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 +16,24 @@ 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: 0.5058
-- Accuracy: 0.8269
+- Loss: 0.5068
+- Accuracy: 0.8288
 
 ## 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
+More information needed
 
-labels = ["low", "medium", "high", "critical"]
+## Intended uses & limitations
 
-model_name = "CIRCL/vulnerability-severity-classification-distilbert-base-uncased"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-model.eval()
+More information needed
 
-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)
+## Training and evaluation data
 
-# Run inference
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+More information needed
 
-# Print results
-print("Predictions:", predictions)
-predicted_class = torch.argmax(predictions, dim=-1).item()
-print("Predicted severity:", labels[predicted_class])
-```
+## Training procedure
 
 ### Training hyperparameters
 
@@ -73,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.6291        | 1.0   | 27084  | 0.6327          | 0.7463   |
-| 0.6025        | 2.0   | 54168  | 0.5640          | 0.7770   |
-| 0.5139        | 3.0   | 81252  | 0.5181          | 0.8016   |
-| 0.3072        | 4.0   | 108336 | 0.4975          | 0.8182   |
-| 0.2318        | 5.0   | 135420 | 0.5058          | 0.8269   |
+| 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   |
 
 
 ### Framework versions
@@ -85,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.51.3
 - Pytorch 2.7.0+cu126
 - Datasets 3.6.0
-- 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
-2025-05-15T16:29:02,codecarbon,a0427070-b2ac-42a3-a26a-afd32efc7e56,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,34918.766422379005,0.7132912489415533,2.0427160579315703e-05,42.5,592.3660989009531,94.34468364715576,0.4118040232016034,5.450364638621572,0.9141083675371564,6.776277029360299,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
+2025-05-19T14:31:46,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

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:c9d79bbe2bf2afe98192880aeb68050576c5e37de34265ddfae9feef4ff95ac1
 size 498618976