| --- |
| library_name: transformers |
| base_model: huggingface/CodeBERTa-small-v1 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - accuracy |
| - precision |
| - recall |
| model-index: |
| - name: CodeBERTa-small-v1-sourcecode-detection-clf |
| 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. --> |
|
|
| # CodeBERTa-small-v1-sourcecode-detection-clf |
|
|
| This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0171 |
| - F1: 0.9975 |
| - Accuracy: 0.9975 |
| - Precision: 0.9975 |
| - Recall: 0.9975 |
|
|
| ## 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: 0.0003 |
| - train_batch_size: 320 |
| - eval_batch_size: 320 |
| - seed: 2024 |
| - 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: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 1 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | |
| |:-------------:|:------:|:----:|:---------------:|:------:|:--------:|:---------:|:------:| |
| | No log | 0 | 0 | 0.6981 | 0.3337 | 0.5001 | 0.6162 | 0.5001 | |
| | 0.0294 | 0.1420 | 1000 | 0.0398 | 0.9947 | 0.9947 | 0.9947 | 0.9947 | |
| | 0.0076 | 0.2841 | 2000 | 0.0211 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | |
| | 0.0053 | 0.4261 | 3000 | 0.0188 | 0.9973 | 0.9973 | 0.9973 | 0.9973 | |
| | 0.0056 | 0.5681 | 4000 | 0.0166 | 0.9976 | 0.9976 | 0.9976 | 0.9976 | |
| | 0.0044 | 0.7101 | 5000 | 0.0172 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | |
| | 0.0009 | 0.8522 | 6000 | 0.0171 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | |
| | 0.0052 | 0.9942 | 7000 | 0.0171 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.46.3 |
| - Pytorch 2.5.1 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.3 |
| |
