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: 0.6576
-- Answer: {'precision': 0.6760869565217391, 'recall': 0.7688504326328801, 'f1': 0.719491035280509, 'number': 809}
-- Header: {'precision': 0.29473684210526313, 'recall': 0.23529411764705882, 'f1': 0.2616822429906542, 'number': 119}
-- Question: {'precision': 0.7385398981324278, 'recall': 0.8169014084507042, 'f1': 0.7757467677218011, 'number': 1065}
-- Overall Precision: 0.6931
-- Overall Recall: 0.7627
-- Overall F1: 0.7262
-- Overall Accuracy: 0.7966
+- Loss: 0.6746
+- Answer: {'precision': 0.6505771248688352, 'recall': 0.7663782447466008, 'f1': 0.7037457434733257, 'number': 809}
+- Header: {'precision': 0.20930232558139536, 'recall': 0.15126050420168066, 'f1': 0.17560975609756097, 'number': 119}
+- Question: {'precision': 0.7188284518828452, 'recall': 0.8065727699530516, 'f1': 0.7601769911504423, 'number': 1065}
+- Overall Precision: 0.6701
+- Overall Recall: 0.7511
+- Overall F1: 0.7083
+- Overall Accuracy: 0.7973
 
 ## Model description
 
@@ -54,18 +54,18 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8473        | 1.0   | 10   | 1.5928          | {'precision': 0.018163471241170535, 'recall': 0.022249690976514216, 'f1': 0.020000000000000004, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.22706209453197404, 'recall': 0.2300469483568075, 'f1': 0.228544776119403, 'number': 1065} | 0.1271            | 0.1320         | 0.1295     | 0.3941           |
-| 1.4704        | 2.0   | 20   | 1.2787          | {'precision': 0.11602870813397129, 'recall': 0.11990111248454882, 'f1': 0.11793313069908813, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.4026946107784431, 'recall': 0.5051643192488263, 'f1': 0.4481466055810079, 'number': 1065} | 0.2924            | 0.3186         | 0.3049     | 0.5625           |
-| 1.1341        | 3.0   | 30   | 1.0026          | {'precision': 0.3333333333333333, 'recall': 0.33127317676143386, 'f1': 0.33230006199628026, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.5989804587935429, 'recall': 0.6619718309859155, 'f1': 0.6289027653880465, 'number': 1065} | 0.4831            | 0.4882         | 0.4857     | 0.6604           |
-| 0.8967        | 4.0   | 40   | 0.8387          | {'precision': 0.571563981042654, 'recall': 0.7453646477132262, 'f1': 0.6469957081545066, 'number': 809}        | {'precision': 0.06976744186046512, 'recall': 0.025210084033613446, 'f1': 0.037037037037037035, 'number': 119} | {'precision': 0.6548748921484038, 'recall': 0.7126760563380282, 'f1': 0.6825539568345323, 'number': 1065} | 0.6048            | 0.6849         | 0.6424     | 0.7382           |
-| 0.723         | 5.0   | 50   | 0.7520          | {'precision': 0.5984174085064293, 'recall': 0.7478368355995055, 'f1': 0.6648351648351648, 'number': 809}       | {'precision': 0.1935483870967742, 'recall': 0.10084033613445378, 'f1': 0.13259668508287292, 'number': 119}    | {'precision': 0.6901041666666666, 'recall': 0.7464788732394366, 'f1': 0.7171853856562922, 'number': 1065} | 0.6346            | 0.7085         | 0.6695     | 0.7621           |
-| 0.6196        | 6.0   | 60   | 0.7171          | {'precision': 0.6231003039513677, 'recall': 0.7601977750309024, 'f1': 0.6848552338530067, 'number': 809}       | {'precision': 0.2125, 'recall': 0.14285714285714285, 'f1': 0.1708542713567839, 'number': 119}                 | {'precision': 0.7221238938053097, 'recall': 0.7661971830985915, 'f1': 0.743507972665148, 'number': 1065}  | 0.6591            | 0.7265         | 0.6912     | 0.7734           |
-| 0.5747        | 7.0   | 70   | 0.6993          | {'precision': 0.6506410256410257, 'recall': 0.7527812113720643, 'f1': 0.6979942693409743, 'number': 809}       | {'precision': 0.2558139534883721, 'recall': 0.18487394957983194, 'f1': 0.21463414634146344, 'number': 119}    | {'precision': 0.6894060995184591, 'recall': 0.8065727699530516, 'f1': 0.7434011250540891, 'number': 1065} | 0.6570            | 0.7476         | 0.6994     | 0.7841           |
-| 0.5292        | 8.0   | 80   | 0.6785          | {'precision': 0.6484536082474227, 'recall': 0.7775030902348579, 'f1': 0.7071388420460932, 'number': 809}       | {'precision': 0.29069767441860467, 'recall': 0.21008403361344538, 'f1': 0.24390243902439027, 'number': 119}   | {'precision': 0.7459893048128342, 'recall': 0.7859154929577464, 'f1': 0.7654320987654322, 'number': 1065} | 0.6846            | 0.7481         | 0.7149     | 0.7893           |
-| 0.4862        | 9.0   | 90   | 0.6637          | {'precision': 0.658008658008658, 'recall': 0.7515451174289246, 'f1': 0.7016733987305251, 'number': 809}        | {'precision': 0.28125, 'recall': 0.226890756302521, 'f1': 0.2511627906976744, 'number': 119}                  | {'precision': 0.7287853577371048, 'recall': 0.8225352112676056, 'f1': 0.7728275253639171, 'number': 1065} | 0.6800            | 0.7582         | 0.7170     | 0.7931           |
