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--- |
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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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- precision |
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- recall |
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model-index: |
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- name: logs |
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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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# logs |
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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: 0.0405 |
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- Accuracy: 0.9950 |
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- Precision: 0.9950 |
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- Recall: 0.9950 |
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- F1 Score: 0.9950 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| 0.1436 | 1.0 | 907 | 0.0851 | 0.9829 | 0.9829 | 0.9829 | 0.9829 | |
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| 0.0737 | 2.0 | 1814 | 0.0548 | 0.9915 | 0.9915 | 0.9915 | 0.9915 | |
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| 0.0216 | 3.0 | 2721 | 0.0469 | 0.9917 | 0.9918 | 0.9917 | 0.9917 | |
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| 0.0143 | 4.0 | 3628 | 0.0405 | 0.9950 | 0.9950 | 0.9950 | 0.9950 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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