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
@@ -18,47 +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: 0.4977
-- Accuracy: 0.8282
+- Loss: 0.5004
+- Accuracy: 0.8293
 
 ## 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
 
@@ -77,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.6104        | 1.0   | 27258  | 0.6445          | 0.7436   |
-| 0.5623        | 2.0   | 54516  | 0.5718          | 0.7731   |
-| 0.4926        | 3.0   | 81774  | 0.5292          | 0.7999   |
-| 0.4963        | 4.0   | 109032 | 0.5039          | 0.8192   |
-| 0.4027        | 5.0   | 136290 | 0.4977          | 0.8282   |
+| 0.6697        | 1.0   | 27326  | 0.6337          | 0.7444   |
+| 0.4882        | 2.0   | 54652  | 0.5695          | 0.7761   |
+| 0.4137        | 3.0   | 81978  | 0.5285          | 0.7983   |
+| 0.3413        | 4.0   | 109304 | 0.5046          | 0.8197   |
+| 0.2704        | 5.0   | 136630 | 0.5004          | 0.8293   |
 
 
 ### Framework versions
@@ -89,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.51.3
 - Pytorch 2.7.0+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
-2025-05-26T19:05:42,codecarbon,e7543fb4-360a-4000-962e-4799d2abaf96,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,23156.837513568928,0.4180810014444384,1.8054322020416673e-05,42.5,297.7508283866558,94.34468364715576,0.2732017867674655,3.092122704807295,0.6064509798106038,3.9717754713853712,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
+2025-06-02T13:59:48,codecarbon,d6d4ebcb-7b64-49bd-a1b9-58ac3cd6d19d,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,32367.434696137905,0.5816748921396911,1.7970991448670377e-05,42.5,399.66151206309655,94.34468364715576,0.3818975383083689,4.29628874675047,0.8477334791478729,5.525919764206711,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