riasharma
/

layoutlm-funsd

@@ -15,14 +15,14 @@ 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 an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6624
-- Answer: {'precision': 0.7003222341568206, 'recall': 0.8059332509270705, 'f1': 0.7494252873563217, 'number': 809}
-- Header: {'precision': 0.3148148148148148, 'recall': 0.2857142857142857, 'f1': 0.29955947136563876, 'number': 119}
-- Question: {'precision': 0.7602441150828247, 'recall': 0.8187793427230047, 'f1': 0.7884267631103073, 'number': 1065}
-- Overall Precision: 0.7127
-- Overall Recall: 0.7817
-- Overall F1: 0.7456
-- Overall Accuracy: 0.8098
 ## Model description
@@ -52,23 +52,23 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8207        | 1.0   | 10   | 1.6331          | {'precision': 0.01676829268292683, 'recall': 0.013597033374536464, 'f1': 0.015017064846416382, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.21189024390243902, 'recall': 0.13051643192488263, 'f1': 0.16153399186519465, 'number': 1065} | 0.1143            | 0.0753         | 0.0908     | 0.3429           |
-| 1.4867        | 2.0   | 20   | 1.3144          | {'precision': 0.13937282229965156, 'recall': 0.14833127317676142, 'f1': 0.14371257485029942, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4092178770949721, 'recall': 0.5502347417840375, 'f1': 0.4693632358830597, 'number': 1065}    | 0.3079            | 0.3542         | 0.3294     | 0.5753           |
-| 1.1706        | 3.0   | 30   | 1.0082          | {'precision': 0.4507042253521127, 'recall': 0.553770086526576, 'f1': 0.4969495285635052, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.5885810243492863, 'recall': 0.6582159624413145, 'f1': 0.6214539007092199, 'number': 1065}    | 0.5237            | 0.5765         | 0.5488     | 0.6721           |
-| 0.8874        | 4.0   | 40   | 0.8115          | {'precision': 0.6029106029106029, 'recall': 0.7169344870210136, 'f1': 0.6549971767363072, 'number': 809}      | {'precision': 0.05714285714285714, 'recall': 0.01680672268907563, 'f1': 0.025974025974025972, 'number': 119} | {'precision': 0.649792531120332, 'recall': 0.7352112676056338, 'f1': 0.6898678414096917, 'number': 1065}     | 0.6199            | 0.6849         | 0.6508     | 0.7517           |
-| 0.7072        | 5.0   | 50   | 0.7206          | {'precision': 0.6341948310139165, 'recall': 0.788627935723115, 'f1': 0.7030303030303031, 'number': 809}       | {'precision': 0.18032786885245902, 'recall': 0.09243697478991597, 'f1': 0.12222222222222223, 'number': 119}  | {'precision': 0.696551724137931, 'recall': 0.7586854460093897, 'f1': 0.7262921348314607, 'number': 1065}     | 0.6542            | 0.7311         | 0.6905     | 0.7725           |
-| 0.5896        | 6.0   | 60   | 0.6813          | {'precision': 0.6571428571428571, 'recall': 0.796044499381953, 'f1': 0.7199552822806037, 'number': 809}       | {'precision': 0.1746031746031746, 'recall': 0.09243697478991597, 'f1': 0.12087912087912087, 'number': 119}   | {'precision': 0.7217981340118744, 'recall': 0.7990610328638498, 'f1': 0.7584670231729055, 'number': 1065}    | 0.6778            | 0.7556         | 0.7146     | 0.7867           |
-| 0.5193        | 7.0   | 70   | 0.6605          | {'precision': 0.6949516648764769, 'recall': 0.799752781211372, 'f1': 0.7436781609195402, 'number': 809}       | {'precision': 0.20618556701030927, 'recall': 0.16806722689075632, 'f1': 0.1851851851851852, 'number': 119}   | {'precision': 0.734468085106383, 'recall': 0.8103286384976526, 'f1': 0.7705357142857142, 'number': 1065}     | 0.6945            | 0.7677         | 0.7293     | 0.7979           |
