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End of training

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  1. README.md +11 -7
  2. emissions.csv +1 -1
  3. metrics.json +7 -7
  4. model.safetensors +1 -1
README.md CHANGED
@@ -18,9 +18,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 4.3345
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- - Accuracy: 0.1685
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- - F1: 0.0060
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  ## Model description
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@@ -40,19 +40,23 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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- - train_batch_size: 32
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- - eval_batch_size: 32
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 1
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  - label_smoothing_factor: 0.1
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 4.5185 | 1.0 | 25 | 4.3345 | 0.1685 | 0.0060 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 3.6247
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+ - Accuracy: 0.4091
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+ - F1: 0.0535
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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+ - train_batch_size: 40
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+ - eval_batch_size: 40
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 5
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  - label_smoothing_factor: 0.1
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 4.4681 | 1.0 | 20 | 4.1164 | 0.2159 | 0.0083 |
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+ | 4.015 | 2.0 | 40 | 4.0152 | 0.3409 | 0.0264 |
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+ | 3.7805 | 3.0 | 60 | 3.7930 | 0.375 | 0.0293 |
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+ | 3.5834 | 4.0 | 80 | 3.6496 | 0.3864 | 0.0288 |
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+ | 3.4556 | 5.0 | 100 | 3.6247 | 0.4091 | 0.0535 |
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  ### Framework versions
emissions.csv CHANGED
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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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+ 2025-08-11T10:19:46,codecarbon,9f55ce0f-9c24-4b8a-87bd-12ffb4735cc0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,33.95569324027747,0.0005908333174972222,1.7400125313783587e-05,42.5,427.54523859736616,94.34468364715576,0.00040055365950037315,0.004323224569674267,0.0008891467064363745,0.005612924935611014,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 CHANGED
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  {
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- "eval_loss": 4.33454704284668,
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- "eval_accuracy": 0.16853932584269662,
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- "eval_f1": 0.006009615384615384,
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- "eval_runtime": 0.288,
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- "eval_samples_per_second": 309.072,
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- "eval_steps_per_second": 10.418,
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- "epoch": 1.0
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  }
 
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  {
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+ "eval_accuracy": 0.4090909090909091,
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+ "eval_f1": 0.05352666218744808,
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+ "eval_runtime": 0.232,
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+ "eval_samples_per_second": 379.332,
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+ "eval_steps_per_second": 12.932,
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+ "epoch": 5.0
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  }
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