End of training

README.md

@@ -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: 1.0844
-- Answer: {'precision': 0.3143100511073254, 'recall': 0.4561186650185414, 'f1': 0.3721633888048412, 'number': 809}
-- Header: {'precision': 0.275, 'recall': 0.18487394957983194, 'f1': 0.22110552763819097, 'number': 119}
-- Question: {'precision': 0.44804716285924834, 'recall': 0.5708920187793427, 'f1': 0.5020644095788603, 'number': 1065}
-- Overall Precision: 0.3826
-- Overall Recall: 0.5013
-- Overall F1: 0.4340
-- Overall Accuracy: 0.5793
+- Loss: 1.1017
+- Answer: {'precision': 0.40439158279963405, 'recall': 0.546353522867738, 'f1': 0.46477392218717145, 'number': 809}
+- Header: {'precision': 0.3368421052631579, 'recall': 0.2689075630252101, 'f1': 0.29906542056074764, 'number': 119}
+- Question: {'precision': 0.5619128949615713, 'recall': 0.6178403755868545, 'f1': 0.5885509838998211, 'number': 1065}
+- Overall Precision: 0.4799
+- Overall Recall: 0.5680
+- Overall F1: 0.5202
+- Overall Accuracy: 0.6339
 
 ## Model description
 
@@ -53,23 +53,23 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7974        | 1.0   | 5    | 1.6082          | {'precision': 0.015957446808510637, 'recall': 0.003708281829419036, 'f1': 0.006018054162487462, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.13218390804597702, 'recall': 0.0215962441314554, 'f1': 0.037126715092816794, 'number': 1065} | 0.0718            | 0.0130         | 0.0221     | 0.2950           |
-| 1.6031        | 2.0   | 10   | 1.4809          | {'precision': 0.09702549575070822, 'recall': 0.16934487021013597, 'f1': 0.12336785231877535, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2448499117127722, 'recall': 0.39061032863849765, 'f1': 0.301013024602026, 'number': 1065}    | 0.1778            | 0.2775         | 0.2167     | 0.3926           |
-| 1.4415        | 3.0   | 15   | 1.3965          | {'precision': 0.15503875968992248, 'recall': 0.32138442521631644, 'f1': 0.20917135961383748, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.25329341317365267, 'recall': 0.3971830985915493, 'f1': 0.3093235831809872, 'number': 1065}   | 0.2041            | 0.3427         | 0.2558     | 0.4162           |
-| 1.3417        | 4.0   | 20   | 1.2882          | {'precision': 0.1925233644859813, 'recall': 0.3819530284301607, 'f1': 0.25600662800331403, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2921832884097035, 'recall': 0.5089201877934272, 'f1': 0.3712328767123288, 'number': 1065}    | 0.2457            | 0.4270         | 0.3120     | 0.4305           |
-| 1.2673        | 5.0   | 25   | 1.2461          | {'precision': 0.2402555910543131, 'recall': 0.4647713226205192, 'f1': 0.3167649536647009, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.3362183754993342, 'recall': 0.47417840375586856, 'f1': 0.39345539540319435, 'number': 1065}  | 0.2828            | 0.4420         | 0.3449     | 0.4621           |
-| 1.1953        | 6.0   | 30   | 1.1667          | {'precision': 0.2396469789545146, 'recall': 0.4363411619283066, 'f1': 0.30937773882559155, 'number': 809}      | {'precision': 0.1038961038961039, 'recall': 0.06722689075630252, 'f1': 0.08163265306122448, 'number': 119}  | {'precision': 0.34711246200607904, 'recall': 0.536150234741784, 'f1': 0.42140221402214023, 'number': 1065}   | 0.2917            | 0.4676         | 0.3593     | 0.5048           |
-| 1.1257        | 7.0   | 35   | 1.1238          | {'precision': 0.271211022480058, 'recall': 0.4622991347342398, 'f1': 0.34186471663619744, 'number': 809}       | {'precision': 0.17708333333333334, 'recall': 0.14285714285714285, 'f1': 0.15813953488372096, 'number': 119} | {'precision': 0.38812154696132595, 'recall': 0.5276995305164319, 'f1': 0.4472741742936729, 'number': 1065}   | 0.3260            | 0.4782         | 0.3877     | 0.5539           |
