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---
license: mit
base_model: kavg/LiLT-SER-JA
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-JA-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: 0.7378410438908659
- name: Recall
type: recall
value: 0.7660098522167488
- name: F1
type: f1
value: 0.7516616314199396
- name: Accuracy
type: accuracy
value: 0.8793659699817646
---
<!-- 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-SIN
This model is a fine-tuned version of [kavg/LiLT-SER-JA](https://huggingface.co/kavg/LiLT-SER-JA) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1113
- Precision: 0.7378
- Recall: 0.7660
- F1: 0.7517
- Accuracy: 0.8794
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0009 | 21.74 | 500 | 0.8785 | 0.6584 | 0.7217 | 0.6886 | 0.8505 |
| 0.0031 | 43.48 | 1000 | 1.0637 | 0.7309 | 0.7291 | 0.7300 | 0.8533 |
| 0.0046 | 65.22 | 1500 | 0.9166 | 0.7219 | 0.7512 | 0.7363 | 0.8729 |
| 0.0002 | 86.96 | 2000 | 1.0366 | 0.7212 | 0.7389 | 0.7299 | 0.8721 |
| 0.0 | 108.7 | 2500 | 1.0535 | 0.7191 | 0.7377 | 0.7283 | 0.8662 |
| 0.0006 | 130.43 | 3000 | 1.1869 | 0.7409 | 0.7291 | 0.7349 | 0.8495 |
| 0.005 | 152.17 | 3500 | 1.2062 | 0.7356 | 0.7401 | 0.7379 | 0.8627 |
| 0.0002 | 173.91 | 4000 | 1.2067 | 0.7011 | 0.7192 | 0.7100 | 0.8451 |
| 0.0002 | 195.65 | 4500 | 1.1819 | 0.7290 | 0.7389 | 0.7339 | 0.8578 |
| 0.0 | 217.39 | 5000 | 1.1699 | 0.7463 | 0.75 | 0.7482 | 0.8632 |
| 0.0 | 239.13 | 5500 | 1.1548 | 0.7267 | 0.7599 | 0.7429 | 0.8637 |
| 0.0 | 260.87 | 6000 | 1.1867 | 0.7227 | 0.7574 | 0.7396 | 0.8651 |
| 0.0 | 282.61 | 6500 | 1.1614 | 0.7222 | 0.7525 | 0.7370 | 0.8721 |
| 0.0 | 304.35 | 7000 | 1.1884 | 0.7146 | 0.7648 | 0.7388 | 0.8681 |
| 0.0 | 326.09 | 7500 | 1.2186 | 0.6975 | 0.7438 | 0.7199 | 0.8582 |
| 0.0001 | 347.83 | 8000 | 1.0423 | 0.7313 | 0.7709 | 0.7506 | 0.8754 |
| 0.0 | 369.57 | 8500 | 1.1254 | 0.7278 | 0.7574 | 0.7423 | 0.8705 |
| 0.0 | 391.3 | 9000 | 1.1113 | 0.7378 | 0.7660 | 0.7517 | 0.8794 |
| 0.0 | 413.04 | 9500 | 1.1517 | 0.7424 | 0.7562 | 0.7492 | 0.8732 |
| 0.0 | 434.78 | 10000 | 1.1568 | 0.7413 | 0.7586 | 0.7498 | 0.8726 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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