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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: layoutlm-funsd |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlm-funsd |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6857 |
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- Answer: {'precision': 0.7176981541802389, 'recall': 0.8170580964153276, 'f1': 0.7641618497109827, 'number': 809} |
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- Header: {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119} |
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- Question: {'precision': 0.7773820124666073, 'recall': 0.819718309859155, 'f1': 0.7979890310786105, 'number': 1065} |
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- Overall Precision: 0.7204 |
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- Overall Recall: 0.7898 |
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- Overall F1: 0.7535 |
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- Overall Accuracy: 0.8139 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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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 | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 1.8064 | 1.0 | 10 | 1.6080 | {'precision': 0.020618556701030927, 'recall': 0.012360939431396786, 'f1': 0.01545595054095827, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2702127659574468, 'recall': 0.11924882629107982, 'f1': 0.16547231270358306, 'number': 1065} | 0.1435 | 0.0687 | 0.0929 | 0.3378 | |
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| 1.4826 | 2.0 | 20 | 1.2520 | {'precision': 0.20166320166320167, 'recall': 0.23980222496909764, 'f1': 0.21908526256352345, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4309507286606523, 'recall': 0.5830985915492958, 'f1': 0.49561053471667993, 'number': 1065} | 0.3392 | 0.4089 | 0.3708 | 0.5993 | |
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| 1.1438 | 3.0 | 30 | 0.9584 | {'precision': 0.463519313304721, 'recall': 0.5339925834363412, 'f1': 0.49626651349798967, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6199664429530202, 'recall': 0.6938967136150235, 'f1': 0.6548515728843598, 'number': 1065} | 0.5492 | 0.5876 | 0.5678 | 0.6897 | |
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| 0.8546 | 4.0 | 40 | 0.7900 | {'precision': 0.5885714285714285, 'recall': 0.7639060568603214, 'f1': 0.6648735879505111, 'number': 809} | {'precision': 0.06666666666666667, 'recall': 0.025210084033613446, 'f1': 0.036585365853658534, 'number': 119} | {'precision': 0.6505823627287853, 'recall': 0.7342723004694836, 'f1': 0.6898985443317159, 'number': 1065} | 0.6108 | 0.7040 | 0.6541 | 0.7537 | |
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| 0.6765 | 5.0 | 50 | 0.7144 | {'precision': 0.6514047866805411, 'recall': 0.7737948084054388, 'f1': 0.7073446327683616, 'number': 809} | {'precision': 0.09230769230769231, 'recall': 0.05042016806722689, 'f1': 0.06521739130434782, 'number': 119} | {'precision': 0.7019810508182601, 'recall': 0.7652582159624414, 'f1': 0.7322551662174304, 'number': 1065} | 0.6616 | 0.7260 | 0.6923 | 0.7773 | |
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| 0.5613 | 6.0 | 60 | 0.6796 | {'precision': 0.6635514018691588, 'recall': 0.7898640296662547, 'f1': 0.7212189616252822, 'number': 809} | {'precision': 0.15306122448979592, 'recall': 0.12605042016806722, 'f1': 0.1382488479262673, 'number': 119} | {'precision': 0.7274320771253286, 'recall': 0.7793427230046949, 'f1': 0.7524932003626473, 'number': 1065} | 0.6739 | 0.7446 | 0.7075 | 0.7927 | |
