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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: 1.5278
  • Answer: {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817}
  • Header: {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119}
  • Question: {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077}
  • Overall Precision: 0.8845
  • Overall Recall: 0.8942
  • Overall F1: 0.8893
  • Overall Accuracy: 0.8213

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: 2500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
0.4258 10.53 200 0.9877 {'precision': 0.8354143019296254, 'recall': 0.9008567931456548, 'f1': 0.866902237926973, 'number': 817} {'precision': 0.5491803278688525, 'recall': 0.5630252100840336, 'f1': 0.5560165975103735, 'number': 119} {'precision': 0.8691756272401434, 'recall': 0.9006499535747446, 'f1': 0.8846329229366164, 'number': 1077} 0.8367 0.8808 0.8582 0.8030
0.0396 21.05 400 1.2891 {'precision': 0.8314855875831486, 'recall': 0.9179926560587516, 'f1': 0.8726003490401396, 'number': 817} {'precision': 0.5289256198347108, 'recall': 0.5378151260504201, 'f1': 0.5333333333333334, 'number': 119} {'precision': 0.9008579599618685, 'recall': 0.8774373259052924, 'f1': 0.8889934148635935, 'number': 1077} 0.8489 0.8738 0.8612 0.8071
0.0145 31.58 600 1.1878 {'precision': 0.8541666666666666, 'recall': 0.9033047735618115, 'f1': 0.8780487804878049, 'number': 817} {'precision': 0.5943396226415094, 'recall': 0.5294117647058824, 'f1': 0.5599999999999999, 'number': 119} {'precision': 0.8793565683646113, 'recall': 0.9136490250696379, 'f1': 0.896174863387978, 'number': 1077} 0.8545 0.8867 0.8703 0.8139
0.0093 42.11 800 1.3968 {'precision': 0.8727272727272727, 'recall': 0.8812729498164015, 'f1': 0.8769792935444579, 'number': 817} {'precision': 0.5454545454545454, 'recall': 0.6050420168067226, 'f1': 0.5737051792828685, 'number': 119} {'precision': 0.8951686417502279, 'recall': 0.9117920148560817, 'f1': 0.9034038638454462, 'number': 1077} 0.8637 0.8813 0.8724 0.8054
0.0042 52.63 1000 1.5509 {'precision': 0.8372093023255814, 'recall': 0.9253365973072215, 'f1': 0.8790697674418605, 'number': 817} {'precision': 0.6304347826086957, 'recall': 0.48739495798319327, 'f1': 0.5497630331753555, 'number': 119} {'precision': 0.9044048734770385, 'recall': 0.8960074280408542, 'f1': 0.9001865671641791, 'number': 1077} 0.8628 0.8838 0.8731 0.8044
0.0026 63.16 1200 1.5696 {'precision': 0.8618266978922716, 'recall': 0.9008567931456548, 'f1': 0.8809096349491322, 'number': 817} {'precision': 0.6666666666666666, 'recall': 0.5210084033613446, 'f1': 0.5849056603773585, 'number': 119} {'precision': 0.8935978358881875, 'recall': 0.9201485608170845, 'f1': 0.9066788655077767, 'number': 1077} 0.8701 0.8887 0.8793 0.8116
0.001 73.68 1400 1.7209 {'precision': 0.8396860986547086, 'recall': 0.9167686658506732, 'f1': 0.8765359859566998, 'number': 817} {'precision': 0.6781609195402298, 'recall': 0.4957983193277311, 'f1': 0.5728155339805825, 'number': 119} {'precision': 0.8969359331476323, 'recall': 0.8969359331476323, 'f1': 0.8969359331476322, 'number': 1077} 0.8628 0.8813 0.8720 0.7977
0.0011 84.21 1600 1.5329 {'precision': 0.8646188850967008, 'recall': 0.9302325581395349, 'f1': 0.8962264150943396, 'number': 817} {'precision': 0.6666666666666666, 'recall': 0.5042016806722689, 'f1': 0.5741626794258373, 'number': 119} {'precision': 0.9050691244239631, 'recall': 0.9117920148560817, 'f1': 0.9084181313598519, 'number': 1077} 0.8773 0.8952 0.8862 0.8267
0.0006 94.74 1800 1.5523 {'precision': 0.8748510131108462, 'recall': 0.8984088127294981, 'f1': 0.8864734299516908, 'number': 817} {'precision': 0.5811965811965812, 'recall': 0.5714285714285714, 'f1': 0.576271186440678, 'number': 119} {'precision': 0.9045412418906394, 'recall': 0.9062209842154132, 'f1': 0.9053803339517627, 'number': 1077} 0.8737 0.8833 0.8785 0.8196
0.0005 105.26 2000 1.5178 {'precision': 0.8758949880668258, 'recall': 0.8984088127294981, 'f1': 0.8870090634441088, 'number': 817} {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} {'precision': 0.8995475113122172, 'recall': 0.9229340761374187, 'f1': 0.9110907424381303, 'number': 1077} 0.8775 0.8897 0.8836 0.8253
0.0004 115.79 2200 1.5493 {'precision': 0.8597701149425288, 'recall': 0.9155446756425949, 'f1': 0.8867812685240072, 'number': 817} {'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119} {'precision': 0.9107635694572217, 'recall': 0.9192200557103064, 'f1': 0.9149722735674676, 'number': 1077} 0.8777 0.8947 0.8861 0.8217
0.0003 126.32 2400 1.5278 {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817} {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119} {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077} 0.8845 0.8942 0.8893 0.8213

Framework versions

  • Transformers 4.28.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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