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# x_rotated

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: 0.1574
- Cer: 0.0377

## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2067        | 1.0   | 500  | 0.2103          | 0.0538 |
| 0.1043        | 2.0   | 1000 | 0.1667          | 0.0445 |
| 0.0667        | 3.0   | 1500 | 0.1571          | 0.0388 |
| 0.0489        | 4.0   | 2000 | 0.1574          | 0.0377 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "model-index": [{"name": "x_rotated", "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 

x\_rotated
==========


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: 0.1574
* Cer: 0.0377


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: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* 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: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3