Benedict-L
/

layoutlm-funsd1

@@ -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: 0.6985
-- Answer: {'precision': 0.7292134831460674, 'recall': 0.8022249690976514, 'f1': 0.7639788110653325, 'number': 809}
-- Header: {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}
-- Question: {'precision': 0.7711267605633803, 'recall': 0.8225352112676056, 'f1': 0.7960018173557474, 'number': 1065}
-- Overall Precision: 0.7242
-- Overall Recall: 0.7852
-- Overall F1: 0.7535
-- Overall Accuracy: 0.8108
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7326        | 1.0   | 10   | 1.5225          | {'precision': 0.0576307363927428, 'recall': 0.06674907292954264, 'f1': 0.06185567010309278, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2126899016979446, 'recall': 0.22347417840375586, 'f1': 0.21794871794871795, 'number': 1065} | 0.1420            | 0.1465         | 0.1442     | 0.4302           |
-| 1.3559        | 2.0   | 20   | 1.1907          | {'precision': 0.2647058823529412, 'recall': 0.22249690976514216, 'f1': 0.24177300201477503, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.48519736842105265, 'recall': 0.5539906103286385, 'f1': 0.5173169662428759, 'number': 1065}  | 0.4055            | 0.3864         | 0.3957     | 0.5967           |
-| 1.0329        | 3.0   | 30   | 0.9021          | {'precision': 0.4879518072289157, 'recall': 0.5006180469715699, 'f1': 0.49420378279438687, 'number': 809}  | {'precision': 0.1, 'recall': 0.04201680672268908, 'f1': 0.059171597633136105, 'number': 119}                | {'precision': 0.647636039250669, 'recall': 0.6816901408450704, 'f1': 0.6642268984446478, 'number': 1065}    | 0.5677            | 0.5700         | 0.5689     | 0.7304           |
-| 0.779         | 4.0   | 40   | 0.7524          | {'precision': 0.6258205689277899, 'recall': 0.7070457354758962, 'f1': 0.6639582124201974, 'number': 809}   | {'precision': 0.25675675675675674, 'recall': 0.15966386554621848, 'f1': 0.19689119170984457, 'number': 119} | {'precision': 0.6596814752724225, 'recall': 0.7389671361502348, 'f1': 0.6970770593445527, 'number': 1065}   | 0.6318            | 0.6914         | 0.6603     | 0.7734           |
-| 0.6249        | 5.0   | 50   | 0.6899          | {'precision': 0.6615553121577218, 'recall': 0.7466007416563659, 'f1': 0.7015098722415796, 'number': 809}   | {'precision': 0.3157894736842105, 'recall': 0.20168067226890757, 'f1': 0.24615384615384614, 'number': 119}  | {'precision': 0.6818181818181818, 'recall': 0.7746478873239436, 'f1': 0.7252747252747253, 'number': 1065}   | 0.6608            | 0.7291         | 0.6932     | 0.7938           |
-| 0.5376        | 6.0   | 60   | 0.6911          | {'precision': 0.6773504273504274, 'recall': 0.7836835599505563, 'f1': 0.7266475644699141, 'number': 809}   | {'precision': 0.29411764705882354, 'recall': 0.21008403361344538, 'f1': 0.2450980392156863, 'number': 119}  | {'precision': 0.7166377816291161, 'recall': 0.7765258215962442, 'f1': 0.7453808021631364, 'number': 1065}   | 0.6832            | 0.7456         | 0.7131     | 0.7926           |
-| 0.4627        | 7.0   | 70   | 0.6573          | {'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809}   | {'precision': 0.2882882882882883, 'recall': 0.2689075630252101, 'f1': 0.2782608695652174, 'number': 119}    | {'precision': 0.735494880546075, 'recall': 0.8093896713615023, 'f1': 0.7706750111756816, 'number': 1065}    | 0.6975            | 0.7727         | 0.7332     | 0.8012           |
