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
@@ -18,44 +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).
-
+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: 0.5063
-- Accuracy: 0.8285
+- Loss: 0.5078
+- 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-scores"
-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
 
@@ -74,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.5765        | 1.0   | 26137  | 0.6326          | 0.7452   |
-| 0.5378        | 2.0   | 52274  | 0.5672          | 0.7718   |
-| 0.3156        | 3.0   | 78411  | 0.5185          | 0.8026   |
-| 0.4264        | 4.0   | 104548 | 0.4989          | 0.8200   |
-| 0.3328        | 5.0   | 130685 | 0.5063          | 0.8285   |
+| 0.7488        | 1.0   | 26165  | 0.6294          | 0.7406   |
+| 0.5932        | 2.0   | 52330  | 0.6006          | 0.7699   |
+| 0.3986        | 3.0   | 78495  | 0.5378          | 0.7952   |
+| 0.4782        | 4.0   | 104660 | 0.5102          | 0.8172   |
+| 0.2724        | 5.0   | 130825 | 0.5078          | 0.8279   |
 
 
 ### Framework versions
@@ -86,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.49.0
 - Pytorch 2.6.0+cu124
 - Datasets 3.4.0
-- 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
-2025-03-18T19:43:33,codecarbon,96adf9c1-8a37-431e-a103-8149fd657b71,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22817.171325219795,0.3990832484971706,1.7490478675420397e-05,42.5,182.5595136863412,94.34470081329346,0.26915968393788736,2.9246688477888085,0.5974680159412475,3.7912965476679417,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
+2025-03-20T13:21:43,codecarbon,dd013514-38bf-4af2-95c1-8ea1bb48f668,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22678.657158810645,0.39802804660826774,1.7550776654059258e-05,42.5,186.2722255320865,94.34470081329346,0.26752504179716097,2.9199066595344902,0.5938404133550726,3.781272114686715,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