--- library_name: transformers license: mit base_model: riteshbehera857/layoutlm-base-uncased-finetuned-invoices-1 tags: - generated_from_trainer model-index: - name: layoutlm-base-uncased-finetuned-invoices-2 results: [] --- # layoutlm-base-uncased-finetuned-invoices-2 This model is a fine-tuned version of [riteshbehera857/layoutlm-base-uncased-finetuned-invoices-1](https://huggingface.co/riteshbehera857/layoutlm-base-uncased-finetuned-invoices-1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0342 - B-adress: {'precision': 0.9669491525423729, 'recall': 0.971063829787234, 'f1': 0.9690021231422504, 'number': 1175} - B-name: {'precision': 0.9795918367346939, 'recall': 0.9739130434782609, 'f1': 0.9767441860465117, 'number': 345} - Gst no: {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} - Invoice no: {'precision': 0.9702970297029703, 'recall': 0.9607843137254902, 'f1': 0.9655172413793103, 'number': 102} - Order date: {'precision': 0.9672131147540983, 'recall': 0.9752066115702479, 'f1': 0.9711934156378601, 'number': 121} - Order id: {'precision': 0.9770992366412213, 'recall': 0.9922480620155039, 'f1': 0.9846153846153846, 'number': 129} - S-adress: {'precision': 0.978021978021978, 'recall': 0.9941489361702127, 'f1': 0.9860195199155896, 'number': 1880} - S-name: {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} - Total gross: {'precision': 0.9298245614035088, 'recall': 1.0, 'f1': 0.9636363636363636, 'number': 53} - Total net: {'precision': 0.9923076923076923, 'recall': 1.0, 'f1': 0.9961389961389961, 'number': 129} - Overall Precision: 0.9761 - Overall Recall: 0.9808 - Overall F1: 0.9784 - Overall Accuracy: 0.9944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | B-adress | B-name | Gst no | Invoice no | Order date | Order id | S-adress | S-name | Total gross | Total net | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | 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| 0.0192 | 1.0 | 19 | 0.0257 | {'precision': 0.9559322033898305, 'recall': 0.96, 'f1': 0.9579617834394905, 'number': 1175} | {'precision': 0.9713467048710601, 'recall': 0.9826086956521739, 'f1': 0.9769452449567723, 'number': 345} | {'precision': 1.0, 'recall': 0.9844961240310077, 'f1': 0.9921875, 'number': 129} | {'precision': 0.9615384615384616, 'recall': 0.9803921568627451, 'f1': 0.970873786407767, 'number': 102} | {'precision': 0.9752066115702479, 'recall': 0.9752066115702479, 'f1': 0.9752066115702479, 'number': 121} | {'precision': 0.9696969696969697, 'recall': 0.9922480620155039, 'f1': 0.9808429118773947, 'number': 129} | {'precision': 0.9789251844046365, 'recall': 0.9882978723404255, 'f1': 0.9835892006352567, 'number': 1880} | {'precision': 0.9899396378269618, 'recall': 0.9498069498069498, 'f1': 0.9694581280788177, 'number': 518} | {'precision': 0.8688524590163934, 'recall': 1.0, 'f1': 0.9298245614035088, 'number': 53} | {'precision': 1.0, 'recall': 0.9922480620155039, 'f1': 0.9961089494163424, 'number': 129} | 0.9726 | 0.9760 | 0.9743 | 0.9932 | | 0.0179 | 2.0 | 38 | 0.0272 | {'precision': 0.9562657695542472, 'recall': 0.9676595744680851, 'f1': 0.9619289340101522, 'number': 1175} | {'precision': 0.9740634005763689, 'recall': 0.9797101449275363, 'f1': 0.976878612716763, 'number': 345} | {'precision': 1.0, 'recall': 0.9844961240310077, 'f1': 0.9921875, 'number': 129} | {'precision': 