End of training

README.md

@@ -0,0 +1,87 @@
+---
+library_name: transformers
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: layoutlm-base-uncased-finetuned-invoices-0
+  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-0
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0543
+- B-adress: {'precision': 0.9086576648133439, 'recall': 0.967032967032967, 'f1': 0.9369369369369369, 'number': 1183}
+- B-name: {'precision': 0.96045197740113, 'recall': 0.9941520467836257, 'f1': 0.9770114942528735, 'number': 342}
+- Gst no: {'precision': 0.952755905511811, 'recall': 0.983739837398374, 'f1': 0.968, 'number': 123}
+- Invoice no: {'precision': 0.94, 'recall': 0.94, 'f1': 0.94, 'number': 100}
+- Order date: {'precision': 1.0, 'recall': 0.983739837398374, 'f1': 0.9918032786885246, 'number': 123}
+- Order id: {'precision': 0.9922480620155039, 'recall': 0.9846153846153847, 'f1': 0.9884169884169884, 'number': 130}
+- S-adress: {'precision': 0.9866666666666667, 'recall': 0.9458077709611452, 'f1': 0.9658052727747326, 'number': 1956}
+- S-name: {'precision': 0.85431654676259, 'recall': 0.9875259875259875, 'f1': 0.9161041465766634, 'number': 481}
+- Total gross: {'precision': 0.8545454545454545, 'recall': 0.8392857142857143, 'f1': 0.8468468468468467, 'number': 56}
+- Total net: {'precision': 0.8768115942028986, 'recall': 0.952755905511811, 'f1': 0.9132075471698112, 'number': 127}
+- Overall Precision: 0.9421
+- Overall Recall: 0.9610
+- Overall F1: 0.9515
+- Overall Accuracy: 0.9872
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.3404        | 1.0   | 19   | 0.5934          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1183}                                               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 342}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 123}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100}                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 123}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 130}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1956}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 481}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 127}                                                 | 0.0               | 0.0            | 0.0        | 0.8274           |
+| 0.4824        | 2.0   | 38   | 0.3290          | {'precision': 0.5835654596100278, 'recall': 0.35418427726120033, 'f1': 0.4408206207259337, 'number': 1183} | {'precision': 0.5, 'recall': 0.0029239766081871343, 'f1': 0.005813953488372093, 'number': 342}           | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 123}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100}                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 123}                                              | {'precision': 1.0, 'recall': 0.4076923076923077, 'f1': 0.5792349726775957, 'number': 130}                | {'precision': 0.8446411012782694, 'recall': 0.878323108384458, 'f1': 0.8611528822055139, 'number': 1956}  | {'precision': 0.825, 'recall': 0.6860706860706861, 'f1': 0.7491486946651532, 'number': 481}              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 127}                                                 | 0.7861            | 0.5456         | 0.6441     | 0.9083           |
