---
license: mit
base_model: kavg/LiLT-SER-DE
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-DE-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: 0.7494033412887828
    - name: Recall
      type: recall
      value: 0.7733990147783252
    - name: F1
      type: f1
      value: 0.7612121212121213
    - name: Accuracy
      type: accuracy
      value: 0.8555197082339739
---

<!-- 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-DE-SIN

This model is a fine-tuned version of [kavg/LiLT-SER-DE](https://huggingface.co/kavg/LiLT-SER-DE) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2864
- Precision: 0.7494
- Recall: 0.7734
- F1: 0.7612
- Accuracy: 0.8555

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0053        | 21.74  | 500   | 0.9346          | 0.6644    | 0.7315 | 0.6964 | 0.8395   |
| 0.0026        | 43.48  | 1000  | 0.9918          | 0.7216    | 0.7278 | 0.7247 | 0.8587   |
| 0.0016        | 65.22  | 1500  | 0.9133          | 0.7098    | 0.7291 | 0.7193 | 0.8603   |
| 0.0001        | 86.96  | 2000  | 1.0041          | 0.7580    | 0.7328 | 0.7451 | 0.8536   |
| 0.0002        | 108.7  | 2500  | 1.1795          | 0.7312    | 0.7438 | 0.7375 | 0.8461   |
| 0.0001        | 130.43 | 3000  | 1.1417          | 0.7169    | 0.7328 | 0.7247 | 0.8642   |
| 0.0           | 152.17 | 3500  | 1.1740          | 0.7336    | 0.7562 | 0.7447 | 0.8453   |
| 0.0           | 173.91 | 4000  | 1.0535          | 0.7350    | 0.7414 | 0.7382 | 0.8635   |
| 0.0           | 195.65 | 4500  | 1.2769          | 0.7443    | 0.7241 | 0.7341 | 0.8502   |
| 0.0           | 217.39 | 5000  | 1.2235          | 0.7148    | 0.7438 | 0.7290 | 0.8363   |
| 0.0004        | 239.13 | 5500  | 1.2500          | 0.7376    | 0.7685 | 0.7527 | 0.8569   |
| 0.0           | 260.87 | 6000  | 1.2864          | 0.7494    | 0.7734 | 0.7612 | 0.8555   |
| 0.0           | 282.61 | 6500  | 1.1766          | 0.7649    | 0.7451 | 0.7548 | 0.8589   |
| 0.0           | 304.35 | 7000  | 1.3060          | 0.7231    | 0.7365 | 0.7297 | 0.8479   |
| 0.0           | 326.09 | 7500  | 1.1780          | 0.7093    | 0.7451 | 0.7267 | 0.8534   |
| 0.0           | 347.83 | 8000  | 1.2882          | 0.7337    | 0.75   | 0.7418 | 0.8614   |
| 0.0           | 369.57 | 8500  | 1.2833          | 0.7436    | 0.75   | 0.7468 | 0.8644   |
| 0.0           | 391.3  | 9000  | 1.4372          | 0.7372    | 0.7463 | 0.7417 | 0.8522   |
| 0.0           | 413.04 | 9500  | 1.4223          | 0.7382    | 0.75   | 0.7440 | 0.8513   |
| 0.0           | 434.78 | 10000 | 1.3686          | 0.7454    | 0.75   | 0.7477 | 0.8554   |


### Framework versions

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