---
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: 1.4801
- Answer: {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817}
- Header: {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119}
- Question: {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077}
- Overall Precision: 0.8720
- Overall Recall: 0.8932
- Overall F1: 0.8825
- Overall Accuracy: 0.8040

## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0015        | 2.67  | 200  | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0011        | 5.33  | 400  | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0011        | 8.0   | 600  | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0008        | 10.67 | 800  | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0011        | 13.33 | 1000 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0011        | 16.0  | 1200 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0017        | 18.67 | 1400 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0008        | 21.33 | 1600 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0008        | 24.0  | 1800 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0009        | 26.67 | 2000 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0012        | 29.33 | 2200 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
| 0.0009        | 32.0  | 2400 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 2.12.0
- Tokenizers 0.13.3