-| 0.4795        | 10.0  | 100  | 0.6576          | {'precision': 0.6760869565217391, 'recall': 0.7688504326328801, 'f1': 0.719491035280509, 'number': 809}        | {'precision': 0.29473684210526313, 'recall': 0.23529411764705882, 'f1': 0.2616822429906542, 'number': 119}    | {'precision': 0.7385398981324278, 'recall': 0.8169014084507042, 'f1': 0.7757467677218011, 'number': 1065} | 0.6931            | 0.7627         | 0.7262     | 0.7966           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8511        | 1.0   | 10   | 1.6077          | {'precision': 0.01362088535754824, 'recall': 0.014833127317676144, 'f1': 0.014201183431952664, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.17871759890859482, 'recall': 0.12300469483568074, 'f1': 0.1457174638487208, 'number': 1065} | 0.0886            | 0.0718         | 0.0793     | 0.3669           |
+| 1.4863        | 2.0   | 20   | 1.2821          | {'precision': 0.14936708860759493, 'recall': 0.14585908529048208, 'f1': 0.14759224515322075, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4211309523809524, 'recall': 0.5314553990610329, 'f1': 0.46990452469904526, 'number': 1065}  | 0.3204            | 0.3432         | 0.3314     | 0.5815           |
+| 1.1566        | 3.0   | 30   | 1.0398          | {'precision': 0.38341968911917096, 'recall': 0.3658838071693449, 'f1': 0.3744465528146742, 'number': 809}     | {'precision': 0.04, 'recall': 0.008403361344537815, 'f1': 0.01388888888888889, 'number': 119}               | {'precision': 0.5764705882352941, 'recall': 0.644131455399061, 'f1': 0.6084257206208424, 'number': 1065}    | 0.4947            | 0.4932         | 0.4940     | 0.6493           |
+| 0.9277        | 4.0   | 40   | 0.8788          | {'precision': 0.5094339622641509, 'recall': 0.6007416563658838, 'f1': 0.5513329551900171, 'number': 809}      | {'precision': 0.19047619047619047, 'recall': 0.06722689075630252, 'f1': 0.09937888198757765, 'number': 119} | {'precision': 0.6472172351885098, 'recall': 0.6769953051643193, 'f1': 0.6617714547957778, 'number': 1065}   | 0.5758            | 0.6096         | 0.5922     | 0.7266           |
+| 0.7448        | 5.0   | 50   | 0.7982          | {'precision': 0.5696594427244582, 'recall': 0.6823238566131026, 'f1': 0.6209223847019122, 'number': 809}      | {'precision': 0.2, 'recall': 0.11764705882352941, 'f1': 0.14814814814814817, 'number': 119}                 | {'precision': 0.6689478186484175, 'recall': 0.7342723004694836, 'f1': 0.7000895255147717, 'number': 1065}   | 0.6105            | 0.6764         | 0.6418     | 0.7475           |
+| 0.6273        | 6.0   | 60   | 0.7378          | {'precision': 0.6345549738219896, 'recall': 0.7490729295426453, 'f1': 0.6870748299319728, 'number': 809}      | {'precision': 0.21052631578947367, 'recall': 0.13445378151260504, 'f1': 0.1641025641025641, 'number': 119}  | {'precision': 0.6871270247229326, 'recall': 0.7568075117370892, 'f1': 0.7202859696157283, 'number': 1065}   | 0.6479            | 0.7165         | 0.6805     | 0.7778           |
+| 0.5778        | 7.0   | 70   | 0.6971          | {'precision': 0.6439075630252101, 'recall': 0.757725587144623, 'f1': 0.6961953435547985, 'number': 809}       | {'precision': 0.20238095238095238, 'recall': 0.14285714285714285, 'f1': 0.16748768472906403, 'number': 119} | {'precision': 0.6765412329863891, 'recall': 0.7934272300469484, 'f1': 0.7303370786516853, 'number': 1065}   | 0.6455            | 0.7401         | 0.6896     | 0.7825           |
+| 0.5262        | 8.0   | 80   | 0.6989          | {'precision': 0.6372141372141372, 'recall': 0.757725587144623, 'f1': 0.6922642574816488, 'number': 809}       | {'precision': 0.20689655172413793, 'recall': 0.15126050420168066, 'f1': 0.17475728155339806, 'number': 119} | {'precision': 0.7364685004436557, 'recall': 0.7793427230046949, 'f1': 0.7572992700729927, 'number': 1065}   | 0.6714            | 0.7331         | 0.7009     | 0.7963           |
+| 0.4867        | 9.0   | 90   | 0.6756          | {'precision': 0.6428571428571429, 'recall': 0.7564894932014833, 'f1': 0.6950596252129472, 'number': 809}      | {'precision': 0.1935483870967742, 'recall': 0.15126050420168066, 'f1': 0.169811320754717, 'number': 119}    | {'precision': 0.7079207920792079, 'recall': 0.8056338028169014, 'f1': 0.7536231884057971, 'number': 1065}   | 0.6593            | 0.7466         | 0.7002     | 0.7951           |
+| 0.4757        | 10.0  | 100  | 0.6746          | {'precision': 0.6505771248688352, 'recall': 0.7663782447466008, 'f1': 0.7037457434733257, 'number': 809}      | {'precision': 0.20930232558139536, 'recall': 0.15126050420168066, 'f1': 0.17560975609756097, 'number': 119} | {'precision': 0.7188284518828452, 'recall': 0.8065727699530516, 'f1': 0.7601769911504423, 'number': 1065}   | 0.6701            | 0.7511         | 0.7083     | 0.7973           |
 
 
 ### Framework versions

logs/events.out.tfevents.1719808398.HCIDC-SV-DMZ-ORC-NODE02.2136861.0

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model.safetensors

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