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README.md

@@ -1,69 +1,50 @@
 ---
 library_name: transformers
-license: cc-by-4.0
+license: mit
 base_model: roberta-base
-metrics:
-- accuracy
 tags:
 - generated_from_trainer
-- text-classification
-- classification
-- nlp
-- vulnerability
+metrics:
+- accuracy
 model-index:
 - name: vulnerability-severity-classification-roberta-base
   results: []
-datasets:
-- CIRCL/vulnerability-scores
 ---
 
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-# VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
-
-# Severity classification
-
-This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the dataset [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores).
-
-The model was presented in the paper [VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification](https://huggingface.co/papers/2507.03607) [[arXiv](https://arxiv.org/abs/2507.03607)].
-
-**Abstract:** VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.
-
-You can read [this page](https://www.vulnerability-lookup.org/user-manual/ai/) for more information.
+# vulnerability-severity-classification-roberta-base
 
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.0444
+- Accuracy: 0.8151
+- F1 Macro: 0.7468
+- Low Precision: 0.6636
+- Low Recall: 0.5090
+- Low F1: 0.5761
+- Medium Precision: 0.8444
+- Medium Recall: 0.8694
+- Medium F1: 0.8567
+- High Precision: 0.8099
+- High Recall: 0.8094
+- High F1: 0.8097
+- Critical Precision: 0.7554
+- Critical Recall: 0.7342
+- Critical F1: 0.7446
 
 ## Model description
 
-It is a classification model and is aimed to assist in classifying vulnerabilities by severity based on their descriptions.
-
-## How to get started with the model
-
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
+More information needed
 
-labels = ["low", "medium", "high", "critical"]
+## Intended uses & limitations
 
-model_name = "CIRCL/vulnerability-severity-classification-roberta-base"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-model.eval()
+More information needed
 
-print("Model revision:", model.config._commit_hash)
+## Training and evaluation data
 
-test_description = "SAP NetWeaver Visual Composer Metadata Uploader is not protected with a proper authorization, allowing unauthenticated agent to upload potentially malicious executable binaries \
-that could severely harm the host system. This could significantly affect the confidentiality, integrity, and availability of the targeted system."
-inputs = tokenizer(test_description, return_tensors="pt", truncation=True, padding=True)
-
-# Run inference
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-
-# Print results
-print("Predictions:", predictions)
-predicted_class = torch.argmax(predictions, dim=-1).item()
-print("Predicted severity:", labels[predicted_class])
-```
+More information needed
 
 ## Training procedure
 
@@ -78,32 +59,15 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5
 
-It achieves the following results on the evaluation set:
-- Loss: 2.0385
-- Accuracy: 0.8186
-- F1 Macro: 0.7478
-- Low Precision: 0.6754
-- Low Recall: 0.4833
-- Low F1: 0.5635
-- Medium Precision: 0.8502
-- Medium Recall: 0.8699
-- Medium F1: 0.8600
-- High Precision: 0.8100
-- High Recall: 0.8134
-- High F1: 0.8117
-- Critical Precision: 0.7563
-- Critical Recall: 0.7558
-- Critical F1: 0.7560
-
 ### Training results
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1 Macro | Low Precision | Low Recall | Low F1 | Medium Precision | Medium Recall | Medium F1 | High Precision | High Recall | High F1 | Critical Precision | Critical Recall | Critical F1 |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------:|:-------:|:------------------:|:---------------:|:-----------:|
-| 2.5276        | 1.0   | 16588 | 2.5819          | 0.7385   | 0.6339   | 0.6151        | 0.2946     | 0.3984 | 0.7507           | 0.8666        | 0.8045    | 0.7470         | 0.6814      | 0.7127  | 0.6711             | 0.5762          | 0.6201      |
-| 2.2893        | 2.0   | 33176 | 2.3169          | 0.7685   | 0.6574   | 0.7390        | 0.2577     | 0.3822 | 0.7870           | 0.8658        | 0.8245    | 0.7531         | 0.7560      | 0.7545  | 0.7351             | 0.6127          | 0.6683      |
-| 1.8541        | 3.0   | 49764 | 2.1505          | 0.7915   | 0.7062   | 0.6939        | 0.3762     | 0.4879 | 0.8098           | 0.8751        | 0.8412    | 0.7994         | 0.7561      | 0.7772  | 0.7122             | 0.7251          | 0.7186      |
-| 1.7567        | 4.0   | 66352 | 2.0204          | 0.8098   | 0.7325   | 0.6776        | 0.4461     | 0.5380 | 0.8347           | 0.8721        | 0.8530    | 0.7962         | 0.8100      | 0.8030  | 0.7790             | 0.6977          | 0.7361      |
-| 1.6384        | 5.0   | 82940 | 2.0385          | 0.8186   | 0.7478   | 0.6754        | 0.4833     | 0.5635 | 0.8502           | 0.8699        | 0.8600    | 0.8100         | 0.8134      | 0.8117  | 0.7563             | 0.7558          | 0.7560      |
+| 2.7608        | 1.0   | 16685 | 2.5243          | 0.7411   | 0.6299   | 0.6253        | 0.2810     | 0.3877 | 0.7719           | 0.8474        | 0.8079    | 0.7165         | 0.7320      | 0.7242  | 0.6957             | 0.5270          | 0.5997      |
+| 2.3464        | 2.0   | 33370 | 2.3441          | 0.7670   | 0.6837   | 0.5518        | 0.4400     | 0.4896 | 0.8160           | 0.8279        | 0.8219    | 0.7451         | 0.7738      | 0.7591  | 0.7014             | 0.6306          | 0.6642      |
+| 1.7701        | 3.0   | 50055 | 2.1916          | 0.7895   | 0.7116   | 0.6944        | 0.4197     | 0.5232 | 0.8338           | 0.8397        | 0.8367    | 0.7599         | 0.8059      | 0.7822  | 0.7311             | 0.6793          | 0.7043      |
+| 1.8485        | 4.0   | 66740 | 2.0622          | 0.8059   | 0.7348   | 0.6377        | 0.5008     | 0.5610 | 0.8259           | 0.8765        | 0.8504    | 0.8062         | 0.7891      | 0.7976  | 0.7674             | 0.6964          | 0.7302      |
+| 1.4786        | 5.0   | 83425 | 2.0444          | 0.8151   | 0.7468   | 0.6636        | 0.5090     | 0.5761 | 0.8444           | 0.8694        | 0.8567    | 0.8099         | 0.8094      | 0.8097  | 0.7554             | 0.7342          | 0.7446      |
 
 
 ### Framework versions

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7634d428e910dcbaf1e052437b7e5ade0adf526018d53d906597228ca3560e84
+oid sha256:b83d92aab85a78469a909f52b19999ff0d89b66bcf87f75be81d042674ee75ee
 size 498618976