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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: microsoft-codebert-base-finetuned-defect-detection |
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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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# microsoft-codebert-base-finetuned-defect-detection |
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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: 0.6197 |
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- Accuracy: 0.7382 |
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- Roc Auc: 0.7394 |
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- Precision: 0.7070 |
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- Recall: 0.7924 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 4711 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 5 |
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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 | Roc Auc | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:---------:|:------:| |
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| 0.6456 | 1.0 | 996 | 0.5435 | 0.6832 | 0.6810 | 0.7151 | 0.5843 | |
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| 0.5086 | 2.0 | 1993 | 0.5373 | 0.7113 | 0.7139 | 0.6654 | 0.8227 | |
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| 0.4173 | 3.0 | 2989 | 0.5476 | 0.7289 | 0.7293 | 0.7125 | 0.7461 | |
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| 0.3543 | 4.0 | 3986 | 0.5803 | 0.7357 | 0.7369 | 0.7051 | 0.7888 | |
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| 0.3059 | 5.0 | 4980 | 0.6197 | 0.7382 | 0.7394 | 0.7070 | 0.7924 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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