End of training

README.md

@@ -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: 0.6924
-- Answer: {'precision': 0.6991525423728814, 'recall': 0.8158220024721878, 'f1': 0.7529948659440959, 'number': 809}
-- Header: {'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119}
-- Question: {'precision': 0.7807250221043325, 'recall': 0.8291079812206573, 'f1': 0.8041894353369764, 'number': 1065}
-- Overall Precision: 0.7197
-- Overall Recall: 0.7948
-- Overall F1: 0.7554
-- Overall Accuracy: 0.8040
+- Loss: 0.6845
+- Answer: {'precision': 0.6932907348242812, 'recall': 0.8046971569839307, 'f1': 0.7448512585812357, 'number': 809}
+- Header: {'precision': 0.3220338983050847, 'recall': 0.31932773109243695, 'f1': 0.32067510548523204, 'number': 119}
+- Question: {'precision': 0.7827225130890052, 'recall': 0.8422535211267606, 'f1': 0.8113975576662144, 'number': 1065}
+- Overall Precision: 0.7199
+- Overall Recall: 0.7958
+- Overall F1: 0.7560
+- Overall Accuracy: 0.8087
 
 ## Model description
 
@@ -52,28 +52,28 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8027        | 1.0   | 10   | 1.6152          | {'precision': 0.0103359173126615, 'recall': 0.004944375772558714, 'f1': 0.006688963210702341, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.18232044198895028, 'recall': 0.061971830985915494, 'f1': 0.09250175192711983, 'number': 1065} | 0.0935            | 0.0351         | 0.0511     | 0.3249           |
-| 1.4899        | 2.0   | 20   | 1.2751          | {'precision': 0.18495297805642633, 'recall': 0.21878862793572312, 'f1': 0.20045300113250283, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4306969459671104, 'recall': 0.5164319248826291, 'f1': 0.46968403074295473, 'number': 1065}    | 0.3254            | 0.3648         | 0.3440     | 0.5899           |
-| 1.1133        | 3.0   | 30   | 0.9514          | {'precision': 0.4911937377690802, 'recall': 0.6205191594561187, 'f1': 0.5483342435827417, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5651144435674822, 'recall': 0.672300469483568, 'f1': 0.614065180102916, 'number': 1065}       | 0.5314            | 0.6111         | 0.5685     | 0.6982           |
-| 0.8513        | 4.0   | 40   | 0.8326          | {'precision': 0.5850746268656717, 'recall': 0.7268232385661311, 'f1': 0.6482910694597575, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.6844003606853021, 'recall': 0.7126760563380282, 'f1': 0.6982520699172033, 'number': 1065}     | 0.6248            | 0.6759         | 0.6493     | 0.7384           |
-| 0.7153        | 5.0   | 50   | 0.7422          | {'precision': 0.6159274193548387, 'recall': 0.7552533992583437, 'f1': 0.6785119378123265, 'number': 809}     | {'precision': 0.08695652173913043, 'recall': 0.05042016806722689, 'f1': 0.06382978723404256, 'number': 119} | {'precision': 0.6826051112943117, 'recall': 0.7774647887323943, 'f1': 0.726953467954346, 'number': 1065}      | 0.6354            | 0.7250         | 0.6773     | 0.7734           |
-| 0.5972        | 6.0   | 60   | 0.7031          | {'precision': 0.6330645161290323, 'recall': 0.7762669962917181, 'f1': 0.6973903387007219, 'number': 809}     | {'precision': 0.15492957746478872, 'recall': 0.09243697478991597, 'f1': 0.11578947368421053, 'number': 119} | {'precision': 0.6802189210320563, 'recall': 0.8169014084507042, 'f1': 0.742320819112628, 'number': 1065}      | 0.6443            | 0.7572         | 0.6962     | 0.7836           |
