---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9402
- Answer: {'precision': 0.4992163009404389, 'recall': 0.7796817625458996, 'f1': 0.608695652173913, 'number': 817}
- Header: {'precision': 0.03125, 'recall': 0.008403361344537815, 'f1': 0.013245033112582781, 'number': 119}
- Question: {'precision': 0.625, 'recall': 0.7753017641597029, 'f1': 0.692084542063821, 'number': 1077}
- Overall Precision: 0.5571
- Overall Recall: 0.7317
- Overall F1: 0.6326
- Overall Accuracy: 0.6359

## 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: 25
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0