End of training

README.md

@@ -9,8 +9,6 @@ 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
@@ -18,46 +16,22 @@ should probably proofread and complete it, then remove this comment. -->
 
 # vulnerability-severity-classification-roberta-base
 
-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).
-
-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: 0.4877
-- Accuracy: 0.8327
+- Loss: 0.5027
+- Accuracy: 0.8290
 
 ## 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-distilbert-base-uncased"
-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)
+## Training and evaluation data
 
-# Print results
-print("Predictions:", predictions)
-predicted_class = torch.argmax(predictions, dim=-1).item()
-print("Predicted severity:", labels[predicted_class])
-```
+More information needed
 
 ## Training procedure
 
@@ -76,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.6796        | 1.0   | 27610  | 0.6247          | 0.7486   |
-| 0.511         | 2.0   | 55220  | 0.5811          | 0.7754   |
-| 0.5079        | 3.0   | 82830  | 0.5179          | 0.8053   |
-| 0.3685        | 4.0   | 110440 | 0.4863          | 0.8231   |
-| 0.4885        | 5.0   | 138050 | 0.4877          | 0.8327   |
+| 0.6731        | 1.0   | 27750  | 0.6511          | 0.7357   |
+| 0.6648        | 2.0   | 55500  | 0.5703          | 0.7746   |
+| 0.427         | 3.0   | 83250  | 0.5215          | 0.8010   |
+| 0.4361        | 4.0   | 111000 | 0.5099          | 0.8179   |
+| 0.3167        | 5.0   | 138750 | 0.5027          | 0.8290   |
 
 
 ### Framework versions
@@ -88,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.51.3
 - Pytorch 2.7.1+cu126
 - Datasets 3.6.0
-- Tokenizers 0.21.1
+- Tokenizers 0.21.1

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
-2025-06-24T15:53:24,codecarbon,dea556a8-0fa6-48cd-83ec-f23d85db7c7c,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,32671.975663511083,0.590254076994602,1.806606625426124e-05,42.5,427.2816674683831,94.34468364715576,0.38548106524080844,4.366259382171364,0.8556816951697346,5.607422142581932,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
+2025-06-30T21:37:53,codecarbon,fdd20567-9b6a-4e5e-b81a-d2f3561abea1,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,31939.66063390905,0.5761457661682887,1.803856881173678e-05,42.5,387.869585627133,94.34468364715576,0.37683512923984447,4.260060606934644,0.8364972525137364,5.473392988688225,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