---
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: 1.1164
- Answer: {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817}
- Header: {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119}
- Question: {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077}
- Overall Precision: 0.8800
- Overall Recall: 0.8892
- Overall F1: 0.8846
- Overall Accuracy: 0.8211

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                  | Header                                                                                                   | Question                                                                                                 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0418        | 1.34  | 200  | 1.1164          | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800            | 0.8892         | 0.8846     | 0.8211           |
| 0.0473        | 2.68  | 400  | 1.1164          | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800            | 0.8892         | 0.8846     | 0.8211           |
| 0.0444        | 4.03  | 600  | 1.1164          | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800            | 0.8892         | 0.8846     | 0.8211           |
| 0.0532        | 5.37  | 800  | 1.1164          | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800            | 0.8892         | 0.8846     | 0.8211           |
| 0.0405        | 6.71  | 1000 | 1.1164          | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800            | 0.8892         | 0.8846     | 0.8211           |
| 0.0383        | 8.05  | 1200 | 1.1164          | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800            | 0.8892         | 0.8846     | 0.8211           |
| 0.0494        | 9.4   | 1400 | 1.1164          | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800            | 0.8892         | 0.8846     | 0.8211           |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3