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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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+ - bleu
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+ - wer
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+ model-index:
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+ - name: donut-base-sroie-bayesian-optimization
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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-bayesian-optimization
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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: 0.1396
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+ - Bleu: 0.0196
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+ - Precisions: [0.9883177570093458, 0.9724655819774718, 0.954177897574124, 0.9328467153284672]
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+ - Brevity Penalty: 0.0203
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+ - Length Ratio: 0.2043
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+ - Translation Length: 856
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+ - Reference Length: 4190
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+ - Cer: 0.8584
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+ - Wer: 1.0
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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: 1.2010406976282324e-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: 5
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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 | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:---:|
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+ | 0.021 | 1.0 | 253 | 0.1656 | 0.0194 | [0.9848130841121495, 0.9649561952440551, 0.9420485175202157, 0.9153284671532846] | 0.0203 | 0.2043 | 856 | 4190 | 0.8596 | 1.0 |
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+ | 0.0353 | 2.0 | 506 | 0.1501 | 0.0195 | [0.9813736903376019, 0.9588528678304239, 0.9328859060402684, 0.9026162790697675] | 0.0207 | 0.2050 | 859 | 4190 | 0.8595 | 1.0 |
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+ | 0.0417 | 3.0 | 759 | 0.1423 | 0.0195 | [0.9871495327102804, 0.9699624530663329, 0.9501347708894878, 0.927007299270073] | 0.0203 | 0.2043 | 856 | 4190 | 0.8586 | 1.0 |
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+ | 0.0308 | 4.0 | 1012 | 0.1403 | 0.0193 | [0.9859649122807017, 0.9674185463659147, 0.9460188933873145, 0.9210526315789473] | 0.0202 | 0.2041 | 855 | 4190 | 0.8593 | 1.0 |
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+ | 0.0464 | 5.0 | 1265 | 0.1396 | 0.0196 | [0.9883177570093458, 0.9724655819774718, 0.954177897574124, 0.9328467153284672] | 0.0203 | 0.2043 | 856 | 4190 | 0.8584 | 1.0 |
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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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