End of training

README.md

@@ -9,63 +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. -->
 
-# 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.
-
-
-## Model description
+# 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: 0.5065
-- Accuracy: 0.8293
-
-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
+- Loss: 0.5104
+- Accuracy: 0.8285
 
-```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()
+## Model description
 
-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)
+## Intended uses & limitations
 
-# Print results
-print("Predictions:", predictions)
-predicted_class = torch.argmax(predictions, dim=-1).item()
-print("Predicted severity:", labels[predicted_class])
-```
+More information needed
 
+## Training and evaluation data
 
+More information needed
 
 ## Training procedure
 
@@ -76,7 +42,7 @@ The following hyperparameters were used during training:
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - num_epochs: 5
 
@@ -84,16 +50,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.5365        | 1.0   | 28319  | 0.6723          | 0.7432   |
-| 0.4935        | 2.0   | 56638  | 0.5961          | 0.7707   |
-| 0.4246        | 3.0   | 84957  | 0.5398          | 0.7981   |
-| 0.4927        | 4.0   | 113276 | 0.5052          | 0.8193   |
-| 0.2864        | 5.0   | 141595 | 0.5065          | 0.8293   |
+| 0.7426        | 1.0   | 28432  | 0.6408          | 0.7432   |
+| 0.451         | 2.0   | 56864  | 0.6088          | 0.7640   |
+| 0.5408        | 3.0   | 85296  | 0.5352          | 0.7990   |
+| 0.4343        | 4.0   | 113728 | 0.5045          | 0.8185   |
+| 0.4341        | 5.0   | 142160 | 0.5104          | 0.8285   |
 
 
 ### Framework versions
 
-- Transformers 4.51.3
-- Pytorch 2.7.1+cu126
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+- Transformers 4.55.2
+- Pytorch 2.8.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.21.4

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
-2025-08-12T14:50:28,codecarbon,8d66914a-9b1d-4e8d-b5b0-197aae7695b1,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,33622.2954240907,0.6071869809020806,1.8059057932940093e-05,42.5,391.41603724835056,94.34468364715576,0.396695449095465,4.49100706696936,0.8805824549323915,5.768284970997222,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-60-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.58582305908203,machine,N,1.0
+2025-08-19T18:23:36,codecarbon,00094c79-e50f-4991-a4b7-c70f8770a685,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,31003.21120994899,0.5509036672436785,1.776924536987615e-05,42.5,315.7355467933024,94.34468507766725,0.3658022772405763,4.055787291849455,0.8120031201139472,5.233592689203979,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:b20dd370cb5fd8c9db9f52e05e23f36afd7e6dee67fc32566427c19c5a0d5c82
+oid sha256:a4b17dd6677761d945b839636cfacd3a6313759aef3982b71b0472114a509449
 size 498618976