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pabloma09
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layoutlm-FUNSDxSynthetic

Token Classification
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layoutlm
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End of training

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  1. README.md +20 -17
  2. logs/events.out.tfevents.1741601290.DESKTOP-HA84SVN.94162.8 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -16,14 +16,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5960
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- - Eader: {'precision': 0.3673469387755102, 'recall': 0.21686746987951808, 'f1': 0.27272727272727276, 'number': 83}
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- - Nswer: {'precision': 0.4124087591240876, 'recall': 0.551219512195122, 'f1': 0.4718162839248434, 'number': 205}
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- - Uestion: {'precision': 0.34296028880866425, 'recall': 0.41125541125541126, 'f1': 0.3740157480314961, 'number': 231}
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- - Overall Precision: 0.3767
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- - Overall Recall: 0.4355
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- - Overall F1: 0.4039
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- - Overall Accuracy: 0.7754
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  ## Model description
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@@ -48,19 +48,22 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 6
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Eader | Nswer | Uestion | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 1.237 | 1.0 | 12 | 1.0264 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83} | {'precision': 0.11783960720130933, 'recall': 0.35121951219512193, 'f1': 0.1764705882352941, 'number': 205} | {'precision': 0.10819672131147541, 'recall': 0.2857142857142857, 'f1': 0.15695600475624258, 'number': 231} | 0.1130 | 0.2659 | 0.1586 | 0.6411 |
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- | 0.9388 | 2.0 | 24 | 0.7817 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83} | {'precision': 0.2572944297082228, 'recall': 0.47317073170731705, 'f1': 0.3333333333333333, 'number': 205} | {'precision': 0.22606382978723405, 'recall': 0.36796536796536794, 'f1': 0.2800658978583196, 'number': 231} | 0.2395 | 0.3507 | 0.2846 | 0.7290 |
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- | 0.7272 | 3.0 | 36 | 0.6507 | {'precision': 0.1935483870967742, 'recall': 0.07228915662650602, 'f1': 0.10526315789473682, 'number': 83} | {'precision': 0.34983498349834985, 'recall': 0.5170731707317073, 'f1': 0.41732283464566927, 'number': 205} | {'precision': 0.33766233766233766, 'recall': 0.45021645021645024, 'f1': 0.3858998144712431, 'number': 231} | 0.3364 | 0.4162 | 0.3721 | 0.7760 |
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- | 0.5653 | 4.0 | 48 | 0.6198 | {'precision': 0.17543859649122806, 'recall': 0.12048192771084337, 'f1': 0.14285714285714288, 'number': 83} | {'precision': 0.3806228373702422, 'recall': 0.5365853658536586, 'f1': 0.44534412955465585, 'number': 205} | {'precision': 0.3413793103448276, 'recall': 0.42857142857142855, 'f1': 0.3800383877159309, 'number': 231} | 0.3443 | 0.4220 | 0.3792 | 0.7709 |
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- | 0.5199 | 5.0 | 60 | 0.5949 | {'precision': 0.3269230769230769, 'recall': 0.20481927710843373, 'f1': 0.2518518518518518, 'number': 83} | {'precision': 0.4124087591240876, 'recall': 0.551219512195122, 'f1': 0.4718162839248434, 'number': 205} | {'precision': 0.3309608540925267, 'recall': 0.4025974025974026, 'f1': 0.36328125000000006, 'number': 231} | 0.3674 | 0.4297 | 0.3961 | 0.7778 |
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- | 0.457 | 6.0 | 72 | 0.5960 | {'precision': 0.3673469387755102, 'recall': 0.21686746987951808, 'f1': 0.27272727272727276, 'number': 83} | {'precision': 0.4124087591240876, 'recall': 0.551219512195122, 'f1': 0.4718162839248434, 'number': 205} | {'precision': 0.34296028880866425, 'recall': 0.41125541125541126, 'f1': 0.3740157480314961, 'number': 231} | 0.3767 | 0.4355 | 0.4039 | 0.7754 |
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6076
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+ - Eader: {'precision': 0.43333333333333335, 'recall': 0.3132530120481928, 'f1': 0.36363636363636365, 'number': 83}
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+ - Nswer: {'precision': 0.4857142857142857, 'recall': 0.5804878048780487, 'f1': 0.5288888888888889, 'number': 205}
