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---
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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: plbart-base-finetuned-detection-bad-good-ut
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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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# plbart-base-finetuned-detection-bad-good-ut
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This model is a fine-tuned version of [uclanlp/plbart-base](https://huggingface.co/uclanlp/plbart-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3264
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- Accuracy: 0.826
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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-06
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- train_batch_size: 8
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- eval_batch_size: 8
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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: 2
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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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| 0.6958 | 0.09 | 100 | 0.7097 | 0.532 |
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| 0.6358 | 0.18 | 200 | 0.4519 | 0.759 |
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| 0.4083 | 0.27 | 300 | 0.3793 | 0.789 |
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| 0.3863 | 0.36 | 400 | 0.3827 | 0.797 |
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| 0.3581 | 0.44 | 500 | 0.3392 | 0.81 |
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| 0.3395 | 0.53 | 600 | 0.3546 | 0.8 |
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| 0.3336 | 0.62 | 700 | 0.3297 | 0.827 |
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| 0.353 | 0.71 | 800 | 0.3645 | 0.803 |
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| 0.3628 | 0.8 | 900 | 0.3400 | 0.824 |
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| 0.3227 | 0.89 | 1000 | 0.3264 | 0.826 |
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| 0.3521 | 0.98 | 1100 | 0.3227 | 0.823 |
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| 0.3556 | 1.07 | 1200 | 0.3211 | 0.821 |
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| 0.3243 | 1.16 | 1300 | 0.3296 | 0.812 |
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| 0.3201 | 1.24 | 1400 | 0.3395 | 0.832 |
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| 0.3127 | 1.33 | 1500 | 0.3365 | 0.83 |
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| 0.3267 | 1.42 | 1600 | 0.3376 | 0.828 |
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| 0.3046 | 1.51 | 1700 | 0.3316 | 0.82 |
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| 0.2903 | 1.6 | 1800 | 0.3418 | 0.835 |
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| 0.3062 | 1.69 | 1900 | 0.3300 | 0.84 |
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| 0.3034 | 1.78 | 2000 | 0.3327 | 0.838 |
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| 0.2828 | 1.87 | 2100 | 0.3342 | 0.825 |
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| 0.3119 | 1.96 | 2200 | 0.3319 | 0.833 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.10.0
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- Tokenizers 0.13.2
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