-| 0.4591        | 8.0   | 80   | 0.6473          | {'precision': 0.6922246220302376, 'recall': 0.792336217552534, 'f1': 0.7389048991354467, 'number': 809}       | {'precision': 0.24, 'recall': 0.20168067226890757, 'f1': 0.2191780821917808, 'number': 119}                  | {'precision': 0.7382154882154882, 'recall': 0.8234741784037559, 'f1': 0.7785175321793164, 'number': 1065}    | 0.6965            | 0.7737         | 0.7331     | 0.8059           |
-| 0.3939        | 9.0   | 90   | 0.6369          | {'precision': 0.6886291179596175, 'recall': 0.8009888751545118, 'f1': 0.7405714285714285, 'number': 809}      | {'precision': 0.2777777777777778, 'recall': 0.25210084033613445, 'f1': 0.2643171806167401, 'number': 119}    | {'precision': 0.7515047291487532, 'recall': 0.8206572769953052, 'f1': 0.784560143626571, 'number': 1065}     | 0.7016            | 0.7787         | 0.7382     | 0.8088           |
-| 0.3604        | 10.0  | 100  | 0.6514          | {'precision': 0.6954643628509719, 'recall': 0.796044499381953, 'f1': 0.7423631123919308, 'number': 809}       | {'precision': 0.29, 'recall': 0.24369747899159663, 'f1': 0.2648401826484018, 'number': 119}                  | {'precision': 0.7665505226480837, 'recall': 0.8262910798122066, 'f1': 0.7953004970628107, 'number': 1065}    | 0.7144            | 0.7792         | 0.7454     | 0.8125           |
-| 0.3344        | 11.0  | 110  | 0.6505          | {'precision': 0.7031419284940412, 'recall': 0.8022249690976514, 'f1': 0.7494226327944574, 'number': 809}      | {'precision': 0.3153153153153153, 'recall': 0.29411764705882354, 'f1': 0.30434782608695654, 'number': 119}   | {'precision': 0.755632582322357, 'recall': 0.8187793427230047, 'f1': 0.7859396124380351, 'number': 1065}     | 0.7112            | 0.7807         | 0.7443     | 0.8087           |
-| 0.3144        | 12.0  | 120  | 0.6461          | {'precision': 0.6973262032085561, 'recall': 0.8059332509270705, 'f1': 0.7477064220183487, 'number': 809}      | {'precision': 0.3119266055045872, 'recall': 0.2857142857142857, 'f1': 0.2982456140350877, 'number': 119}     | {'precision': 0.7590051457975986, 'recall': 0.8309859154929577, 'f1': 0.7933662034961901, 'number': 1065}    | 0.7109            | 0.7883         | 0.7476     | 0.8137           |
-| 0.2976        | 13.0  | 130  | 0.6569          | {'precision': 0.6925531914893617, 'recall': 0.8046971569839307, 'f1': 0.7444253859348199, 'number': 809}      | {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}     | {'precision': 0.7586805555555556, 'recall': 0.8206572769953052, 'f1': 0.7884528642309426, 'number': 1065}    | 0.7060            | 0.7832         | 0.7426     | 0.8094           |
-| 0.2876        | 14.0  | 140  | 0.6629          | {'precision': 0.7034632034632035, 'recall': 0.8034610630407911, 'f1': 0.7501442585112521, 'number': 809}      | {'precision': 0.3148148148148148, 'recall': 0.2857142857142857, 'f1': 0.29955947136563876, 'number': 119}    | {'precision': 0.7657894736842106, 'recall': 0.819718309859155, 'f1': 0.7918367346938776, 'number': 1065}     | 0.7169            | 0.7812         | 0.7477     | 0.8104           |
-| 0.2877        | 15.0  | 150  | 0.6624          | {'precision': 0.7003222341568206, 'recall': 0.8059332509270705, 'f1': 0.7494252873563217, 'number': 809}      | {'precision': 0.3148148148148148, 'recall': 0.2857142857142857, 'f1': 0.29955947136563876, 'number': 119}    | {'precision': 0.7602441150828247, 'recall': 0.8187793427230047, 'f1': 0.7884267631103073, 'number': 1065}    | 0.7127            | 0.7817         | 0.7456     | 0.8098           |
 ### Framework versions

 This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.7207
