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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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base_model: naver-clova-ix/donut-base |
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datasets: |
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- imagefolder |
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model-index: |
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- name: invoice_extraction_20240809_base_non_0_aug4x_retrain |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# donut-base-invoice-resize_splitbydate_roc_240401 |
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This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. |
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## Model description |
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Trained from Donut base model (naver-clova-ix/donut-base) with non-type-0 invoice data with original Gregorian date instead of ROC date with Chinese characters |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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Train data: 1124 samples of non type 0 images with invoice date between 2024/07/01 and 2024/07/15 (original 281 samples + 3 times augmented data) |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 (checkpoint-5620) |
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### Training results |
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TrainOutput(global_step=2420, training_loss=1.0457300442309418, metrics={'train_runtime': 9844.8278, 'train_samples_per_second': 0.49, 'train_steps_per_second': 0.246, 'total_flos': 6.47612717723136e+18, 'train_loss': 1.0457300442309418, 'epoch': 20.0}) |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.15.2 |
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