
<!-- 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

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "plbart-base-finetuned-detection-bad-good-ut", "results": []}]}
transformers pytorch tensorboard plbart text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #plbart #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

plbart-base-finetuned-detection-bad-good-ut
===========================================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #plbart #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #plbart #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2