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: 0.6746
-- Answer: {'precision': 0.7057569296375267, 'recall': 0.8182941903584673, 'f1': 0.7578706353749285, 'number': 809}
-- Header: {'precision': 0.3508771929824561, 'recall': 0.33613445378151263, 'f1': 0.34334763948497854, 'number': 119}
-- Question: {'precision': 0.7793345008756567, 'recall': 0.8356807511737089, 'f1': 0.8065246941549614, 'number': 1065}
-- Overall Precision: 0.7256
-- Overall Recall: 0.7988
-- Overall F1: 0.7604
-- Overall Accuracy: 0.8085
+- Loss: 0.7055
+- Answer: {'precision': 0.7035830618892508, 'recall': 0.8009888751545118, 'f1': 0.7491329479768787, 'number': 809}
+- Header: {'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119}
+- Question: {'precision': 0.7775816416593115, 'recall': 0.8272300469483568, 'f1': 0.8016378525932666, 'number': 1065}
+- Overall Precision: 0.7216
+- Overall Recall: 0.7883
+- Overall F1: 0.7535
+- Overall Accuracy: 0.8028
 
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8024        | 1.0   | 10   | 1.6086          | {'precision': 0.009900990099009901, 'recall': 0.007416563658838072, 'f1': 0.008480565371024736, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.20625, 'recall': 0.12394366197183099, 'f1': 0.15483870967741936, 'number': 1065}           | 0.1108            | 0.0692         | 0.0852     | 0.3458           |
-| 1.4593        | 2.0   | 20   | 1.2405          | {'precision': 0.13250283125707815, 'recall': 0.1446229913473424, 'f1': 0.13829787234042556, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.42007168458781363, 'recall': 0.5502347417840375, 'f1': 0.4764227642276423, 'number': 1065} | 0.3086            | 0.3527         | 0.3292     | 0.5822           |
-| 1.1064        | 3.0   | 30   | 0.9251          | {'precision': 0.46214355948869223, 'recall': 0.580964153275649, 'f1': 0.5147864184008761, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.550321199143469, 'recall': 0.723943661971831, 'f1': 0.6253041362530414, 'number': 1065}    | 0.5111            | 0.6227         | 0.5614     | 0.7118           |
-| 0.8548        | 4.0   | 40   | 0.7690          | {'precision': 0.5675413022351797, 'recall': 0.7218788627935723, 'f1': 0.6354733405875952, 'number': 809}       | {'precision': 0.02564102564102564, 'recall': 0.008403361344537815, 'f1': 0.012658227848101267, 'number': 119} | {'precision': 0.6504534212695795, 'recall': 0.7408450704225352, 'f1': 0.6927129060579456, 'number': 1065}  | 0.6024            | 0.6894         | 0.6430     | 0.7602           |
-| 0.6855        | 5.0   | 50   | 0.7230          | {'precision': 0.6310572687224669, 'recall': 0.7082818294190358, 'f1': 0.6674432149097262, 'number': 809}       | {'precision': 0.2054794520547945, 'recall': 0.12605042016806722, 'f1': 0.15625, 'number': 119}                | {'precision': 0.6592818945760123, 'recall': 0.8103286384976526, 'f1': 0.7270429654591406, 'number': 1065}  | 0.6336            | 0.7280         | 0.6776     | 0.7785           |
-| 0.5838        | 6.0   | 60   | 0.6791          | {'precision': 0.6316297010607522, 'recall': 0.8096415327564895, 'f1': 0.7096424702058506, 'number': 809}       | {'precision': 0.25, 'recall': 0.15126050420168066, 'f1': 0.18848167539267013, 'number': 119}                  | {'precision': 0.7356521739130435, 'recall': 0.7943661971830986, 'f1': 0.7638826185101579, 'number': 1065}  | 0.6724            | 0.7622         | 0.7145     | 0.7886           |
