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--- |
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library_name: transformers |
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base_model: huggingface/CodeBERTa-small-v1 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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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: CodeBERTa-small-v1-sourcecode-detection-clf |
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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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# CodeBERTa-small-v1-sourcecode-detection-clf |
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This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0171 |
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- F1: 0.9975 |
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- Accuracy: 0.9975 |
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- Precision: 0.9975 |
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- Recall: 0.9975 |
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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: 0.0003 |
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- train_batch_size: 320 |
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- eval_batch_size: 320 |
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- seed: 2024 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH 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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:--------:|:---------:|:------:| |
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| No log | 0 | 0 | 0.6981 | 0.3337 | 0.5001 | 0.6162 | 0.5001 | |
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| 0.0294 | 0.1420 | 1000 | 0.0398 | 0.9947 | 0.9947 | 0.9947 | 0.9947 | |
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| 0.0076 | 0.2841 | 2000 | 0.0211 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | |
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| 0.0053 | 0.4261 | 3000 | 0.0188 | 0.9973 | 0.9973 | 0.9973 | 0.9973 | |
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| 0.0056 | 0.5681 | 4000 | 0.0166 | 0.9976 | 0.9976 | 0.9976 | 0.9976 | |
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| 0.0044 | 0.7101 | 5000 | 0.0172 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | |
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| 0.0009 | 0.8522 | 6000 | 0.0171 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | |
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| 0.0052 | 0.9942 | 7000 | 0.0171 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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