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---
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-JA
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xfun
type: xfun
config: xfun.ja
split: validation
args: xfun.ja
metrics:
- name: Precision
type: precision
value: 0.7244408945686901
- name: Recall
type: recall
value: 0.8754826254826255
- name: F1
type: f1
value: 0.7928321678321678
- name: Accuracy
type: accuracy
value: 0.7835245046923879
---
<!-- 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
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: 2.3482
- Precision: 0.7244
- Recall: 0.8755
- F1: 0.7928
- Accuracy: 0.7835
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0726 | 10.2 | 500 | 1.0347 | 0.6824 | 0.8359 | 0.7514 | 0.7829 |
| 0.0015 | 20.41 | 1000 | 1.6415 | 0.6828 | 0.8808 | 0.7692 | 0.7700 |
| 0.0062 | 30.61 | 1500 | 1.7000 | 0.7063 | 0.8427 | 0.7685 | 0.7828 |
| 0.0145 | 40.82 | 2000 | 1.9098 | 0.6979 | 0.8885 | 0.7817 | 0.7729 |
| 0.0014 | 51.02 | 2500 | 1.6868 | 0.7117 | 0.8509 | 0.7751 | 0.7859 |
| 0.0009 | 61.22 | 3000 | 1.8930 | 0.7087 | 0.8441 | 0.7705 | 0.7782 |
| 0.0001 | 71.43 | 3500 | 2.0325 | 0.7217 | 0.8736 | 0.7904 | 0.7845 |
| 0.0006 | 81.63 | 4000 | 1.8854 | 0.7032 | 0.8769 | 0.7805 | 0.7904 |
| 0.0001 | 91.84 | 4500 | 2.2205 | 0.6977 | 0.8721 | 0.7752 | 0.7577 |
| 0.0002 | 102.04 | 5000 | 2.1731 | 0.7090 | 0.8702 | 0.7814 | 0.7786 |
| 0.0 | 112.24 | 5500 | 2.3198 | 0.7150 | 0.8707 | 0.7852 | 0.7681 |
| 0.0003 | 122.45 | 6000 | 1.9680 | 0.7188 | 0.8649 | 0.7851 | 0.7896 |
| 0.0 | 132.65 | 6500 | 2.2202 | 0.7316 | 0.8523 | 0.7873 | 0.7815 |
| 0.0 | 142.86 | 7000 | 2.2800 | 0.7013 | 0.8818 | 0.7813 | 0.7727 |
| 0.0 | 153.06 | 7500 | 2.2149 | 0.7202 | 0.8784 | 0.7915 | 0.7790 |
| 0.0 | 163.27 | 8000 | 2.2384 | 0.7264 | 0.8663 | 0.7902 | 0.7834 |
| 0.0001 | 173.47 | 8500 | 2.2177 | 0.7269 | 0.8682 | 0.7913 | 0.7842 |
| 0.0 | 183.67 | 9000 | 2.2768 | 0.7333 | 0.8731 | 0.7971 | 0.7872 |
| 0.0 | 193.88 | 9500 | 2.2996 | 0.7344 | 0.8716 | 0.7972 | 0.7878 |
| 0.0 | 204.08 | 10000 | 2.3482 | 0.7244 | 0.8755 | 0.7928 | 0.7835 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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