End of training

README.md

@@ -0,0 +1,87 @@
+---
+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: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 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

config.json

@@ -0,0 +1,52 @@
+{
+  "_name_or_path": "riteshbehera857/layoutlm-base-uncased-finetuned-invoices-1",
+  "architectures": [
+    "LayoutLMForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "Invoice no",
+    "2": "Order id",
+    "3": "Order date",
+    "4": "GST no",
+    "5": "Total net",
+    "6": "Total gross",
+    "7": "S-name",
+    "8": "B-name",
+    "9": "S-adress",
+    "10": "B-adress"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-adress": 10,
+    "B-name": 8,
+    "GST no": 4,
+    "Invoice no": 1,
+    "O": 0,
+    "Order date": 3,
+    "Order id": 2,
+    "S-adress": 9,
+    "S-name": 7,
+    "Total gross": 6,
+    "Total net": 5
+  },
+  "layer_norm_eps": 1e-12,
+  "max_2d_position_embeddings": 1024,
+  "max_position_embeddings": 512,
+  "model_type": "layoutlm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d6103613111ddd3bbb8b736aadd2fa860ddee045806f7592352e9ca4cf2ed07d
+size 450570516

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8c9c4a2b145445e7c7083dd9d6a2585893db9fb6ff1959c27746e082e3580f47
+size 5176