End of training

README.md

@@ -16,14 +16,14 @@ 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: 0.7040
-- Answer: {'precision': 0.6568109820485745, 'recall': 0.7688504326328801, 'f1': 0.7084282460136675, 'number': 809}
-- Header: {'precision': 0.2803738317757009, 'recall': 0.25210084033613445, 'f1': 0.2654867256637167, 'number': 119}
-- Question: {'precision': 0.7009113504556752, 'recall': 0.7943661971830986, 'f1': 0.744718309859155, 'number': 1065}
-- Overall Precision: 0.6625
-- Overall Recall: 0.7516
-- Overall F1: 0.7043
-- Overall Accuracy: 0.7902
+- Loss: 0.6680
+- Answer: {'precision': 0.6523109243697479, 'recall': 0.7676143386897404, 'f1': 0.7052810902896083, 'number': 809}
+- Header: {'precision': 0.23300970873786409, 'recall': 0.20168067226890757, 'f1': 0.21621621621621623, 'number': 119}
+- Question: {'precision': 0.7324786324786324, 'recall': 0.8046948356807512, 'f1': 0.766890380313199, 'number': 1065}
+- Overall Precision: 0.6751
+- Overall Recall: 0.7536
+- Overall F1: 0.7122
+- Overall Accuracy: 0.7957
 
