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: 0.6801
-- Answer: {'precision': 0.6998916576381365, 'recall': 0.7985166872682324, 'f1': 0.745958429561201, 'number': 809}
-- Header: {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119}
-- Question: {'precision': 0.7674216027874564, 'recall': 0.8272300469483568, 'f1': 0.7962042476276546, 'number': 1065}
-- Overall Precision: 0.7123
-- Overall Recall: 0.7852
-- Overall F1: 0.7470
-- Overall Accuracy: 0.8102
+- Loss: 0.6689
+- Answer: {'precision': 0.7029063509149623, 'recall': 0.8071693448702101, 'f1': 0.7514384349827388, 'number': 809}
+- Header: {'precision': 0.3412698412698413, 'recall': 0.36134453781512604, 'f1': 0.35102040816326535, 'number': 119}
+- Question: {'precision': 0.7777777777777778, 'recall': 0.828169014084507, 'f1': 0.8021828103683492, 'number': 1065}
+- Overall Precision: 0.7209
+- Overall Recall: 0.7918
+- Overall F1: 0.7547
+- Overall Accuracy: 0.8158
 
 ## Model description
 
@@ -52,28 +52,28 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8269        | 1.0   | 10   | 1.5983          | {'precision': 0.0274949083503055, 'recall': 0.03337453646477132, 'f1': 0.03015075376884422, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.10761154855643044, 'recall': 0.07699530516431925, 'f1': 0.08976464148877941, 'number': 1065} | 0.0625            | 0.0547         | 0.0583     | 0.3800           |
-| 1.4969        | 2.0   | 20   | 1.3148          | {'precision': 0.14439140811455847, 'recall': 0.14956736711990112, 'f1': 0.14693381906496664, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.3501683501683502, 'recall': 0.39061032863849765, 'f1': 0.36928539724811366, 'number': 1065}  | 0.2651            | 0.2694         | 0.2672     | 0.5643           |
-| 1.1839        | 3.0   | 30   | 1.0447          | {'precision': 0.40492957746478875, 'recall': 0.4264524103831891, 'f1': 0.41541240216736913, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5461672473867596, 'recall': 0.5887323943661972, 'f1': 0.5666516041572526, 'number': 1065}    | 0.486             | 0.4877         | 0.4869     | 0.6655           |
-| 0.9261        | 4.0   | 40   | 0.8452          | {'precision': 0.5813715455475946, 'recall': 0.7021013597033374, 'f1': 0.6360582306830906, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.6362068965517241, 'recall': 0.6929577464788732, 'f1': 0.6633707865168539, 'number': 1065}    | 0.6035            | 0.6553         | 0.6283     | 0.7335           |
-| 0.7296        | 5.0   | 50   | 0.7494          | {'precision': 0.6223849372384938, 'recall': 0.7354758961681088, 'f1': 0.6742209631728047, 'number': 809}    | {'precision': 0.16666666666666666, 'recall': 0.06722689075630252, 'f1': 0.09580838323353293, 'number': 119} | {'precision': 0.6773356401384083, 'recall': 0.7352112676056338, 'f1': 0.7050877982890591, 'number': 1065}    | 0.6417            | 0.6954         | 0.6675     | 0.7676           |
-| 0.6079        | 6.0   | 60   | 0.6998          | {'precision': 0.629399585921325, 'recall': 0.7515451174289246, 'f1': 0.6850704225352113, 'number': 809}     | {'precision': 0.12222222222222222, 'recall': 0.09243697478991597, 'f1': 0.10526315789473684, 'number': 119} | {'precision': 0.6630686198920586, 'recall': 0.8075117370892019, 'f1': 0.7281964436917865, 'number': 1065}    | 0.6286            | 0.7421         | 0.6806     | 0.7881           |