+| 0.2716        | 3.0   | 57   | 0.2123          | {'precision': 0.6143583227445998, 'recall': 0.8174133558748944, 'f1': 0.701487123685165, 'number': 1183}   | {'precision': 0.856140350877193, 'recall': 0.7134502923976608, 'f1': 0.7783094098883573, 'number': 342}  | {'precision': 0.8910891089108911, 'recall': 0.7317073170731707, 'f1': 0.8035714285714285, 'number': 123} | {'precision': 0.98, 'recall': 0.49, 'f1': 0.6533333333333333, 'number': 100}               | {'precision': 0.9375, 'recall': 0.6097560975609756, 'f1': 0.7389162561576355, 'number': 123}             | {'precision': 0.9223300970873787, 'recall': 0.7307692307692307, 'f1': 0.815450643776824, 'number': 130}  | {'precision': 0.9909255898366606, 'recall': 0.8374233128834356, 'f1': 0.9077306733167083, 'number': 1956} | {'precision': 0.9159836065573771, 'recall': 0.9293139293139293, 'f1': 0.9226006191950464, 'number': 481} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 127}                                                 | 0.8318            | 0.7801         | 0.8051     | 0.9439           |
+| 0.1817        | 4.0   | 76   | 0.1527          | {'precision': 0.7367706919945726, 'recall': 0.9180050718512257, 'f1': 0.8174633044787355, 'number': 1183}  | {'precision': 0.8218085106382979, 'recall': 0.9035087719298246, 'f1': 0.8607242339832869, 'number': 342} | {'precision': 0.8928571428571429, 'recall': 0.8130081300813008, 'f1': 0.8510638297872342, 'number': 123} | {'precision': 0.8651685393258427, 'recall': 0.77, 'f1': 0.8148148148148148, 'number': 100} | {'precision': 0.9484536082474226, 'recall': 0.7479674796747967, 'f1': 0.8363636363636364, 'number': 123} | {'precision': 0.96, 'recall': 0.7384615384615385, 'f1': 0.8347826086956522, 'number': 130}               | {'precision': 0.9706510138740662, 'recall': 0.929959100204499, 'f1': 0.9498694516971279, 'number': 1956}  | {'precision': 0.9447852760736196, 'recall': 0.9604989604989606, 'f1': 0.9525773195876289, 'number': 481} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 127}                                                 | 0.8764            | 0.8745         | 0.8754     | 0.9615           |
+| 0.1376        | 5.0   | 95   | 0.1115          | {'precision': 0.8572664359861591, 'recall': 0.8377007607776839, 'f1': 0.84737067122702, 'number': 1183}    | {'precision': 0.8636363636363636, 'recall': 0.9444444444444444, 'f1': 0.9022346368715084, 'number': 342} | {'precision': 0.9363636363636364, 'recall': 0.8373983739837398, 'f1': 0.8841201716738197, 'number': 123} | {'precision': 0.875, 'recall': 0.84, 'f1': 0.8571428571428572, 'number': 100}              | {'precision': 0.8899082568807339, 'recall': 0.7886178861788617, 'f1': 0.8362068965517241, 'number': 123} | {'precision': 0.9702970297029703, 'recall': 0.7538461538461538, 'f1': 0.8484848484848484, 'number': 130} | {'precision': 0.9693665628245067, 'recall': 0.9544989775051125, 'f1': 0.9618753219989695, 'number': 1956} | {'precision': 0.9665970772442589, 'recall': 0.9625779625779626, 'f1': 0.9645833333333335, 'number': 481} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 127}                                                 | 0.9253            | 0.8712         | 0.8975     | 0.9683           |
+| 0.1027        | 6.0   | 114  | 0.0891          | {'precision': 0.8551068883610451, 'recall': 0.9129332206255283, 'f1': 0.8830744071954211, 'number': 1183}  | {'precision': 0.9029649595687331, 'recall': 0.97953216374269, 'f1': 0.9396914446002805, 'number': 342}   | {'precision': 0.9568965517241379, 'recall': 0.9024390243902439, 'f1': 0.9288702928870294, 'number': 123} | {'precision': 0.9052631578947369, 'recall': 0.86, 'f1': 0.8820512820512821, 'number': 100} | {'precision': 0.9067796610169492, 'recall': 0.8699186991869918, 'f1': 0.8879668049792531, 'number': 123} | {'precision': 0.9902912621359223, 'recall': 0.7846153846153846, 'f1': 0.8755364806866953, 'number': 130} | {'precision': 0.9579789894947474, 'recall': 0.9790388548057259, 'f1': 0.9683944374209861, 'number': 1956} | {'precision': 0.9747368421052631, 'recall': 0.9625779625779626, 'f1': 0.9686192468619247, 'number': 481} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56}                                                  | {'precision': 0.6666666666666666, 'recall': 0.015748031496062992, 'f1': 0.03076923076923077, 'number': 127} | 0.9247            | 0.9091         | 0.9168     | 0.9740           |
