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: 0.0363
+- Address: {'precision': 0.901685393258427, 'recall': 0.9250720461095101, 'f1': 0.9132290184921764, 'number': 347}
+- Company: {'precision': 0.904891304347826, 'recall': 0.9596541786743515, 'f1': 0.9314685314685315, 'number': 347}
+- Date: {'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347}
+- Total: {'precision': 0.8155080213903744, 'recall': 0.8789625360230547, 'f1': 0.8460471567267684, 'number': 347}
+- Overall Precision: 0.9017
+- Overall Recall: 0.9388
+- Overall F1: 0.9199
+- Overall Accuracy: 0.9930
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.5189        | 1.0   | 40   | 0.1280          | {'precision': 0.7891891891891892, 'recall': 0.8414985590778098, 'f1': 0.8145048814504882, 'number': 347} | {'precision': 0.6987012987012987, 'recall': 0.7752161383285303, 'f1': 0.7349726775956286, 'number': 347} | {'precision': 0.6651982378854625, 'recall': 0.8703170028818443, 'f1': 0.7540574282147315, 'number': 347} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 347}                                               | 0.7138            | 0.6218         | 0.6646     | 0.9650           |
+| 0.0849        | 2.0   | 80   | 0.0558          | {'precision': 0.8753462603878116, 'recall': 0.9106628242074928, 'f1': 0.8926553672316384, 'number': 347} | {'precision': 0.8102189781021898, 'recall': 0.9596541786743515, 'f1': 0.8786279683377309, 'number': 347} | {'precision': 0.9178082191780822, 'recall': 0.9654178674351584, 'f1': 0.9410112359550562, 'number': 347} | {'precision': 0.5179282868525896, 'recall': 0.3746397694524496, 'f1': 0.43478260869565216, 'number': 347} | 0.8026            | 0.8026         | 0.8026     | 0.9851           |
+| 0.0447        | 3.0   | 120  | 0.0435          | {'precision': 0.8997214484679665, 'recall': 0.930835734870317, 'f1': 0.9150141643059491, 'number': 347}  | {'precision': 0.8954423592493298, 'recall': 0.962536023054755, 'f1': 0.9277777777777777, 'number': 347}  | {'precision': 0.96045197740113, 'recall': 0.9798270893371758, 'f1': 0.9700427960057061, 'number': 347}   | {'precision': 0.6222222222222222, 'recall': 0.6455331412103746, 'f1': 0.6336633663366337, 'number': 347}  | 0.8444            | 0.8797         | 0.8617     | 0.9890           |
+| 0.0318        | 4.0   | 160  | 0.0347          | {'precision': 0.8777777777777778, 'recall': 0.9106628242074928, 'f1': 0.8939179632248939, 'number': 347} | {'precision': 0.9153005464480874, 'recall': 0.9654178674351584, 'f1': 0.9396914446002805, 'number': 347} | {'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347} | {'precision': 0.671957671957672, 'recall': 0.7319884726224783, 'f1': 0.7006896551724137, 'number': 347}   | 0.8608            | 0.8999         | 0.8799     | 0.9910           |
+| 0.0245        | 5.0   | 200  | 0.0360          | {'precision': 0.8885793871866295, 'recall': 0.9193083573487032, 'f1': 0.9036827195467422, 'number': 347} | {'precision': 0.8909574468085106, 'recall': 0.9654178674351584, 'f1': 0.9266943291839556, 'number': 347} | {'precision': 0.9913294797687862, 'recall': 0.9884726224783862, 'f1': 0.98989898989899, 'number': 347}   | {'precision': 0.7873754152823921, 'recall': 0.6829971181556196, 'f1': 0.7314814814814815, 'number': 347}  | 0.8929            | 0.8890         | 0.8910     | 0.9910           |
+| 0.0171        | 6.0   | 240  | 0.0325          | {'precision': 0.8932584269662921, 'recall': 0.9164265129682997, 'f1': 0.9046941678520626, 'number': 347} | {'precision': 0.912568306010929, 'recall': 0.962536023054755, 'f1': 0.9368863955119215, 'number': 347}   | {'precision': 0.991304347826087, 'recall': 0.9855907780979827, 'f1': 0.9884393063583815, 'number': 347}  | {'precision': 0.823170731707317, 'recall': 0.7780979827089337, 'f1': 0.8, 'number': 347}                  | 0.9061            | 0.9107         | 0.9084     | 0.9926           |
+| 0.0133        | 7.0   | 280  | 0.0352          | {'precision': 0.8969359331476323, 'recall': 0.9279538904899135, 'f1': 0.9121813031161472, 'number': 347} | {'precision': 0.9103260869565217, 'recall': 0.9654178674351584, 'f1': 0.937062937062937, 'number': 347}  | {'precision': 0.9885057471264368, 'recall': 0.9913544668587896, 'f1': 0.9899280575539569, 'number': 347} | {'precision': 0.7801608579088471, 'recall': 0.8386167146974063, 'f1': 0.8083333333333332, 'number': 347}  | 0.8923            | 0.9308         | 0.9111     | 0.9922           |
