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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: 0.5526
  • Answer: {'precision': 0.8434684684684685, 'recall': 0.9167686658506732, 'f1': 0.8785923753665689, 'number': 817}
  • Header: {'precision': 0.6494845360824743, 'recall': 0.5294117647058824, 'f1': 0.5833333333333334, 'number': 119}
  • Question: {'precision': 0.8929219600725953, 'recall': 0.9136490250696379, 'f1': 0.9031665901789812, 'number': 1077}
  • Overall Precision: 0.8606
  • Overall Recall: 0.8922
  • Overall F1: 0.8761
  • Overall Accuracy: 0.7988

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
0.1122 10.53 200 0.3663 {'precision': 0.7907488986784141, 'recall': 0.8788249694002448, 'f1': 0.832463768115942, 'number': 817} {'precision': 0.4888888888888889, 'recall': 0.5546218487394958, 'f1': 0.5196850393700787, 'number': 119} {'precision': 0.8885630498533724, 'recall': 0.8440111420612814, 'f1': 0.8657142857142858, 'number': 1077} 0.8195 0.8410 0.8301 0.7813
0.0121 21.05 400 0.3856 {'precision': 0.8256880733944955, 'recall': 0.8812729498164015, 'f1': 0.8525754884547069, 'number': 817} {'precision': 0.6021505376344086, 'recall': 0.47058823529411764, 'f1': 0.5283018867924528, 'number': 119} {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} 0.8417 0.8713 0.8562 0.8060
0.0038 31.58 600 0.4187 {'precision': 0.8352534562211982, 'recall': 0.8873929008567931, 'f1': 0.8605341246290802, 'number': 817} {'precision': 0.59, 'recall': 0.4957983193277311, 'f1': 0.5388127853881278, 'number': 119} {'precision': 0.8956197576887233, 'recall': 0.8922934076137419, 'f1': 0.8939534883720931, 'number': 1077} 0.8550 0.8669 0.8609 0.8082
0.0018 42.11 800 0.4984 {'precision': 0.8299319727891157, 'recall': 0.8959608323133414, 'f1': 0.8616833431430253, 'number': 817} {'precision': 0.6111111111111112, 'recall': 0.46218487394957986, 'f1': 0.5263157894736842, 'number': 119} {'precision': 0.8860055607043559, 'recall': 0.8876508820798514, 'f1': 0.8868274582560296, 'number': 1077} 0.8498 0.8659 0.8578 0.7950
0.0015 52.63 1000 0.4974 {'precision': 0.830316742081448, 'recall': 0.8984088127294981, 'f1': 0.8630217519106408, 'number': 817} {'precision': 0.6161616161616161, 'recall': 0.5126050420168067, 'f1': 0.5596330275229358, 'number': 119} {'precision': 0.9015151515151515, 'recall': 0.8839368616527391, 'f1': 0.8926394749179559, 'number': 1077} 0.8568 0.8679 0.8623 0.7888
0.0012 63.16 1200 0.5089 {'precision': 0.8566473988439306, 'recall': 0.9069767441860465, 'f1': 0.8810939357907253, 'number': 817} {'precision': 0.5981308411214953, 'recall': 0.5378151260504201, 'f1': 0.5663716814159291, 'number': 119} {'precision': 0.9059590316573557, 'recall': 0.903435468895079, 'f1': 0.904695490469549, 'number': 1077} 0.8690 0.8833 0.8761 0.8067
0.0006 73.68 1400 0.5012 {'precision': 0.8409090909090909, 'recall': 0.9057527539779682, 'f1': 0.8721272834413673, 'number': 817} {'precision': 0.6043956043956044, 'recall': 0.46218487394957986, 'f1': 0.5238095238095237, 'number': 119} {'precision': 0.8840182648401826, 'recall': 0.8987929433611885, 'f1': 0.8913443830570903, 'number': 1077} 0.8533 0.8758 0.8644 0.7997
0.0003 84.21 1600 0.5316 {'precision': 0.8506944444444444, 'recall': 0.8996328029375765, 'f1': 0.874479476502082, 'number': 817} {'precision': 0.5775862068965517, 'recall': 0.5630252100840336, 'f1': 0.5702127659574467, 'number': 119} {'precision': 0.8932481751824818, 'recall': 0.9090064995357474, 'f1': 0.9010584445467096, 'number': 1077} 0.8579 0.8847 0.8711 0.8003
0.0002 94.74 1800 0.5347 {'precision': 0.8617401668653158, 'recall': 0.8849449204406364, 'f1': 0.8731884057971013, 'number': 817} {'precision': 0.5317460317460317, 'recall': 0.5630252100840336, 'f1': 0.5469387755102041, 'number': 119} {'precision': 0.8999081726354453, 'recall': 0.9099350046425255, 'f1': 0.9048938134810711, 'number': 1077} 0.8617 0.8793 0.8704 0.7979
0.0002 105.26 2000 0.5526 {'precision': 0.8434684684684685, 'recall': 0.9167686658506732, 'f1': 0.8785923753665689, 'number': 817} {'precision': 0.6494845360824743, 'recall': 0.5294117647058824, 'f1': 0.5833333333333334, 'number': 119} {'precision': 0.8929219600725953, 'recall': 0.9136490250696379, 'f1': 0.9031665901789812, 'number': 1077} 0.8606 0.8922 0.8761 0.7988
0.0001 115.79 2200 0.5488 {'precision': 0.8368953880764904, 'recall': 0.9106487148102815, 'f1': 0.8722157092614302, 'number': 817} {'precision': 0.5833333333333334, 'recall': 0.5294117647058824, 'f1': 0.5550660792951542, 'number': 119} {'precision': 0.8835740072202166, 'recall': 0.9090064995357474, 'f1': 0.8961098398169337, 'number': 1077} 0.8485 0.8872 0.8674 0.8017
0.0001 126.32 2400 0.5510 {'precision': 0.8372615039281706, 'recall': 0.9130966952264382, 'f1': 0.8735362997658079, 'number': 817} {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119} {'precision': 0.895264116575592, 'recall': 0.9127205199628597, 'f1': 0.9039080459770115, 'number': 1077} 0.8559 0.8912 0.8732 0.8024

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0
  • Datasets 2.9.0
  • Tokenizers 0.14.1
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