-| 0.5209        | 7.0   | 70   | 0.6902          | {'precision': 0.6597510373443983, 'recall': 0.7861557478368356, 'f1': 0.7174280879864636, 'number': 809}     | {'precision': 0.2755102040816326, 'recall': 0.226890756302521, 'f1': 0.2488479262672811, 'number': 119}     | {'precision': 0.7128463476070529, 'recall': 0.7971830985915493, 'f1': 0.7526595744680851, 'number': 1065}     | 0.6711            | 0.7587         | 0.7122     | 0.7902           |
-| 0.4673        | 8.0   | 80   | 0.6693          | {'precision': 0.6649642492339122, 'recall': 0.8046971569839307, 'f1': 0.7281879194630874, 'number': 809}     | {'precision': 0.28, 'recall': 0.23529411764705882, 'f1': 0.2557077625570776, 'number': 119}                 | {'precision': 0.7322314049586777, 'recall': 0.831924882629108, 'f1': 0.7789010989010988, 'number': 1065}      | 0.6837            | 0.7852         | 0.7310     | 0.7965           |
-| 0.4151        | 9.0   | 90   | 0.6684          | {'precision': 0.6839323467230444, 'recall': 0.799752781211372, 'f1': 0.7373219373219373, 'number': 809}      | {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}  | {'precision': 0.7363560033585222, 'recall': 0.8234741784037559, 'f1': 0.7774822695035462, 'number': 1065}     | 0.6910            | 0.7832         | 0.7342     | 0.8017           |
-| 0.3689        | 10.0  | 100  | 0.6742          | {'precision': 0.6954643628509719, 'recall': 0.796044499381953, 'f1': 0.7423631123919308, 'number': 809}      | {'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119}   | {'precision': 0.7483221476510067, 'recall': 0.8375586854460094, 'f1': 0.7904297740363314, 'number': 1065}     | 0.7043            | 0.7888         | 0.7441     | 0.8000           |
-| 0.3327        | 11.0  | 110  | 0.6861          | {'precision': 0.6843198338525441, 'recall': 0.8145859085290482, 'f1': 0.7437923250564334, 'number': 809}     | {'precision': 0.32456140350877194, 'recall': 0.31092436974789917, 'f1': 0.31759656652360513, 'number': 119} | {'precision': 0.7709790209790209, 'recall': 0.828169014084507, 'f1': 0.7985513807152557, 'number': 1065}      | 0.7105            | 0.7918         | 0.7489     | 0.8031           |
-| 0.3167        | 12.0  | 120  | 0.6912          | {'precision': 0.6896186440677966, 'recall': 0.8046971569839307, 'f1': 0.7427267541357673, 'number': 809}     | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}   | {'precision': 0.7727272727272727, 'recall': 0.8300469483568075, 'f1': 0.8003621548211861, 'number': 1065}     | 0.7138            | 0.7908         | 0.7503     | 0.8013           |
-| 0.3012        | 13.0  | 130  | 0.6878          | {'precision': 0.7015086206896551, 'recall': 0.8046971569839307, 'f1': 0.7495682210708117, 'number': 809}     | {'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119}  | {'precision': 0.7694974003466204, 'recall': 0.8338028169014085, 'f1': 0.8003605227579991, 'number': 1065}     | 0.7162            | 0.7928         | 0.7526     | 0.8073           |
-| 0.2882        | 14.0  | 140  | 0.6930          | {'precision': 0.6997885835095138, 'recall': 0.8182941903584673, 'f1': 0.7544159544159544, 'number': 809}     | {'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119}  | {'precision': 0.7793468667255075, 'recall': 0.8291079812206573, 'f1': 0.8034576888080072, 'number': 1065}     | 0.7199            | 0.7958         | 0.7560     | 0.8024           |
