---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- darentang/sroie
model-index:
- name: donut-base-sroie
  results: []
---

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# donut-base-sroie

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.

## Model description

Donut 🍩, Document understanding transformer, is a new method of document understanding
that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR
engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks,
such as visual document classification or information extraction (a.k.a. document parsing). 

## Intended uses & limitations

Basic Donut model



## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3