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---
license: mit
base_model: kavg/LiLT-SER-IT
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-IT-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: 0.7651331719128329
- name: Recall
type: recall
value: 0.7783251231527094
- name: F1
type: f1
value: 0.7716727716727716
- name: Accuracy
type: accuracy
value: 0.8705288259222892
---
<!-- 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-IT-SIN
This model is a fine-tuned version of [kavg/LiLT-SER-IT](https://huggingface.co/kavg/LiLT-SER-IT) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2031
- Precision: 0.7651
- Recall: 0.7783
- F1: 0.7717
- Accuracy: 0.8705
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0301 | 21.74 | 500 | 1.0148 | 0.7146 | 0.7586 | 0.7360 | 0.8470 |
| 0.0058 | 43.48 | 1000 | 0.9498 | 0.7121 | 0.7401 | 0.7258 | 0.8566 |
| 0.0008 | 65.22 | 1500 | 1.0385 | 0.7310 | 0.7833 | 0.7562 | 0.8559 |
| 0.0004 | 86.96 | 2000 | 1.2165 | 0.7484 | 0.7032 | 0.7251 | 0.8512 |
| 0.0017 | 108.7 | 2500 | 1.0999 | 0.7252 | 0.7734 | 0.7485 | 0.8726 |
| 0.0018 | 130.43 | 3000 | 1.1872 | 0.7293 | 0.7697 | 0.7490 | 0.8564 |
| 0.0001 | 152.17 | 3500 | 1.2632 | 0.7386 | 0.7377 | 0.7381 | 0.8457 |
| 0.0008 | 173.91 | 4000 | 1.0687 | 0.7337 | 0.7635 | 0.7483 | 0.8691 |
| 0.0 | 195.65 | 4500 | 1.0346 | 0.7205 | 0.7746 | 0.7466 | 0.8684 |
| 0.0 | 217.39 | 5000 | 1.1440 | 0.7158 | 0.7537 | 0.7343 | 0.8686 |
| 0.0 | 239.13 | 5500 | 1.3391 | 0.7690 | 0.7586 | 0.7638 | 0.8578 |
| 0.0 | 260.87 | 6000 | 1.0498 | 0.7482 | 0.7722 | 0.7600 | 0.8761 |
| 0.0 | 282.61 | 6500 | 1.0602 | 0.7301 | 0.7894 | 0.7586 | 0.8787 |
| 0.0 | 304.35 | 7000 | 1.1634 | 0.7355 | 0.7328 | 0.7341 | 0.8613 |
| 0.0 | 326.09 | 7500 | 1.1705 | 0.7680 | 0.7746 | 0.7713 | 0.8754 |
| 0.0 | 347.83 | 8000 | 1.2455 | 0.7616 | 0.7709 | 0.7662 | 0.8687 |
| 0.0 | 369.57 | 8500 | 1.2259 | 0.7327 | 0.7562 | 0.7442 | 0.8665 |
| 0.0 | 391.3 | 9000 | 1.1737 | 0.7577 | 0.7857 | 0.7715 | 0.8690 |
| 0.0 | 413.04 | 9500 | 1.2174 | 0.7636 | 0.7796 | 0.7715 | 0.8704 |
| 0.0 | 434.78 | 10000 | 1.2031 | 0.7651 | 0.7783 | 0.7717 | 0.8705 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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