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End of training

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: naver-clova-ix/donut-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: donut-base-sroie-metrics-combined-new
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # donut-base-sroie-metrics-combined-new
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+
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+ This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1662
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+ - Bleu score: 0.0215
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+ - Precisions: [0.9469914040114613, 0.9204368174726989, 0.8938356164383562, 0.872865275142315]
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+ - Brevity penalty: 0.0237
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+ - Length ratio: 0.2109
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+ - Translation length: 698
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+ - Reference length: 3310
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+ - Cer: 0.7917
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+ - Wer: 0.8253
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+ - Cer Hugging Face: 0.7954
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+ - Wer Hugging Face: 0.8274
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 2
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 4
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Bleu score | Precisions | Brevity penalty | Length ratio | Translation length | Reference length | Cer | Wer | Cer Hugging Face | Wer Hugging Face |
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+ |:-------------:|:-----:|:----:|:---------------:|:----------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|:----------------:|:----------------:|
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+ | 0.5956 | 1.0 | 253 | 0.2372 | 0.0231 | [0.9258741258741259, 0.8890577507598785, 0.8519134775374376, 0.8180147058823529] | 0.0265 | 0.2160 | 715 | 3310 | 0.7922 | 0.8383 | 0.7969 | 0.8412 |
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+ | 0.2509 | 2.0 | 506 | 0.1730 | 0.0213 | [0.9425287356321839, 0.9217527386541471, 0.8969072164948454, 0.88] | 0.0234 | 0.2103 | 696 | 3310 | 0.7928 | 0.8285 | 0.7966 | 0.8306 |
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+ | 0.22 | 3.0 | 759 | 0.1777 | 0.0215 | [0.9469914040114613, 0.9188767550702028, 0.8921232876712328, 0.872865275142315] | 0.0237 | 0.2109 | 698 | 3310 | 0.7914 | 0.8282 | 0.7948 | 0.8306 |
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+ | 0.1687 | 4.0 | 1012 | 0.1662 | 0.0215 | [0.9469914040114613, 0.9204368174726989, 0.8938356164383562, 0.872865275142315] | 0.0237 | 0.2109 | 698 | 3310 | 0.7917 | 0.8253 | 0.7954 | 0.8274 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.0.dev0
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+ - Pytorch 2.1.0
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+ - Datasets 2.19.0
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+ - Tokenizers 0.19.1
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