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donut_experiment_bayesian_trial_7
davelotito/donut_experiment_bayesian_trial_7
donut_experiment_bayesian_trial_7 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_7
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: 0.3786
- Bleu: 0.0669
- Precisions: [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332]
- Brevity Penalty: 0.0870
- Length Ratio: 0.2905
- Translation Length: 473
- Reference Length: 1628
- Cer: 0.7532
- Wer: 0.8192
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: 3.540464175534869e-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: 5
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5323 | 1.0 | 253 | 0.4204 | 0.0580 | [0.7710084033613446, 0.6778042959427207, 0.6132596685082873, 0.5639344262295082] | 0.0889 | 0.2924 | 476 | 1628 | 0.7617 | 0.8431 |
| 0.2487 | 2.0 | 506 | 0.3788 | 0.0609 | [0.8123667377398721, 0.7402912621359223, 0.6929577464788732, 0.6476510067114094] | 0.0845 | 0.2881 | 469 | 1628 | 0.7561 | 0.8279 |
| 0.1746 | 3.0 | 759 | 0.3551 | 0.0652 | [0.836864406779661, 0.7759036144578313, 0.729050279329609, 0.6843853820598007] | 0.0864 | 0.2899 | 472 | 1628 | 0.7541 | 0.8213 |
| 0.1191 | 4.0 | 1012 | 0.3690 | 0.0680 | [0.8547368421052631, 0.784688995215311, 0.7451523545706371, 0.7039473684210527] | 0.0883 | 0.2918 | 475 | 1628 | 0.7514 | 0.8192 |
| 0.1072 | 5.0 | 1265 | 0.3786 | 0.0669 | [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332] | 0.0870 | 0.2905 | 473 | 1628 | 0.7532 | 0.8192 |
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 4, 2024
- Updated
- Jun 4, 2024