0.9705882352941176, 'recall': 0.9705882352941176, 'f1': 0.9705882352941176, 'number': 102} | {'precision': 0.9754098360655737, 'recall': 0.9834710743801653, 'f1': 0.9794238683127573, 'number': 121} | {'precision': 0.9770992366412213, 'recall': 0.9922480620155039, 'f1': 0.9846153846153846, 'number': 129} | {'precision': 0.982086406743941, 'recall': 0.9914893617021276, 'f1': 0.9867654843832716, 'number': 1880} | {'precision': 0.9900199600798403, 'recall': 0.9575289575289575, 'f1': 0.9735034347399412, 'number': 518} | {'precision': 0.8833333333333333, 'recall': 1.0, 'f1': 0.9380530973451328, 'number': 53} | {'precision': 1.0, 'recall': 0.9922480620155039, 'f1': 0.9961089494163424, 'number': 129} | 0.9748 | 0.9799 | 0.9774 | 0.9940 | | 0.0149 | 3.0 | 57 | 0.0284 | {'precision': 0.9647766323024055, 'recall': 0.9557446808510638, 'f1': 0.960239418554938, 'number': 1175} | {'precision': 0.9794117647058823, 'recall': 0.9652173913043478, 'f1': 0.9722627737226278, 'number': 345} | {'precision': 1.0, 'recall': 0.9844961240310077, 'f1': 0.9921875, 'number': 129} | {'precision': 0.9611650485436893, 'recall': 0.9705882352941176, 'f1': 0.9658536585365853, 'number': 102} | {'precision': 0.9754098360655737, 'recall': 0.9834710743801653, 'f1': 0.9794238683127573, 'number': 121} | {'precision': 0.9770992366412213, 'recall': 0.9922480620155039, 'f1': 0.9846153846153846, 'number': 129} | {'precision': 0.9744791666666667, 'recall': 0.9952127659574468, 'f1': 0.9847368421052631, 'number': 1880} | {'precision': 0.99, 'recall': 0.9555984555984556, 'f1': 0.9724950884086443, 'number': 518} | {'precision': 0.9298245614035088, 'recall': 1.0, 'f1': 0.9636363636363636, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9743 | 0.9773 | 0.9758 | 0.9935 | | 0.0135 | 4.0 | 76 | 0.0295 | {'precision': 0.9531380753138076, 'recall': 0.9693617021276596, 'f1': 0.9611814345991562, 'number': 1175} | {'precision': 0.9796511627906976, 'recall': 0.9768115942028985, 'f1': 0.9782293178519593, 'number': 345} | {'precision': 1.0, 'recall': 0.9767441860465116, 'f1': 0.988235294117647, 'number': 129} | {'precision': 0.9705882352941176, 'recall': 0.9705882352941176, 'f1': 0.9705882352941176, 'number': 102} | {'precision': 0.9672131147540983, 'recall': 0.9752066115702479, 'f1': 0.9711934156378601, 'number': 121} | {'precision': 0.9552238805970149, 'recall': 0.9922480620155039, 'f1': 0.9733840304182508, 'number': 129} | {'precision': 0.9760166840458812, 'recall': 0.9957446808510638, 'f1': 0.985781990521327, 'number': 1880} | {'precision': 0.988, 'recall': 0.9536679536679536, 'f1': 0.9705304518664047, 'number': 518} | {'precision': 0.9137931034482759, 'recall': 1.0, 'f1': 0.9549549549549551, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9708 | 0.9812 | 0.9760 | 0.9936 | | 0.01 | 5.0 | 95 | 0.0308 | {'precision': 0.9619611158072696, 'recall': 0.9685106382978723, 'f1': 0.9652247667514843, 'number': 1175} | {'precision': 0.9795918367346939, 'recall': 0.9739130434782609, 'f1': 0.9767441860465117, 'number': 345} | {'precision': 1.0, 'recall': 0.9767441860465116, 'f1': 0.988235294117647, 'number': 129} | {'precision': 0.9519230769230769, 'recall': 0.9705882352941176, 'f1': 0.9611650485436893, 'number': 102} | {'precision': 0.9752066115702479, 'recall': 0.9752066115702479, 'f1': 0.9752066115702479, 'number': 121} | {'precision': 