+| 0.0774        | 7.0   | 133  | 0.0761          | {'precision': 0.883495145631068, 'recall': 0.9230769230769231, 'f1': 0.9028524183546921, 'number': 1183}   | {'precision': 0.9786585365853658, 'recall': 0.9385964912280702, 'f1': 0.9582089552238806, 'number': 342} | {'precision': 0.9576271186440678, 'recall': 0.9186991869918699, 'f1': 0.9377593360995852, 'number': 123} | {'precision': 0.9368421052631579, 'recall': 0.89, 'f1': 0.9128205128205129, 'number': 100} | {'precision': 0.9652173913043478, 'recall': 0.9024390243902439, 'f1': 0.9327731092436974, 'number': 123} | {'precision': 0.9908256880733946, 'recall': 0.8307692307692308, 'f1': 0.9037656903765691, 'number': 130} | {'precision': 0.9897739504843919, 'recall': 0.9401840490797546, 'f1': 0.9643418982695334, 'number': 1956} | {'precision': 0.9019230769230769, 'recall': 0.975051975051975, 'f1': 0.9370629370629371, 'number': 481}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56}                                                  | {'precision': 0.5984251968503937, 'recall': 0.5984251968503937, 'f1': 0.5984251968503937, 'number': 127}    | 0.9361            | 0.9128         | 0.9243     | 0.9783           |
+| 0.0604        | 8.0   | 152  | 0.0642          | {'precision': 0.8949511400651465, 'recall': 0.9289940828402367, 'f1': 0.9116549149730402, 'number': 1183}  | {'precision': 0.9598853868194842, 'recall': 0.97953216374269, 'f1': 0.9696092619392185, 'number': 342}   | {'precision': 0.9603174603174603, 'recall': 0.983739837398374, 'f1': 0.9718875502008032, 'number': 123}  | {'precision': 0.9468085106382979, 'recall': 0.89, 'f1': 0.9175257731958764, 'number': 100} | {'precision': 0.9910714285714286, 'recall': 0.9024390243902439, 'f1': 0.9446808510638298, 'number': 123} | {'precision': 1.0, 'recall': 0.8615384615384616, 'f1': 0.9256198347107438, 'number': 130}                | {'precision': 0.9896061269146609, 'recall': 0.9248466257668712, 'f1': 0.9561310782241015, 'number': 1956} | {'precision': 0.8571428571428571, 'recall': 0.9854469854469855, 'f1': 0.9168278529980657, 'number': 481} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56}                                                  | {'precision': 0.5363128491620112, 'recall': 0.7559055118110236, 'f1': 0.627450980392157, 'number': 127}     | 0.9269            | 0.9188         | 0.9228     | 0.9798           |
+| 0.0472        | 9.0   | 171  | 0.0684          | {'precision': 0.8824439288476411, 'recall': 0.9644970414201184, 'f1': 0.9216478190630049, 'number': 1183}  | {'precision': 0.9629629629629629, 'recall': 0.9883040935672515, 'f1': 0.9754689754689754, 'number': 342} | {'precision': 0.9596774193548387, 'recall': 0.967479674796748, 'f1': 0.9635627530364373, 'number': 123}  | {'precision': 0.9479166666666666, 'recall': 0.91, 'f1': 0.9285714285714285, 'number': 100} | {'precision': 0.990909090909091, 'recall': 0.8861788617886179, 'f1': 0.9356223175965666, 'number': 123}  | {'precision': 1.0, 'recall': 0.8615384615384616, 'f1': 0.9256198347107438, 'number': 130}                | {'precision': 0.987417943107221, 'recall': 0.9228016359918201, 'f1': 0.9540169133192389, 'number': 1956}  | {'precision': 0.8166089965397924, 'recall': 0.9812889812889813, 'f1': 0.8914069877242682, 'number': 481} | {'precision': 0.09523809523809523, 'recall': 0.03571428571428571, 'f1': 0.051948051948051945, 'number': 56} | {'precision': 0.42907801418439717, 'recall': 0.952755905511811, 'f1': 0.5916870415647921, 'number': 127}    | 0.8989            | 0.9327         | 0.9155     | 0.9780           |
