End of training

README.md

@@ -0,0 +1,79 @@
+---
+tags:
+- generated_from_trainer
+datasets:
+- sroie
+model-index:
+- name: layoutlm-sroie
+  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-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

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