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lilt-ru-bio

This model was trained from scratch on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4705
  • Answer: {'precision': 0.8711583924349882, 'recall': 0.9020807833537332, 'f1': 0.8863499699338545, 'number': 817}
  • Header: {'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119}
  • Question: {'precision': 0.8966455122393472, 'recall': 0.9182915506035283, 'f1': 0.9073394495412844, 'number': 1077}
  • Overall Precision: 0.8732
  • Overall Recall: 0.8892
  • Overall F1: 0.8811
  • Overall Accuracy: 0.8223

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
0.0199 5.26 100 1.3310 {'precision': 0.8788627935723115, 'recall': 0.8702570379436965, 'f1': 0.8745387453874538, 'number': 817} {'precision': 0.6288659793814433, 'recall': 0.5126050420168067, 'f1': 0.5648148148148148, 'number': 119} {'precision': 0.8519148936170213, 'recall': 0.9294336118848654, 'f1': 0.8889875666074601, 'number': 1077} 0.8520 0.8808 0.8661 0.8038
0.0085 10.53 200 1.5426 {'precision': 0.8631578947368421, 'recall': 0.9033047735618115, 'f1': 0.8827751196172249, 'number': 817} {'precision': 0.5641025641025641, 'recall': 0.5546218487394958, 'f1': 0.559322033898305, 'number': 119} {'precision': 0.899812734082397, 'recall': 0.8922934076137419, 'f1': 0.8960372960372962, 'number': 1077} 0.8652 0.8768 0.8710 0.8120
0.0047 15.79 300 1.5043 {'precision': 0.8698710433763188, 'recall': 0.9082007343941249, 'f1': 0.8886227544910178, 'number': 817} {'precision': 0.5508474576271186, 'recall': 0.5462184873949579, 'f1': 0.5485232067510548, 'number': 119} {'precision': 0.8980716253443526, 'recall': 0.9080779944289693, 'f1': 0.9030470914127423, 'number': 1077} 0.8665 0.8867 0.8765 0.8086
0.0017 21.05 400 1.4705 {'precision': 0.8711583924349882, 'recall': 0.9020807833537332, 'f1': 0.8863499699338545, 'number': 817} {'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119} {'precision': 0.8966455122393472, 'recall': 0.9182915506035283, 'f1': 0.9073394495412844, 'number': 1077} 0.8732 0.8892 0.8811 0.8223
0.0012 26.32 500 1.5088 {'precision': 0.8744075829383886, 'recall': 0.9033047735618115, 'f1': 0.8886213124623721, 'number': 817} {'precision': 0.5904761904761905, 'recall': 0.5210084033613446, 'f1': 0.5535714285714286, 'number': 119} {'precision': 0.8935395814376706, 'recall': 0.9117920148560817, 'f1': 0.9025735294117648, 'number': 1077} 0.8701 0.8852 0.8776 0.8174

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.12.1
  • Datasets 2.8.0
  • Tokenizers 0.13.2
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