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donut_experiment_bayesian_trial_11
davelotito/donut_experiment_bayesian_trial_11
donut_experiment_bayesian_trial_11 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_11
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: 0.4496
- Bleu: 0.0662
- Precisions: [0.8162839248434238, 0.7440758293838863, 0.7013698630136986, 0.6623376623376623]
- Brevity Penalty: 0.0908
- Length Ratio: 0.2942
- Translation Length: 479
- Reference Length: 1628
- Cer: 0.7615
- Wer: 0.8328
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: 0.00012678733283601488
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5513 | 1.0 | 253 | 0.5209 | 0.0666 | [0.7287128712871287, 0.6450892857142857, 0.578005115089514, 0.5269461077844312] | 0.1082 | 0.3102 | 505 | 1628 | 0.7584 | 0.8461 |
| 0.2612 | 2.0 | 506 | 0.5017 | 0.0648 | [0.7962577962577962, 0.7287735849056604, 0.6784741144414169, 0.6225806451612903] | 0.0921 | 0.2955 | 481 | 1628 | 0.7542 | 0.8349 |
| 0.1638 | 3.0 | 759 | 0.4666 | 0.0615 | [0.7995824634655533, 0.6990521327014217, 0.6356164383561644, 0.5909090909090909] | 0.0908 | 0.2942 | 479 | 1628 | 0.7636 | 0.8392 |
| 0.059 | 4.0 | 1012 | 0.4496 | 0.0662 | [0.8162839248434238, 0.7440758293838863, 0.7013698630136986, 0.6623376623376623] | 0.0908 | 0.2942 | 479 | 1628 | 0.7615 | 0.8328 |
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