+- Answer: {'precision': 0.7114754098360656, 'recall': 0.8046971569839307, 'f1': 0.7552204176334106, 'number': 809}
+- Header: {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}
+- Question: {'precision': 0.7707061900610288, 'recall': 0.8300469483568075, 'f1': 0.7992766726943942, 'number': 1065}
+- Overall Precision: 0.7203
+- Overall Recall: 0.7883
+- Overall F1: 0.7528
+- Overall Accuracy: 0.7971
 ## Model description
 ### Training results
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                      | Header                                                                                                       | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8417        | 1.0   | 10   | 1.6166          | {'precision': 0.028741328047571853, 'recall': 0.03584672435105068, 'f1': 0.0319031903190319, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.16827852998065765, 'recall': 0.16338028169014085, 'f1': 0.16579323487374942, 'number': 1065} | 0.0993            | 0.1019         | 0.1006     | 0.3810           |
+| 1.4476        | 2.0   | 20   | 1.2599          | {'precision': 0.15711947626841244, 'recall': 0.11866501854140915, 'f1': 0.13521126760563382, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.46172441579371476, 'recall': 0.5380281690140845, 'f1': 0.4969644405897658, 'number': 1065}   | 0.3612            | 0.3357         | 0.3480     | 0.5747           |
+| 1.1227        | 3.0   | 30   | 0.9780          | {'precision': 0.4661214953271028, 'recall': 0.4932014833127318, 'f1': 0.4792792792792793, 'number': 809}    | {'precision': 0.16279069767441862, 'recall': 0.058823529411764705, 'f1': 0.08641975308641975, 'number': 119} | {'precision': 0.5979020979020979, 'recall': 0.6422535211267606, 'f1': 0.6192847442281576, 'number': 1065}    | 0.5335            | 0.5469         | 0.5401     | 0.7036           |
+| 0.8596        | 4.0   | 40   | 0.7946          | {'precision': 0.5789473684210527, 'recall': 0.6526576019777504, 'f1': 0.6135967460778617, 'number': 809}    | {'precision': 0.24193548387096775, 'recall': 0.12605042016806722, 'f1': 0.16574585635359115, 'number': 119}  | {'precision': 0.6658141517476556, 'recall': 0.7333333333333333, 'f1': 0.6979445933869526, 'number': 1065}    | 0.6167            | 0.6643         | 0.6396     | 0.7589           |
+| 0.6705        | 5.0   | 50   | 0.7132          | {'precision': 0.6424759871931697, 'recall': 0.7441285537700866, 'f1': 0.6895761741122567, 'number': 809}    | {'precision': 0.3333333333333333, 'recall': 0.21008403361344538, 'f1': 0.2577319587628866, 'number': 119}    | {'precision': 0.6747376916868443, 'recall': 0.7849765258215963, 'f1': 0.7256944444444444, 'number': 1065}    | 0.6499            | 0.7341         | 0.6894     | 0.7767           |
+| 0.5653        | 6.0   | 60   | 0.6840          | {'precision': 0.653125, 'recall': 0.7750309023485785, 'f1': 0.7088750706613907, 'number': 809}              | {'precision': 0.30952380952380953, 'recall': 0.2184873949579832, 'f1': 0.2561576354679803, 'number': 119}    | {'precision': 0.7077814569536424, 'recall': 0.8028169014084507, 'f1': 0.7523097228332599, 'number': 1065}    | 0.6696            | 0.7566         | 0.7105     | 0.7846           |
+| 0.4959        | 7.0   | 70   | 0.6684          | {'precision': 0.6872964169381107, 'recall': 0.7824474660074165, 'f1': 0.7317919075144509, 'number': 809}    | {'precision': 0.2815533980582524, 'recall': 0.24369747899159663, 'f1': 0.26126126126126126, 'number': 119}   | {'precision': 0.734006734006734, 'recall': 0.8187793427230047, 'f1': 0.7740790057700843, 'number': 1065}     | 0.6935            | 0.7697         | 0.7296     | 0.7950           |
