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Benedict-L
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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.1718872065.HCIDC-SV-DMZ-ORC-NODE02.3913563.0 +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: 0.6965
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- - Answer: {'precision': 0.7010869565217391, 'recall': 0.7972805933250927, 'f1': 0.746096009253904, 'number': 809}
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- - Header: {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}
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- - Question: {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065}
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- - Overall Precision: 0.7162
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- - Overall Recall: 0.7852
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- - Overall F1: 0.7492
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- - Overall Accuracy: 0.8006
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  ## Model description
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@@ -54,23 +54,23 @@ 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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- | 1.8343 | 1.0 | 10 | 1.5921 | {'precision': 0.006666666666666667, 'recall': 0.006180469715698393, 'f1': 0.006414368184733804, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.22067901234567902, 'recall': 0.13427230046948357, 'f1': 0.166958552247519, 'number': 1065} | 0.1059 | 0.0743 | 0.0873 | 0.3510 |
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- | 1.4828 | 2.0 | 20 | 1.2849 | {'precision': 0.2738799661876585, 'recall': 0.4004944375772559, 'f1': 0.32530120481927705, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.38058114812189936, 'recall': 0.504225352112676, 'f1': 0.43376413570274636, 'number': 1065} | 0.3314 | 0.4320 | 0.3751 | 0.5951 |
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- | 1.1444 | 3.0 | 30 | 0.9563 | {'precision': 0.4725897920604915, 'recall': 0.6180469715698393, 'f1': 0.5356186395286556, 'number': 809} | {'precision': 0.041666666666666664, 'recall': 0.01680672268907563, 'f1': 0.02395209580838323, 'number': 119} | {'precision': 0.5378020265003897, 'recall': 0.647887323943662, 'f1': 0.5877342419080069, 'number': 1065} | 0.4990 | 0.5981 | 0.5440 | 0.6955 |
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- | 0.8658 | 4.0 | 40 | 0.7885 | {'precision': 0.5757009345794393, 'recall': 0.761433868974042, 'f1': 0.6556679084619478, 'number': 809} | {'precision': 0.1388888888888889, 'recall': 0.08403361344537816, 'f1': 0.10471204188481677, 'number': 119} | {'precision': 0.6567299006323396, 'recall': 0.6826291079812207, 'f1': 0.6694290976058932, 'number': 1065} | 0.6016 | 0.6789 | 0.6379 | 0.7601 |
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- | 0.6833 | 5.0 | 50 | 0.7124 | {'precision': 0.64375, 'recall': 0.7639060568603214, 'f1': 0.6986998304126625, 'number': 809} | {'precision': 0.35, 'recall': 0.23529411764705882, 'f1': 0.28140703517587945, 'number': 119} | {'precision': 0.6729354047424366, 'recall': 0.7727699530516432, 'f1': 0.7194055944055944, 'number': 1065} | 0.6491 | 0.7371 | 0.6903 | 0.7810 |
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- | 0.5898 | 6.0 | 60 | 0.6874 | {'precision': 0.6227141482194418, 'recall': 0.799752781211372, 'f1': 0.7002164502164502, 'number': 809} | {'precision': 0.3411764705882353, 'recall': 0.24369747899159663, 'f1': 0.28431372549019607, 'number': 119} | {'precision': 0.7236492471213464, 'recall': 0.7671361502347418, 'f1': 0.7447584320875114, 'number': 1065} | 0.6627 | 0.7491 | 0.7033 | 0.7851 |
