---
license: mit
base_model: kavg/LiLT-RE-FR
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-FR](https://huggingface.co/kavg/LiLT-RE-FR) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.3604
- Recall: 0.5707
- F1: 0.4418
- Loss: 0.2693

## 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 |
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:|
| 0.0868        | 41.67  | 500   | 0.3688    | 0.2803 | 0.3185 | 0.1679          |
| 0.0557        | 83.33  | 1000  | 0.3604    | 0.5707 | 0.4418 | 0.2693          |
| 0.0513        | 125.0  | 1500  | 0.3962    | 0.5833 | 0.4719 | 0.3008          |
| 0.0248        | 166.67 | 2000  | 0.4043    | 0.6237 | 0.4906 | 0.4857          |
| 0.0139        | 208.33 | 2500  | 0.4296    | 0.6010 | 0.5011 | 0.4227          |
| 0.004         | 250.0  | 3000  | 0.4177    | 0.6212 | 0.4995 | 0.5369          |
| 0.0084        | 291.67 | 3500  | 0.4255    | 0.6490 | 0.514  | 0.5332          |
| 0.0067        | 333.33 | 4000  | 0.4259    | 0.6389 | 0.5111 | 0.4978          |
| 0.0008        | 375.0  | 4500  | 0.4189    | 0.6263 | 0.5020 | 0.4567          |
| 0.0116        | 416.67 | 5000  | 0.4336    | 0.6515 | 0.5207 | 0.5514          |
| 0.0007        | 458.33 | 5500  | 0.4394    | 0.6414 | 0.5216 | 0.5703          |
| 0.0004        | 500.0  | 6000  | 0.4504    | 0.6540 | 0.5335 | 0.6107          |
| 0.002         | 541.67 | 6500  | 0.4480    | 0.6414 | 0.5275 | 0.5859          |
| 0.0059        | 583.33 | 7000  | 0.4526    | 0.6263 | 0.5254 | 0.6033          |
| 0.0023        | 625.0  | 7500  | 0.4379    | 0.6414 | 0.5205 | 0.6440          |
| 0.0007        | 666.67 | 8000  | 0.4499    | 0.6237 | 0.5228 | 0.5594          |
| 0.003         | 708.33 | 8500  | 0.4393    | 0.6490 | 0.5240 | 0.6276          |
| 0.0001        | 750.0  | 9000  | 0.4410    | 0.6515 | 0.5260 | 0.6132          |
| 0.001         | 791.67 | 9500  | 0.4376    | 0.6288 | 0.5161 | 0.6312          |
| 0.0001        | 833.33 | 10000 | 0.4415    | 0.6389 | 0.5222 | 0.6304          |


### Framework versions

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