ManelR
/

layoutlm-funsd

@@ -15,14 +15,14 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6633
-- Answer: {'precision': 0.7068004459308808, 'recall': 0.7836835599505563, 'f1': 0.7432590855803048, 'number': 809}
-- Header: {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}
-- Question: {'precision': 0.757679180887372, 'recall': 0.8338028169014085, 'f1': 0.7939204291461779, 'number': 1065}
-- Overall Precision: 0.7121
-- Overall Recall: 0.7817
-- Overall F1: 0.7453
-- Overall Accuracy: 0.8174
 ## Model description
@@ -52,23 +52,23 @@ The following hyperparameters were used during training:
 ### Training results
-| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                       | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8218        | 1.0   | 10   | 1.6340          | {'precision': 0.012857142857142857, 'recall': 0.011124845488257108, 'f1': 0.011928429423459244, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.22849807445442877, 'recall': 0.1671361502347418, 'f1': 0.19305856832971802, 'number': 1065} | 0.1264            | 0.0938         | 0.1077     | 0.3314           |
-| 1.4842        | 2.0   | 20   | 1.2777          | {'precision': 0.18856447688564476, 'recall': 0.1915945611866502, 'f1': 0.19006744328632738, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.44694533762057875, 'recall': 0.5220657276995305, 'f1': 0.48159376353399735, 'number': 1065} | 0.3441            | 0.3567         | 0.3503     | 0.5691           |
-| 1.1045        | 3.0   | 30   | 0.9751          | {'precision': 0.44747612551159616, 'recall': 0.4054388133498146, 'f1': 0.42542153047989617, 'number': 809}     | {'precision': 0.05555555555555555, 'recall': 0.01680672268907563, 'f1': 0.025806451612903226, 'number': 119} | {'precision': 0.6208445642407907, 'recall': 0.6488262910798122, 'f1': 0.6345270890725436, 'number': 1065}   | 0.5425            | 0.5123         | 0.5270     | 0.6860           |
-| 0.833         | 4.0   | 40   | 0.7763          | {'precision': 0.6252609603340292, 'recall': 0.7404202719406675, 'f1': 0.677985285795133, 'number': 809}        | {'precision': 0.1935483870967742, 'recall': 0.10084033613445378, 'f1': 0.13259668508287292, 'number': 119}   | {'precision': 0.6614583333333334, 'recall': 0.7154929577464789, 'f1': 0.6874154262516915, 'number': 1065}   | 0.6321            | 0.6889         | 0.6593     | 0.7559           |
-| 0.6773        | 5.0   | 50   | 0.7051          | {'precision': 0.6295918367346939, 'recall': 0.7626699629171817, 'f1': 0.6897708216880939, 'number': 809}       | {'precision': 0.29069767441860467, 'recall': 0.21008403361344538, 'f1': 0.24390243902439027, 'number': 119}  | {'precision': 0.6980802792321117, 'recall': 0.7511737089201878, 'f1': 0.7236544549977386, 'number': 1065}   | 0.6519            | 0.7235         | 0.6859     | 0.7788           |
-| 0.5627        | 6.0   | 60   | 0.6598          | {'precision': 0.6423432682425488, 'recall': 0.7725587144622992, 'f1': 0.7014590347923682, 'number': 809}       | {'precision': 0.32098765432098764, 'recall': 0.2184873949579832, 'f1': 0.26, 'number': 119}                  | {'precision': 0.7032878909382518, 'recall': 0.8234741784037559, 'f1': 0.7586505190311419, 'number': 1065}   | 0.6641            | 0.7667         | 0.7117     | 0.7947           |
-| 0.4959        | 7.0   | 70   | 0.6625          | {'precision': 0.6652267818574514, 'recall': 0.761433868974042, 'f1': 0.7100864553314121, 'number': 809}        | {'precision': 0.2761904761904762, 'recall': 0.24369747899159663, 'f1': 0.2589285714285714, 'number': 119}    | {'precision': 0.7452504317789291, 'recall': 0.8103286384976526, 'f1': 0.7764282501124606, 'number': 1065}   | 0.6889            | 0.7566         | 0.7212     | 0.7945           |
