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: 0.7028
-- Answer: {'precision': 0.7362637362637363, 'recall': 0.8281829419035847, 'f1': 0.779522978475858, 'number': 809}
-- Header: {'precision': 0.33093525179856115, 'recall': 0.3865546218487395, 'f1': 0.35658914728682173, 'number': 119}
-- Question: {'precision': 0.7906360424028268, 'recall': 0.8403755868544601, 'f1': 0.81474738279472, 'number': 1065}
-- Overall Precision: 0.7387
-- Overall Recall: 0.8083
-- Overall F1: 0.7719
-- Overall Accuracy: 0.8094
+- Loss: 0.6771
+- Answer: {'precision': 0.7107258938244854, 'recall': 0.8108776266996292, 'f1': 0.7575057736720554, 'number': 809}
+- Header: {'precision': 0.3543307086614173, 'recall': 0.37815126050420167, 'f1': 0.3658536585365853, 'number': 119}
+- Question: {'precision': 0.7716814159292036, 'recall': 0.8187793427230047, 'f1': 0.7945330296127562, 'number': 1065}
+- Overall Precision: 0.7216
+- Overall Recall: 0.7893
+- Overall F1: 0.7539
+- Overall Accuracy: 0.8139
 
 ## Model description
 
@@ -54,23 +54,23 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8038        | 1.0   | 10   | 1.5774          | {'precision': 0.020026702269692925, 'recall': 0.018541409147095178, 'f1': 0.01925545571245186, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.15968586387434555, 'recall': 0.05727699530516432, 'f1': 0.08431237042156187, 'number': 1065} | 0.0672            | 0.0381         | 0.0487     | 0.3785           |
-| 1.4615        | 2.0   | 20   | 1.2492          | {'precision': 0.13497652582159625, 'recall': 0.14215080346106304, 'f1': 0.13847080072245638, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4067796610169492, 'recall': 0.4732394366197183, 'f1': 0.4375, 'number': 1065}                | 0.2960            | 0.3106         | 0.3031     | 0.5839           |
-| 1.121         | 3.0   | 30   | 0.9545          | {'precision': 0.4633642930856553, 'recall': 0.5550061804697157, 'f1': 0.5050618672665917, 'number': 809}      | {'precision': 0.1111111111111111, 'recall': 0.008403361344537815, 'f1': 0.015625, 'number': 119}            | {'precision': 0.5644546147978642, 'recall': 0.6948356807511737, 'f1': 0.6228956228956228, 'number': 1065}    | 0.5199            | 0.5971         | 0.5558     | 0.7097           |
-| 0.8446        | 4.0   | 40   | 0.7984          | {'precision': 0.5806451612903226, 'recall': 0.6897404202719407, 'f1': 0.6305084745762712, 'number': 809}      | {'precision': 0.04878048780487805, 'recall': 0.01680672268907563, 'f1': 0.025, 'number': 119}               | {'precision': 0.6506024096385542, 'recall': 0.7605633802816901, 'f1': 0.7012987012987013, 'number': 1065}    | 0.6097            | 0.6874         | 0.6462     | 0.7567           |
-| 0.6751        | 5.0   | 50   | 0.7137          | {'precision': 0.6598049837486457, 'recall': 0.7527812113720643, 'f1': 0.7032332563510393, 'number': 809}      | {'precision': 0.18666666666666668, 'recall': 0.11764705882352941, 'f1': 0.14432989690721648, 'number': 119} | {'precision': 0.694006309148265, 'recall': 0.8262910798122066, 'f1': 0.7543934847835405, 'number': 1065}     | 0.6633            | 0.7541         | 0.7058     | 0.7900           |
-| 0.5714        | 6.0   | 60   | 0.6914          | {'precision': 0.6613065326633166, 'recall': 0.8133498145859085, 'f1': 0.729490022172949, 'number': 809}       | {'precision': 0.26153846153846155, 'recall': 0.14285714285714285, 'f1': 0.18478260869565216, 'number': 119} | {'precision': 0.7305699481865285, 'recall': 0.7943661971830986, 'f1': 0.7611336032388665, 'number': 1065}    | 0.6858            | 0.7632         | 0.7224     | 0.7861           |
