---
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-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: 0.7058139534883721
    - name: Recall
      type: recall
      value: 0.7475369458128078
    - name: F1
      type: f1
      value: 0.7260765550239234
    - name: Accuracy
      type: accuracy
      value: 0.8621124982465984
---

<!-- 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-SIN

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: 1.1967
- Precision: 0.7058
- Recall: 0.7475
- F1: 0.7261
- Accuracy: 0.8621

## 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 |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0739        | 21.74  | 500   | 0.8268   | 0.5620 | 0.7143          | 0.5       | 0.6416 |
| 0.0509        | 43.48  | 1000  | 0.8324   | 0.5839 | 0.8499          | 0.5348    | 0.6429 |
| 0.0004        | 65.22  | 1500  | 0.8398   | 0.6521 | 0.9889          | 0.6256    | 0.6810 |
| 0.0004        | 86.96  | 2000  | 0.8461   | 0.6678 | 1.0577          | 0.6251    | 0.7167 |
| 0.003         | 108.7  | 2500  | 0.8561   | 0.6929 | 1.0734          | 0.6532    | 0.7377 |
| 0.0006        | 130.43 | 3000  | 0.8569   | 0.6924 | 1.1114          | 0.6686    | 0.7180 |
| 0.0022        | 152.17 | 3500  | 0.8245   | 0.6749 | 1.4184          | 0.6774    | 0.6724 |
| 0.0001        | 173.91 | 4000  | 0.8502   | 0.6937 | 1.0524          | 0.6546    | 0.7377 |
| 0.001         | 195.65 | 4500  | 0.8493   | 0.6900 | 1.1949          | 0.6663    | 0.7155 |
| 0.0001        | 217.39 | 5000  | 0.8460   | 0.6885 | 1.1462          | 0.6790    | 0.6983 |
| 0.0001        | 239.13 | 5500  | 0.8641   | 0.6970 | 1.1296          | 0.6697    | 0.7266 |
| 0.0           | 260.87 | 6000  | 0.8529   | 0.7046 | 1.2585          | 0.6929    | 0.7167 |
| 0.0037        | 282.61 | 6500  | 0.8634   | 0.7139 | 1.2292          | 0.6917    | 0.7377 |
| 0.0           | 304.35 | 7000  | 0.8621   | 0.7261 | 1.1967          | 0.7058    | 0.7475 |
| 0.0           | 326.09 | 7500  | 0.8585   | 0.7230 | 1.2144          | 0.7089    | 0.7377 |
| 0.0           | 347.83 | 8000  | 0.8609   | 0.7180 | 1.2117          | 0.6918    | 0.7463 |
| 0.0           | 369.57 | 8500  | 0.8628   | 0.7135 | 1.1961          | 0.6755    | 0.7562 |
| 0.0           | 391.3  | 9000  | 0.8624   | 0.7220 | 1.2292          | 0.7059    | 0.7389 |
| 0.0           | 413.04 | 9500  | 0.8611   | 0.7262 | 1.2278          | 0.7071    | 0.7463 |
| 0.0           | 434.78 | 10000 | 0.8609   | 0.7242 | 1.2317          | 0.7056    | 0.7438 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2