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: 2.0802
+- Accuracy: 0.8101
+- F1 Macro: 0.7378
+- Low Precision: 0.6473
+- Low Recall: 0.4729
+- Low F1: 0.5465
+- Medium Precision: 0.8264
+- Medium Recall: 0.8726
+- Medium F1: 0.8489
+- High Precision: 0.8190
+- High Recall: 0.7991
+- High F1: 0.8089
+- Critical Precision: 0.7599
+- Critical Recall: 0.7339
+- Critical F1: 0.7467
 
 ## 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: 2.0373
-- Accuracy: 0.8137
-- F1 Macro: 0.7424
-- Low Precision: 0.6660
-- Low Recall: 0.4757
-- Low F1: 0.5550
-- Medium Precision: 0.8348
-- Medium Recall: 0.8692
-- Medium F1: 0.8516
-- High Precision: 0.8163
-- High Recall: 0.8102
-- High F1: 0.8132
-- Critical Precision: 0.7602
-- Critical Recall: 0.7395
-- Critical F1: 0.7497
-
 ### 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 |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------:|:-------:|:------------------:|:---------------:|:-----------:|
-| 2.6518        | 1.0   | 19049 | 2.6882          | 0.7235   | 0.6396   | 0.4169        | 0.4805     | 0.4464 | 0.7792           | 0.7764        | 0.7778    | 0.7148         | 0.7409      | 0.7276  | 0.6632             | 0.5592          | 0.6068      |
-| 2.5168        | 2.0   | 38098 | 2.3584          | 0.7578   | 0.6749   | 0.6350        | 0.3686     | 0.4664 | 0.8087           | 0.8128        | 0.8108    | 0.7431         | 0.7566      | 0.7498  | 0.6498             | 0.6971          | 0.6726      |
-| 1.9209        | 3.0   | 57147 | 2.1623          | 0.7839   | 0.6967   | 0.7067        | 0.3504     | 0.4685 | 0.8078           | 0.8563        | 0.8314    | 0.7898         | 0.7654      | 0.7774  | 0.6907             | 0.7288          | 0.7093      |
-| 1.4313        | 4.0   | 76196 | 2.1291          | 0.8001   | 0.7300   | 0.6197        | 0.4962     | 0.5511 | 0.8262           | 0.8599        | 0.8427    | 0.8112         | 0.7838      | 0.7973  | 0.7193             | 0.7391          | 0.7290      |
-| 1.6812        | 5.0   | 95245 | 2.0373          | 0.8137   | 0.7424   | 0.6660        | 0.4757     | 0.5550 | 0.8348           | 0.8692        | 0.8516    | 0.8163         | 0.8102      | 0.8132  | 0.7602             | 0.7395          | 0.7497      |
+| 2.6673        | 1.0   | 19209 | 2.6270          | 0.7275   | 0.6229   | 0.5891        | 0.2786     | 0.3783 | 0.7994           | 0.7680        | 0.7833    | 0.6809         | 0.7859      | 0.7296  | 0.6556             | 0.5536          | 0.6003      |
+| 2.2508        | 2.0   | 38418 | 2.3707          | 0.7599   | 0.6789   | 0.5790        | 0.4020     | 0.4745 | 0.7871           | 0.8371        | 0.8113    | 0.7704         | 0.7325      | 0.7510  | 0.6695             | 0.6882          | 0.6787      |
+| 1.5071        | 3.0   | 57627 | 2.2419          | 0.7817   | 0.6942   | 0.6690        | 0.3588     | 0.4671 | 0.8150           | 0.8384        | 0.8265    | 0.7653         | 0.7944      | 0.7796  | 0.7271             | 0.6819          | 0.7038      |
+| 1.7185        | 4.0   | 76836 | 2.0814          | 0.8013   | 0.7238   | 0.6724        | 0.4270     | 0.5223 | 0.8264           | 0.8579        | 0.8418    | 0.7933         | 0.8072      | 0.8002  | 0.7571             | 0.7067          | 0.7310      |
+| 1.4517        | 5.0   | 96045 | 2.0802          | 0.8101   | 0.7378   | 0.6473        | 0.4729     | 0.5465 | 0.8264           | 0.8726        | 0.8489    | 0.8190         | 0.7991      | 0.8089  | 0.7599             | 0.7339          | 0.7467      |
 
 
 ### Framework versions

emissions.csv

@@ -1,2 +1,2 @@
 timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,water_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,cpu_utilization_percent,gpu_utilization_percent,ram_utilization_percent,ram_used_gb,on_cloud,pue,wue
-2026-09-21T11:05:12,VulnTrain,60e8811e-1383-484d-bb61-9ae96509c8fa,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,17099.910114891,0.5195390783635757,3.038256194757123e-05,70.00020808185151,900.8442818908296,70.0,0.3324570481294704,4.271542157508771,0.33162944225868307,4.93562864789694,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-139-generic-x86_64-with-glibc2.39,3.12.3,3.3.1,224,Intel(R) Xeon(R) Platinum 8480+,4,4 x NVIDIA L40S,6.1327,49.6098,2015.3354606628418,machine,1.0324861422337541,76.20092286826277,1.057760349097771,21.178276900399307,N,1.0,0.0
+2026-09-29T12:11:39,VulnTrain,6f754b7c-7147-4fa0-a032-65d6ce7b7a72,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,17152.291897212,0.5226085257974022,3.046872854830258e-05,70.00021089819239,903.3599994824584,70.0,0.3334758593117621,4.298650257806345,0.332662325139881,4.9647884422579835,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-142-generic-x86_64-with-glibc2.39,3.12.3,3.3.1,224,Intel(R) Xeon(R) Platinum 8480+,4,4 x NVIDIA L40S,6.1327,49.6098,2015.3354301452637,machine,1.0355438287892114,76.39231896804456,1.0096100850190561,21.07601278165819,N,1.0,0.0

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:e9a3c415f5df1d793f5862da7cfb2bf245c1f6515a2ce414bb7a1fea90c22f64
 size 498618976