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| 0.4872 | 7.0 | 70 | 0.6554 | {'precision': 0.6592517694641051, 'recall': 0.8059332509270705, 'f1': 0.7252502780867631, 'number': 809} | {'precision': 0.22549019607843138, 'recall': 0.19327731092436976, 'f1': 0.20814479638009048, 'number': 119} | {'precision': 0.7383177570093458, 'recall': 0.815962441314554, 'f1': 0.775200713648528, 'number': 1065} | 0.6808 | 0.7747 | 0.7247 | 0.7997 | |
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| 0.4334 | 8.0 | 80 | 0.6526 | {'precision': 0.6941176470588235, 'recall': 0.8022249690976514, 'f1': 0.7442660550458714, 'number': 809} | {'precision': 0.24545454545454545, 'recall': 0.226890756302521, 'f1': 0.23580786026200873, 'number': 119} | {'precision': 0.7493627867459643, 'recall': 0.828169014084507, 'f1': 0.7867975022301517, 'number': 1065} | 0.7012 | 0.7817 | 0.7393 | 0.8035 | |
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| 0.3941 | 9.0 | 90 | 0.6694 | {'precision': 0.7048997772828508, 'recall': 0.7824474660074165, 'f1': 0.741652021089631, 'number': 809} | {'precision': 0.22099447513812154, 'recall': 0.33613445378151263, 'f1': 0.26666666666666666, 'number': 119} | {'precision': 0.7218984179850125, 'recall': 0.8140845070422535, 'f1': 0.76522506619594, 'number': 1065} | 0.6754 | 0.7727 | 0.7208 | 0.8007 | |
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| 0.3556 | 10.0 | 100 | 0.6607 | {'precision': 0.694006309148265, 'recall': 0.8158220024721878, 'f1': 0.75, 'number': 809} | {'precision': 0.25, 'recall': 0.2773109243697479, 'f1': 0.26294820717131473, 'number': 119} | {'precision': 0.7846153846153846, 'recall': 0.8140845070422535, 'f1': 0.7990783410138248, 'number': 1065} | 0.7130 | 0.7827 | 0.7462 | 0.8068 | |
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| 0.3245 | 11.0 | 110 | 0.6728 | {'precision': 0.6990595611285266, 'recall': 0.826946847960445, 'f1': 0.7576443941109853, 'number': 809} | {'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119} | {'precision': 0.7817703768624014, 'recall': 0.8375586854460094, 'f1': 0.8087035358114233, 'number': 1065} | 0.7192 | 0.8008 | 0.7578 | 0.8089 | |
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| 0.3113 | 12.0 | 120 | 0.6799 | {'precision': 0.71875, 'recall': 0.796044499381953, 'f1': 0.755425219941349, 'number': 809} | {'precision': 0.25903614457831325, 'recall': 0.36134453781512604, 'f1': 0.3017543859649123, 'number': 119} | {'precision': 0.775330396475771, 'recall': 0.8262910798122066, 'f1': 0.8, 'number': 1065} | 0.7132 | 0.7863 | 0.7480 | 0.8106 | |
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| 0.2921 | 13.0 | 130 | 0.6836 | {'precision': 0.7070063694267515, 'recall': 0.823238566131026, 'f1': 0.7607081667618503, 'number': 809} | {'precision': 0.32432432432432434, 'recall': 0.3025210084033613, 'f1': 0.31304347826086953, 'number': 119} | {'precision': 0.7976513098464318, 'recall': 0.8291079812206573, 'f1': 0.8130755064456722, 'number': 1065} | 0.7338 | 0.7953 | 0.7633 | 0.8122 | |
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| 0.2841 | 14.0 | 140 | 0.6848 | {'precision': 0.7150537634408602, 'recall': 0.8220024721878862, 'f1': 0.7648073605520415, 'number': 809} | {'precision': 0.26666666666666666, 'recall': 0.33613445378151263, 'f1': 0.2973977695167286, 'number': 119} | {'precision': 0.7841726618705036, 'recall': 0.8187793427230047, 'f1': 0.8011024345429489, 'number': 1065} | 0.7194 | 0.7913 | 0.7536 | 0.8127 | |
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| 0.2793 | 15.0 | 150 | 0.6857 | {'precision': 0.7176981541802389, 'recall': 0.8170580964153276, 'f1': 0.7641618497109827, 'number': 809} | {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119} | {'precision': 0.7773820124666073, 'recall': 0.819718309859155, 'f1': 0.7979890310786105, 'number': 1065} | 0.7204 | 0.7898 | 0.7535 | 0.8139 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 1.8.0+cu101 |
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- Tokenizers 0.13.2 |
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