-| 0.5267        | 7.0   | 70   | 0.6732          | {'precision': 0.6366427840327533, 'recall': 0.7688504326328801, 'f1': 0.696528555431131, 'number': 809}     | {'precision': 0.20652173913043478, 'recall': 0.15966386554621848, 'f1': 0.1800947867298578, 'number': 119}  | {'precision': 0.7107296137339055, 'recall': 0.7774647887323943, 'f1': 0.7426008968609865, 'number': 1065}    | 0.6576            | 0.7371         | 0.6951     | 0.7864           |
-| 0.4762        | 8.0   | 80   | 0.6606          | {'precision': 0.6577319587628866, 'recall': 0.788627935723115, 'f1': 0.71725688589095, 'number': 809}       | {'precision': 0.25663716814159293, 'recall': 0.24369747899159663, 'f1': 0.25, 'number': 119}                | {'precision': 0.731665228645384, 'recall': 0.7962441314553991, 'f1': 0.762589928057554, 'number': 1065}      | 0.6757            | 0.7602         | 0.7155     | 0.7936           |
-| 0.4175        | 9.0   | 90   | 0.6566          | {'precision': 0.6815761448349308, 'recall': 0.7911001236093943, 'f1': 0.7322654462242563, 'number': 809}    | {'precision': 0.25892857142857145, 'recall': 0.24369747899159663, 'f1': 0.2510822510822511, 'number': 119}  | {'precision': 0.7513089005235603, 'recall': 0.8084507042253521, 'f1': 0.7788331071913162, 'number': 1065}    | 0.6964            | 0.7677         | 0.7303     | 0.8021           |
-| 0.374         | 10.0  | 100  | 0.6592          | {'precision': 0.6956521739130435, 'recall': 0.7911001236093943, 'f1': 0.7403123192596877, 'number': 809}    | {'precision': 0.319672131147541, 'recall': 0.3277310924369748, 'f1': 0.32365145228215775, 'number': 119}    | {'precision': 0.7637931034482759, 'recall': 0.831924882629108, 'f1': 0.7964044943820225, 'number': 1065}     | 0.7107            | 0.7852         | 0.7461     | 0.8070           |
-| 0.3406        | 11.0  | 110  | 0.6666          | {'precision': 0.7, 'recall': 0.796044499381953, 'f1': 0.7449392712550607, 'number': 809}                    | {'precision': 0.3305084745762712, 'recall': 0.3277310924369748, 'f1': 0.32911392405063294, 'number': 119}   | {'precision': 0.7656387665198238, 'recall': 0.815962441314554, 'f1': 0.79, 'number': 1065}                   | 0.7142            | 0.7787         | 0.7451     | 0.8071           |
-| 0.332         | 12.0  | 120  | 0.6704          | {'precision': 0.6941798941798942, 'recall': 0.8108776266996292, 'f1': 0.7480045610034207, 'number': 809}    | {'precision': 0.3220338983050847, 'recall': 0.31932773109243695, 'f1': 0.32067510548523204, 'number': 119}  | {'precision': 0.7660311958405546, 'recall': 0.8300469483568075, 'f1': 0.7967552951780081, 'number': 1065}    | 0.7118            | 0.7918         | 0.7496     | 0.8078           |
-| 0.3061        | 13.0  | 130  | 0.6787          | {'precision': 0.6908108108108109, 'recall': 0.7898640296662547, 'f1': 0.7370242214532873, 'number': 809}    | {'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119}   | {'precision': 0.7669305189094108, 'recall': 0.8187793427230047, 'f1': 0.7920072661217076, 'number': 1065}    | 0.7093            | 0.7787         | 0.7424     | 0.8091           |
-| 0.2879        | 14.0  | 140  | 0.6781          | {'precision': 0.6951871657754011, 'recall': 0.8034610630407911, 'f1': 0.7454128440366973, 'number': 809}    | {'precision': 0.3333333333333333, 'recall': 0.33613445378151263, 'f1': 0.33472803347280333, 'number': 119}  | {'precision': 0.7746478873239436, 'recall': 0.8262910798122066, 'f1': 0.7996365288505224, 'number': 1065}    | 0.7166            | 0.7878         | 0.7505     | 0.8099           |
