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: 0.6859
-- Answer: {'precision': 0.7175572519083969, 'recall': 0.8133498145859085, 'f1': 0.7624565469293164, 'number': 809}
-- Header: {'precision': 0.29411764705882354, 'recall': 0.33613445378151263, 'f1': 0.3137254901960785, 'number': 119}
-- Question: {'precision': 0.7724867724867724, 'recall': 0.8225352112676056, 'f1': 0.7967257844474761, 'number': 1065}
-- Overall Precision: 0.7197
-- Overall Recall: 0.7898
-- Overall F1: 0.7531
-- Overall Accuracy: 0.8101
+- Loss: 0.6891
+- Answer: {'precision': 0.7296996662958843, 'recall': 0.8108776266996292, 'f1': 0.7681498829039812, 'number': 809}
+- Header: {'precision': 0.3620689655172414, 'recall': 0.35294117647058826, 'f1': 0.3574468085106383, 'number': 119}
+- Question: {'precision': 0.7939609236234458, 'recall': 0.8394366197183099, 'f1': 0.8160657234139663, 'number': 1065}
+- Overall Precision: 0.7436
+- Overall Recall: 0.7988
+- Overall F1: 0.7702
+- 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8268        | 1.0   | 10   | 1.5857          | {'precision': 0.015523932729624839, 'recall': 0.014833127317676144, 'f1': 0.015170670037926676, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.17011834319526628, 'recall': 0.107981220657277, 'f1': 0.1321079839172889, 'number': 1065}  | 0.0876            | 0.0637         | 0.0738     | 0.3586           |
-| 1.4514        | 2.0   | 20   | 1.2482          | {'precision': 0.28865979381443296, 'recall': 0.311495673671199, 'f1': 0.29964328180737215, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.38357142857142856, 'recall': 0.504225352112676, 'f1': 0.43569979716024343, 'number': 1065} | 0.3471            | 0.3959         | 0.3699     | 0.5859           |
-| 1.1188        | 3.0   | 30   | 0.9477          | {'precision': 0.5157232704402516, 'recall': 0.6081582200247219, 'f1': 0.5581395348837209, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.5390879478827362, 'recall': 0.6215962441314554, 'f1': 0.5774095071958134, 'number': 1065}  | 0.5215            | 0.5790         | 0.5487     | 0.7076           |
-| 0.8437        | 4.0   | 40   | 0.7798          | {'precision': 0.5986124876114965, 'recall': 0.7466007416563659, 'f1': 0.6644664466446645, 'number': 809}       | {'precision': 0.1875, 'recall': 0.07563025210084033, 'f1': 0.10778443113772454, 'number': 119}             | {'precision': 0.6486718080548415, 'recall': 0.7107981220657277, 'f1': 0.6783154121863798, 'number': 1065}  | 0.6160            | 0.6874         | 0.6498     | 0.7580           |
-| 0.6804        | 5.0   | 50   | 0.7073          | {'precision': 0.6413502109704642, 'recall': 0.7515451174289246, 'f1': 0.6920887877063175, 'number': 809}       | {'precision': 0.3, 'recall': 0.17647058823529413, 'f1': 0.22222222222222224, 'number': 119}                | {'precision': 0.6712662337662337, 'recall': 0.7765258215962442, 'f1': 0.7200696560731389, 'number': 1065}  | 0.6471            | 0.7306         | 0.6863     | 0.7850           |
-| 0.5726        | 6.0   | 60   | 0.6805          | {'precision': 0.643141153081511, 'recall': 0.799752781211372, 'f1': 0.7129476584022039, 'number': 809}         | {'precision': 0.3142857142857143, 'recall': 0.18487394957983194, 'f1': 0.23280423280423282, 'number': 119} | {'precision': 0.709372312983663, 'recall': 0.7746478873239436, 'f1': 0.7405745062836624, 'number': 1065}   | 0.6673            | 0.7496         | 0.7060     | 0.7854           |
