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donut-base-sroie-v2
davelotito/donut-base-sroie-v2
donut-base-sroie-v2 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-v2
This model is a fine-tuned version of davelotito/donut-base-sroie on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4355
- Bleu: 0.8879
- Precisions: [0.943646408839779, 0.9119229045271179, 0.8854285064787452, 0.860009225092251]
- Brevity Penalty: 0.9868
- Length Ratio: 0.9869
- Translation Length: 4525
- Reference Length: 4585
- Cer: 0.0857
- Wer: 0.2978
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 | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer |
|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 0.99 | 62 | 0.4638 | 0.8823 | [0.9399823477493381, 0.9044528977399866, 0.8772128915115751, 0.8514851485148515] | 0.9884 | 0.9884 | 4532 | 4585 | 0.0912 | 0.3085 |
| 0.0043 | 2.0 | 125 | 0.4421 | 0.8853 | [0.9405155320555189, 0.9059428060768543, 0.8794470881486517, 0.8537931034482759] | 0.9899 | 0.9900 | 4539 | 4585 | 0.0889 | 0.3050 |
| 0.0043 | 2.99 | 187 | 0.4328 | 0.8904 | [0.9399122807017544, 0.9068267734044919, 0.8809201623815968, 0.8558682223747426] | 0.9945 | 0.9945 | 4560 | 4585 | 0.0842 | 0.2939 |
| 0.0106 | 3.97 | 248 | 0.4355 | 0.8879 | [0.943646408839779, 0.9119229045271179, 0.8854285064787452, 0.860009225092251] | 0.9868 | 0.9869 | 4525 | 4585 | 0.0857 | 0.2978 |
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
- Feb 27, 2024
- Updated
- Apr 3, 2024