Update README.md

README.md

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----
-license: mit
----
+---
+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