-| 0.2811        | 15.0  | 150  | 0.6924          | {'precision': 0.6991525423728814, 'recall': 0.8158220024721878, 'f1': 0.7529948659440959, 'number': 809}     | {'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119}   | {'precision': 0.7807250221043325, 'recall': 0.8291079812206573, 'f1': 0.8041894353369764, 'number': 1065}     | 0.7197            | 0.7948         | 0.7554     | 0.8040           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                     | Question                                                                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7948        | 1.0   | 10   | 1.5982          | {'precision': 0.019115890083632018, 'recall': 0.019777503090234856, 'f1': 0.01944106925880923, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.1559202813599062, 'recall': 0.12488262910798122, 'f1': 0.1386861313868613, 'number': 1065} | 0.0882            | 0.0748         | 0.0809     | 0.3666           |
+| 1.4548        | 2.0   | 20   | 1.2137          | {'precision': 0.18571428571428572, 'recall': 0.19283065512978986, 'f1': 0.18920557913887204, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.5027844073190135, 'recall': 0.5934272300469483, 'f1': 0.5443583118001722, 'number': 1065}  | 0.3758            | 0.3954         | 0.3853     | 0.6060           |
+| 1.0759        | 3.0   | 30   | 0.9074          | {'precision': 0.45133689839572194, 'recall': 0.5216316440049443, 'f1': 0.48394495412844035, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.6371453138435081, 'recall': 0.6957746478873239, 'f1': 0.6651705565529622, 'number': 1065}  | 0.5491            | 0.5835         | 0.5658     | 0.7138           |
+| 0.818         | 4.0   | 40   | 0.7636          | {'precision': 0.601010101010101, 'recall': 0.7354758961681088, 'f1': 0.6614785992217899, 'number': 809}       | {'precision': 0.22, 'recall': 0.09243697478991597, 'f1': 0.13017751479289943, 'number': 119}               | {'precision': 0.6860670194003528, 'recall': 0.7305164319248826, 'f1': 0.707594361073215, 'number': 1065}   | 0.6366            | 0.6944         | 0.6643     | 0.7580           |
+| 0.6744        | 5.0   | 50   | 0.6948          | {'precision': 0.6172106824925816, 'recall': 0.7713226205191595, 'f1': 0.6857142857142857, 'number': 809}      | {'precision': 0.2608695652173913, 'recall': 0.15126050420168066, 'f1': 0.19148936170212766, 'number': 119} | {'precision': 0.7063758389261745, 'recall': 0.7906103286384977, 'f1': 0.7461231723526807, 'number': 1065}  | 0.6532            | 0.7446         | 0.6959     | 0.7803           |
+| 0.5678        | 6.0   | 60   | 0.6772          | {'precision': 0.6684100418410042, 'recall': 0.7898640296662547, 'f1': 0.7240793201133144, 'number': 809}      | {'precision': 0.32857142857142857, 'recall': 0.19327731092436976, 'f1': 0.2433862433862434, 'number': 119} | {'precision': 0.7155309033280507, 'recall': 0.847887323943662, 'f1': 0.7761065749892565, 'number': 1065}   | 0.6840            | 0.7852         | 0.7311     | 0.7902           |
+| 0.4886        | 7.0   | 70   | 0.6596          | {'precision': 0.6836518046709129, 'recall': 0.796044499381953, 'f1': 0.7355796687607081, 'number': 809}       | {'precision': 0.30952380952380953, 'recall': 0.2184873949579832, 'f1': 0.2561576354679803, 'number': 119}  | {'precision': 0.75, 'recall': 0.8422535211267606, 'f1': 0.793454223794781, 'number': 1065}                 | 0.7052            | 0.7863         | 0.7435     | 0.7931           |
+| 0.4432        | 8.0   | 80   | 0.6525          | {'precision': 0.6792849631966351, 'recall': 0.7985166872682324, 'f1': 0.734090909090909, 'number': 809}       | {'precision': 0.2736842105263158, 'recall': 0.2184873949579832, 'f1': 0.2429906542056075, 'number': 119}   | {'precision': 0.7472984206151289, 'recall': 0.844131455399061, 'f1': 0.7927689594356261, 'number': 1065}   | 0.6985            | 0.7883         | 0.7407     | 0.7965           |
