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--- |
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license: mit |
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base_model: naver-clova-ix/donut-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- darentang/sroie |
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model-index: |
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- name: donut-base-sroie |
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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-sroie |
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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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Donut 🍩, Document understanding transformer, is a new method of document understanding |
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that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR |
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engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, |
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such as visual document classification or information extraction (a.k.a. document parsing). |
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## Intended uses & limitations |
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Basic Donut model |
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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-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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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: 3 |
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### Framework versions |
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- Transformers 4.33.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
