---
license: mit
base_model: kavg/LiLT-RE-ES
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-ES](https://huggingface.co/kavg/LiLT-RE-ES) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.2886
- Recall: 0.3586
- F1: 0.3198
- Loss: 0.2312

## 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 |
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:|
| 0.1103        | 41.67  | 500   | 0.4808    | 0.0631 | 0.1116 | 0.2442          |
| 0.0871        | 83.33  | 1000  | 0.2886    | 0.3586 | 0.3198 | 0.2312          |
| 0.0905        | 125.0  | 1500  | 0.2904    | 0.5177 | 0.3721 | 0.2402          |
| 0.0521        | 166.67 | 2000  | 0.3065    | 0.5581 | 0.3957 | 0.2793          |
| 0.0508        | 208.33 | 2500  | 0.3080    | 0.6136 | 0.4101 | 0.4084          |
| 0.0509        | 250.0  | 3000  | 0.3250    | 0.5934 | 0.4200 | 0.4008          |
| 0.0406        | 291.67 | 3500  | 0.3290    | 0.5808 | 0.4201 | 0.4593          |
| 0.0333        | 333.33 | 4000  | 0.3488    | 0.5884 | 0.4380 | 0.4806          |
| 0.0358        | 375.0  | 4500  | 0.3456    | 0.5682 | 0.4298 | 0.6472          |
| 0.0289        | 416.67 | 5000  | 0.3657    | 0.5808 | 0.4488 | 0.6532          |
| 0.0255        | 458.33 | 5500  | 0.3601    | 0.5783 | 0.4438 | 0.7617          |
| 0.0183        | 500.0  | 6000  | 0.3736    | 0.5859 | 0.4562 | 0.7025          |
| 0.0213        | 541.67 | 6500  | 0.3606    | 0.5783 | 0.4442 | 0.8442          |
| 0.0296        | 583.33 | 7000  | 0.3621    | 0.5505 | 0.4369 | 0.7416          |
| 0.0418        | 625.0  | 7500  | 0.3659    | 0.5682 | 0.4451 | 0.7372          |
| 0.0225        | 666.67 | 8000  | 0.3729    | 0.5556 | 0.4462 | 0.8660          |
| 0.0225        | 708.33 | 8500  | 0.3723    | 0.5707 | 0.4506 | 0.8646          |
| 0.0128        | 750.0  | 9000  | 0.375     | 0.5606 | 0.4494 | 0.7905          |
| 0.0182        | 791.67 | 9500  | 0.3758    | 0.5657 | 0.4516 | 0.8551          |
| 0.0061        | 833.33 | 10000 | 0.3788    | 0.5606 | 0.4521 | 0.8355          |


### Framework versions

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