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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- funsd-layoutlmv3 |
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model-index: |
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- name: lilt-form-read |
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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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# lilt-form-read |
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7208 |
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- Answer: {'precision': 0.8635321100917431, 'recall': 0.9216646266829865, 'f1': 0.8916518650088809, 'number': 817} |
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- Header: {'precision': 0.6813186813186813, 'recall': 0.5210084033613446, 'f1': 0.5904761904761905, 'number': 119} |
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- Question: {'precision': 0.9005424954792043, 'recall': 0.924791086350975, 'f1': 0.9125057260650481, 'number': 1077} |
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- Overall Precision: 0.8753 |
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- Overall Recall: 0.8997 |
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- Overall F1: 0.8873 |
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- Overall Accuracy: 0.8077 |
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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: 5e-05 |
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- train_batch_size: 8 |
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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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- training_steps: 2500 |
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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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| 0.4555 | 10.53 | 200 | 0.9514 | {'precision': 0.8207440811724915, 'recall': 0.8910648714810282, 'f1': 0.8544600938967137, 'number': 817} | {'precision': 0.6233766233766234, 'recall': 0.40336134453781514, 'f1': 0.48979591836734687, 'number': 119} | {'precision': 0.8611825192802056, 'recall': 0.9331476323119777, 'f1': 0.8957219251336899, 'number': 1077} | 0.8358 | 0.8847 | 0.8596 | 0.7991 | |
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| 0.0457 | 21.05 | 400 | 1.4096 | {'precision': 0.8654088050314466, 'recall': 0.8421052631578947, 'f1': 0.8535980148883374, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.5294117647058824, 'f1': 0.5550660792951542, 'number': 119} | {'precision': 0.8606837606837607, 'recall': 0.9350046425255338, 'f1': 0.8963061860258122, 'number': 1077} | 0.8480 | 0.8733 | 0.8605 | 0.7914 | |
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| 0.0144 | 31.58 | 600 | 1.4435 | {'precision': 0.8720095693779905, 'recall': 0.8922888616891065, 'f1': 0.8820326678765881, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} | {'precision': 0.8682581786030061, 'recall': 0.9117920148560817, 'f1': 0.8894927536231884, 'number': 1077} | 0.8591 | 0.8813 | 0.8700 | 0.8033 | |
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| 0.008 | 42.11 | 800 | 1.5197 | {'precision': 0.8660287081339713, 'recall': 0.8861689106487148, 'f1': 0.8759830611010284, 'number': 817} | {'precision': 0.5798319327731093, 'recall': 0.5798319327731093, 'f1': 0.5798319327731093, 'number': 119} | {'precision': 0.8838248436103664, 'recall': 0.9182915506035283, 'f1': 0.9007285974499089, 'number': 1077} | 0.8592 | 0.8852 | 0.8720 | 0.7921 | |
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| 0.0039 | 52.63 | 1000 | 1.4373 | {'precision': 0.8733727810650888, 'recall': 0.9033047735618115, 'f1': 0.888086642599278, 'number': 817} | {'precision': 0.6019417475728155, 'recall': 0.5210084033613446, 'f1': 0.5585585585585585, 'number': 119} | {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077} | 0.8664 | 0.8927 | 0.8794 | 0.8096 | |
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| 0.0028 | 63.16 | 1200 | 1.7146 | {'precision': 0.8490351872871736, 'recall': 0.9155446756425949, 'f1': 0.8810365135453475, 'number': 817} | {'precision': 0.6941176470588235, 'recall': 0.4957983193277311, 'f1': 0.5784313725490197, 'number': 119} | {'precision': 0.8852313167259787, 'recall': 0.9238625812441968, 'f1': 0.9041344843253067, 'number': 1077} | 0.8622 | 0.8952 | 0.8784 | 0.7971 | |
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| 0.0022 | 73.68 | 1400 | 1.5638 | {'precision': 0.8608893956670467, 'recall': 0.9241126070991432, 'f1': 0.8913813459268004, 'number': 817} | {'precision': 0.6565656565656566, 'recall': 0.5462184873949579, 'f1': 0.5963302752293578, 'number': 119} | {'precision': 0.8993536472760849, 'recall': 0.904363974001857, 'f1': 0.9018518518518519, 'number': 1077} | 0.8713 | 0.8912 | 0.8811 | 0.8051 | |
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| 0.0009 | 84.21 | 1600 | 1.7113 | {'precision': 0.8682080924855491, 'recall': 0.9192166462668299, 'f1': 0.8929845422116528, 'number': 817} | {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119} | {'precision': 0.9085027726432532, 'recall': 0.9127205199628597, 'f1': 0.9106067623899953, 'number': 1077} | 0.8796 | 0.8927 | 0.8861 | 0.8039 | |
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| 0.0009 | 94.74 | 1800 | 1.6397 | {'precision': 0.8767942583732058, 'recall': 0.8971848225214198, 'f1': 0.8868723532970357, 'number': 817} | {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119} | {'precision': 0.898458748866727, 'recall': 0.9201485608170845, 'f1': 0.9091743119266055, 'number': 1077} | 0.8760 | 0.8882 | 0.8821 | 0.8042 | |
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| 0.0004 | 105.26 | 2000 | 1.7362 | {'precision': 0.8690614136732329, 'recall': 0.9179926560587516, 'f1': 0.8928571428571428, 'number': 817} | {'precision': 0.6458333333333334, 'recall': 0.5210084033613446, 'f1': 0.5767441860465117, 'number': 119} | {'precision': 0.8928892889288929, 'recall': 0.9210770659238626, 'f1': 0.9067641681901281, 'number': 1077} | 0.8715 | 0.8962 | 0.8837 | 0.8040 | |
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| 0.0003 | 115.79 | 2200 | 1.7208 | {'precision': 0.8635321100917431, 'recall': 0.9216646266829865, 'f1': 0.8916518650088809, 'number': 817} | {'precision': 0.6813186813186813, 'recall': 0.5210084033613446, 'f1': 0.5904761904761905, 'number': 119} | {'precision': 0.9005424954792043, 'recall': 0.924791086350975, 'f1': 0.9125057260650481, 'number': 1077} | 0.8753 | 0.8997 | 0.8873 | 0.8077 | |
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| 0.0002 | 126.32 | 2400 | 1.7281 | {'precision': 0.8819362455726092, 'recall': 0.9143206854345165, 'f1': 0.8978365384615384, 'number': 817} | {'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119} | {'precision': 0.8917710196779964, 'recall': 0.9257195914577531, 'f1': 0.9084282460136676, 'number': 1077} | 0.8772 | 0.8977 | 0.8873 | 0.8060 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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