---
license: mit
base_model: kavg/LiLT-SER-EN
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-EN-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: 0.7420494699646644
    - name: Recall
      type: recall
      value: 0.7758620689655172
    - name: F1
      type: f1
      value: 0.7585791691751957
    - name: Accuracy
      type: accuracy
      value: 0.8473839248141394
---

<!-- 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-EN-SIN

This model is a fine-tuned version of [kavg/LiLT-SER-EN](https://huggingface.co/kavg/LiLT-SER-EN) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3790
- Precision: 0.7420
- Recall: 0.7759
- F1: 0.7586
- Accuracy: 0.8474

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0089        | 21.74  | 500   | 0.8362          | 0.6606    | 0.7118 | 0.6852 | 0.8545   |
| 0.0013        | 43.48  | 1000  | 1.3605          | 0.7269    | 0.7081 | 0.7174 | 0.8230   |
| 0.0051        | 65.22  | 1500  | 0.9220          | 0.7113    | 0.7525 | 0.7313 | 0.8725   |
| 0.0054        | 86.96  | 2000  | 1.2086          | 0.6965    | 0.7291 | 0.7124 | 0.8467   |
| 0.0001        | 108.7  | 2500  | 1.1308          | 0.6843    | 0.7315 | 0.7071 | 0.8449   |
| 0.0001        | 130.43 | 3000  | 1.0934          | 0.7362    | 0.7044 | 0.7199 | 0.8606   |
| 0.0           | 152.17 | 3500  | 1.0390          | 0.7297    | 0.7512 | 0.7403 | 0.8590   |
| 0.0001        | 173.91 | 4000  | 1.1448          | 0.7128    | 0.7672 | 0.7390 | 0.8599   |
| 0.0           | 195.65 | 4500  | 1.1902          | 0.7393    | 0.7229 | 0.7310 | 0.8551   |
| 0.0001        | 217.39 | 5000  | 1.1164          | 0.7141    | 0.7783 | 0.7448 | 0.8555   |
| 0.0001        | 239.13 | 5500  | 1.4359          | 0.7197    | 0.7241 | 0.7219 | 0.8313   |
| 0.0           | 260.87 | 6000  | 1.4027          | 0.7256    | 0.7426 | 0.7340 | 0.8376   |
| 0.0           | 282.61 | 6500  | 1.4112          | 0.7085    | 0.7574 | 0.7321 | 0.8524   |
| 0.0           | 304.35 | 7000  | 1.5045          | 0.7627    | 0.7599 | 0.7613 | 0.8432   |
| 0.0           | 326.09 | 7500  | 1.4482          | 0.7390    | 0.7672 | 0.7529 | 0.8398   |
| 0.0           | 347.83 | 8000  | 1.5717          | 0.7155    | 0.7525 | 0.7335 | 0.8360   |
| 0.0           | 369.57 | 8500  | 1.3845          | 0.7348    | 0.7746 | 0.7542 | 0.8422   |
| 0.0           | 391.3  | 9000  | 1.3238          | 0.7283    | 0.7660 | 0.7467 | 0.8499   |
| 0.0           | 413.04 | 9500  | 1.3677          | 0.7321    | 0.7672 | 0.7492 | 0.8492   |
| 0.0001        | 434.78 | 10000 | 1.3790          | 0.7420    | 0.7759 | 0.7586 | 0.8474   |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1