-| 0.5064        | 7.0   | 70   | 0.6675          | {'precision': 0.6880927291886196, 'recall': 0.8071693448702101, 'f1': 0.7428896473265074, 'number': 809}      | {'precision': 0.2125984251968504, 'recall': 0.226890756302521, 'f1': 0.21951219512195122, 'number': 119}    | {'precision': 0.7450812660393499, 'recall': 0.8178403755868544, 'f1': 0.7797672336615935, 'number': 1065}    | 0.6909            | 0.7782         | 0.7319     | 0.7985           |
-| 0.4472        | 8.0   | 80   | 0.6608          | {'precision': 0.6995798319327731, 'recall': 0.823238566131026, 'f1': 0.7563884156729132, 'number': 809}       | {'precision': 0.23529411764705882, 'recall': 0.23529411764705882, 'f1': 0.23529411764705882, 'number': 119} | {'precision': 0.7419087136929461, 'recall': 0.8394366197183099, 'f1': 0.7876651982378854, 'number': 1065}    | 0.6977            | 0.7968         | 0.7440     | 0.8048           |
-| 0.3918        | 9.0   | 90   | 0.6697          | {'precision': 0.7226435536294691, 'recall': 0.8244746600741656, 'f1': 0.7702078521939953, 'number': 809}      | {'precision': 0.2647058823529412, 'recall': 0.3025210084033613, 'f1': 0.2823529411764706, 'number': 119}    | {'precision': 0.7542808219178082, 'recall': 0.8272300469483568, 'f1': 0.7890729959695477, 'number': 1065}    | 0.7113            | 0.7948         | 0.7507     | 0.8038           |
-| 0.3803        | 10.0  | 100  | 0.6890          | {'precision': 0.7176724137931034, 'recall': 0.823238566131026, 'f1': 0.7668393782383419, 'number': 809}       | {'precision': 0.2890625, 'recall': 0.31092436974789917, 'f1': 0.29959514170040485, 'number': 119}           | {'precision': 0.7899910634495085, 'recall': 0.8300469483568075, 'f1': 0.8095238095238095, 'number': 1065}    | 0.7297            | 0.7963         | 0.7615     | 0.8062           |
-| 0.323         | 11.0  | 110  | 0.6893          | {'precision': 0.7279651795429815, 'recall': 0.826946847960445, 'f1': 0.7743055555555555, 'number': 809}       | {'precision': 0.3161764705882353, 'recall': 0.36134453781512604, 'f1': 0.3372549019607843, 'number': 119}   | {'precision': 0.7728055077452668, 'recall': 0.8431924882629108, 'f1': 0.8064660978895375, 'number': 1065}    | 0.7262            | 0.8078         | 0.7648     | 0.8068           |
-| 0.3093        | 12.0  | 120  | 0.6906          | {'precision': 0.7358490566037735, 'recall': 0.8195302843016069, 'f1': 0.775438596491228, 'number': 809}       | {'precision': 0.33093525179856115, 'recall': 0.3865546218487395, 'f1': 0.35658914728682173, 'number': 119}  | {'precision': 0.7902654867256638, 'recall': 0.8384976525821596, 'f1': 0.8136674259681094, 'number': 1065}    | 0.7382            | 0.8038         | 0.7696     | 0.8096           |
-| 0.2963        | 13.0  | 130  | 0.6953          | {'precision': 0.7271739130434782, 'recall': 0.826946847960445, 'f1': 0.7738577212261423, 'number': 809}       | {'precision': 0.3181818181818182, 'recall': 0.35294117647058826, 'f1': 0.3346613545816733, 'number': 119}   | {'precision': 0.7943262411347518, 'recall': 0.8413145539906103, 'f1': 0.8171454628362973, 'number': 1065}    | 0.7372            | 0.8063         | 0.7702     | 0.8099           |
-| 0.2773        | 14.0  | 140  | 0.6991          | {'precision': 0.7343578485181119, 'recall': 0.826946847960445, 'f1': 0.7779069767441861, 'number': 809}       | {'precision': 0.3283582089552239, 'recall': 0.3697478991596639, 'f1': 0.34782608695652173, 'number': 119}   | {'precision': 0.7982222222222223, 'recall': 0.8431924882629108, 'f1': 0.8200913242009132, 'number': 1065}    | 0.7424            | 0.8083         | 0.7740     | 0.8099           |