-| 0.4473        | 8.0   | 80   | 0.6402          | {'precision': 0.6684491978609626, 'recall': 0.7725587144622992, 'f1': 0.7167431192660552, 'number': 809}       | {'precision': 0.25961538461538464, 'recall': 0.226890756302521, 'f1': 0.242152466367713, 'number': 119}      | {'precision': 0.7415540540540541, 'recall': 0.8244131455399061, 'f1': 0.7807914628723877, 'number': 1065}   | 0.6883            | 0.7677         | 0.7258     | 0.8046           |
-| 0.3997        | 9.0   | 90   | 0.6381          | {'precision': 0.6879120879120879, 'recall': 0.7737948084054388, 'f1': 0.7283304246655031, 'number': 809}       | {'precision': 0.27350427350427353, 'recall': 0.2689075630252101, 'f1': 0.2711864406779661, 'number': 119}    | {'precision': 0.7418817651956703, 'recall': 0.8366197183098592, 'f1': 0.7864077669902912, 'number': 1065}   | 0.6952            | 0.7772         | 0.7339     | 0.8095           |
-| 0.3597        | 10.0  | 100  | 0.6481          | {'precision': 0.6959910913140311, 'recall': 0.7725587144622992, 'f1': 0.7322788517867603, 'number': 809}       | {'precision': 0.25984251968503935, 'recall': 0.2773109243697479, 'f1': 0.2682926829268293, 'number': 119}    | {'precision': 0.7495769881556683, 'recall': 0.831924882629108, 'f1': 0.7886070315976857, 'number': 1065}    | 0.6996            | 0.7747         | 0.7352     | 0.8094           |
-| 0.3241        | 11.0  | 110  | 0.6649          | {'precision': 0.6960893854748603, 'recall': 0.7700865265760197, 'f1': 0.7312206572769954, 'number': 809}       | {'precision': 0.32075471698113206, 'recall': 0.2857142857142857, 'f1': 0.30222222222222217, 'number': 119}   | {'precision': 0.7689625108979947, 'recall': 0.828169014084507, 'f1': 0.7974683544303798, 'number': 1065}    | 0.7165            | 0.7722         | 0.7433     | 0.8115           |
-| 0.3111        | 12.0  | 120  | 0.6584          | {'precision': 0.7083333333333334, 'recall': 0.7985166872682324, 'f1': 0.7507263219058687, 'number': 809}       | {'precision': 0.29310344827586204, 'recall': 0.2857142857142857, 'f1': 0.2893617021276596, 'number': 119}    | {'precision': 0.7658833768494343, 'recall': 0.8262910798122066, 'f1': 0.7949412827461607, 'number': 1065}   | 0.7166            | 0.7827         | 0.7482     | 0.8134           |
-| 0.2896        | 13.0  | 130  | 0.6736          | {'precision': 0.7007963594994312, 'recall': 0.761433868974042, 'f1': 0.7298578199052134, 'number': 809}        | {'precision': 0.2536231884057971, 'recall': 0.29411764705882354, 'f1': 0.2723735408560311, 'number': 119}    | {'precision': 0.7527993109388458, 'recall': 0.8206572769953052, 'f1': 0.7852650494159928, 'number': 1065}   | 0.7002            | 0.7652         | 0.7312     | 0.8091           |
-| 0.278         | 14.0  | 140  | 0.6619          | {'precision': 0.7066666666666667, 'recall': 0.7861557478368356, 'f1': 0.7442949093036864, 'number': 809}       | {'precision': 0.30973451327433627, 'recall': 0.29411764705882354, 'f1': 0.3017241379310345, 'number': 119}   | {'precision': 0.7631806395851339, 'recall': 0.8291079812206573, 'f1': 0.7947794779477948, 'number': 1065}   | 0.7161            | 0.7797         | 0.7466     | 0.8172           |
-| 0.2785        | 15.0  | 150  | 0.6633          | {'precision': 0.7068004459308808, 'recall': 0.7836835599505563, 'f1': 0.7432590855803048, 'number': 809}       | {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}     | {'precision': 0.757679180887372, 'recall': 0.8338028169014085, 'f1': 0.7939204291461779, 'number': 1065}    | 0.7121            | 0.7817         | 0.7453     | 0.8174           |
