|
--- |
|
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 |
|
|
