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SeanLB
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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.1751940015.7a9bc2c7ed3e.19205.0 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -16,14 +16,14 @@ 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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7253
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- - Answer: {'precision': 0.70995670995671, 'recall': 0.8108776266996292, 'f1': 0.7570686670513561, 'number': 809}
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- - Header: {'precision': 0.3333333333333333, 'recall': 0.3445378151260504, 'f1': 0.33884297520661155, 'number': 119}
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- - Question: {'precision': 0.7931034482758621, 'recall': 0.8422535211267606, 'f1': 0.8169398907103825, 'number': 1065}
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- - Overall Precision: 0.7319
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- - Overall Recall: 0.7998
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- - Overall F1: 0.7643
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- - Overall Accuracy: 0.8025
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  ## Model description
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@@ -53,23 +53,23 @@ 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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- | 1.8167 | 1.0 | 10 | 1.6314 | {'precision': 0.01308139534883721, 'recall': 0.011124845488257108, 'f1': 0.012024048096192386, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.14820143884892087, 'recall': 0.09671361502347418, 'f1': 0.11704545454545455, 'number': 1065} | 0.0810 | 0.0562 | 0.0664 | 0.3378 |
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- | 1.4882 | 2.0 | 20 | 1.3037 | {'precision': 0.1090686274509804, 'recall': 0.1100123609394314, 'f1': 0.10953846153846154, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.39293598233995586, 'recall': 0.5014084507042254, 'f1': 0.4405940594059406, 'number': 1065} | 0.2864 | 0.3126 | 0.2989 | 0.5571 |
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- | 1.1329 | 3.0 | 30 | 0.9825 | {'precision': 0.46, 'recall': 0.511742892459827, 'f1': 0.4844938560561732, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5010366275051832, 'recall': 0.6807511737089202, 'f1': 0.5772292993630574, 'number': 1065} | 0.4774 | 0.5715 | 0.5202 | 0.6939 |
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- | 0.8727 | 4.0 | 40 | 0.8129 | {'precision': 0.5495951417004049, 'recall': 0.6711990111248455, 'f1': 0.6043405676126878, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.626418152350081, 'recall': 0.7258215962441315, 'f1': 0.6724662896911702, 'number': 1065} | 0.5774 | 0.6603 | 0.6161 | 0.7443 |
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- | 0.6968 | 5.0 | 50 | 0.7437 | {'precision': 0.5873362445414847, 'recall': 0.6650185414091471, 'f1': 0.6237681159420291, 'number': 809} | {'precision': 0.1717171717171717, 'recall': 0.14285714285714285, 'f1': 0.15596330275229356, 'number': 119} | {'precision': 0.6337295690936107, 'recall': 0.8009389671361502, 'f1': 0.7075902115304852, 'number': 1065} | 0.5964 | 0.7065 | 0.6468 | 0.7722 |
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- | 0.5935 | 6.0 | 60 | 0.6977 | {'precision': 0.6307541625857003, 'recall': 0.796044499381953, 'f1': 0.7038251366120217, 'number': 809} | {'precision': 0.23170731707317074, 'recall': 0.15966386554621848, 'f1': 0.1890547263681592, 'number': 119} | {'precision': 0.7163920208152645, 'recall': 0.7755868544600939, 'f1': 0.7448151487826872, 'number': 1065} | 0.6600 | 0.7471 | 0.7009 | 0.7850 |
