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: 0.6975
-- Answer: {'precision': 0.7111834961997828, 'recall': 0.8096415327564895, 'f1': 0.7572254335260116, 'number': 809}
-- Header: {'precision': 0.3046875, 'recall': 0.3277310924369748, 'f1': 0.31578947368421056, 'number': 119}
-- Question: {'precision': 0.7682819383259912, 'recall': 0.8187793427230047, 'f1': 0.7927272727272727, 'number': 1065}
-- Overall Precision: 0.7170
-- Overall Recall: 0.7858
-- Overall F1: 0.7498
-- Overall Accuracy: 0.8006
+- Loss: 0.7079
+- Answer: {'precision': 0.7141316073354909, 'recall': 0.8182941903584673, 'f1': 0.7626728110599078, 'number': 809}
+- Header: {'precision': 0.312, 'recall': 0.3277310924369748, 'f1': 0.31967213114754095, 'number': 119}
+- Question: {'precision': 0.7706502636203867, 'recall': 0.8234741784037559, 'f1': 0.7961870177031322, 'number': 1065}
+- Overall Precision: 0.7205
+- Overall Recall: 0.7918
+- Overall F1: 0.7545
+- Overall Accuracy: 0.8059
 
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8205        | 1.0   | 10   | 1.6197          | {'precision': 0.018433179723502304, 'recall': 0.019777503090234856, 'f1': 0.019081693500298154, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.20243362831858408, 'recall': 0.17183098591549295, 'f1': 0.18588115794819704, 'number': 1065} | 0.1123            | 0.0998         | 0.1057     | 0.3687           |
-| 1.4872        | 2.0   | 20   | 1.2615          | {'precision': 0.11558441558441558, 'recall': 0.1100123609394314, 'f1': 0.11272957568081064, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.4534979423868313, 'recall': 0.5173708920187794, 'f1': 0.48333333333333334, 'number': 1065}   | 0.3223            | 0.3211         | 0.3217     | 0.5529           |
-| 1.1097        | 3.0   | 30   | 0.9582          | {'precision': 0.45864661654135336, 'recall': 0.45241038318912236, 'f1': 0.4555071561916615, 'number': 809}     | {'precision': 0.08823529411764706, 'recall': 0.025210084033613446, 'f1': 0.039215686274509796, 'number': 119} | {'precision': 0.6037234042553191, 'recall': 0.6394366197183099, 'f1': 0.6210670314637483, 'number': 1065}    | 0.5357            | 0.5268         | 0.5312     | 0.6937           |
-| 0.8429        | 4.0   | 40   | 0.7753          | {'precision': 0.6193353474320241, 'recall': 0.7601977750309024, 'f1': 0.6825749167591565, 'number': 809}       | {'precision': 0.2553191489361702, 'recall': 0.10084033613445378, 'f1': 0.14457831325301204, 'number': 119}    | {'precision': 0.6663701067615658, 'recall': 0.7032863849765258, 'f1': 0.6843307446322522, 'number': 1065}    | 0.6359            | 0.6904         | 0.6620     | 0.7557           |
-| 0.6833        | 5.0   | 50   | 0.7026          | {'precision': 0.6558441558441559, 'recall': 0.7490729295426453, 'f1': 0.6993652625504905, 'number': 809}       | {'precision': 0.3064516129032258, 'recall': 0.15966386554621848, 'f1': 0.20994475138121546, 'number': 119}    | {'precision': 0.7068376068376069, 'recall': 0.7765258215962442, 'f1': 0.7400447427293065, 'number': 1065}    | 0.6735            | 0.7285         | 0.6999     | 0.7858           |
-| 0.5554        | 6.0   | 60   | 0.6718          | {'precision': 0.6733403582718651, 'recall': 0.7898640296662547, 'f1': 0.726962457337884, 'number': 809}        | {'precision': 0.29411764705882354, 'recall': 0.16806722689075632, 'f1': 0.21390374331550802, 'number': 119}   | {'precision': 0.7259816207184628, 'recall': 0.815962441314554, 'f1': 0.7683465959328029, 'number': 1065}     | 0.6902            | 0.7667         | 0.7264     | 0.7943           |