0.9770992366412213, 'recall': 0.9922480620155039, 'f1': 0.9846153846153846, 'number': 129} | {'precision': 0.9739311783107404, 'recall': 0.9936170212765958, 'f1': 0.9836756187467087, 'number': 1880} | {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} | {'precision': 0.9298245614035088, 'recall': 1.0, 'f1': 0.9636363636363636, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9727 | 0.9804 | 0.9765 | 0.9938 | | 0.009 | 6.0 | 114 | 0.0315 | {'precision': 0.9667235494880546, 'recall': 0.9642553191489361, 'f1': 0.9654878568385172, 'number': 1175} | {'precision': 0.9795918367346939, 'recall': 0.9739130434782609, 'f1': 0.9767441860465117, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9705882352941176, 'recall': 0.9705882352941176, 'f1': 0.9705882352941176, 'number': 102} | {'precision': 0.9672131147540983, 'recall': 0.9752066115702479, 'f1': 0.9711934156378601, 'number': 121} | {'precision': 0.9624060150375939, 'recall': 0.9922480620155039, 'f1': 0.9770992366412213, 'number': 129} | {'precision': 0.9714434060228453, 'recall': 0.9952127659574468, 'f1': 0.9831844456121913, 'number': 1880} | {'precision': 0.9860557768924303, 'recall': 0.9555984555984556, 'f1': 0.9705882352941176, 'number': 518} | {'precision': 0.9298245614035088, 'recall': 1.0, 'f1': 0.9636363636363636, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9727 | 0.9795 | 0.9761 | 0.9936 | | 0.0081 | 7.0 | 133 | 0.0322 | {'precision': 0.966893039049236, 'recall': 0.9693617021276596, 'f1': 0.9681257968550786, 'number': 1175} | {'precision': 0.9739130434782609, 'recall': 0.9739130434782609, 'f1': 0.9739130434782609, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9607843137254902, 'recall': 0.9607843137254902, 'f1': 0.9607843137254902, 'number': 102} | {'precision': 0.9672131147540983, 'recall': 0.9752066115702479, 'f1': 0.9711934156378601, 'number': 121} | {'precision': 0.9624060150375939, 'recall': 0.9922480620155039, 'f1': 0.9770992366412213, 'number': 129} | {'precision': 0.9785002621919245, 'recall': 0.9925531914893617, 'f1': 0.9854766305782942, 'number': 1880} | {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} | {'precision': 0.9298245614035088, 'recall': 1.0, 'f1': 0.9636363636363636, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9750 | 0.9797 | 0.9774 | 0.9940 | | 0.0069 | 8.0 | 152 | 0.0324 | {'precision': 0.9658994032395567, 'recall': 0.9642553191489361, 'f1': 0.9650766609880749, 'number': 1175} | {'precision': 0.9767441860465116, 'recall': 0.9739130434782609, 'f1': 0.9753265602322205, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9702970297029703, 'recall': 0.9607843137254902, 'f1': 0.9655172413793103, 'number': 102} | {'precision': 0.959349593495935, 'recall': 0.9752066115702479, 'f1': 0.9672131147540983, 'number': 121} | {'precision': 0.9696969696969697, 'recall': 0.9922480620155039, 'f1': 0.9808429118773947, 'number': 129} | {'precision': 0.9769633507853404, 'recall': 0.9925531914893617, 'f1': 0.9846965699208443, 'number': 1880} | {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} | {'precision': 0.9298245614035088, 'recall': 1.0, 'f1': 0.9636363636363636, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9746 | 0.9784 | 0.9765 | 0.9938 | | 0.0064 | 9.0 | 171 | 0.0344 | {'precision': 0.9636209813874789, 'recall': 0.9693617021276596, 'f1': 0.9664828171404328, 