-| 0.2687        | 15.0  | 150  | 0.7028          | {'precision': 0.7362637362637363, 'recall': 0.8281829419035847, 'f1': 0.779522978475858, 'number': 809}       | {'precision': 0.33093525179856115, 'recall': 0.3865546218487395, 'f1': 0.35658914728682173, 'number': 119}  | {'precision': 0.7906360424028268, 'recall': 0.8403755868544601, 'f1': 0.81474738279472, 'number': 1065}      | 0.7387            | 0.8083         | 0.7719     | 0.8094           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8027        | 1.0   | 10   | 1.5884          | {'precision': 0.01997780244173141, 'recall': 0.022249690976514216, 'f1': 0.02105263157894737, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.18858307849133538, 'recall': 0.17370892018779344, 'f1': 0.18084066471163246, 'number': 1065} | 0.1079            | 0.1019         | 0.1048     | 0.3753           |
+| 1.4071        | 2.0   | 20   | 1.2076          | {'precision': 0.23890339425587467, 'recall': 0.22620519159456118, 'f1': 0.23238095238095238, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.41302791696492486, 'recall': 0.5417840375586854, 'f1': 0.4687246141348498, 'number': 1065}   | 0.3512            | 0.3813         | 0.3656     | 0.5772           |
+| 1.0593        | 3.0   | 30   | 0.9154          | {'precision': 0.4750542299349241, 'recall': 0.5414091470951793, 'f1': 0.5060658578856152, 'number': 809}     | {'precision': 0.11363636363636363, 'recall': 0.04201680672268908, 'f1': 0.06134969325153375, 'number': 119} | {'precision': 0.5922493681550126, 'recall': 0.6600938967136151, 'f1': 0.6243339253996447, 'number': 1065}    | 0.5323            | 0.5750         | 0.5528     | 0.7136           |
+| 0.802         | 4.0   | 40   | 0.7552          | {'precision': 0.5981404958677686, 'recall': 0.715698393077874, 'f1': 0.6516601012943164, 'number': 809}      | {'precision': 0.20253164556962025, 'recall': 0.13445378151260504, 'f1': 0.1616161616161616, 'number': 119}  | {'precision': 0.6680707666385847, 'recall': 0.7446009389671362, 'f1': 0.7042628774422734, 'number': 1065}    | 0.6213            | 0.6964         | 0.6567     | 0.7659           |
+| 0.6561        | 5.0   | 50   | 0.7030          | {'precision': 0.6381856540084389, 'recall': 0.7478368355995055, 'f1': 0.6886738759248718, 'number': 809}     | {'precision': 0.3, 'recall': 0.226890756302521, 'f1': 0.25837320574162675, 'number': 119}                   | {'precision': 0.6780766096169519, 'recall': 0.7812206572769953, 'f1': 0.7260034904013962, 'number': 1065}    | 0.6464            | 0.7346         | 0.6876     | 0.7889           |
+| 0.5591        | 6.0   | 60   | 0.6842          | {'precision': 0.6502100840336135, 'recall': 0.765142150803461, 'f1': 0.7030096536059057, 'number': 809}      | {'precision': 0.3132530120481928, 'recall': 0.2184873949579832, 'f1': 0.25742574257425743, 'number': 119}   | {'precision': 0.7165820642978004, 'recall': 0.7953051643192488, 'f1': 0.7538940809968847, 'number': 1065}    | 0.6730            | 0.7486         | 0.7088     | 0.7942           |
+| 0.4858        | 7.0   | 70   | 0.6508          | {'precision': 0.6569948186528497, 'recall': 0.7836835599505563, 'f1': 0.7147688838782412, 'number': 809}     | {'precision': 0.34210526315789475, 'recall': 0.3277310924369748, 'f1': 0.33476394849785407, 'number': 119}  | {'precision': 0.7205503009458297, 'recall': 0.7868544600938967, 'f1': 0.7522441651705565, 'number': 1065}    | 0.6740            | 0.7582         | 0.7136     | 0.8063           |
+| 0.431         | 8.0   | 80   | 0.6674          | {'precision': 0.6578140960163432, 'recall': 0.796044499381953, 'f1': 0.7203579418344519, 'number': 809}      | {'precision': 0.35964912280701755, 'recall': 0.3445378151260504, 'f1': 0.351931330472103, 'number': 119}    | {'precision': 0.7482517482517482, 'recall': 0.8037558685446009, 'f1': 0.775011317338162, 'number': 1065}     | 0.6889            | 0.7732         | 0.7286     | 0.7969           |
