---
license: mit
base_model: kavg/LiLT-RE-IT
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-IT](https://huggingface.co/kavg/LiLT-RE-IT) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.4898
- Recall: 0.6641
- F1: 0.5638
- Loss: 0.6049

## 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 |
|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:|
| 0.061         | 41.67  | 500   | 0.4764 | 0.2580          | 0.4142    | 0.5606 |
| 0.0332        | 83.33  | 1000  | 0.4906 | 0.3439          | 0.4181    | 0.5934 |
| 0.0264        | 125.0  | 1500  | 0.5194 | 0.3892          | 0.4436    | 0.6263 |
| 0.0104        | 166.67 | 2000  | 0.5250 | 0.4165          | 0.4468    | 0.6364 |
| 0.0064        | 208.33 | 2500  | 0.5245 | 0.4479          | 0.4460    | 0.6364 |
| 0.0013        | 250.0  | 3000  | 0.5204 | 0.4655          | 0.4532    | 0.6111 |
| 0.0019        | 291.67 | 3500  | 0.5342 | 0.4859          | 0.4630    | 0.6313 |
| 0.0009        | 333.33 | 4000  | 0.5420 | 0.5162          | 0.4640    | 0.6515 |
| 0.0006        | 375.0  | 4500  | 0.5515 | 0.5724          | 0.4795    | 0.6490 |
| 0.0039        | 416.67 | 5000  | 0.5470 | 0.5687          | 0.4662    | 0.6616 |
| 0.0012        | 458.33 | 5500  | 0.5595 | 0.5582          | 0.4860    | 0.6591 |
| 0.0001        | 500.0  | 6000  | 0.5730 | 0.5709          | 0.4981    | 0.6742 |
| 0.0022        | 541.67 | 6500  | 0.5578 | 0.5795          | 0.4877    | 0.6515 |
| 0.0012        | 583.33 | 7000  | 0.5674 | 0.5710          | 0.4953    | 0.6641 |
| 0.0009        | 625.0  | 7500  | 0.5607 | 0.5994          | 0.4879    | 0.6591 |
| 0.0002        | 666.67 | 8000  | 0.5616 | 0.5865          | 0.4879    | 0.6616 |
| 0.0016        | 708.33 | 8500  | 0.4972 | 0.6717          | 0.5714    | 0.5878 |
| 0.0           | 750.0  | 9000  | 0.4898 | 0.6641          | 0.5638    | 0.6049 |
| 0.0002        | 791.67 | 9500  | 0.4826 | 0.6641          | 0.5590    | 0.6223 |
| 0.0014        | 833.33 | 10000 | 0.4890 | 0.6742          | 0.5669    | 0.6318 |


### Framework versions

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