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- | 0.5126 | 7.0 | 70 | 0.6599 | {'precision': 0.6705632306057385, 'recall': 0.7799752781211372, 'f1': 0.7211428571428572, 'number': 809} | {'precision': 0.32673267326732675, 'recall': 0.2773109243697479, 'f1': 0.30000000000000004, 'number': 119} | {'precision': 0.7427821522309711, 'recall': 0.7971830985915493, 'f1': 0.7690217391304347, 'number': 1065} | 0.6924 | 0.7592 | 0.7243 | 0.7963 |
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- | 0.4534 | 8.0 | 80 | 0.6562 | {'precision': 0.670490093847758, 'recall': 0.7948084054388134, 'f1': 0.7273755656108597, 'number': 809} | {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119} | {'precision': 0.7476475620188195, 'recall': 0.8206572769953052, 'f1': 0.7824529991047449, 'number': 1065} | 0.6910 | 0.7787 | 0.7322 | 0.7954 |
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- | 0.3984 | 9.0 | 90 | 0.6561 | {'precision': 0.6838709677419355, 'recall': 0.7861557478368356, 'f1': 0.7314548591144335, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3025210084033613, 'f1': 0.3171806167400881, 'number': 119} | {'precision': 0.7555555555555555, 'recall': 0.8300469483568075, 'f1': 0.7910514541387024, 'number': 1065} | 0.7047 | 0.7807 | 0.7408 | 0.7986 |
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- | 0.3865 | 10.0 | 100 | 0.6673 | {'precision': 0.6877005347593583, 'recall': 0.7948084054388134, 'f1': 0.7373853211009175, 'number': 809} | {'precision': 0.31666666666666665, 'recall': 0.31932773109243695, 'f1': 0.3179916317991632, 'number': 119} | {'precision': 0.7613240418118467, 'recall': 0.8206572769953052, 'f1': 0.7898779936737461, 'number': 1065} | 0.7059 | 0.7802 | 0.7412 | 0.8019 |
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- | 0.3343 | 11.0 | 110 | 0.6761 | {'precision': 0.6853220696937699, 'recall': 0.8022249690976514, 'f1': 0.7391799544419134, 'number': 809} | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119} | {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065} | 0.7110 | 0.7863 | 0.7467 | 0.7998 |
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- | 0.314 | 12.0 | 120 | 0.6772 | {'precision': 0.6989130434782609, 'recall': 0.7948084054388134, 'f1': 0.7437825332562175, 'number': 809} | {'precision': 0.34545454545454546, 'recall': 0.31932773109243695, 'f1': 0.3318777292576419, 'number': 119} | {'precision': 0.7698343504795118, 'recall': 0.8291079812206573, 'f1': 0.7983725135623869, 'number': 1065} | 0.7184 | 0.7847 | 0.7501 | 0.8053 |
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- | 0.3008 | 13.0 | 130 | 0.6878 | {'precision': 0.7048648648648649, 'recall': 0.8059332509270705, 'f1': 0.7520184544405998, 'number': 809} | {'precision': 0.33620689655172414, 'recall': 0.3277310924369748, 'f1': 0.33191489361702126, 'number': 119} | {'precision': 0.7689594356261023, 'recall': 0.8187793427230047, 'f1': 0.793087767166894, 'number': 1065} | 0.7186 | 0.7842 | 0.75 | 0.8033 |
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- | 0.2797 | 14.0 | 140 | 0.6948 | {'precision': 0.7027322404371584, 'recall': 0.7948084054388134, 'f1': 0.7459396751740139, 'number': 809} | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119} | {'precision': 0.7661996497373029, 'recall': 0.8215962441314554, 'f1': 0.7929315813321249, 'number': 1065} | 0.7137 | 0.7817 | 0.7462 | 0.8017 |
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- | 0.2722 | 15.0 | 150 | 0.6965 | {'precision': 0.7010869565217391, 'recall': 0.7972805933250927, 'f1': 0.746096009253904, 'number': 809} | {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119} | {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065} | 0.7162 | 0.7852 | 0.7492 | 0.8006 |
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  ### Framework versions
 
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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:
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+ - Loss: 0.6582
