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: 0.6633
-- 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
+- Loss: 0.6659
+- Answer: {'precision': 0.7130434782608696, 'recall': 0.8108776266996292, 'f1': 0.7588201272411799, 'number': 809}
+- Header: {'precision': 0.30578512396694213, 'recall': 0.31092436974789917, 'f1': 0.30833333333333335, 'number': 119}
+- Question: {'precision': 0.7858407079646018, 'recall': 0.8338028169014085, 'f1': 0.8091116173120729, 'number': 1065}
+- Overall Precision: 0.7282
+- Overall Recall: 0.7933
+- Overall F1: 0.7594
+- Overall Accuracy: 0.8113
 
 ## Model description
 
@@ -51,23 +51,23 @@ 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           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7894        | 1.0   | 10   | 1.6087          | {'precision': 0.022050716648291068, 'recall': 0.024721878862793572, 'f1': 0.023310023310023312, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.21468926553672316, 'recall': 0.2140845070422535, 'f1': 0.21438645980253881, 'number': 1065} | 0.1260            | 0.1244         | 0.1252     | 0.3753           |
+| 1.4429        | 2.0   | 20   | 1.2246          | {'precision': 0.2103861517976032, 'recall': 0.19530284301606923, 'f1': 0.20256410256410257, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4474885844748858, 'recall': 0.5521126760563381, 'f1': 0.4943253467843632, 'number': 1065}   | 0.3613            | 0.3743         | 0.3677     | 0.5866           |
+| 1.0606        | 3.0   | 30   | 0.9253          | {'precision': 0.5022075055187638, 'recall': 0.5624227441285538, 'f1': 0.5306122448979591, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.6054006968641115, 'recall': 0.6525821596244131, 'f1': 0.6281066425666515, 'number': 1065}   | 0.5518            | 0.5770         | 0.5641     | 0.7066           |
+| 0.8153        | 4.0   | 40   | 0.7559          | {'precision': 0.6192893401015228, 'recall': 0.754017305315204, 'f1': 0.6800445930880714, 'number': 809}        | {'precision': 0.21153846153846154, 'recall': 0.09243697478991597, 'f1': 0.1286549707602339, 'number': 119}  | {'precision': 0.6809480401093893, 'recall': 0.7014084507042253, 'f1': 0.6910268270120259, 'number': 1065}   | 0.6410            | 0.6864         | 0.6630     | 0.7565           |
+| 0.6686        | 5.0   | 50   | 0.6983          | {'precision': 0.6512378902045209, 'recall': 0.7478368355995055, 'f1': 0.6962025316455697, 'number': 809}       | {'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119} | {'precision': 0.6876075731497419, 'recall': 0.7502347417840376, 'f1': 0.7175572519083969, 'number': 1065}   | 0.6555            | 0.7150         | 0.6839     | 0.7797           |
+| 0.5578        | 6.0   | 60   | 0.6618          | {'precision': 0.6344969199178645, 'recall': 0.7639060568603214, 'f1': 0.6932136848008974, 'number': 809}       | {'precision': 0.27586206896551724, 'recall': 0.20168067226890757, 'f1': 0.23300970873786409, 'number': 119} | {'precision': 0.6968724939855654, 'recall': 0.815962441314554, 'f1': 0.7517301038062284, 'number': 1065}    | 0.6547            | 0.7582         | 0.7026     | 0.7895           |
+| 0.4916        | 7.0   | 70   | 0.6501          | {'precision': 0.6787234042553192, 'recall': 0.788627935723115, 'f1': 0.729559748427673, 'number': 809}         | {'precision': 0.2523364485981308, 'recall': 0.226890756302521, 'f1': 0.23893805309734512, 'number': 119}    | {'precision': 0.7281964436917866, 'recall': 0.8075117370892019, 'f1': 0.7658058771148708, 'number': 1065}   | 0.6845            | 0.7652         | 0.7226     | 0.7975           |