 ### Framework versions

 This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.6865
+- Answer: {'precision': 0.6990185387131952, 'recall': 0.792336217552534, 'f1': 0.7427578215527232, 'number': 809}
+- Header: {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}
+- Question: {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065}
+- Overall Precision: 0.7268
+- Overall Recall: 0.7888
+- Overall F1: 0.7565
+- Overall Accuracy: 0.8047
 ## Model description
 ### Training results
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                        | Question                                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7784        | 1.0   | 10   | 1.6271          | {'precision': 0.01841620626151013, 'recall': 0.012360939431396786, 'f1': 0.014792899408284023, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.11462450592885376, 'recall': 0.054460093896713614, 'f1': 0.07383831954169319, 'number': 1065} | 0.0648            | 0.0341         | 0.0447     | 0.3258           |
+| 1.4893        | 2.0   | 20   | 1.2865          | {'precision': 0.18452935694315004, 'recall': 0.24474660074165636, 'f1': 0.21041445270988307, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.4293563579277865, 'recall': 0.5136150234741784, 'f1': 0.4677212483967507, 'number': 1065}     | 0.3174            | 0.3738         | 0.3433     | 0.5703           |
+| 1.1173        | 3.0   | 30   | 0.9566          | {'precision': 0.4382845188284519, 'recall': 0.5179233621755254, 'f1': 0.4747875354107649, 'number': 809}      | {'precision': 0.045454545454545456, 'recall': 0.01680672268907563, 'f1': 0.024539877300613498, 'number': 119} | {'precision': 0.5686113393590797, 'recall': 0.6497652582159624, 'f1': 0.6064855390008765, 'number': 1065}     | 0.5020            | 0.5585         | 0.5287     | 0.6883           |
+| 0.8579        | 4.0   | 40   | 0.8042          | {'precision': 0.5834932821497121, 'recall': 0.7515451174289246, 'f1': 0.6569421934089681, 'number': 809}      | {'precision': 0.18055555555555555, 'recall': 0.1092436974789916, 'f1': 0.13612565445026178, 'number': 119}    | {'precision': 0.6401480111008325, 'recall': 0.6497652582159624, 'f1': 0.6449207828518173, 'number': 1065}     | 0.5982            | 0.6588         | 0.6270     | 0.7438           |
+| 0.711         | 5.0   | 50   | 0.7251          | {'precision': 0.6355140186915887, 'recall': 0.7564894932014833, 'f1': 0.6907449209932279, 'number': 809}      | {'precision': 0.25252525252525254, 'recall': 0.21008403361344538, 'f1': 0.22935779816513763, 'number': 119}   | {'precision': 0.6740237691001698, 'recall': 0.7455399061032864, 'f1': 0.7079803834150691, 'number': 1065}     | 0.6388            | 0.7180         | 0.6761     | 0.7764           |
+| 0.5916        | 6.0   | 60   | 0.6914          | {'precision': 0.6471204188481675, 'recall': 0.7639060568603214, 'f1': 0.7006802721088435, 'number': 809}      | {'precision': 0.325, 'recall': 0.2184873949579832, 'f1': 0.26130653266331655, 'number': 119}                  | {'precision': 0.6792452830188679, 'recall': 0.8112676056338028, 'f1': 0.7394094993581515, 'number': 1065}     | 0.6537            | 0.7566         | 0.7014     | 0.7820           |
+| 0.5253        | 7.0   | 70   | 0.6778          | {'precision': 0.6542056074766355, 'recall': 0.7787391841779975, 'f1': 0.711060948081264, 'number': 809}       | {'precision': 0.3047619047619048, 'recall': 0.2689075630252101, 'f1': 0.28571428571428575, 'number': 119}     | {'precision': 0.739247311827957, 'recall': 0.7746478873239436, 'f1': 0.7565337001375517, 'number': 1065}      | 0.6809            | 0.7461         | 0.7120     | 0.7896           |