-| 0.5005        | 7.0   | 70   | 0.6536          | {'precision': 0.6701680672268907, 'recall': 0.788627935723115, 'f1': 0.7245883021010789, 'number': 809}        | {'precision': 0.27450980392156865, 'recall': 0.23529411764705882, 'f1': 0.2533936651583711, 'number': 119} | {'precision': 0.743103448275862, 'recall': 0.8093896713615023, 'f1': 0.7748314606741572, 'number': 1065}   | 0.6902            | 0.7667         | 0.7264     | 0.7982           |
-| 0.444         | 8.0   | 80   | 0.6526          | {'precision': 0.6802935010482181, 'recall': 0.8022249690976514, 'f1': 0.7362450368689732, 'number': 809}       | {'precision': 0.26956521739130435, 'recall': 0.2605042016806723, 'f1': 0.264957264957265, 'number': 119}   | {'precision': 0.7400690846286702, 'recall': 0.8046948356807512, 'f1': 0.7710301394511921, 'number': 1065}  | 0.6902            | 0.7712         | 0.7284     | 0.8022           |
-| 0.3904        | 9.0   | 90   | 0.6549          | {'precision': 0.6905781584582441, 'recall': 0.7972805933250927, 'f1': 0.7401032702237521, 'number': 809}       | {'precision': 0.26666666666666666, 'recall': 0.2689075630252101, 'f1': 0.26778242677824265, 'number': 119} | {'precision': 0.7554019014693172, 'recall': 0.8206572769953052, 'f1': 0.7866786678667866, 'number': 1065}  | 0.7015            | 0.7782         | 0.7379     | 0.8073           |
-| 0.3778        | 10.0  | 100  | 0.6593          | {'precision': 0.6996805111821086, 'recall': 0.8121137206427689, 'f1': 0.7517162471395881, 'number': 809}       | {'precision': 0.3018867924528302, 'recall': 0.2689075630252101, 'f1': 0.28444444444444444, 'number': 119}  | {'precision': 0.7707231040564374, 'recall': 0.8206572769953052, 'f1': 0.7949067758071852, 'number': 1065}  | 0.7173            | 0.7842         | 0.7493     | 0.8096           |
-| 0.3205        | 11.0  | 110  | 0.6673          | {'precision': 0.7185104052573932, 'recall': 0.8108776266996292, 'f1': 0.761904761904762, 'number': 809}        | {'precision': 0.26277372262773724, 'recall': 0.3025210084033613, 'f1': 0.28125000000000006, 'number': 119} | {'precision': 0.7557643040136636, 'recall': 0.8309859154929577, 'f1': 0.7915921288014313, 'number': 1065}  | 0.7100            | 0.7913         | 0.7485     | 0.8077           |
-| 0.3107        | 12.0  | 120  | 0.6723          | {'precision': 0.7185104052573932, 'recall': 0.8108776266996292, 'f1': 0.761904761904762, 'number': 809}        | {'precision': 0.2803030303030303, 'recall': 0.31092436974789917, 'f1': 0.29482071713147406, 'number': 119} | {'precision': 0.7740213523131673, 'recall': 0.8169014084507042, 'f1': 0.7948835084513477, 'number': 1065}  | 0.7206            | 0.7842         | 0.7511     | 0.8102           |
-| 0.2906        | 13.0  | 130  | 0.6774          | {'precision': 0.7175324675324676, 'recall': 0.8195302843016069, 'f1': 0.7651471436814773, 'number': 809}       | {'precision': 0.2824427480916031, 'recall': 0.31092436974789917, 'f1': 0.29600000000000004, 'number': 119} | {'precision': 0.7678883071553229, 'recall': 0.8262910798122066, 'f1': 0.7960199004975125, 'number': 1065}  | 0.7179            | 0.7928         | 0.7535     | 0.8111           |
-| 0.2684        | 14.0  | 140  | 0.6829          | {'precision': 0.716304347826087, 'recall': 0.8145859085290482, 'f1': 0.7622903412377097, 'number': 809}        | {'precision': 0.2900763358778626, 'recall': 0.31932773109243695, 'f1': 0.304, 'number': 119}               | {'precision': 0.7742504409171076, 'recall': 0.8244131455399061, 'f1': 0.7985447930877672, 'number': 1065}  | 0.7208            | 0.7903         | 0.7539     | 0.8115           |
