lilt-en-funsd
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:
- Loss: 1.7566
- Answer: {'precision': 0.8818713450292398, 'recall': 0.9228886168910648, 'f1': 0.9019138755980862, 'number': 817}
- Header: {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119}
- Question: {'precision': 0.8944494995450409, 'recall': 0.9127205199628597, 'f1': 0.9034926470588234, 'number': 1077}
- Overall Precision: 0.8781
- Overall Recall: 0.8947
- Overall F1: 0.8863
- Overall Accuracy: 0.7939
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
---|---|---|---|---|---|---|---|---|---|---|
0.4354 | 10.53 | 200 | 1.0094 | {'precision': 0.8219954648526077, 'recall': 0.8873929008567931, 'f1': 0.8534432018834609, 'number': 817} | {'precision': 0.5617977528089888, 'recall': 0.42016806722689076, 'f1': 0.4807692307692308, 'number': 119} | {'precision': 0.8493723849372385, 'recall': 0.9424326833797586, 'f1': 0.8934859154929579, 'number': 1077} | 0.8264 | 0.8892 | 0.8567 | 0.7972 |
0.0503 | 21.05 | 400 | 1.2949 | {'precision': 0.8543577981651376, 'recall': 0.9118727050183598, 'f1': 0.8821788040260509, 'number': 817} | {'precision': 0.5658914728682171, 'recall': 0.6134453781512605, 'f1': 0.5887096774193549, 'number': 119} | {'precision': 0.9066147859922179, 'recall': 0.8653667595171773, 'f1': 0.8855106888361044, 'number': 1077} | 0.8625 | 0.8693 | 0.8659 | 0.8117 |
0.0143 | 31.58 | 600 | 1.3527 | {'precision': 0.8726190476190476, 'recall': 0.8971848225214198, 'f1': 0.8847314423657212, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119} | {'precision': 0.8533674339300937, 'recall': 0.9294336118848654, 'f1': 0.8897777777777779, 'number': 1077} | 0.8520 | 0.8952 | 0.8731 | 0.8116 |
0.0064 | 42.11 | 800 | 1.6567 | {'precision': 0.8483466362599772, 'recall': 0.9106487148102815, 'f1': 0.8783943329397875, 'number': 817} | {'precision': 0.5564516129032258, 'recall': 0.5798319327731093, 'f1': 0.5679012345679013, 'number': 119} | {'precision': 0.8949814126394052, 'recall': 0.8941504178272981, 'f1': 0.8945657222480261, 'number': 1077} | 0.8551 | 0.8823 | 0.8685 | 0.7982 |
0.0051 | 52.63 | 1000 | 1.6856 | {'precision': 0.8542141230068337, 'recall': 0.9179926560587516, 'f1': 0.8849557522123894, 'number': 817} | {'precision': 0.66, 'recall': 0.5546218487394958, 'f1': 0.6027397260273973, 'number': 119} | {'precision': 0.9025069637883009, 'recall': 0.9025069637883009, 'f1': 0.9025069637883009, 'number': 1077} | 0.8701 | 0.8882 | 0.8791 | 0.7925 |
0.0029 | 63.16 | 1200 | 1.5031 | {'precision': 0.8860294117647058, 'recall': 0.8849449204406364, 'f1': 0.8854868340477648, 'number': 817} | {'precision': 0.6147540983606558, 'recall': 0.6302521008403361, 'f1': 0.6224066390041495, 'number': 119} | {'precision': 0.8724890829694323, 'recall': 0.9275766016713092, 'f1': 0.8991899189918992, 'number': 1077} | 0.8627 | 0.8927 | 0.8774 | 0.8117 |
0.0015 | 73.68 | 1400 | 1.6708 | {'precision': 0.8720657276995305, 'recall': 0.9094247246022031, 'f1': 0.89035350509287, 'number': 817} | {'precision': 0.5286624203821656, 'recall': 0.6974789915966386, 'f1': 0.6014492753623188, 'number': 119} | {'precision': 0.8897126969416126, 'recall': 0.8913649025069638, 'f1': 0.8905380333951762, 'number': 1077} | 0.8554 | 0.8872 | 0.8710 | 0.7958 |
0.0012 | 84.21 | 1600 | 1.7566 | {'precision': 0.8818713450292398, 'recall': 0.9228886168910648, 'f1': 0.9019138755980862, 'number': 817} | {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119} | {'precision': 0.8944494995450409, 'recall': 0.9127205199628597, 'f1': 0.9034926470588234, 'number': 1077} | 0.8781 | 0.8947 | 0.8863 | 0.7939 |
0.0006 | 94.74 | 1800 | 1.8482 | {'precision': 0.8781362007168458, 'recall': 0.8996328029375765, 'f1': 0.8887545344619106, 'number': 817} | {'precision': 0.5862068965517241, 'recall': 0.5714285714285714, 'f1': 0.5787234042553192, 'number': 119} | {'precision': 0.8949814126394052, 'recall': 0.8941504178272981, 'f1': 0.8945657222480261, 'number': 1077} | 0.8704 | 0.8773 | 0.8738 | 0.7913 |
0.0006 | 105.26 | 2000 | 1.7763 | {'precision': 0.8747072599531616, 'recall': 0.9143206854345165, 'f1': 0.8940754039497306, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.8859489051094891, 'recall': 0.9015784586815228, 'f1': 0.8936953520478601, 'number': 1077} | 0.8672 | 0.8852 | 0.8761 | 0.7964 |
0.0003 | 115.79 | 2200 | 1.9186 | {'precision': 0.8813953488372093, 'recall': 0.9277845777233782, 'f1': 0.9039952295766249, 'number': 817} | {'precision': 0.6190476190476191, 'recall': 0.5462184873949579, 'f1': 0.5803571428571429, 'number': 119} | {'precision': 0.9025304592314901, 'recall': 0.8941504178272981, 'f1': 0.898320895522388, 'number': 1077} | 0.8789 | 0.8872 | 0.8831 | 0.7971 |
0.0002 | 126.32 | 2400 | 1.8948 | {'precision': 0.8780487804878049, 'recall': 0.9253365973072215, 'f1': 0.901072705601907, 'number': 817} | {'precision': 0.6261682242990654, 'recall': 0.5630252100840336, 'f1': 0.5929203539823009, 'number': 119} | {'precision': 0.9033457249070632, 'recall': 0.9025069637883009, 'f1': 0.9029261495587553, 'number': 1077} | 0.8782 | 0.8917 | 0.8849 | 0.7978 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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