-| 0.4934        | 7.0   | 70   | 0.6565          | {'precision': 0.6820566631689402, 'recall': 0.8034610630407911, 'f1': 0.7377979568671965, 'number': 809}       | {'precision': 0.2826086956521739, 'recall': 0.2184873949579832, 'f1': 0.24644549763033172, 'number': 119}     | {'precision': 0.7274247491638796, 'recall': 0.8169014084507042, 'f1': 0.7695709862892524, 'number': 1065}    | 0.6899            | 0.7757         | 0.7303     | 0.7962           |
-| 0.4473        | 8.0   | 80   | 0.6501          | {'precision': 0.6903719912472648, 'recall': 0.7799752781211372, 'f1': 0.7324434126523505, 'number': 809}       | {'precision': 0.2653061224489796, 'recall': 0.2184873949579832, 'f1': 0.23963133640552997, 'number': 119}     | {'precision': 0.7345731191885038, 'recall': 0.815962441314554, 'f1': 0.7731316725978647, 'number': 1065}     | 0.6952            | 0.7657         | 0.7287     | 0.8047           |
-| 0.3997        | 9.0   | 90   | 0.6500          | {'precision': 0.7120708748615725, 'recall': 0.7948084054388134, 'f1': 0.7511682242990654, 'number': 809}       | {'precision': 0.2608695652173913, 'recall': 0.25210084033613445, 'f1': 0.2564102564102564, 'number': 119}     | {'precision': 0.7440068493150684, 'recall': 0.815962441314554, 'f1': 0.7783251231527093, 'number': 1065}     | 0.7054            | 0.7737         | 0.7380     | 0.8078           |
-| 0.3553        | 10.0  | 100  | 0.6592          | {'precision': 0.7158351409978309, 'recall': 0.8158220024721878, 'f1': 0.7625649913344889, 'number': 809}       | {'precision': 0.3148148148148148, 'recall': 0.2857142857142857, 'f1': 0.29955947136563876, 'number': 119}     | {'precision': 0.7599653379549394, 'recall': 0.8234741784037559, 'f1': 0.790446146913024, 'number': 1065}     | 0.7193            | 0.7883         | 0.7522     | 0.8078           |
-| 0.3215        | 11.0  | 110  | 0.6783          | {'precision': 0.701063829787234, 'recall': 0.8145859085290482, 'f1': 0.7535734705546027, 'number': 809}        | {'precision': 0.29914529914529914, 'recall': 0.29411764705882354, 'f1': 0.29661016949152547, 'number': 119}   | {'precision': 0.7712014134275619, 'recall': 0.819718309859155, 'f1': 0.7947200728265817, 'number': 1065}     | 0.7159            | 0.7863         | 0.7494     | 0.8034           |
-| 0.3131        | 12.0  | 120  | 0.6804          | {'precision': 0.7120879120879121, 'recall': 0.8009888751545118, 'f1': 0.7539267015706805, 'number': 809}       | {'precision': 0.3125, 'recall': 0.29411764705882354, 'f1': 0.30303030303030304, 'number': 119}                | {'precision': 0.7834224598930482, 'recall': 0.8253521126760563, 'f1': 0.8038408779149521, 'number': 1065}    | 0.7285            | 0.7837         | 0.7551     | 0.8076           |
-| 0.2891        | 13.0  | 130  | 0.6889          | {'precision': 0.7045454545454546, 'recall': 0.8046971569839307, 'f1': 0.7512983266012695, 'number': 809}       | {'precision': 0.3135593220338983, 'recall': 0.31092436974789917, 'f1': 0.31223628691983124, 'number': 119}    | {'precision': 0.7616580310880829, 'recall': 0.828169014084507, 'f1': 0.7935222672064778, 'number': 1065}     | 0.7136            | 0.7878         | 0.7489     | 0.8034           |
-| 0.2819        | 14.0  | 140  | 0.6979          | {'precision': 0.7063236870310825, 'recall': 0.8145859085290482, 'f1': 0.7566016073478761, 'number': 809}       | {'precision': 0.3023255813953488, 'recall': 0.3277310924369748, 'f1': 0.314516129032258, 'number': 119}       | {'precision': 0.7682819383259912, 'recall': 0.8187793427230047, 'f1': 0.7927272727272727, 'number': 1065}    | 0.7146            | 0.7878         | 0.7494     | 0.8001           |
