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layoutlmv3-real_triplet

Token Classification
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layoutlmv3
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  1. README.md +21 -21
  2. logs/events.out.tfevents.1680085487.138-2-233-57.251686.0 +2 -2
  3. pytorch_model.bin +1 -1
README.md CHANGED
@@ -14,15 +14,15 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0166
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- - Item: {'precision': 0.944560669456067, 'recall': 0.9495268138801262, 'f1': 0.9470372312532774, 'number': 951}
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- - Aption: {'precision': 0.9277436946148603, 'recall': 0.9249065579340808, 'f1': 0.9263229538880381, 'number': 2943}
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- - Ootnote: {'precision': 0.8297872340425532, 'recall': 0.8068965517241379, 'f1': 0.8181818181818181, 'number': 145}
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- - Ormula: {'precision': 0.9745836985100789, 'recall': 0.9754385964912281, 'f1': 0.9750109601052169, 'number': 2280}
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- - Overall Precision: 0.9450
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- - Overall Recall: 0.9441
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- - Overall F1: 0.9446
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- - Overall Accuracy: 0.9980
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  ## Model description
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@@ -51,18 +51,18 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Item | Aption | Ootnote | Ormula | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:-----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 0.0173 | 1.0 | 8507 | 0.0103 | {'precision': 0.9440175631174533, 'recall': 0.9043112513144059, 'f1': 0.9237379162191193, 'number': 951} | {'precision': 0.9030054644808743, 'recall': 0.8984029901461094, 'f1': 0.9006983478112757, 'number': 2943} | {'precision': 0.7337662337662337, 'recall': 0.7793103448275862, 'f1': 0.7558528428093645, 'number': 145} | {'precision': 0.9664195377235063, 'recall': 0.9719298245614035, 'f1': 0.9691668488956922, 'number': 2280} | 0.9279 | 0.9231 | 0.9255 | 0.9975 |
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- | 0.0072 | 2.0 | 17014 | 0.0121 | {'precision': 0.9393305439330544, 'recall': 0.9442691903259727, 'f1': 0.9417933927635028, 'number': 951} | {'precision': 0.9193493730938664, 'recall': 0.9218484539585458, 'f1': 0.9205972175093315, 'number': 2943} | {'precision': 0.7651006711409396, 'recall': 0.7862068965517242, 'f1': 0.7755102040816326, 'number': 145} | {'precision': 0.9686820356676816, 'recall': 0.9767543859649123, 'f1': 0.9727014632015725, 'number': 2280} | 0.9366 | 0.9419 | 0.9392 | 0.9977 |
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- | 0.0056 | 3.0 | 25521 | 0.0104 | {'precision': 0.9389067524115756, 'recall': 0.9211356466876972, 'f1': 0.9299363057324842, 'number': 951} | {'precision': 0.9154160982264665, 'recall': 0.9119945633707102, 'f1': 0.9137021276595745, 'number': 2943} | {'precision': 0.6842105263157895, 'recall': 0.8068965517241379, 'f1': 0.7405063291139241, 'number': 145} | {'precision': 0.973568281938326, 'recall': 0.9692982456140351, 'f1': 0.9714285714285714, 'number': 2280} | 0.9336 | 0.9316 | 0.9326 | 0.9978 |