-| 0.2659        | 15.0  | 150  | 0.6859          | {'precision': 0.7175572519083969, 'recall': 0.8133498145859085, 'f1': 0.7624565469293164, 'number': 809}       | {'precision': 0.29411764705882354, 'recall': 0.33613445378151263, 'f1': 0.3137254901960785, 'number': 119} | {'precision': 0.7724867724867724, 'recall': 0.8225352112676056, 'f1': 0.7967257844474761, 'number': 1065}  | 0.7197            | 0.7898         | 0.7531     | 0.8101           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                     | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8006        | 1.0   | 10   | 1.5983          | {'precision': 0.015552099533437015, 'recall': 0.012360939431396786, 'f1': 0.013774104683195591, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.20512820512820512, 'recall': 0.12018779342723004, 'f1': 0.15156897572528122, 'number': 1065} | 0.1089            | 0.0692         | 0.0847     | 0.3439           |
+| 1.4817        | 2.0   | 20   | 1.2758          | {'precision': 0.23966065747614, 'recall': 0.27935723114956734, 'f1': 0.2579908675799087, 'number': 809}        | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.4002998500749625, 'recall': 0.5014084507042254, 'f1': 0.4451854939558149, 'number': 1065}    | 0.3338            | 0.3813         | 0.3560     | 0.5953           |
+| 1.1479        | 3.0   | 30   | 0.9589          | {'precision': 0.48957298907646474, 'recall': 0.6093943139678616, 'f1': 0.5429515418502202, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.5823389021479713, 'recall': 0.6873239436619718, 'f1': 0.6304909560723513, 'number': 1065}    | 0.5361            | 0.6147         | 0.5727     | 0.6955           |
+| 0.872         | 4.0   | 40   | 0.8144          | {'precision': 0.574447646493756, 'recall': 0.7391841779975278, 'f1': 0.6464864864864865, 'number': 809}        | {'precision': 0.075, 'recall': 0.025210084033613446, 'f1': 0.03773584905660377, 'number': 119}             | {'precision': 0.6640281442392261, 'recall': 0.7089201877934272, 'f1': 0.6857402361489555, 'number': 1065}    | 0.6114            | 0.6804         | 0.6440     | 0.7492           |
+| 0.6944        | 5.0   | 50   | 0.7241          | {'precision': 0.638682252922423, 'recall': 0.7428924598269468, 'f1': 0.6868571428571428, 'number': 809}        | {'precision': 0.1875, 'recall': 0.12605042016806722, 'f1': 0.1507537688442211, 'number': 119}              | {'precision': 0.674493927125506, 'recall': 0.7821596244131456, 'f1': 0.7243478260869566, 'number': 1065}     | 0.6423            | 0.7270         | 0.6820     | 0.7876           |
+| 0.588         | 6.0   | 60   | 0.6902          | {'precision': 0.6445115810674723, 'recall': 0.7911001236093943, 'f1': 0.7103218645948945, 'number': 809}       | {'precision': 0.265625, 'recall': 0.14285714285714285, 'f1': 0.18579234972677594, 'number': 119}           | {'precision': 0.7232219365895458, 'recall': 0.7924882629107981, 'f1': 0.7562724014336917, 'number': 1065}    | 0.6749            | 0.7531         | 0.7119     | 0.7922           |
+| 0.5155        | 7.0   | 70   | 0.6651          | {'precision': 0.6762820512820513, 'recall': 0.7824474660074165, 'f1': 0.7255014326647564, 'number': 809}       | {'precision': 0.22115384615384615, 'recall': 0.19327731092436976, 'f1': 0.2062780269058296, 'number': 119} | {'precision': 0.7402597402597403, 'recall': 0.8028169014084507, 'f1': 0.7702702702702703, 'number': 1065}    | 0.6884            | 0.7582         | 0.7216     | 0.7979           |
+| 0.4567        | 8.0   | 80   | 0.6544          | {'precision': 0.682062298603652, 'recall': 0.7849196538936959, 'f1': 0.7298850574712644, 'number': 809}        | {'precision': 0.21359223300970873, 'recall': 0.18487394957983194, 'f1': 0.1981981981981982, 'number': 119} | {'precision': 0.759515570934256, 'recall': 0.8244131455399061, 'f1': 0.790634849167042, 'number': 1065}      | 0.7009            | 0.7702         | 0.7339     | 0.8047           |
