End of training
Browse files- README.md +11 -7
- emissions.csv +1 -1
- metrics.json +7 -7
- model.safetensors +1 -1
README.md
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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:
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- Accuracy: 0.
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- F1: 0.
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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:
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- eval_batch_size:
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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:
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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.
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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
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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
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2025-08-
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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
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metrics.json
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{
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"eval_loss":
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"eval_accuracy": 0.
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"eval_f1": 0.
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"eval_runtime": 0.
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"eval_samples_per_second":
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"eval_steps_per_second":
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"epoch":
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}
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{
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"eval_loss": 3.6246719360351562,
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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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model.safetensors
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