+| 0.3878        | 9.0   | 90   | 0.6526          | {'precision': 0.6787564766839378, 'recall': 0.8096415327564895, 'f1': 0.7384441939120632, 'number': 809}     | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119}   | {'precision': 0.7586206896551724, 'recall': 0.7849765258215963, 'f1': 0.7715736040609138, 'number': 1065}    | 0.7014            | 0.7672         | 0.7328     | 0.8073           |
+| 0.3744        | 10.0  | 100  | 0.6519          | {'precision': 0.6854410201912858, 'recall': 0.7972805933250927, 'f1': 0.7371428571428571, 'number': 809}     | {'precision': 0.3130434782608696, 'recall': 0.3025210084033613, 'f1': 0.3076923076923077, 'number': 119}    | {'precision': 0.7611940298507462, 'recall': 0.8140845070422535, 'f1': 0.7867513611615246, 'number': 1065}    | 0.7052            | 0.7767         | 0.7393     | 0.8120           |
+| 0.3161        | 11.0  | 110  | 0.6696          | {'precision': 0.6948257655755016, 'recall': 0.8133498145859085, 'f1': 0.7494305239179954, 'number': 809}     | {'precision': 0.3283582089552239, 'recall': 0.3697478991596639, 'f1': 0.34782608695652173, 'number': 119}   | {'precision': 0.7604166666666666, 'recall': 0.8225352112676056, 'f1': 0.7902571041948578, 'number': 1065}    | 0.7067            | 0.7918         | 0.7468     | 0.8060           |
+| 0.3039        | 12.0  | 120  | 0.6656          | {'precision': 0.7007534983853606, 'recall': 0.8046971569839307, 'f1': 0.7491369390103566, 'number': 809}     | {'precision': 0.3524590163934426, 'recall': 0.36134453781512604, 'f1': 0.35684647302904565, 'number': 119}  | {'precision': 0.7695769576957696, 'recall': 0.8028169014084507, 'f1': 0.7858455882352942, 'number': 1065}    | 0.7165            | 0.7772         | 0.7456     | 0.8131           |
+| 0.2877        | 13.0  | 130  | 0.6742          | {'precision': 0.6927138331573389, 'recall': 0.8108776266996292, 'f1': 0.7471526195899771, 'number': 809}     | {'precision': 0.32592592592592595, 'recall': 0.3697478991596639, 'f1': 0.3464566929133859, 'number': 119}   | {'precision': 0.7651715039577837, 'recall': 0.8169014084507042, 'f1': 0.7901907356948229, 'number': 1065}    | 0.7075            | 0.7878         | 0.7455     | 0.8109           |
+| 0.2681        | 14.0  | 140  | 0.6743          | {'precision': 0.7128927410617552, 'recall': 0.8133498145859085, 'f1': 0.7598152424942264, 'number': 809}     | {'precision': 0.36220472440944884, 'recall': 0.3865546218487395, 'f1': 0.37398373983739847, 'number': 119}  | {'precision': 0.7734513274336283, 'recall': 0.8206572769953052, 'f1': 0.7963553530751709, 'number': 1065}    | 0.7239            | 0.7918         | 0.7563     | 0.8148           |
+| 0.2609        | 15.0  | 150  | 0.6771          | {'precision': 0.7107258938244854, 'recall': 0.8108776266996292, 'f1': 0.7575057736720554, 'number': 809}     | {'precision': 0.3543307086614173, 'recall': 0.37815126050420167, 'f1': 0.3658536585365853, 'number': 119}   | {'precision': 0.7716814159292036, 'recall': 0.8187793427230047, 'f1': 0.7945330296127562, 'number': 1065}    | 0.7216            | 0.7893         | 0.7539     | 0.8139           |
 
 
 ### Framework versions

logs/events.out.tfevents.1714566395.DESKTOP-3CFO1LV.7959.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 12645
+oid sha256:46d4dc7f4adb4e904d5c8d2c743c2ed6e6fec1bb56e0ea4620a18ddd11d4f4ae
+size 15846

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:db08656fdb18250052dfae254c96f7242651534d614aefedf4647180247d85b1
+oid sha256:0c429d11154c9df7233166d57cf1450c9eb9e63ecbb128a9826e0039d2c91c93
 size 450558212