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donut-base-sroie-metrics-combined

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metadata
license: mit
base_model: naver-clova-ix/donut-base
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - wer
model-index:
  - name: donut-base-sroie-metrics-combined
    results: []

donut-base-sroie-metrics-combined

This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2827
  • Bleu score: 0.0762
  • Precisions: [0.8396396396396396, 0.7886178861788617, 0.7435897435897436, 0.7049180327868853]
  • Brevity penalty: 0.0993
  • Length ratio: 0.3021
  • Translation length: 555
  • Reference length: 1837
  • Cer: 0.7452
  • Wer: 0.8162
  • Cer Hugging Face: 0.7544
  • Wer Hugging Face: 0.8233

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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • 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
No log 0.99 62 0.3478 0.0756 [0.8178571428571428, 0.7625754527162978, 0.7142857142857143, 0.6711590296495957] 0.1022 0.3048 560 1837 0.7474 0.8243 0.7570 0.8333
0.2634 2.0 125 0.2873 0.0763 [0.8345323741007195, 0.7829614604462475, 0.7418604651162791, 0.7029972752043597] 0.0999 0.3027 556 1837 0.7435 0.8219 0.7527 0.8288
0.2634 2.99 187 0.2817 0.0777 [0.8369175627240143, 0.7838383838383839, 0.7476851851851852, 0.7127371273712737] 0.1011 0.3038 558 1837 0.7407 0.8152 0.7498 0.8215
0.263 3.97 248 0.2827 0.0762 [0.8396396396396396, 0.7886178861788617, 0.7435897435897436, 0.7049180327868853] 0.0993 0.3021 555 1837 0.7452 0.8162 0.7544 0.8233

Framework versions

  • Transformers 4.40.0.dev0
  • Pytorch 2.1.0
  • Datasets 2.18.0
  • Tokenizers 0.15.2