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donut_experiment_bayesian_trial_17
davelotito/donut_experiment_bayesian_trial_17
donut_experiment_bayesian_trial_17 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_17
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: 0.4635
- Bleu: 0.0675
- Precisions: [0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307]
- Brevity Penalty: 0.0895
- Length Ratio: 0.2930
- Translation Length: 477
- Reference Length: 1628
- Cer: 0.7603
- Wer: 0.8297
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: 0.00018015728878154226
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.8044 | 1.0 | 253 | 0.7112 | 0.0610 | [0.7535641547861507, 0.6497695852534562, 0.5809018567639257, 0.5125] | 0.0987 | 0.3016 | 491 | 1628 | 0.7647 | 0.8548 |
| 0.3513 | 2.0 | 506 | 0.5640 | 0.0632 | [0.7908902691511387, 0.7089201877934272, 0.6449864498644986, 0.5801282051282052] | 0.0934 | 0.2967 | 483 | 1628 | 0.7549 | 0.8416 |
| 0.2101 | 3.0 | 759 | 0.4754 | 0.0666 | [0.8198757763975155, 0.744131455399061, 0.6802168021680217, 0.6217948717948718] | 0.0934 | 0.2967 | 483 | 1628 | 0.7508 | 0.8282 |
| 0.0756 | 4.0 | 1012 | 0.4635 | 0.0675 | [0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307] | 0.0895 | 0.2930 | 477 | 1628 | 0.7603 | 0.8297 |
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
- Jul 1, 2024
- Updated
- Jul 1, 2024