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# donut_pdf_ocr

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0443

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0829        | 1.0   | 47   | 0.1157          |
| 0.0184        | 2.0   | 94   | 0.1660          |
| 0.0533        | 3.0   | 141  | 0.0765          |
| 0.0765        | 4.0   | 188  | 0.0530          |
| 0.101         | 5.0   | 235  | 0.0481          |
| 0.0936        | 6.0   | 282  | 0.0494          |
| 0.1032        | 7.0   | 329  | 0.0524          |
| 0.0033        | 8.0   | 376  | 0.0460          |
| 0.0185        | 9.0   | 423  | 0.0440          |
| 0.0044        | 10.0  | 470  | 0.0443          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "model-index": [{"name": "donut_pdf_ocr", "results": []}]}
transformers pytorch tensorboard vision-encoder-decoder generated_from_trainer dataset:imagefolder endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #endpoints_compatible #region-us 

donut\_pdf\_ocr
===============


This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0443


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3