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donut_experiment_bayesian_trial_20
davelotito/donut_experiment_bayesian_trial_20
donut_experiment_bayesian_trial_20 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_20
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: 0.6042
- Bleu: 0.0711
- Precisions: [0.8440748440748441, 0.7924528301886793, 0.7493188010899182, 0.7064516129032258]
- Brevity Penalty: 0.0921
- Length Ratio: 0.2955
- Translation Length: 481
- Reference Length: 1628
- Cer: 0.7518
- Wer: 0.8208
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: 1.4151037707088747e-05
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0012 | 1.0 | 253 | 0.6392 | 0.0724 | [0.8333333333333334, 0.7762237762237763, 0.7365591397849462, 0.6952380952380952] | 0.0954 | 0.2985 | 486 | 1628 | 0.7482 | 0.8193 |
| 0.0068 | 2.0 | 506 | 0.6212 | 0.0697 | [0.8413361169102297, 0.7867298578199052, 0.7452054794520548, 0.7012987012987013] | 0.0908 | 0.2942 | 479 | 1628 | 0.7527 | 0.8230 |
| 0.0087 | 3.0 | 759 | 0.6105 | 0.0687 | [0.83125, 0.7706855791962175, 0.726775956284153, 0.6828478964401294] | 0.0915 | 0.2948 | 480 | 1628 | 0.7573 | 0.8282 |
| 0.0056 | 4.0 | 1012 | 0.6042 | 0.0711 | [0.8440748440748441, 0.7924528301886793, 0.7493188010899182, 0.7064516129032258] | 0.0921 | 0.2955 | 481 | 1628 | 0.7518 | 0.8208 |
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