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).
-
+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: 0.5087
-- Accuracy: 0.8286
+- Loss: 0.4956
+- Accuracy: 0.8294
 
 ## 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-distilbert-base-uncased"
-... tokenizer = AutoTokenizer.from_pretrained(model_name)
-... model = AutoModelForSequenceClassification.from_pretrained(model_name)
-... model.eval()
-... 
-... 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])
-... 
-Predictions: tensor([[4.9335e-04, 3.4782e-02, 2.6257e-01, 7.0215e-01]])
-Predicted severity: critical
-```
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
 
 ## Training procedure
 
@@ -77,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.6379        | 1.0   | 26871  | 0.6473          | 0.7292   |
-| 0.4942        | 2.0   | 53742  | 0.5829          | 0.7669   |
-| 0.4624        | 3.0   | 80613  | 0.5428          | 0.7982   |
-| 0.3467        | 4.0   | 107484 | 0.5104          | 0.8187   |
-| 0.4102        | 5.0   | 134355 | 0.5087          | 0.8286   |
+| 0.6174        | 1.0   | 26913  | 0.6369          | 0.7439   |
+| 0.5776        | 2.0   | 53826  | 0.5643          | 0.7777   |
+| 0.5285        | 3.0   | 80739  | 0.5198          | 0.8026   |
+| 0.4074        | 4.0   | 107652 | 0.4993          | 0.8198   |
+| 0.2624        | 5.0   | 134565 | 0.4956          | 0.8294   |
 
 
 ### Framework versions
@@ -89,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.51.3
 - Pytorch 2.7.0+cu126
 - Datasets 3.5.0
-- Tokenizers 0.21.1
+- Tokenizers 0.21.1

emissions.csv

@@ -1,2 +1,2 @@
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+2025-05-05T13:04:52,codecarbon,4e93da60-c02f-48cb-81ee-52aeae364584,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22289.27175961435,0.39885643060794007,1.7894547426651375e-05,42.5,330.69648896037273,94.34470081329346,0.2629468895621533,2.942507891504448,0.5836869934214994,3.789141774488092,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-48-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.58586883544922,machine,N,1.0