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: 1.1684
-- Answer: {'precision': 0.2061711079943899, 'recall': 0.18170580964153277, 'f1': 0.19316688567674112, 'number': 809}
+- Loss: 1.1386
+- Answer: {'precision': 0.30710659898477155, 'recall': 0.29913473423980225, 'f1': 0.3030682529743269, 'number': 809}
 - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
-- Question: {'precision': 0.4875900720576461, 'recall': 0.571830985915493, 'f1': 0.5263612791702679, 'number': 1065}
-- Overall Precision: 0.3849
-- Overall Recall: 0.3793
-- Overall F1: 0.3821
-- Overall Accuracy: 0.6163
+- Question: {'precision': 0.5028901734104047, 'recall': 0.571830985915493, 'f1': 0.5351493848857645, 'number': 1065}
+- Overall Precision: 0.4247
+- Overall Recall: 0.4270
+- Overall F1: 0.4258
+- Overall Accuracy: 0.6220
 
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.98          | 1.0   | 2    | 1.8269          | {'precision': 0.028085735402808575, 'recall': 0.04697156983930779, 'f1': 0.03515263644773358, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.04985337243401759, 'recall': 0.06384976525821597, 'f1': 0.05599011939069576, 'number': 1065} | 0.0306            | 0.0532         | 0.0388     | 0.2940           |
-| 1.8519        | 2.0   | 4    | 1.7295          | {'precision': 0.023269689737470168, 'recall': 0.048207663782447466, 'f1': 0.03138832997987928, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0860323886639676, 'recall': 0.07981220657276995, 'f1': 0.08280565026790063, 'number': 1065}  | 0.0454            | 0.0622         | 0.0525     | 0.3751           |
-| 1.7541        | 3.0   | 6    | 1.6598          | {'precision': 0.012596899224806201, 'recall': 0.016069221260815822, 'f1': 0.014122759369907657, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.16825396825396827, 'recall': 0.09953051643192488, 'f1': 0.12507374631268436, 'number': 1065} | 0.0714            | 0.0597         | 0.0650     | 0.3817           |
-| 1.6489        | 4.0   | 8    | 1.6110          | {'precision': 0.021822849807445442, 'recall': 0.021013597033374538, 'f1': 0.02141057934508816, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.229607250755287, 'recall': 0.14272300469483568, 'f1': 0.17602779386218878, 'number': 1065}   | 0.1173            | 0.0848         | 0.0984     | 0.3789           |
-| 1.6038        | 5.0   | 10   | 1.5620          | {'precision': 0.03172737955346651, 'recall': 0.03337453646477132, 'f1': 0.0325301204819277, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2894736842105263, 'recall': 0.26854460093896715, 'f1': 0.2786166585484657, 'number': 1065}   | 0.1702            | 0.1570         | 0.1634     | 0.4121           |
-| 1.5457        | 6.0   | 12   | 1.4998          | {'precision': 0.05321507760532151, 'recall': 0.059332509270704575, 'f1': 0.05610753945061367, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3017176997759522, 'recall': 0.37934272300469485, 'f1': 0.3361064891846922, 'number': 1065}   | 0.2017            | 0.2268         | 0.2135     | 0.4609           |
-| 1.4809        | 7.0   | 14   | 1.4337          | {'precision': 0.06864988558352403, 'recall': 0.07416563658838071, 'f1': 0.07130124777183601, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.32998661311914324, 'recall': 0.4629107981220657, 'f1': 0.3853067604533021, 'number': 1065}   | 0.2335            | 0.2775         | 0.2536     | 0.5084           |
-| 1.4273        | 8.0   | 16   | 1.3705          | {'precision': 0.08483290488431877, 'recall': 0.0815822002472188, 'f1': 0.0831758034026465, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3624393624393624, 'recall': 0.49107981220657276, 'f1': 0.41706539074960125, 'number': 1065}  | 0.2652            | 0.2955         | 0.2795     | 0.5411           |
-| 1.3793        | 9.0   | 18   | 1.3172          | {'precision': 0.09651474530831099, 'recall': 0.08899876390605686, 'f1': 0.09260450160771704, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4089191232048375, 'recall': 0.507981220657277, 'f1': 0.45309882747068675, 'number': 1065}    | 0.2963            | 0.3076         | 0.3018     | 0.5622           |
