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
@@ -18,45 +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: 1.1168
-- Accuracy: 0.4499
+- Loss: 0.5153
+- Accuracy: 0.8281
 
 ## 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()
-
-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)
+More information needed
 
-# Run inference
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+## Intended uses & limitations
 
+More information needed
 
-# Print results
-print("Predictions:", predictions)
-predicted_class = torch.argmax(predictions, dim=-1).item()
-print("Predicted severity:", labels[predicted_class])
-```
+## Training and evaluation data
 
+More information needed
 
 ## Training procedure
 
@@ -75,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 1.1123        | 1.0   | 26248  | 1.1174          | 0.4499   |
-| 1.076         | 2.0   | 52496  | 1.1196          | 0.4499   |
-| 1.0503        | 3.0   | 78744  | 1.1173          | 0.4499   |
-| 1.1249        | 4.0   | 104992 | 1.1166          | 0.4499   |
-| 1.0597        | 5.0   | 131240 | 1.1168          | 0.4499   |
+| 0.6466        | 1.0   | 26311  | 0.6492          | 0.7420   |
+| 0.6188        | 2.0   | 52622  | 0.5752          | 0.7695   |
+| 0.4497        | 3.0   | 78933  | 0.5307          | 0.8002   |
+| 0.4025        | 4.0   | 105244 | 0.5029          | 0.8168   |
+| 0.3657        | 5.0   | 131555 | 0.5153          | 0.8281   |
 
 
 ### Framework versions
@@ -87,4 +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
-2025-03-24T13:07:56,codecarbon,96a6fcc0-faaa-4e28-9045-c84b72eb293c,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22816.120048392564,0.4001257415247599,1.7536975641612187e-05,42.5,183.77782432692922,94.34470081329346,0.26914087106201057,2.934635983540261,0.5974233916833254,3.801200246285588,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
+2025-03-27T12:03:03,codecarbon,4a13a9e8-510b-4d67-b2e8-a69b4e6f8bfe,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,21638.455213064328,0.3872766296384803,1.789760987210677e-05,42.5,182.12426234884637,94.34470081329346,0.2552900867056506,2.8571545184995557,0.5666888950606045,3.679133500265813,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:efbf0247cf7dbad148ebdea774171136d1ca37395c8959b6ec7bbbbdff87cf43
 size 498618976