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donut_experiment_bayesian_trial_6
davelotito/donut_experiment_bayesian_trial_6
donut_experiment_bayesian_trial_6 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_6
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: 0.5515
- Bleu: 0.0683
- Precisions: [0.8127572016460906, 0.7412587412587412, 0.6854838709677419, 0.638095238095238]
- Brevity Penalty: 0.0954
- Length Ratio: 0.2985
- Translation Length: 486
- Reference Length: 1628
- Cer: 0.7532
- Wer: 0.8274
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: 0.00016063260663724173
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3276 | 1.0 | 253 | 0.6672 | 0.0589 | [0.76875, 0.6737588652482269, 0.6092896174863388, 0.5436893203883495] | 0.0915 | 0.2948 | 480 | 1628 | 0.7586 | 0.8473 |
| 0.2008 | 2.0 | 506 | 0.5780 | 0.0662 | [0.7905544147843943, 0.7069767441860465, 0.6595174262734584, 0.6107594936708861] | 0.0960 | 0.2991 | 487 | 1628 | 0.7559 | 0.8374 |
| 0.1356 | 3.0 | 759 | 0.5355 | 0.0651 | [0.8238993710691824, 0.7452380952380953, 0.6942148760330579, 0.6535947712418301] | 0.0895 | 0.2930 | 477 | 1628 | 0.7580 | 0.8299 |
| 0.0394 | 4.0 | 1012 | 0.5515 | 0.0683 | [0.8127572016460906, 0.7412587412587412, 0.6854838709677419, 0.638095238095238] | 0.0954 | 0.2985 | 486 | 1628 | 0.7532 | 0.8274 |
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 30, 2024
- Updated
- May 30, 2024