---
license: mit
base_model: kavg/LiLT-RE-JA
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
model-index:
- name: checkpoints
  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. -->

# checkpoints

This model is a fine-tuned version of [kavg/LiLT-RE-JA](https://huggingface.co/kavg/LiLT-RE-JA) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.4744
- Recall: 0.6540
- F1: 0.5499
- Loss: 0.5293

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 10000

### Training results

| Training Loss | Epoch  | Step  | F1     | Validation Loss | Precision | Recall |
|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:|
| 0.0815        | 41.67  | 500   | 0.4149 | 0.1502          | 0.3521    | 0.5051 |
| 0.0408        | 83.33  | 1000  | 0.4931 | 0.1593          | 0.4244    | 0.5884 |
| 0.0435        | 125.0  | 1500  | 0.5041 | 0.2311          | 0.4218    | 0.6263 |
| 0.0168        | 166.67 | 2000  | 0.5097 | 0.3195          | 0.4286    | 0.6288 |
| 0.0073        | 208.33 | 2500  | 0.5088 | 0.3313          | 0.4308    | 0.6212 |
| 0.0051        | 250.0  | 3000  | 0.5264 | 0.3939          | 0.4349    | 0.6667 |
| 0.0038        | 291.67 | 3500  | 0.5252 | 0.3958          | 0.4435    | 0.6439 |
| 0.0016        | 333.33 | 4000  | 0.5335 | 0.4708          | 0.4606    | 0.6338 |
| 0.0082        | 375.0  | 4500  | 0.5340 | 0.4429          | 0.4562    | 0.6439 |
| 0.0079        | 416.67 | 5000  | 0.5305 | 0.4498          | 0.4601    | 0.6263 |
| 0.0028        | 458.33 | 5500  | 0.5352 | 0.4993          | 0.4578    | 0.6439 |
| 0.0003        | 500.0  | 6000  | 0.5422 | 0.5253          | 0.4695    | 0.6414 |
| 0.0014        | 541.67 | 6500  | 0.5437 | 0.5134          | 0.4705    | 0.6439 |
| 0.0043        | 583.33 | 7000  | 0.5393 | 0.5308          | 0.4652    | 0.6414 |
| 0.0002        | 625.0  | 7500  | 0.5378 | 0.5572          | 0.4604    | 0.6465 |
| 0.0014        | 666.67 | 8000  | 0.5386 | 0.5451          | 0.4591    | 0.6515 |
| 0.0027        | 708.33 | 8500  | 0.4629 | 0.6465          | 0.5395    | 0.5747 |
| 0.0036        | 750.0  | 9000  | 0.4744 | 0.6540          | 0.5499    | 0.5293 |
| 0.0021        | 791.67 | 9500  | 0.4610 | 0.6566          | 0.5417    | 0.5391 |
| 0.0002        | 833.33 | 10000 | 0.4625 | 0.6540          | 0.5418    | 0.5359 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1