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: 0.6896
  • Answer: {'precision': 0.7152245345016429, 'recall': 0.8071693448702101, 'f1': 0.7584204413472706, 'number': 809}
  • Header: {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}
  • Question: {'precision': 0.7833333333333333, 'recall': 0.8384976525821596, 'f1': 0.8099773242630386, 'number': 1065}
  • Overall Precision: 0.7320
  • Overall Recall: 0.7978
  • Overall F1: 0.7635
  • Overall Accuracy: 0.8098

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • 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
1.7932 1.0 10 1.6151 {'precision': 0.013916500994035786, 'recall': 0.00865265760197775, 'f1': 0.010670731707317074, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.24816176470588236, 'recall': 0.1267605633802817, 'f1': 0.1678060907395898, 'number': 1065} 0.1356 0.0712 0.0934 0.3264
1.4724 2.0 20 1.2863 {'precision': 0.11666666666666667, 'recall': 0.1211372064276885, 'f1': 0.11885991510006065, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.3751733703190014, 'recall': 0.507981220657277, 'f1': 0.43159154367770247, 'number': 1065} 0.2800 0.3206 0.2989 0.5749
1.1346 3.0 30 0.9582 {'precision': 0.44279176201373, 'recall': 0.4783683559950556, 'f1': 0.45989304812834225, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.5849647611589663, 'recall': 0.7014084507042253, 'f1': 0.6379163108454312, 'number': 1065} 0.5202 0.5690 0.5435 0.7037
0.8689 4.0 40 0.7730 {'precision': 0.6069182389937107, 'recall': 0.715698393077874, 'f1': 0.6568349404424276, 'number': 809} {'precision': 0.15384615384615385, 'recall': 0.06722689075630252, 'f1': 0.0935672514619883, 'number': 119} {'precision': 0.6672519754170325, 'recall': 0.7136150234741784, 'f1': 0.6896551724137931, 'number': 1065} 0.6280 0.6759 0.6510 0.7590
0.6834 5.0 50 0.6983 {'precision': 0.6349206349206349, 'recall': 0.7416563658838071, 'f1': 0.6841505131128848, 'number': 809} {'precision': 0.273972602739726, 'recall': 0.16806722689075632, 'f1': 0.20833333333333331, 'number': 119} {'precision': 0.6741214057507987, 'recall': 0.7924882629107981, 'f1': 0.7285282693137678, 'number': 1065} 0.6449 0.7346 0.6868 0.7813
0.5771 6.0 60 0.6775 {'precision': 0.6438631790744467, 'recall': 0.7911001236093943, 'f1': 0.7099278979478648, 'number': 809} {'precision': 0.3424657534246575, 'recall': 0.21008403361344538, 'f1': 0.2604166666666667, 'number': 119} {'precision': 0.7335640138408305, 'recall': 0.7962441314553991, 'f1': 0.7636199909950472, 'number': 1065} 0.6806 0.7592 0.7177 0.7871
0.5055 7.0 70 0.6602 {'precision': 0.6920529801324503, 'recall': 0.7750309023485785, 'f1': 0.7311953352769679, 'number': 809} {'precision': 0.3069306930693069, 'recall': 0.2605042016806723, 'f1': 0.28181818181818186, 'number': 119} {'precision': 0.7590788308237378, 'recall': 0.8046948356807512, 'f1': 0.781221513217867, 'number': 1065} 0.7093 0.7602 0.7338 0.7950
0.4549 8.0 80 0.6456 {'precision': 0.6804670912951167, 'recall': 0.792336217552534, 'f1': 0.7321530553969159, 'number': 809} {'precision': 0.2831858407079646, 'recall': 0.2689075630252101, 'f1': 0.27586206896551724, 'number': 119} {'precision': 0.7497865072587532, 'recall': 0.8244131455399061, 'f1': 0.7853309481216457, 'number': 1065} 0.6968 0.7782 0.7352 0.8069
0.3945 9.0 90 0.6484 {'precision': 0.6906552094522019, 'recall': 0.7948084054388134, 'f1': 0.7390804597701149, 'number': 809} {'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119} {'precision': 0.7660869565217391, 'recall': 0.8272300469483568, 'f1': 0.7954853273137698, 'number': 1065} 0.7092 0.7832 0.7444 0.8067
0.3887 10.0 100 0.6674 {'precision': 0.6968085106382979, 'recall': 0.8096415327564895, 'f1': 0.7489994282447112, 'number': 809} {'precision': 0.31, 'recall': 0.2605042016806723, 'f1': 0.2831050228310502, 'number': 119} {'precision': 0.790990990990991, 'recall': 0.8244131455399061, 'f1': 0.8073563218390805, 'number': 1065} 0.7274 0.7847 0.7550 0.8115
0.3299 11.0 110 0.6748 {'precision': 0.7125550660792952, 'recall': 0.799752781211372, 'f1': 0.7536400698893418, 'number': 809} {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119} {'precision': 0.7663230240549829, 'recall': 0.8375586854460094, 'f1': 0.800358905338717, 'number': 1065} 0.7200 0.7923 0.7544 0.8053
0.3088 12.0 120 0.6757 {'precision': 0.7155361050328227, 'recall': 0.8084054388133498, 'f1': 0.759141033081834, 'number': 809} {'precision': 0.3904761904761905, 'recall': 0.3445378151260504, 'f1': 0.36607142857142855, 'number': 119} {'precision': 0.7783641160949868, 'recall': 0.8309859154929577, 'f1': 0.8038147138964576, 'number': 1065} 0.7328 0.7928 0.7616 0.8076
0.2922 13.0 130 0.6892 {'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809} {'precision': 0.38461538461538464, 'recall': 0.37815126050420167, 'f1': 0.38135593220338987, 'number': 119} {'precision': 0.7850133809099019, 'recall': 0.8262910798122066, 'f1': 0.8051235132662397, 'number': 1065} 0.7332 0.7928 0.7618 0.8076
0.2692 14.0 140 0.6906 {'precision': 0.7212389380530974, 'recall': 0.8059332509270705, 'f1': 0.7612375948628138, 'number': 809} {'precision': 0.375, 'recall': 0.37815126050420167, 'f1': 0.37656903765690375, 'number': 119} {'precision': 0.7841409691629956, 'recall': 0.8356807511737089, 'f1': 0.8090909090909091, 'number': 1065} 0.7351 0.7963 0.7645 0.8087
0.2735 15.0 150 0.6896 {'precision': 0.7152245345016429, 'recall': 0.8071693448702101, 'f1': 0.7584204413472706, 'number': 809} {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119} {'precision': 0.7833333333333333, 'recall': 0.8384976525821596, 'f1': 0.8099773242630386, 'number': 1065} 0.7320 0.7978 0.7635 0.8098

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0
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