| --- |
| library_name: transformers |
| base_model: microsoft/codebert-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: codebert-java-inconsistency |
| 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. --> |
|
|
| # codebert-java-inconsistency |
|
|
| This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3543 |
| - Accuracy: 0.9167 |
| - F1: 0.9183 |
| - Precision: 0.9235 |
| - Recall: 0.9167 |
|
|
| ## 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: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 64 |
| - 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: cosine |
| - num_epochs: 30 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 1.4625 | 3.1290 | 50 | 0.8954 | 0.7531 | 0.7554 | 0.7765 | 0.7531 | |
| | 0.5834 | 6.2581 | 100 | 0.5559 | 0.8189 | 0.8241 | 0.8483 | 0.8189 | |
| | 0.2858 | 9.3871 | 150 | 0.4046 | 0.8930 | 0.8945 | 0.8995 | 0.8930 | |
| | 0.1624 | 12.5161 | 200 | 0.4461 | 0.8642 | 0.8661 | 0.8750 | 0.8642 | |
| | 0.1084 | 15.6452 | 250 | 0.4012 | 0.9012 | 0.9038 | 0.9123 | 0.9012 | |
| | 0.074 | 18.7742 | 300 | 0.4689 | 0.8765 | 0.8817 | 0.8972 | 0.8765 | |
| | 0.0574 | 21.9032 | 350 | 0.4885 | 0.8807 | 0.8845 | 0.8970 | 0.8807 | |
| | 0.0452 | 25.0 | 400 | 0.4900 | 0.8848 | 0.8888 | 0.9011 | 0.8848 | |
| | 0.0396 | 28.1290 | 450 | 0.4896 | 0.8765 | 0.8805 | 0.8934 | 0.8765 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.51.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.5.0 |
| - Tokenizers 0.21.1 |
|
|