-| 1.3187        | 10.0  | 20   | 1.2753          | {'precision': 0.11297071129707113, 'recall': 0.10012360939431397, 'f1': 0.10615989515072084, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4332273449920509, 'recall': 0.5117370892018779, 'f1': 0.469220835126991, 'number': 1065}     | 0.3170            | 0.3141         | 0.3155     | 0.5747           |
-| 1.2744        | 11.0  | 22   | 1.2405          | {'precision': 0.12750716332378223, 'recall': 0.1100123609394314, 'f1': 0.11811546118115461, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4627964022894522, 'recall': 0.5314553990610329, 'f1': 0.4947552447552448, 'number': 1065}    | 0.3410            | 0.3287         | 0.3347     | 0.5858           |
-| 1.2732        | 12.0  | 24   | 1.2117          | {'precision': 0.14942528735632185, 'recall': 0.12855377008652658, 'f1': 0.1382059800664452, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4745484400656814, 'recall': 0.5427230046948357, 'f1': 0.5063512921594393, 'number': 1065}    | 0.3563            | 0.3422         | 0.3491     | 0.5968           |
-| 1.2123        | 13.0  | 26   | 1.1894          | {'precision': 0.18555240793201133, 'recall': 0.1619283065512979, 'f1': 0.17293729372937294, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.48222940226171246, 'recall': 0.5605633802816902, 'f1': 0.518454190186713, 'number': 1065}    | 0.3745            | 0.3653         | 0.3698     | 0.6064           |
-| 1.1913        | 14.0  | 28   | 1.1748          | {'precision': 0.19915254237288135, 'recall': 0.17428924598269468, 'f1': 0.18589321028345415, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.48672566371681414, 'recall': 0.568075117370892, 'f1': 0.5242634315424609, 'number': 1065}    | 0.3824            | 0.3743         | 0.3783     | 0.6127           |
-| 1.1601        | 15.0  | 30   | 1.1684          | {'precision': 0.2061711079943899, 'recall': 0.18170580964153277, 'f1': 0.19316688567674112, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4875900720576461, 'recall': 0.571830985915493, 'f1': 0.5263612791702679, 'number': 1065}     | 0.3849            | 0.3793         | 0.3821     | 0.6163           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.9493        | 1.0   | 2    | 1.8316          | {'precision': 0.04491161012900143, 'recall': 0.1161928306551298, 'f1': 0.0647829083390765, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.04848966613672496, 'recall': 0.11455399061032864, 'f1': 0.06813739179000279, 'number': 1065} | 0.0459            | 0.1084         | 0.0645     | 0.2414           |
+| 1.8128        | 2.0   | 4    | 1.7172          | {'precision': 0.043029259896729774, 'recall': 0.09270704573547589, 'f1': 0.05877742946708463, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.06945337620578779, 'recall': 0.10140845070422536, 'f1': 0.08244274809160307, 'number': 1065} | 0.0554            | 0.0918         | 0.0691     | 0.3412           |
+| 1.7055        | 3.0   | 6    | 1.6336          | {'precision': 0.026881720430107527, 'recall': 0.037082818294190356, 'f1': 0.03116883116883117, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.13069544364508393, 'recall': 0.10234741784037558, 'f1': 0.11479726171669301, 'number': 1065} | 0.0713            | 0.0697         | 0.0705     | 0.3750           |
+| 1.618         | 4.0   | 8    | 1.5747          | {'precision': 0.028535980148883373, 'recall': 0.02843016069221261, 'f1': 0.02848297213622291, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2175925925925926, 'recall': 0.1323943661971831, 'f1': 0.1646234676007005, 'number': 1065}    | 0.1128            | 0.0823         | 0.0952     | 0.3794           |
+| 1.5703        | 5.0   | 10   | 1.5192          | {'precision': 0.03393939393939394, 'recall': 0.034610630407911, 'f1': 0.03427172582619339, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2924187725631769, 'recall': 0.22816901408450704, 'f1': 0.25632911392405067, 'number': 1065}  | 0.1636            | 0.1360         | 0.1485     | 0.4119           |
