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layoutlm-funsd

Token Classification
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layoutlm
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End of training

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  1. README.md +25 -25
  2. logs/events.out.tfevents.1688559629.ddc1b2f452a2.1418.0 +2 -2
  3. pytorch_model.bin +1 -1
  4. tokenizer.json +16 -2
README.md CHANGED
@@ -15,14 +15,14 @@ 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: 0.6650
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- - Answer: {'precision': 0.7083786724700761, 'recall': 0.8046971569839307, 'f1': 0.7534722222222221, 'number': 809}
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- - Header: {'precision': 0.30708661417322836, 'recall': 0.3277310924369748, 'f1': 0.3170731707317073, 'number': 119}
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- - Question: {'precision': 0.7877145438121048, 'recall': 0.8187793427230047, 'f1': 0.8029465930018417, 'number': 1065}
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- - Overall Precision: 0.7255
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- - Overall Recall: 0.7837
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- - Overall F1: 0.7535
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- - Overall Accuracy: 0.8109
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  ## Model description
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@@ -51,23 +51,23 @@ The following hyperparameters were used during training:
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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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- | 1.808 | 1.0 | 10 | 1.6430 | {'precision': 0.01707317073170732, 'recall': 0.00865265760197775, 'f1': 0.011484823625922888, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.22358722358722358, 'recall': 0.08544600938967137, 'f1': 0.1236413043478261, 'number': 1065} | 0.1200 | 0.0492 | 0.0698 | 0.3237 |
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- | 1.4971 | 2.0 | 20 | 1.3040 | {'precision': 0.1444906444906445, 'recall': 0.17181705809641531, 'f1': 0.15697346132128742, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3913978494623656, 'recall': 0.5126760563380282, 'f1': 0.4439024390243903, 'number': 1065} | 0.2906 | 0.3437 | 0.3149 | 0.5650 |
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- | 1.1406 | 3.0 | 30 | 0.9802 | {'precision': 0.43251859723698194, 'recall': 0.5030902348578492, 'f1': 0.4651428571428572, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5981228668941979, 'recall': 0.6582159624413145, 'f1': 0.6267322306660705, 'number': 1065} | 0.5231 | 0.5559 | 0.5390 | 0.6959 |
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- | 0.8596 | 4.0 | 40 | 0.7764 | {'precision': 0.5982404692082112, 'recall': 0.7564894932014833, 'f1': 0.6681222707423581, 'number': 809} | {'precision': 0.0975609756097561, 'recall': 0.03361344537815126, 'f1': 0.05, 'number': 119} | {'precision': 0.7104779411764706, 'recall': 0.7258215962441315, 'f1': 0.7180678123548537, 'number': 1065} | 0.6454 | 0.6969 | 0.6702 | 0.7560 |
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- | 0.6884 | 5.0 | 50 | 0.7000 | {'precision': 0.6383419689119171, 'recall': 0.761433868974042, 'f1': 0.6944757609921082, 'number': 809} | {'precision': 0.19736842105263158, 'recall': 0.12605042016806722, 'f1': 0.15384615384615385, 'number': 119} | {'precision': 0.7349726775956285, 'recall': 0.7577464788732394, 'f1': 0.7461858529819695, 'number': 1065} | 0.6723 | 0.7215 | 0.6960 | 0.7759 |
