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# donut-base-hangul-handwritten-KMOU

This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5986        | 1.0   | 157  | 2.9011          |
| 0.6912        | 2.0   | 314  | 0.4334          |
| 0.3317        | 3.0   | 471  | 0.2250          |
| 0.2029        | 4.0   | 628  | 0.1379          |
| 0.1587        | 5.0   | 785  | 0.0901          |
| 0.0963        | 6.0   | 942  | 0.0609          |
| 0.0812        | 7.0   | 1099 | 0.0410          |
| 0.0755        | 8.0   | 1256 | 0.0289          |
| 0.0471        | 9.0   | 1413 | 0.0191          |
| 0.0368        | 10.0  | 1570 | 0.0140          |
| 0.0247        | 11.0  | 1727 | 0.0088          |
| 0.023         | 12.0  | 1884 | 0.0065          |
| 0.0239        | 13.0  | 2041 | 0.0045          |
| 0.0139        | 14.0  | 2198 | 0.0036          |
| 0.0166        | 15.0  | 2355 | 0.0025          |
| 0.0144        | 16.0  | 2512 | 0.0020          |
| 0.0144        | 17.0  | 2669 | 0.0017          |
| 0.012         | 18.0  | 2826 | 0.0012          |
| 0.0085        | 19.0  | 2983 | 0.0008          |
| 0.0063        | 20.0  | 3140 | 0.0010          |
| 0.0054        | 21.0  | 3297 | 0.0007          |
| 0.0074        | 22.0  | 3454 | 0.0008          |
| 0.0056        | 23.0  | 3611 | 0.0006          |
| 0.0048        | 24.0  | 3768 | 0.0004          |
| 0.0055        | 25.0  | 3925 | 0.0003          |
| 0.0053        | 26.0  | 4082 | 0.0003          |
| 0.0054        | 27.0  | 4239 | 0.0003          |
| 0.0067        | 28.0  | 4396 | 0.0004          |
| 0.0042        | 29.0  | 4553 | 0.0002          |
| 0.0028        | 30.0  | 4710 | 0.0003          |
| 0.0036        | 31.0  | 4867 | 0.0002          |
| 0.004         | 32.0  | 5024 | 0.0003          |
| 0.0041        | 33.0  | 5181 | 0.0001          |
| 0.005         | 34.0  | 5338 | 0.0002          |
| 0.0025        | 35.0  | 5495 | 0.0002          |
| 0.004         | 36.0  | 5652 | 0.0001          |
| 0.0038        | 37.0  | 5809 | 0.0001          |
| 0.0033        | 38.0  | 5966 | 0.0001          |
| 0.0026        | 39.0  | 6123 | 0.0001          |
| 0.0052        | 40.0  | 6280 | 0.0001          |
| 0.0051        | 41.0  | 6437 | 0.0001          |
| 0.0019        | 42.0  | 6594 | 0.0001          |
| 0.0036        | 43.0  | 6751 | 0.0001          |
| 0.0053        | 44.0  | 6908 | 0.0001          |
| 0.0018        | 45.0  | 7065 | 0.0001          |
| 0.0033        | 46.0  | 7222 | 0.0001          |
| 0.0061        | 47.0  | 7379 | 0.0001          |
| 0.0023        | 48.0  | 7536 | 0.0000          |
| 0.0023        | 49.0  | 7693 | 0.0000          |
| 0.0048        | 50.0  | 7850 | 0.0000          |


### Framework versions

- Transformers 4.28.1
- Pytorch 1.10.0+cu111
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "model-index": [{"name": "donut-base-hangul-handwritten-KMOU", "results": []}]}
transformers pytorch tensorboard vision-encoder-decoder generated_from_trainer dataset:imagefolder license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #license-mit #endpoints_compatible #region-us 

donut-base-hangul-handwritten-KMOU
==================================


This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0000


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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 1.10.0+cu111
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.10.0+cu111
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.10.0+cu111
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #license-mit #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 1.10.0+cu111
* Datasets 2.11.0
* Tokenizers 0.13.3