layoutlm-funsd / README.md
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metadata
base_model: microsoft/layoutlm-base-uncased
tags:
  - generated_from_trainer
datasets:
  - funsd
model-index:
  - name: layoutlm-funsd
    results: []

layoutlm-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: 0.7011
  • Answer: {'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809}
  • Header: {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}
  • Question: {'precision': 0.7859712230215827, 'recall': 0.8206572769953052, 'f1': 0.8029398254478639, 'number': 1065}
  • Overall Precision: 0.7250
  • Overall Recall: 0.7873
  • Overall F1: 0.7549
  • Overall Accuracy: 0.8102

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

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
1.7566 1.0 10 1.5349 {'precision': 0.03646308113035551, 'recall': 0.049443757725587144, 'f1': 0.04197271773347323, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.16700819672131148, 'recall': 0.15305164319248826, 'f1': 0.15972562469377757, 'number': 1065} 0.0979 0.1019 0.0999 0.4336
1.4057 2.0 20 1.1865 {'precision': 0.17656500802568217, 'recall': 0.13597033374536466, 'f1': 0.15363128491620115, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.471847739888977, 'recall': 0.5586854460093896, 'f1': 0.5116079105760963, 'number': 1065} 0.3742 0.3537 0.3637 0.6016
1.0729 3.0 30 0.9241 {'precision': 0.49693251533742333, 'recall': 0.5006180469715699, 'f1': 0.4987684729064039, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.6378708551483421, 'recall': 0.6863849765258216, 'f1': 0.6612392582541836, 'number': 1065} 0.5691 0.5700 0.5696 0.7181
0.8134 4.0 40 0.7831 {'precision': 0.6211640211640211, 'recall': 0.7255871446229913, 'f1': 0.669327251995439, 'number': 809} {'precision': 0.09375, 'recall': 0.05042016806722689, 'f1': 0.0655737704918033, 'number': 119} {'precision': 0.6889081455805892, 'recall': 0.7464788732394366, 'f1': 0.7165389815232085, 'number': 1065} 0.6417 0.6964 0.6679 0.7640
0.6582 5.0 50 0.7298 {'precision': 0.6422018348623854, 'recall': 0.7787391841779975, 'f1': 0.7039106145251396, 'number': 809} {'precision': 0.2361111111111111, 'recall': 0.14285714285714285, 'f1': 0.17801047120418848, 'number': 119} {'precision': 0.7311233885819521, 'recall': 0.7455399061032864, 'f1': 0.7382612738261274, 'number': 1065} 0.6737 0.7230 0.6975 0.7761
0.553 6.0 60 0.6763 {'precision': 0.6673532440782698, 'recall': 0.8009888751545118, 'f1': 0.7280898876404494, 'number': 809} {'precision': 0.25806451612903225, 'recall': 0.20168067226890757, 'f1': 0.22641509433962265, 'number': 119} {'precision': 0.735445205479452, 'recall': 0.8065727699530516, 'f1': 0.7693685624720108, 'number': 1065} 0.6859 0.7682 0.7247 0.7962
0.4805 7.0 70 0.6797 {'precision': 0.6904255319148936, 'recall': 0.8022249690976514, 'f1': 0.7421383647798742, 'number': 809} {'precision': 0.25925925925925924, 'recall': 0.23529411764705882, 'f1': 0.24669603524229072, 'number': 119} {'precision': 0.7363945578231292, 'recall': 0.8131455399061033, 'f1': 0.7728692547969657, 'number': 1065} 0.6938 0.7742 0.7318 0.7970
0.4259 8.0 80 0.6726 {'precision': 0.689401888772298, 'recall': 0.8121137206427689, 'f1': 0.7457434733257663, 'number': 809} {'precision': 0.24786324786324787, 'recall': 0.24369747899159663, 'f1': 0.24576271186440676, 'number': 119} {'precision': 0.7463581833761782, 'recall': 0.8178403755868544, 'f1': 0.7804659498207885, 'number': 1065} 0.6960 0.7812 0.7362 0.8020
0.3787 9.0 90 0.6784 {'precision': 0.7043956043956044, 'recall': 0.792336217552534, 'f1': 0.7457824316463061, 'number': 809} {'precision': 0.26229508196721313, 'recall': 0.2689075630252101, 'f1': 0.26556016597510373, 'number': 119} {'precision': 0.779707495429616, 'recall': 0.8009389671361502, 'f1': 0.7901806391848076, 'number': 1065} 0.7178 0.7657 0.7410 0.8026
0.3411 10.0 100 0.6821 {'precision': 0.7015086206896551, 'recall': 0.8046971569839307, 'f1': 0.7495682210708117, 'number': 809} {'precision': 0.2708333333333333, 'recall': 0.3277310924369748, 'f1': 0.2965779467680608, 'number': 119} {'precision': 0.775200713648528, 'recall': 0.815962441314554, 'f1': 0.7950594693504116, 'number': 1065} 0.7109 0.7822 0.7449 0.8047
0.313 11.0 110 0.7129 {'precision': 0.7111111111111111, 'recall': 0.7911001236093943, 'f1': 0.7489760093622002, 'number': 809} {'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119} {'precision': 0.7816711590296496, 'recall': 0.8169014084507042, 'f1': 0.7988980716253444, 'number': 1065} 0.7210 0.7767 0.7478 0.7994
0.297 12.0 120 0.6955 {'precision': 0.708779443254818, 'recall': 0.8182941903584673, 'f1': 0.759609868043603, 'number': 809} {'precision': 0.291044776119403, 'recall': 0.3277310924369748, 'f1': 0.308300395256917, 'number': 119} {'precision': 0.783978397839784, 'recall': 0.8178403755868544, 'f1': 0.8005514705882352, 'number': 1065} 0.7214 0.7888 0.7536 0.8103
0.2907 13.0 130 0.7098 {'precision': 0.7092511013215859, 'recall': 0.796044499381953, 'f1': 0.7501456027955737, 'number': 809} {'precision': 0.3142857142857143, 'recall': 0.3697478991596639, 'f1': 0.33976833976833976, 'number': 119} {'precision': 0.7896678966789668, 'recall': 0.8037558685446009, 'f1': 0.796649604467194, 'number': 1065} 0.7242 0.7747 0.7486 0.8052
0.2701 14.0 140 0.7006 {'precision': 0.7133479212253829, 'recall': 0.8059332509270705, 'f1': 0.7568195008705745, 'number': 809} {'precision': 0.3037037037037037, 'recall': 0.3445378151260504, 'f1': 0.3228346456692913, 'number': 119} {'precision': 0.7894736842105263, 'recall': 0.8169014084507042, 'f1': 0.8029533917858791, 'number': 1065} 0.7266 0.7842 0.7543 0.8091
0.2649 15.0 150 0.7011 {'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809} {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119} {'precision': 0.7859712230215827, 'recall': 0.8206572769953052, 'f1': 0.8029398254478639, 'number': 1065} 0.7250 0.7873 0.7549 0.8102

Framework versions

  • Transformers 4.32.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3