Edit model card

layoutmlv2_funsd_rjz

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: 0.9422
  • Answer: {'precision': 0.7382857142857143, 'recall': 0.7985166872682324, 'f1': 0.7672209026128266, 'number': 809}
  • Header: {'precision': 0.42758620689655175, 'recall': 0.5210084033613446, 'f1': 0.4696969696969697, 'number': 119}
  • Question: {'precision': 0.8075160403299725, 'recall': 0.8272300469483568, 'f1': 0.8172541743970314, 'number': 1065}
  • Overall Precision: 0.7527
  • Overall Recall: 0.7973
  • Overall F1: 0.7744
  • Overall Accuracy: 0.8096

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
0.3143 1.0 10 0.7685 {'precision': 0.7, 'recall': 0.7700865265760197, 'f1': 0.7333725721012359, 'number': 809} {'precision': 0.2986111111111111, 'recall': 0.36134453781512604, 'f1': 0.32699619771863114, 'number': 119} {'precision': 0.7693032015065914, 'recall': 0.7671361502347418, 'f1': 0.768218147625764, 'number': 1065} 0.7075 0.7441 0.7254 0.7924
0.2816 2.0 20 0.7829 {'precision': 0.7162315550510783, 'recall': 0.7799752781211372, 'f1': 0.7467455621301775, 'number': 809} {'precision': 0.33152173913043476, 'recall': 0.5126050420168067, 'f1': 0.40264026402640263, 'number': 119} {'precision': 0.7855839416058394, 'recall': 0.8084507042253521, 'f1': 0.7968533086534013, 'number': 1065} 0.7186 0.7792 0.7477 0.7976
0.2216 3.0 30 0.7825 {'precision': 0.7016806722689075, 'recall': 0.8257107540173053, 'f1': 0.7586598523566157, 'number': 809} {'precision': 0.35570469798657717, 'recall': 0.44537815126050423, 'f1': 0.39552238805970147, 'number': 119} {'precision': 0.7851985559566786, 'recall': 0.8169014084507042, 'f1': 0.8007363092498849, 'number': 1065} 0.7202 0.7983 0.7573 0.7942
0.1973 4.0 40 0.7683 {'precision': 0.7095032397408207, 'recall': 0.8121137206427689, 'f1': 0.7573487031700288, 'number': 809} {'precision': 0.3968253968253968, 'recall': 0.42016806722689076, 'f1': 0.40816326530612246, 'number': 119} {'precision': 0.802367941712204, 'recall': 0.8272300469483568, 'f1': 0.8146093388811835, 'number': 1065} 0.7386 0.7968 0.7666 0.8143
0.1671 5.0 50 0.7918 {'precision': 0.7269585253456221, 'recall': 0.7799752781211372, 'f1': 0.7525342874180083, 'number': 809} {'precision': 0.4076923076923077, 'recall': 0.44537815126050423, 'f1': 0.42570281124497994, 'number': 119} {'precision': 0.7848888888888889, 'recall': 0.8291079812206573, 'f1': 0.8063926940639269, 'number': 1065} 0.7381 0.7863 0.7614 0.8139
0.1342 6.0 60 0.8295 {'precision': 0.7234972677595628, 'recall': 0.8182941903584673, 'f1': 0.7679814385150812, 'number': 809} {'precision': 0.37857142857142856, 'recall': 0.44537815126050423, 'f1': 0.4092664092664093, 'number': 119} {'precision': 0.7939339875111507, 'recall': 0.8356807511737089, 'f1': 0.8142726440988106, 'number': 1065} 0.7376 0.8053 0.7700 0.8120
0.1212 7.0 70 0.8632 {'precision': 0.7337883959044369, 'recall': 0.7972805933250927, 'f1': 0.764218009478673, 'number': 809} {'precision': 0.4084507042253521, 'recall': 0.48739495798319327, 'f1': 0.4444444444444445, 'number': 119} {'precision': 0.8137347130761995, 'recall': 0.812206572769953, 'f1': 0.8129699248120301, 'number': 1065} 0.7524 0.7868 0.7692 0.8082
