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: 2.0378
+- 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
 
 ## 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: 2.0236
-- Accuracy: 0.8186
-- F1 Macro: 0.7510
-- Low Precision: 0.6773
-- Low Recall: 0.5058
-- Low F1: 0.5791
-- Medium Precision: 0.8501
-- Medium Recall: 0.8656
-- Medium F1: 0.8578
-- High Precision: 0.8109
-- High Recall: 0.8214
-- High F1: 0.8162
-- Critical Precision: 0.7575
-- Critical Recall: 0.7447
-- Critical F1: 0.7510
-
 ### 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 |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------:|:-------:|:------------------:|:---------------:|:-----------:|
-| 2.5373        | 1.0   | 17620 | 2.5717          | 0.7372   | 0.6397   | 0.5989        | 0.3038     | 0.4031 | 0.7881           | 0.8179        | 0.8027    | 0.7286         | 0.7113      | 0.7199  | 0.6012             | 0.6683          | 0.6330      |
-| 2.0841        | 2.0   | 35240 | 2.3156          | 0.7658   | 0.6802   | 0.6251        | 0.3867     | 0.4778 | 0.8150           | 0.8241        | 0.8195    | 0.7404         | 0.7772      | 0.7584  | 0.6855             | 0.6458          | 0.6651      |
-| 1.9451        | 3.0   | 52860 | 2.1746          | 0.7897   | 0.7152   | 0.6160        | 0.4740     | 0.5358 | 0.8102           | 0.8710        | 0.8395    | 0.8044         | 0.7546      | 0.7787  | 0.7101             | 0.7031          | 0.7066      |
-| 1.8354        | 4.0   | 70480 | 2.0207          | 0.8059   | 0.7345   | 0.6308        | 0.5018     | 0.5590 | 0.8223           | 0.8780        | 0.8493    | 0.8119         | 0.7884      | 0.8000  | 0.7678             | 0.6952          | 0.7297      |
-| 1.6430        | 5.0   | 88100 | 2.0236          | 0.8186   | 0.7510   | 0.6773        | 0.5058     | 0.5791 | 0.8501           | 0.8656        | 0.8578    | 0.8109         | 0.8214      | 0.8162  | 0.7575             | 0.7447          | 0.7510      |
+| 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      |
 
 
 ### 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
-2026-07-22T10:32:32,VulnTrain,07e4bcd3-dc27-4d56-8dca-2abdf599dbec,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,17101.49111927,0.49649144895604547,2.903205606419886e-05,70.00020223721837,854.506252984916,70.0,0.3324758516935228,4.0525399956405135,0.33165999276215513,4.716675840096194,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-136-generic-x86_64-with-glibc2.39,3.12.3,3.2.9,224,Intel(R) Xeon(R) Platinum 8480+,4,4 x NVIDIA L40S,6.1327,49.6098,2015.335433959961,machine,0.9794609829135107,71.30325582760848,1.0,20.34628215353247,N,1.0,0.0
+2026-08-10T10:57:27,VulnTrain,bbe287dc-2b90-4133-be54-f49439361a46,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,17412.237488129,0.5062978248175911,2.9077126082318003e-05,70.00020254254338,855.9715475520533,70.0,0.338522028198362,4.133602226045805,0.3377122962777855,4.80983655052194,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-137-generic-x86_64-with-glibc2.39,3.12.3,3.3.0,224,Intel(R) Xeon(R) Platinum 8480+,4,4 x NVIDIA L40S,6.1327,49.6098,2015.3354377746582,machine,0.9477997577714978,71.24766422515717,1.0,20.362818210125933,N,1.0,0.0

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c85cfd9b4ff3d4f3b4e83a532f58aebbb6a3a39e7d1bc9cd42706fe9c15b0bef
+oid sha256:1355f24f024bb12965b7c7c80a6b0902b5e20eee936e5704e67b23eca84aa4f6
 size 498618976