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- | 0.004 | 4.0 | 34028 | 0.0120 | {'precision': 0.9771428571428571, 'recall': 0.8990536277602523, 'f1': 0.9364731653888281, 'number': 951} | {'precision': 0.9220199244245963, 'recall': 0.9119945633707102, 'f1': 0.9169798428425008, 'number': 2943} | {'precision': 0.8214285714285714, 'recall': 0.7931034482758621, 'f1': 0.8070175438596492, 'number': 145} | {'precision': 0.9733158355205599, 'recall': 0.9758771929824561, 'f1': 0.9745948313622427, 'number': 2280} | 0.9464 | 0.9304 | 0.9383 | 0.9981 |
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- | 0.0028 | 5.0 | 42535 | 0.0122 | {'precision': 0.9588431590656284, 'recall': 0.9064143007360673, 'f1': 0.9318918918918919, 'number': 951} | {'precision': 0.924378453038674, 'recall': 0.909616038056405, 'f1': 0.9169378318205172, 'number': 2943} | {'precision': 0.8984375, 'recall': 0.7931034482758621, 'f1': 0.8424908424908425, 'number': 145} | {'precision': 0.971453667105841, 'recall': 0.9701754385964912, 'f1': 0.9708141321044547, 'number': 2280} | 0.9461 | 0.9283 | 0.9371 | 0.9980 |
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- | 0.0022 | 6.0 | 51042 | 0.0161 | {'precision': 0.899009900990099, 'recall': 0.9547844374342797, 'f1': 0.9260581336053034, 'number': 951} | {'precision': 0.9120916133378242, 'recall': 0.9201495073054706, 'f1': 0.9161028416779432, 'number': 2943} | {'precision': 0.8538461538461538, 'recall': 0.7655172413793103, 'f1': 0.8072727272727271, 'number': 145} | {'precision': 0.9733275032794053, 'recall': 0.9763157894736842, 'f1': 0.9748193562513685, 'number': 2280} | 0.9307 | 0.9421 | 0.9364 | 0.9978 |
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- | 0.0015 | 7.0 | 59549 | 0.0187 | {'precision': 0.9438444924406048, 'recall': 0.9190325972660357, 'f1': 0.9312733084709643, 'number': 951} | {'precision': 0.9206730769230769, 'recall': 0.9109751953788651, 'f1': 0.9157984628522631, 'number': 2943} | {'precision': 0.875968992248062, 'recall': 0.7793103448275862, 'f1': 0.8248175182481752, 'number': 145} | {'precision': 0.9741681260945709, 'recall': 0.9758771929824561, 'f1': 0.9750219106047328, 'number': 2280} | 0.9427 | 0.9326 | 0.9376 | 0.9981 |
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- | 0.0012 | 8.0 | 68056 | 0.0145 | {'precision': 0.9401260504201681, 'recall': 0.9411146161934806, 'f1': 0.9406200735680504, 'number': 951} | {'precision': 0.9231032125768968, 'recall': 0.9177709819911655, 'f1': 0.9204293746805248, 'number': 2943} | {'precision': 0.8041958041958042, 'recall': 0.7931034482758621, 'f1': 0.7986111111111112, 'number': 145} | {'precision': 0.9753629564452265, 'recall': 0.9723684210526315, 'f1': 0.9738633867779486, 'number': 2280} | 0.9418 | 0.9381 | 0.9400 | 0.9981 |
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- | 0.0009 | 9.0 | 76563 | 0.0140 | {'precision': 0.9475890985324947, 'recall': 0.9505783385909569, 'f1': 0.9490813648293962, 'number': 951} | {'precision': 0.9295003422313484, 'recall': 0.9228678219503907, 'f1': 0.9261722080136402, 'number': 2943} | {'precision': 0.8740740740740741, 'recall': 0.8137931034482758, 'f1': 0.8428571428571429, 'number': 145} | {'precision': 0.9771025979744606, 'recall': 0.9732456140350877, 'f1': 0.975170292243463, 'number': 2280} | 0.9483 | 0.9427 | 0.9455 | 0.9980 |
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- | 0.0007 | 10.0 | 85070 | 0.0166 | {'precision': 0.944560669456067, 'recall': 0.9495268138801262, 'f1': 0.9470372312532774, 'number': 951} | {'precision': 0.9277436946148603, 'recall': 0.9249065579340808, 'f1': 0.9263229538880381, 'number': 2943} | {'precision': 0.8297872340425532, 'recall': 0.8068965517241379, 'f1': 0.8181818181818181, 'number': 145} | {'precision': 0.9745836985100789, 'recall': 0.9754385964912281, 'f1': 0.9750109601052169, 'number': 2280} | 0.9450 | 0.9441 | 0.9446 | 0.9980 |
