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donut-finetuned – AI Model by Tuan-Bao | AlphaNeural AI
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donut-finetuned
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transformers
tensorboard
safetensors
vision-encoder-decoder
generated_from_trainer
naver-clova-ix/donut-base
finetune
mit
endpoints_compatible
us
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donut-finetuned
This model is a fine-tuned version of
naver-clova-ix/donut-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1691
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: 1
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
85.1472
0.5073
130
4.4339
18.4994
1.0117
260
1.0729
7.8035
1.5190
390
0.3930
3.7089
2.0234
520
0.2107
2.8292
2.5307
650
0.1691
Framework versions
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2
