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layoutlm-funsd3

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  1. README.md +31 -23
  2. logs/events.out.tfevents.1718875275.HCIDC-SV-DMZ-ORC-NODE02.4012038.1 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -17,14 +17,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1833
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- - Answer: {'precision': 0.2526041666666667, 'recall': 0.23980222496909764, 'f1': 0.24603677869372226, 'number': 809}
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- - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
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- - Question: {'precision': 0.4935064935064935, 'recall': 0.5352112676056338, 'f1': 0.5135135135135136, 'number': 1065}
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- - Overall Precision: 0.3973
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- - Overall Recall: 0.3833
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- - Overall F1: 0.3902
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- - Overall Accuracy: 0.6048
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  ## Model description
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@@ -50,25 +50,33 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 500
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- - num_epochs: 12
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 1.9597 | 1.0 | 10 | 1.9692 | {'precision': 0.024831867563372995, 'recall': 0.059332509270704575, 'f1': 0.0350109409190372, 'number': 809} | {'precision': 0.0054894784995425435, 'recall': 0.05042016806722689, 'f1': 0.009900990099009901, 'number': 119} | {'precision': 0.05862516212710765, 'recall': 0.21220657276995306, 'f1': 0.091869918699187, 'number': 1065} | 0.0407 | 0.1405 | 0.0631 | 0.1655 |
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- | 1.9429 | 2.0 | 20 | 1.9517 | {'precision': 0.02355889724310777, 'recall': 0.0580964153275649, 'f1': 0.033523537803138374, 'number': 809} | {'precision': 0.006984866123399301, 'recall': 0.05042016806722689, 'f1': 0.012269938650306747, 'number': 119} | {'precision': 0.06357435197817189, 'recall': 0.2187793427230047, 'f1': 0.09852008456659618, 'number': 1065} | 0.0439 | 0.1435 | 0.0672 | 0.1837 |
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- | 1.9283 | 3.0 | 30 | 1.9222 | {'precision': 0.02506265664160401, 'recall': 0.06180469715698393, 'f1': 0.035663338088445073, 'number': 809} | {'precision': 0.005802707930367505, 'recall': 0.025210084033613446, 'f1': 0.009433962264150943, 'number': 119} | {'precision': 0.0683998761993191, 'recall': 0.20751173708920187, 'f1': 0.10288640595903166, 'number': 1065} | 0.0477 | 0.1375 | 0.0708 | 0.2076 |
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- | 1.8979 | 4.0 | 40 | 1.8822 | {'precision': 0.026082130965593784, 'recall': 0.0580964153275649, 'f1': 0.03600153198008425, 'number': 809} | {'precision': 0.01327433628318584, 'recall': 0.025210084033613446, 'f1': 0.017391304347826087, 'number': 119} | {'precision': 0.07303807303807304, 'recall': 0.17652582159624414, 'f1': 0.10332508931025007, 'number': 1065} | 0.0517 | 0.1194 | 0.0722 | 0.2412 |
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- | 1.8452 | 5.0 | 50 | 1.8330 | {'precision': 0.02404809619238477, 'recall': 0.04449938195302843, 'f1': 0.03122289679098005, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.08863636363636364, 'recall': 0.14647887323943662, 'f1': 0.11044247787610618, 'number': 1065} | 0.0577 | 0.0963 | 0.0722 | 0.2719 |
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- | 1.7986 | 6.0 | 60 | 1.7759 | {'precision': 0.017241379310344827, 'recall': 0.022249690976514216, 'f1': 0.019427954668105776, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1244196843082637, 'recall': 0.12582159624413145, 'f1': 0.12511671335200747, 'number': 1065} | 0.0713 | 0.0763 | 0.0737 | 0.3012 |
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- | 1.7397 | 7.0 | 70 | 1.7107 | {'precision': 0.02045728038507822, 'recall': 0.021013597033374538, 'f1': 0.02073170731707317, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18026315789473685, 'recall': 0.12863849765258217, 'f1': 0.15013698630136987, 'number': 1065} | 0.0967 | 0.0773 | 0.0859 | 0.3270 |
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- | 1.6707 | 8.0 | 80 | 1.6298 | {'precision': 0.03066271018793274, 'recall': 0.038318912237330034, 'f1': 0.03406593406593406, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.20087815587266739, 'recall': 0.17183098591549295, 'f1': 0.18522267206477733, 'number': 1065} | 0.1113 | 0.1074 | 0.1093 | 0.3654 |
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- | 1.5891 | 9.0 | 90 | 1.5416 | {'precision': 0.047619047619047616, 'recall': 0.06674907292954264, 'f1': 0.055584148224395266, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2087421944692239, 'recall': 0.21971830985915494, 'f1': 0.2140896614821592, 'number': 1065} | 0.1277 | 0.1445 | 0.1356 | 0.4183 |
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- | 1.516 | 10.0 | 100 | 1.4443 | {'precision': 0.06370070778564206, 'recall': 0.07787391841779975, 'f1': 0.07007786429365963, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2838983050847458, 'recall': 0.3145539906103286, 'f1': 0.2984409799554566, 'number': 1065} | 0.1835 | 0.1997 | 0.1913 | 0.4720 |
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- | 1.3887 | 11.0 | 110 | 1.3259 | {'precision': 0.11662531017369727, 'recall': 0.1161928306551298, 'f1': 0.11640866873065016, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.39842381786339753, 'recall': 0.4272300469483568, 'f1': 0.412324422292705, 'number': 1065} | 0.2818 | 0.2755 | 0.2786 | 0.5434 |
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- | 1.261 | 12.0 | 120 | 1.1833 | {'precision': 0.2526041666666667, 'recall': 0.23980222496909764, 'f1': 0.24603677869372226, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4935064935064935, 'recall': 0.5352112676056338, 'f1': 0.5135135135135136, 'number': 1065} | 0.3973 | 0.3833 | 0.3902 | 0.6048 |
 
