| --- |
| base_model: microsoft/unixcoder-base |
| library_name: peft |
| license: apache-2.0 |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: CodeGenDetect-Unixcoder_Lora |
| 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. --> |
|
|
| # CodeGenDetect-Unixcoder_Lora |
| |
| This model is a fine-tuned version of [microsoft/unixcoder-base](https://huggingface.co/microsoft/unixcoder-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0266 |
| - Accuracy: 0.9927 |
| - F1: 0.9927 |
| - Precision: 0.9927 |
| - Recall: 0.9927 |
| |
| ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.0349 | 1.02 | 4000 | 0.0342 | 0.9887 | 0.9887 | 0.9887 | 0.9887 | |
| | 0.0244 | 2.05 | 8000 | 0.0279 | 0.9916 | 0.9916 | 0.9916 | 0.9916 | |
| | 0.0234 | 3.07 | 12000 | 0.0260 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | |
| | 0.0249 | 4.1 | 16000 | 0.0266 | 0.9927 | 0.9927 | 0.9927 | 0.9927 | |
|
|
|
|
| ### Framework versions |
|
|
| - PEFT 0.9.0 |
| - Transformers 4.38.2 |
| - Pytorch 2.5.1+rocm6.2 |
| - Datasets 2.21.0 |
| - Tokenizers 0.15.2 |
