---
license: mit
base_model: kavg/LiLT-RE-ZH
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-ZH](https://huggingface.co/kavg/LiLT-RE-ZH) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.4183
- Recall: 0.5884
- F1: 0.4890
- Loss: 0.4704

## 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 |
|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:|
| 0.0761        | 41.67  | 500   | 0.3965 | 0.1487          | 0.3487    | 0.4596 |
| 0.0508        | 83.33  | 1000  | 0.4620 | 0.2456          | 0.4049    | 0.5379 |
| 0.0422        | 125.0  | 1500  | 0.4876 | 0.2545          | 0.4137    | 0.5934 |
| 0.0186        | 166.67 | 2000  | 0.4984 | 0.2960          | 0.4258    | 0.6010 |
| 0.0147        | 208.33 | 2500  | 0.4933 | 0.3388          | 0.4171    | 0.6035 |
| 0.0054        | 250.0  | 3000  | 0.5010 | 0.3819          | 0.4283    | 0.6035 |
| 0.0059        | 291.67 | 3500  | 0.5115 | 0.4177          | 0.4373    | 0.6162 |
| 0.006         | 333.33 | 4000  | 0.4974 | 0.3875          | 0.4281    | 0.5934 |
| 0.0026        | 375.0  | 4500  | 0.4910 | 0.4209          | 0.4226    | 0.5859 |
| 0.0078        | 416.67 | 5000  | 0.4952 | 0.3861          | 0.4275    | 0.5884 |
| 0.0035        | 458.33 | 5500  | 0.4890 | 0.4193          | 0.4183    | 0.5884 |
| 0.0022        | 500.0  | 6000  | 0.5059 | 0.4399          | 0.4395    | 0.5960 |
| 0.0042        | 541.67 | 6500  | 0.4979 | 0.4653          | 0.4288    | 0.5934 |
| 0.0079        | 583.33 | 7000  | 0.5037 | 0.4514          | 0.4309    | 0.6061 |
| 0.0047        | 625.0  | 7500  | 0.4937 | 0.4701          | 0.4227    | 0.5934 |
| 0.0032        | 666.67 | 8000  | 0.4937 | 0.4733          | 0.4227    | 0.5934 |
| 0.0048        | 708.33 | 8500  | 0.4339 | 0.6136          | 0.5084    | 0.5080 |
| 0.0004        | 750.0  | 9000  | 0.4183 | 0.5884          | 0.4890    | 0.4704 |
| 0.0044        | 791.67 | 9500  | 0.4213 | 0.5884          | 0.4910    | 0.4843 |
| 0.0014        | 833.33 | 10000 | 0.4234 | 0.5934          | 0.4942    | 0.5084 |


### Framework versions

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