metadata
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_5
results: []
donut_experiment_5
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: 0.3987
- Bleu: 0.0661
- Precisions: [0.8020833333333334, 0.7375886524822695, 0.6994535519125683, 0.6601941747572816]
- Brevity Penalty: 0.0915
- Length Ratio: 0.2948
- Translation Length: 480
- Reference Length: 1628
- Cer: 0.7576
- Wer: 0.8280
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3274 | 1.0 | 253 | 0.4698 | 0.0586 | [0.7707006369426752, 0.6956521739130435, 0.6582633053221288, 0.62] | 0.0857 | 0.2893 | 471 | 1628 | 0.7660 | 0.8432 |
| 0.2539 | 2.0 | 506 | 0.4198 | 0.0643 | [0.799163179916318, 0.7315914489311164, 0.6868131868131868, 0.6416938110749185] | 0.0902 | 0.2936 | 478 | 1628 | 0.7605 | 0.8313 |
| 0.224 | 3.0 | 759 | 0.3941 | 0.0658 | [0.8075313807531381, 0.7387173396674585, 0.7060439560439561, 0.6710097719869706] | 0.0902 | 0.2936 | 478 | 1628 | 0.7573 | 0.8283 |
| 0.1566 | 4.0 | 1012 | 0.3987 | 0.0661 | [0.8020833333333334, 0.7375886524822695, 0.6994535519125683, 0.6601941747572816] | 0.0915 | 0.2948 | 480 | 1628 | 0.7576 | 0.8280 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1