-| 0.4082        | 8.0   | 80   | 0.6650          | {'precision': 0.6871741397288843, 'recall': 0.8145859085290482, 'f1': 0.7454751131221721, 'number': 809}   | {'precision': 0.28440366972477066, 'recall': 0.2605042016806723, 'f1': 0.2719298245614035, 'number': 119}   | {'precision': 0.7446626814688301, 'recall': 0.8187793427230047, 'f1': 0.7799642218246869, 'number': 1065}   | 0.6976            | 0.7837         | 0.7382     | 0.8040           |
-| 0.3665        | 9.0   | 90   | 0.6682          | {'precision': 0.7011995637949836, 'recall': 0.7948084054388134, 'f1': 0.7450753186558517, 'number': 809}   | {'precision': 0.3076923076923077, 'recall': 0.3025210084033613, 'f1': 0.30508474576271183, 'number': 119}   | {'precision': 0.7519582245430809, 'recall': 0.8112676056338028, 'f1': 0.7804878048780487, 'number': 1065}   | 0.7068            | 0.7742         | 0.7390     | 0.8071           |
-| 0.3554        | 10.0  | 100  | 0.6680          | {'precision': 0.7168338907469343, 'recall': 0.7948084054388134, 'f1': 0.753810082063306, 'number': 809}    | {'precision': 0.3333333333333333, 'recall': 0.35294117647058826, 'f1': 0.34285714285714286, 'number': 119}  | {'precision': 0.7586206896551724, 'recall': 0.8262910798122066, 'f1': 0.7910112359550561, 'number': 1065}   | 0.7169            | 0.7852         | 0.7495     | 0.8101           |
-| 0.3056        | 11.0  | 110  | 0.6786          | {'precision': 0.707027027027027, 'recall': 0.8084054388133498, 'f1': 0.7543252595155711, 'number': 809}    | {'precision': 0.296, 'recall': 0.31092436974789917, 'f1': 0.30327868852459017, 'number': 119}               | {'precision': 0.7668393782383419, 'recall': 0.8338028169014085, 'f1': 0.7989203778677464, 'number': 1065}   | 0.7151            | 0.7923         | 0.7517     | 0.8087           |
-| 0.2977        | 12.0  | 120  | 0.6900          | {'precision': 0.7291196388261851, 'recall': 0.7985166872682324, 'f1': 0.7622418879056048, 'number': 809}   | {'precision': 0.32575757575757575, 'recall': 0.36134453781512604, 'f1': 0.3426294820717131, 'number': 119}  | {'precision': 0.7726872246696035, 'recall': 0.8234741784037559, 'f1': 0.7972727272727272, 'number': 1065}   | 0.7274            | 0.7858         | 0.7554     | 0.8097           |
-| 0.2788        | 13.0  | 130  | 0.6937          | {'precision': 0.7224669603524229, 'recall': 0.8108776266996292, 'f1': 0.7641234711706465, 'number': 809}   | {'precision': 0.3023255813953488, 'recall': 0.3277310924369748, 'f1': 0.314516129032258, 'number': 119}     | {'precision': 0.7724867724867724, 'recall': 0.8225352112676056, 'f1': 0.7967257844474761, 'number': 1065}   | 0.7236            | 0.7883         | 0.7546     | 0.8099           |
-| 0.2593        | 14.0  | 140  | 0.6981          | {'precision': 0.7278835386338186, 'recall': 0.8034610630407911, 'f1': 0.7638072855464161, 'number': 809}   | {'precision': 0.29850746268656714, 'recall': 0.33613445378151263, 'f1': 0.31620553359683795, 'number': 119} | {'precision': 0.7715289982425307, 'recall': 0.8244131455399061, 'f1': 0.7970948706309579, 'number': 1065}   | 0.7242            | 0.7868         | 0.7542     | 0.8110           |
-| 0.2581        | 15.0  | 150  | 0.6985          | {'precision': 0.7292134831460674, 'recall': 0.8022249690976514, 'f1': 0.7639788110653325, 'number': 809}   | {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}  | {'precision': 0.7711267605633803, 'recall': 0.8225352112676056, 'f1': 0.7960018173557474, 'number': 1065}   | 0.7242            | 0.7852         | 0.7535     | 0.8108           |
 ### Framework versions

 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: 0.6794
