---
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: 0.8811
- Answer: {'precision': 0.8316268486916951, 'recall': 0.8947368421052632, 'f1': 0.8620283018867924, 'number': 817}
- Header: {'precision': 0.4777777777777778, 'recall': 0.36134453781512604, 'f1': 0.41148325358851673, 'number': 119}
- Question: {'precision': 0.8480349344978166, 'recall': 0.9015784586815228, 'f1': 0.873987398739874, 'number': 1077}
- Overall Precision: 0.8254
- Overall Recall: 0.8669
- Overall F1: 0.8457
- Overall Accuracy: 0.7806

## 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: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                     | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.7083        | 5.26  | 100  | 0.7032          | {'precision': 0.8124293785310734, 'recall': 0.8800489596083231, 'f1': 0.8448883666274971, 'number': 817} | {'precision': 0.4852941176470588, 'recall': 0.2773109243697479, 'f1': 0.35294117647058826, 'number': 119}  | {'precision': 0.8360375747224594, 'recall': 0.9090064995357474, 'f1': 0.8709964412811388, 'number': 1077} | 0.8150            | 0.8599         | 0.8368     | 0.8089           |
| 0.1639        | 10.53 | 200  | 0.8811          | {'precision': 0.8316268486916951, 'recall': 0.8947368421052632, 'f1': 0.8620283018867924, 'number': 817} | {'precision': 0.4777777777777778, 'recall': 0.36134453781512604, 'f1': 0.41148325358851673, 'number': 119} | {'precision': 0.8480349344978166, 'recall': 0.9015784586815228, 'f1': 0.873987398739874, 'number': 1077}  | 0.8254            | 0.8669         | 0.8457     | 0.7806           |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2