End of training

README.md

@@ -9,51 +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. -->
 
 # 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).
-
+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: 0.4974
-- Accuracy: 0.8307
+- Loss: 0.5055
+- Accuracy: 0.8279
 
 ## 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"]
-
-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
 
-test_description = "langchain_experimental 0.0.14 allows an attacker to bypass the CVE-2023-36258 fix and execute arbitrary code via the PALChain in the python exec method."
-inputs = tokenizer(test_description, return_tensors="pt", truncation=True, padding=True)
+## Intended uses & limitations
 
-# Run inference
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+More information needed
 
+## 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
 
@@ -72,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.4708        | 1.0   | 25773  | 0.6487          | 0.7430   |
-| 0.5266        | 2.0   | 51546  | 0.5670          | 0.7753   |
-| 0.5546        | 3.0   | 77319  | 0.5139          | 0.8031   |
-| 0.4507        | 4.0   | 103092 | 0.5080          | 0.8189   |
-| 0.4545        | 5.0   | 128865 | 0.4974          | 0.8307   |
+| 0.6309        | 1.0   | 25825  | 0.6348          | 0.7432   |
+| 0.5293        | 2.0   | 51650  | 0.5614          | 0.7708   |
+| 0.5551        | 3.0   | 77475  | 0.5202          | 0.7954   |
+| 0.4509        | 4.0   | 103300 | 0.5038          | 0.8168   |
+| 0.2226        | 5.0   | 129125 | 0.5055          | 0.8279   |
 
 
 ### Framework versions
@@ -84,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.49.0
 - Pytorch 2.6.0+cu124
 - Datasets 3.3.2
-- Tokenizers 0.21.0
+- Tokenizers 0.21.0

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
-2025-03-11T20:15:08,codecarbon,b7c3a2f3-9527-4fc3-a183-71f65a632d44,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22397.0022299774,0.39311459333379095,1.75521076123136e-05,42.5,187.2567482976426,94.34470081329346,0.26418021162066835,2.8840004138650954,0.5864136130766143,3.7345942385623716,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-48-generic-x86_64-with-glibc2.39,3.12.3,2.8.3,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.58586883544922,machine,N,1.0
+2025-03-13T12:22:26,codecarbon,bd683362-ba46-4aa3-ac20-26f10f217d73,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22453.898297887295,0.3941647645265368,1.7554402326816636e-05,42.5,180.95528060003272,94.34470081329346,0.2648649747194387,2.8917746178622963,0.5879312872766858,3.744570879858419,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-48-generic-x86_64-with-glibc2.39,3.12.3,2.8.3,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.58586883544922,machine,N,1.0

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:a55c4e1960815f42e04965b354f6e82ea561e2260b20956d5f64ae078c9d7708
 size 498618976