File size: 1,701 Bytes
568fccc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
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
should probably proofread and complete it, then remove this comment. -->
# 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
|