--- tags: - generated_from_trainer datasets: - sroie model-index: - name: layoutlm-sroie results: [] --- # layoutlm-sroie This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the sroie dataset. It achieves the following results on the evaluation set: - Loss: 0.0320 - Address: {'precision': 0.9154929577464789, 'recall': 0.9365994236311239, 'f1': 0.925925925925926, 'number': 347} - Company: {'precision': 0.9228650137741047, 'recall': 0.9654178674351584, 'f1': 0.9436619718309859, 'number': 347} - Date: {'precision': 0.9884393063583815, 'recall': 0.9855907780979827, 'f1': 0.987012987012987, 'number': 347} - Total: {'precision': 0.8431372549019608, 'recall': 0.8674351585014409, 'f1': 0.8551136363636365, 'number': 347} - Overall Precision: 0.9170 - Overall Recall: 0.9388 - Overall F1: 0.9277 - Overall Accuracy: 0.9942 ## 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: 3e-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 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Address | Company | Date | Total | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.5392 | 1.0 | 40 | 0.1200 | {'precision': 0.8346883468834688, 'recall': 0.8876080691642652, 'f1': 0.8603351955307261, 'number': 347} | {'precision': 0.75, 'recall': 0.8126801152737753, 'f1': 0.7800829875518672, 'number': 347} | {'precision': 0.648068669527897, 'recall': 0.8703170028818443, 'f1': 0.7429274292742927, 'number': 347} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 347} | 0.7366 | 0.6427 | 0.6864 | 0.9712 | | 0.0803 | 2.0 | 80 | 0.0485 | {'precision': 0.896358543417367, 'recall': 0.9221902017291066, 'f1': 0.9090909090909091, 'number': 347} | {'precision': 0.8897849462365591, 'recall': 0.9538904899135446, 'f1': 0.9207232267037552, 'number': 347} | {'precision': 0.9628571428571429, 'recall': 0.9711815561959655, 'f1': 0.9670014347202295, 'number': 347} | {'precision': 0.5219298245614035, 'recall': 0.34293948126801155, 'f1': 0.41391304347826086, 'number': 347} | 0.8470 | 0.7976 | 0.8215 | 0.9876 | | 0.0441 | 3.0 | 120 | 0.0373 | {'precision': 0.9131652661064426, 'recall': 0.9394812680115274, 'f1': 0.9261363636363636, 'number': 347} | {'precision': 0.9277777777777778, 'recall': 0.962536023054755, 'f1': 0.9448373408769449, 'number': 347} | {'precision': 0.9826589595375722, 'recall': 0.9798270893371758, 'f1': 0.9812409812409811, 'number': 347} | {'precision': 0.5418502202643172, 'recall': 0.7089337175792507, 'f1': 0.6142322097378278, 'number': 347} | 0.8214 | 0.8977 | 0.8578 | 0.9897 | | 0.0309 | 4.0 | 160 | 0.0333 | {'precision': 0.8864265927977839, 'recall': 0.9221902017291066, 'f1': 0.903954802259887, 'number': 347} | {'precision': 0.915068493150685, 'recall': 0.962536023054755, 'f1': 0.9382022471910113, 'number': 347} | {'precision': 0.9912790697674418, 'recall': 0.9827089337175793, 'f1': 0.9869753979739508, 'number': 347} | {'precision': 0.6556122448979592, 'recall': 0.7406340057636888, 'f1': 0.6955345060893099, 'number': 347} | 0.8564 | 0.9020 | 0.8786 | 0.9916 | | 0.0249 | 5.0 | 200 | 0.0318 | {'precision': 0.907563025210084, 'recall': 0.9337175792507204, 'f1': 0.9204545454545455, 'number': 347} | {'precision': 0.9054054054054054, 'recall': 0.9654178674351584, 'f1': 0.9344490934449093, 'number': 347} | {'precision': 0.9912536443148688, 'recall': 0.9798270893371758, 'f1': 0.9855072463768115, 'number': 347} | {'precision': 0.7186700767263428, 'recall': 0.8097982708933718, 'f1': 0.7615176151761518, 'number': 347} | 0.8761 | 0.9222 | 0.8986 | 0.9924 | | 0.0183 | 6.0 | 240 | 0.0294 | {'precision': 0.9103641456582633, 'recall': 0.9365994236311239, 'f1': 0.9232954545454545, 'number': 347} | {'precision': 0.9201101928374655, 'recall': 0.962536023054755, 'f1': 0.9408450704225352, 'number': 347} | {'precision': 0.991304347826087, 'recall': 0.9855907780979827, 'f1': 0.9884393063583815, 'number': 347} | {'precision': 0.8397435897435898, 'recall': 0.7550432276657061, 'f1': 0.795144157814871, 'number': 347} | 0.9172 | 0.9099 | 0.9136 | 0.9936 | | 0.0155 | 7.0 | 280 | 0.0290 | {'precision': 0.9129213483146067, 'recall': 0.9365994236311239, 'f1': 0.9246088193456615, 'number': 347} | {'precision': 0.9155313351498637, 'recall': 0.968299711815562, 'f1': 0.9411764705882353, 'number': 347} | {'precision': 