+| 0.013         | 8.0   | 320  | 0.0338          | {'precision': 0.889196675900277, 'recall': 0.9250720461095101, 'f1': 0.9067796610169492, 'number': 347}  | {'precision': 0.9103260869565217, 'recall': 0.9654178674351584, 'f1': 0.937062937062937, 'number': 347}  | {'precision': 0.9885057471264368, 'recall': 0.9913544668587896, 'f1': 0.9899280575539569, 'number': 347} | {'precision': 0.7887700534759359, 'recall': 0.8501440922190202, 'f1': 0.8183079056865465, 'number': 347}  | 0.8925            | 0.9330         | 0.9123     | 0.9927           |
+| 0.0105        | 9.0   | 360  | 0.0378          | {'precision': 0.8885793871866295, 'recall': 0.9193083573487032, 'f1': 0.9036827195467422, 'number': 347} | {'precision': 0.9081081081081082, 'recall': 0.968299711815562, 'f1': 0.9372384937238494, 'number': 347}  | {'precision': 0.9913294797687862, 'recall': 0.9884726224783862, 'f1': 0.98989898989899, 'number': 347}   | {'precision': 0.8096590909090909, 'recall': 0.8213256484149856, 'f1': 0.8154506437768241, 'number': 347}  | 0.8991            | 0.9244         | 0.9115     | 0.9923           |
+| 0.0094        | 10.0  | 400  | 0.0353          | {'precision': 0.901685393258427, 'recall': 0.9250720461095101, 'f1': 0.9132290184921764, 'number': 347}  | {'precision': 0.904891304347826, 'recall': 0.9596541786743515, 'f1': 0.9314685314685315, 'number': 347}  | {'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347} | {'precision': 0.8142076502732241, 'recall': 0.8587896253602305, 'f1': 0.8359046283309958, 'number': 347}  | 0.9019            | 0.9337         | 0.9175     | 0.9929           |
+| 0.0078        | 11.0  | 440  | 0.0373          | {'precision': 0.8938547486033519, 'recall': 0.9221902017291066, 'f1': 0.9078014184397163, 'number': 347} | {'precision': 0.9098360655737705, 'recall': 0.9596541786743515, 'f1': 0.9340813464235624, 'number': 347} | {'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347} | {'precision': 0.8150134048257373, 'recall': 0.8760806916426513, 'f1': 0.8444444444444444, 'number': 347}  | 0.9010            | 0.9373         | 0.9188     | 0.9928           |
+| 0.0074        | 12.0  | 480  | 0.0379          | {'precision': 0.8994413407821229, 'recall': 0.9279538904899135, 'f1': 0.9134751773049646, 'number': 347} | {'precision': 0.9128065395095368, 'recall': 0.9654178674351584, 'f1': 0.938375350140056, 'number': 347}  | {'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347} | {'precision': 0.835195530726257, 'recall': 0.861671469740634, 'f1': 0.8482269503546098, 'number': 347}    | 0.9091            | 0.9366         | 0.9226     | 0.9931           |
+| 0.007         | 13.0  | 520  | 0.0357          | {'precision': 0.9019607843137255, 'recall': 0.9279538904899135, 'f1': 0.9147727272727272, 'number': 347} | {'precision': 0.9024390243902439, 'recall': 0.9596541786743515, 'f1': 0.9301675977653631, 'number': 347} | {'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347} | {'precision': 0.8328767123287671, 'recall': 0.8760806916426513, 'f1': 0.853932584269663, 'number': 347}   | 0.9061            | 0.9388         | 0.9222     | 0.9932           |
+| 0.0069        | 14.0  | 560  | 0.0361          | {'precision': 0.901685393258427, 'recall': 0.9250720461095101, 'f1': 0.9132290184921764, 'number': 347}  | {'precision': 0.9051490514905149, 'recall': 0.962536023054755, 'f1': 0.9329608938547486, 'number': 347}  | {'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347} | {'precision': 0.8046875, 'recall': 0.8904899135446686, 'f1': 0.8454172366621068, 'number': 347}           | 0.8984            | 0.9424         | 0.9198     | 0.9930           |
+| 0.0065        | 15.0  | 600  | 0.0363          | {'precision': 0.901685393258427, 'recall': 0.9250720461095101, 'f1': 0.9132290184921764, 'number': 347}  | {'precision': 0.904891304347826, 'recall': 0.9596541786743515, 'f1': 0.9314685314685315, 'number': 347}  | {'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347} | {'precision': 0.8155080213903744, 'recall': 0.8789625360230547, 'f1': 0.8460471567267684, 'number': 347}  | 0.9017            | 0.9388         | 0.9199     | 0.9930           |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.12.1

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