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+ - Answer: {'precision': 0.6929547844374343, 'recall': 0.8145859085290482, 'f1': 0.7488636363636364, 'number': 809}
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+ - Header: {'precision': 0.3050847457627119, 'recall': 0.3025210084033613, 'f1': 0.3037974683544304, 'number': 119}
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+ - Question: {'precision': 0.7621107266435986, 'recall': 0.8272300469483568, 'f1': 0.7933363349842413, 'number': 1065}
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+ - Overall Precision: 0.7083
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+ - Overall Recall: 0.7908
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+ - Overall F1: 0.7473
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+ - Overall Accuracy: 0.8124
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  ## Model description
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55
  ### Training results
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57
+ | 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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+ | 1.759 | 1.0 | 10 | 1.5816 | {'precision': 0.01859799713876967, 'recall': 0.016069221260815822, 'f1': 0.01724137931034483, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.20221169036334913, 'recall': 0.12018779342723004, 'f1': 0.1507656065959953, 'number': 1065} | 0.1059 | 0.0707 | 0.0848 | 0.3558 |
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+ | 1.4623 | 2.0 | 20 | 1.2724 | {'precision': 0.20780711825487944, 'recall': 0.22373300370828184, 'f1': 0.21547619047619046, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.41998306519898393, 'recall': 0.46572769953051646, 'f1': 0.4416740872662512, 'number': 1065} | 0.3299 | 0.3397 | 0.3347 | 0.5858 |
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+ | 1.1305 | 3.0 | 30 | 0.9617 | {'precision': 0.44331210191082804, 'recall': 0.43016069221260816, 'f1': 0.4366373902132999, 'number': 809} | {'precision': 0.1111111111111111, 'recall': 0.03361344537815126, 'f1': 0.05161290322580645, 'number': 119} | {'precision': 0.6134868421052632, 'recall': 0.7004694835680751, 'f1': 0.6540990793511618, 'number': 1065} | 0.5390 | 0.5509 | 0.5449 | 0.6969 |
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+ | 0.865 | 4.0 | 40 | 0.7941 | {'precision': 0.6445916114790287, 'recall': 0.7218788627935723, 'f1': 0.6810495626822157, 'number': 809} | {'precision': 0.2857142857142857, 'recall': 0.11764705882352941, 'f1': 0.16666666666666666, 'number': 119} | {'precision': 0.6923076923076923, 'recall': 0.7436619718309859, 'f1': 0.7170665459483929, 'number': 1065} | 0.6622 | 0.6974 | 0.6794 | 0.7551 |
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+ | 0.6881 | 5.0 | 50 | 0.7089 | {'precision': 0.6280041797283177, 'recall': 0.7428924598269468, 'f1': 0.680634201585504, 'number': 809} | {'precision': 0.2753623188405797, 'recall': 0.15966386554621848, 'f1': 0.20212765957446807, 'number': 119} | {'precision': 0.6679596586501164, 'recall': 0.8084507042253521, 'f1': 0.7315208156329652, 'number': 1065} | 0.6397 | 0.7431 | 0.6876 | 0.7760 |
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+ | 0.5841 | 6.0 | 60 | 0.6779 | {'precision': 0.6493775933609959, 'recall': 0.7737948084054388, 'f1': 0.7061477721376199, 'number': 809} | {'precision': 0.30434782608695654, 'recall': 0.17647058823529413, 'f1': 0.22340425531914895, 'number': 119} | {'precision': 0.7071547420965059, 'recall': 0.7981220657276995, 'f1': 0.7498897220996913, 'number': 1065} | 0.6698 | 0.7511 | 0.7081 | 0.7920 |
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+ | 0.5038 | 7.0 | 70 | 0.6612 | {'precision': 0.6738197424892703, 'recall': 0.7762669962917181, 'f1': 0.7214244686961515, 'number': 809} | {'precision': 0.25892857142857145, 'recall': 0.24369747899159663, 'f1': 0.2510822510822511, 'number': 119} | {'precision': 0.7261802575107296, 'recall': 0.7943661971830986, 'f1': 0.758744394618834, 'number': 1065} | 0.6804 | 0.7541 | 0.7154 | 0.7942 |
