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---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
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: 1.5815
- Answer: {'precision': 0.8604118993135011, 'recall': 0.9204406364749081, 'f1': 0.8894145476049675, 'number': 817}
- Header: {'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119}
- Question: {'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077}
- Overall Precision: 0.8745
- Overall Recall: 0.8962
- Overall F1: 0.8852
- Overall Accuracy: 0.8209
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4131 | 10.53 | 200 | 0.9920 | {'precision': 0.7944444444444444, 'recall': 0.8751529987760098, 'f1': 0.8328479906814211, 'number': 817} | {'precision': 0.5267857142857143, 'recall': 0.4957983193277311, 'f1': 0.5108225108225107, 'number': 119} | {'precision': 0.8690265486725663, 'recall': 0.9117920148560817, 'f1': 0.8898957861350248, 'number': 1077} | 0.8198 | 0.8723 | 0.8452 | 0.7912 |
| 0.0453 | 21.05 | 400 | 1.3055 | {'precision': 0.8215077605321508, 'recall': 0.9069767441860465, 'f1': 0.8621291448516578, 'number': 817} | {'precision': 0.5961538461538461, 'recall': 0.5210084033613446, 'f1': 0.5560538116591929, 'number': 119} | {'precision': 0.8818755635707844, 'recall': 0.9080779944289693, 'f1': 0.8947849954254347, 'number': 1077} | 0.8421 | 0.8847 | 0.8629 | 0.7971 |
| 0.0129 | 31.58 | 600 | 1.6559 | {'precision': 0.8261826182618262, 'recall': 0.9192166462668299, 'f1': 0.8702201622247971, 'number': 817} | {'precision': 0.4957983193277311, 'recall': 0.4957983193277311, 'f1': 0.4957983193277311, 'number': 119} | {'precision': 0.9050814956855225, 'recall': 0.8765088207985144, 'f1': 0.8905660377358492, 'number': 1077} | 0.8469 | 0.8713 | 0.8590 | 0.7952 |
| 0.0083 | 42.11 | 800 | 1.6136 | {'precision': 0.8760529482551144, 'recall': 0.8910648714810282, 'f1': 0.883495145631068, 'number': 817} | {'precision': 0.6145833333333334, 'recall': 0.4957983193277311, 'f1': 0.5488372093023256, 'number': 119} | {'precision': 0.8963922294172063, 'recall': 0.8997214484679665, 'f1': 0.8980537534754401, 'number': 1077} | 0.8745 | 0.8723 | 0.8734 | 0.8060 |
| 0.0058 | 52.63 | 1000 | 1.6826 | {'precision': 0.8553386911595867, 'recall': 0.9118727050183598, 'f1': 0.8827014218009479, 'number': 817} | {'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119} | {'precision': 0.8902991840435177, 'recall': 0.9117920148560817, 'f1': 0.9009174311926607, 'number': 1077} | 0.8626 | 0.8917 | 0.8769 | 0.7928 |
| 0.0027 | 63.16 | 1200 | 1.5511 | {'precision': 0.8640661938534279, 'recall': 0.8947368421052632, 'f1': 0.8791340950090198, 'number': 817} | {'precision': 0.576, 'recall': 0.6050420168067226, 'f1': 0.5901639344262294, 'number': 119} | {'precision': 0.8985374771480804, 'recall': 0.9127205199628597, 'f1': 0.9055734684477199, 'number': 1077} | 0.8649 | 0.8872 | 0.8759 | 0.8110 |
| 0.0014 | 73.68 | 1400 | 1.5130 | {'precision': 0.8801452784503632, 'recall': 0.8898408812729498, 'f1': 0.8849665246500303, 'number': 817} | {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119} | {'precision': 0.8748906386701663, 'recall': 0.9285051067780873, 'f1': 0.900900900900901, 'number': 1077} | 0.8644 | 0.8897 | 0.8769 | 0.8092 |
| 0.001 | 84.21 | 1600 | 1.5433 | {'precision': 0.8373893805309734, 'recall': 0.9265605875152999, 'f1': 0.8797210923881464, 'number': 817} | {'precision': 0.6033057851239669, 'recall': 0.6134453781512605, 'f1': 0.6083333333333334, 'number': 119} | {'precision': 0.9138257575757576, 'recall': 0.8960074280408542, 'f1': 0.9048288795124239, 'number': 1077} | 0.8626 | 0.8917 | 0.8769 | 0.8139 |
| 0.0006 | 94.74 | 1800 | 1.5585 | {'precision': 0.8500576701268743, 'recall': 0.9020807833537332, 'f1': 0.8752969121140143, 'number': 817} | {'precision': 0.6371681415929203, 'recall': 0.6050420168067226, 'f1': 0.6206896551724138, 'number': 119} | {'precision': 0.8933454876937101, 'recall': 0.9099350046425255, 'f1': 0.9015639374425023, 'number': 1077} | 0.8613 | 0.8887 | 0.8748 | 0.8197 |
| 0.0003 | 105.26 | 2000 | 1.5719 | {'precision': 0.8505096262740657, 'recall': 0.9192166462668299, 'f1': 0.8835294117647059, 'number': 817} | {'precision': 0.6605504587155964, 'recall': 0.6050420168067226, 'f1': 0.6315789473684209, 'number': 119} | {'precision': 0.9113805970149254, 'recall': 0.9071494893221913, 'f1': 0.9092601209865054, 'number': 1077} | 0.8721 | 0.8942 | 0.8830 | 0.8246 |
| 0.0004 | 115.79 | 2200 | 1.5578 | {'precision': 0.8554913294797688, 'recall': 0.9057527539779682, 'f1': 0.8799048751486326, 'number': 817} | {'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119} | {'precision': 0.9059907834101383, 'recall': 0.9127205199628597, 'f1': 0.9093432007400555, 'number': 1077} | 0.8696 | 0.8912 | 0.8803 | 0.8194 |
| 0.0003 | 126.32 | 2400 | 1.5815 | {'precision': 0.8604118993135011, 'recall': 0.9204406364749081, 'f1': 0.8894145476049675, 'number': 817} | {'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119} | {'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077} | 0.8745 | 0.8962 | 0.8852 | 0.8209 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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