0.1131 8.0 80 0.9081 {'precision': 0.7244785949506037, 'recall': 0.8158220024721878, 'f1': 0.7674418604651163, 'number': 809} {'precision': 0.40131578947368424, 'recall': 0.5126050420168067, 'f1': 0.4501845018450184, 'number': 119} {'precision': 0.8097876269621422, 'recall': 0.8234741784037559, 'f1': 0.8165735567970206, 'number': 1065} 0.7446 0.8018 0.7722 0.8011
0.1043 9.0 90 0.9021 {'precision': 0.7308132875143184, 'recall': 0.788627935723115, 'f1': 0.7586206896551724, 'number': 809} {'precision': 0.425531914893617, 'recall': 0.5042016806722689, 'f1': 0.4615384615384615, 'number': 119} {'precision': 0.7914818101153505, 'recall': 0.8375586854460094, 'f1': 0.8138686131386863, 'number': 1065} 0.7426 0.7978 0.7692 0.8075
0.0884 10.0 100 0.9126 {'precision': 0.7231450719822813, 'recall': 0.8071693448702101, 'f1': 0.7628504672897196, 'number': 809} {'precision': 0.40939597315436244, 'recall': 0.5126050420168067, 'f1': 0.4552238805970149, 'number': 119} {'precision': 0.819718309859155, 'recall': 0.819718309859155, 'f1': 0.819718309859155, 'number': 1065} 0.7496 0.7963 0.7723 0.8094
0.084 11.0 110 0.9354 {'precision': 0.7502944640753828, 'recall': 0.7873918417799752, 'f1': 0.7683956574185766, 'number': 809} {'precision': 0.4140127388535032, 'recall': 0.5462184873949579, 'f1': 0.47101449275362317, 'number': 119} {'precision': 0.7946428571428571, 'recall': 0.8356807511737089, 'f1': 0.8146453089244852, 'number': 1065} 0.7488 0.7988 0.7730 0.8064
0.0794 12.0 120 0.9323 {'precision': 0.7244785949506037, 'recall': 0.8158220024721878, 'f1': 0.7674418604651163, 'number': 809} {'precision': 0.4172661870503597, 'recall': 0.48739495798319327, 'f1': 0.4496124031007752, 'number': 119} {'precision': 0.8152985074626866, 'recall': 0.8206572769953052, 'f1': 0.8179691155825924, 'number': 1065} 0.7502 0.7988 0.7738 0.8094
0.0803 13.0 130 0.9429 {'precision': 0.7401129943502824, 'recall': 0.8096415327564895, 'f1': 0.7733175914994096, 'number': 809} {'precision': 0.42592592592592593, 'recall': 0.5798319327731093, 'f1': 0.49110320284697506, 'number': 119} {'precision': 0.8110599078341014, 'recall': 0.8262910798122066, 'f1': 0.8186046511627907, 'number': 1065} 0.7523 0.8048 0.7777 0.8085
0.0754 14.0 140 0.9393 {'precision': 0.7425629290617849, 'recall': 0.8022249690976514, 'f1': 0.7712418300653594, 'number': 809} {'precision': 0.4225352112676056, 'recall': 0.5042016806722689, 'f1': 0.45977011494252873, 'number': 119} {'precision': 0.8018099547511313, 'recall': 0.831924882629108, 'f1': 0.816589861751152, 'number': 1065} 0.7520 0.8003 0.7754 0.8106
0.0732 15.0 150 0.9422 {'precision': 0.7382857142857143, 'recall': 0.7985166872682324, 'f1': 0.7672209026128266, 'number': 809} {'precision': 0.42758620689655175, 'recall': 0.5210084033613446, 'f1': 0.4696969696969697, 'number': 119} {'precision': 0.8075160403299725, 'recall': 0.8272300469483568, 'f1': 0.8172541743970314, 'number': 1065} 0.7527 0.7973 0.7744 0.8096

Framework versions

  • Transformers 4.27.2
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.1
  • Tokenizers 0.13.2
Downloads last month
1
Safetensors
Model size
113M params
Tensor type
I64
·
F32
·
Inference API
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.