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---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: donut-base-sroie-metrics-combined-new
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-sroie-metrics-combined-new
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.
It achieves the following results on the evaluation set:
- Loss: 0.3400
- Bleu score: 0.0856
- Precisions: [0.8478260869565217, 0.8017817371937639, 0.7755102040816326, 0.755223880597015]
- Brevity penalty: 0.1078
- Length ratio: 0.3099
- Translation length: 506
- Reference length: 1633
- Cer: 0.7597
- Wer: 0.8305
- Cer Hugging Face: 0.7664
- Wer Hugging Face: 0.8347
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|:----------------:|:----------------:|
| 0.9692 | 1.0 | 253 | 0.4901 | 0.0746 | [0.8011928429423459, 0.726457399103139, 0.6760925449871465, 0.6295180722891566] | 0.1058 | 0.3080 | 503 | 1633 | 0.7672 | 0.8440 | 0.7741 | 0.8478 |
| 0.437 | 2.0 | 506 | 0.3906 | 0.0824 | [0.8382642998027613, 0.7755555555555556, 0.7353689567430025, 0.6964285714285714] | 0.1085 | 0.3105 | 507 | 1633 | 0.7611 | 0.8328 | 0.7675 | 0.8367 |
| 0.2997 | 3.0 | 759 | 0.3565 | 0.0858 | [0.828125, 0.778021978021978, 0.7462311557788944, 0.718475073313783] | 0.1120 | 0.3135 | 512 | 1633 | 0.7640 | 0.8363 | 0.7703 | 0.8397 |
| 0.2168 | 4.0 | 1012 | 0.3400 | 0.0856 | [0.8478260869565217, 0.8017817371937639, 0.7755102040816326, 0.755223880597015] | 0.1078 | 0.3099 | 506 | 1633 | 0.7597 | 0.8305 | 0.7664 | 0.8347 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.1.0
- Datasets 2.19.0
- Tokenizers 0.19.1
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