'number': 1175} | {'precision': 0.9767441860465116, 'recall': 0.9739130434782609, 'f1': 0.9753265602322205, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9705882352941176, 'recall': 0.9705882352941176, 'f1': 0.9705882352941176, 'number': 102} | {'precision': 0.9672131147540983, 'recall': 0.9752066115702479, 'f1': 0.9711934156378601, 'number': 121} | {'precision': 0.9696969696969697, 'recall': 0.9922480620155039, 'f1': 0.9808429118773947, 'number': 129} | {'precision': 0.9744791666666667, 'recall': 0.9952127659574468, 'f1': 0.9847368421052631, 'number': 1880} | {'precision': 0.9860557768924303, 'recall': 0.9555984555984556, 'f1': 0.9705882352941176, 'number': 518} | {'precision': 0.9137931034482759, 'recall': 1.0, 'f1': 0.9549549549549551, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9729 | 0.9808 | 0.9768 | 0.9940 | | 0.0058 | 10.0 | 190 | 0.0338 | {'precision': 0.9652836579170194, 'recall': 0.9702127659574468, 'f1': 0.9677419354838709, 'number': 1175} | {'precision': 0.9795918367346939, 'recall': 0.9739130434782609, 'f1': 0.9767441860465117, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9607843137254902, 'recall': 0.9607843137254902, 'f1': 0.9607843137254902, 'number': 102} | {'precision': 0.959349593495935, 'recall': 0.9752066115702479, 'f1': 0.9672131147540983, 'number': 121} | {'precision': 0.9624060150375939, 'recall': 0.9922480620155039, 'f1': 0.9770992366412213, 'number': 129} | {'precision': 0.9785115303983228, 'recall': 0.9930851063829788, 'f1': 0.9857444561774024, 'number': 1880} | {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} | {'precision': 0.9298245614035088, 'recall': 1.0, 'f1': 0.9636363636363636, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9748 | 0.9801 | 0.9775 | 0.9941 | | 0.0057 | 11.0 | 209 | 0.0342 | {'precision': 0.9669491525423729, 'recall': 0.971063829787234, 'f1': 0.9690021231422504, 'number': 1175} | {'precision': 0.9795918367346939, 'recall': 0.9739130434782609, 'f1': 0.9767441860465117, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9702970297029703, 'recall': 0.9607843137254902, 'f1': 0.9655172413793103, 'number': 102} | {'precision': 0.9672131147540983, 'recall': 0.9752066115702479, 'f1': 0.9711934156378601, 'number': 121} | {'precision': 0.9770992366412213, 'recall': 0.9922480620155039, 'f1': 0.9846153846153846, 'number': 129} | {'precision': 0.978021978021978, 'recall': 0.9941489361702127, 'f1': 0.9860195199155896, 'number': 1880} | {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} | {'precision': 0.9298245614035088, 'recall': 1.0, 'f1': 0.9636363636363636, 'number': 53} | {'precision': 0.9923076923076923, 'recall': 1.0, 'f1': 0.9961389961389961, 'number': 129} | 0.9761 | 0.9808 | 0.9784 | 0.9944 | | 0.0051 | 12.0 | 228 | 0.0352 | {'precision': 0.964527027027027, 'recall': 0.9719148936170213, 'f1': 0.9682068673166595, 'number': 1175} | {'precision': 0.9739130434782609, 'recall': 0.9739130434782609, 'f1': 0.9739130434782609, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9702970297029703, 'recall': 0.9607843137254902, 'f1': 0.9655172413793103, 'number': 102} | {'precision': 0.9672131147540983, 'recall': 0.9752066115702479, 'f1': 0.9711934156378601, 'number': 121} | {'precision': 0.9770992366412213, 'recall': 0.9922480620155039, 'f1': 0.9846153846153846, 'number': 129} | {'precision': 0.9785340314136126, 