+| 0.4705        | 8.0   | 80   | 0.6586          | {'precision': 0.6659751037344398, 'recall': 0.7935723114956736, 'f1': 0.7241962774957698, 'number': 809}      | {'precision': 0.30392156862745096, 'recall': 0.2605042016806723, 'f1': 0.28054298642533937, 'number': 119}    | {'precision': 0.7257093723129837, 'recall': 0.7924882629107981, 'f1': 0.7576301615798923, 'number': 1065}     | 0.6806            | 0.7612         | 0.7186     | 0.7966           |
+| 0.4214        | 9.0   | 90   | 0.6588          | {'precision': 0.6852846401718582, 'recall': 0.788627935723115, 'f1': 0.7333333333333334, 'number': 809}       | {'precision': 0.2755905511811024, 'recall': 0.29411764705882354, 'f1': 0.2845528455284553, 'number': 119}     | {'precision': 0.7396907216494846, 'recall': 0.8084507042253521, 'f1': 0.7725437415881561, 'number': 1065}     | 0.6904            | 0.7697         | 0.7279     | 0.7992           |
+| 0.3765        | 10.0  | 100  | 0.6598          | {'precision': 0.6825053995680346, 'recall': 0.7812113720642769, 'f1': 0.7285302593659942, 'number': 809}      | {'precision': 0.32142857142857145, 'recall': 0.3025210084033613, 'f1': 0.3116883116883117, 'number': 119}     | {'precision': 0.7658833768494343, 'recall': 0.8262910798122066, 'f1': 0.7949412827461607, 'number': 1065}     | 0.7078            | 0.7767         | 0.7407     | 0.8013           |
+| 0.3331        | 11.0  | 110  | 0.6659          | {'precision': 0.6778947368421052, 'recall': 0.796044499381953, 'f1': 0.7322342239909039, 'number': 809}       | {'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119}     | {'precision': 0.772566371681416, 'recall': 0.819718309859155, 'f1': 0.7954441913439636, 'number': 1065}       | 0.7078            | 0.7792         | 0.7418     | 0.8033           |
+| 0.3192        | 12.0  | 120  | 0.6782          | {'precision': 0.6885069817400644, 'recall': 0.792336217552534, 'f1': 0.7367816091954023, 'number': 809}       | {'precision': 0.3170731707317073, 'recall': 0.3277310924369748, 'f1': 0.32231404958677684, 'number': 119}     | {'precision': 0.7828418230563002, 'recall': 0.8225352112676056, 'f1': 0.8021978021978022, 'number': 1065}     | 0.7161            | 0.7807         | 0.7470     | 0.8015           |
+| 0.3012        | 13.0  | 130  | 0.6835          | {'precision': 0.6929637526652452, 'recall': 0.8034610630407911, 'f1': 0.7441327990841443, 'number': 809}      | {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}     | {'precision': 0.7847652790079717, 'recall': 0.831924882629108, 'f1': 0.8076572470373746, 'number': 1065}      | 0.7196            | 0.7908         | 0.7535     | 0.8025           |
+| 0.2867        | 14.0  | 140  | 0.6851          | {'precision': 0.7003257328990228, 'recall': 0.7972805933250927, 'f1': 0.7456647398843931, 'number': 809}      | {'precision': 0.3445378151260504, 'recall': 0.3445378151260504, 'f1': 0.3445378151260504, 'number': 119}      | {'precision': 0.7884444444444444, 'recall': 0.8328638497652582, 'f1': 0.8100456621004566, 'number': 1065}     | 0.7266            | 0.7893         | 0.7566     | 0.8029           |
+| 0.2827        | 15.0  | 150  | 0.6865          | {'precision': 0.6990185387131952, 'recall': 0.792336217552534, 'f1': 0.7427578215527232, 'number': 809}       | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}     | {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065}     | 0.7268            | 0.7888         | 0.7565     | 0.8047           |
 ### Framework versions

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