure
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  ### Training hyperparameters
 
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  ---
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  library_name: transformers
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+ license: cc-by-4.0
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  base_model: roberta-base
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  tags:
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  - generated_from_trainer
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+ - text-classification
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+ - classification
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+ - nlp
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+ - vulnerability
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+ - CWE
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  metrics:
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  - accuracy
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  model-index:
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  - name: cwe-parent-vulnerability-classification-roberta-base
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  results: []
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+ datasets:
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+ - CIRCL/vulnerability-cwe-patch
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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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+ # CWE guessing
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+
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the
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+ [CIRCL/vulnerability-cwe-patch](https://huggingface.co/datasets/CIRCL/vulnerability-cwe-patch) dataset.
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+
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+ The goal is to predict CWE categories from Git commit messages and vulnerability descriptions. Predicted child CWEs are mapped to their parent CWEs if applicable.
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  It achieves the following results on the evaluation set:
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  - Loss: 1.7510
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  - Accuracy: 0.5455
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  - F1 Macro: 0.3776
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  ## Training procedure
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  ### Training hyperparameters