'recall': 0.9941489361702127, 'f1': 0.9862796833773088, 'number': 1880} | {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} | {'precision': 0.9137931034482759, 'recall': 1.0, 'f1': 0.9549549549549551, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9748 | 0.9810 | 0.9779 | 0.9943 | | 0.0051 | 13.0 | 247 | 0.0352 | {'precision': 0.9638047138047138, 'recall': 0.9744680851063829, 'f1': 0.9691070672873465, 'number': 1175} | {'precision': 0.9795918367346939, 'recall': 0.9739130434782609, 'f1': 0.9767441860465117, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9702970297029703, 'recall': 0.9607843137254902, 'f1': 0.9655172413793103, 'number': 102} | {'precision': 0.9291338582677166, 'recall': 0.9752066115702479, 'f1': 0.9516129032258065, 'number': 121} | {'precision': 0.9624060150375939, 'recall': 0.9922480620155039, 'f1': 0.9770992366412213, 'number': 129} | {'precision': 0.9790356394129979, 'recall': 0.9936170212765958, 'f1': 0.9862724392819429, 'number': 1880} | {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} | {'precision': 0.9298245614035088, 'recall': 1.0, 'f1': 0.9636363636363636, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9740 | 0.9814 | 0.9777 | 0.9942 | | 0.0046 | 14.0 | 266 | 0.0356 | {'precision': 0.9605373635600336, 'recall': 0.9736170212765958, 'f1': 0.967032967032967, 'number': 1175} | {'precision': 0.9795918367346939, 'recall': 0.9739130434782609, 'f1': 0.9767441860465117, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9702970297029703, 'recall': 0.9607843137254902, 'f1': 0.9655172413793103, 'number': 102} | {'precision': 0.959349593495935, 'recall': 0.9752066115702479, 'f1': 0.9672131147540983, 'number': 121} | {'precision': 0.9624060150375939, 'recall': 0.9922480620155039, 'f1': 0.9770992366412213, 'number': 129} | {'precision': 0.9790246460409019, 'recall': 0.9930851063829788, 'f1': 0.9860047531027197, 'number': 1880} | {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} | {'precision': 0.9137931034482759, 'recall': 1.0, 'f1': 0.9549549549549551, 'number': 53} | {'precision': 0.9847328244274809, 'recall': 1.0, 'f1': 0.9923076923076923, 'number': 129} | 0.9738 | 0.9810 | 0.9774 | 0.9941 | | 0.0044 | 15.0 | 285 | 0.0355 | {'precision': 0.9613445378151261, 'recall': 0.9736170212765958, 'f1': 0.9674418604651164, 'number': 1175} | {'precision': 0.9795918367346939, 'recall': 0.9739130434782609, 'f1': 0.9767441860465117, 'number': 345} | {'precision': 1.0, 'recall': 0.9689922480620154, 'f1': 0.9842519685039369, 'number': 129} | {'precision': 0.9702970297029703, 'recall': 0.9607843137254902, 'f1': 0.9655172413793103, 'number': 102} | {'precision': 0.959349593495935, 'recall': 0.9752066115702479, 'f1': 0.9672131147540983, 'number': 121} | {'precision': 0.9624060150375939, 'recall': 0.9922480620155039, 'f1': 0.9770992366412213, 'number': 129} | {'precision': 0.9790246460409019, 'recall': 0.9930851063829788, 'f1': 0.9860047531027197, 'number': 1880} | {'precision': 0.9860834990059643, 'recall': 0.9575289575289575, 'f1': 0.9715964740450539, 'number': 518} | {'precision': 0.9137931034482759, 'recall': 1.0, 'f1': 0.9549549549549551, 'number': 53} | {'precision': 0.9923076923076923, 'recall': 1.0, 'f1': 0.9961389961389961, 'number': 129} | 0.9742 | 0.9810 | 0.9776 | 0.9942 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1