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  ### Framework versions
 
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15
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0279
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+ - Item: {'precision': 0.9425511197663097, 'recall': 0.7378048780487805, 'f1': 0.827704147071398, 'number': 2624}
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+ - Aption: {'precision': 0.8349913494809689, 'recall': 0.7924876847290641, 'f1': 0.81318449873631, 'number': 4872}
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+ - Ootnote: {'precision': 0.7846153846153846, 'recall': 0.8360655737704918, 'f1': 0.8095238095238095, 'number': 122}
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+ - Ormula: {'precision': 0.9865976241242765, 'recall': 0.9920367534456356, 'f1': 0.9893097128894318, 'number': 3265}
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+ - Overall Precision: 0.9056
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+ - Overall Recall: 0.8397
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+ - Overall F1: 0.8714
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+ - Overall Accuracy: 0.9961
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Item | Aption | Ootnote | Ormula | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.0166 | 1.0 | 8507 | 0.0252 | {'precision': 0.9114948731786292, 'recall': 0.6436737804878049, 'f1': 0.7545231181594819, 'number': 2624} | {'precision': 0.778075463273052, 'recall': 0.715311986863711, 'f1': 0.7453748262217944, 'number': 4872} | {'precision': 0.8598130841121495, 'recall': 0.7540983606557377, 'f1': 0.8034934497816593, 'number': 122} | {'precision': 0.976365868631062, 'recall': 0.9742725880551302, 'f1': 0.9753181051663345, 'number': 3265} | 0.8711 | 0.7762 | 0.8209 | 0.9948 |
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+ | 0.0075 | 2.0 | 17014 | 0.0318 | {'precision': 0.9079837618403248, 'recall': 0.5114329268292683, 'f1': 0.6543149683081424, 'number': 2624} | {'precision': 0.7194696441032798, 'recall': 0.6348522167487685, 'f1': 0.6745175008177952, 'number': 4872} | {'precision': 0.9207920792079208, 'recall': 0.7622950819672131, 'f1': 0.8340807174887892, 'number': 122} | {'precision': 0.9831132944427388, 'recall': 0.9807044410413476, 'f1': 0.9819073903710519, 'number': 3265} | 0.8462 | 0.7103 | 0.7723 | 0.9938 |
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+ | 0.0057 | 3.0 | 25521 | 0.0338 | {'precision': 0.9227359088030399, 'recall': 0.5552591463414634, 'f1': 0.6933142993100166, 'number': 2624} | {'precision': 0.7442236598890942, 'recall': 0.6611247947454844, 'f1': 0.7002173913043479, 'number': 4872} | {'precision': 0.8859649122807017, 'recall': 0.8278688524590164, 'f1': 0.8559322033898306, 'number': 122} | {'precision': 0.9791538933169834, 'recall': 0.9782542113323124, 'f1': 0.9787038455645779, 'number': 3265} | 0.8589 | 0.7326 | 0.7907 | 0.9942 |
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+ | 0.004 | 4.0 | 34028 | 0.0615 | {'precision': 0.9321486268174475, 'recall': 0.43978658536585363, 'f1': 0.5976178146038322, 'number': 2624} | {'precision': 0.6900404088424055, 'recall': 0.5958538587848933, 'f1': 0.639497742042075, 'number': 4872} | {'precision': 0.8738738738738738, 'recall': 0.7950819672131147, 'f1': 0.832618025751073, 'number': 122} | {'precision': 0.9880660954712362, 'recall': 0.9889739663093415, 'f1': 0.9885198224399205, 'number': 3265} | 0.8367 | 0.6784 | 0.7493 | 0.9932 |
