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: 2.0532
+- Accuracy: 0.8158
+- F1 Macro: 0.7498
+- Low Precision: 0.6832
+- Low Recall: 0.4988
+- Low F1: 0.5766
+- Medium Precision: 0.8349
+- Medium Recall: 0.8687
+- Medium F1: 0.8514
+- High Precision: 0.8180
+- High Recall: 0.8128
+- High F1: 0.8154
+- Critical Precision: 0.7683
+- Critical Recall: 0.7435
+- Critical F1: 0.7557
 
 ## 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
 
@@ -78,37 +59,20 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5
 
-It achieves the following results on the evaluation set:
-- Loss: 2.0411
-- Accuracy: 0.8164
-- F1 Macro: 0.7491
-- Low Precision: 0.6536
-- Low Recall: 0.5148
-- Low F1: 0.5759
-- Medium Precision: 0.8377
-- Medium Recall: 0.8712
-- Medium F1: 0.8541
-- High Precision: 0.8236
-- High Recall: 0.8077
-- High F1: 0.8156
-- Critical Precision: 0.7561
-- Critical Recall: 0.7457
-- Critical F1: 0.7509
-
 ### 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 |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------:|:-------:|:------------------:|:---------------:|:-----------:|
-| 2.6595        | 1.0   | 18151 | 2.5896          | 0.7341   | 0.6285   | 0.6716        | 0.2567     | 0.3714 | 0.7917           | 0.7991        | 0.7954    | 0.7002         | 0.7492      | 0.7239  | 0.6326             | 0.6141          | 0.6232      |
-| 2.4978        | 2.0   | 36302 | 2.3990          | 0.7614   | 0.6814   | 0.5762        | 0.4255     | 0.4895 | 0.7779           | 0.8556        | 0.8149    | 0.7881         | 0.7133      | 0.7488  | 0.6681             | 0.6771          | 0.6726      |
-| 1.7617        | 3.0   | 54453 | 2.1975          | 0.7879   | 0.7097   | 0.6682        | 0.4156     | 0.5125 | 0.8119           | 0.8615        | 0.8360    | 0.8056         | 0.7546      | 0.7793  | 0.6775             | 0.7484          | 0.7112      |
-| 1.7304        | 4.0   | 72604 | 2.0677          | 0.8075   | 0.7366   | 0.6663        | 0.4761     | 0.5554 | 0.8389           | 0.8588        | 0.8487    | 0.8077         | 0.8021      | 0.8049  | 0.7248             | 0.7506          | 0.7375      |
-| 1.4189        | 5.0   | 90755 | 2.0411          | 0.8164   | 0.7491   | 0.6536        | 0.5148     | 0.5759 | 0.8377           | 0.8712        | 0.8541    | 0.8236         | 0.8077      | 0.8156  | 0.7561             | 0.7457          | 0.7509      |
+| 2.7510        | 1.0   | 18405 | 2.5883          | 0.7325   | 0.6230   | 0.6335        | 0.2451     | 0.3534 | 0.7813           | 0.8031        | 0.7920    | 0.7066         | 0.7464      | 0.7259  | 0.6394             | 0.6027          | 0.6205      |
+| 2.1876        | 2.0   | 36810 | 2.3209          | 0.7617   | 0.6768   | 0.6014        | 0.3890     | 0.4724 | 0.7807           | 0.8477        | 0.8128    | 0.7610         | 0.7464      | 0.7536  | 0.7190             | 0.6244          | 0.6683      |
+| 1.9450        | 3.0   | 55215 | 2.2040          | 0.7853   | 0.7005   | 0.6855        | 0.3683     | 0.4792 | 0.8019           | 0.8620        | 0.8309    | 0.7950         | 0.7654      | 0.7799  | 0.7092             | 0.7147          | 0.7119      |
+| 1.6939        | 4.0   | 73620 | 2.0452          | 0.8060   | 0.7346   | 0.6528        | 0.4713     | 0.5474 | 0.8262           | 0.8633        | 0.8443    | 0.8097         | 0.8011      | 0.8054  | 0.7539             | 0.7294          | 0.7414      |
+| 1.4585        | 5.0   | 92025 | 2.0532          | 0.8158   | 0.7498   | 0.6832        | 0.4988     | 0.5766 | 0.8349           | 0.8687        | 0.8514    | 0.8180         | 0.8128      | 0.8154  | 0.7683             | 0.7435          | 0.7557      |
 
 
 ### Framework versions
 
-- Transformers 5.15.0
+- Transformers 5.16.1
 - Pytorch 2.13.0+cu130
 - Datasets 4.8.5
-- Tokenizers 0.22.2
+- Tokenizers 0.23.1

config.json

@@ -34,7 +34,7 @@
   "pad_token_id": 1,
   "problem_type": "single_label_classification",
   "tie_word_embeddings": true,
-  "transformers_version": "5.15.0",
+  "transformers_version": "5.16.1",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 50265

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
-2026-08-18T10:32:57,VulnTrain,0a366a90-c25c-437b-a16b-4a478dcc1c0c,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,16404.755511164,0.49881326542091087,3.0406625998263205e-05,70.00022302887778,901.5634496219493,70.0,0.3189415151946195,4.101619937404433,0.318171676999283,4.738733129598346,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-138-generic-x86_64-with-glibc2.39,3.12.3,3.3.0,224,Intel(R) Xeon(R) Platinum 8480+,4,4 x NVIDIA L40S,6.1327,49.6098,2015.3353958129883,machine,1.0084411205786796,76.34296266781095,1.0,20.37703774285654,N,1.0,0.0
+2026-08-27T16:17:18,VulnTrain,2585f4d4-09c0-4e3f-9611-66fccb17caff,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,16542.748394694,0.5043625208971754,3.0488435709929975e-05,70.00020829888082,904.2931710572431,70.0,0.32162915407029274,4.1489424610957615,0.32087952053381347,4.7914511356998695,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-138-generic-x86_64-with-glibc2.39,3.12.3,3.3.0,224,Intel(R) Xeon(R) Platinum 8480+,4,4 x NVIDIA L40S,6.1327,49.6098,2015.3354301452637,machine,1.004560975609756,75.97385670731707,1.021170731707317,21.07486058142127,N,1.0,0.0

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:719be9f179bf8d338644875512b60b330566d1b8df28376252399ebe04f94505
 size 498618976