+- Answer: {'precision': 0.7130242825607064, 'recall': 0.7985166872682324, 'f1': 0.7533527696793003, 'number': 809}
+- Header: {'precision': 0.2907801418439716, 'recall': 0.3445378151260504, 'f1': 0.3153846153846154, 'number': 119}
+- Question: {'precision': 0.773286467486819, 'recall': 0.8262910798122066, 'f1': 0.7989105764866091, 'number': 1065}
+- Overall Precision: 0.7172
+- Overall Recall: 0.7863
+- Overall F1: 0.7501
+- Overall Accuracy: 0.8053
 ## Model description
 ### Training results
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                      | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8172        | 1.0   | 10   | 1.5984          | {'precision': 0.02287581699346405, 'recall': 0.0173053152039555, 'f1': 0.019704433497536946, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2019704433497537, 'recall': 0.11549295774647887, 'f1': 0.14695340501792115, 'number': 1065} | 0.1122            | 0.0687         | 0.0853     | 0.3383           |
+| 1.4573        | 2.0   | 20   | 1.2552          | {'precision': 0.21509106678230702, 'recall': 0.3065512978986403, 'f1': 0.2528032619775739, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4427123928293063, 'recall': 0.5333333333333333, 'f1': 0.4838160136286201, 'number': 1065}   | 0.3350            | 0.4094         | 0.3685     | 0.5671           |
+| 1.1187        | 3.0   | 30   | 0.9227          | {'precision': 0.47129909365558914, 'recall': 0.5784919653893696, 'f1': 0.5194228634850167, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5402558314522197, 'recall': 0.6741784037558686, 'f1': 0.5998329156223893, 'number': 1065}   | 0.5081            | 0.5951         | 0.5482     | 0.6953           |
+| 0.8526        | 4.0   | 40   | 0.7688          | {'precision': 0.6256410256410256, 'recall': 0.754017305315204, 'f1': 0.6838565022421524, 'number': 809}     | {'precision': 0.2564102564102564, 'recall': 0.08403361344537816, 'f1': 0.12658227848101264, 'number': 119}  | {'precision': 0.6581125827814569, 'recall': 0.7464788732394366, 'f1': 0.6995160580730313, 'number': 1065}   | 0.6368            | 0.7100         | 0.6714     | 0.7562           |
+| 0.6873        | 5.0   | 50   | 0.6983          | {'precision': 0.6456776947705443, 'recall': 0.7478368355995055, 'f1': 0.693012600229095, 'number': 809}     | {'precision': 0.22916666666666666, 'recall': 0.18487394957983194, 'f1': 0.2046511627906977, 'number': 119}  | {'precision': 0.6671814671814672, 'recall': 0.8112676056338028, 'f1': 0.7322033898305085, 'number': 1065}   | 0.6405            | 0.7481         | 0.6901     | 0.7729           |
+| 0.5884        | 6.0   | 60   | 0.6816          | {'precision': 0.6539256198347108, 'recall': 0.7824474660074165, 'f1': 0.7124366910523354, 'number': 809}    | {'precision': 0.273972602739726, 'recall': 0.16806722689075632, 'f1': 0.20833333333333331, 'number': 119}   | {'precision': 0.7033613445378152, 'recall': 0.7859154929577464, 'f1': 0.7423503325942351, 'number': 1065}   | 0.6679            | 0.7476         | 0.7055     | 0.7799           |
+| 0.5091        | 7.0   | 70   | 0.6491          | {'precision': 0.6754478398314014, 'recall': 0.792336217552534, 'f1': 0.7292377701934016, 'number': 809}     | {'precision': 0.256, 'recall': 0.2689075630252101, 'f1': 0.26229508196721313, 'number': 119}                | {'precision': 0.7409326424870466, 'recall': 0.8056338028169014, 'f1': 0.7719298245614035, 'number': 1065}   | 0.6859            | 0.7682         | 0.7247     | 0.7920           |