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+ | 0.0027 | 5.0 | 42535 | 0.0227 | {'precision': 0.9356973995271868, 'recall': 0.7541920731707317, 'f1': 0.8351972990082295, 'number': 2624} | {'precision': 0.843103448275862, 'recall': 0.8029556650246306, 'f1': 0.8225399495374264, 'number': 4872} | {'precision': 0.8571428571428571, 'recall': 0.7868852459016393, 'f1': 0.8205128205128205, 'number': 122} | {'precision': 0.9850655288021944, 'recall': 0.9898928024502297, 'f1': 0.9874732661167125, 'number': 3265} | 0.9085 | 0.8471 | 0.8767 | 0.9963 |
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+ | 0.0021 | 6.0 | 51042 | 0.0165 | {'precision': 0.9341987466427932, 'recall': 0.7953506097560976, 'f1': 0.859201317414574, 'number': 2624} | {'precision': 0.856687898089172, 'recall': 0.8282019704433498, 'f1': 0.8422041327489042, 'number': 4872} | {'precision': 0.9174311926605505, 'recall': 0.819672131147541, 'f1': 0.8658008658008659, 'number': 122} | {'precision': 0.9736523319200484, 'recall': 0.9846860643185299, 'f1': 0.9791381148165067, 'number': 3265} | 0.9113 | 0.8671 | 0.8887 | 0.9966 |
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+ | 0.0015 | 7.0 | 59549 | 0.0271 | {'precision': 0.9294605809128631, 'recall': 0.6829268292682927, 'f1': 0.7873462214411249, 'number': 2624} | {'precision': 0.8111135515045025, 'recall': 0.7580049261083743, 'f1': 0.7836604774535808, 'number': 4872} | {'precision': 0.8389830508474576, 'recall': 0.8114754098360656, 'f1': 0.825, 'number': 122} | {'precision': 0.9880879657910813, 'recall': 0.9908116385911179, 'f1': 0.9894479278177092, 'number': 3265} | 0.8932 | 0.8103 | 0.8498 | 0.9956 |
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+ | 0.0012 | 8.0 | 68056 | 0.0231 | {'precision': 0.9250706880301602, 'recall': 0.7480945121951219, 'f1': 0.8272229245680573, 'number': 2624} | {'precision': 0.8451156812339332, 'recall': 0.8097290640394089, 'f1': 0.8270440251572327, 'number': 4872} | {'precision': 0.8962264150943396, 'recall': 0.7786885245901639, 'f1': 0.8333333333333333, 'number': 122} | {'precision': 0.9889739663093415, 'recall': 0.9889739663093415, 'f1': 0.9889739663093415, 'number': 3265} | 0.9086 | 0.8483 | 0.8774 | 0.9962 |
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+ | 0.0009 | 9.0 | 76563 | 0.0224 | {'precision': 0.9263715110683349, 'recall': 0.7336128048780488, 'f1': 0.8188005104210974, 'number': 2624} | {'precision': 0.835820895522388, 'recall': 0.7931034482758621, 'f1': 0.8139020537124803, 'number': 4872} | {'precision': 0.832, 'recall': 0.8524590163934426, 'f1': 0.8421052631578947, 'number': 122} | {'precision': 0.9829787234042553, 'recall': 0.9905053598774886, 'f1': 0.9867276887871854, 'number': 3265} | 0.9022 | 0.8386 | 0.8693 | 0.9960 |
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+ | 0.0007 | 10.0 | 85070 | 0.0279 | {'precision': 0.9425511197663097, 'recall': 0.7378048780487805, 'f1': 0.827704147071398, 'number': 2624} | {'precision': 0.8349913494809689, 'recall': 0.7924876847290641, 'f1': 0.81318449873631, 'number': 4872} | {'precision': 0.7846153846153846, 'recall': 0.8360655737704918, 'f1': 0.8095238095238095, 'number': 122} | {'precision': 0.9865976241242765, 'recall': 0.9920367534456356, 'f1': 0.9893097128894318, 'number': 3265} | 0.9056 | 0.8397 | 0.8714 | 0.9961 |
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  ### Framework versions
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