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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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model-index: |
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- name: CodeBertForDefect-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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# CodeBertForDefect-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: 0.9039 |
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- Accuracy: 0.6435 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 13112.4 |
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- num_epochs: 10 |
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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 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.6483 | 1.0 | 1366 | 0.6494 | 0.5637 | |
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| 0.6213 | 2.0 | 2732 | 0.5968 | 0.6380 | |
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| 0.5927 | 3.0 | 4098 | 0.5767 | 0.6457 | |
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| 0.5615 | 4.0 | 5464 | 0.5855 | 0.6669 | |
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| 0.5271 | 5.0 | 6830 | 0.6677 | 0.6643 | |
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| 0.4488 | 6.0 | 8196 | 0.7177 | 0.6237 | |
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| 0.4576 | 7.0 | 9562 | 0.6643 | 0.6398 | |
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| 0.45 | 8.0 | 10928 | 0.7414 | 0.6479 | |
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| 0.4156 | 9.0 | 12294 | 0.9852 | 0.6519 | |
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| 0.3362 | 10.0 | 13660 | 0.9039 | 0.6435 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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