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- | 0.5725 | 6.0 | 60 | 0.6576 | {'precision': 0.6304347826086957, 'recall': 0.788627935723115, 'f1': 0.700713893465129, 'number': 809} | {'precision': 0.24358974358974358, 'recall': 0.15966386554621848, 'f1': 0.1928934010152284, 'number': 119} | {'precision': 0.735494880546075, 'recall': 0.8093896713615023, 'f1': 0.7706750111756816, 'number': 1065} | 0.6715 | 0.7622 | 0.7140 | 0.7888 |
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- | 0.5037 | 7.0 | 70 | 0.6605 | {'precision': 0.6878363832077503, 'recall': 0.7898640296662547, 'f1': 0.7353279631760644, 'number': 809} | {'precision': 0.2616822429906542, 'recall': 0.23529411764705882, 'f1': 0.24778761061946902, 'number': 119} | {'precision': 0.7627416520210897, 'recall': 0.8150234741784037, 'f1': 0.7880163413527008, 'number': 1065} | 0.7061 | 0.7702 | 0.7367 | 0.7923 |
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- | 0.4537 | 8.0 | 80 | 0.6406 | {'precision': 0.6794055201698513, 'recall': 0.7911001236093943, 'f1': 0.7310108509423187, 'number': 809} | {'precision': 0.26785714285714285, 'recall': 0.25210084033613445, 'f1': 0.2597402597402597, 'number': 119} | {'precision': 0.7596153846153846, 'recall': 0.815962441314554, 'f1': 0.7867813490267089, 'number': 1065} | 0.7002 | 0.7722 | 0.7344 | 0.8001 |
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- | 0.4015 | 9.0 | 90 | 0.6439 | {'precision': 0.7077777777777777, 'recall': 0.7873918417799752, 'f1': 0.7454651843183148, 'number': 809} | {'precision': 0.3, 'recall': 0.3025210084033613, 'f1': 0.301255230125523, 'number': 119} | {'precision': 0.7617391304347826, 'recall': 0.8225352112676056, 'f1': 0.7909706546275395, 'number': 1065} | 0.7138 | 0.7772 | 0.7442 | 0.8056 |
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- | 0.3645 | 10.0 | 100 | 0.6387 | {'precision': 0.6993464052287581, 'recall': 0.7935723114956736, 'f1': 0.7434858135495078, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.29411764705882354, 'f1': 0.3125, 'number': 119} | {'precision': 0.773403324584427, 'recall': 0.8300469483568075, 'f1': 0.8007246376811593, 'number': 1065} | 0.7207 | 0.7832 | 0.7507 | 0.8087 |
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- | 0.3282 | 11.0 | 110 | 0.6538 | {'precision': 0.6973969631236443, 'recall': 0.7948084054388134, 'f1': 0.7429231658001155, 'number': 809} | {'precision': 0.3103448275862069, 'recall': 0.3025210084033613, 'f1': 0.30638297872340425, 'number': 119} | {'precision': 0.775022143489814, 'recall': 0.8215962441314554, 'f1': 0.7976298997265269, 'number': 1065} | 0.7171 | 0.7797 | 0.7471 | 0.8096 |
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- | 0.3113 | 12.0 | 120 | 0.6569 | {'precision': 0.6995708154506438, 'recall': 0.8059332509270705, 'f1': 0.7489948305571511, 'number': 809} | {'precision': 0.3220338983050847, 'recall': 0.31932773109243695, 'f1': 0.32067510548523204, 'number': 119} | {'precision': 0.7868852459016393, 'recall': 0.8112676056338028, 'f1': 0.7988904299583911, 'number': 1065} | 0.7235 | 0.7797 | 0.7505 | 0.8092 |
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- | 0.2955 | 13.0 | 130 | 0.6552 | {'precision': 0.7004357298474946, 'recall': 0.7948084054388134, 'f1': 0.7446438911407064, 'number': 809} | {'precision': 0.3023255813953488, 'recall': 0.3277310924369748, 'f1': 0.314516129032258, 'number': 119} | {'precision': 0.7834681042228212, 'recall': 0.8187793427230047, 'f1': 0.800734618916437, 'number': 1065} | 0.7194 | 0.7797 | 0.7484 | 0.8105 |
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- | 0.2828 | 14.0 | 140 | 0.6645 | {'precision': 0.703344120819849, 'recall': 0.8059332509270705, 'f1': 0.751152073732719, 'number': 809} | {'precision': 0.3023255813953488, 'recall': 0.3277310924369748, 'f1': 0.314516129032258, 'number': 119} | {'precision': 0.7845601436265709, 'recall': 0.8206572769953052, 'f1': 0.8022028453419, 'number': 1065} | 0.7212 | 0.7852 | 0.7519 | 0.8108 |
