---
license: mit
base_model: kavg/LiLT-SER-JA
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-JA-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: 0.7378410438908659
    - name: Recall
      type: recall
      value: 0.7660098522167488
    - name: F1
      type: f1
      value: 0.7516616314199396
    - name: Accuracy
      type: accuracy
      value: 0.8793659699817646
---

<!-- 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-JA-SIN

This model is a fine-tuned version of [kavg/LiLT-SER-JA](https://huggingface.co/kavg/LiLT-SER-JA) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1113
- Precision: 0.7378
- Recall: 0.7660
- F1: 0.7517
- Accuracy: 0.8794

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0009        | 21.74  | 500   | 0.8785          | 0.6584    | 0.7217 | 0.6886 | 0.8505   |
| 0.0031        | 43.48  | 1000  | 1.0637          | 0.7309    | 0.7291 | 0.7300 | 0.8533   |
| 0.0046        | 65.22  | 1500  | 0.9166          | 0.7219    | 0.7512 | 0.7363 | 0.8729   |
| 0.0002        | 86.96  | 2000  | 1.0366          | 0.7212    | 0.7389 | 0.7299 | 0.8721   |
| 0.0           | 108.7  | 2500  | 1.0535          | 0.7191    | 0.7377 | 0.7283 | 0.8662   |
| 0.0006        | 130.43 | 3000  | 1.1869          | 0.7409    | 0.7291 | 0.7349 | 0.8495   |
| 0.005         | 152.17 | 3500  | 1.2062          | 0.7356    | 0.7401 | 0.7379 | 0.8627   |
| 0.0002        | 173.91 | 4000  | 1.2067          | 0.7011    | 0.7192 | 0.7100 | 0.8451   |
| 0.0002        | 195.65 | 4500  | 1.1819          | 0.7290    | 0.7389 | 0.7339 | 0.8578   |
| 0.0           | 217.39 | 5000  | 1.1699          | 0.7463    | 0.75   | 0.7482 | 0.8632   |
| 0.0           | 239.13 | 5500  | 1.1548          | 0.7267    | 0.7599 | 0.7429 | 0.8637   |
| 0.0           | 260.87 | 6000  | 1.1867          | 0.7227    | 0.7574 | 0.7396 | 0.8651   |
| 0.0           | 282.61 | 6500  | 1.1614          | 0.7222    | 0.7525 | 0.7370 | 0.8721   |
| 0.0           | 304.35 | 7000  | 1.1884          | 0.7146    | 0.7648 | 0.7388 | 0.8681   |
| 0.0           | 326.09 | 7500  | 1.2186          | 0.6975    | 0.7438 | 0.7199 | 0.8582   |
| 0.0001        | 347.83 | 8000  | 1.0423          | 0.7313    | 0.7709 | 0.7506 | 0.8754   |
| 0.0           | 369.57 | 8500  | 1.1254          | 0.7278    | 0.7574 | 0.7423 | 0.8705   |
| 0.0           | 391.3  | 9000  | 1.1113          | 0.7378    | 0.7660 | 0.7517 | 0.8794   |
| 0.0           | 413.04 | 9500  | 1.1517          | 0.7424    | 0.7562 | 0.7492 | 0.8732   |
| 0.0           | 434.78 | 10000 | 1.1568          | 0.7413    | 0.7586 | 0.7498 | 0.8726   |


### Framework versions

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