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: 2.0411
+- Accuracy: 0.8164
+- F1 Macro: 0.7491
+- Low Precision: 0.6536
+- Low Recall: 0.5148
+- Low F1: 0.5759
+- Medium Precision: 0.8377
+- Medium Recall: 0.8712
+- Medium F1: 0.8541
+- High Precision: 0.8236
+- High Recall: 0.8077
+- High F1: 0.8156
+- Critical Precision: 0.7561
+- Critical Recall: 0.7457
+- Critical F1: 0.7509
 
 ## 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
-
-labels = ["low", "medium", "high", "critical"]
-
-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)
+## Intended uses & limitations
 
-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)
+More information needed
 
-# Run inference
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+## Training and evaluation data
 
-# Print results
-print("Predictions:", predictions)
-predicted_class = torch.argmax(predictions, dim=-1).item()
-print("Predicted severity:", labels[predicted_class])
-```
+More information needed
 
 ## Training procedure
 
@@ -78,41 +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: 2.0378
-- Accuracy: 0.8152
-- F1 Macro: 0.7487
-- Low Precision: 0.6544
-- Low Recall: 0.5105
-- Low F1: 0.5736
-- Medium Precision: 0.8440
-- Medium Recall: 0.8653
-- Medium F1: 0.8545
-- High Precision: 0.8110
-- High Recall: 0.8111
-- High F1: 0.8111
-- Critical Precision: 0.7619
-- Critical Recall: 0.7493
-- Critical F1: 0.7556
-
 ### 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 |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------:|:-------:|:------------------:|:---------------:|:-----------:|
-| 2.3504        | 1.0   | 17926 | 2.6006          | 0.7357   | 0.6372   | 0.5440        | 0.3233     | 0.4056 | 0.7788           | 0.8171        | 0.7975    | 0.7123         | 0.7369      | 0.7244  | 0.6738             | 0.5764          | 0.6213      |
-| 2.4658        | 2.0   | 35852 | 2.3303          | 0.7636   | 0.6543   | 0.7435        | 0.2598     | 0.3850 | 0.7912           | 0.8500        | 0.8195    | 0.7377         | 0.7716      | 0.7543  | 0.7336             | 0.5973          | 0.6585      |
-| 2.0186        | 3.0   | 53778 | 2.1500          | 0.7856   | 0.7041   | 0.6568        | 0.3937     | 0.4923 | 0.8148           | 0.8526        | 0.8333    | 0.7805         | 0.7728      | 0.7767  | 0.7146             | 0.7138          | 0.7142      |
-| 1.7673        | 4.0   | 71704 | 2.0545          | 0.8046   | 0.7352   | 0.5998        | 0.5178     | 0.5558 | 0.8334           | 0.8602        | 0.8466    | 0.8018         | 0.7995      | 0.8007  | 0.7623             | 0.7150          | 0.7379      |
-| 1.2162        | 5.0   | 89630 | 2.0378          | 0.8152   | 0.7487   | 0.6544        | 0.5105     | 0.5736 | 0.8440           | 0.8653        | 0.8545    | 0.8110         | 0.8111      | 0.8111  | 0.7619             | 0.7493          | 0.7556      |
+| 2.6595        | 1.0   | 18151 | 2.5896          | 0.7341   | 0.6285   | 0.6716        | 0.2567     | 0.3714 | 0.7917           | 0.7991        | 0.7954    | 0.7002         | 0.7492      | 0.7239  | 0.6326             | 0.6141          | 0.6232      |
+| 2.4978        | 2.0   | 36302 | 2.3990          | 0.7614   | 0.6814   | 0.5762        | 0.4255     | 0.4895 | 0.7779           | 0.8556        | 0.8149    | 0.7881         | 0.7133      | 0.7488  | 0.6681             | 0.6771          | 0.6726      |
+| 1.7617        | 3.0   | 54453 | 2.1975          | 0.7879   | 0.7097   | 0.6682        | 0.4156     | 0.5125 | 0.8119           | 0.8615        | 0.8360    | 0.8056         | 0.7546      | 0.7793  | 0.6775             | 0.7484          | 0.7112      |
+| 1.7304        | 4.0   | 72604 | 2.0677          | 0.8075   | 0.7366   | 0.6663        | 0.4761     | 0.5554 | 0.8389           | 0.8588        | 0.8487    | 0.8077         | 0.8021      | 0.8049  | 0.7248             | 0.7506          | 0.7375      |
+| 1.4189        | 5.0   | 90755 | 2.0411          | 0.8164   | 0.7491   | 0.6536        | 0.5148     | 0.5759 | 0.8377           | 0.8712        | 0.8541    | 0.8236         | 0.8077      | 0.8156  | 0.7561             | 0.7457          | 0.7509      |
 
 
 ### Framework versions
 
-- Transformers 5.14.1
+- Transformers 5.15.0
 - Pytorch 2.13.0+cu130
 - Datasets 4.8.5
 - Tokenizers 0.22.2
-
-## Funding
-
-[AIPITCH](https://aipitch.eu/) (AI-Powered Innovative Toolkit for Cybersecurity Hubs) is a co-funded EU project supported by the European Cybersecurity Competence Centre (ECCC) under the DIGITAL-ECCC-2024-DEPLOY-CYBER-06-ENABLINGTECH program and [CIRCL](https://www.circl.lu/).

config.json

@@ -34,7 +34,7 @@
   "pad_token_id": 1,
   "problem_type": "single_label_classification",
   "tie_word_embeddings": true,
-  "transformers_version": "5.14.1",
+  "transformers_version": "5.15.0",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 50265

emissions.csv

@@ -1,2 +1,2 @@
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+2026-08-18T10:32:57,VulnTrain,0a366a90-c25c-437b-a16b-4a478dcc1c0c,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,16404.755511164,0.49881326542091087,3.0406625998263205e-05,70.00022302887778,901.5634496219493,70.0,0.3189415151946195,4.101619937404433,0.318171676999283,4.738733129598346,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-138-generic-x86_64-with-glibc2.39,3.12.3,3.3.0,224,Intel(R) Xeon(R) Platinum 8480+,4,4 x NVIDIA L40S,6.1327,49.6098,2015.3353958129883,machine,1.0084411205786796,76.34296266781095,1.0,20.37703774285654,N,1.0,0.0

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