---
license: mit
base_model: kavg/LiLT-SER-ZH
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-ZH-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: 0.7417061611374408
    - name: Recall
      type: recall
      value: 0.770935960591133
    - name: F1
      type: f1
      value: 0.7560386473429951
    - name: Accuracy
      type: accuracy
      value: 0.8558002524898303
---

<!-- 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-ZH-SIN

This model is a fine-tuned version of [kavg/LiLT-SER-ZH](https://huggingface.co/kavg/LiLT-SER-ZH) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2037
- Precision: 0.7417
- Recall: 0.7709
- F1: 0.7560
- Accuracy: 0.8558

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0013        | 21.74  | 500   | 0.9018          | 0.6843    | 0.7475 | 0.7145 | 0.8599   |
| 0.012         | 43.48  | 1000  | 1.0791          | 0.7115    | 0.7623 | 0.7360 | 0.8561   |
| 0.0002        | 65.22  | 1500  | 1.0060          | 0.7360    | 0.7623 | 0.7489 | 0.8565   |
| 0.03          | 86.96  | 2000  | 1.1521          | 0.7282    | 0.6700 | 0.6979 | 0.8313   |
| 0.0013        | 108.7  | 2500  | 1.1517          | 0.7240    | 0.7463 | 0.7350 | 0.8579   |
| 0.0016        | 130.43 | 3000  | 0.9393          | 0.7319    | 0.7697 | 0.7503 | 0.8732   |
| 0.0021        | 152.17 | 3500  | 0.9972          | 0.7249    | 0.7562 | 0.7402 | 0.8635   |
| 0.0001        | 173.91 | 4000  | 1.0485          | 0.7049    | 0.7796 | 0.7404 | 0.8583   |
| 0.0002        | 195.65 | 4500  | 1.0827          | 0.7055    | 0.7315 | 0.7183 | 0.8433   |
| 0.0           | 217.39 | 5000  | 1.0528          | 0.7354    | 0.7599 | 0.7474 | 0.8586   |
| 0.0001        | 239.13 | 5500  | 1.1183          | 0.7001    | 0.7131 | 0.7065 | 0.8465   |
| 0.0002        | 260.87 | 6000  | 1.1749          | 0.7231    | 0.7685 | 0.7451 | 0.8520   |
| 0.0           | 282.61 | 6500  | 1.1206          | 0.7315    | 0.7685 | 0.7495 | 0.8611   |
| 0.0           | 304.35 | 7000  | 1.2037          | 0.7417    | 0.7709 | 0.7560 | 0.8558   |
| 0.0           | 326.09 | 7500  | 1.3737          | 0.7391    | 0.75   | 0.7445 | 0.8513   |
| 0.0           | 347.83 | 8000  | 1.2926          | 0.7221    | 0.7648 | 0.7428 | 0.8475   |
| 0.0           | 369.57 | 8500  | 1.4108          | 0.6966    | 0.7549 | 0.7246 | 0.8293   |
| 0.0           | 391.3  | 9000  | 1.4346          | 0.7222    | 0.7586 | 0.7399 | 0.8303   |
| 0.0           | 413.04 | 9500  | 1.4146          | 0.7225    | 0.7599 | 0.7407 | 0.8363   |
| 0.0           | 434.78 | 10000 | 1.4097          | 0.7121    | 0.7586 | 0.7346 | 0.8346   |


### Framework versions

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