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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: plbart-base-finetuned-detection-bad-good-ut
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# plbart-base-finetuned-detection-bad-good-ut
This model is a fine-tuned version of [uclanlp/plbart-base](https://huggingface.co/uclanlp/plbart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3264
- Accuracy: 0.826
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6958 | 0.09 | 100 | 0.7097 | 0.532 |
| 0.6358 | 0.18 | 200 | 0.4519 | 0.759 |
| 0.4083 | 0.27 | 300 | 0.3793 | 0.789 |
| 0.3863 | 0.36 | 400 | 0.3827 | 0.797 |
| 0.3581 | 0.44 | 500 | 0.3392 | 0.81 |
| 0.3395 | 0.53 | 600 | 0.3546 | 0.8 |
| 0.3336 | 0.62 | 700 | 0.3297 | 0.827 |
| 0.353 | 0.71 | 800 | 0.3645 | 0.803 |
| 0.3628 | 0.8 | 900 | 0.3400 | 0.824 |
| 0.3227 | 0.89 | 1000 | 0.3264 | 0.826 |
| 0.3521 | 0.98 | 1100 | 0.3227 | 0.823 |
| 0.3556 | 1.07 | 1200 | 0.3211 | 0.821 |
| 0.3243 | 1.16 | 1300 | 0.3296 | 0.812 |
| 0.3201 | 1.24 | 1400 | 0.3395 | 0.832 |
| 0.3127 | 1.33 | 1500 | 0.3365 | 0.83 |
| 0.3267 | 1.42 | 1600 | 0.3376 | 0.828 |
| 0.3046 | 1.51 | 1700 | 0.3316 | 0.82 |
| 0.2903 | 1.6 | 1800 | 0.3418 | 0.835 |
| 0.3062 | 1.69 | 1900 | 0.3300 | 0.84 |
| 0.3034 | 1.78 | 2000 | 0.3327 | 0.838 |
| 0.2828 | 1.87 | 2100 | 0.3342 | 0.825 |
| 0.3119 | 1.96 | 2200 | 0.3319 | 0.833 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
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