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+ - Uestion: {'precision': 0.358695652173913, 'recall': 0.42857142857142855, 'f1': 0.3905325443786982, 'number': 231}
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+ - Overall Precision: 0.4200
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+ - Overall Recall: 0.4701
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+ - Overall F1: 0.4436
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+ - Overall Accuracy: 0.7970
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  ## Model description
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 9
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Eader | Nswer | Uestion | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.3184 | 1.0 | 12 | 1.0718 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83} | {'precision': 0.0707482993197279, 'recall': 0.25365853658536586, 'f1': 0.11063829787234042, 'number': 205} | {'precision': 0.09251700680272108, 'recall': 0.2943722943722944, 'f1': 0.14078674948240166, 'number': 231} | 0.0816 | 0.2312 | 0.1207 | 0.6133 |
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+ | 0.9674 | 2.0 | 24 | 0.7899 | {'precision': 0.16, 'recall': 0.04819277108433735, 'f1': 0.07407407407407407, 'number': 83} | {'precision': 0.23114355231143552, 'recall': 0.4634146341463415, 'f1': 0.30844155844155846, 'number': 205} | {'precision': 0.22518159806295399, 'recall': 0.4025974025974026, 'f1': 0.2888198757763975, 'number': 231} | 0.2261 | 0.3699 | 0.2807 | 0.7268 |
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+ | 0.7106 | 3.0 | 36 | 0.6643 | {'precision': 0.3181818181818182, 'recall': 0.1686746987951807, 'f1': 0.2204724409448819, 'number': 83} | {'precision': 0.40892193308550184, 'recall': 0.5365853658536586, 'f1': 0.4641350210970464, 'number': 205} | {'precision': 0.3263157894736842, 'recall': 0.4025974025974026, 'f1': 0.36046511627906974, 'number': 231} | 0.3629 | 0.4181 | 0.3885 | 0.7812 |
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+ | 0.5308 | 4.0 | 48 | 0.6111 | {'precision': 0.3125, 'recall': 0.24096385542168675, 'f1': 0.27210884353741494, 'number': 83} | {'precision': 0.4338235294117647, 'recall': 0.5756097560975609, 'f1': 0.4947589098532495, 'number': 205} | {'precision': 0.33448275862068966, 'recall': 0.4199134199134199, 'f1': 0.3723608445297505, 'number': 231} | 0.3754 | 0.4528 | 0.4105 | 0.7867 |
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+ | 0.4626 | 5.0 | 60 | 0.5787 | {'precision': 0.4230769230769231, 'recall': 0.26506024096385544, 'f1': 0.32592592592592595, 'number': 83} | {'precision': 0.4580152671755725, 'recall': 0.5853658536585366, 'f1': 0.5139186295503212, 'number': 205} | {'precision': 0.34657039711191334, 'recall': 0.4155844155844156, 'f1': 0.3779527559055118, 'number': 231} | 0.4027 | 0.4586 | 0.4288 | 0.7938 |
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+ | 0.3703 | 6.0 | 72 | 0.5845 | {'precision': 0.4339622641509434, 'recall': 0.27710843373493976, 'f1': 0.3382352941176471, 'number': 83} | {'precision': 0.46360153256704983, 'recall': 0.5902439024390244, 'f1': 0.5193133047210301, 'number': 205} | {'precision': 0.3506944444444444, 'recall': 0.43722943722943725, 'f1': 0.3892100192678227, 'number': 231} | 0.4070 | 0.4721 | 0.4371 | 0.8011 |
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+ | 0.33 | 7.0 | 84 | 0.6011 | {'precision': 0.45614035087719296, 'recall': 0.3132530120481928, 'f1': 0.37142857142857144, 'number': 83} | {'precision': 0.4878048780487805, 'recall': 0.5853658536585366, 'f1': 0.532150776053215, 'number': 205} | {'precision': 0.37362637362637363, 'recall': 0.44155844155844154, 'f1': 0.4047619047619048, 'number': 231} | 0.4306 | 0.4778 | 0.4530 | 0.7934 |
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+ | 0.2903 | 8.0 | 96 | 0.6063 | {'precision': 0.4727272727272727, 'recall': 0.3132530120481928, 'f1': 0.3768115942028985, 'number': 83} | {'precision': 0.5, 'recall': 0.5853658536585366, 'f1': 0.5393258426966292, 'number': 205} | {'precision': 0.36900369003690037, 'recall': 0.4329004329004329, 'f1': 0.398406374501992, 'number': 231} | 0.4346 | 0.4740 | 0.4535 | 0.7972 |
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+ | 0.2723 | 9.0 | 108 | 0.6076 | {'precision': 0.43333333333333335, 'recall': 0.3132530120481928, 'f1': 0.36363636363636365, 'number': 83} | {'precision': 0.4857142857142857, 'recall': 0.5804878048780487, 'f1': 0.5288888888888889, 'number': 205} | {'precision': 0.358695652173913, 'recall': 0.42857142857142855, 'f1': 0.3905325443786982, 'number': 231} | 0.4200 | 0.4701 | 0.4436 | 0.7970 |
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  ### Framework versions
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