---
license: mit
base_model: kavg/LiLT-RE-PT
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-PT](https://huggingface.co./kavg/LiLT-RE-PT) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.3631
- Recall: 0.4823
- F1: 0.4143
- Loss: 0.1671

## 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  | Precision | Recall | F1     | Validation Loss |
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:|
| 0.0907        | 41.67  | 500   | 0.3315    | 0.3106 | 0.3207 | 0.2039          |
| 0.0766        | 83.33  | 1000  | 0.3631    | 0.4823 | 0.4143 | 0.1671          |
| 0.0639        | 125.0  | 1500  | 0.3640    | 0.6086 | 0.4556 | 0.2525          |
| 0.0309        | 166.67 | 2000  | 0.3973    | 0.6010 | 0.4784 | 0.2339          |
| 0.0318        | 208.33 | 2500  | 0.4045    | 0.6414 | 0.4961 | 0.3325          |
| 0.0144        | 250.0  | 3000  | 0.4268    | 0.6187 | 0.5052 | 0.3513          |
| 0.0163        | 291.67 | 3500  | 0.4273    | 0.6086 | 0.5021 | 0.2880          |
| 0.0062        | 333.33 | 4000  | 0.4368    | 0.6288 | 0.5155 | 0.3064          |
| 0.0115        | 375.0  | 4500  | 0.4386    | 0.6313 | 0.5176 | 0.3283          |
| 0.0168        | 416.67 | 5000  | 0.4373    | 0.6162 | 0.5115 | 0.3258          |
| 0.0062        | 458.33 | 5500  | 0.4530    | 0.6086 | 0.5194 | 0.3467          |
| 0.0074        | 500.0  | 6000  | 0.4569    | 0.6162 | 0.5247 | 0.3401          |
| 0.0037        | 541.67 | 6500  | 0.4559    | 0.6136 | 0.5231 | 0.3526          |
| 0.008         | 583.33 | 7000  | 0.4650    | 0.6035 | 0.5253 | 0.3076          |
| 0.0045        | 625.0  | 7500  | 0.4610    | 0.6111 | 0.5255 | 0.3799          |
| 0.0045        | 666.67 | 8000  | 0.4551    | 0.6136 | 0.5226 | 0.3692          |
| 0.0052        | 708.33 | 8500  | 0.4535    | 0.6162 | 0.5225 | 0.3492          |
| 0.0002        | 750.0  | 9000  | 0.4537    | 0.6061 | 0.5189 | 0.4075          |
| 0.0027        | 791.67 | 9500  | 0.4581    | 0.6212 | 0.5273 | 0.3816          |
| 0.0009        | 833.33 | 10000 | 0.4569    | 0.6162 | 0.5247 | 0.3834          |


### Framework versions

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