+| 1.499         | 6.0   | 12   | 1.4574          | {'precision': 0.05172413793103448, 'recall': 0.05562422744128554, 'f1': 0.053603335318642045, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3345132743362832, 'recall': 0.35492957746478876, 'f1': 0.34441913439635535, 'number': 1065}  | 0.2115            | 0.2122         | 0.2119     | 0.4623           |
+| 1.4485        | 7.0   | 14   | 1.3976          | {'precision': 0.06690561529271206, 'recall': 0.069221260815822, 'f1': 0.06804374240583232, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.35199386503067487, 'recall': 0.4309859154929577, 'f1': 0.3875052764879696, 'number': 1065}   | 0.2405            | 0.2584         | 0.2492     | 0.5090           |
+| 1.4014        | 8.0   | 16   | 1.3413          | {'precision': 0.10366624525916561, 'recall': 0.10135970333745364, 'f1': 0.1025, 'number': 809}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3722576079263977, 'recall': 0.49389671361502346, 'f1': 0.4245359160613398, 'number': 1065}   | 0.2759            | 0.3051         | 0.2897     | 0.5445           |
+| 1.3465        | 9.0   | 18   | 1.2908          | {'precision': 0.14323962516733602, 'recall': 0.13226205191594562, 'f1': 0.13753213367609254, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.40387374461979914, 'recall': 0.5286384976525822, 'f1': 0.45790971939812936, 'number': 1065}  | 0.3129            | 0.3362         | 0.3241     | 0.5679           |
+| 1.2943        | 10.0  | 20   | 1.2491          | {'precision': 0.18072289156626506, 'recall': 0.1668726823238566, 'f1': 0.17352185089974292, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4293233082706767, 'recall': 0.536150234741784, 'f1': 0.47682672233820456, 'number': 1065}    | 0.3399            | 0.3542         | 0.3469     | 0.5816           |
+| 1.2334        | 11.0  | 22   | 1.2138          | {'precision': 0.21903520208604954, 'recall': 0.207663782447466, 'f1': 0.2131979695431472, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.46141732283464565, 'recall': 0.5502347417840375, 'f1': 0.5019271948608137, 'number': 1065}   | 0.3702            | 0.3783         | 0.3742     | 0.5934           |
+| 1.2339        | 12.0  | 24   | 1.1840          | {'precision': 0.24804177545691905, 'recall': 0.23485784919653893, 'f1': 0.24126984126984127, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.48806584362139915, 'recall': 0.5568075117370892, 'f1': 0.5201754385964912, 'number': 1065}   | 0.3945            | 0.3929         | 0.3937     | 0.6030           |
+| 1.1924        | 13.0  | 26   | 1.1607          | {'precision': 0.2782051282051282, 'recall': 0.26823238566131025, 'f1': 0.27312775330396477, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.49793899422918386, 'recall': 0.5671361502347417, 'f1': 0.5302897278314311, 'number': 1065}   | 0.4111            | 0.4119         | 0.4115     | 0.6143           |
+| 1.1666        | 14.0  | 28   | 1.1454          | {'precision': 0.2970550576184379, 'recall': 0.2867737948084054, 'f1': 0.2918238993710692, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5008250825082509, 'recall': 0.5699530516431925, 'f1': 0.5331576635924462, 'number': 1065}    | 0.4199            | 0.4210         | 0.4204     | 0.6193           |
+| 1.1426        | 15.0  | 30   | 1.1386          | {'precision': 0.30710659898477155, 'recall': 0.29913473423980225, 'f1': 0.3030682529743269, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5028901734104047, 'recall': 0.571830985915493, 'f1': 0.5351493848857645, 'number': 1065}     | 0.4247            | 0.4270         | 0.4258     | 0.6220           |
 
 
 ### Framework versions

logs/events.out.tfevents.1664933849.AiD-DLS-1.55836.0

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pytorch_model.bin

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