End of training

README.md

@@ -9,56 +9,29 @@ metrics:
 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. -->
 
+# vulnerability-severity-classification-roberta-base
 
-# 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.
-
+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: 0.5052
+- Accuracy: 0.8289
 
 ## 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
 
@@ -73,19 +46,15 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5
 
-It achieves the following results on the evaluation set:
-- Loss: 0.5072
-- Accuracy: 0.8282
-
 ### Training results
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.5519        | 1.0   | 28760  | 0.6553          | 0.7357   |
-| 0.5365        | 2.0   | 57520  | 0.5647          | 0.7746   |
-| 0.3656        | 3.0   | 86280  | 0.5397          | 0.7997   |
-| 0.4367        | 4.0   | 115040 | 0.4903          | 0.8191   |
-| 0.3609        | 5.0   | 143800 | 0.5072          | 0.8282   |
+| 0.7443        | 1.0   | 28795  | 0.6486          | 0.7343   |
+| 0.6199        | 2.0   | 57590  | 0.5670          | 0.7743   |
+| 0.4911        | 3.0   | 86385  | 0.5249          | 0.7958   |
+| 0.3388        | 4.0   | 115180 | 0.4890          | 0.8185   |
+| 0.4451        | 5.0   | 143975 | 0.5052          | 0.8289   |
 
 
 ### 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,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,on_cloud,pue
-2025-09-11T12:32:12,codecarbon,81764e46-1456-449e-a3dd-290dd2a3688d,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22484.098965471145,0.40757034411397625,1.812704813032012e-05,42.5,322.7434811553208,94.34468507766725,0.2652669085793128,3.017823348701171,0.5888338196884041,3.8719240769688894,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-71-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.5858268737793,machine,N,1.0
+2025-09-15T13:56:20,codecarbon,91da8172-a405-4d50-8389-85b2c93bf918,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22496.31638219813,0.40783754608547185,1.8129081186296057e-05,42.5,279.6408161707655,94.34468507766725,0.2654148633706176,3.0198868070186364,0.589160829116472,3.8744624995057313,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-71-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.5858268737793,machine,N,1.0

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a2076f78529936f77a851e407eea6755302b5ddc35a93d175bf52c3f4bcf7d12
+oid sha256:636d0e927f426e03fe11457e4a56df3256c3a41550aedcb07d7267187509e65e
 size 498618976