metadata
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 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