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donut_experiment_bayesian_trial_2
davelotito/donut_experiment_bayesian_trial_2
donut_experiment_bayesian_trial_2 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_2
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: 0.4983
- Bleu: 0.0695
- Precisions: [0.8257261410788381, 0.7717647058823529, 0.7255434782608695, 0.6816720257234726]
- Brevity Penalty: 0.0928
- Length Ratio: 0.2961
- Translation Length: 482
- Reference Length: 1628
- Cer: 0.7610
- Wer: 0.8275
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: 0.00015752383448484097
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3017 | 1.0 | 253 | 0.7248 | 0.0641 | [0.7525150905432596, 0.65, 0.587467362924282, 0.5276073619631901] | 0.1027 | 0.3053 | 497 | 1628 | 0.7622 | 0.8495 |
| 0.1875 | 2.0 | 506 | 0.6129 | 0.0670 | [0.7914110429447853, 0.7152777777777778, 0.6613333333333333, 0.60062893081761] | 0.0974 | 0.3004 | 489 | 1628 | 0.7565 | 0.8375 |
| 0.1171 | 3.0 | 759 | 0.5027 | 0.0697 | [0.8202479338842975, 0.7587822014051522, 0.7162162162162162, 0.6741214057507987] | 0.0941 | 0.2973 | 484 | 1628 | 0.7563 | 0.8293 |
| 0.0432 | 4.0 | 1012 | 0.4983 | 0.0695 | [0.8257261410788381, 0.7717647058823529, 0.7255434782608695, 0.6816720257234726] | 0.0928 | 0.2961 | 482 | 1628 | 0.7610 | 0.8275 |
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