jaeger1
/

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: 0.7079
-- 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8289        | 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

 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: 0.6657
+- Answer: {'precision': 0.7226519337016575, 'recall': 0.8084054388133498, 'f1': 0.763127187864644, 'number': 809}
+- Header: {'precision': 0.29545454545454547, 'recall': 0.3277310924369748, 'f1': 0.3107569721115538, 'number': 119}
+- Question: {'precision': 0.7931960608773501, 'recall': 0.831924882629108, 'f1': 0.8120989917506873, 'number': 1065}
+- Overall Precision: 0.7331
+- Overall Recall: 0.7923
+- Overall F1: 0.7615
+- Overall Accuracy: 0.8136
 ## Model description
 ### Training results
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                     | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7892        | 1.0   | 10   | 1.6086          | {'precision': 0.020948180815876516, 'recall': 0.023485784919653894, 'f1': 0.022144522144522148, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.20356472795497185, 'recall': 0.20375586854460093, 'f1': 0.20366025340215863, 'number': 1065} | 0.1196            | 0.1184         | 0.1190     | 0.3742           |
+| 1.4438        | 2.0   | 20   | 1.2175          | {'precision': 0.22015915119363394, 'recall': 0.20519159456118666, 'f1': 0.2124120281509917, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.4544037412314887, 'recall': 0.5474178403755868, 'f1': 0.4965928449744464, 'number': 1065}    | 0.3677            | 0.3758         | 0.3717     | 0.5883           |
+| 1.0512        | 3.0   | 30   | 0.9159          | {'precision': 0.5192519251925193, 'recall': 0.5834363411619283, 'f1': 0.5494761350407451, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.6103327495621717, 'recall': 0.6544600938967137, 'f1': 0.6316266425011329, 'number': 1065}    | 0.5615            | 0.5866         | 0.5737     | 0.7102           |
+| 0.8045        | 4.0   | 40   | 0.7549          | {'precision': 0.6132264529058116, 'recall': 0.7564894932014833, 'f1': 0.6773657996679578, 'number': 809}       | {'precision': 0.22, 'recall': 0.09243697478991597, 'f1': 0.13017751479289943, 'number': 119}               | {'precision': 0.6795580110497238, 'recall': 0.6929577464788732, 'f1': 0.6861924686192468, 'number': 1065}    | 0.6378            | 0.6829         | 0.6596     | 0.7538           |
+| 0.6559        | 5.0   | 50   | 0.6887          | {'precision': 0.6546227417640808, 'recall': 0.761433868974042, 'f1': 0.704, 'number': 809}                     | {'precision': 0.25, 'recall': 0.16806722689075632, 'f1': 0.20100502512562815, 'number': 119}               | {'precision': 0.6964285714285714, 'recall': 0.7323943661971831, 'f1': 0.7139588100686498, 'number': 1065}    | 0.6614            | 0.7105         | 0.6851     | 0.7764           |
+| 0.547         | 6.0   | 60   | 0.6515          | {'precision': 0.6659793814432989, 'recall': 0.7985166872682324, 'f1': 0.7262507026419337, 'number': 809}       | {'precision': 0.2891566265060241, 'recall': 0.20168067226890757, 'f1': 0.23762376237623764, 'number': 119} | {'precision': 0.7140439932318104, 'recall': 0.7924882629107981, 'f1': 0.7512238540275923, 'number': 1065}    | 0.6774            | 0.7597         | 0.7162     | 0.7928           |
+| 0.4923        | 7.0   | 70   | 0.6337          | {'precision': 0.6784188034188035, 'recall': 0.7849196538936959, 'f1': 0.7277936962750717, 'number': 809}       | {'precision': 0.2761904761904762, 'recall': 0.24369747899159663, 'f1': 0.2589285714285714, 'number': 119}  | {'precision': 0.7371575342465754, 'recall': 0.8084507042253521, 'f1': 0.7711598746081505, 'number': 1065}    | 0.6904            | 0.7652         | 0.7258     | 0.8052           |
