---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-ZH
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xfun
      type: xfun
      config: xfun.zh
      split: validation
      args: xfun.zh
    metrics:
    - name: Precision
      type: precision
      value: 0.8408488063660478
    - name: Recall
      type: recall
      value: 0.9347968545216252
    - name: F1
      type: f1
      value: 0.8853374709076804
    - name: Accuracy
      type: accuracy
      value: 0.8116519985331867
---

<!-- 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

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8792
- Precision: 0.8408
- Recall: 0.9348
- F1: 0.8853
- Accuracy: 0.8117

## 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  | Accuracy | F1     | Validation Loss | Precision | Recall |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.2166        | 10.64  | 500   | 0.7724   | 0.8544 | 1.1239          | 0.7932    | 0.9260 |
| 0.0238        | 21.28  | 1000  | 0.8201   | 0.8624 | 1.0535          | 0.8572    | 0.8676 |
| 0.0034        | 31.91  | 1500  | 0.8057   | 0.8599 | 1.4675          | 0.8088    | 0.9178 |
| 0.0163        | 42.55  | 2000  | 0.8232   | 0.8729 | 1.2837          | 0.8572    | 0.8893 |
| 0.0037        | 53.19  | 2500  | 0.8114   | 0.8627 | 1.5315          | 0.8142    | 0.9174 |
| 0.0003        | 63.83  | 3000  | 0.8137   | 0.8652 | 1.4604          | 0.8471    | 0.8840 |
| 0.0005        | 74.47  | 3500  | 0.8115   | 0.8767 | 1.5980          | 0.8409    | 0.9158 |
| 0.0005        | 85.11  | 4000  | 0.8129   | 0.8634 | 1.5108          | 0.8261    | 0.9043 |
| 0.0004        | 95.74  | 4500  | 0.8161   | 0.8817 | 1.7719          | 0.8397    | 0.9282 |
| 0.0001        | 106.38 | 5000  | 0.8203   | 0.8813 | 1.4313          | 0.8600    | 0.9037 |
| 0.0001        | 117.02 | 5500  | 0.8181   | 0.8832 | 1.5232          | 0.8509    | 0.9181 |
| 0.0           | 127.66 | 6000  | 0.8069   | 0.8808 | 1.6845          | 0.8532    | 0.9102 |
| 0.0179        | 138.3  | 6500  | 0.8192   | 0.8793 | 1.6293          | 0.8398    | 0.9227 |
| 0.0           | 148.94 | 7000  | 0.8081   | 0.8815 | 1.8209          | 0.8381    | 0.9296 |
| 0.0           | 159.57 | 7500  | 1.8224   | 0.8443 | 0.9184          | 0.8798    | 0.8070 |
| 0.0           | 170.21 | 8000  | 1.7810   | 0.8450 | 0.9305          | 0.8857    | 0.8127 |
| 0.0           | 180.85 | 8500  | 1.7531   | 0.8454 | 0.9230          | 0.8825    | 0.8088 |
| 0.0           | 191.49 | 9000  | 1.8757   | 0.8394 | 0.9302          | 0.8825    | 0.8070 |
| 0.0           | 202.13 | 9500  | 1.8757   | 0.8417 | 0.9338          | 0.8854    | 0.8123 |
| 0.0           | 212.77 | 10000 | 1.8792   | 0.8408 | 0.9348          | 0.8853    | 0.8117 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1