End of training

README.md

@@ -1,67 +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: 2.0132
+- Accuracy: 0.8191
+- F1 Macro: 0.7488
+- Low Precision: 0.6601
+- Low Recall: 0.5006
+- Low F1: 0.5694
+- Medium Precision: 0.8440
+- Medium Recall: 0.8767
+- Medium F1: 0.8601
+- High Precision: 0.8195
+- High Recall: 0.8112
+- High F1: 0.8153
+- Critical Precision: 0.7618
+- Critical Recall: 0.7392
+- Critical F1: 0.7503
 
 ## 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
 
-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)
+## Training and evaluation data
 
-# 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
 
@@ -76,37 +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: 2.0407
-- Accuracy: 0.8185
-- F1 Macro: 0.7505
-- Low Precision: 0.6657
-- Low Recall: 0.5010
-- Low F1: 0.5718
-- Medium Precision: 0.8460
-- Medium Recall: 0.8703
-- Medium F1: 0.8580
-- High Precision: 0.8113
-- High Recall: 0.8131
-- High F1: 0.8122
-- Critical Precision: 0.7715
-- Critical Recall: 0.7487
-- Critical F1: 0.7600
-
 ### 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 |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------:|:-------:|:------------------:|:---------------:|:-----------:|
-| 2.4052        | 1.0   | 15960 | 2.5659          | 0.7361   | 0.6356   | 0.6322        | 0.2785     | 0.3867 | 0.7888           | 0.8197        | 0.8039    | 0.7427         | 0.6876      | 0.7141  | 0.5704             | 0.7236          | 0.6379      |
-| 2.4105        | 2.0   | 31920 | 2.3267          | 0.7650   | 0.6895   | 0.5665        | 0.4571     | 0.5060 | 0.7853           | 0.8612        | 0.8215    | 0.7845         | 0.7028      | 0.7414  | 0.6847             | 0.6933          | 0.6890      |
-| 1.6032        | 3.0   | 47880 | 2.1721          | 0.7914   | 0.7087   | 0.6868        | 0.3825     | 0.4913 | 0.8350           | 0.8420        | 0.8385    | 0.7696         | 0.7942      | 0.7817  | 0.7144             | 0.7325          | 0.7233      |
-| 1.6972        | 4.0   | 63840 | 2.0397          | 0.8094   | 0.7359   | 0.6748        | 0.4567     | 0.5447 | 0.8360           | 0.8704        | 0.8528    | 0.8095         | 0.7912      | 0.8003  | 0.7370             | 0.7545          | 0.7456      |
-| 1.5162        | 5.0   | 79800 | 2.0407          | 0.8185   | 0.7505   | 0.6657        | 0.5010     | 0.5718 | 0.8460           | 0.8703        | 0.8580    | 0.8113         | 0.8131      | 0.8122  | 0.7715             | 0.7487          | 0.7600      |
+| 2.3936        | 1.0   | 16180 | 2.5423          | 0.7404   | 0.6271   | 0.6925        | 0.2372     | 0.3534 | 0.7777           | 0.8359        | 0.8057    | 0.7237         | 0.7233      | 0.7235  | 0.6416             | 0.6110          | 0.6259      |
+| 2.5847        | 2.0   | 32360 | 2.2926          | 0.7674   | 0.6790   | 0.6162        | 0.3880     | 0.4762 | 0.7899           | 0.8604        | 0.8237    | 0.7640         | 0.7458      | 0.7548  | 0.7115             | 0.6175          | 0.6612      |
+| 2.0935        | 3.0   | 48540 | 2.1257          | 0.7920   | 0.7086   | 0.6727        | 0.4017     | 0.5030 | 0.8166           | 0.8670        | 0.8411    | 0.7907         | 0.7774      | 0.7840  | 0.7206             | 0.6927          | 0.7064      |
+| 1.4077        | 4.0   | 64720 | 2.0427          | 0.8080   | 0.7367   | 0.5928        | 0.5203     | 0.5542 | 0.8334           | 0.8691        | 0.8509    | 0.8127         | 0.7952      | 0.8038  | 0.7583             | 0.7185          | 0.7379      |
+| 1.0097        | 5.0   | 80900 | 2.0132          | 0.8191   | 0.7488   | 0.6601        | 0.5006     | 0.5694 | 0.8440           | 0.8767        | 0.8601    | 0.8195         | 0.8112      | 0.8153  | 0.7618             | 0.7392          | 0.7503      |
 
 
 ### Framework versions
 
-- Transformers 5.5.4
+- Transformers 5.6.2
 - Pytorch 2.11.0+cu130
-- Datasets 4.8.4
+- 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": "5.5.4",
+  "transformers_version": "5.6.2",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 50265

model.safetensors

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+size 498618976

tokenizer_config.json

@@ -6,6 +6,7 @@
   "eos_token": "</s>",
   "errors": "replace",
   "is_local": false,
+  "local_files_only": false,
   "mask_token": "<mask>",
   "model_max_length": 512,
   "pad_token": "<pad>",

training_args.bin

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+oid sha256:4fed503167938b6f5ae1dcf6f5ee14cb3ed1c9517c6dec60fa2d4cdf1068e8e0
+size 5329