 
 
 
 
 
 
 
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  ### Framework versions
 
17
 
18
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 0.6751
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+ - Answer: {'precision': 0.6893617021276596, 'recall': 0.8009888751545118, 'f1': 0.7409948542024015, 'number': 809}
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+ - Header: {'precision': 0.29, 'recall': 0.24369747899159663, 'f1': 0.2648401826484018, 'number': 119}
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+ - Question: {'precision': 0.7557840616966581, 'recall': 0.828169014084507, 'f1': 0.7903225806451614, 'number': 1065}
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+ - Overall Precision: 0.7064
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+ - Overall Recall: 0.7822
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+ - Overall F1: 0.7424
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+ - Overall Accuracy: 0.8022
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29
  ## Model description
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50
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
  - lr_scheduler_type: linear
52
  - lr_scheduler_warmup_steps: 500
53
+ - num_epochs: 20
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  - mixed_precision_training: Native AMP
55
 
56
  ### Training results
57
 
58
+ | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 2.0338 | 1.0 | 10 | 2.0357 | {'precision': 0.037227214377406934, 'recall': 0.03584672435105068, 'f1': 0.03652392947103275, 'number': 809} | {'precision': 0.004568527918781726, 'recall': 0.15126050420168066, 'f1': 0.008869179600886918, 'number': 119} | {'precision': 0.052884615384615384, 'recall': 0.15492957746478872, 'f1': 0.07885304659498207, 'number': 1065} | 0.0270 | 0.1064 | 0.0431 | 0.0892 |
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+ | 2.0223 | 2.0 | 20 | 2.0181 | {'precision': 0.03938730853391685, 'recall': 0.04449938195302843, 'f1': 0.04178757980266977, 'number': 809} | {'precision': 0.004824063564131668, 'recall': 0.14285714285714285, 'f1': 0.009332967334614329, 'number': 119} | {'precision': 0.05484848484848485, 'recall': 0.1699530516431925, 'f1': 0.08293241695303552, 'number': 1065} | 0.0302 | 0.1174 | 0.0481 | 0.1005 |
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+ | 1.9986 | 3.0 | 30 | 1.9858 | {'precision': 0.04096989966555184, 'recall': 0.06056860321384425, 'f1': 0.048877805486284294, 'number': 809} | {'precision': 0.004677941705649514, 'recall': 0.1092436974789916, 'f1': 0.008971704623878536, 'number': 119} | {'precision': 0.05835468260745801, 'recall': 0.19248826291079812, 'f1': 0.08955875928352992, 'number': 1065} | 0.0357 | 0.1340 | 0.0563 | 0.1256 |
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+ | 1.9605 | 4.0 | 40 | 1.9419 | {'precision': 0.03710462287104623, 'recall': 0.0754017305315204, 'f1': 0.04973501834488382, 'number': 809} | {'precision': 0.006282124500285551, 'recall': 0.09243697478991597, 'f1': 0.011764705882352943, 'number': 119} | {'precision': 0.06151288445552785, 'recall': 0.2084507042253521, 'f1': 0.09499358151476253, 'number': 1065} | 0.0420 | 0.1475 | 0.0654 | 0.1633 |