-| 1.0703        | 8.0   | 40   | 1.0882          | {'precision': 0.2758340113913751, 'recall': 0.41903584672435107, 'f1': 0.33267909715407257, 'number': 809}     | {'precision': 0.1919191919191919, 'recall': 0.15966386554621848, 'f1': 0.17431192660550457, 'number': 119}  | {'precision': 0.4045307443365696, 'recall': 0.5868544600938967, 'f1': 0.47892720306513414, 'number': 1065}   | 0.3422            | 0.4932         | 0.4040     | 0.5809           |
-| 1.0172        | 9.0   | 45   | 1.0768          | {'precision': 0.277602523659306, 'recall': 0.43510506798516685, 'f1': 0.3389504092441021, 'number': 809}       | {'precision': 0.24096385542168675, 'recall': 0.16806722689075632, 'f1': 0.19801980198019803, 'number': 119} | {'precision': 0.40967092008059103, 'recall': 0.5727699530516432, 'f1': 0.4776820673453407, 'number': 1065}   | 0.3458            | 0.4927         | 0.4064     | 0.5803           |
-| 0.9713        | 10.0  | 50   | 1.0884          | {'precision': 0.3041700735895339, 'recall': 0.45982694684796044, 'f1': 0.3661417322834645, 'number': 809}      | {'precision': 0.2631578947368421, 'recall': 0.16806722689075632, 'f1': 0.20512820512820512, 'number': 119}  | {'precision': 0.4506024096385542, 'recall': 0.5267605633802817, 'f1': 0.4857142857142857, 'number': 1065}    | 0.3746            | 0.4782         | 0.4201     | 0.5781           |
-| 0.9434        | 11.0  | 55   | 1.1220          | {'precision': 0.29082426127527217, 'recall': 0.4622991347342398, 'f1': 0.35704057279236273, 'number': 809}     | {'precision': 0.2727272727272727, 'recall': 0.17647058823529413, 'f1': 0.21428571428571427, 'number': 119}  | {'precision': 0.4404934687953556, 'recall': 0.5699530516431925, 'f1': 0.4969300040933278, 'number': 1065}    | 0.3656            | 0.5028         | 0.4233     | 0.5669           |
-| 0.9288        | 12.0  | 60   | 1.0876          | {'precision': 0.298372513562387, 'recall': 0.4079110012360939, 'f1': 0.34464751958224543, 'number': 809}       | {'precision': 0.23958333333333334, 'recall': 0.19327731092436976, 'f1': 0.21395348837209302, 'number': 119} | {'precision': 0.4299933642999336, 'recall': 0.6084507042253521, 'f1': 0.5038880248833593, 'number': 1065}    | 0.3695            | 0.5023         | 0.4258     | 0.5784           |
-| 0.9043        | 13.0  | 65   | 1.1185          | {'precision': 0.31703204047217537, 'recall': 0.4647713226205192, 'f1': 0.3769423558897243, 'number': 809}      | {'precision': 0.2894736842105263, 'recall': 0.18487394957983194, 'f1': 0.22564102564102564, 'number': 119}  | {'precision': 0.4605263157894737, 'recall': 0.5258215962441315, 'f1': 0.49101271372205174, 'number': 1065}   | 0.3866            | 0.4807         | 0.4285     | 0.5679           |
-| 0.8884        | 14.0  | 70   | 1.1097          | {'precision': 0.31260364842454397, 'recall': 0.46600741656365885, 'f1': 0.37419354838709684, 'number': 809}    | {'precision': 0.29333333333333333, 'recall': 0.18487394957983194, 'f1': 0.2268041237113402, 'number': 119}  | {'precision': 0.4597791798107255, 'recall': 0.5474178403755868, 'f1': 0.4997856836690956, 'number': 1065}    | 0.3852            | 0.4927         | 0.4324     | 0.5710           |
