invoice_extraction_20240808_base_non_0_retrain
This model is a fine-tuned version of
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: 281 samples of non type 0 images with invoice date between 2024/07/01 and 2024/07/15
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: 20
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
