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donut_experiment_bayesian_trial_15
davelotito/donut_experiment_bayesian_trial_15
donut_experiment_bayesian_trial_15 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_15
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: 0.5777
- Bleu: 0.0659
- Precisions: [0.8158995815899581, 0.7434679334916865, 0.7060439560439561, 0.6644951140065146]
- Brevity Penalty: 0.0902
- Length Ratio: 0.2936
- Translation Length: 478
- Reference Length: 1628
- Cer: 0.7557
- Wer: 0.8239
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: 2.349414650597281e-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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0066 | 1.0 | 253 | 0.5790 | 0.0648 | [0.8305084745762712, 0.7686746987951807, 0.7262569832402235, 0.6843853820598007] | 0.0864 | 0.2899 | 472 | 1628 | 0.7593 | 0.8258 |
| 0.0143 | 2.0 | 506 | 0.5824 | 0.0663 | [0.8225469728601252, 0.7511848341232228, 0.7041095890410959, 0.6525974025974026] | 0.0908 | 0.2942 | 479 | 1628 | 0.7577 | 0.8265 |
| 0.009 | 3.0 | 759 | 0.5826 | 0.0640 | [0.8185654008438819, 0.7458033573141487, 0.7055555555555556, 0.6600660066006601] | 0.0876 | 0.2912 | 474 | 1628 | 0.7553 | 0.8248 |
| 0.0103 | 4.0 | 1012 | 0.5777 | 0.0659 | [0.8158995815899581, 0.7434679334916865, 0.7060439560439561, 0.6644951140065146] | 0.0902 | 0.2936 | 478 | 1628 | 0.7557 | 0.8239 |
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