|
--- |
|
license: mit |
|
base_model: naver-clova-ix/donut-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- bleu |
|
- wer |
|
model-index: |
|
- name: donut_experiment_bayesian_trial_16 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# donut_experiment_bayesian_trial_16 |
|
|
|
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5541 |
|
- Bleu: 0.0670 |
|
- Precisions: [0.8417721518987342, 0.7841726618705036, 0.7388888888888889, 0.6996699669966997] |
|
- Brevity Penalty: 0.0876 |
|
- Length Ratio: 0.2912 |
|
- Translation Length: 474 |
|
- Reference Length: 1628 |
|
- Cer: 0.7567 |
|
- Wer: 0.8224 |
|
|
|
## 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: 0.00011219603369833024 |
|
- 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:| |
|
| 0.0965 | 1.0 | 253 | 0.5550 | 0.0624 | [0.7995824634655533, 0.7085308056872038, 0.6520547945205479, 0.6038961038961039] | 0.0908 | 0.2942 | 479 | 1628 | 0.7576 | 0.8347 | |
|
| 0.0844 | 2.0 | 506 | 0.5896 | 0.0651 | [0.8218029350104822, 0.7476190476190476, 0.696969696969697, 0.6535947712418301] | 0.0895 | 0.2930 | 477 | 1628 | 0.7557 | 0.8302 | |
|
| 0.0539 | 3.0 | 759 | 0.5594 | 0.0666 | [0.8322851153039832, 0.7642857142857142, 0.7134986225895317, 0.673202614379085] | 0.0895 | 0.2930 | 477 | 1628 | 0.7552 | 0.8223 | |
|
| 0.023 | 4.0 | 1012 | 0.5541 | 0.0670 | [0.8417721518987342, 0.7841726618705036, 0.7388888888888889, 0.6996699669966997] | 0.0876 | 0.2912 | 474 | 1628 | 0.7567 | 0.8224 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.0 |
|
- Pytorch 2.1.0 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.19.1 |
|
|
