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---
library_name: transformers
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: lilt-en-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6340
- Answer: {'precision': 0.8372352285395763, 'recall': 0.9192166462668299, 'f1': 0.8763127187864644, 'number': 817}
- Header: {'precision': 0.5963302752293578, 'recall': 0.5462184873949579, 'f1': 0.5701754385964912, 'number': 119}
- Question: {'precision': 0.9025974025974026, 'recall': 0.903435468895079, 'f1': 0.9030162412993039, 'number': 1077}
- Overall Precision: 0.8584
- Overall Recall: 0.8887
- Overall F1: 0.8733
- Overall Accuracy: 0.8024
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.411 | 10.5263 | 200 | 0.9921 | {'precision': 0.8178770949720671, 'recall': 0.8959608323133414, 'f1': 0.8551401869158878, 'number': 817} | {'precision': 0.5328467153284672, 'recall': 0.6134453781512605, 'f1': 0.5703125, 'number': 119} | {'precision': 0.8800738007380073, 'recall': 0.8857938718662952, 'f1': 0.882924571957427, 'number': 1077} | 0.8313 | 0.8738 | 0.8520 | 0.7889 |
| 0.0439 | 21.0526 | 400 | 1.1727 | {'precision': 0.8136511375947996, 'recall': 0.9192166462668299, 'f1': 0.8632183908045976, 'number': 817} | {'precision': 0.5728155339805825, 'recall': 0.4957983193277311, 'f1': 0.5315315315315315, 'number': 119} | {'precision': 0.8545135845749343, 'recall': 0.9052924791086351, 'f1': 0.87917042380523, 'number': 1077} | 0.8237 | 0.8867 | 0.8541 | 0.7946 |
| 0.0137 | 31.5789 | 600 | 1.2732 | {'precision': 0.8581730769230769, 'recall': 0.8739290085679314, 'f1': 0.865979381443299, 'number': 817} | {'precision': 0.5147058823529411, 'recall': 0.5882352941176471, 'f1': 0.5490196078431372, 'number': 119} | {'precision': 0.8467400508044031, 'recall': 0.9285051067780873, 'f1': 0.8857395925597875, 'number': 1077} | 0.8302 | 0.8862 | 0.8573 | 0.7981 |
| 0.0075 | 42.1053 | 800 | 1.4152 | {'precision': 0.8164627363737486, 'recall': 0.8984088127294981, 'f1': 0.8554778554778554, 'number': 817} | {'precision': 0.5897435897435898, 'recall': 0.5798319327731093, 'f1': 0.5847457627118645, 'number': 119} | {'precision': 0.8922934076137419, 'recall': 0.8922934076137419, 'f1': 0.8922934076137419, 'number': 1077} | 0.8428 | 0.8763 | 0.8592 | 0.7992 |
| 0.0047 | 52.6316 | 1000 | 1.5824 | {'precision': 0.8137254901960784, 'recall': 0.9143206854345165, 'f1': 0.8610951008645533, 'number': 817} | {'precision': 0.627906976744186, 'recall': 0.453781512605042, 'f1': 0.526829268292683, 'number': 119} | {'precision': 0.8995348837209303, 'recall': 0.8978644382544104, 'f1': 0.8986988847583643, 'number': 1077} | 0.8504 | 0.8783 | 0.8641 | 0.7952 |
| 0.0018 | 63.1579 | 1200 | 1.6941 | {'precision': 0.8627450980392157, 'recall': 0.9155446756425949, 'f1': 0.8883610451306414, 'number': 817} | {'precision': 0.5193798449612403, 'recall': 0.5630252100840336, 'f1': 0.5403225806451614, 'number': 119} | {'precision': 0.9080568720379147, 'recall': 0.8895078922934077, 'f1': 0.8986866791744841, 'number': 1077} | 0.8645 | 0.8808 | 0.8725 | 0.7959 |
| 0.0017 | 73.6842 | 1400 | 1.6340 | {'precision': 0.8372352285395763, 'recall': 0.9192166462668299, 'f1': 0.8763127187864644, 'number': 817} | {'precision': 0.5963302752293578, 'recall': 0.5462184873949579, 'f1': 0.5701754385964912, 'number': 119} | {'precision': 0.9025974025974026, 'recall': 0.903435468895079, 'f1': 0.9030162412993039, 'number': 1077} | 0.8584 | 0.8887 | 0.8733 | 0.8024 |
| 0.0011 | 84.2105 | 1600 | 1.5738 | {'precision': 0.8417508417508418, 'recall': 0.9179926560587516, 'f1': 0.8782201405152226, 'number': 817} | {'precision': 0.5545454545454546, 'recall': 0.5126050420168067, 'f1': 0.5327510917030567, 'number': 119} | {'precision': 0.8979779411764706, 'recall': 0.9071494893221913, 'f1': 0.902540415704388, 'number': 1077} | 0.8559 | 0.8882 | 0.8718 | 0.8095 |
| 0.0005 | 94.7368 | 1800 | 1.5766 | {'precision': 0.8384180790960452, 'recall': 0.9082007343941249, 'f1': 0.871915393654524, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.47058823529411764, 'f1': 0.5161290322580646, 'number': 119} | {'precision': 0.8916211293260473, 'recall': 0.9090064995357474, 'f1': 0.9002298850574713, 'number': 1077} | 0.8539 | 0.8828 | 0.8681 | 0.8108 |
| 0.0005 | 105.2632 | 2000 | 1.7020 | {'precision': 0.8291347207009858, 'recall': 0.9265605875152999, 'f1': 0.8751445086705203, 'number': 817} | {'precision': 0.5648148148148148, 'recall': 0.5126050420168067, 'f1': 0.5374449339207047, 'number': 119} | {'precision': 0.8938134810710988, 'recall': 0.8987929433611885, 'f1': 0.8962962962962964, 'number': 1077} | 0.8489 | 0.8872 | 0.8676 | 0.7844 |
| 0.0002 | 115.7895 | 2200 | 1.7386 | {'precision': 0.8264192139737991, 'recall': 0.9265605875152999, 'f1': 0.8736295441431046, 'number': 817} | {'precision': 0.58, 'recall': 0.48739495798319327, 'f1': 0.5296803652968036, 'number': 119} | {'precision': 0.891643709825528, 'recall': 0.9015784586815228, 'f1': 0.8965835641735919, 'number': 1077} | 0.8485 | 0.8872 | 0.8674 | 0.7826 |
| 0.0003 | 126.3158 | 2400 | 1.7405 | {'precision': 0.8422818791946308, 'recall': 0.9216646266829865, 'f1': 0.880187025131502, 'number': 817} | {'precision': 0.5576923076923077, 'recall': 0.48739495798319327, 'f1': 0.5201793721973094, 'number': 119} | {'precision': 0.8932842686292548, 'recall': 0.9015784586815228, 'f1': 0.8974121996303143, 'number': 1077} | 0.8547 | 0.8852 | 0.8697 | 0.7796 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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