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vulnerability-severity-classification-roberta-base

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
classification
nlp
vulnerability
Model card Files
xet
Community
cedricbonhomme commited on
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End of training

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  1. README.md +7 -7
  2. emissions.csv +1 -1
README.md CHANGED
@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5008
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- - Accuracy: 0.8272
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  ## Model description
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@@ -50,11 +50,11 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:------:|:---------------:|:--------:|
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- | 0.7147 | 1.0 | 26851 | 0.6525 | 0.7324 |
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- | 0.5133 | 2.0 | 53702 | 0.5803 | 0.7693 |
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- | 0.4534 | 3.0 | 80553 | 0.5314 | 0.7969 |
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- | 0.3721 | 4.0 | 107404 | 0.5030 | 0.8183 |
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- | 0.2978 | 5.0 | 134255 | 0.5008 | 0.8272 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5087
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+ - Accuracy: 0.8286
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:------:|:---------------:|:--------:|
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+ | 0.6379 | 1.0 | 26871 | 0.6473 | 0.7292 |
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+ | 0.4942 | 2.0 | 53742 | 0.5829 | 0.7669 |
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+ | 0.4624 | 3.0 | 80613 | 0.5428 | 0.7982 |
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+ | 0.3467 | 4.0 | 107484 | 0.5104 | 0.8187 |
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+ | 0.4102 | 5.0 | 134355 | 0.5087 | 0.8286 |
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  ### Framework versions
emissions.csv CHANGED
@@ -1,2 +1,2 @@
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  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
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- 2025-04-28T13:40:46,codecarbon,c715ed1f-a988-4f23-a458-9522ca614c8a,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22246.894596571103,0.39704568373654014,1.784724074692818e-05,42.5,175.54676399984314,94.34470081329346,0.2624518737546004,2.926899614017856,0.5825881656341594,3.771939653406611,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
 
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  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
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+ 2025-04-30T12:03:15,codecarbon,d4239479-931a-44ec-b579-857001bd3485,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22291.09947314672,0.3980622433512762,1.785745220108175e-05,42.5,350.6976920332212,94.34470081329346,0.26297261152170354,2.9348807245693536,0.5837436481415417,3.7815969842326003,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