---
license: mit
base_model: kavg/LiLT-RE-DE
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-DE](https://huggingface.co/kavg/LiLT-RE-DE) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.2952
- Recall: 0.4167
- F1: 0.3455
- Loss: 0.3186

## 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 |
|:-------------:|:------:|:-----:|:---------:|:------:|:------:|:---------------:|
| 0.1035        | 41.67  | 500   | 0.2905    | 0.1540 | 0.2013 | 0.2291          |
| 0.0691        | 83.33  | 1000  | 0.2952    | 0.4167 | 0.3455 | 0.3186          |
| 0.0442        | 125.0  | 1500  | 0.2970    | 0.5909 | 0.3953 | 0.2765          |
| 0.024         | 166.67 | 2000  | 0.3227    | 0.5884 | 0.4168 | 0.4144          |
| 0.0216        | 208.33 | 2500  | 0.3234    | 0.6035 | 0.4211 | 0.4036          |
| 0.0096        | 250.0  | 3000  | 0.3534    | 0.6364 | 0.4545 | 0.5716          |
| 0.0079        | 291.67 | 3500  | 0.3456    | 0.5934 | 0.4368 | 0.6643          |
| 0.0045        | 333.33 | 4000  | 0.3427    | 0.6187 | 0.4410 | 0.6955          |
| 0.0017        | 375.0  | 4500  | 0.3587    | 0.6187 | 0.4541 | 0.8144          |
| 0.0147        | 416.67 | 5000  | 0.3407    | 0.6212 | 0.4401 | 0.8101          |
| 0.0027        | 458.33 | 5500  | 0.3491    | 0.6162 | 0.4457 | 0.8809          |
| 0.0079        | 500.0  | 6000  | 0.3183    | 0.6061 | 0.4174 | 0.8863          |
| 0.0028        | 541.67 | 6500  | 0.3506    | 0.5985 | 0.4422 | 0.9944          |
| 0.0075        | 583.33 | 7000  | 0.3476    | 0.5960 | 0.4391 | 0.9920          |
| 0.0002        | 625.0  | 7500  | 0.3448    | 0.6061 | 0.4396 | 0.9752          |
| 0.0025        | 666.67 | 8000  | 0.3456    | 0.6162 | 0.4428 | 0.9866          |
| 0.0037        | 708.33 | 8500  | 0.3465    | 0.6187 | 0.4442 | 1.0153          |
| 0.0041        | 750.0  | 9000  | 0.3442    | 0.6136 | 0.4410 | 1.1227          |
| 0.0023        | 791.67 | 9500  | 0.3450    | 0.6237 | 0.4442 | 1.0995          |
| 0.0007        | 833.33 | 10000 | 0.3408    | 0.6162 | 0.4388 | 1.1097          |


### Framework versions

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