+| 0.3961        | 9.0   | 90   | 0.6515          | {'precision': 0.6940540540540541, 'recall': 0.7935723114956736, 'f1': 0.740484429065744, 'number': 809}       | {'precision': 0.2803738317757009, 'recall': 0.25210084033613445, 'f1': 0.2654867256637167, 'number': 119}  | {'precision': 0.7613344739093242, 'recall': 0.8356807511737089, 'f1': 0.7967770814682185, 'number': 1065}  | 0.7097            | 0.7837         | 0.7449     | 0.8019           |
+| 0.3531        | 10.0  | 100  | 0.6628          | {'precision': 0.697452229299363, 'recall': 0.8121137206427689, 'f1': 0.750428326670474, 'number': 809}        | {'precision': 0.2962962962962963, 'recall': 0.2689075630252101, 'f1': 0.28193832599118945, 'number': 119}  | {'precision': 0.7577276524644946, 'recall': 0.8516431924882629, 'f1': 0.801945181255526, 'number': 1065}   | 0.7103            | 0.8008         | 0.7528     | 0.8034           |
+| 0.3201        | 11.0  | 110  | 0.6678          | {'precision': 0.6915005246589717, 'recall': 0.8145859085290482, 'f1': 0.7480136208853576, 'number': 809}      | {'precision': 0.2909090909090909, 'recall': 0.2689075630252101, 'f1': 0.2794759825327511, 'number': 119}   | {'precision': 0.7679794520547946, 'recall': 0.8422535211267606, 'f1': 0.8034034930586654, 'number': 1065}  | 0.7118            | 0.7968         | 0.7519     | 0.8071           |
+| 0.3055        | 12.0  | 120  | 0.6760          | {'precision': 0.6869747899159664, 'recall': 0.8084054388133498, 'f1': 0.7427597955706984, 'number': 809}      | {'precision': 0.296, 'recall': 0.31092436974789917, 'f1': 0.30327868852459017, 'number': 119}              | {'precision': 0.7839506172839507, 'recall': 0.8347417840375587, 'f1': 0.8085493406093679, 'number': 1065}  | 0.7146            | 0.7928         | 0.7517     | 0.8047           |
+| 0.29          | 13.0  | 130  | 0.6844          | {'precision': 0.7013963480128894, 'recall': 0.8071693448702101, 'f1': 0.7505747126436783, 'number': 809}      | {'precision': 0.28346456692913385, 'recall': 0.3025210084033613, 'f1': 0.2926829268292683, 'number': 119}  | {'precision': 0.7771084337349398, 'recall': 0.847887323943662, 'f1': 0.8109564436461607, 'number': 1065}   | 0.7171            | 0.7988         | 0.7558     | 0.8041           |
+| 0.2797        | 14.0  | 140  | 0.6841          | {'precision': 0.6956055734190782, 'recall': 0.8022249690976514, 'f1': 0.7451205510907002, 'number': 809}      | {'precision': 0.3064516129032258, 'recall': 0.31932773109243695, 'f1': 0.31275720164609055, 'number': 119} | {'precision': 0.7750865051903114, 'recall': 0.8413145539906103, 'f1': 0.8068437640702386, 'number': 1065}  | 0.7153            | 0.7943         | 0.7527     | 0.8070           |
+| 0.2733        | 15.0  | 150  | 0.6845          | {'precision': 0.6932907348242812, 'recall': 0.8046971569839307, 'f1': 0.7448512585812357, 'number': 809}      | {'precision': 0.3220338983050847, 'recall': 0.31932773109243695, 'f1': 0.32067510548523204, 'number': 119} | {'precision': 0.7827225130890052, 'recall': 0.8422535211267606, 'f1': 0.8113975576662144, 'number': 1065}  | 0.7199            | 0.7958         | 0.7560     | 0.8087           |
 
 
 ### Framework versions
 
 - Transformers 4.26.1
 - Pytorch 1.13.1+cu116
-- Datasets 2.9.1.dev0
+- Datasets 2.10.0
 - Tokenizers 0.13.2

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