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- | 0.2826 | 15.0 | 150 | 0.6650 | {'precision': 0.7083786724700761, 'recall': 0.8046971569839307, 'f1': 0.7534722222222221, 'number': 809} | {'precision': 0.30708661417322836, 'recall': 0.3277310924369748, 'f1': 0.3170731707317073, 'number': 119} | {'precision': 0.7877145438121048, 'recall': 0.8187793427230047, 'f1': 0.8029465930018417, 'number': 1065} | 0.7255 | 0.7837 | 0.7535 | 0.8109 |
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  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.6847
19
+ - Answer: {'precision': 0.7144432194046306, 'recall': 0.8009888751545118, 'f1': 0.7552447552447553, 'number': 809}
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+ - Header: {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}
21
+ - Question: {'precision': 0.7912966252220248, 'recall': 0.8366197183098592, 'f1': 0.81332724783204, 'number': 1065}
22
+ - Overall Precision: 0.7309
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+ - Overall Recall: 0.7918
24
+ - Overall F1: 0.7601
25
+ - Overall Accuracy: 0.8132
26
 
27
  ## Model description
28
 
 
51
 
52
  ### Training results
53
 
54
+ | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
55
+ |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.8057 | 1.0 | 10 | 1.5966 | {'precision': 0.008733624454148471, 'recall': 0.009888751545117428, 'f1': 0.009275362318840578, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.14909090909090908, 'recall': 0.11549295774647887, 'f1': 0.13015873015873014, 'number': 1065} | 0.0752 | 0.0657 | 0.0702 | 0.3764 |
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+ | 1.4635 | 2.0 | 20 | 1.2374 | {'precision': 0.14137483787289234, 'recall': 0.13473423980222496, 'f1': 0.1379746835443038, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.42204995693367786, 'recall': 0.460093896713615, 'f1': 0.440251572327044, 'number': 1065} | 0.3100 | 0.3006 | 0.3052 | 0.6035 |
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+ | 1.1031 | 3.0 | 30 | 0.9623 | {'precision': 0.4551451187335092, 'recall': 0.4264524103831891, 'f1': 0.44033184428844924, 'number': 809} | {'precision': 0.13157894736842105, 'recall': 0.04201680672268908, 'f1': 0.06369426751592357, 'number': 119} | {'precision': 0.630297565374211, 'recall': 0.6563380281690141, 'f1': 0.6430542778288868, 'number': 1065} | 0.5507 | 0.5263 | 0.5382 | 0.7016 |
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+ | 0.8514 | 4.0 | 40 | 0.7967 | {'precision': 0.6146682188591386, 'recall': 0.6526576019777504, 'f1': 0.6330935251798562, 'number': 809} | {'precision': 0.23333333333333334, 'recall': 0.11764705882352941, 'f1': 0.1564245810055866, 'number': 119} | {'precision': 0.6810422282120395, 'recall': 0.711737089201878, 'f1': 0.6960514233241506, 'number': 1065} | 0.6398 | 0.6523 | 0.6460 | 0.7495 |
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+ | 0.6854 | 5.0 | 50 | 0.7228 | {'precision': 0.6617647058823529, 'recall': 0.723114956736712, 'f1': 0.6910809214412286, 'number': 809} | {'precision': 0.25806451612903225, 'recall': 0.20168067226890757, 'f1': 0.22641509433962265, 'number': 119} | {'precision': 0.697751873438801, 'recall': 0.7868544600938967, 'f1': 0.7396293027360988, 'number': 1065} | 0.6644 | 0.7260 | 0.6938 | 0.7818 |
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+ | 0.5608 | 6.0 | 60 | 0.6733 | {'precision': 0.6585879873551106, 'recall': 0.7725587144622992, 'f1': 0.7110352673492606, 'number': 809} | {'precision': 0.25, 'recall': 0.17647058823529413, 'f1': 0.20689655172413793, 'number': 119} | {'precision': 0.7112561174551386, 'recall': 0.8187793427230047, 'f1': 0.7612396333478829, 'number': 1065} | 0.6720 | 0.7617 | 0.7140 | 0.7976 |
