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---
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: 0.9065
- Answer: {'precision': 0.834096109839817, 'recall': 0.8922888616891065, 'f1': 0.8622117090479007, 'number': 817}
- Header: {'precision': 0.5319148936170213, 'recall': 0.42016806722689076, 'f1': 0.4694835680751173, 'number': 119}
- Question: {'precision': 0.8570175438596491, 'recall': 0.9071494893221913, 'f1': 0.8813712223725756, 'number': 1077}
- Overall Precision: 0.8330
- Overall Recall: 0.8723
- Overall F1: 0.8522
- Overall Accuracy: 0.7918
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.7017 | 5.26 | 100 | 0.7391 | {'precision': 0.8216340621403913, 'recall': 0.8739290085679314, 'f1': 0.8469750889679716, 'number': 817} | {'precision': 0.4533333333333333, 'recall': 0.2857142857142857, 'f1': 0.3505154639175258, 'number': 119} | {'precision': 0.8234323432343235, 'recall': 0.9266480965645311, 'f1': 0.8719965050240279, 'number': 1077} | 0.8098 | 0.8674 | 0.8376 | 0.8073 |
| 0.1656 | 10.53 | 200 | 0.9065 | {'precision': 0.834096109839817, 'recall': 0.8922888616891065, 'f1': 0.8622117090479007, 'number': 817} | {'precision': 0.5319148936170213, 'recall': 0.42016806722689076, 'f1': 0.4694835680751173, 'number': 119} | {'precision': 0.8570175438596491, 'recall': 0.9071494893221913, 'f1': 0.8813712223725756, 'number': 1077} | 0.8330 | 0.8723 | 0.8522 | 0.7918 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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