---
license: mit
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
model-index:
- name: lilt-en-funsd-2
  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-2

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: 1.7012
- Answer: {'precision': 0.901985111662531, 'recall': 0.8898408812729498, 'f1': 0.8958718422674061, 'number': 817}
- Header: {'precision': 0.6371681415929203, 'recall': 0.6050420168067226, 'f1': 0.6206896551724138, 'number': 119}
- Question: {'precision': 0.8736027515047291, 'recall': 0.9433611884865367, 'f1': 0.9071428571428571, 'number': 1077}
- Overall Precision: 0.8718
- Overall Recall: 0.9016
- Overall F1: 0.8864
- Overall Accuracy: 0.8041

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4401        | 10.53  | 200  | 0.9136          | {'precision': 0.8668252080856124, 'recall': 0.8922888616891065, 'f1': 0.879372738238842, 'number': 817}  | {'precision': 0.512, 'recall': 0.5378151260504201, 'f1': 0.5245901639344263, 'number': 119}               | {'precision': 0.8825622775800712, 'recall': 0.9210770659238626, 'f1': 0.9014084507042255, 'number': 1077} | 0.8541            | 0.8867         | 0.8701     | 0.8093           |
| 0.0458        | 21.05  | 400  | 1.2043          | {'precision': 0.879415347137637, 'recall': 0.8837209302325582, 'f1': 0.8815628815628815, 'number': 817}  | {'precision': 0.6153846153846154, 'recall': 0.5378151260504201, 'f1': 0.5739910313901345, 'number': 119}  | {'precision': 0.8856121537086684, 'recall': 0.9201485608170845, 'f1': 0.9025500910746811, 'number': 1077} | 0.8694            | 0.8828         | 0.8760     | 0.8042           |
| 0.0127        | 31.58  | 600  | 1.3936          | {'precision': 0.880722891566265, 'recall': 0.8947368421052632, 'f1': 0.8876745598057073, 'number': 817}  | {'precision': 0.6019417475728155, 'recall': 0.5210084033613446, 'f1': 0.5585585585585585, 'number': 119}  | {'precision': 0.8826714801444043, 'recall': 0.9080779944289693, 'f1': 0.8951945080091533, 'number': 1077} | 0.8677            | 0.8798         | 0.8737     | 0.8098           |
| 0.0082        | 42.11  | 800  | 1.3872          | {'precision': 0.8771498771498771, 'recall': 0.8739290085679314, 'f1': 0.8755364806866953, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}  | {'precision': 0.8669565217391304, 'recall': 0.9257195914577531, 'f1': 0.895374943870678, 'number': 1077}  | 0.8603            | 0.8813         | 0.8707     | 0.8067           |
| 0.0035        | 52.63  | 1000 | 1.6235          | {'precision': 0.8825665859564165, 'recall': 0.8922888616891065, 'f1': 0.887401095556908, 'number': 817}  | {'precision': 0.49044585987261147, 'recall': 0.6470588235294118, 'f1': 0.5579710144927537, 'number': 119} | {'precision': 0.8833333333333333, 'recall': 0.8857938718662952, 'f1': 0.8845618915159944, 'number': 1077} | 0.8531            | 0.8743         | 0.8636     | 0.7953           |
| 0.0031        | 63.16  | 1200 | 1.6677          | {'precision': 0.9051833122629582, 'recall': 0.8763769889840881, 'f1': 0.890547263681592, 'number': 817}  | {'precision': 0.48484848484848486, 'recall': 0.6722689075630253, 'f1': 0.5633802816901409, 'number': 119} | {'precision': 0.8893023255813953, 'recall': 0.8876508820798514, 'f1': 0.8884758364312269, 'number': 1077} | 0.8626            | 0.8703         | 0.8665     | 0.7994           |
| 0.0014        | 73.68  | 1400 | 1.7012          | {'precision': 0.901985111662531, 'recall': 0.8898408812729498, 'f1': 0.8958718422674061, 'number': 817}  | {'precision': 0.6371681415929203, 'recall': 0.6050420168067226, 'f1': 0.6206896551724138, 'number': 119}  | {'precision': 0.8736027515047291, 'recall': 0.9433611884865367, 'f1': 0.9071428571428571, 'number': 1077} | 0.8718            | 0.9016         | 0.8864     | 0.8041           |
| 0.0011        | 84.21  | 1600 | 1.6779          | {'precision': 0.8715814506539834, 'recall': 0.8971848225214198, 'f1': 0.8841978287092883, 'number': 817} | {'precision': 0.6413043478260869, 'recall': 0.4957983193277311, 'f1': 0.5592417061611374, 'number': 119}  | {'precision': 0.8754448398576512, 'recall': 0.9136490250696379, 'f1': 0.894139027714675, 'number': 1077}  | 0.8634            | 0.8823         | 0.8727     | 0.8067           |
| 0.0009        | 94.74  | 1800 | 1.6159          | {'precision': 0.8729216152019003, 'recall': 0.8996328029375765, 'f1': 0.8860759493670887, 'number': 817} | {'precision': 0.5740740740740741, 'recall': 0.5210084033613446, 'f1': 0.5462555066079295, 'number': 119}  | {'precision': 0.8681898066783831, 'recall': 0.9173630454967502, 'f1': 0.8920993227990971, 'number': 1077} | 0.8549            | 0.8867         | 0.8705     | 0.8060           |
| 0.0007        | 105.26 | 2000 | 1.5876          | {'precision': 0.8733572281959379, 'recall': 0.8947368421052632, 'f1': 0.8839177750906893, 'number': 817} | {'precision': 0.5982142857142857, 'recall': 0.5630252100840336, 'f1': 0.5800865800865801, 'number': 119}  | {'precision': 0.8783783783783784, 'recall': 0.9052924791086351, 'f1': 0.8916323731138546, 'number': 1077} | 0.8611            | 0.8808         | 0.8708     | 0.8091           |
| 0.0003        | 115.79 | 2200 | 1.6529          | {'precision': 0.8662721893491124, 'recall': 0.8959608323133414, 'f1': 0.8808664259927798, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.5378151260504201, 'f1': 0.554112554112554, 'number': 119}   | {'precision': 0.8662587412587412, 'recall': 0.9201485608170845, 'f1': 0.8923908149482216, 'number': 1077} | 0.8505            | 0.8877         | 0.8687     | 0.8039           |
| 0.0002        | 126.32 | 2400 | 1.6602          | {'precision': 0.8699763593380615, 'recall': 0.9008567931456548, 'f1': 0.8851473241130486, 'number': 817} | {'precision': 0.5775862068965517, 'recall': 0.5630252100840336, 'f1': 0.5702127659574467, 'number': 119}  | {'precision': 0.8725663716814159, 'recall': 0.9155060352831941, 'f1': 0.8935206162211146, 'number': 1077} | 0.8552            | 0.8887         | 0.8716     | 0.8024           |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3