Improve model card: Add pipeline tag, paper, project, code links, and descriptive tags
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nielsr
HF Staff
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README.md
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library_name: transformers
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license: apache-2.0
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base_model: distilbert-base-uncased
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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: vulnerability-severity-classification-distilbert-base-uncased
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results: []
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datasets:
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- CIRCL/vulnerability-scores
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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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# vulnerability-severity-classification-distilbert-base-uncased
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the dataset [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores).
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It achieves the following results on the evaluation set:
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- Loss: 0.6447
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- Accuracy: 0.7595
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base_model: distilbert-base-uncased
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datasets:
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- CIRCL/vulnerability-scores
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library_name: transformers
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license: apache-2.0
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metrics:
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- accuracy
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pipeline_tag: text-classification
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tags:
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- generated_from_trainer
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- security
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- vulnerability
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- classification
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- distilbert
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model-index:
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- name: vulnerability-severity-classification-distilbert-base-uncased
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results: []
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---
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# vulnerability-severity-classification-distilbert-base-uncased
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the dataset [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores).
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This model is part of the work presented in the paper [VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification](https://huggingface.co/papers/2507.03607).
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**Project Page**: [https://vulnerability.circl.lu](https://vulnerability.circl.lu)
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**Code Repository**: [https://github.com/vulnerability-lookup/ML-Gateway](https://github.com/vulnerability-lookup/ML-Gateway)
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It achieves the following results on the evaluation set:
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- Loss: 0.6447
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- Accuracy: 0.7595
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