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--- |
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library_name: peft |
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base_model: microsoft/codebert-base |
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tags: |
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- base_model:adapter:microsoft/codebert-base |
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- lora |
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- transformers |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: CodeGenDetect-CodeBert_Lora |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# CodeGenDetect-CodeBert_Lora |
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This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0384 |
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- Accuracy: 0.9907 |
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- F1: 0.9907 |
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- Precision: 0.9907 |
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- Recall: 0.9907 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:| |
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| 0.1381 | 0.128 | 4000 | 0.9586 | 0.9586 | 0.1627 | 0.9599 | 0.9586 | |
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| 0.0821 | 0.256 | 8000 | 0.9761 | 0.9761 | 0.1081 | 0.9761 | 0.9761 | |
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| 0.0667 | 0.384 | 12000 | 0.9786 | 0.9786 | 0.1008 | 0.9787 | 0.9786 | |
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| 0.0754 | 0.512 | 16000 | 0.9820 | 0.9820 | 0.0779 | 0.9821 | 0.9820 | |
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| 0.0776 | 0.64 | 20000 | 0.9846 | 0.9846 | 0.0617 | 0.9847 | 0.9846 | |
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| 0.0643 | 0.768 | 24000 | 0.9831 | 0.9831 | 0.0761 | 0.9832 | 0.9831 | |
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| 0.064 | 0.896 | 28000 | 0.9878 | 0.9878 | 0.0495 | 0.9878 | 0.9878 | |
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| 0.0477 | 1.024 | 32000 | 0.9879 | 0.9879 | 0.0480 | 0.9880 | 0.9879 | |
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| 0.0427 | 1.152 | 36000 | 0.9894 | 0.9894 | 0.0424 | 0.9894 | 0.9894 | |
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| 0.0381 | 1.28 | 40000 | 0.9880 | 0.9880 | 0.0484 | 0.9880 | 0.9880 | |
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| 0.0423 | 1.408 | 44000 | 0.9901 | 0.9901 | 0.0399 | 0.9901 | 0.9901 | |
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| 0.0389 | 1.536 | 48000 | 0.9888 | 0.9888 | 0.0513 | 0.9889 | 0.9888 | |
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| 0.0416 | 1.6640 | 52000 | 0.9908 | 0.9908 | 0.0358 | 0.9908 | 0.9908 | |
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| 0.0374 | 1.792 | 56000 | 0.0370 | 0.9905 | 0.9905 | 0.9905 | 0.9905 | |
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| 0.0441 | 1.92 | 60000 | 0.0355 | 0.9905 | 0.9905 | 0.9905 | 0.9905 | |
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| 0.0358 | 2.048 | 64000 | 0.0384 | 0.9907 | 0.9907 | 0.9907 | 0.9907 | |
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### Framework versions |
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- PEFT 0.18.0 |
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- Transformers 4.57.3 |
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- Pytorch 2.9.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.1 |