+| 0.0399        | 10.0  | 190  | 0.0571          | {'precision': 0.8933649289099526, 'recall': 0.9560439560439561, 'f1': 0.9236423029808084, 'number': 1183}  | {'precision': 0.9659090909090909, 'recall': 0.9941520467836257, 'f1': 0.9798270893371758, 'number': 342} | {'precision': 0.9603174603174603, 'recall': 0.983739837398374, 'f1': 0.9718875502008032, 'number': 123}  | {'precision': 0.94, 'recall': 0.94, 'f1': 0.94, 'number': 100}                             | {'precision': 1.0, 'recall': 0.9349593495934959, 'f1': 0.9663865546218486, 'number': 123}                | {'precision': 1.0, 'recall': 0.9153846153846154, 'f1': 0.9558232931726908, 'number': 130}                | {'precision': 0.9876277568585261, 'recall': 0.9386503067484663, 'f1': 0.9625163826998688, 'number': 1956} | {'precision': 0.8497316636851521, 'recall': 0.9875259875259875, 'f1': 0.9134615384615384, 'number': 481} | {'precision': 0.7368421052631579, 'recall': 0.5, 'f1': 0.5957446808510638, 'number': 56}                    | {'precision': 0.5757575757575758, 'recall': 0.8976377952755905, 'f1': 0.7015384615384614, 'number': 127}    | 0.9241            | 0.9463         | 0.9351     | 0.9831           |
+| 0.0348        | 11.0  | 209  | 0.0652          | {'precision': 0.8785549577248271, 'recall': 0.9661876584953508, 'f1': 0.9202898550724637, 'number': 1183}  | {'precision': 0.9550561797752809, 'recall': 0.9941520467836257, 'f1': 0.9742120343839542, 'number': 342} | {'precision': 0.9453125, 'recall': 0.983739837398374, 'f1': 0.9641434262948206, 'number': 123}           | {'precision': 0.94, 'recall': 0.94, 'f1': 0.94, 'number': 100}                             | {'precision': 0.9918032786885246, 'recall': 0.983739837398374, 'f1': 0.9877551020408164, 'number': 123}  | {'precision': 0.984375, 'recall': 0.9692307692307692, 'f1': 0.9767441860465116, 'number': 130}           | {'precision': 0.9912087912087912, 'recall': 0.9222903885480572, 'f1': 0.9555084745762713, 'number': 1956} | {'precision': 0.8191126279863481, 'recall': 0.997920997920998, 'f1': 0.8997188378631678, 'number': 481}  | {'precision': 0.8367346938775511, 'recall': 0.7321428571428571, 'f1': 0.7809523809523811, 'number': 56}     | {'precision': 0.711764705882353, 'recall': 0.952755905511811, 'f1': 0.8148148148148149, 'number': 127}      | 0.9225            | 0.9502         | 0.9361     | 0.9827           |
+| 0.0302        | 12.0  | 228  | 0.0513          | {'precision': 0.8866615265998458, 'recall': 0.9721048182586645, 'f1': 0.9274193548387096, 'number': 1183}  | {'precision': 0.9798270893371758, 'recall': 0.9941520467836257, 'f1': 0.9869375907111755, 'number': 342} | {'precision': 0.9453125, 'recall': 0.983739837398374, 'f1': 0.9641434262948206, 'number': 123}           | {'precision': 0.9393939393939394, 'recall': 0.93, 'f1': 0.9346733668341709, 'number': 100} | {'precision': 1.0, 'recall': 0.983739837398374, 'f1': 0.9918032786885246, 'number': 123}                 | {'precision': 0.9921875, 'recall': 0.9769230769230769, 'f1': 0.9844961240310077, 'number': 130}          | {'precision': 0.9872, 'recall': 0.946319018404908, 'f1': 0.9663273296789351, 'number': 1956}              | {'precision': 0.8574007220216606, 'recall': 0.9875259875259875, 'f1': 0.9178743961352657, 'number': 481} | {'precision': 0.8653846153846154, 'recall': 0.8035714285714286, 'f1': 0.8333333333333334, 'number': 56}     | {'precision': 0.8345323741007195, 'recall': 0.9133858267716536, 'f1': 0.8721804511278195, 'number': 127}    | 0.9365            | 0.9606         | 0.9484     | 0.9861           |
