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@@ -17,14 +17,14 @@ 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: 0.6696
-- Answer: {'precision': 0.7092896174863388, 'recall': 0.8022249690976514, 'f1': 0.7529002320185615, 'number': 809}
-- Header: {'precision': 0.26618705035971224, 'recall': 0.31092436974789917, 'f1': 0.2868217054263566, 'number': 119}
-- Question: {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065}
-- Overall Precision: 0.7170
-- Overall Recall: 0.7842
-- Overall F1: 0.7491
-- Overall Accuracy: 0.8090
 ## Model description
@@ -54,28 +54,28 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7756        | 1.0   | 10   | 1.5443          | {'precision': 0.022977022977022976, 'recall': 0.02843016069221261, 'f1': 0.025414364640883976, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.18486486486486486, 'recall': 0.16056338028169015, 'f1': 0.17185929648241205, 'number': 1065} | 0.1007            | 0.0973         | 0.0990     | 0.3934           |
-| 1.4156        | 2.0   | 20   | 1.2218          | {'precision': 0.2844311377245509, 'recall': 0.3522867737948084, 'f1': 0.3147432357813363, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4658151765589782, 'recall': 0.5821596244131455, 'f1': 0.5175292153589315, 'number': 1065}    | 0.3879            | 0.4541         | 0.4184     | 0.5974           |
-| 1.0947        | 3.0   | 30   | 0.9351          | {'precision': 0.45348837209302323, 'recall': 0.5784919653893696, 'f1': 0.5084193373166758, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.5731800766283525, 'recall': 0.7023474178403756, 'f1': 0.6312236286919831, 'number': 1065}    | 0.5155            | 0.6101         | 0.5588     | 0.7042           |
-| 0.8401        | 4.0   | 40   | 0.8011          | {'precision': 0.5671361502347417, 'recall': 0.7466007416563659, 'f1': 0.6446104589114193, 'number': 809}      | {'precision': 0.03636363636363636, 'recall': 0.01680672268907563, 'f1': 0.022988505747126436, 'number': 119} | {'precision': 0.6641285956006768, 'recall': 0.7370892018779343, 'f1': 0.6987093902981754, 'number': 1065}    | 0.6043            | 0.6979         | 0.6477     | 0.7447           |
-| 0.6784        | 5.0   | 50   | 0.7088          | {'precision': 0.6298568507157464, 'recall': 0.761433868974042, 'f1': 0.689423614997202, 'number': 809}        | {'precision': 0.13333333333333333, 'recall': 0.08403361344537816, 'f1': 0.10309278350515463, 'number': 119}  | {'precision': 0.6869009584664537, 'recall': 0.8075117370892019, 'f1': 0.7423392317652138, 'number': 1065}    | 0.6447            | 0.7456         | 0.6915     | 0.7832           |
-| 0.5803        | 6.0   | 60   | 0.6837          | {'precision': 0.632512315270936, 'recall': 0.7935723114956736, 'f1': 0.7039473684210525, 'number': 809}       | {'precision': 0.17, 'recall': 0.14285714285714285, 'f1': 0.15525114155251143, 'number': 119}                 | {'precision': 0.7255244755244755, 'recall': 0.7793427230046949, 'f1': 0.7514712539610684, 'number': 1065}    | 0.6591            | 0.7471         | 0.7004     | 0.7899           |
-| 0.5058        | 7.0   | 70   | 0.6616          | {'precision': 0.6632337796086509, 'recall': 0.796044499381953, 'f1': 0.7235955056179776, 'number': 809}       | {'precision': 0.22935779816513763, 'recall': 0.21008403361344538, 'f1': 0.2192982456140351, 'number': 119}   | {'precision': 0.7556917688266199, 'recall': 0.8103286384976526, 'f1': 0.7820570910738559, 'number': 1065}    | 0.6895            | 0.7687         | 0.7269     | 0.8049           |
