End of training

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: 1.9981
+- Accuracy: 0.8188
+- F1 Macro: 0.7502
+- Low Precision: 0.6490
+- Low Recall: 0.5113
+- Low F1: 0.572
+- Medium Precision: 0.8488
+- Medium Recall: 0.8681
+- Medium F1: 0.8584
+- High Precision: 0.8148
+- High Recall: 0.8129
+- High F1: 0.8138
+- Critical Precision: 0.7592
+- Critical Recall: 0.7543
+- Critical F1: 0.7567
 
 ## 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
 
@@ -78,37 +59,20 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5
 
-It achieves the following results on the evaluation set:
-- Loss: 2.0444
-- 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
-
 ### 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 |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------:|:-------:|:------------------:|:---------------:|:-----------:|
-| 2.7608        | 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      |
+| 2.9393        | 1.0   | 16760 | 2.5264          | 0.7363   | 0.6358   | 0.5562        | 0.3500     | 0.4297 | 0.7994           | 0.7985        | 0.7990    | 0.6842         | 0.7790      | 0.7286  | 0.7063             | 0.5008          | 0.5861      |
+| 2.3025        | 2.0   | 33520 | 2.2776          | 0.7710   | 0.6785   | 0.6646        | 0.3369     | 0.4471 | 0.7978           | 0.8607        | 0.8280    | 0.7771         | 0.7286      | 0.7520  | 0.6673             | 0.7073          | 0.6867      |
+| 1.7004        | 3.0   | 50280 | 2.1303          | 0.7911   | 0.7164   | 0.5839        | 0.4895     | 0.5325 | 0.8184           | 0.8623        | 0.8398    | 0.7968         | 0.7644      | 0.7803  | 0.7224             | 0.7041          | 0.7131      |
+| 1.3977        | 4.0   | 67040 | 2.0143          | 0.8087   | 0.7335   | 0.6898        | 0.4418     | 0.5386 | 0.8412           | 0.8622        | 0.8516    | 0.7999         | 0.8029      | 0.8014  | 0.7357             | 0.7489          | 0.7422      |
+| 1.5018        | 5.0   | 83800 | 1.9981          | 0.8188   | 0.7502   | 0.6490        | 0.5113     | 0.572  | 0.8488           | 0.8681        | 0.8584    | 0.8148         | 0.8129      | 0.8138  | 0.7592             | 0.7543          | 0.7567      |
 
 
 ### Framework versions
 
-- Transformers 5.9.0
+- Transformers 5.10.2
 - Pytorch 2.12.0+cu130
 - Datasets 4.8.5
 - Tokenizers 0.22.2

config.json

@@ -34,7 +34,7 @@
   "pad_token_id": 1,
   "problem_type": "single_label_classification",
   "tie_word_embeddings": true,
-  "transformers_version": "5.9.0",
+  "transformers_version": "5.10.2",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 50265

model.safetensors

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 size 498618976

training_args.bin

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