+| 0.4044        | 9.0   | 90   | 0.6556          | {'precision': 0.7029379760609358, 'recall': 0.7985166872682324, 'f1': 0.7476851851851851, 'number': 809}       | {'precision': 0.2621359223300971, 'recall': 0.226890756302521, 'f1': 0.24324324324324326, 'number': 119}   | {'precision': 0.7710320901994796, 'recall': 0.8347417840375587, 'f1': 0.8016230838593327, 'number': 1065}    | 0.7182            | 0.7837         | 0.7495     | 0.8089           |
+| 0.3974        | 10.0  | 100  | 0.6652          | {'precision': 0.7141292442497261, 'recall': 0.8059332509270705, 'f1': 0.7572590011614402, 'number': 809}       | {'precision': 0.30357142857142855, 'recall': 0.2857142857142857, 'f1': 0.2943722943722944, 'number': 119}  | {'precision': 0.7917414721723519, 'recall': 0.828169014084507, 'f1': 0.8095456631482332, 'number': 1065}     | 0.7331            | 0.7868         | 0.7590     | 0.8094           |
+| 0.3327        | 11.0  | 110  | 0.6705          | {'precision': 0.720620842572062, 'recall': 0.8034610630407911, 'f1': 0.7597895967270601, 'number': 809}        | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119}  | {'precision': 0.7891939769707705, 'recall': 0.8366197183098592, 'f1': 0.812215132178669, 'number': 1065}     | 0.7365            | 0.7923         | 0.7634     | 0.8096           |
+| 0.3194        | 12.0  | 120  | 0.6719          | {'precision': 0.7239057239057239, 'recall': 0.7972805933250927, 'f1': 0.7588235294117647, 'number': 809}       | {'precision': 0.3853211009174312, 'recall': 0.35294117647058826, 'f1': 0.36842105263157904, 'number': 119} | {'precision': 0.7911111111111111, 'recall': 0.8356807511737089, 'f1': 0.812785388127854, 'number': 1065}     | 0.7421            | 0.7913         | 0.7659     | 0.8115           |
+| 0.301         | 13.0  | 130  | 0.6828          | {'precision': 0.7256637168141593, 'recall': 0.8108776266996292, 'f1': 0.7659077641564506, 'number': 809}       | {'precision': 0.41414141414141414, 'recall': 0.3445378151260504, 'f1': 0.3761467889908257, 'number': 119}  | {'precision': 0.8005415162454874, 'recall': 0.8328638497652582, 'f1': 0.8163828808099403, 'number': 1065}    | 0.7504            | 0.7948         | 0.7719     | 0.8099           |
+| 0.286         | 14.0  | 140  | 0.6856          | {'precision': 0.7279821627647715, 'recall': 0.8071693448702101, 'f1': 0.7655334114888628, 'number': 809}       | {'precision': 0.3853211009174312, 'recall': 0.35294117647058826, 'f1': 0.36842105263157904, 'number': 119} | {'precision': 0.7931034482758621, 'recall': 0.8422535211267606, 'f1': 0.8169398907103825, 'number': 1065}    | 0.7450            | 0.7988         | 0.7709     | 0.8108           |
+| 0.2789        | 15.0  | 150  | 0.6891          | {'precision': 0.7296996662958843, 'recall': 0.8108776266996292, 'f1': 0.7681498829039812, 'number': 809}       | {'precision': 0.3620689655172414, 'recall': 0.35294117647058826, 'f1': 0.3574468085106383, 'number': 119}  | {'precision': 0.7939609236234458, 'recall': 0.8394366197183099, 'f1': 0.8160657234139663, 'number': 1065}    | 0.7436            | 0.7988         | 0.7702     | 0.8108           |
 
 
 ### Framework versions

logs/events.out.tfevents.1714565161.DESKTOP-3CFO1LV.2689.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:11dcbae81a6ac50453347349f0d7010ee3dc8c8e3e9a7a640aeb6839877af30d
-size 12645
+oid sha256:7f8972c5c1bee27cf65b39b95b514083a16d50b445f7ecf2d77ba61346d53929
+size 15846

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:16f48160736c0faaa37504aca1ef4d75991f3ebfa66a13836b6624e5156bf46c
+oid sha256:a9c4a5ea9f31994c950d421141af42e6b2b37f84af7bb9482eff8c68eb226fdc
 size 450558212