+| 0.0269        | 13.0  | 247  | 0.0543          | {'precision': 0.9086576648133439, 'recall': 0.967032967032967, 'f1': 0.9369369369369369, 'number': 1183}   | {'precision': 0.96045197740113, 'recall': 0.9941520467836257, 'f1': 0.9770114942528735, 'number': 342}   | {'precision': 0.952755905511811, 'recall': 0.983739837398374, 'f1': 0.968, 'number': 123}                | {'precision': 0.94, 'recall': 0.94, 'f1': 0.94, 'number': 100}                             | {'precision': 1.0, 'recall': 0.983739837398374, 'f1': 0.9918032786885246, 'number': 123}                 | {'precision': 0.9922480620155039, 'recall': 0.9846153846153847, 'f1': 0.9884169884169884, 'number': 130} | {'precision': 0.9866666666666667, 'recall': 0.9458077709611452, 'f1': 0.9658052727747326, 'number': 1956} | {'precision': 0.85431654676259, 'recall': 0.9875259875259875, 'f1': 0.9161041465766634, 'number': 481}   | {'precision': 0.8545454545454545, 'recall': 0.8392857142857143, 'f1': 0.8468468468468467, 'number': 56}     | {'precision': 0.8768115942028986, 'recall': 0.952755905511811, 'f1': 0.9132075471698112, 'number': 127}     | 0.9421            | 0.9610         | 0.9515     | 0.9872           |
+| 0.0251        | 14.0  | 266  | 0.0549          | {'precision': 0.8923315259488769, 'recall': 0.9737954353338969, 'f1': 0.931285367825384, 'number': 1183}   | {'precision': 0.9855072463768116, 'recall': 0.9941520467836257, 'f1': 0.9898107714701602, 'number': 342} | {'precision': 0.9603174603174603, 'recall': 0.983739837398374, 'f1': 0.9718875502008032, 'number': 123}  | {'precision': 0.9405940594059405, 'recall': 0.95, 'f1': 0.9452736318407959, 'number': 100} | {'precision': 1.0, 'recall': 0.983739837398374, 'f1': 0.9918032786885246, 'number': 123}                 | {'precision': 0.9922480620155039, 'recall': 0.9846153846153847, 'f1': 0.9884169884169884, 'number': 130} | {'precision': 0.98718633208756, 'recall': 0.9452965235173824, 'f1': 0.9657874118568818, 'number': 1956}   | {'precision': 0.85431654676259, 'recall': 0.9875259875259875, 'f1': 0.9161041465766634, 'number': 481}   | {'precision': 0.8421052631578947, 'recall': 0.8571428571428571, 'f1': 0.8495575221238938, 'number': 56}     | {'precision': 0.7610062893081762, 'recall': 0.952755905511811, 'f1': 0.8461538461538461, 'number': 127}     | 0.9353            | 0.9630         | 0.9489     | 0.9862           |
+| 0.0242        | 15.0  | 285  | 0.0543          | {'precision': 0.902668759811617, 'recall': 0.9721048182586645, 'f1': 0.9361009361009361, 'number': 1183}   | {'precision': 0.9855072463768116, 'recall': 0.9941520467836257, 'f1': 0.9898107714701602, 'number': 342} | {'precision': 0.9603174603174603, 'recall': 0.983739837398374, 'f1': 0.9718875502008032, 'number': 123}  | {'precision': 0.9405940594059405, 'recall': 0.95, 'f1': 0.9452736318407959, 'number': 100} | {'precision': 1.0, 'recall': 0.983739837398374, 'f1': 0.9918032786885246, 'number': 123}                 | {'precision': 0.9922480620155039, 'recall': 0.9846153846153847, 'f1': 0.9884169884169884, 'number': 130} | {'precision': 0.9887580299785867, 'recall': 0.9442740286298569, 'f1': 0.9660041841004183, 'number': 1956} | {'precision': 0.8530465949820788, 'recall': 0.9896049896049897, 'f1': 0.9162656400384984, 'number': 481} | {'precision': 0.8421052631578947, 'recall': 0.8571428571428571, 'f1': 0.8495575221238938, 'number': 56}     | {'precision': 0.7960526315789473, 'recall': 0.952755905511811, 'f1': 0.8673835125448028, 'number': 127}     | 0.9400            | 0.9623         | 0.9510     | 0.9870           |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 3.0.0
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,52 @@
+{
+  "_name_or_path": "microsoft/layoutlm-base-uncased",
+  "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 sha256:7488aa380f5fc4dd52e7a5e6b3afeb6c5d46f59965d60113b8e3b394cfc2ef75
+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 sha256:290b8e6708c244c1a969f5642e2b2e2ca63c555c3c62c691a29aa603bca0d3a3
+size 5176