+| 0.4501        | 8.0   | 80   | 0.6401          | {'precision': 0.6938110749185668, 'recall': 0.7898640296662547, 'f1': 0.738728323699422, 'number': 809}        | {'precision': 0.26126126126126126, 'recall': 0.24369747899159663, 'f1': 0.25217391304347825, 'number': 119} | {'precision': 0.7434154630416313, 'recall': 0.8215962441314554, 'f1': 0.7805530776092775, 'number': 1065}   | 0.6985            | 0.7742         | 0.7344     | 0.8066           |
+| 0.3986        | 9.0   | 90   | 0.6403          | {'precision': 0.7054945054945055, 'recall': 0.7935723114956736, 'f1': 0.7469458987783596, 'number': 809}       | {'precision': 0.2537313432835821, 'recall': 0.2857142857142857, 'f1': 0.26877470355731226, 'number': 119}   | {'precision': 0.7491496598639455, 'recall': 0.8272300469483568, 'f1': 0.786256135653726, 'number': 1065}    | 0.7014            | 0.7812         | 0.7391     | 0.8069           |
+| 0.3621        | 10.0  | 100  | 0.6501          | {'precision': 0.7071038251366121, 'recall': 0.799752781211372, 'f1': 0.7505800464037122, 'number': 809}        | {'precision': 0.29245283018867924, 'recall': 0.2605042016806723, 'f1': 0.27555555555555555, 'number': 119}  | {'precision': 0.7715289982425307, 'recall': 0.8244131455399061, 'f1': 0.7970948706309579, 'number': 1065}   | 0.7207            | 0.7807         | 0.7495     | 0.8085           |
+| 0.328         | 11.0  | 110  | 0.6625          | {'precision': 0.707742639040349, 'recall': 0.8022249690976514, 'f1': 0.7520278099652375, 'number': 809}        | {'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119}  | {'precision': 0.7820738137082601, 'recall': 0.8356807511737089, 'f1': 0.8079891057648662, 'number': 1065}   | 0.7230            | 0.7898         | 0.7549     | 0.8075           |
+| 0.3134        | 12.0  | 120  | 0.6655          | {'precision': 0.711038961038961, 'recall': 0.8121137206427689, 'f1': 0.7582227351413734, 'number': 809}        | {'precision': 0.3135593220338983, 'recall': 0.31092436974789917, 'f1': 0.31223628691983124, 'number': 119}  | {'precision': 0.7838078291814946, 'recall': 0.8272300469483568, 'f1': 0.8049337597076289, 'number': 1065}   | 0.7271            | 0.7903         | 0.7574     | 0.8089           |
+| 0.2962        | 13.0  | 130  | 0.6583          | {'precision': 0.7161716171617162, 'recall': 0.8046971569839307, 'f1': 0.7578579743888243, 'number': 809}       | {'precision': 0.3064516129032258, 'recall': 0.31932773109243695, 'f1': 0.31275720164609055, 'number': 119}  | {'precision': 0.7808098591549296, 'recall': 0.8328638497652582, 'f1': 0.8059972739663789, 'number': 1065}   | 0.7266            | 0.7908         | 0.7573     | 0.8089           |
+| 0.2823        | 14.0  | 140  | 0.6638          | {'precision': 0.7167755991285403, 'recall': 0.8133498145859085, 'f1': 0.7620150550086855, 'number': 809}       | {'precision': 0.3135593220338983, 'recall': 0.31092436974789917, 'f1': 0.31223628691983124, 'number': 119}  | {'precision': 0.7834960070984915, 'recall': 0.8291079812206573, 'f1': 0.8056569343065694, 'number': 1065}   | 0.7295            | 0.7918         | 0.7594     | 0.8102           |
+| 0.2796        | 15.0  | 150  | 0.6659          | {'precision': 0.7130434782608696, 'recall': 0.8108776266996292, 'f1': 0.7588201272411799, 'number': 809}       | {'precision': 0.30578512396694213, 'recall': 0.31092436974789917, 'f1': 0.30833333333333335, 'number': 119} | {'precision': 0.7858407079646018, 'recall': 0.8338028169014085, 'f1': 0.8091116173120729, 'number': 1065}   | 0.7282            | 0.7933         | 0.7594     | 0.8113           |
 
 
 ### Framework versions

logs/events.out.tfevents.1688792637.02076f391228.2329.1

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:367d8c6e42db5cd7bdf167c7d73ffe70a1b415b923f6ff5ad8681f0dcabfe61e
-size 13375
+oid sha256:8d50c5f90a6a76a8f051fbd86fd4dd2ab8df8c1f7173ad8d46cc765a3419adcc
+size 14390