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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: 1.7487
- Answer: {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817}
- Header: {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}
- Question: {'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077}
- Overall Precision: 0.8731
- Overall Recall: 0.8952
- Overall F1: 0.8840
- Overall Accuracy: 0.7977
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4352 | 10.53 | 200 | 0.9574 | {'precision': 0.8385167464114832, 'recall': 0.8580171358629131, 'f1': 0.8481548699334543, 'number': 817} | {'precision': 0.5673076923076923, 'recall': 0.4957983193277311, 'f1': 0.5291479820627802, 'number': 119} | {'precision': 0.8394534585824082, 'recall': 0.9127205199628597, 'f1': 0.8745551601423487, 'number': 1077} | 0.8257 | 0.8659 | 0.8453 | 0.7896 |
| 0.0467 | 21.05 | 400 | 1.3446 | {'precision': 0.8343057176196033, 'recall': 0.8751529987760098, 'f1': 0.8542413381123058, 'number': 817} | {'precision': 0.5789473684210527, 'recall': 0.46218487394957986, 'f1': 0.514018691588785, 'number': 119} | {'precision': 0.8543859649122807, 'recall': 0.904363974001857, 'f1': 0.8786648624267027, 'number': 1077} | 0.8337 | 0.8664 | 0.8497 | 0.7969 |
| 0.0125 | 31.58 | 600 | 1.4274 | {'precision': 0.8556461001164144, 'recall': 0.8996328029375765, 'f1': 0.8770883054892601, 'number': 817} | {'precision': 0.568, 'recall': 0.5966386554621849, 'f1': 0.5819672131147541, 'number': 119} | {'precision': 0.8916211293260473, 'recall': 0.9090064995357474, 'f1': 0.9002298850574713, 'number': 1077} | 0.8573 | 0.8867 | 0.8718 | 0.8010 |
| 0.0071 | 42.11 | 800 | 1.4147 | {'precision': 0.865265760197775, 'recall': 0.8567931456548348, 'f1': 0.8610086100861009, 'number': 817} | {'precision': 0.6888888888888889, 'recall': 0.5210084033613446, 'f1': 0.5933014354066986, 'number': 119} | {'precision': 0.8798206278026905, 'recall': 0.9108635097493036, 'f1': 0.8950729927007299, 'number': 1077} | 0.8654 | 0.8659 | 0.8657 | 0.8055 |
| 0.0067 | 52.63 | 1000 | 1.5877 | {'precision': 0.8747016706443914, 'recall': 0.8971848225214198, 'f1': 0.8858006042296073, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119} | {'precision': 0.8936363636363637, 'recall': 0.9127205199628597, 'f1': 0.9030776297657327, 'number': 1077} | 0.8709 | 0.8847 | 0.8778 | 0.8080 |
| 0.003 | 63.16 | 1200 | 1.5406 | {'precision': 0.875, 'recall': 0.8996328029375765, 'f1': 0.8871454435727218, 'number': 817} | {'precision': 0.584070796460177, 'recall': 0.5546218487394958, 'f1': 0.5689655172413793, 'number': 119} | {'precision': 0.8858695652173914, 'recall': 0.9080779944289693, 'f1': 0.8968363136176066, 'number': 1077} | 0.8649 | 0.8838 | 0.8742 | 0.8183 |
| 0.0011 | 73.68 | 1400 | 1.6131 | {'precision': 0.8686987104337632, 'recall': 0.9069767441860465, 'f1': 0.8874251497005988, 'number': 817} | {'precision': 0.7011494252873564, 'recall': 0.5126050420168067, 'f1': 0.5922330097087377, 'number': 119} | {'precision': 0.8821966341895483, 'recall': 0.924791086350975, 'f1': 0.9029918404351769, 'number': 1077} | 0.8690 | 0.8932 | 0.8809 | 0.8111 |
| 0.0008 | 84.21 | 1600 | 1.7487 | {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817} | {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119} | {'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077} | 0.8731 | 0.8952 | 0.8840 | 0.7977 |
| 0.0007 | 94.74 | 1800 | 1.8317 | {'precision': 0.8605990783410138, 'recall': 0.9143206854345165, 'f1': 0.886646884272997, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.9026876737720111, 'recall': 0.904363974001857, 'f1': 0.9035250463821892, 'number': 1077} | 0.8699 | 0.8867 | 0.8782 | 0.7934 |
| 0.0005 | 105.26 | 2000 | 1.8600 | {'precision': 0.8669778296382731, 'recall': 0.9094247246022031, 'f1': 0.8876941457586618, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} | {'precision': 0.8928247048138056, 'recall': 0.9127205199628597, 'f1': 0.9026629935720845, 'number': 1077} | 0.8701 | 0.8887 | 0.8793 | 0.7897 |
| 0.0005 | 115.79 | 2200 | 1.7672 | {'precision': 0.8781362007168458, 'recall': 0.8996328029375765, 'f1': 0.8887545344619106, 'number': 817} | {'precision': 0.6568627450980392, 'recall': 0.5630252100840336, 'f1': 0.6063348416289592, 'number': 119} | {'precision': 0.8939256572982774, 'recall': 0.9155060352831941, 'f1': 0.9045871559633027, 'number': 1077} | 0.8756 | 0.8882 | 0.8819 | 0.8074 |
| 0.0002 | 126.32 | 2400 | 1.8044 | {'precision': 0.8604382929642446, 'recall': 0.9130966952264382, 'f1': 0.8859857482185273, 'number': 817} | {'precision': 0.6391752577319587, 'recall': 0.5210084033613446, 'f1': 0.5740740740740741, 'number': 119} | {'precision': 0.9022140221402214, 'recall': 0.9080779944289693, 'f1': 0.9051365108745951, 'number': 1077} | 0.8721 | 0.8872 | 0.8796 | 0.8000 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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