+| 0.4463        | 8.0   | 80   | 0.6478          | {'precision': 0.7045454545454546, 'recall': 0.7663782447466008, 'f1': 0.7341622261693309, 'number': 809}       | {'precision': 0.2831858407079646, 'recall': 0.2689075630252101, 'f1': 0.27586206896551724, 'number': 119}  | {'precision': 0.751937984496124, 'recall': 0.819718309859155, 'f1': 0.7843665768194069, 'number': 1065}      | 0.7080            | 0.7652         | 0.7355     | 0.8048           |
+| 0.3974        | 9.0   | 90   | 0.6389          | {'precision': 0.7029379760609358, 'recall': 0.7985166872682324, 'f1': 0.7476851851851851, 'number': 809}       | {'precision': 0.2748091603053435, 'recall': 0.3025210084033613, 'f1': 0.288, 'number': 119}                | {'precision': 0.7609254498714653, 'recall': 0.8338028169014085, 'f1': 0.7956989247311828, 'number': 1065}    | 0.7082            | 0.7878         | 0.7458     | 0.8060           |
+| 0.3599        | 10.0  | 100  | 0.6429          | {'precision': 0.7177777777777777, 'recall': 0.7985166872682324, 'f1': 0.7559976594499708, 'number': 809}       | {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119}  | {'precision': 0.7795275590551181, 'recall': 0.8366197183098592, 'f1': 0.8070652173913043, 'number': 1065}    | 0.7274            | 0.7888         | 0.7569     | 0.8139           |
+| 0.3227        | 11.0  | 110  | 0.6510          | {'precision': 0.710239651416122, 'recall': 0.8059332509270705, 'f1': 0.755066589461494, 'number': 809}         | {'precision': 0.28205128205128205, 'recall': 0.2773109243697479, 'f1': 0.2796610169491525, 'number': 119}  | {'precision': 0.7882037533512064, 'recall': 0.828169014084507, 'f1': 0.8076923076923077, 'number': 1065}     | 0.7275            | 0.7863         | 0.7557     | 0.8111           |
+| 0.3156        | 12.0  | 120  | 0.6579          | {'precision': 0.7245575221238938, 'recall': 0.8096415327564895, 'f1': 0.7647402218330415, 'number': 809}       | {'precision': 0.2920353982300885, 'recall': 0.2773109243697479, 'f1': 0.28448275862068967, 'number': 119}  | {'precision': 0.7926391382405745, 'recall': 0.8291079812206573, 'f1': 0.8104635153740247, 'number': 1065}    | 0.7372            | 0.7883         | 0.7619     | 0.8123           |
+| 0.2935        | 13.0  | 130  | 0.6596          | {'precision': 0.7119386637458927, 'recall': 0.8034610630407911, 'f1': 0.7549361207897795, 'number': 809}       | {'precision': 0.2846715328467153, 'recall': 0.3277310924369748, 'f1': 0.3046875, 'number': 119}            | {'precision': 0.7852112676056338, 'recall': 0.8375586854460094, 'f1': 0.8105406633348478, 'number': 1065}    | 0.7232            | 0.7933         | 0.7566     | 0.8131           |
+| 0.2814        | 14.0  | 140  | 0.6629          | {'precision': 0.7189901207464325, 'recall': 0.8096415327564895, 'f1': 0.7616279069767442, 'number': 809}       | {'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119}  | {'precision': 0.7924528301886793, 'recall': 0.828169014084507, 'f1': 0.8099173553719008, 'number': 1065}     | 0.7312            | 0.7903         | 0.7596     | 0.8132           |
+| 0.2762        | 15.0  | 150  | 0.6657          | {'precision': 0.7226519337016575, 'recall': 0.8084054388133498, 'f1': 0.763127187864644, 'number': 809}        | {'precision': 0.29545454545454547, 'recall': 0.3277310924369748, 'f1': 0.3107569721115538, 'number': 119}  | {'precision': 0.7931960608773501, 'recall': 0.831924882629108, 'f1': 0.8120989917506873, 'number': 1065}     | 0.7331            | 0.7923         | 0.7615     | 0.8136           |
 ### Framework versions

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