-| 0.2779        | 15.0  | 150  | 0.6975          | {'precision': 0.7111834961997828, 'recall': 0.8096415327564895, 'f1': 0.7572254335260116, 'number': 809}       | {'precision': 0.3046875, 'recall': 0.3277310924369748, 'f1': 0.31578947368421056, 'number': 119}              | {'precision': 0.7682819383259912, 'recall': 0.8187793427230047, 'f1': 0.7927272727272727, 'number': 1065}    | 0.7170            | 0.7858         | 0.7498     | 0.8006           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8289        | 1.0   | 10   | 1.6208          | {'precision': 0.012987012987012988, 'recall': 0.016069221260815822, 'f1': 0.014364640883977901, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.1291005291005291, 'recall': 0.11455399061032864, 'f1': 0.12139303482587065, 'number': 1065} | 0.0694            | 0.0677         | 0.0685     | 0.3686           |
+| 1.4956        | 2.0   | 20   | 1.2610          | {'precision': 0.13918305597579425, 'recall': 0.11372064276885044, 'f1': 0.1251700680272109, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.45817912657290893, 'recall': 0.5812206572769953, 'f1': 0.5124172185430463, 'number': 1065}  | 0.3534            | 0.3567         | 0.3551     | 0.5813           |
+| 1.1192        | 3.0   | 30   | 0.9572          | {'precision': 0.464327485380117, 'recall': 0.4907292954264524, 'f1': 0.47716346153846156, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5908720456397718, 'recall': 0.6807511737089202, 'f1': 0.6326352530541013, 'number': 1065}   | 0.5315            | 0.5630         | 0.5468     | 0.6904           |
+| 0.856         | 4.0   | 40   | 0.7960          | {'precision': 0.6064864864864865, 'recall': 0.6934487021013597, 'f1': 0.6470588235294117, 'number': 809}       | {'precision': 0.15789473684210525, 'recall': 0.07563025210084033, 'f1': 0.10227272727272725, 'number': 119} | {'precision': 0.6779059449866903, 'recall': 0.7173708920187793, 'f1': 0.6970802919708029, 'number': 1065}   | 0.6325            | 0.6693         | 0.6504     | 0.7536           |
+| 0.689         | 5.0   | 50   | 0.7273          | {'precision': 0.635593220338983, 'recall': 0.7416563658838071, 'f1': 0.6845407872219051, 'number': 809}        | {'precision': 0.24719101123595505, 'recall': 0.18487394957983194, 'f1': 0.21153846153846156, 'number': 119} | {'precision': 0.7046046915725456, 'recall': 0.7615023474178404, 'f1': 0.7319494584837544, 'number': 1065}   | 0.6561            | 0.7190         | 0.6861     | 0.7783           |
+| 0.566         | 6.0   | 60   | 0.6986          | {'precision': 0.6628029504741834, 'recall': 0.7775030902348579, 'f1': 0.715585893060296, 'number': 809}        | {'precision': 0.3132530120481928, 'recall': 0.2184873949579832, 'f1': 0.25742574257425743, 'number': 119}   | {'precision': 0.698220064724919, 'recall': 0.8103286384976526, 'f1': 0.7501086484137333, 'number': 1065}    | 0.6693            | 0.7617         | 0.7125     | 0.7867           |
+| 0.5056        | 7.0   | 70   | 0.6756          | {'precision': 0.65625, 'recall': 0.7787391841779975, 'f1': 0.7122668174109665, 'number': 809}                  | {'precision': 0.32558139534883723, 'recall': 0.23529411764705882, 'f1': 0.2731707317073171, 'number': 119}  | {'precision': 0.7231298366294067, 'recall': 0.7896713615023474, 'f1': 0.7549371633752243, 'number': 1065}   | 0.6786            | 0.7521         | 0.7135     | 0.7954           |