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+ | 0.486 | 7.0 | 70 | 0.6683 | {'precision': 0.670514165792235, 'recall': 0.7898640296662547, 'f1': 0.7253121452894438, 'number': 809} | {'precision': 0.25688073394495414, 'recall': 0.23529411764705882, 'f1': 0.24561403508771928, 'number': 119} | {'precision': 0.7351398601398601, 'recall': 0.7896713615023474, 'f1': 0.7614305115436849, 'number': 1065} | 0.6836 | 0.7566 | 0.7183 | 0.7992 |
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+ | 0.4391 | 8.0 | 80 | 0.6590 | {'precision': 0.6809623430962343, 'recall': 0.8046971569839307, 'f1': 0.7376770538243627, 'number': 809} | {'precision': 0.2641509433962264, 'recall': 0.23529411764705882, 'f1': 0.24888888888888888, 'number': 119} | {'precision': 0.7585324232081911, 'recall': 0.8347417840375587, 'f1': 0.7948144836835047, 'number': 1065} | 0.7019 | 0.7868 | 0.7419 | 0.8042 |
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+ | 0.3834 | 9.0 | 90 | 0.6569 | {'precision': 0.7043189368770764, 'recall': 0.7861557478368356, 'f1': 0.7429906542056073, 'number': 809} | {'precision': 0.2619047619047619, 'recall': 0.2773109243697479, 'f1': 0.2693877551020408, 'number': 119} | {'precision': 0.7637457044673539, 'recall': 0.8347417840375587, 'f1': 0.7976671152983401, 'number': 1065} | 0.7104 | 0.7817 | 0.7444 | 0.8099 |
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+ | 0.3489 | 10.0 | 100 | 0.6655 | {'precision': 0.6984649122807017, 'recall': 0.7873918417799752, 'f1': 0.7402672864613596, 'number': 809} | {'precision': 0.2714285714285714, 'recall': 0.31932773109243695, 'f1': 0.29343629343629346, 'number': 119} | {'precision': 0.7745614035087719, 'recall': 0.8291079812206573, 'f1': 0.800907029478458, 'number': 1065} | 0.7108 | 0.7817 | 0.7446 | 0.8126 |
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+ | 0.3103 | 11.0 | 110 | 0.6682 | {'precision': 0.6981934112646121, 'recall': 0.8121137206427689, 'f1': 0.7508571428571429, 'number': 809} | {'precision': 0.3173076923076923, 'recall': 0.2773109243697479, 'f1': 0.29596412556053814, 'number': 119} | {'precision': 0.7878521126760564, 'recall': 0.8403755868544601, 'f1': 0.8132666969559291, 'number': 1065} | 0.7267 | 0.7953 | 0.7595 | 0.8148 |
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+ | 0.293 | 12.0 | 120 | 0.6739 | {'precision': 0.7123893805309734, 'recall': 0.796044499381953, 'f1': 0.7518972562755399, 'number': 809} | {'precision': 0.328, 'recall': 0.3445378151260504, 'f1': 0.33606557377049184, 'number': 119} | {'precision': 0.7863475177304965, 'recall': 0.8328638497652582, 'f1': 0.8089375284997721, 'number': 1065} | 0.7288 | 0.7888 | 0.7576 | 0.8167 |
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+ | 0.2633 | 14.0 | 140 | 0.6849 | {'precision': 0.7085590465872156, 'recall': 0.8084054388133498, 'f1': 0.7551963048498845, 'number': 809} | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119} | {'precision': 0.7883082373782108, 'recall': 0.8356807511737089, 'f1': 0.8113035551504102, 'number': 1065} | 0.7273 | 0.7948 | 0.7595 | 0.8125 |
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+ | 0.2632 | 15.0 | 150 | 0.6847 | {'precision': 0.7144432194046306, 'recall': 0.8009888751545118, 'f1': 0.7552447552447553, 'number': 809} | {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119} | {'precision': 0.7912966252220248, 'recall': 0.8366197183098592, 'f1': 0.81332724783204, 'number': 1065} | 0.7309 | 0.7918 | 0.7601 | 0.8132 |
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  ### Framework versions
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