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- | 0.5186 | 7.0 | 70 | 0.6795 | {'precision': 0.6838709677419355, 'recall': 0.7861557478368356, 'f1': 0.7314548591144335, 'number': 809} | {'precision': 0.31958762886597936, 'recall': 0.2605042016806723, 'f1': 0.28703703703703703, 'number': 119} | {'precision': 0.7390557939914163, 'recall': 0.8084507042253521, 'f1': 0.7721973094170403, 'number': 1065} | 0.6971 | 0.7667 | 0.7302 | 0.7968 |
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- | 0.4576 | 8.0 | 80 | 0.6670 | {'precision': 0.6711340206185566, 'recall': 0.8046971569839307, 'f1': 0.7318718381112984, 'number': 809} | {'precision': 0.25225225225225223, 'recall': 0.23529411764705882, 'f1': 0.2434782608695652, 'number': 119} | {'precision': 0.7412765957446809, 'recall': 0.8178403755868544, 'f1': 0.7776785714285714, 'number': 1065} | 0.6871 | 0.7777 | 0.7296 | 0.8010 |
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- | 0.3975 | 9.0 | 90 | 0.6732 | {'precision': 0.6915584415584416, 'recall': 0.7898640296662547, 'f1': 0.7374495095210617, 'number': 809} | {'precision': 0.272, 'recall': 0.2857142857142857, 'f1': 0.27868852459016397, 'number': 119} | {'precision': 0.7510548523206751, 'recall': 0.8356807511737089, 'f1': 0.7911111111111111, 'number': 1065} | 0.6996 | 0.7842 | 0.7395 | 0.8006 |
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- | 0.3865 | 10.0 | 100 | 0.6818 | {'precision': 0.6941798941798942, 'recall': 0.8108776266996292, 'f1': 0.7480045610034207, 'number': 809} | {'precision': 0.28695652173913044, 'recall': 0.2773109243697479, 'f1': 0.2820512820512821, 'number': 119} | {'precision': 0.7790492957746479, 'recall': 0.8309859154929577, 'f1': 0.8041799182189914, 'number': 1065} | 0.7168 | 0.7898 | 0.7515 | 0.8007 |
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- | 0.3278 | 11.0 | 110 | 0.6996 | {'precision': 0.7050053248136315, 'recall': 0.8182941903584673, 'f1': 0.7574370709382151, 'number': 809} | {'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119} | {'precision': 0.7785527462946817, 'recall': 0.8384976525821596, 'f1': 0.8074141048824593, 'number': 1065} | 0.7226 | 0.7998 | 0.7592 | 0.8015 |
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- | 0.3097 | 12.0 | 120 | 0.7068 | {'precision': 0.7093649085037675, 'recall': 0.8145859085290482, 'f1': 0.7583429228998849, 'number': 809} | {'precision': 0.32456140350877194, 'recall': 0.31092436974789917, 'f1': 0.31759656652360513, 'number': 119} | {'precision': 0.7866666666666666, 'recall': 0.8309859154929577, 'f1': 0.8082191780821917, 'number': 1065} | 0.7292 | 0.7933 | 0.7599 | 0.8008 |
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- | 0.2962 | 13.0 | 130 | 0.7236 | {'precision': 0.7118463180362861, 'recall': 0.8244746600741656, 'f1': 0.7640320733104239, 'number': 809} | {'precision': 0.3217391304347826, 'recall': 0.31092436974789917, 'f1': 0.3162393162393162, 'number': 119} | {'precision': 0.7907801418439716, 'recall': 0.8375586854460094, 'f1': 0.8134974920200638, 'number': 1065} | 0.7321 | 0.8008 | 0.7649 | 0.8050 |
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- | 0.2711 | 14.0 | 140 | 0.7240 | {'precision': 0.7082429501084598, 'recall': 0.8071693448702101, 'f1': 0.754477180820335, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3445378151260504, 'f1': 0.33884297520661155, 'number': 119} | {'precision': 0.7931034482758621, 'recall': 0.8422535211267606, 'f1': 0.8169398907103825, 'number': 1065} | 0.7312 | 0.7983 | 0.7633 | 0.8017 |
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- | 0.2721 | 15.0 | 150 | 0.7253 | {'precision': 0.70995670995671, 'recall': 0.8108776266996292, 'f1': 0.7570686670513561, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3445378151260504, 'f1': 0.33884297520661155, 'number': 119} | {'precision': 0.7931034482758621, 'recall': 0.8422535211267606, 'f1': 0.8169398907103825, 'number': 1065} | 0.7319 | 0.7998 | 0.7643 | 0.8025 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6840