-| 0.499         | 7.0   | 70   | 0.6482          | {'precision': 0.6722689075630253, 'recall': 0.7911001236093943, 'f1': 0.7268597387847815, 'number': 809}       | {'precision': 0.30612244897959184, 'recall': 0.25210084033613445, 'f1': 0.2764976958525346, 'number': 119}    | {'precision': 0.7367972742759795, 'recall': 0.812206572769953, 'f1': 0.7726663689146941, 'number': 1065}   | 0.6902            | 0.7702         | 0.7280     | 0.8001           |
-| 0.4429        | 8.0   | 80   | 0.6642          | {'precision': 0.6596596596596597, 'recall': 0.8145859085290482, 'f1': 0.7289823008849557, 'number': 809}       | {'precision': 0.26605504587155965, 'recall': 0.24369747899159663, 'f1': 0.2543859649122807, 'number': 119}    | {'precision': 0.7445193929173693, 'recall': 0.8291079812206573, 'f1': 0.7845402043536206, 'number': 1065}  | 0.6848            | 0.7883         | 0.7329     | 0.7998           |
-| 0.387         | 9.0   | 90   | 0.6536          | {'precision': 0.6892177589852009, 'recall': 0.8059332509270705, 'f1': 0.743019943019943, 'number': 809}        | {'precision': 0.3269230769230769, 'recall': 0.2857142857142857, 'f1': 0.30493273542600896, 'number': 119}     | {'precision': 0.757912745936698, 'recall': 0.831924882629108, 'f1': 0.7931960608773501, 'number': 1065}    | 0.7084            | 0.7888         | 0.7464     | 0.8018           |
-| 0.3798        | 10.0  | 100  | 0.6564          | {'precision': 0.6893305439330544, 'recall': 0.8145859085290482, 'f1': 0.746742209631728, 'number': 809}        | {'precision': 0.3055555555555556, 'recall': 0.2773109243697479, 'f1': 0.2907488986784141, 'number': 119}      | {'precision': 0.7616580310880829, 'recall': 0.828169014084507, 'f1': 0.7935222672064778, 'number': 1065}   | 0.7084            | 0.7898         | 0.7469     | 0.8132           |
-| 0.3185        | 11.0  | 110  | 0.6684          | {'precision': 0.690700104493208, 'recall': 0.8170580964153276, 'f1': 0.7485843714609287, 'number': 809}        | {'precision': 0.3230769230769231, 'recall': 0.35294117647058826, 'f1': 0.3373493975903615, 'number': 119}     | {'precision': 0.761168384879725, 'recall': 0.831924882629108, 'f1': 0.7949753252579633, 'number': 1065}    | 0.7059            | 0.7973         | 0.7488     | 0.8018           |
-| 0.3035        | 12.0  | 120  | 0.6603          | {'precision': 0.69989281886388, 'recall': 0.8071693448702101, 'f1': 0.7497129735935705, 'number': 809}         | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119}     | {'precision': 0.7688966116420504, 'recall': 0.8309859154929577, 'f1': 0.7987364620938627, 'number': 1065}  | 0.7173            | 0.7908         | 0.7523     | 0.8129           |
-| 0.2848        | 13.0  | 130  | 0.6748          | {'precision': 0.695697796432319, 'recall': 0.8195302843016069, 'f1': 0.7525539160045404, 'number': 809}        | {'precision': 0.3474576271186441, 'recall': 0.3445378151260504, 'f1': 0.3459915611814346, 'number': 119}      | {'precision': 0.7705061082024433, 'recall': 0.8291079812206573, 'f1': 0.798733604703754, 'number': 1065}   | 0.7158            | 0.7963         | 0.7539     | 0.8063           |
-| 0.2628        | 14.0  | 140  | 0.6744          | {'precision': 0.7089151450053706, 'recall': 0.8158220024721878, 'f1': 0.7586206896551725, 'number': 809}       | {'precision': 0.358974358974359, 'recall': 0.35294117647058826, 'f1': 0.35593220338983056, 'number': 119}     | {'precision': 0.7739965095986039, 'recall': 0.8328638497652582, 'f1': 0.8023518769787427, 'number': 1065}  | 0.7242            | 0.7973         | 0.7590     | 0.8092           |
