donut_experiment_bayesian_trial_0

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: 0.4050
• Bleu: 0.0639
• Precisions: [0.79957805907173, 0.7386091127098321, 0.7083333333333334, 0.6765676567656765]
• Brevity Penalty: 0.0876
• Length Ratio: 0.2912
• Translation Length: 474
• Reference Length: 1628
• Cer: 0.7653
• Wer: 0.8371

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: 1.2045081648781836e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 2
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Precisions	Brevity Penalty	Length Ratio	Translation Length	Reference Length	Cer	Wer
0.9353	1.0	253	0.6228	0.0486	[0.7096774193548387, 0.6053921568627451, 0.5612535612535613, 0.5102040816326531]	0.0820	0.2856	465	1628	0.7751	0.8592
0.462	2.0	506	0.4846	0.0568	[0.7913978494623656, 0.7058823529411765, 0.6609686609686609, 0.6224489795918368]	0.0820	0.2856	465	1628	0.7650	0.8423
0.4071	3.0	759	0.4226	0.0626	[0.7899159663865546, 0.711217183770883, 0.6767955801104972, 0.6459016393442623]	0.0889	0.2924	476	1628	0.7685	0.8436
0.3007	4.0	1012	0.4092	0.0638	[0.7957894736842105, 0.7344497607655502, 0.7008310249307479, 0.6644736842105263]	0.0883	0.2918	475	1628	0.7640	0.8397
0.3114	5.0	1265	0.4050	0.0639	[0.79957805907173, 0.7386091127098321, 0.7083333333333334, 0.6765676567656765]	0.0876	0.2912	474	1628	0.7653	0.8371

Framework versions

• Transformers 4.40.0
• Pytorch 2.1.0
• Datasets 2.18.0
• Tokenizers 0.19.1