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donut-base-sroie-metrics-combined-new

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metadata
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: []

donut-base-sroie-metrics-combined-new

This model is a fine-tuned version of 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