+| 0.455         | 8.0   | 80   | 0.6797          | {'precision': 0.6945975744211687, 'recall': 0.7787391841779975, 'f1': 0.7342657342657343, 'number': 809}       | {'precision': 0.32075471698113206, 'recall': 0.2857142857142857, 'f1': 0.30222222222222217, 'number': 119}  | {'precision': 0.732606873428332, 'recall': 0.8206572769953052, 'f1': 0.7741364038972542, 'number': 1065}    | 0.6972            | 0.7717         | 0.7326     | 0.8034           |
+| 0.4034        | 9.0   | 90   | 0.6765          | {'precision': 0.7026737967914438, 'recall': 0.8121137206427689, 'f1': 0.7534403669724771, 'number': 809}       | {'precision': 0.33962264150943394, 'recall': 0.3025210084033613, 'f1': 0.32, 'number': 119}                 | {'precision': 0.7434599156118143, 'recall': 0.8272300469483568, 'f1': 0.7831111111111111, 'number': 1065}   | 0.7071            | 0.7898         | 0.7461     | 0.8034           |
+| 0.362         | 10.0  | 100  | 0.6767          | {'precision': 0.694591728525981, 'recall': 0.8096415327564895, 'f1': 0.7477168949771691, 'number': 809}        | {'precision': 0.3394495412844037, 'recall': 0.31092436974789917, 'f1': 0.324561403508772, 'number': 119}    | {'precision': 0.7523645743766122, 'recall': 0.8215962441314554, 'f1': 0.7854578096947935, 'number': 1065}   | 0.7074            | 0.7863         | 0.7448     | 0.8090           |
+| 0.3283        | 11.0  | 110  | 0.6988          | {'precision': 0.7081545064377682, 'recall': 0.8158220024721878, 'f1': 0.7581849511774841, 'number': 809}       | {'precision': 0.3557692307692308, 'recall': 0.31092436974789917, 'f1': 0.33183856502242154, 'number': 119}  | {'precision': 0.7775800711743772, 'recall': 0.8206572769953052, 'f1': 0.7985381452718137, 'number': 1065}   | 0.7273            | 0.7883         | 0.7566     | 0.8092           |
+| 0.3196        | 12.0  | 120  | 0.6961          | {'precision': 0.7017167381974249, 'recall': 0.8084054388133498, 'f1': 0.7512923607122343, 'number': 809}       | {'precision': 0.3333333333333333, 'recall': 0.31092436974789917, 'f1': 0.32173913043478264, 'number': 119}  | {'precision': 0.7717013888888888, 'recall': 0.8347417840375587, 'f1': 0.8019846639603068, 'number': 1065}   | 0.7198            | 0.7928         | 0.7545     | 0.8102           |
+| 0.2943        | 13.0  | 130  | 0.7015          | {'precision': 0.7037433155080214, 'recall': 0.8133498145859085, 'f1': 0.7545871559633027, 'number': 809}       | {'precision': 0.3391304347826087, 'recall': 0.3277310924369748, 'f1': 0.3333333333333333, 'number': 119}    | {'precision': 0.7720524017467248, 'recall': 0.8300469483568075, 'f1': 0.7999999999999999, 'number': 1065}   | 0.7203            | 0.7933         | 0.7550     | 0.8074           |
+| 0.2843        | 14.0  | 140  | 0.7059          | {'precision': 0.7074468085106383, 'recall': 0.8220024721878862, 'f1': 0.7604345340194397, 'number': 809}       | {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}  | {'precision': 0.7664618086040387, 'recall': 0.819718309859155, 'f1': 0.7921960072595282, 'number': 1065}    | 0.7152            | 0.7913         | 0.7513     | 0.8063           |
+| 0.2779        | 15.0  | 150  | 0.7079          | {'precision': 0.7141316073354909, 'recall': 0.8182941903584673, 'f1': 0.7626728110599078, 'number': 809}       | {'precision': 0.312, 'recall': 0.3277310924369748, 'f1': 0.31967213114754095, 'number': 119}                | {'precision': 0.7706502636203867, 'recall': 0.8234741784037559, 'f1': 0.7961870177031322, 'number': 1065}   | 0.7205            | 0.7918         | 0.7545     | 0.8059           |
 
 
 ### Framework versions

logs/events.out.tfevents.1685714174.kimv.11340.0

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+size 14394

pytorch_model.bin

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