End of training

README.md

@@ -16,46 +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 post](https://www.vulnerability-lookup.org/2025/02/26/exploring-llm-in-vulnerability-lookup/) 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: 0.5068
-- Accuracy: 0.8288
+- Loss: 0.5141
+- Accuracy: 0.8267
 
 ## 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
 
@@ -74,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.7177        | 1.0   | 27141  | 0.6449          | 0.7401   |
-| 0.449         | 2.0   | 54282  | 0.5911          | 0.7727   |
-| 0.4575        | 3.0   | 81423  | 0.5174          | 0.8015   |
-| 0.4397        | 4.0   | 108564 | 0.4977          | 0.8193   |
-| 0.3868        | 5.0   | 135705 | 0.5068          | 0.8288   |
+| 0.688         | 1.0   | 27243  | 0.6592          | 0.7350   |
+| 0.5601        | 2.0   | 54486  | 0.5602          | 0.7764   |
+| 0.4741        | 3.0   | 81729  | 0.5347          | 0.8005   |
+| 0.3537        | 4.0   | 108972 | 0.5066          | 0.8164   |
+| 0.4263        | 5.0   | 136215 | 0.5141          | 0.8267   |
 
 
 ### Framework versions

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
-2025-05-19T14:31:46,codecarbon,b0549cdd-487c-4c25-aced-7547e421e1c7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,30200.185689591046,0.5953149219658064,1.9712293430400685e-05,42.5,183.42897859440293,94.34468364715576,0.3562902715484039,4.508317828318042,0.7908921420590646,5.655500241925523,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
+2025-05-23T14:06:40,codecarbon,502c031d-5242-4691-b790-8ba670fb6f5f,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22568.717941163108,0.4037375703996053,1.788925589181244e-05,42.5,226.35816568313916,94.34468364715576,0.2662543288831904,2.9782288748034134,0.5910294683786624,3.8355126720652577,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

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:5fba2e0dbc9f6db68753883443dfe7f3db7ddefebbdcbda56f02ebdc60132adc
 size 498618976