Edit model card

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: 1.6122
  • Answer: {'precision': 0.8905882352941177, 'recall': 0.9265605875152999, 'f1': 0.9082183563287344, 'number': 817}
  • Header: {'precision': 0.6379310344827587, 'recall': 0.6218487394957983, 'f1': 0.6297872340425532, 'number': 119}
  • Question: {'precision': 0.8964252978918423, 'recall': 0.9080779944289693, 'f1': 0.9022140221402215, 'number': 1077}
  • Overall Precision: 0.8794
  • Overall Recall: 0.8987
  • Overall F1: 0.8889
  • Overall Accuracy: 0.8020

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

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
0.4109 10.53 200 0.9316 {'precision': 0.8169491525423729, 'recall': 0.8849449204406364, 'f1': 0.8495887191539365, 'number': 817} {'precision': 0.5258620689655172, 'recall': 0.5126050420168067, 'f1': 0.5191489361702126, 'number': 119} {'precision': 0.8695652173913043, 'recall': 0.8913649025069638, 'f1': 0.8803301237964236, 'number': 1077} 0.8285 0.8664 0.8470 0.7998
0.0432 21.05 400 1.6192 {'precision': 0.8022099447513812, 'recall': 0.8886168910648715, 'f1': 0.843205574912892, 'number': 817} {'precision': 0.5585585585585585, 'recall': 0.5210084033613446, 'f1': 0.5391304347826087, 'number': 119} {'precision': 0.9002932551319648, 'recall': 0.8551532033426184, 'f1': 0.8771428571428571, 'number': 1077} 0.8382 0.8490 0.8435 0.7705
0.0145 31.58 600 1.4845 {'precision': 0.8556701030927835, 'recall': 0.9143206854345165, 'f1': 0.8840236686390532, 'number': 817} {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119} {'precision': 0.8772241992882562, 'recall': 0.9155060352831941, 'f1': 0.8959563834620626, 'number': 1077} 0.8561 0.8927 0.8740 0.7985
0.0063 42.11 800 1.4909 {'precision': 0.8667452830188679, 'recall': 0.8996328029375765, 'f1': 0.8828828828828829, 'number': 817} {'precision': 0.5766423357664233, 'recall': 0.6638655462184874, 'f1': 0.6171874999999999, 'number': 119} {'precision': 0.891444342226311, 'recall': 0.8997214484679665, 'f1': 0.8955637707948244, 'number': 1077} 0.8605 0.8857 0.8729 0.8117
0.0041 52.63 1000 1.6197 {'precision': 0.834056399132321, 'recall': 0.9412484700122399, 'f1': 0.8844163312248418, 'number': 817} {'precision': 0.5945945945945946, 'recall': 0.5546218487394958, 'f1': 0.5739130434782609, 'number': 119} {'precision': 0.8972407231208372, 'recall': 0.8755803156917363, 'f1': 0.8862781954887218, 'number': 1077} 0.8532 0.8833 0.8680 0.7912
0.0043 63.16 1200 1.6797 {'precision': 0.8763005780346821, 'recall': 0.9277845777233782, 'f1': 0.901307966706302, 'number': 817} {'precision': 0.6134453781512605, 'recall': 0.6134453781512605, 'f1': 0.6134453781512605, 'number': 119} {'precision': 0.8998144712430427, 'recall': 0.9006499535747446, 'f1': 0.9002320185614848, 'number': 1077} 0.8734 0.8947 0.8839 0.8046
0.0015 73.68 1400 1.5586 {'precision': 0.8683314415437003, 'recall': 0.9363525091799265, 'f1': 0.9010600706713782, 'number': 817} {'precision': 0.6486486486486487, 'recall': 0.6050420168067226, 'f1': 0.6260869565217391, 'number': 119} {'precision': 0.9055555555555556, 'recall': 0.9080779944289693, 'f1': 0.9068150208623087, 'number': 1077} 0.8760 0.9016 0.8886 0.8093
0.001 84.21 1600 1.5060 {'precision': 0.8894230769230769, 'recall': 0.9057527539779682, 'f1': 0.8975136446331109, 'number': 817} {'precision': 0.711340206185567, 'recall': 0.5798319327731093, 'f1': 0.638888888888889, 'number': 119} {'precision': 0.8927927927927928, 'recall': 0.9201485608170845, 'f1': 0.9062642889803384, 'number': 1077} 0.8828 0.8942 0.8885 0.8202
0.0004 94.74 1800 1.6122 {'precision': 0.8905882352941177, 'recall': 0.9265605875152999, 'f1': 0.9082183563287344, 'number': 817} {'precision': 0.6379310344827587, 'recall': 0.6218487394957983, 'f1': 0.6297872340425532, 'number': 119} {'precision': 0.8964252978918423, 'recall': 0.9080779944289693, 'f1': 0.9022140221402215, 'number': 1077} 0.8794 0.8987 0.8889 0.8020
0.0004 105.26 2000 1.6057 {'precision': 0.8791079812206573, 'recall': 0.9167686658506732, 'f1': 0.8975434391851408, 'number': 817} {'precision': 0.6260869565217392, 'recall': 0.6050420168067226, 'f1': 0.6153846153846154, 'number': 119} {'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077} 0.8720 0.8967 0.8842 0.8057
0.0004 115.79 2200 1.6179 {'precision': 0.8848413631022327, 'recall': 0.9216646266829865, 'f1': 0.9028776978417267, 'number': 817} {'precision': 0.6206896551724138, 'recall': 0.6050420168067226, 'f1': 0.6127659574468085, 'number': 119} {'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077} 0.8739 0.8987 0.8861 0.8070
0.0002 126.32 2400 1.6142 {'precision': 0.8826291079812206, 'recall': 0.9204406364749081, 'f1': 0.9011384062312762, 'number': 817} {'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119} {'precision': 0.8965201465201466, 'recall': 0.9090064995357474, 'f1': 0.9027201475334256, 'number': 1077} 0.8743 0.8952 0.8846 0.8074

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3
Downloads last month
1