-| 0.262         | 15.0  | 150  | 0.6746          | {'precision': 0.7057569296375267, 'recall': 0.8182941903584673, 'f1': 0.7578706353749285, 'number': 809}       | {'precision': 0.3508771929824561, 'recall': 0.33613445378151263, 'f1': 0.34334763948497854, 'number': 119}    | {'precision': 0.7793345008756567, 'recall': 0.8356807511737089, 'f1': 0.8065246941549614, 'number': 1065}  | 0.7256            | 0.7988         | 0.7604     | 0.8085           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8301        | 1.0   | 10   | 1.5849          | {'precision': 0.008086253369272238, 'recall': 0.007416563658838072, 'f1': 0.007736943907156674, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.22358346094946402, 'recall': 0.13708920187793427, 'f1': 0.16996507566938301, 'number': 1065} | 0.1090            | 0.0763         | 0.0897     | 0.3514           |
+| 1.4704        | 2.0   | 20   | 1.2710          | {'precision': 0.2843881856540084, 'recall': 0.41656365883807167, 'f1': 0.3380140421263791, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.3906474820143885, 'recall': 0.5098591549295775, 'f1': 0.44236252545824845, 'number': 1065}   | 0.3408            | 0.4415         | 0.3847     | 0.6020           |
+| 1.1259        | 3.0   | 30   | 0.9451          | {'precision': 0.47373447946513847, 'recall': 0.6131025957972805, 'f1': 0.5344827586206896, 'number': 809}      | {'precision': 0.0625, 'recall': 0.025210084033613446, 'f1': 0.035928143712574856, 'number': 119}            | {'precision': 0.5223654283548143, 'recall': 0.6469483568075117, 'f1': 0.5780201342281879, 'number': 1065}    | 0.4921            | 0.5961         | 0.5391     | 0.7000           |
+| 0.8549        | 4.0   | 40   | 0.7891          | {'precision': 0.5652985074626866, 'recall': 0.7490729295426453, 'f1': 0.6443381180223287, 'number': 809}       | {'precision': 0.20833333333333334, 'recall': 0.12605042016806722, 'f1': 0.15706806282722513, 'number': 119} | {'precision': 0.6485013623978202, 'recall': 0.6704225352112676, 'f1': 0.6592797783933518, 'number': 1065}    | 0.5947            | 0.6698         | 0.6300     | 0.7562           |
+| 0.6872        | 5.0   | 50   | 0.7203          | {'precision': 0.6393617021276595, 'recall': 0.7428924598269468, 'f1': 0.6872498570611778, 'number': 809}       | {'precision': 0.358974358974359, 'recall': 0.23529411764705882, 'f1': 0.28426395939086296, 'number': 119}   | {'precision': 0.6650563607085346, 'recall': 0.7755868544600939, 'f1': 0.716081491114001, 'number': 1065}     | 0.6438            | 0.7301         | 0.6842     | 0.7798           |
+| 0.5872        | 6.0   | 60   | 0.6889          | {'precision': 0.6236559139784946, 'recall': 0.788627935723115, 'f1': 0.6965065502183407, 'number': 809}        | {'precision': 0.35802469135802467, 'recall': 0.24369747899159663, 'f1': 0.29000000000000004, 'number': 119} | {'precision': 0.7190517998244074, 'recall': 0.7690140845070422, 'f1': 0.7431941923774955, 'number': 1065}    | 0.6625            | 0.7456         | 0.7016     | 0.7797           |
+| 0.5065        | 7.0   | 70   | 0.6618          | {'precision': 0.681283422459893, 'recall': 0.7873918417799752, 'f1': 0.7305045871559632, 'number': 809}        | {'precision': 0.336734693877551, 'recall': 0.2773109243697479, 'f1': 0.30414746543778803, 'number': 119}    | {'precision': 0.748471615720524, 'recall': 0.8046948356807512, 'f1': 0.7755656108597285, 'number': 1065}     | 0.7011            | 0.7662         | 0.7322     | 0.7934           |