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+ - Answer: {'precision': 0.7045203969128997, 'recall': 0.7898640296662547, 'f1': 0.7447552447552448, 'number': 809}
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+ - Header: {'precision': 0.3465346534653465, 'recall': 0.29411764705882354, 'f1': 0.3181818181818182, 'number': 119}
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+ - Question: {'precision': 0.7655709342560554, 'recall': 0.8309859154929577, 'f1': 0.7969383160738407, 'number': 1065}
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+ - Overall Precision: 0.7204
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+ - Overall Recall: 0.7822
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+ - Overall F1: 0.7501
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+ - Overall Accuracy: 0.8036
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  ## Model description
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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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+ | 1.81 | 1.0 | 10 | 1.6151 | {'precision': 0.004709576138147566, 'recall': 0.003708281829419036, 'f1': 0.004149377593360995, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1045016077170418, 'recall': 0.06103286384976526, 'f1': 0.07705986959098991, 'number': 1065} | 0.0540 | 0.0341 | 0.0418 | 0.3338 |
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+ | 1.4675 | 2.0 | 20 | 1.2857 | {'precision': 0.20516129032258065, 'recall': 0.1965389369592089, 'f1': 0.20075757575757577, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4057971014492754, 'recall': 0.5258215962441315, 'f1': 0.45807770961145194, 'number': 1065} | 0.3336 | 0.3608 | 0.3467 | 0.5615 |
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+ | 1.1095 | 3.0 | 30 | 0.9749 | {'precision': 0.4744136460554371, 'recall': 0.5500618046971569, 'f1': 0.5094447624499141, 'number': 809} | {'precision': 0.02857142857142857, 'recall': 0.008403361344537815, 'f1': 0.012987012987012986, 'number': 119} | {'precision': 0.5271260997067448, 'recall': 0.6751173708920187, 'f1': 0.592013174145739, 'number': 1065} | 0.4985 | 0.5845 | 0.5381 | 0.6871 |
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+ | 0.8396 | 4.0 | 40 | 0.7887 | {'precision': 0.6004098360655737, 'recall': 0.7243510506798516, 'f1': 0.6565826330532212, 'number': 809} | {'precision': 0.1016949152542373, 'recall': 0.05042016806722689, 'f1': 0.06741573033707866, 'number': 119} | {'precision': 0.6529942575881871, 'recall': 0.7474178403755869, 'f1': 0.6970227670753065, 'number': 1065} | 0.6158 | 0.6964 | 0.6536 | 0.7516 |
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+ | 0.6706 | 5.0 | 50 | 0.7238 | {'precision': 0.6403887688984882, 'recall': 0.7330037082818294, 'f1': 0.6835734870317004, 'number': 809} | {'precision': 0.1875, 'recall': 0.12605042016806722, 'f1': 0.1507537688442211, 'number': 119} | {'precision': 0.6530303030303031, 'recall': 0.8093896713615023, 'f1': 0.7228511530398324, 'number': 1065} | 0.6320 | 0.7376 | 0.6807 | 0.7773 |
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+ | 0.5725 | 6.0 | 60 | 0.6919 | {'precision': 0.6587473002159827, 'recall': 0.754017305315204, 'f1': 0.7031700288184438, 'number': 809} | {'precision': 0.24050632911392406, 'recall': 0.15966386554621848, 'f1': 0.1919191919191919, 'number': 119} | {'precision': 0.7182978723404255, 'recall': 0.7924882629107981, 'f1': 0.7535714285714286, 'number': 1065} | 0.6757 | 0.7391 | 0.7060 | 0.7819 |
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+ | 0.4918 | 7.0 | 70 | 0.6613 | {'precision': 0.685807150595883, 'recall': 0.7824474660074165, 'f1': 0.7309468822170901, 'number': 809} | {'precision': 0.32941176470588235, 'recall': 0.23529411764705882, 'f1': 0.2745098039215686, 'number': 119} | {'precision': 0.730092204526404, 'recall': 0.8178403755868544, 'f1': 0.7714791851195748, 'number': 1065} | 0.6960 | 0.7687 | 0.7306 | 0.7954 |