 ## Model description
 
@@ -54,16 +54,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8156        | 1.0   | 10   | 1.6000          | {'precision': 0.016967126193001062, 'recall': 0.019777503090234856, 'f1': 0.0182648401826484, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.19947159841479525, 'recall': 0.14178403755868543, 'f1': 0.16575192096597147, 'number': 1065} | 0.0982            | 0.0838         | 0.0904     | 0.3885           |
-| 1.4929        | 2.0   | 20   | 1.2928          | {'precision': 0.2471213463241807, 'recall': 0.34487021013597036, 'f1': 0.2879256965944273, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.37381275440976935, 'recall': 0.5173708920187794, 'f1': 0.43402914533280823, 'number': 1065}  | 0.3189            | 0.4165         | 0.3612     | 0.5918           |
-| 1.1481        | 3.0   | 30   | 1.0078          | {'precision': 0.37816979051819183, 'recall': 0.42398022249690975, 'f1': 0.3997668997668998, 'number': 809}   | {'precision': 0.14705882352941177, 'recall': 0.04201680672268908, 'f1': 0.06535947712418301, 'number': 119} | {'precision': 0.5541346973572038, 'recall': 0.6103286384976526, 'f1': 0.580875781948168, 'number': 1065}     | 0.4721            | 0.5008         | 0.4860     | 0.6646           |
-| 0.9026        | 4.0   | 40   | 0.8847          | {'precision': 0.5041322314049587, 'recall': 0.6786155747836835, 'f1': 0.5785036880927291, 'number': 809}     | {'precision': 0.16, 'recall': 0.06722689075630252, 'f1': 0.09467455621301775, 'number': 119}                | {'precision': 0.6154513888888888, 'recall': 0.6657276995305165, 'f1': 0.6396030672079386, 'number': 1065}    | 0.5526            | 0.6352         | 0.5910     | 0.7209           |
-| 0.7479        | 5.0   | 50   | 0.7907          | {'precision': 0.6089324618736384, 'recall': 0.6909765142150803, 'f1': 0.6473653734800232, 'number': 809}     | {'precision': 0.23076923076923078, 'recall': 0.15126050420168066, 'f1': 0.18274111675126906, 'number': 119} | {'precision': 0.6239600665557404, 'recall': 0.704225352112676, 'f1': 0.6616674018526687, 'number': 1065}     | 0.6037            | 0.6658         | 0.6333     | 0.7576           |
-| 0.651         | 6.0   | 60   | 0.7416          | {'precision': 0.604040404040404, 'recall': 0.7391841779975278, 'f1': 0.6648137854363535, 'number': 809}      | {'precision': 0.20238095238095238, 'recall': 0.14285714285714285, 'f1': 0.16748768472906403, 'number': 119} | {'precision': 0.6520376175548589, 'recall': 0.7812206572769953, 'f1': 0.7108073472874841, 'number': 1065}    | 0.6157            | 0.7260         | 0.6664     | 0.7732           |
-| 0.5864        | 7.0   | 70   | 0.7379          | {'precision': 0.6485355648535565, 'recall': 0.7663782447466008, 'f1': 0.7025495750708215, 'number': 809}     | {'precision': 0.22772277227722773, 'recall': 0.19327731092436976, 'f1': 0.2090909090909091, 'number': 119}  | {'precision': 0.7006861063464837, 'recall': 0.7671361502347418, 'f1': 0.7324069923800985, 'number': 1065}    | 0.6568            | 0.7326         | 0.6926     | 0.7746           |
-| 0.5425        | 8.0   | 80   | 0.7093          | {'precision': 0.6484210526315789, 'recall': 0.761433868974042, 'f1': 0.7003979533826037, 'number': 809}      | {'precision': 0.25925925925925924, 'recall': 0.23529411764705882, 'f1': 0.24669603524229072, 'number': 119} | {'precision': 0.6843800322061192, 'recall': 0.7981220657276995, 'f1': 0.7368877329865627, 'number': 1065}    | 0.6496            | 0.7496         | 0.6960     | 0.7901           |
-| 0.4986        | 9.0   | 90   | 0.7080          | {'precision': 0.6553911205073996, 'recall': 0.7663782447466008, 'f1': 0.7065527065527065, 'number': 809}     | {'precision': 0.2857142857142857, 'recall': 0.25210084033613445, 'f1': 0.26785714285714285, 'number': 119}  | {'precision': 0.7062761506276151, 'recall': 0.7924882629107981, 'f1': 0.7469026548672565, 'number': 1065}    | 0.6652            | 0.7496         | 0.7049     | 0.7881           |
-| 0.481         | 10.0  | 100  | 0.7040          | {'precision': 0.6568109820485745, 'recall': 0.7688504326328801, 'f1': 0.7084282460136675, 'number': 809}     | {'precision': 0.2803738317757009, 'recall': 0.25210084033613445, 'f1': 0.2654867256637167, 'number': 119}   | {'precision': 0.7009113504556752, 'recall': 0.7943661971830986, 'f1': 0.744718309859155, 'number': 1065}     | 0.6625            | 0.7516         | 0.7043     | 0.7902           |