-| 0.4504        | 8.0   | 80   | 0.6497          | {'precision': 0.6694045174537988, 'recall': 0.8059332509270705, 'f1': 0.7313516545148627, 'number': 809}      | {'precision': 0.24778761061946902, 'recall': 0.23529411764705882, 'f1': 0.2413793103448276, 'number': 119}   | {'precision': 0.7757255936675461, 'recall': 0.828169014084507, 'f1': 0.8010899182561309, 'number': 1065}     | 0.7023            | 0.7837         | 0.7408     | 0.8126           |
-| 0.4046        | 9.0   | 90   | 0.6455          | {'precision': 0.6864406779661016, 'recall': 0.8009888751545118, 'f1': 0.7393040501996578, 'number': 809}      | {'precision': 0.25396825396825395, 'recall': 0.2689075630252101, 'f1': 0.2612244897959184, 'number': 119}    | {'precision': 0.7812223206377326, 'recall': 0.828169014084507, 'f1': 0.8040109389243391, 'number': 1065}     | 0.7103            | 0.7837         | 0.7452     | 0.8152           |
-| 0.3936        | 10.0  | 100  | 0.6659          | {'precision': 0.6867088607594937, 'recall': 0.8046971569839307, 'f1': 0.7410358565737052, 'number': 809}      | {'precision': 0.24193548387096775, 'recall': 0.25210084033613445, 'f1': 0.2469135802469136, 'number': 119}   | {'precision': 0.7786811201445348, 'recall': 0.8093896713615023, 'f1': 0.7937384898710865, 'number': 1065}    | 0.7081            | 0.7742         | 0.7397     | 0.8078           |
-| 0.3364        | 11.0  | 110  | 0.6591          | {'precision': 0.6890308839190629, 'recall': 0.799752781211372, 'f1': 0.7402745995423341, 'number': 809}       | {'precision': 0.2824427480916031, 'recall': 0.31092436974789917, 'f1': 0.29600000000000004, 'number': 119}   | {'precision': 0.7735682819383259, 'recall': 0.8244131455399061, 'f1': 0.7981818181818181, 'number': 1065}    | 0.7084            | 0.7837         | 0.7442     | 0.8115           |
-| 0.3265        | 12.0  | 120  | 0.6682          | {'precision': 0.6912393162393162, 'recall': 0.799752781211372, 'f1': 0.7415472779369628, 'number': 809}       | {'precision': 0.26666666666666666, 'recall': 0.3025210084033613, 'f1': 0.28346456692913385, 'number': 119}   | {'precision': 0.7784697508896797, 'recall': 0.8215962441314554, 'f1': 0.7994518044769301, 'number': 1065}    | 0.7098            | 0.7817         | 0.7440     | 0.8077           |
-| 0.3079        | 13.0  | 130  | 0.6711          | {'precision': 0.7035830618892508, 'recall': 0.8009888751545118, 'f1': 0.7491329479768787, 'number': 809}      | {'precision': 0.26717557251908397, 'recall': 0.29411764705882354, 'f1': 0.28, 'number': 119}                 | {'precision': 0.7762114537444934, 'recall': 0.8272300469483568, 'f1': 0.8009090909090909, 'number': 1065}    | 0.7151            | 0.7847         | 0.7483     | 0.8090           |
-| 0.2868        | 14.0  | 140  | 0.6677          | {'precision': 0.704225352112676, 'recall': 0.8034610630407911, 'f1': 0.7505773672055426, 'number': 809}       | {'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119}   | {'precision': 0.7804444444444445, 'recall': 0.8244131455399061, 'f1': 0.8018264840182647, 'number': 1065}    | 0.7177            | 0.7858         | 0.7502     | 0.8111           |
-| 0.2863        | 15.0  | 150  | 0.6696          | {'precision': 0.7092896174863388, 'recall': 0.8022249690976514, 'f1': 0.7529002320185615, 'number': 809}      | {'precision': 0.26618705035971224, 'recall': 0.31092436974789917, 'f1': 0.2868217054263566, 'number': 119}   | {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065}    | 0.7170            | 0.7842         | 0.7491     | 0.8090           |
 ### Framework versions
-- Transformers 4.43.2
-- Pytorch 2.3.1+cu121
 - Datasets 2.20.0
 - Tokenizers 0.19.1