+| 0.4343        | 8.0   | 80   | 0.6696          | {'precision': 0.6898395721925134, 'recall': 0.7972805933250927, 'f1': 0.7396788990825688, 'number': 809}    | {'precision': 0.26956521739130435, 'recall': 0.2605042016806723, 'f1': 0.264957264957265, 'number': 119}     | {'precision': 0.7495769881556683, 'recall': 0.831924882629108, 'f1': 0.7886070315976857, 'number': 1065}     | 0.6998            | 0.7837         | 0.7394     | 0.7987           |
+| 0.375         | 9.0   | 90   | 0.6760          | {'precision': 0.7105549510337323, 'recall': 0.8071693448702101, 'f1': 0.755787037037037, 'number': 809}     | {'precision': 0.25, 'recall': 0.2605042016806723, 'f1': 0.25514403292181076, 'number': 119}                  | {'precision': 0.7758007117437722, 'recall': 0.8187793427230047, 'f1': 0.7967108268615805, 'number': 1065}    | 0.7180            | 0.7807         | 0.7481     | 0.7992           |
+| 0.3532        | 10.0  | 100  | 0.6802          | {'precision': 0.7147470398277718, 'recall': 0.8207663782447466, 'f1': 0.7640966628308401, 'number': 809}    | {'precision': 0.3018867924528302, 'recall': 0.2689075630252101, 'f1': 0.28444444444444444, 'number': 119}    | {'precision': 0.7761061946902655, 'recall': 0.8234741784037559, 'f1': 0.7990888382687927, 'number': 1065}    | 0.7266            | 0.7893         | 0.7566     | 0.8046           |
+| 0.3265        | 11.0  | 110  | 0.6995          | {'precision': 0.6968716289104638, 'recall': 0.7985166872682324, 'f1': 0.7442396313364056, 'number': 809}    | {'precision': 0.308411214953271, 'recall': 0.2773109243697479, 'f1': 0.29203539823008845, 'number': 119}     | {'precision': 0.7589134125636672, 'recall': 0.8394366197183099, 'f1': 0.7971466785555059, 'number': 1065}    | 0.7111            | 0.7893         | 0.7482     | 0.7973           |
+| 0.3023        | 12.0  | 120  | 0.7053          | {'precision': 0.7027896995708155, 'recall': 0.8096415327564895, 'f1': 0.7524411257897761, 'number': 809}    | {'precision': 0.30303030303030304, 'recall': 0.33613445378151263, 'f1': 0.3187250996015936, 'number': 119}   | {'precision': 0.769434628975265, 'recall': 0.8178403755868544, 'f1': 0.7928994082840236, 'number': 1065}     | 0.7131            | 0.7858         | 0.7477     | 0.7991           |
+| 0.2927        | 13.0  | 130  | 0.7080          | {'precision': 0.7024704618689581, 'recall': 0.8084054388133498, 'f1': 0.7517241379310345, 'number': 809}    | {'precision': 0.3125, 'recall': 0.29411764705882354, 'f1': 0.30303030303030304, 'number': 119}               | {'precision': 0.7679033649698016, 'recall': 0.8356807511737089, 'f1': 0.8003597122302158, 'number': 1065}    | 0.7171            | 0.7923         | 0.7528     | 0.7999           |
+| 0.2756        | 14.0  | 140  | 0.7128          | {'precision': 0.7081967213114754, 'recall': 0.8009888751545118, 'f1': 0.7517401392111368, 'number': 809}    | {'precision': 0.32142857142857145, 'recall': 0.3025210084033613, 'f1': 0.3116883116883117, 'number': 119}    | {'precision': 0.7720524017467248, 'recall': 0.8300469483568075, 'f1': 0.7999999999999999, 'number': 1065}    | 0.7219            | 0.7868         | 0.7529     | 0.7984           |
+| 0.2741        | 15.0  | 150  | 0.7207          | {'precision': 0.7114754098360656, 'recall': 0.8046971569839307, 'f1': 0.7552204176334106, 'number': 809}    | {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}     | {'precision': 0.7707061900610288, 'recall': 0.8300469483568075, 'f1': 0.7992766726943942, 'number': 1065}    | 0.7203            | 0.7883         | 0.7528     | 0.7971           |
 ### Framework versions

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