---
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-JA
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xfun
      type: xfun
      config: xfun.ja
      split: validation
      args: xfun.ja
    metrics:
    - name: Precision
      type: precision
      value: 0.7244408945686901
    - name: Recall
      type: recall
      value: 0.8754826254826255
    - name: F1
      type: f1
      value: 0.7928321678321678
    - name: Accuracy
      type: accuracy
      value: 0.7835245046923879
---

<!-- 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

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: 2.3482
- Precision: 0.7244
- Recall: 0.8755
- F1: 0.7928
- Accuracy: 0.7835

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0726        | 10.2   | 500   | 1.0347          | 0.6824    | 0.8359 | 0.7514 | 0.7829   |
| 0.0015        | 20.41  | 1000  | 1.6415          | 0.6828    | 0.8808 | 0.7692 | 0.7700   |
| 0.0062        | 30.61  | 1500  | 1.7000          | 0.7063    | 0.8427 | 0.7685 | 0.7828   |
| 0.0145        | 40.82  | 2000  | 1.9098          | 0.6979    | 0.8885 | 0.7817 | 0.7729   |
| 0.0014        | 51.02  | 2500  | 1.6868          | 0.7117    | 0.8509 | 0.7751 | 0.7859   |
| 0.0009        | 61.22  | 3000  | 1.8930          | 0.7087    | 0.8441 | 0.7705 | 0.7782   |
| 0.0001        | 71.43  | 3500  | 2.0325          | 0.7217    | 0.8736 | 0.7904 | 0.7845   |
| 0.0006        | 81.63  | 4000  | 1.8854          | 0.7032    | 0.8769 | 0.7805 | 0.7904   |
| 0.0001        | 91.84  | 4500  | 2.2205          | 0.6977    | 0.8721 | 0.7752 | 0.7577   |
| 0.0002        | 102.04 | 5000  | 2.1731          | 0.7090    | 0.8702 | 0.7814 | 0.7786   |
| 0.0           | 112.24 | 5500  | 2.3198          | 0.7150    | 0.8707 | 0.7852 | 0.7681   |
| 0.0003        | 122.45 | 6000  | 1.9680          | 0.7188    | 0.8649 | 0.7851 | 0.7896   |
| 0.0           | 132.65 | 6500  | 2.2202          | 0.7316    | 0.8523 | 0.7873 | 0.7815   |
| 0.0           | 142.86 | 7000  | 2.2800          | 0.7013    | 0.8818 | 0.7813 | 0.7727   |
| 0.0           | 153.06 | 7500  | 2.2149          | 0.7202    | 0.8784 | 0.7915 | 0.7790   |
| 0.0           | 163.27 | 8000  | 2.2384          | 0.7264    | 0.8663 | 0.7902 | 0.7834   |
| 0.0001        | 173.47 | 8500  | 2.2177          | 0.7269    | 0.8682 | 0.7913 | 0.7842   |
| 0.0           | 183.67 | 9000  | 2.2768          | 0.7333    | 0.8731 | 0.7971 | 0.7872   |
| 0.0           | 193.88 | 9500  | 2.2996          | 0.7344    | 0.8716 | 0.7972 | 0.7878   |
| 0.0           | 204.08 | 10000 | 2.3482          | 0.7244    | 0.8755 | 0.7928 | 0.7835   |


### Framework versions

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