| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: CodeDetect-BERT-T2 |
| 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. --> |
|
|
| # CodeDetect-BERT-T2 |
|
|
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.2616 |
| - Accuracy: 0.9178 |
| - F1: 0.4680 |
| - Precision: 0.4960 |
| - Recall: 0.4600 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 128 |
| - eval_batch_size: 128 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 2.1731 | 1.2798 | 5000 | 2.2130 | 0.9197 | 0.3964 | 0.4880 | 0.3890 | |
| | 1.8504 | 2.5595 | 10000 | 2.1255 | 0.9216 | 0.4423 | 0.5119 | 0.4239 | |
| | 1.5734 | 3.8393 | 15000 | 2.1121 | 0.9226 | 0.4766 | 0.5175 | 0.4584 | |
| | 1.4124 | 5.0 | 19535 | 2.2616 | 0.9178 | 0.4680 | 0.4960 | 0.4600 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.13.1 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