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+ | 0.4476 | 8.0 | 80 | 0.6504 | {'precision': 0.6548223350253807, 'recall': 0.7972805933250927, 'f1': 0.7190635451505016, 'number': 809} | {'precision': 0.25, 'recall': 0.2184873949579832, 'f1': 0.23318385650224216, 'number': 119} | {'precision': 0.7463706233988044, 'recall': 0.8206572769953052, 'f1': 0.7817531305903399, 'number': 1065} | 0.6836 | 0.7752 | 0.7265 | 0.7941 |
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+ | 0.3962 | 9.0 | 90 | 0.6375 | {'precision': 0.6781609195402298, 'recall': 0.8022249690976514, 'f1': 0.7349943374858438, 'number': 809} | {'precision': 0.2761904761904762, 'recall': 0.24369747899159663, 'f1': 0.2589285714285714, 'number': 119} | {'precision': 0.7482817869415808, 'recall': 0.8178403755868544, 'f1': 0.781516375056079, 'number': 1065} | 0.6959 | 0.7772 | 0.7343 | 0.8080 |
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+ | 0.3791 | 10.0 | 100 | 0.6459 | {'precision': 0.6928879310344828, 'recall': 0.7948084054388134, 'f1': 0.740356937248129, 'number': 809} | {'precision': 0.30357142857142855, 'recall': 0.2857142857142857, 'f1': 0.2943722943722944, 'number': 119} | {'precision': 0.7443037974683544, 'recall': 0.828169014084507, 'f1': 0.784, 'number': 1065} | 0.7007 | 0.7822 | 0.7392 | 0.8098 |
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+ | 0.328 | 11.0 | 110 | 0.6524 | {'precision': 0.6830543933054394, 'recall': 0.8071693448702101, 'f1': 0.739943342776204, 'number': 809} | {'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119} | {'precision': 0.7478777589134126, 'recall': 0.8272300469483568, 'f1': 0.7855550601872493, 'number': 1065} | 0.6931 | 0.7888 | 0.7379 | 0.8050 |
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+ | 0.3164 | 12.0 | 120 | 0.6502 | {'precision': 0.6967741935483871, 'recall': 0.8009888751545118, 'f1': 0.7452558941920645, 'number': 809} | {'precision': 0.34951456310679613, 'recall': 0.3025210084033613, 'f1': 0.32432432432432434, 'number': 119} | {'precision': 0.7677475898334793, 'recall': 0.8225352112676056, 'f1': 0.7941976427923844, 'number': 1065} | 0.7176 | 0.7827 | 0.7487 | 0.8140 |
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+ | 0.301 | 13.0 | 130 | 0.6594 | {'precision': 0.6896918172157279, 'recall': 0.8022249690976514, 'f1': 0.7417142857142858, 'number': 809} | {'precision': 0.304, 'recall': 0.31932773109243695, 'f1': 0.31147540983606553, 'number': 119} | {'precision': 0.758147512864494, 'recall': 0.8300469483568075, 'f1': 0.7924697445091887, 'number': 1065} | 0.7039 | 0.7883 | 0.7437 | 0.8125 |
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+ | 0.2805 | 14.0 | 140 | 0.6586 | {'precision': 0.6912539515279241, 'recall': 0.8108776266996292, 'f1': 0.7463026166097838, 'number': 809} | {'precision': 0.3076923076923077, 'recall': 0.3025210084033613, 'f1': 0.30508474576271183, 'number': 119} | {'precision': 0.7634315424610052, 'recall': 0.8272300469483568, 'f1': 0.7940513744930149, 'number': 1065} | 0.7086 | 0.7893 | 0.7467 | 0.8124 |
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+ | 0.2783 | 15.0 | 150 | 0.6582 | {'precision': 0.6929547844374343, 'recall': 0.8145859085290482, 'f1': 0.7488636363636364, 'number': 809} | {'precision': 0.3050847457627119, 'recall': 0.3025210084033613, 'f1': 0.3037974683544304, 'number': 119} | {'precision': 0.7621107266435986, 'recall': 0.8272300469483568, 'f1': 0.7933363349842413, 'number': 1065} | 0.7083 | 0.7908 | 0.7473 | 0.8124 |
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  ### Framework versions
logs/events.out.tfevents.1718872065.HCIDC-SV-DMZ-ORC-NODE02.3913563.0 CHANGED
@@ -1,3 +1,3 @@
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