---
license: mit
base_model: kavg/LiLT-SER-IT
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-IT-SIN
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xfun
      type: xfun
      config: xfun.sin
      split: validation
      args: xfun.sin
    metrics:
    - name: Precision
      type: precision
      value: 0.7651331719128329
    - name: Recall
      type: recall
      value: 0.7783251231527094
    - name: F1
      type: f1
      value: 0.7716727716727716
    - name: Accuracy
      type: accuracy
      value: 0.8705288259222892
---

<!-- 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. -->

# LiLT-SER-IT-SIN

This model is a fine-tuned version of [kavg/LiLT-SER-IT](https://huggingface.co/kavg/LiLT-SER-IT) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2031
- Precision: 0.7651
- Recall: 0.7783
- F1: 0.7717
- Accuracy: 0.8705

## 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: 5e-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
- training_steps: 10000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0301        | 21.74  | 500   | 1.0148          | 0.7146    | 0.7586 | 0.7360 | 0.8470   |
| 0.0058        | 43.48  | 1000  | 0.9498          | 0.7121    | 0.7401 | 0.7258 | 0.8566   |
| 0.0008        | 65.22  | 1500  | 1.0385          | 0.7310    | 0.7833 | 0.7562 | 0.8559   |
| 0.0004        | 86.96  | 2000  | 1.2165          | 0.7484    | 0.7032 | 0.7251 | 0.8512   |
| 0.0017        | 108.7  | 2500  | 1.0999          | 0.7252    | 0.7734 | 0.7485 | 0.8726   |
| 0.0018        | 130.43 | 3000  | 1.1872          | 0.7293    | 0.7697 | 0.7490 | 0.8564   |
| 0.0001        | 152.17 | 3500  | 1.2632          | 0.7386    | 0.7377 | 0.7381 | 0.8457   |
| 0.0008        | 173.91 | 4000  | 1.0687          | 0.7337    | 0.7635 | 0.7483 | 0.8691   |
| 0.0           | 195.65 | 4500  | 1.0346          | 0.7205    | 0.7746 | 0.7466 | 0.8684   |
| 0.0           | 217.39 | 5000  | 1.1440          | 0.7158    | 0.7537 | 0.7343 | 0.8686   |
| 0.0           | 239.13 | 5500  | 1.3391          | 0.7690    | 0.7586 | 0.7638 | 0.8578   |
| 0.0           | 260.87 | 6000  | 1.0498          | 0.7482    | 0.7722 | 0.7600 | 0.8761   |
| 0.0           | 282.61 | 6500  | 1.0602          | 0.7301    | 0.7894 | 0.7586 | 0.8787   |
| 0.0           | 304.35 | 7000  | 1.1634          | 0.7355    | 0.7328 | 0.7341 | 0.8613   |
| 0.0           | 326.09 | 7500  | 1.1705          | 0.7680    | 0.7746 | 0.7713 | 0.8754   |
| 0.0           | 347.83 | 8000  | 1.2455          | 0.7616    | 0.7709 | 0.7662 | 0.8687   |
| 0.0           | 369.57 | 8500  | 1.2259          | 0.7327    | 0.7562 | 0.7442 | 0.8665   |
| 0.0           | 391.3  | 9000  | 1.1737          | 0.7577    | 0.7857 | 0.7715 | 0.8690   |
| 0.0           | 413.04 | 9500  | 1.2174          | 0.7636    | 0.7796 | 0.7715 | 0.8704   |
| 0.0           | 434.78 | 10000 | 1.2031          | 0.7651    | 0.7783 | 0.7717 | 0.8705   |


### Framework versions

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