+| 0.4527        | 8.0   | 80   | 0.6639          | {'precision': 0.671161825726141, 'recall': 0.799752781211372, 'f1': 0.7298364354201917, 'number': 809}         | {'precision': 0.3170731707317073, 'recall': 0.3277310924369748, 'f1': 0.32231404958677684, 'number': 119}   | {'precision': 0.7473867595818815, 'recall': 0.8056338028169014, 'f1': 0.7754179846362403, 'number': 1065}    | 0.6908            | 0.7747         | 0.7304     | 0.7955           |
+| 0.3952        | 9.0   | 90   | 0.6666          | {'precision': 0.686358754027927, 'recall': 0.7898640296662547, 'f1': 0.7344827586206897, 'number': 809}        | {'precision': 0.3523809523809524, 'recall': 0.31092436974789917, 'f1': 0.33035714285714285, 'number': 119}  | {'precision': 0.7519247219846023, 'recall': 0.8253521126760563, 'f1': 0.7869292748433303, 'number': 1065}    | 0.7052            | 0.7802         | 0.7408     | 0.7969           |
+| 0.3863        | 10.0  | 100  | 0.6806          | {'precision': 0.6849894291754757, 'recall': 0.8009888751545118, 'f1': 0.7384615384615385, 'number': 809}       | {'precision': 0.3333333333333333, 'recall': 0.31932773109243695, 'f1': 0.3261802575107296, 'number': 119}   | {'precision': 0.7670157068062827, 'recall': 0.8253521126760563, 'f1': 0.7951153324287653, 'number': 1065}    | 0.7094            | 0.7852         | 0.7454     | 0.7985           |
+| 0.3307        | 11.0  | 110  | 0.6859          | {'precision': 0.6938775510204082, 'recall': 0.7985166872682324, 'f1': 0.7425287356321839, 'number': 809}       | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}   | {'precision': 0.764402407566638, 'recall': 0.8347417840375587, 'f1': 0.7980251346499103, 'number': 1065}     | 0.7118            | 0.7908         | 0.7492     | 0.8004           |
+| 0.3126        | 12.0  | 120  | 0.6896          | {'precision': 0.697198275862069, 'recall': 0.799752781211372, 'f1': 0.7449625791594704, 'number': 809}         | {'precision': 0.36283185840707965, 'recall': 0.3445378151260504, 'f1': 0.35344827586206895, 'number': 119}  | {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065}    | 0.7222            | 0.7852         | 0.7524     | 0.8012           |
+| 0.2979        | 13.0  | 130  | 0.6997          | {'precision': 0.6992399565689468, 'recall': 0.796044499381953, 'f1': 0.7445086705202313, 'number': 809}        | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}   | {'precision': 0.7763157894736842, 'recall': 0.8309859154929577, 'f1': 0.802721088435374, 'number': 1065}     | 0.7199            | 0.7878         | 0.7523     | 0.8007           |
+| 0.2712        | 14.0  | 140  | 0.7039          | {'precision': 0.7083333333333334, 'recall': 0.7985166872682324, 'f1': 0.7507263219058687, 'number': 809}       | {'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119}                | {'precision': 0.7771929824561403, 'recall': 0.831924882629108, 'f1': 0.8036281179138323, 'number': 1065}     | 0.7230            | 0.7898         | 0.7549     | 0.8028           |
+| 0.2738        | 15.0  | 150  | 0.7055          | {'precision': 0.7035830618892508, 'recall': 0.8009888751545118, 'f1': 0.7491329479768787, 'number': 809}       | {'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119} | {'precision': 0.7775816416593115, 'recall': 0.8272300469483568, 'f1': 0.8016378525932666, 'number': 1065}    | 0.7216            | 0.7883         | 0.7535     | 0.8028           |
 
 
 ### Framework versions

logs/events.out.tfevents.1717255017.280ef54c6982.10819.1

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:17655b237e93755331e1fdfa328c86ad95c002bf0e2a75d8cfe88e85d8424eaf
-size 14915
+oid sha256:173f8cc2410f97be70c73046909af45382553160cd10974d5cd82dcb07b75bb6
+size 15984