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+ | 1.9119 | 5.0 | 50 | 1.8881 | {'precision': 0.03694581280788178, 'recall': 0.09270704573547589, 'f1': 0.0528355054596689, 'number': 809} | {'precision': 0.002574002574002574, 'recall': 0.01680672268907563, 'f1': 0.004464285714285714, 'number': 119} | {'precision': 0.0671203216826477, 'recall': 0.20375586854460093, 'f1': 0.10097719869706841, 'number': 1065} | 0.0487 | 0.1475 | 0.0732 | 0.2224 |
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+ | 1.8502 | 6.0 | 60 | 1.8264 | {'precision': 0.03467062902426944, 'recall': 0.0865265760197775, 'f1': 0.04950495049504951, 'number': 809} | {'precision': 0.010101010101010102, 'recall': 0.01680672268907563, 'f1': 0.012618296529968456, 'number': 119} | {'precision': 0.08910070451719851, 'recall': 0.20187793427230047, 'f1': 0.1236342725704428, 'number': 1065} | 0.0620 | 0.1440 | 0.0867 | 0.2866 |
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+ | 1.7869 | 7.0 | 70 | 1.7587 | {'precision': 0.026297085998578537, 'recall': 0.04573547589616811, 'f1': 0.033393501805054154, 'number': 809} | {'precision': 0.05555555555555555, 'recall': 0.008403361344537815, 'f1': 0.014598540145985401, 'number': 119} | {'precision': 0.13085399449035812, 'recall': 0.1784037558685446, 'f1': 0.15097338100913787, 'number': 1065} | 0.0792 | 0.1144 | 0.0936 | 0.3296 |
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+ | 1.7064 | 8.0 | 80 | 1.6779 | {'precision': 0.018147086914995225, 'recall': 0.023485784919653894, 'f1': 0.020474137931034486, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18514946962391515, 'recall': 0.18028169014084508, 'f1': 0.18268315889628925, 'number': 1065} | 0.1011 | 0.1059 | 0.1034 | 0.3535 |
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+ | 1.6169 | 9.0 | 90 | 1.5857 | {'precision': 0.03464419475655431, 'recall': 0.04573547589616811, 'f1': 0.03942461374533831, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2270450751252087, 'recall': 0.25539906103286386, 'f1': 0.2403888643393725, 'number': 1065} | 0.1364 | 0.1550 | 0.1451 | 0.3995 |
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+ | 1.5331 | 10.0 | 100 | 1.4740 | {'precision': 0.06180344478216818, 'recall': 0.0754017305315204, 'f1': 0.06792873051224943, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3259207783182766, 'recall': 0.4403755868544601, 'f1': 0.3746006389776358, 'number': 1065} | 0.2185 | 0.2659 | 0.2399 | 0.4731 |
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+ | 1.3817 | 11.0 | 110 | 1.3317 | {'precision': 0.1468609865470852, 'recall': 0.1619283065512979, 'f1': 0.1540270429159318, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.42608089260808923, 'recall': 0.5737089201877934, 'f1': 0.48899559823929567, 'number': 1065} | 0.3190 | 0.3723 | 0.3436 | 0.5459 |
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+ | 1.2192 | 12.0 | 120 | 1.1630 | {'precision': 0.2839506172839506, 'recall': 0.2843016069221261, 'f1': 0.28412600370599134, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5201238390092879, 'recall': 0.6309859154929578, 'f1': 0.5702163767501062, 'number': 1065} | 0.4283 | 0.4526 | 0.4401 | 0.6106 |
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+ | 1.068 | 13.0 | 130 | 1.0176 | {'precision': 0.41460905349794236, 'recall': 0.49814585908529047, 'f1': 0.45255474452554745, 'number': 809} | {'precision': 0.05263157894736842, 'recall': 0.008403361344537815, 'f1': 0.014492753623188406, 'number': 119} | {'precision': 0.54477050413845, 'recall': 0.67981220657277, 'f1': 0.6048454469507102, 'number': 1065} | 0.4862 | 0.5660 | 0.5231 | 0.6807 |
