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donut-base-sroie-metrics-combined
davelotito/donut-base-sroie-metrics-combined
donut-base-sroie-metrics-combined 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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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-base-sroie-metrics-combined
This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2827
- Bleu score: 0.0762
- Precisions: [0.8396396396396396, 0.7886178861788617, 0.7435897435897436, 0.7049180327868853]
- Brevity penalty: 0.0993
- Length ratio: 0.3021
- Translation length: 555
- Reference length: 1837
- Cer: 0.7452
- Wer: 0.8162
- Cer Hugging Face: 0.7544
- Wer Hugging Face: 0.8233
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 score | Precisions | Brevity penalty | Length ratio | Translation length | Reference length | Cer | Wer | Cer Hugging Face | Wer Hugging Face |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 0.99 | 62 | 0.3478 | 0.0756 | [0.8178571428571428, 0.7625754527162978, 0.7142857142857143, 0.6711590296495957] | 0.1022 | 0.3048 | 560 | 1837 | 0.7474 | 0.8243 | 0.7570 | 0.8333 |
| 0.2634 | 2.0 | 125 | 0.2873 | 0.0763 | [0.8345323741007195, 0.7829614604462475, 0.7418604651162791, 0.7029972752043597] | 0.0999 | 0.3027 | 556 | 1837 | 0.7435 | 0.8219 | 0.7527 | 0.8288 |
| 0.2634 | 2.99 | 187 | 0.2817 | 0.0777 | [0.8369175627240143, 0.7838383838383839, 0.7476851851851852, 0.7127371273712737] | 0.1011 | 0.3038 | 558 | 1837 | 0.7407 | 0.8152 | 0.7498 | 0.8215 |
| 0.263 | 3.97 | 248 | 0.2827 | 0.0762 | [0.8396396396396396, 0.7886178861788617, 0.7435897435897436, 0.7049180327868853] | 0.0993 | 0.3021 | 555 | 1837 | 0.7452 | 0.8162 | 0.7544 | 0.8233 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- mit
- Created
- Apr 5, 2024
- Updated
- Apr 5, 2024