-| 0.8759        | 15.0  | 75   | 1.0844          | {'precision': 0.3143100511073254, 'recall': 0.4561186650185414, 'f1': 0.3721633888048412, 'number': 809}       | {'precision': 0.275, 'recall': 0.18487394957983194, 'f1': 0.22110552763819097, 'number': 119}               | {'precision': 0.44804716285924834, 'recall': 0.5708920187793427, 'f1': 0.5020644095788603, 'number': 1065}   | 0.3826            | 0.5013         | 0.4340     | 0.5793           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                     | Header                                                                                                      | Question                                                                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.3745        | 1.0   | 5    | 1.1446          | {'precision': 0.24631396357328708, 'recall': 0.3510506798516687, 'f1': 0.28950050968399593, 'number': 809} | {'precision': 0.20930232558139536, 'recall': 0.226890756302521, 'f1': 0.21774193548387097, 'number': 119}   | {'precision': 0.4135151890886547, 'recall': 0.6262910798122066, 'f1': 0.4981329350261389, 'number': 1065}  | 0.3378            | 0.4907         | 0.4002     | 0.5475           |
+| 1.0195        | 2.0   | 10   | 1.0518          | {'precision': 0.29006968641114983, 'recall': 0.411619283065513, 'f1': 0.340316811446091, 'number': 809}    | {'precision': 0.2815533980582524, 'recall': 0.24369747899159663, 'f1': 0.26126126126126126, 'number': 119}  | {'precision': 0.42618741976893454, 'recall': 0.6234741784037559, 'f1': 0.5062905070529927, 'number': 1065} | 0.3653            | 0.5148         | 0.4273     | 0.5967           |
+| 0.8996        | 3.0   | 15   | 1.0952          | {'precision': 0.3147887323943662, 'recall': 0.5525339925834364, 'f1': 0.4010767160161508, 'number': 809}   | {'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119} | {'precision': 0.4714285714285714, 'recall': 0.5267605633802817, 'f1': 0.4975609756097561, 'number': 1065}  | 0.3821            | 0.5163         | 0.4392     | 0.5831           |
+| 0.8294        | 4.0   | 20   | 1.0418          | {'precision': 0.3429571303587052, 'recall': 0.484548825710754, 'f1': 0.4016393442622951, 'number': 809}    | {'precision': 0.32, 'recall': 0.20168067226890757, 'f1': 0.24742268041237112, 'number': 119}                | {'precision': 0.49588815789473684, 'recall': 0.5661971830985916, 'f1': 0.5287154756685665, 'number': 1065} | 0.4187            | 0.5113         | 0.4604     | 0.6110           |
+| 0.773         | 5.0   | 25   | 1.0412          | {'precision': 0.34150772025431425, 'recall': 0.4647713226205192, 'f1': 0.393717277486911, 'number': 809}   | {'precision': 0.2857142857142857, 'recall': 0.20168067226890757, 'f1': 0.23645320197044337, 'number': 119}  | {'precision': 0.4541223404255319, 'recall': 0.6413145539906103, 'f1': 0.5317244063838069, 'number': 1065}  | 0.4028            | 0.5434         | 0.4626     | 0.6114           |
+| 0.731         | 6.0   | 30   | 1.0832          | {'precision': 0.352991452991453, 'recall': 0.5105067985166872, 'f1': 0.4173825164224356, 'number': 809}    | {'precision': 0.2708333333333333, 'recall': 0.2184873949579832, 'f1': 0.24186046511627907, 'number': 119}   | {'precision': 0.5029686174724343, 'recall': 0.5568075117370892, 'f1': 0.5285204991087344, 'number': 1065}  | 0.4221            | 0.5178         | 0.4651     | 0.6014           |
+| 0.6884        | 7.0   | 35   | 1.1304          | {'precision': 0.3588709677419355, 'recall': 0.5500618046971569, 'f1': 0.4343582235236701, 'number': 809}   | {'precision': 0.36619718309859156, 'recall': 0.2184873949579832, 'f1': 0.2736842105263158, 'number': 119}   | {'precision': 0.5510204081632653, 'recall': 0.5577464788732395, 'f1': 0.5543630424638357, 'number': 1065}  | 0.4458            | 0.5344         | 0.4861     | 0.6078           |
