End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: cwe-parent-vulnerability-classification-roberta-base
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# cwe-parent-vulnerability-classification-roberta-base
+
+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: 3.6317
+- Accuracy: 0.4111
+- F1: 0.0069
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 40
+- eval_batch_size: 40
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+- label_smoothing_factor: 0.1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 4.5723        | 1.0   | 20   | 4.0892          | 0.2397   | 0.0022 |
+| 4.0579        | 2.0   | 40   | 3.9983          | 0.2397   | 0.0022 |
+| 3.9765        | 3.0   | 60   | 3.9314          | 0.2448   | 0.0025 |
+| 3.9012        | 4.0   | 80   | 3.7501          | 0.3776   | 0.0064 |
+| 3.7555        | 5.0   | 100  | 3.6317          | 0.4111   | 0.0069 |
+
+
+### Framework versions
+
+- Transformers 4.55.0
+- Pytorch 2.7.1+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.2

emissions.csv

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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
+2025-08-13T10:10:27,codecarbon,b5c70249-ce94-4ba6-87bc-ddbee31a9e67,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,72.31097214482725,0.0013173524712290327,1.821787803641455e-05,42.5,450.87281593019804,94.34468364715576,0.0008532083166213449,0.00976771086979511,0.0018939480625602314,0.012514867248976684,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

metrics.json

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+{
+    "eval_loss": 3.6316609382629395,
+    "eval_accuracy": 0.4110824742268041,
+    "eval_f1": 0.006900071113956835,
+    "eval_runtime": 3.6016,
+    "eval_samples_per_second": 215.462,
+    "eval_steps_per_second": 5.553,
+    "epoch": 5.0
+}