---
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: 0.9151
- Answer: {'precision': 0.8149779735682819, 'recall': 0.9057527539779682, 'f1': 0.8579710144927536, 'number': 817}
- Header: {'precision': 0.49523809523809526, 'recall': 0.4369747899159664, 'f1': 0.4642857142857143, 'number': 119}
- Question: {'precision': 0.8627272727272727, 'recall': 0.8811513463324049, 'f1': 0.8718419843821773, 'number': 1077}
- Overall Precision: 0.8239
- Overall Recall: 0.8649
- Overall F1: 0.8439
- Overall Accuracy: 0.7891

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.7154        | 5.26  | 100  | 0.7542          | {'precision': 0.8251173708920188, 'recall': 0.8604651162790697, 'f1': 0.8424206111443978, 'number': 817} | {'precision': 0.45054945054945056, 'recall': 0.3445378151260504, 'f1': 0.3904761904761904, 'number': 119} | {'precision': 0.8157248157248157, 'recall': 0.924791086350975, 'f1': 0.866840731070496, 'number': 1077}   | 0.8041            | 0.8644         | 0.8331     | 0.7915           |
| 0.1665        | 10.53 | 200  | 0.9151          | {'precision': 0.8149779735682819, 'recall': 0.9057527539779682, 'f1': 0.8579710144927536, 'number': 817} | {'precision': 0.49523809523809526, 'recall': 0.4369747899159664, 'f1': 0.4642857142857143, 'number': 119} | {'precision': 0.8627272727272727, 'recall': 0.8811513463324049, 'f1': 0.8718419843821773, 'number': 1077} | 0.8239            | 0.8649         | 0.8439     | 0.7891           |


### Framework versions

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