 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: 0.6748
+- Answer: {'precision': 0.7245575221238938, 'recall': 0.8096415327564895, 'f1': 0.7647402218330415, 'number': 809}
+- Header: {'precision': 0.3464566929133858, 'recall': 0.3697478991596639, 'f1': 0.35772357723577236, 'number': 119}
+- Question: {'precision': 0.7756183745583038, 'recall': 0.8244131455399061, 'f1': 0.7992717341829768, 'number': 1065}
+- Overall Precision: 0.7291
+- Overall Recall: 0.7913
+- Overall F1: 0.7589
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7656        | 1.0   | 10   | 1.5482          | {'precision': 0.030390738060781478, 'recall': 0.02595797280593325, 'f1': 0.028, 'number': 809}              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.31988041853512705, 'recall': 0.20093896713615023, 'f1': 0.24682814302191464, 'number': 1065} | 0.1728            | 0.1179         | 0.1402     | 0.3707           |
+| 1.4041        | 2.0   | 20   | 1.1664          | {'precision': 0.15247252747252749, 'recall': 0.13720642768850433, 'f1': 0.14443721535458687, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.46790409899458624, 'recall': 0.568075117370892, 'f1': 0.5131467345207804, 'number': 1065}    | 0.3539            | 0.3593         | 0.3566     | 0.6164           |
+| 1.0549        | 3.0   | 30   | 0.8895          | {'precision': 0.521044992743106, 'recall': 0.4437577255871446, 'f1': 0.479305740987984, 'number': 809}      | {'precision': 0.25, 'recall': 0.08403361344537816, 'f1': 0.12578616352201258, 'number': 119}                | {'precision': 0.5932336742722266, 'recall': 0.707981220657277, 'f1': 0.6455479452054794, 'number': 1065}     | 0.5615            | 0.5635         | 0.5625     | 0.7226           |
+| 0.8144        | 4.0   | 40   | 0.7445          | {'precision': 0.621978021978022, 'recall': 0.6996291718170581, 'f1': 0.658522396742292, 'number': 809}      | {'precision': 0.2753623188405797, 'recall': 0.15966386554621848, 'f1': 0.20212765957446807, 'number': 119}  | {'precision': 0.6641477749790092, 'recall': 0.7427230046948357, 'f1': 0.7012411347517731, 'number': 1065}    | 0.6341            | 0.6904         | 0.6611     | 0.7620           |
+| 0.6601        | 5.0   | 50   | 0.6786          | {'precision': 0.6608505997818975, 'recall': 0.7490729295426453, 'f1': 0.7022016222479722, 'number': 809}    | {'precision': 0.34615384615384615, 'recall': 0.226890756302521, 'f1': 0.27411167512690354, 'number': 119}   | {'precision': 0.6853932584269663, 'recall': 0.8018779342723005, 'f1': 0.7390739939420164, 'number': 1065}    | 0.6635            | 0.7461         | 0.7024     | 0.7912           |
+| 0.558         | 6.0   | 60   | 0.6751          | {'precision': 0.6495375128468653, 'recall': 0.7812113720642769, 'f1': 0.7093153759820426, 'number': 809}    | {'precision': 0.34615384615384615, 'recall': 0.226890756302521, 'f1': 0.27411167512690354, 'number': 119}   | {'precision': 0.7348017621145374, 'recall': 0.7830985915492957, 'f1': 0.7581818181818181, 'number': 1065}    | 0.6830            | 0.7491         | 0.7145     | 0.7873           |
+| 0.4876        | 7.0   | 70   | 0.6439          | {'precision': 0.6867469879518072, 'recall': 0.7750309023485785, 'f1': 0.7282229965156795, 'number': 809}    | {'precision': 0.2672413793103448, 'recall': 0.2605042016806723, 'f1': 0.26382978723404255, 'number': 119}   | {'precision': 0.735144312393888, 'recall': 0.8131455399061033, 'f1': 0.7721801159161837, 'number': 1065}     | 0.6905            | 0.7647         | 0.7257     | 0.8059           |
