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donut_experiment_bayesian_trial_16
davelotito/donut_experiment_bayesian_trial_16
donut_experiment_bayesian_trial_16 is a image-text-to-text model from davelotito. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- mit
- Created
- Jun 26, 2024
- Updated
- Jun 26, 2024