+| 0.6731        | 8.0   | 40   | 1.0667          | {'precision': 0.3651096282173499, 'recall': 0.47342398022249693, 'f1': 0.41227125941872983, 'number': 809} | {'precision': 0.29, 'recall': 0.24369747899159663, 'f1': 0.2648401826484018, 'number': 119}                 | {'precision': 0.49964912280701756, 'recall': 0.6685446009389672, 'f1': 0.5718875502008032, 'number': 1065} | 0.4367            | 0.5640         | 0.4922     | 0.6205           |
+| 0.6441        | 9.0   | 45   | 1.0893          | {'precision': 0.3948576675849403, 'recall': 0.5315203955500618, 'f1': 0.45310853530031614, 'number': 809}  | {'precision': 0.3238095238095238, 'recall': 0.2857142857142857, 'f1': 0.30357142857142855, 'number': 119}   | {'precision': 0.5439367311072056, 'recall': 0.5812206572769953, 'f1': 0.5619609623241035, 'number': 1065}  | 0.4644            | 0.5434         | 0.5008     | 0.6241           |
+| 0.6139        | 10.0  | 50   | 1.0987          | {'precision': 0.37037037037037035, 'recall': 0.5562422744128553, 'f1': 0.44466403162055335, 'number': 809} | {'precision': 0.313953488372093, 'recall': 0.226890756302521, 'f1': 0.2634146341463415, 'number': 119}      | {'precision': 0.533678756476684, 'recall': 0.5802816901408451, 'f1': 0.5560053981106613, 'number': 1065}   | 0.4453            | 0.5494         | 0.4919     | 0.6253           |
+| 0.6007        | 11.0  | 55   | 1.0803          | {'precision': 0.40096618357487923, 'recall': 0.5129789864029666, 'f1': 0.45010845986984815, 'number': 809} | {'precision': 0.29591836734693877, 'recall': 0.24369747899159663, 'f1': 0.26728110599078336, 'number': 119} | {'precision': 0.5409054805401112, 'recall': 0.6394366197183099, 'f1': 0.5860585197934596, 'number': 1065}  | 0.4703            | 0.5645         | 0.5131     | 0.6317           |
+| 0.5985        | 12.0  | 60   | 1.0997          | {'precision': 0.4080846968238691, 'recall': 0.5241038318912238, 'f1': 0.45887445887445893, 'number': 809}  | {'precision': 0.31683168316831684, 'recall': 0.2689075630252101, 'f1': 0.29090909090909095, 'number': 119}  | {'precision': 0.5536303630363036, 'recall': 0.6300469483568075, 'f1': 0.5893719806763285, 'number': 1065}  | 0.4792            | 0.5655         | 0.5188     | 0.6323           |
+| 0.5828        | 13.0  | 65   | 1.0996          | {'precision': 0.40275229357798165, 'recall': 0.5426452410383189, 'f1': 0.46234860452869925, 'number': 809} | {'precision': 0.33695652173913043, 'recall': 0.2605042016806723, 'f1': 0.29383886255924174, 'number': 119}  | {'precision': 0.5685936151855048, 'recall': 0.6187793427230047, 'f1': 0.5926258992805755, 'number': 1065}  | 0.4823            | 0.5665         | 0.5210     | 0.6345           |
+| 0.5656        | 14.0  | 70   | 1.1065          | {'precision': 0.40542986425339367, 'recall': 0.553770086526576, 'f1': 0.46812957157784746, 'number': 809}  | {'precision': 0.32967032967032966, 'recall': 0.25210084033613445, 'f1': 0.28571428571428575, 'number': 119} | {'precision': 0.5730735163861824, 'recall': 0.6075117370892019, 'f1': 0.5897903372835004, 'number': 1065}  | 0.4839            | 0.5645         | 0.5211     | 0.6338           |
+| 0.5625        | 15.0  | 75   | 1.1017          | {'precision': 0.40439158279963405, 'recall': 0.546353522867738, 'f1': 0.46477392218717145, 'number': 809}  | {'precision': 0.3368421052631579, 'recall': 0.2689075630252101, 'f1': 0.29906542056074764, 'number': 119}   | {'precision': 0.5619128949615713, 'recall': 0.6178403755868545, 'f1': 0.5885509838998211, 'number': 1065}  | 0.4799            | 0.5680         | 0.5202     | 0.6339           |
 
 
 ### Framework versions

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:e15b84a697580a711fe6dfac34e292516ceda8f6c331ddf8b17c4e056f8517f4
 size 450603969