metadata

license: mit
base_model: naver-clova-ix/donut-base
tags:
  - generated_from_trainer
metrics:
  - bleu
  - wer
model-index:
  - name: donut-base-sroie-metrics-combined-new
    results: []

donut-base-sroie-metrics-combined-new

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: 0.4671
Bleu: 0.0662
Precisions: [0.785140562248996, 0.6825396825396826, 0.6197916666666666, 0.5626911314984709]
Brevity Penalty: 0.1007
Length Ratio: 0.3035
Translation Length: 498
Reference Length: 1641
Cer: 0.7528
Wer: 0.8385

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: 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: 4
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
3.6559	1.0	253	1.5613	0.0007	[0.5056179775280899, 0.1943127962085308, 0.07692307692307693, 0.02830188679245283]	0.0058	0.1627	267	1641	0.8768	0.9436
1.2493	2.0	506	0.6697	0.0409	[0.6560509554140127, 0.5048309178743962, 0.4481792717086835, 0.39]	0.0834	0.2870	471	1641	0.7766	0.8837
0.9257	3.0	759	0.5168	0.0594	[0.75, 0.6275862068965518, 0.5714285714285714, 0.5264797507788161]	0.0968	0.2998	492	1641	0.7570	0.8499
0.6416	4.0	1012	0.4671	0.0662	[0.785140562248996, 0.6825396825396826, 0.6197916666666666, 0.5626911314984709]	0.1007	0.3035	498	1641	0.7528	0.8385

Framework versions

Transformers 4.41.0.dev0
Pytorch 2.1.0
Datasets 2.19.1
Tokenizers 0.19.1