-| 0.2831        | 15.0  | 150  | 0.6801          | {'precision': 0.6998916576381365, 'recall': 0.7985166872682324, 'f1': 0.745958429561201, 'number': 809}     | {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119}   | {'precision': 0.7674216027874564, 'recall': 0.8272300469483568, 'f1': 0.7962042476276546, 'number': 1065}    | 0.7123            | 0.7852         | 0.7470     | 0.8102           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                       | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8306        | 1.0   | 10   | 1.6060          | {'precision': 0.026582278481012658, 'recall': 0.02595797280593325, 'f1': 0.026266416510318948, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.21528861154446177, 'recall': 0.1295774647887324, 'f1': 0.16178194607268465, 'number': 1065} | 0.1111            | 0.0798         | 0.0929     | 0.3733           |
+| 1.4787        | 2.0   | 20   | 1.2612          | {'precision': 0.20019627085377822, 'recall': 0.2521631644004944, 'f1': 0.22319474835886213, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.419710544452102, 'recall': 0.571830985915493, 'f1': 0.4841017488076311, 'number': 1065}     | 0.3291            | 0.4079         | 0.3643     | 0.5976           |
+| 1.1115        | 3.0   | 30   | 0.9517          | {'precision': 0.466, 'recall': 0.5760197775030902, 'f1': 0.5152017689331123, 'number': 809}                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.5697115384615384, 'recall': 0.6676056338028169, 'f1': 0.6147859922178989, 'number': 1065}   | 0.5201            | 0.5906         | 0.5531     | 0.6834           |
+| 0.8531        | 4.0   | 40   | 0.8275          | {'precision': 0.5730337078651685, 'recall': 0.7564894932014833, 'f1': 0.65210442194992, 'number': 809}        | {'precision': 0.06521739130434782, 'recall': 0.025210084033613446, 'f1': 0.03636363636363636, 'number': 119} | {'precision': 0.6735751295336787, 'recall': 0.7323943661971831, 'f1': 0.7017543859649122, 'number': 1065}   | 0.6140            | 0.6999         | 0.6542     | 0.7393           |
+| 0.7059        | 5.0   | 50   | 0.7345          | {'precision': 0.6333687566418703, 'recall': 0.7367119901112484, 'f1': 0.6811428571428572, 'number': 809}      | {'precision': 0.2, 'recall': 0.14285714285714285, 'f1': 0.16666666666666666, 'number': 119}                  | {'precision': 0.6966386554621848, 'recall': 0.7784037558685446, 'f1': 0.7352549889135255, 'number': 1065}   | 0.6507            | 0.7235         | 0.6852     | 0.7712           |
+| 0.5949        | 6.0   | 60   | 0.6931          | {'precision': 0.6376050420168067, 'recall': 0.7503090234857849, 'f1': 0.689381033503691, 'number': 809}       | {'precision': 0.20430107526881722, 'recall': 0.15966386554621848, 'f1': 0.1792452830188679, 'number': 119}   | {'precision': 0.6931637519872814, 'recall': 0.8187793427230047, 'f1': 0.7507533362031856, 'number': 1065}   | 0.6505            | 0.7516         | 0.6974     | 0.7836           |
+| 0.5143        | 7.0   | 70   | 0.6674          | {'precision': 0.6688172043010753, 'recall': 0.7688504326328801, 'f1': 0.7153536515238643, 'number': 809}      | {'precision': 0.23478260869565218, 'recall': 0.226890756302521, 'f1': 0.23076923076923078, 'number': 119}    | {'precision': 0.7146341463414634, 'recall': 0.8253521126760563, 'f1': 0.7660130718954249, 'number': 1065}   | 0.6716            | 0.7667         | 0.7160     | 0.7933           |
+| 0.4641        | 8.0   | 80   | 0.6507          | {'precision': 0.667016806722689, 'recall': 0.7849196538936959, 'f1': 0.7211811470755252, 'number': 809}       | {'precision': 0.3142857142857143, 'recall': 0.2773109243697479, 'f1': 0.29464285714285715, 'number': 119}    | {'precision': 0.7347280334728034, 'recall': 0.8244131455399061, 'f1': 0.7769911504424779, 'number': 1065}   | 0.6865            | 0.7757         | 0.7284     | 0.8029           |
