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