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
3
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.