End of training

README.md

@@ -16,15 +16,15 @@ 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: 0.1783
-- Ddress: {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54}
-- Eller: {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55}
-- Imestamp: {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54}
-- Otal Cost: {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55}
-- Overall Precision: 0.9585
-- Overall Recall: 0.9541
-- Overall F1: 0.9563
-- Overall Accuracy: 0.9787
+- Loss: 0.0293
+- Ddress: {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217}
+- Eller: {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234}
+- Imestamp: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}
+- Otal Cost: {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}
+- Overall Precision: 0.9909
+- Overall Recall: 0.992
+- Overall F1: 0.9914
+- Overall Accuracy: 0.9960
 
 ## Model description
 
@@ -54,23 +54,23 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Ddress                                                                                                  | Eller                                                                                                   | Imestamp                                                                                 | Otal Cost                                                                                               | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.0001        | 1.0   | 7    | 0.1773          | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767            | 0.9633         | 0.9700     | 0.9817           |
-| 0.0           | 2.0   | 14   | 0.1568          | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767            | 0.9633         | 0.9700     | 0.9817           |
-| 0.0           | 3.0   | 21   | 0.1664          | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767            | 0.9633         | 0.9700     | 0.9817           |
-| 0.0           | 4.0   | 28   | 0.1649          | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767            | 0.9633         | 0.9700     | 0.9817           |
-| 0.0           | 5.0   | 35   | 0.1713          | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767            | 0.9633         | 0.9700     | 0.9817           |
-| 0.0           | 6.0   | 42   | 0.1678          | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9722            | 0.9633         | 0.9677     | 0.9817           |
-| 0.0           | 7.0   | 49   | 0.1669          | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9722            | 0.9633         | 0.9677     | 0.9817           |
-| 0.0           | 8.0   | 56   | 0.1690          | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9722            | 0.9633         | 0.9677     | 0.9817           |
-| 0.0           | 9.0   | 63   | 0.1686          | {'precision': 0.9629629629629629, 'recall': 0.9629629629629629, 'f1': 0.9629629629629629, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9767            | 0.9633         | 0.9700     | 0.9848           |
-| 0.0           | 10.0  | 70   | 0.1718          | {'precision': 0.9444444444444444, 'recall': 0.9444444444444444, 'f1': 0.9444444444444444, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9676            | 0.9587         | 0.9631     | 0.9817           |
-| 0.0           | 11.0  | 77   | 0.1893          | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585            | 0.9541         | 0.9563     | 0.9787           |
-| 0.0           | 12.0  | 84   | 0.1943          | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585            | 0.9541         | 0.9563     | 0.9787           |
-| 0.0           | 13.0  | 91   | 0.1914          | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585            | 0.9541         | 0.9563     | 0.9787           |
-| 0.0           | 14.0  | 98   | 0.1835          | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585            | 0.9541         | 0.9563     | 0.9787           |
-| 0.0           | 15.0  | 105  | 0.1783          | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585            | 0.9541         | 0.9563     | 0.9787           |
+| Training Loss | Epoch | Step | Validation Loss | Ddress                                                                                                   | Eller                                                                                                    | Imestamp                                                                                              | Otal Cost                                                                                                | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.3063        | 1.0   | 55   | 0.0304          | {'precision': 0.9585253456221198, 'recall': 0.9585253456221198, 'f1': 0.9585253456221198, 'number': 217} | {'precision': 0.991304347826087, 'recall': 0.9743589743589743, 'f1': 0.9827586206896551, 'number': 234}  | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.986046511627907, 'recall': 0.9953051643192489, 'f1': 0.9906542056074766, 'number': 213}  | 0.9828            | 0.9806         | 0.9817     | 0.9912           |
+| 0.0332        | 2.0   | 110  | 0.0303          | {'precision': 0.967741935483871, 'recall': 0.967741935483871, 'f1': 0.967741935483871, 'number': 217}    | {'precision': 0.991304347826087, 'recall': 0.9743589743589743, 'f1': 0.9827586206896551, 'number': 234}  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}                                           | {'precision': 0.9906976744186047, 'recall': 1.0, 'f1': 0.9953271028037384, 'number': 213}                | 0.9874            | 0.9851         | 0.9863     | 0.9928           |
+| 0.0174        | 3.0   | 165  | 0.0252          | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9863            | 0.9886         | 0.9874     | 0.9944           |
+| 0.0145        | 4.0   | 220  | 0.0271          | {'precision': 0.967741935483871, 'recall': 0.967741935483871, 'f1': 0.967741935483871, 'number': 217}    | {'precision': 0.9913793103448276, 'recall': 0.9829059829059829, 'f1': 0.9871244635193134, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9863            | 0.9851         | 0.9857     | 0.9936           |
+| 0.0114        | 5.0   | 275  | 0.0254          | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9886            | 0.9897         | 0.9891     | 0.9952           |
+| 0.0079        | 6.0   | 330  | 0.0273          | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9863            | 0.9886         | 0.9874     | 0.9944           |
+| 0.0053        | 7.0   | 385  | 0.0259          | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}                                           | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}                | 0.9909            | 0.992          | 0.9914     | 0.9960           |
+| 0.005         | 8.0   | 440  | 0.0255          | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9863            | 0.9886         | 0.9874     | 0.9944           |
+| 0.0034        | 9.0   | 495  | 0.0281          | {'precision': 0.9768518518518519, 'recall': 0.9723502304147466, 'f1': 0.97459584295612, 'number': 217}   | {'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}                                           | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}                | 0.9897            | 0.9909         | 0.9903     | 0.9952           |
+| 0.0032        | 10.0  | 550  | 0.0290          | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9914163090128756, 'recall': 0.9871794871794872, 'f1': 0.9892933618843683, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9874            | 0.9874         | 0.9874     | 0.9944           |
+| 0.0032        | 11.0  | 605  | 0.0306          | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9913793103448276, 'recall': 0.9829059829059829, 'f1': 0.9871244635193134, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.986046511627907, 'recall': 0.9953051643192489, 'f1': 0.9906542056074766, 'number': 213}  | 0.9863            | 0.9863         | 0.9863     | 0.9936           |
+| 0.0018        | 12.0  | 660  | 0.0273          | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}                                           | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}                | 0.9909            | 0.992          | 0.9914     | 0.9960           |
+| 0.0007        | 13.0  | 715  | 0.0266          | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}                                           | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}                | 0.9909            | 0.992          | 0.9914     | 0.9960           |
+| 0.0006        | 14.0  | 770  | 0.0292          | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}                                           | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}                | 0.9909            | 0.992          | 0.9914     | 0.9960           |
+| 0.0006        | 15.0  | 825  | 0.0293          | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}                                           | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}                | 0.9909            | 0.992          | 0.9914     | 0.9960           |
 
 
 ### Framework versions

logs/events.out.tfevents.1733255564.63d9064179a3.23.4

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+size 16233

model.safetensors

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 version https://git-lfs.github.com/spec/v1
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