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donut_experiment_bayesian_trial_3
davelotito/donut_experiment_bayesian_trial_3
donut_experiment_bayesian_trial_3 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_3
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: 0.5840
- Bleu: 0.0667
- Precisions: [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846]
- Brevity Penalty: 0.0934
- Length Ratio: 0.2967
- Translation Length: 483
- Reference Length: 1628
- Cer: 0.7599
- 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: 0.00017060423589132634
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.2012 | 1.0 | 253 | 0.6694 | 0.0547 | [0.7680851063829788, 0.6682808716707022, 0.6067415730337079, 0.5484949832775919] | 0.0851 | 0.2887 | 470 | 1628 | 0.7597 | 0.8411 |
| 0.127 | 2.0 | 506 | 0.6071 | 0.0638 | [0.7818930041152263, 0.6876456876456877, 0.6370967741935484, 0.5841269841269842] | 0.0954 | 0.2985 | 486 | 1628 | 0.7570 | 0.8360 |
| 0.0766 | 3.0 | 759 | 0.5786 | 0.0655 | [0.8125, 0.735224586288416, 0.6885245901639344, 0.6407766990291263] | 0.0915 | 0.2948 | 480 | 1628 | 0.7564 | 0.8319 |
| 0.0259 | 4.0 | 1012 | 0.5840 | 0.0667 | [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846] | 0.0934 | 0.2967 | 483 | 1628 | 0.7599 | 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
- May 20, 2024
- Updated
- May 20, 2024