+| 0.431         | 8.0   | 80   | 0.6333          | {'precision': 0.7019650655021834, 'recall': 0.7948084054388134, 'f1': 0.7455072463768115, 'number': 809}    | {'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119}   | {'precision': 0.7440878378378378, 'recall': 0.8272300469483568, 'f1': 0.7834593152512227, 'number': 1065}    | 0.7046            | 0.7827         | 0.7416     | 0.8119           |
+| 0.3849        | 9.0   | 90   | 0.6338          | {'precision': 0.713495575221239, 'recall': 0.7972805933250927, 'f1': 0.7530647985989491, 'number': 809}     | {'precision': 0.3465346534653465, 'recall': 0.29411764705882354, 'f1': 0.3181818181818182, 'number': 119}   | {'precision': 0.7697022767075307, 'recall': 0.8253521126760563, 'f1': 0.7965564114182148, 'number': 1065}    | 0.7261            | 0.7822         | 0.7531     | 0.8189           |
+| 0.3741        | 10.0  | 100  | 0.6533          | {'precision': 0.7054429028815368, 'recall': 0.8170580964153276, 'f1': 0.7571592210767468, 'number': 809}    | {'precision': 0.31092436974789917, 'recall': 0.31092436974789917, 'f1': 0.31092436974789917, 'number': 119} | {'precision': 0.7736185383244206, 'recall': 0.8150234741784037, 'f1': 0.7937814357567444, 'number': 1065}    | 0.7190            | 0.7858         | 0.7509     | 0.8133           |
+| 0.3184        | 11.0  | 110  | 0.6556          | {'precision': 0.7065803667745415, 'recall': 0.8096415327564895, 'f1': 0.7546082949308756, 'number': 809}    | {'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119}            | {'precision': 0.7630901287553649, 'recall': 0.8347417840375587, 'f1': 0.7973094170403587, 'number': 1065}    | 0.7140            | 0.7953         | 0.7524     | 0.8104           |
+| 0.3038        | 12.0  | 120  | 0.6681          | {'precision': 0.72271714922049, 'recall': 0.8022249690976514, 'f1': 0.7603983596953721, 'number': 809}      | {'precision': 0.3305084745762712, 'recall': 0.3277310924369748, 'f1': 0.32911392405063294, 'number': 119}   | {'precision': 0.7851387645478961, 'recall': 0.8234741784037559, 'f1': 0.8038496791934006, 'number': 1065}    | 0.7337            | 0.7852         | 0.7586     | 0.8155           |
+| 0.2922        | 13.0  | 130  | 0.6667          | {'precision': 0.7233809001097695, 'recall': 0.8145859085290482, 'f1': 0.7662790697674419, 'number': 809}    | {'precision': 0.36036036036036034, 'recall': 0.33613445378151263, 'f1': 0.34782608695652173, 'number': 119} | {'precision': 0.7810599478714162, 'recall': 0.844131455399061, 'f1': 0.8113718411552348, 'number': 1065}     | 0.7354            | 0.8018         | 0.7672     | 0.8150           |
+| 0.2685        | 14.0  | 140  | 0.6738          | {'precision': 0.7296996662958843, 'recall': 0.8108776266996292, 'f1': 0.7681498829039812, 'number': 809}    | {'precision': 0.3384615384615385, 'recall': 0.3697478991596639, 'f1': 0.35341365461847385, 'number': 119}   | {'precision': 0.7788546255506608, 'recall': 0.8300469483568075, 'f1': 0.8036363636363637, 'number': 1065}    | 0.7320            | 0.7948         | 0.7621     | 0.8131           |
+| 0.2668        | 15.0  | 150  | 0.6748          | {'precision': 0.7245575221238938, 'recall': 0.8096415327564895, 'f1': 0.7647402218330415, 'number': 809}    | {'precision': 0.3464566929133858, 'recall': 0.3697478991596639, 'f1': 0.35772357723577236, 'number': 119}   | {'precision': 0.7756183745583038, 'recall': 0.8244131455399061, 'f1': 0.7992717341829768, 'number': 1065}    | 0.7291            | 0.7913         | 0.7589     | 0.8136           |
 ### Framework versions
+- Transformers 4.43.3
+- Pytorch 2.4.0+cu121
 - Datasets 2.20.0
 - Tokenizers 0.19.1

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