---
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-PT
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xfun
      type: xfun
      config: xfun.pt
      split: validation
      args: xfun.pt
    metrics:
    - name: Precision
      type: precision
      value: 0.6997755331088664
    - name: Recall
      type: recall
      value: 0.7550711474417197
    - name: F1
      type: f1
      value: 0.72637250618902
    - name: Accuracy
      type: accuracy
      value: 0.7709534665415047
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# LiLT-SER-PT

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: 2.1403
- Precision: 0.6998
- Recall: 0.7551
- F1: 0.7264
- Accuracy: 0.7710

## 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 |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0838        | 8.47   | 500   | 0.7697   | 0.6542 | 1.0006          | 0.6081    | 0.7078 |
| 0.0366        | 16.95  | 1000  | 0.7606   | 0.6795 | 1.4063          | 0.6533    | 0.7078 |
| 0.0173        | 25.42  | 1500  | 0.7848   | 0.7047 | 1.4681          | 0.6752    | 0.7369 |
| 0.0036        | 33.9   | 2000  | 0.7706   | 0.7003 | 1.6267          | 0.6577    | 0.7487 |
| 0.0023        | 42.37  | 2500  | 1.6728   | 0.6839 | 0.7172          | 0.7002    | 0.7698 |
| 0.0001        | 50.85  | 3000  | 1.6210   | 0.6742 | 0.7493          | 0.7098    | 0.7941 |
| 0.0001        | 59.32  | 3500  | 1.6883   | 0.6962 | 0.7505          | 0.7223    | 0.7929 |
| 0.0007        | 67.8   | 4000  | 1.8709   | 0.6730 | 0.7590          | 0.7134    | 0.7811 |
| 0.0003        | 76.27  | 4500  | 1.9387   | 0.6884 | 0.7151          | 0.7015    | 0.7690 |
| 0.0034        | 84.75  | 5000  | 1.8042   | 0.6927 | 0.7554          | 0.7227    | 0.7787 |
| 0.0           | 93.22  | 5500  | 2.0395   | 0.6954 | 0.7596          | 0.7261    | 0.7527 |
| 0.0003        | 101.69 | 6000  | 1.9295   | 0.6861 | 0.7511          | 0.7172    | 0.7790 |
| 0.0001        | 110.17 | 6500  | 1.9690   | 0.6813 | 0.7611          | 0.7190    | 0.7694 |
| 0.0           | 118.64 | 7000  | 1.9217   | 0.6974 | 0.7520          | 0.7237    | 0.7754 |
| 0.0001        | 127.12 | 7500  | 2.0703   | 0.6885 | 0.7536          | 0.7196    | 0.7694 |
| 0.0002        | 135.59 | 8000  | 2.0438   | 0.6915 | 0.7635          | 0.7258    | 0.7770 |
| 0.0           | 144.07 | 8500  | 2.0429   | 0.6980 | 0.7599          | 0.7276    | 0.7782 |
| 0.0           | 152.54 | 9000  | 2.1403   | 0.6998 | 0.7551          | 0.7264    | 0.7710 |
| 0.0           | 161.02 | 9500  | 2.1786   | 0.6986 | 0.7578          | 0.7270    | 0.7726 |
| 0.0           | 169.49 | 10000 | 2.1782   | 0.6965 | 0.7560          | 0.7250    | 0.7721 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1