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# Donut_ocr_chart_10k

This model is a fine-tuned version of [aravind-selvam/donut_finetuned_chart](https://huggingface.co/aravind-selvam/donut_finetuned_chart) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2031
- eval_cer: 0.8316
- eval_runtime: 222.4582
- eval_samples_per_second: 4.495
- eval_steps_per_second: 0.283
- epoch: 4.0
- step: 1257

## 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: 2.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "model-index": [{"name": "Donut_ocr_chart_10k", "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_ocr_chart_10k

This model is a fine-tuned version of aravind-selvam/donut_finetuned_chart on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2031
- eval_cer: 0.8316
- eval_runtime: 222.4582
- eval_samples_per_second: 4.495
- eval_steps_per_second: 0.283
- epoch: 4.0
- step: 1257

## 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: 2.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Framework versions

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

# Donut_ocr_chart_10k

This model is a fine-tuned version of aravind-selvam/donut_finetuned_chart on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2031
- eval_cer: 0.8316
- eval_runtime: 222.4582
- eval_samples_per_second: 4.495
- eval_steps_per_second: 0.283
- epoch: 4.0
- step: 1257 ## 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: 2.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP ### 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 #license-mit #endpoints_compatible #region-us 
 # Donut_ocr_chart_10k

This model is a fine-tuned version of aravind-selvam/donut_finetuned_chart on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2031
- eval_cer: 0.8316
- eval_runtime: 222.4582
- eval_samples_per_second: 4.495
- eval_steps_per_second: 0.283
- epoch: 4.0
- step: 1257 ## 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: 2.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP ### 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 #license-mit #endpoints_compatible #region-us 
# Donut_ocr_chart_10k

This model is a fine-tuned version of aravind-selvam/donut_finetuned_chart on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2031
- eval_cer: 0.8316
- eval_runtime: 222.4582
- eval_samples_per_second: 4.495
- eval_steps_per_second: 0.283
- epoch: 4.0
- step: 1257## 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: 2.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP### Framework versions

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