layoutlm-finetuned-funsd

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6806
  • Answer: {'precision': 0.7069154774972558, 'recall': 0.796044499381953, 'f1': 0.7488372093023256, 'number': 809}
  • Header: {'precision': 0.36752136752136755, 'recall': 0.36134453781512604, 'f1': 0.3644067796610169, 'number': 119}
  • Question: {'precision': 0.7866549604916594, 'recall': 0.8413145539906103, 'f1': 0.8130671506352087, 'number': 1065}
  • Overall Precision: 0.7305
  • Overall Recall: 0.7943
  • Overall F1: 0.7611
  • Overall Accuracy: 0.8085

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
1.8023 1.0 10 1.6152 {'precision': 0.009448818897637795, 'recall': 0.007416563658838072, 'f1': 0.008310249307479225, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.12998266897746968, 'recall': 0.07042253521126761, 'f1': 0.09135200974421437, 'number': 1065} 0.0668 0.0406 0.0505 0.3276
1.4614 2.0 20 1.2547 {'precision': 0.2211253701875617, 'recall': 0.276885043263288, 'f1': 0.24588364434687154, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.43353028064992616, 'recall': 0.5511737089201878, 'f1': 0.4853245142620918, 'number': 1065} 0.3420 0.4069 0.3717 0.5926
1.0795 3.0 30 0.9150 {'precision': 0.49837486457204766, 'recall': 0.5686032138442522, 'f1': 0.5311778290993071, 'number': 809} {'precision': 0.04081632653061224, 'recall': 0.01680672268907563, 'f1': 0.023809523809523808, 'number': 119} {'precision': 0.5953338696701529, 'recall': 0.6948356807511737, 'f1': 0.6412478336221837, 'number': 1065} 0.5427 0.6031 0.5713 0.7145
0.8025 4.0 40 0.7686 {'precision': 0.6056622851365016, 'recall': 0.7404202719406675, 'f1': 0.6662958843159066, 'number': 809} {'precision': 0.10975609756097561, 'recall': 0.07563025210084033, 'f1': 0.08955223880597014, 'number': 119} {'precision': 0.6737400530503979, 'recall': 0.7154929577464789, 'f1': 0.6939890710382515, 'number': 1065} 0.6222 0.6874 0.6532 0.7506
0.6638 5.0 50 0.7034 {'precision': 0.644535240040858, 'recall': 0.7799752781211372, 'f1': 0.7058165548098434, 'number': 809} {'precision': 0.23711340206185566, 'recall': 0.19327731092436976, 'f1': 0.21296296296296294, 'number': 119} {'precision': 0.7158992180712423, 'recall': 0.7737089201877935, 'f1': 0.7436823104693141, 'number': 1065} 0.6637 0.7416 0.7005 0.7841
0.5567 6.0 60 0.6784 {'precision': 0.6687435098650052, 'recall': 0.796044499381953, 'f1': 0.7268623024830699, 'number': 809} {'precision': 0.2857142857142857, 'recall': 0.2184873949579832, 'f1': 0.24761904761904763, 'number': 119} {'precision': 0.7160392798690671, 'recall': 0.8215962441314554, 'f1': 0.765194578049847, 'number': 1065} 0.6788 0.7752 0.7238 0.7903
0.4925 7.0 70 0.6815 {'precision': 0.6839779005524862, 'recall': 0.765142150803461, 'f1': 0.7222870478413069, 'number': 809} {'precision': 0.2894736842105263, 'recall': 0.2773109243697479, 'f1': 0.2832618025751073, 'number': 119} {'precision': 0.7233169129720853, 'recall': 0.8272300469483568, 'f1': 0.7717915024091108, 'number': 1065} 0.6853 0.7692 0.7248 0.7913
0.4494 8.0 80 0.6765 {'precision': 0.6962305986696231, 'recall': 0.7762669962917181, 'f1': 0.734073641145529, 'number': 809} {'precision': 0.28, 'recall': 0.29411764705882354, 'f1': 0.28688524590163933, 'number': 119} {'precision': 0.7360066833751044, 'recall': 0.8272300469483568, 'f1': 0.7789566755083996, 'number': 1065} 0.6942 0.7747 0.7323 0.8004
0.3986 9.0 90 0.6587 {'precision': 0.7077777777777777, 'recall': 0.7873918417799752, 'f1': 0.7454651843183148, 'number': 809} {'precision': 0.3274336283185841, 'recall': 0.31092436974789917, 'f1': 0.3189655172413793, 'number': 119} {'precision': 0.7487266553480475, 'recall': 0.828169014084507, 'f1': 0.7864467231386535, 'number': 1065} 0.7102 0.7807 0.7438 0.8019
0.3597 10.0 100 0.6607 {'precision': 0.7054945054945055, 'recall': 0.7935723114956736, 'f1': 0.7469458987783596, 'number': 809} {'precision': 0.3305084745762712, 'recall': 0.3277310924369748, 'f1': 0.32911392405063294, 'number': 119} {'precision': 0.7600685518423308, 'recall': 0.8328638497652582, 'f1': 0.7948028673835125, 'number': 1065} 0.7144 0.7868 0.7488 0.8048
0.3266 11.0 110 0.6751 {'precision': 0.7050279329608938, 'recall': 0.7799752781211372, 'f1': 0.7406103286384977, 'number': 809} {'precision': 0.3684210526315789, 'recall': 0.35294117647058826, 'f1': 0.3605150214592275, 'number': 119} {'precision': 0.7711571675302246, 'recall': 0.8384976525821596, 'f1': 0.8034188034188035, 'number': 1065} 0.7227 0.7858 0.7529 0.8056
0.3103 12.0 120 0.6799 {'precision': 0.7047413793103449, 'recall': 0.8084054388133498, 'f1': 0.7530224525043179, 'number': 809} {'precision': 0.3474576271186441, 'recall': 0.3445378151260504, 'f1': 0.3459915611814346, 'number': 119} {'precision': 0.7799295774647887, 'recall': 0.831924882629108, 'f1': 0.8050885960926851, 'number': 1065} 0.7246 0.7933 0.7574 0.8034
0.2893 13.0 130 0.6773 {'precision': 0.7191392978482446, 'recall': 0.7849196538936959, 'f1': 0.7505910165484633, 'number': 809} {'precision': 0.35833333333333334, 'recall': 0.36134453781512604, 'f1': 0.35983263598326365, 'number': 119} {'precision': 0.7861524978089395, 'recall': 0.8422535211267606, 'f1': 0.8132366273798729, 'number': 1065} 0.7346 0.7903 0.7614 0.8050
0.2743 14.0 140 0.6788 {'precision': 0.7063318777292577, 'recall': 0.799752781211372, 'f1': 0.750144927536232, 'number': 809} {'precision': 0.37168141592920356, 'recall': 0.35294117647058826, 'f1': 0.36206896551724144, 'number': 119} {'precision': 0.7879858657243817, 'recall': 0.8375586854460094, 'f1': 0.812016385980883, 'number': 1065} 0.7316 0.7933 0.7612 0.8085
0.2756 15.0 150 0.6806 {'precision': 0.7069154774972558, 'recall': 0.796044499381953, 'f1': 0.7488372093023256, 'number': 809} {'precision': 0.36752136752136755, 'recall': 0.36134453781512604, 'f1': 0.3644067796610169, 'number': 119} {'precision': 0.7866549604916594, 'recall': 0.8413145539906103, 'f1': 0.8130671506352087, 'number': 1065} 0.7305 0.7943 0.7611 0.8085

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2
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