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--- |
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license: mit |
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base_model: davelotito/donut-base-sroie |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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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-v2 |
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results: [] |
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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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# donut-base-sroie-v2 |
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This model is a fine-tuned version of [davelotito/donut-base-sroie](https://huggingface.co/davelotito/donut-base-sroie) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4355 |
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- Bleu: 0.8879 |
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- Precisions: [0.943646408839779, 0.9119229045271179, 0.8854285064787452, 0.860009225092251] |
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- Brevity Penalty: 0.9868 |
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- Length Ratio: 0.9869 |
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- Translation Length: 4525 |
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- Reference Length: 4585 |
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- Cer: 0.0857 |
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- Wer: 0.2978 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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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### Training results |
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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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| No log | 0.99 | 62 | 0.4638 | 0.8823 | [0.9399823477493381, 0.9044528977399866, 0.8772128915115751, 0.8514851485148515] | 0.9884 | 0.9884 | 4532 | 4585 | 0.0912 | 0.3085 | |
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| 0.0043 | 2.0 | 125 | 0.4421 | 0.8853 | [0.9405155320555189, 0.9059428060768543, 0.8794470881486517, 0.8537931034482759] | 0.9899 | 0.9900 | 4539 | 4585 | 0.0889 | 0.3050 | |
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| 0.0043 | 2.99 | 187 | 0.4328 | 0.8904 | [0.9399122807017544, 0.9068267734044919, 0.8809201623815968, 0.8558682223747426] | 0.9945 | 0.9945 | 4560 | 4585 | 0.0842 | 0.2939 | |
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| 0.0106 | 3.97 | 248 | 0.4355 | 0.8879 | [0.943646408839779, 0.9119229045271179, 0.8854285064787452, 0.860009225092251] | 0.9868 | 0.9869 | 4525 | 4585 | 0.0857 | 0.2978 | |
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### Framework versions |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.1.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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