---
license: mit
tags:
- generated_from_trainer
base_model: naver-clova-ix/donut-base
datasets:
- imagefolder
model-index:
- name: invoice_extraction_20240809_base_non_0_aug4x_retrain
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# donut-base-invoice-resize_splitbydate_roc_240401

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

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

## Intended uses & limitations

More information needed

## Training and evaluation data

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)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10 (checkpoint-5620)

### Training results

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})

### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.2