+| 0.452         | 8.0   | 80   | 0.6574          | {'precision': 0.6897654584221748, 'recall': 0.799752781211372, 'f1': 0.7406983400114482, 'number': 809}     | {'precision': 0.21705426356589147, 'recall': 0.23529411764705882, 'f1': 0.22580645161290322, 'number': 119} | {'precision': 0.7427597955706985, 'recall': 0.8187793427230047, 'f1': 0.7789191603394373, 'number': 1065}   | 0.6903            | 0.7762         | 0.7308     | 0.7949           |
+| 0.3956        | 9.0   | 90   | 0.6481          | {'precision': 0.6923890063424947, 'recall': 0.8096415327564895, 'f1': 0.7464387464387465, 'number': 809}    | {'precision': 0.2748091603053435, 'recall': 0.3025210084033613, 'f1': 0.288, 'number': 119}                 | {'precision': 0.7578671328671329, 'recall': 0.8140845070422535, 'f1': 0.7849705749207787, 'number': 1065}   | 0.7015            | 0.7817         | 0.7394     | 0.8006           |
+| 0.377         | 10.0  | 100  | 0.6458          | {'precision': 0.7069716775599129, 'recall': 0.8022249690976514, 'f1': 0.751592356687898, 'number': 809}     | {'precision': 0.30578512396694213, 'recall': 0.31092436974789917, 'f1': 0.30833333333333335, 'number': 119} | {'precision': 0.7688888888888888, 'recall': 0.812206572769953, 'f1': 0.7899543378995433, 'number': 1065}    | 0.7167            | 0.7782         | 0.7462     | 0.8054           |
+| 0.3216        | 11.0  | 110  | 0.6550          | {'precision': 0.7024972855591748, 'recall': 0.799752781211372, 'f1': 0.7479768786127167, 'number': 809}     | {'precision': 0.2814814814814815, 'recall': 0.31932773109243695, 'f1': 0.2992125984251969, 'number': 119}   | {'precision': 0.7577054794520548, 'recall': 0.8309859154929577, 'f1': 0.7926556202418271, 'number': 1065}   | 0.7059            | 0.7878         | 0.7446     | 0.8031           |
+| 0.3083        | 12.0  | 120  | 0.6539          | {'precision': 0.7086527929901424, 'recall': 0.799752781211372, 'f1': 0.751451800232288, 'number': 809}      | {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119}  | {'precision': 0.7714033539276258, 'recall': 0.8206572769953052, 'f1': 0.7952684258416743, 'number': 1065}   | 0.7170            | 0.7817         | 0.7480     | 0.8066           |
+| 0.2867        | 13.0  | 130  | 0.6673          | {'precision': 0.7047930283224401, 'recall': 0.799752781211372, 'f1': 0.7492762015055008, 'number': 809}     | {'precision': 0.26666666666666666, 'recall': 0.3025210084033613, 'f1': 0.28346456692913385, 'number': 119}  | {'precision': 0.7573402417962003, 'recall': 0.8234741784037559, 'f1': 0.7890238416554206, 'number': 1065}   | 0.7056            | 0.7827         | 0.7422     | 0.8055           |
+| 0.2718        | 14.0  | 140  | 0.6770          | {'precision': 0.7106430155210643, 'recall': 0.792336217552534, 'f1': 0.7492694330800702, 'number': 809}     | {'precision': 0.3, 'recall': 0.35294117647058826, 'f1': 0.3243243243243243, 'number': 119}                  | {'precision': 0.7730870712401056, 'recall': 0.8253521126760563, 'f1': 0.798365122615804, 'number': 1065}    | 0.7168            | 0.7837         | 0.7488     | 0.8053           |
+| 0.2715        | 15.0  | 150  | 0.6794          | {'precision': 0.7130242825607064, 'recall': 0.7985166872682324, 'f1': 0.7533527696793003, 'number': 809}    | {'precision': 0.2907801418439716, 'recall': 0.3445378151260504, 'f1': 0.3153846153846154, 'number': 119}    | {'precision': 0.773286467486819, 'recall': 0.8262910798122066, 'f1': 0.7989105764866091, 'number': 1065}    | 0.7172            | 0.7863         | 0.7501     | 0.8053           |
 ### Framework versions

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