---
license: mit
base_model: kavg/LiLT-SER-PT
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-PT-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: 0.7639225181598063
    - name: Recall
      type: recall
      value: 0.7770935960591133
    - name: F1
      type: f1
      value: 0.7704517704517705
    - name: Accuracy
      type: accuracy
      value: 0.8626735867583111
---

<!-- 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-PT-SIN

This model is a fine-tuned version of [kavg/LiLT-SER-PT](https://huggingface.co/kavg/LiLT-SER-PT) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2074
- Precision: 0.7639
- Recall: 0.7771
- F1: 0.7705
- Accuracy: 0.8627

## 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 |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0124        | 21.74  | 500   | 0.8590   | 0.7403 | 0.8082          | 0.7381    | 0.7426 |
| 0.0017        | 43.48  | 1000  | 0.8301   | 0.7272 | 1.2608          | 0.75      | 0.7057 |
| 0.0004        | 65.22  | 1500  | 0.8694   | 0.7323 | 0.8843          | 0.7098    | 0.7562 |
| 0.0           | 86.96  | 2000  | 0.8617   | 0.7532 | 1.0638          | 0.7419    | 0.7648 |
| 0.0001        | 108.7  | 2500  | 0.8580   | 0.7674 | 1.1504          | 0.7689    | 0.7660 |
| 0.0006        | 130.43 | 3000  | 0.8677   | 0.7479 | 0.9865          | 0.7230    | 0.7746 |
| 0.0           | 152.17 | 3500  | 0.8617   | 0.7558 | 1.1492          | 0.7494    | 0.7623 |
| 0.0001        | 173.91 | 4000  | 0.8385   | 0.7590 | 1.3124          | 0.7485    | 0.7697 |
| 0.0055        | 195.65 | 4500  | 1.1331   | 0.7295 | 0.7869          | 0.7571    | 0.8479 |
| 0.0           | 217.39 | 5000  | 1.2061   | 0.7392 | 0.7611          | 0.7500    | 0.8500 |
| 0.0001        | 239.13 | 5500  | 1.2572   | 0.7253 | 0.7672          | 0.7457    | 0.8482 |
| 0.0           | 260.87 | 6000  | 1.3558   | 0.7494 | 0.7734          | 0.7612    | 0.8569 |
| 0.0           | 282.61 | 6500  | 1.4382   | 0.7598 | 0.7672          | 0.7635    | 0.8589 |
| 0.0           | 304.35 | 7000  | 1.4720   | 0.7537 | 0.7574          | 0.7555    | 0.8533 |
| 0.0           | 326.09 | 7500  | 1.3835   | 0.7524 | 0.7783          | 0.7651    | 0.8579 |
| 0.0           | 347.83 | 8000  | 1.2693   | 0.7534 | 0.7599          | 0.7566    | 0.8599 |
| 0.0           | 369.57 | 8500  | 1.2005   | 0.7417 | 0.7709          | 0.7560    | 0.8600 |
| 0.0           | 391.3  | 9000  | 1.2175   | 0.7560 | 0.7820          | 0.7688    | 0.8601 |
| 0.0           | 413.04 | 9500  | 1.2339   | 0.7556 | 0.7845          | 0.7698    | 0.8601 |
| 0.0           | 434.78 | 10000 | 1.2074   | 0.7639 | 0.7771          | 0.7705    | 0.8627 |


### Framework versions

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