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
@@ -18,46 +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 page](https://www.vulnerability-lookup.org/user-manual/ai/) 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: 0.5091
-- Accuracy: 0.8246
+- Loss: 1.1088
+- Accuracy: 0.4592
 
 ## 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-roberta-base"
-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
 
@@ -76,16 +50,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.744         | 1.0   | 27380  | 0.6382          | 0.7456   |
-| 0.5737        | 2.0   | 54760  | 0.5687          | 0.7722   |
-| 0.5525        | 3.0   | 82140  | 0.5280          | 0.7964   |
-| 0.4037        | 4.0   | 109520 | 0.5090          | 0.8141   |
-| 0.2646        | 5.0   | 136900 | 0.5091          | 0.8246   |
+| 1.2002        | 1.0   | 27520  | 1.1105          | 0.4592   |
+| 1.0249        | 2.0   | 55040  | 1.1088          | 0.4592   |
+| 1.1372        | 3.0   | 82560  | 1.1086          | 0.4592   |
+| 1.0043        | 4.0   | 110080 | 1.1090          | 0.4592   |
+| 1.0767        | 5.0   | 137600 | 1.1088          | 0.4592   |
 
 
 ### Framework versions
 
 - Transformers 4.51.3
-- Pytorch 2.7.0+cu126
+- Pytorch 2.7.1+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
-2025-06-06T16:41:30,codecarbon,3873d026-9eaf-43d9-8dbc-c99c09d26094,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22914.07296414394,0.4089048731829486,1.7845141447476625e-05,42.5,214.69427455783162,94.34468364715576,0.2703376020925034,3.0141753816161483,0.6000891384325414,3.8846021221411946,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
+2025-06-12T13:17:11,codecarbon,a6a9167b-c2cf-4888-8c56-0a43ea894935,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,27431.534481129143,0.4913074246354181,1.7910315041741507e-05,42.5,416.33074039387833,94.34468364715576,0.32364457372366556,3.6253598397299953,0.7184231217241113,4.667427535177774,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:dbc54e261374636e2af87b32188f4c53f4e71fb80b9592408713cf0b799cacb4
 size 498618976