layoutlm-funsd / README.md
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metadata
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: 0.6866
  • Answer: {'precision': 0.7130339539978094, 'recall': 0.8046971569839307, 'f1': 0.7560975609756098, 'number': 809}
  • Header: {'precision': 0.3384615384615385, 'recall': 0.3697478991596639, 'f1': 0.35341365461847385, 'number': 119}
  • Question: {'precision': 0.7763975155279503, 'recall': 0.8215962441314554, 'f1': 0.7983576642335766, 'number': 1065}
  • Overall Precision: 0.7235
  • Overall Recall: 0.7878
  • Overall F1: 0.7543
  • Overall Accuracy: 0.8126

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
1.7968 1.0 10 1.5972 {'precision': 0.011235955056179775, 'recall': 0.011124845488257108, 'f1': 0.011180124223602483, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.1959544879898862, 'recall': 0.14553990610328638, 'f1': 0.1670258620689655, 'number': 1065} 0.1030 0.0823 0.0915 0.3535
1.4694 2.0 20 1.2467 {'precision': 0.2002053388090349, 'recall': 0.24103831891223734, 'f1': 0.21873247335950646, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.43186895011169024, 'recall': 0.5446009389671361, 'f1': 0.48172757475083056, 'number': 1065} 0.3345 0.3889 0.3596 0.6093
1.0892 3.0 30 0.9301 {'precision': 0.49691991786447637, 'recall': 0.5982694684796045, 'f1': 0.5429052159282108, 'number': 809} {'precision': 0.08108108108108109, 'recall': 0.025210084033613446, 'f1': 0.038461538461538464, 'number': 119} {'precision': 0.5869205298013245, 'recall': 0.6657276995305165, 'f1': 0.62384513858337, 'number': 1065} 0.5390 0.6001 0.5679 0.7041
0.8148 4.0 40 0.7921 {'precision': 0.5805243445692884, 'recall': 0.7663782447466008, 'f1': 0.660628662759723, 'number': 809} {'precision': 0.2, 'recall': 0.12605042016806722, 'f1': 0.15463917525773196, 'number': 119} {'precision': 0.6657534246575343, 'recall': 0.6845070422535211, 'f1': 0.6749999999999999, 'number': 1065} 0.6095 0.6844 0.6448 0.7498
0.6789 5.0 50 0.7126 {'precision': 0.6466942148760331, 'recall': 0.7737948084054388, 'f1': 0.7045582442318515, 'number': 809} {'precision': 0.23809523809523808, 'recall': 0.21008403361344538, 'f1': 0.22321428571428573, 'number': 119} {'precision': 0.6851535836177475, 'recall': 0.7539906103286385, 'f1': 0.7179257934734019, 'number': 1065} 0.6477 0.7296 0.6862 0.7822
0.5701 6.0 60 0.6734 {'precision': 0.6524390243902439, 'recall': 0.7935723114956736, 'f1': 0.7161182375906302, 'number': 809} {'precision': 0.25, 'recall': 0.18487394957983194, 'f1': 0.21256038647342995, 'number': 119} {'precision': 0.6886564762670957, 'recall': 0.8037558685446009, 'f1': 0.7417677642980937, 'number': 1065} 0.6566 0.7627 0.7057 0.7949
0.497 7.0 70 0.6688 {'precision': 0.6719745222929936, 'recall': 0.7824474660074165, 'f1': 0.7230154197601371, 'number': 809} {'precision': 0.2857142857142857, 'recall': 0.2689075630252101, 'f1': 0.277056277056277, 'number': 119} {'precision': 0.7403267411865864, 'recall': 0.8084507042253521, 'f1': 0.7728904847396768, 'number': 1065} 0.6883 0.7657 0.7249 0.7976
0.4549 8.0 80 0.6561 {'precision': 0.6881028938906752, 'recall': 0.7935723114956736, 'f1': 0.7370838117106774, 'number': 809} {'precision': 0.25, 'recall': 0.25210084033613445, 'f1': 0.2510460251046025, 'number': 119} {'precision': 0.7432784041630529, 'recall': 0.8046948356807512, 'f1': 0.7727682596934174, 'number': 1065} 0.6931 0.7672 0.7283 0.8045
0.4095 9.0 90 0.6514 {'precision': 0.694206008583691, 'recall': 0.799752781211372, 'f1': 0.7432510051694429, 'number': 809} {'precision': 0.29411764705882354, 'recall': 0.29411764705882354, 'f1': 0.29411764705882354, 'number': 119} {'precision': 0.7452830188679245, 'recall': 0.815962441314554, 'f1': 0.7790228597041686, 'number': 1065} 0.6996 0.7782 0.7368 0.8027
0.3629 10.0 100 0.6616 {'precision': 0.7035010940919038, 'recall': 0.7948084054388134, 'f1': 0.7463726059199072, 'number': 809} {'precision': 0.29927007299270075, 'recall': 0.3445378151260504, 'f1': 0.3203125, 'number': 119} {'precision': 0.7564216120460585, 'recall': 0.8018779342723005, 'f1': 0.7784867821330903, 'number': 1065} 0.7055 0.7717 0.7371 0.8075
0.3322 11.0 110 0.6668 {'precision': 0.7112068965517241, 'recall': 0.8158220024721878, 'f1': 0.75993091537133, 'number': 809} {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119} {'precision': 0.783273381294964, 'recall': 0.8178403755868544, 'f1': 0.8001837390904916, 'number': 1065} 0.7288 0.7873 0.7569 0.8120
0.3188 12.0 120 0.6768 {'precision': 0.7225305216426193, 'recall': 0.8046971569839307, 'f1': 0.7614035087719299, 'number': 809} {'precision': 0.33076923076923076, 'recall': 0.36134453781512604, 'f1': 0.34538152610441764, 'number': 119} {'precision': 0.7759078830823738, 'recall': 0.8225352112676056, 'f1': 0.7985414767547857, 'number': 1065} 0.7269 0.7878 0.7561 0.8119
0.2936 13.0 130 0.6787 {'precision': 0.7122692725298588, 'recall': 0.8108776266996292, 'f1': 0.7583815028901735, 'number': 809} {'precision': 0.35384615384615387, 'recall': 0.3865546218487395, 'f1': 0.3694779116465864, 'number': 119} {'precision': 0.7807486631016043, 'recall': 0.8225352112676056, 'f1': 0.8010973936899862, 'number': 1065} 0.7262 0.7918 0.7576 0.8133
0.2894 14.0 140 0.6863 {'precision': 0.7113289760348583, 'recall': 0.8071693448702101, 'f1': 0.7562246670526924, 'number': 809} {'precision': 0.34108527131782945, 'recall': 0.3697478991596639, 'f1': 0.35483870967741943, 'number': 119} {'precision': 0.7852650494159928, 'recall': 0.8206572769953052, 'f1': 0.8025711662075299, 'number': 1065} 0.7273 0.7883 0.7566 0.8111
0.2813 15.0 150 0.6866 {'precision': 0.7130339539978094, 'recall': 0.8046971569839307, 'f1': 0.7560975609756098, 'number': 809} {'precision': 0.3384615384615385, 'recall': 0.3697478991596639, 'f1': 0.35341365461847385, 'number': 119} {'precision': 0.7763975155279503, 'recall': 0.8215962441314554, 'f1': 0.7983576642335766, 'number': 1065} 0.7235 0.7878 0.7543 0.8126

Framework versions

  • Transformers 4.27.1
  • Pytorch 1.12.1
  • Datasets 2.6.1
  • Tokenizers 0.13.2