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lilt-en-funsd

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metadata
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
  - generated_from_trainer
datasets:
  - funsd-layoutlmv3
model-index:
  - name: lilt-en-funsd
    results: []

lilt-en-funsd

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0001
  • Account Name.key: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
  • Account Name.value: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
  • Account No.key: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
  • Account No.value: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
  • Overall Precision: 1.0
  • Overall Recall: 1.0
  • Overall F1: 1.0
  • Overall Accuracy: 1.0

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: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 2500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Account Name.key Account Name.value Account No.key Account No.value Overall Precision Overall Recall Overall F1 Overall Accuracy
0.0635 100.0 200 0.0001 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0002 200.0 400 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0001 300.0 600 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0001 400.0 800 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0001 500.0 1000 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0 600.0 1200 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0 700.0 1400 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0 800.0 1600 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0 900.0 1800 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0 1000.0 2000 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0 1100.0 2200 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0
0.0 1200.0 2400 0.0000 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} 1.0 1.0 1.0 1.0

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1