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---
license: mit
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
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 the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4588
- Answer: {'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817}
- Header: {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}
- Question: {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077}
- Overall Precision: 0.8705
- Overall Recall: 0.8917
- Overall F1: 0.8810
- Overall Accuracy: 0.8222
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4177 | 10.53 | 200 | 0.9741 | {'precision': 0.834106728538283, 'recall': 0.8800489596083231, 'f1': 0.8564621798689696, 'number': 817} | {'precision': 0.6363636363636364, 'recall': 0.4117647058823529, 'f1': 0.5, 'number': 119} | {'precision': 0.8831985624438454, 'recall': 0.9127205199628597, 'f1': 0.897716894977169, 'number': 1077} | 0.8533 | 0.8698 | 0.8615 | 0.8139 |
| 0.0528 | 21.05 | 400 | 1.2793 | {'precision': 0.8391845979614949, 'recall': 0.9069767441860465, 'f1': 0.871764705882353, 'number': 817} | {'precision': 0.5480769230769231, 'recall': 0.4789915966386555, 'f1': 0.5112107623318385, 'number': 119} | {'precision': 0.878645343367827, 'recall': 0.8672237697307336, 'f1': 0.8728971962616822, 'number': 1077} | 0.8449 | 0.8604 | 0.8526 | 0.8057 |
| 0.0154 | 31.58 | 600 | 1.3635 | {'precision': 0.8705463182897862, 'recall': 0.8971848225214198, 'f1': 0.8836648583484027, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} | {'precision': 0.8776041666666666, 'recall': 0.9387186629526463, 'f1': 0.9071332436069988, 'number': 1077} | 0.8638 | 0.8977 | 0.8804 | 0.8164 |
| 0.0082 | 42.11 | 800 | 1.4185 | {'precision': 0.8700361010830325, 'recall': 0.8849449204406364, 'f1': 0.8774271844660194, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.6050420168067226, 'f1': 0.6233766233766234, 'number': 119} | {'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077} | 0.8695 | 0.8838 | 0.8766 | 0.8212 |
| 0.0038 | 52.63 | 1000 | 1.4588 | {'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817} | {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119} | {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077} | 0.8705 | 0.8917 | 0.8810 | 0.8222 |
| 0.0026 | 63.16 | 1200 | 1.5730 | {'precision': 0.8666666666666667, 'recall': 0.8910648714810282, 'f1': 0.8786964393482196, 'number': 817} | {'precision': 0.7073170731707317, 'recall': 0.48739495798319327, 'f1': 0.5771144278606964, 'number': 119} | {'precision': 0.8887884267631103, 'recall': 0.9127205199628597, 'f1': 0.9005955107650022, 'number': 1077} | 0.8723 | 0.8788 | 0.8755 | 0.8139 |
| 0.0015 | 73.68 | 1400 | 1.6294 | {'precision': 0.837471783295711, 'recall': 0.9082007343941249, 'f1': 0.8714034057545508, 'number': 817} | {'precision': 0.6530612244897959, 'recall': 0.5378151260504201, 'f1': 0.5898617511520737, 'number': 119} | {'precision': 0.9039179104477612, 'recall': 0.8997214484679665, 'f1': 0.9018147975802697, 'number': 1077} | 0.8633 | 0.8818 | 0.8725 | 0.8173 |
| 0.001 | 84.21 | 1600 | 1.6406 | {'precision': 0.8434684684684685, 'recall': 0.9167686658506732, 'f1': 0.8785923753665689, 'number': 817} | {'precision': 0.6260869565217392, 'recall': 0.6050420168067226, 'f1': 0.6153846153846154, 'number': 119} | {'precision': 0.9001865671641791, 'recall': 0.8960074280408542, 'f1': 0.8980921358771522, 'number': 1077} | 0.8607 | 0.8872 | 0.8738 | 0.8140 |
| 0.0006 | 94.74 | 1800 | 1.6743 | {'precision': 0.8525714285714285, 'recall': 0.9130966952264382, 'f1': 0.8817966903073285, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5042016806722689, 'f1': 0.5741626794258373, 'number': 119} | {'precision': 0.8982584784601283, 'recall': 0.9099350046425255, 'f1': 0.904059040590406, 'number': 1077} | 0.8687 | 0.8872 | 0.8779 | 0.8082 |
| 0.0003 | 105.26 | 2000 | 1.7003 | {'precision': 0.8696682464454977, 'recall': 0.8984088127294981, 'f1': 0.8838049367850691, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8927272727272727, 'recall': 0.9117920148560817, 'f1': 0.9021589343132751, 'number': 1077} | 0.8721 | 0.8808 | 0.8764 | 0.8110 |
| 0.0002 | 115.79 | 2200 | 1.7767 | {'precision': 0.8564867967853043, 'recall': 0.9130966952264382, 'f1': 0.8838862559241707, 'number': 817} | {'precision': 0.64, 'recall': 0.5378151260504201, 'f1': 0.5844748858447488, 'number': 119} | {'precision': 0.9077212806026366, 'recall': 0.8950789229340761, 'f1': 0.9013557737260401, 'number': 1077} | 0.8726 | 0.8813 | 0.8769 | 0.8004 |
| 0.0002 | 126.32 | 2400 | 1.7093 | {'precision': 0.8546910755148741, 'recall': 0.9143206854345165, 'f1': 0.8835008870490834, 'number': 817} | {'precision': 0.6413043478260869, 'recall': 0.4957983193277311, 'f1': 0.5592417061611374, 'number': 119} | {'precision': 0.8956602031394275, 'recall': 0.9006499535747446, 'f1': 0.8981481481481481, 'number': 1077} | 0.8668 | 0.8823 | 0.8744 | 0.8027 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
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