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+ | 0.901 | 14.0 | 140 | 0.9007 | {'precision': 0.484472049689441, 'recall': 0.5784919653893696, 'f1': 0.5273239436619719, 'number': 809} | {'precision': 0.023255813953488372, 'recall': 0.008403361344537815, 'f1': 0.01234567901234568, 'number': 119} | {'precision': 0.6263463131731566, 'recall': 0.7098591549295775, 'f1': 0.665492957746479, 'number': 1065} | 0.5528 | 0.6147 | 0.5821 | 0.7168 |
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+ | 0.7884 | 15.0 | 150 | 0.8050 | {'precision': 0.5395833333333333, 'recall': 0.6402966625463535, 'f1': 0.5856416054267948, 'number': 809} | {'precision': 0.11940298507462686, 'recall': 0.06722689075630252, 'f1': 0.08602150537634408, 'number': 119} | {'precision': 0.6304176516942475, 'recall': 0.7511737089201878, 'f1': 0.6855184233076264, 'number': 1065} | 0.5775 | 0.6653 | 0.6183 | 0.7537 |
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+ | 0.7027 | 16.0 | 160 | 0.7470 | {'precision': 0.6069246435845214, 'recall': 0.7367119901112484, 'f1': 0.6655499720826354, 'number': 809} | {'precision': 0.2236842105263158, 'recall': 0.14285714285714285, 'f1': 0.17435897435897438, 'number': 119} | {'precision': 0.6496465043205027, 'recall': 0.7765258215962442, 'f1': 0.7074422583404619, 'number': 1065} | 0.6178 | 0.7225 | 0.6660 | 0.7717 |
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+ | 0.6177 | 17.0 | 170 | 0.7266 | {'precision': 0.6294691224268689, 'recall': 0.7181705809641533, 'f1': 0.6709006928406466, 'number': 809} | {'precision': 0.2777777777777778, 'recall': 0.16806722689075632, 'f1': 0.20942408376963353, 'number': 119} | {'precision': 0.6956875508543532, 'recall': 0.8028169014084507, 'f1': 0.7454228421970358, 'number': 1065} | 0.6547 | 0.7306 | 0.6905 | 0.7750 |
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+ | 0.5539 | 18.0 | 180 | 0.6824 | {'precision': 0.6402805611222445, 'recall': 0.7898640296662547, 'f1': 0.7072495849474266, 'number': 809} | {'precision': 0.27710843373493976, 'recall': 0.19327731092436976, 'f1': 0.22772277227722776, 'number': 119} | {'precision': 0.7141687141687142, 'recall': 0.8187793427230047, 'f1': 0.762904636920385, 'number': 1065} | 0.6664 | 0.7697 | 0.7143 | 0.7913 |
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+ | 0.499 | 19.0 | 190 | 0.6764 | {'precision': 0.6718266253869969, 'recall': 0.8046971569839307, 'f1': 0.732283464566929, 'number': 809} | {'precision': 0.3068181818181818, 'recall': 0.226890756302521, 'f1': 0.2608695652173913, 'number': 119} | {'precision': 0.7504378283712785, 'recall': 0.8046948356807512, 'f1': 0.7766198459447213, 'number': 1065} | 0.6980 | 0.7702 | 0.7323 | 0.7961 |
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+ | 0.4355 | 20.0 | 200 | 0.6751 | {'precision': 0.6893617021276596, 'recall': 0.8009888751545118, 'f1': 0.7409948542024015, 'number': 809} | {'precision': 0.29, 'recall': 0.24369747899159663, 'f1': 0.2648401826484018, 'number': 119} | {'precision': 0.7557840616966581, 'recall': 0.828169014084507, 'f1': 0.7903225806451614, 'number': 1065} | 0.7064 | 0.7822 | 0.7424 | 0.8022 |
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  ### Framework versions
logs/events.out.tfevents.1718875275.HCIDC-SV-DMZ-ORC-NODE02.4012038.1 CHANGED
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