+| 1.8904        | 1.0   | 10   | 1.6569          | {'precision': 0.0226628895184136, 'recall': 0.029666254635352288, 'f1': 0.025695931477516063, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.13233724653148346, 'recall': 0.11643192488262911, 'f1': 0.12387612387612387, 'number': 1065} | 0.074             | 0.0743         | 0.0741     | 0.3562           |
+| 1.5103        | 2.0   | 20   | 1.3215          | {'precision': 0.16376306620209058, 'recall': 0.17428924598269468, 'f1': 0.1688622754491018, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.37047970479704795, 'recall': 0.47136150234741786, 'f1': 0.4148760330578512, 'number': 1065}  | 0.2902            | 0.3226         | 0.3055     | 0.5678           |
+| 1.1593        | 3.0   | 30   | 0.9985          | {'precision': 0.45689655172413796, 'recall': 0.5241038318912238, 'f1': 0.4881980426021877, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5746268656716418, 'recall': 0.6507042253521127, 'f1': 0.6103038309114928, 'number': 1065}    | 0.5176            | 0.5605         | 0.5382     | 0.6952           |
+| 0.8944        | 4.0   | 40   | 0.8291          | {'precision': 0.5676982591876208, 'recall': 0.7255871446229913, 'f1': 0.6370048833423766, 'number': 809}     | {'precision': 0.125, 'recall': 0.05042016806722689, 'f1': 0.07185628742514971, 'number': 119}               | {'precision': 0.648323301805675, 'recall': 0.707981220657277, 'f1': 0.6768402154398564, 'number': 1065}      | 0.6               | 0.6759         | 0.6357     | 0.7440           |
+| 0.7344        | 5.0   | 50   | 0.7416          | {'precision': 0.6231422505307855, 'recall': 0.7255871446229913, 'f1': 0.670474014848658, 'number': 809}      | {'precision': 0.21686746987951808, 'recall': 0.15126050420168066, 'f1': 0.1782178217821782, 'number': 119}  | {'precision': 0.6871794871794872, 'recall': 0.7549295774647887, 'f1': 0.7194630872483222, 'number': 1065}    | 0.6419            | 0.7070         | 0.6729     | 0.7718           |
+| 0.6312        | 6.0   | 60   | 0.7028          | {'precision': 0.616956077630235, 'recall': 0.7466007416563659, 'f1': 0.6756152125279643, 'number': 809}      | {'precision': 0.2413793103448276, 'recall': 0.17647058823529413, 'f1': 0.2038834951456311, 'number': 119}   | {'precision': 0.7020033388981636, 'recall': 0.7896713615023474, 'f1': 0.7432611577551921, 'number': 1065}    | 0.6475            | 0.7356         | 0.6887     | 0.7880           |
+| 0.5603        | 7.0   | 70   | 0.6980          | {'precision': 0.6331550802139038, 'recall': 0.7317676143386898, 'f1': 0.6788990825688073, 'number': 809}     | {'precision': 0.2604166666666667, 'recall': 0.21008403361344538, 'f1': 0.23255813953488375, 'number': 119}  | {'precision': 0.6994219653179191, 'recall': 0.7953051643192488, 'f1': 0.7442882249560634, 'number': 1065}    | 0.6530            | 0.7346         | 0.6914     | 0.7861           |
+| 0.5272        | 8.0   | 80   | 0.6733          | {'precision': 0.6592827004219409, 'recall': 0.7725587144622992, 'f1': 0.7114399544678428, 'number': 809}     | {'precision': 0.25, 'recall': 0.20168067226890757, 'f1': 0.22325581395348837, 'number': 119}                | {'precision': 0.7175188600167645, 'recall': 0.8037558685446009, 'f1': 0.758193091231178, 'number': 1065}     | 0.6728            | 0.7551         | 0.7116     | 0.7927           |
+| 0.4849        | 9.0   | 90   | 0.6716          | {'precision': 0.6549145299145299, 'recall': 0.757725587144623, 'f1': 0.7025787965616046, 'number': 809}      | {'precision': 0.23809523809523808, 'recall': 0.21008403361344538, 'f1': 0.22321428571428573, 'number': 119} | {'precision': 0.7216666666666667, 'recall': 0.8131455399061033, 'f1': 0.7646799116997792, 'number': 1065}    | 0.6711            | 0.7546         | 0.7104     | 0.7961           |
+| 0.4695        | 10.0  | 100  | 0.6680          | {'precision': 0.6523109243697479, 'recall': 0.7676143386897404, 'f1': 0.7052810902896083, 'number': 809}     | {'precision': 0.23300970873786409, 'recall': 0.20168067226890757, 'f1': 0.21621621621621623, 'number': 119} | {'precision': 0.7324786324786324, 'recall': 0.8046948356807512, 'f1': 0.766890380313199, 'number': 1065}     | 0.6751            | 0.7536         | 0.7122     | 0.7957           |
 
 
 ### Framework versions

logs/events.out.tfevents.1690807831.762bcd528089.9286.2

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3d2293d3f8f717f4a7bb2d711ac7b732ebd5d8667becf07729f877cc121626fc
-size 10788
+oid sha256:bce2e92794202976e434db43e5bf71d81fb9023702ead9873f965da6b3060af1
+size 11136