+| 0.4063        | 9.0   | 90   | 0.6671          | {'precision': 0.6574074074074074, 'recall': 0.7898640296662547, 'f1': 0.7175743964065132, 'number': 809}      | {'precision': 0.3114754098360656, 'recall': 0.31932773109243695, 'f1': 0.3153526970954357, 'number': 119}    | {'precision': 0.747008547008547, 'recall': 0.8206572769953052, 'f1': 0.782102908277405, 'number': 1065}     | 0.6851            | 0.7782         | 0.7287     | 0.8017           |
+| 0.3643        | 10.0  | 100  | 0.6603          | {'precision': 0.6851063829787234, 'recall': 0.796044499381953, 'f1': 0.7364208118925099, 'number': 809}       | {'precision': 0.3669724770642202, 'recall': 0.33613445378151263, 'f1': 0.3508771929824562, 'number': 119}    | {'precision': 0.7674825174825175, 'recall': 0.8244131455399061, 'f1': 0.7949298325033951, 'number': 1065}   | 0.7123            | 0.7837         | 0.7463     | 0.8069           |
+| 0.3331        | 11.0  | 110  | 0.6691          | {'precision': 0.6928879310344828, 'recall': 0.7948084054388134, 'f1': 0.740356937248129, 'number': 809}       | {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119}    | {'precision': 0.7666666666666667, 'recall': 0.8206572769953052, 'f1': 0.7927437641723357, 'number': 1065}   | 0.7088            | 0.7802         | 0.7428     | 0.8071           |
+| 0.3193        | 12.0  | 120  | 0.6597          | {'precision': 0.6932059447983014, 'recall': 0.8071693448702101, 'f1': 0.7458595088520845, 'number': 809}      | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}    | {'precision': 0.7721739130434783, 'recall': 0.8338028169014085, 'f1': 0.801805869074492, 'number': 1065}    | 0.7152            | 0.7938         | 0.7524     | 0.8112           |
+| 0.2972        | 13.0  | 130  | 0.6679          | {'precision': 0.7011866235167206, 'recall': 0.8034610630407911, 'f1': 0.7488479262672811, 'number': 809}      | {'precision': 0.344, 'recall': 0.36134453781512604, 'f1': 0.3524590163934426, 'number': 119}                 | {'precision': 0.7716814159292036, 'recall': 0.8187793427230047, 'f1': 0.7945330296127562, 'number': 1065}   | 0.7172            | 0.7852         | 0.7497     | 0.8145           |
+| 0.2833        | 14.0  | 140  | 0.6684          | {'precision': 0.703023758099352, 'recall': 0.8046971569839307, 'f1': 0.7504322766570604, 'number': 809}       | {'precision': 0.3412698412698413, 'recall': 0.36134453781512604, 'f1': 0.35102040816326535, 'number': 119}   | {'precision': 0.7769973661106233, 'recall': 0.8309859154929577, 'f1': 0.8030852994555354, 'number': 1065}   | 0.7207            | 0.7923         | 0.7548     | 0.8163           |
+| 0.2765        | 15.0  | 150  | 0.6689          | {'precision': 0.7029063509149623, 'recall': 0.8071693448702101, 'f1': 0.7514384349827388, 'number': 809}      | {'precision': 0.3412698412698413, 'recall': 0.36134453781512604, 'f1': 0.35102040816326535, 'number': 119}   | {'precision': 0.7777777777777778, 'recall': 0.828169014084507, 'f1': 0.8021828103683492, 'number': 1065}    | 0.7209            | 0.7918         | 0.7547     | 0.8158           |
 
 
 ### Framework versions
 
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
-- Datasets 2.14.2
+- Datasets 2.14.3
 - Tokenizers 0.13.3

logs/events.out.tfevents.1691115886.555594e3d69c.1834.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:48efe3484b6a283250e67411aa80c861469c3ac519aa8d6e35e88b85d94a60b2
-size 13408
+oid sha256:c6ca6e904ea55d1587a0b401fcf6c3a9d83b6982f8c7d90dc1edc3bd819e9956
+size 14423