0.9855907780979827, 'recall': 0.9855907780979827, 'f1': 0.9855907780979827, 'number': 347} | {'precision': 0.8228882833787466, 'recall': 0.8703170028818443, 'f1': 0.8459383753501399, 'number': 347} | 0.9081 | 0.9402 | 0.9239 | 0.9940 | | 0.0125 | 8.0 | 320 | 0.0322 | {'precision': 0.9103641456582633, 'recall': 0.9365994236311239, 'f1': 0.9232954545454545, 'number': 347} | {'precision': 0.9027027027027027, 'recall': 0.962536023054755, 'f1': 0.9316596931659692, 'number': 347} | {'precision': 0.9884393063583815, 'recall': 0.9855907780979827, 'f1': 0.987012987012987, 'number': 347} | {'precision': 0.8269230769230769, 'recall': 0.8674351585014409, 'f1': 0.8466947960618847, 'number': 347} | 0.9061 | 0.9380 | 0.9218 | 0.9936 | | 0.0108 | 9.0 | 360 | 0.0302 | {'precision': 0.9078212290502793, 'recall': 0.9365994236311239, 'f1': 0.921985815602837, 'number': 347} | {'precision': 0.9205479452054794, 'recall': 0.968299711815562, 'f1': 0.9438202247191011, 'number': 347} | {'precision': 0.9884393063583815, 'recall': 0.9855907780979827, 'f1': 0.987012987012987, 'number': 347} | {'precision': 0.8386167146974063, 'recall': 0.8386167146974063, 'f1': 0.8386167146974063, 'number': 347} | 0.9138 | 0.9323 | 0.9230 | 0.9941 | | 0.0099 | 10.0 | 400 | 0.0304 | {'precision': 0.8833333333333333, 'recall': 0.9164265129682997, 'f1': 0.8995756718528995, 'number': 347} | {'precision': 0.9226519337016574, 'recall': 0.962536023054755, 'f1': 0.9421720733427363, 'number': 347} | {'precision': 0.9884393063583815, 'recall': 0.9855907780979827, 'f1': 0.987012987012987, 'number': 347} | {'precision': 0.8472222222222222, 'recall': 0.8789625360230547, 'f1': 0.8628005657708628, 'number': 347} | 0.9097 | 0.9359 | 0.9226 | 0.9943 | | 0.0079 | 11.0 | 440 | 0.0312 | {'precision': 0.9206798866855525, 'recall': 0.9365994236311239, 'f1': 0.9285714285714285, 'number': 347} | {'precision': 0.9203296703296703, 'recall': 0.9654178674351584, 'f1': 0.9423347398030942, 'number': 347} | {'precision': 0.9884393063583815, 'recall': 0.9855907780979827, 'f1': 0.987012987012987, 'number': 347} | {'precision': 0.8515406162464986, 'recall': 0.8760806916426513, 'f1': 0.8636363636363638, 'number': 347} | 0.9197 | 0.9409 | 0.9302 | 0.9945 | | 0.0079 | 12.0 | 480 | 0.0322 | {'precision': 0.9101123595505618, 'recall': 0.9337175792507204, 'f1': 0.9217638691322901, 'number': 347} | {'precision': 0.9153005464480874, 'recall': 0.9654178674351584, 'f1': 0.9396914446002805, 'number': 347} | {'precision': 0.9884393063583815, 'recall': 0.9855907780979827, 'f1': 0.987012987012987, 'number': 347} | {'precision': 0.8310626702997275, 'recall': 0.8789625360230547, 'f1': 0.8543417366946778, 'number': 347} | 0.9101 | 0.9409 | 0.9253 | 0.9941 | | 0.0068 | 13.0 | 520 | 0.0311 | {'precision': 0.9101123595505618, 'recall': 0.9337175792507204, 'f1': 0.9217638691322901, 'number': 347} | {'precision': 0.9226519337016574, 'recall': 0.962536023054755, 'f1': 0.9421720733427363, 'number': 347} | {'precision': 0.991304347826087, 'recall': 0.9855907780979827, 'f1': 0.9884393063583815, 'number': 347} | {'precision': 0.8467966573816156, 'recall': 0.8760806916426513, 'f1': 0.8611898016997168, 'number': 347} | 0.9170 | 0.9395 | 0.9281 | 0.9943 | | 0.0068 | 14.0 | 560 | 0.0318 | {'precision': 0.9101123595505618, 'recall': 0.9337175792507204, 'f1': 0.9217638691322901, 'number': 347} | {'precision': 0.9201101928374655, 'recall': 0.962536023054755, 'f1': 0.9408450704225352, 'number': 347} | {'precision': 0.9884393063583815, 'recall': 0.9855907780979827, 'f1': 0.987012987012987, 'number': 347} | {'precision': 0.837465564738292, 'recall': 0.8760806916426513, 'f1': 0.8563380281690142, 'number': 347} | 0.9132 | 0.9395 | 0.9261 | 0.9943 | | 0.0061 | 15.0 | 600 | 0.0320 | {'precision': 0.9154929577464789, 'recall': 0.9365994236311239, 'f1': 0.925925925925926, 'number': 347} | {'precision': 0.9228650137741047, 'recall': 0.9654178674351584, 'f1': 0.9436619718309859, 'number': 347} | {'precision': 0.9884393063583815, 'recall': 0.9855907780979827, 'f1': 0.987012987012987, 'number': 347} | {'precision': 0.8431372549019608, 'recall': 0.8674351585014409, 'f1': 0.8551136363636365, 'number': 347} | 0.9170 | 0.9388 | 0.9277 | 0.9942 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.13.3