End of training

README.md

@@ -9,65 +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: 0.5093
+- Accuracy: 0.8249
 
 ## 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"]
+More information needed
 
-model_name = "CIRCL/vulnerability-severity-classification-roberta-base"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-model.eval()
+## 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)
-
-# Print results
-print("Predictions:", predictions)
-predicted_class = torch.argmax(predictions, dim=-1).item()
-print("Predicted severity:", labels[predicted_class])
-```
-
-It achieves the following results on the evaluation set:
-- Loss: 0.5055
-- Accuracy: 0.8292
+## Training and evaluation data
 
+More information needed
 
 ## Training procedure
 
@@ -86,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.7509        | 1.0   | 28516  | 0.6343          | 0.7430   |
-| 0.4746        | 2.0   | 57032  | 0.5834          | 0.7715   |
-| 0.507         | 3.0   | 85548  | 0.5317          | 0.7974   |
-| 0.3822        | 4.0   | 114064 | 0.5055          | 0.8171   |
-| 0.3155        | 5.0   | 142580 | 0.5055          | 0.8292   |
+| 0.6783        | 1.0   | 28699  | 0.6468          | 0.7397   |
+| 0.6438        | 2.0   | 57398  | 0.5705          | 0.7716   |
+| 0.5289        | 3.0   | 86097  | 0.5305          | 0.7943   |
+| 0.4965        | 4.0   | 114796 | 0.5140          | 0.8150   |
+| 0.4104        | 5.0   | 143495 | 0.5093          | 0.8249   |
 
 
 ### 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
-2025-08-25T19:38:50,codecarbon,7b19d616-e6d0-42b8-9642-31e36e1630b1,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22265.02864354197,0.40295547151395544,1.809813398245206e-05,42.5,183.5258822134907,94.34468507766725,0.26267707134264057,2.982324163079646,0.5830814870843721,3.8280827215066586,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
+2025-09-08T12:24:11,codecarbon,66f91a74-0db9-475f-857e-dd7952a75d4c,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22385.4896657723,0.40553103793852857,1.811579929647868e-05,42.5,181.22844401520283,94.34468507766725,0.26411038263759956,3.00217460423778,0.5862656523666008,3.85255063924198,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