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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: 0.5091
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- - Accuracy: 0.8220
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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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- | 0.6771 | 1.0 | 26510 | 0.6352 | 0.7360 |
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- | 0.6633 | 2.0 | 53020 | 0.5819 | 0.7646 |
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- | 0.5362 | 3.0 | 79530 | 0.5248 | 0.7932 |
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- | 0.4466 | 4.0 | 106040 | 0.5198 | 0.8121 |
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- | 0.3886 | 5.0 | 132550 | 0.5091 | 0.8220 |
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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: 0.5004
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+ - Accuracy: 0.8269
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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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+ | 0.6222 | 1.0 | 26721 | 0.6425 | 0.7424 |
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+ | 0.5253 | 2.0 | 53442 | 0.5840 | 0.7720 |
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+ | 0.4666 | 3.0 | 80163 | 0.5480 | 0.7959 |
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+ | 0.2633 | 4.0 | 106884 | 0.5039 | 0.8181 |
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+ | 0.2508 | 5.0 | 133605 | 0.5004 | 0.8269 |
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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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- 2025-04-07T11:05:46,codecarbon,736c09be-51f2-4270-8b9b-dc0b1e387939,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,21793.828189492226,0.39047380406020327,1.7916714799489336e-05,42.5,186.42425294851307,94.34470081329346,0.25708567687020156,2.8817499934536954,0.5706710325080956,3.709506702831985,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
 
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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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+ 2025-04-17T20:43:34,codecarbon,b3b235ad-c7c2-4aae-9bf8-6f06a3844b82,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,21964.696577124298,0.3937361711972296,1.7925864343935274e-05,42.5,181.96671727190795,94.34470081329346,0.25912773687640994,2.9061641099294206,0.5752073903167013,3.7404992371225365,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