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+ | 0.4364 | 8.0 | 80 | 0.6622 | {'precision': 0.6994475138121546, 'recall': 0.7824474660074165, 'f1': 0.7386231038506416, 'number': 809} | {'precision': 0.25961538461538464, 'recall': 0.226890756302521, 'f1': 0.242152466367713, 'number': 119} | {'precision': 0.7337826453243471, 'recall': 0.8178403755868544, 'f1': 0.7735346358792186, 'number': 1065} | 0.6972 | 0.7682 | 0.7310 | 0.7961 |
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+ | 0.3848 | 9.0 | 90 | 0.6633 | {'precision': 0.7046460176991151, 'recall': 0.7873918417799752, 'f1': 0.7437244600116752, 'number': 809} | {'precision': 0.3106796116504854, 'recall': 0.2689075630252101, 'f1': 0.28828828828828823, 'number': 119} | {'precision': 0.7519446845289542, 'recall': 0.8169014084507042, 'f1': 0.7830783078307832, 'number': 1065} | 0.7112 | 0.7722 | 0.7404 | 0.7973 |
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+ | 0.3806 | 10.0 | 100 | 0.6619 | {'precision': 0.6894679695982627, 'recall': 0.7849196538936959, 'f1': 0.7341040462427746, 'number': 809} | {'precision': 0.29896907216494845, 'recall': 0.24369747899159663, 'f1': 0.2685185185185185, 'number': 119} | {'precision': 0.7575236457437661, 'recall': 0.8272300469483568, 'f1': 0.7908438061041293, 'number': 1065} | 0.7084 | 0.7752 | 0.7403 | 0.8005 |
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+ | 0.3245 | 11.0 | 110 | 0.6781 | {'precision': 0.7051569506726457, 'recall': 0.7775030902348579, 'f1': 0.7395649617871839, 'number': 809} | {'precision': 0.32710280373831774, 'recall': 0.29411764705882354, 'f1': 0.3097345132743363, 'number': 119} | {'precision': 0.7420701168614358, 'recall': 0.8347417840375587, 'f1': 0.7856827220503757, 'number': 1065} | 0.7069 | 0.7792 | 0.7413 | 0.7994 |
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+ | 0.3037 | 12.0 | 120 | 0.6741 | {'precision': 0.7049723756906078, 'recall': 0.788627935723115, 'f1': 0.7444574095682615, 'number': 809} | {'precision': 0.32, 'recall': 0.2689075630252101, 'f1': 0.2922374429223744, 'number': 119} | {'precision': 0.7791519434628975, 'recall': 0.828169014084507, 'f1': 0.8029130632680928, 'number': 1065} | 0.7263 | 0.7787 | 0.7516 | 0.8001 |
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+ | 0.2917 | 13.0 | 130 | 0.6849 | {'precision': 0.7, 'recall': 0.7787391841779975, 'f1': 0.7372732592159158, 'number': 809} | {'precision': 0.32673267326732675, 'recall': 0.2773109243697479, 'f1': 0.30000000000000004, 'number': 119} | {'precision': 0.7703056768558952, 'recall': 0.828169014084507, 'f1': 0.7981900452488688, 'number': 1065} | 0.7199 | 0.7752 | 0.7466 | 0.8011 |
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+ | 0.2692 | 14.0 | 140 | 0.6823 | {'precision': 0.7019867549668874, 'recall': 0.7861557478368356, 'f1': 0.7416909620991254, 'number': 809} | {'precision': 0.35051546391752575, 'recall': 0.2857142857142857, 'f1': 0.3148148148148148, 'number': 119} | {'precision': 0.7642980935875217, 'recall': 0.828169014084507, 'f1': 0.7949526813880126, 'number': 1065} | 0.7195 | 0.7787 | 0.7480 | 0.8019 |
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+ | 0.2721 | 15.0 | 150 | 0.6840 | {'precision': 0.7045203969128997, 'recall': 0.7898640296662547, 'f1': 0.7447552447552448, 'number': 809} | {'precision': 0.3465346534653465, 'recall': 0.29411764705882354, 'f1': 0.3181818181818182, 'number': 119} | {'precision': 0.7655709342560554, 'recall': 0.8309859154929577, 'f1': 0.7969383160738407, 'number': 1065} | 0.7204 | 0.7822 | 0.7501 | 0.8036 |
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  ### Framework versions
logs/events.out.tfevents.1751940015.7a9bc2c7ed3e.19205.0 CHANGED
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