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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-ZH
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xfun
type: xfun
config: xfun.zh
split: validation
args: xfun.zh
metrics:
- name: Precision
type: precision
value: 0.8408488063660478
- name: Recall
type: recall
value: 0.9347968545216252
- name: F1
type: f1
value: 0.8853374709076804
- name: Accuracy
type: accuracy
value: 0.8116519985331867
---
<!-- 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-ZH
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: 1.8792
- Precision: 0.8408
- Recall: 0.9348
- F1: 0.8853
- Accuracy: 0.8117
## 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 |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.2166 | 10.64 | 500 | 0.7724 | 0.8544 | 1.1239 | 0.7932 | 0.9260 |
| 0.0238 | 21.28 | 1000 | 0.8201 | 0.8624 | 1.0535 | 0.8572 | 0.8676 |
| 0.0034 | 31.91 | 1500 | 0.8057 | 0.8599 | 1.4675 | 0.8088 | 0.9178 |
| 0.0163 | 42.55 | 2000 | 0.8232 | 0.8729 | 1.2837 | 0.8572 | 0.8893 |
| 0.0037 | 53.19 | 2500 | 0.8114 | 0.8627 | 1.5315 | 0.8142 | 0.9174 |
| 0.0003 | 63.83 | 3000 | 0.8137 | 0.8652 | 1.4604 | 0.8471 | 0.8840 |
| 0.0005 | 74.47 | 3500 | 0.8115 | 0.8767 | 1.5980 | 0.8409 | 0.9158 |
| 0.0005 | 85.11 | 4000 | 0.8129 | 0.8634 | 1.5108 | 0.8261 | 0.9043 |
| 0.0004 | 95.74 | 4500 | 0.8161 | 0.8817 | 1.7719 | 0.8397 | 0.9282 |
| 0.0001 | 106.38 | 5000 | 0.8203 | 0.8813 | 1.4313 | 0.8600 | 0.9037 |
| 0.0001 | 117.02 | 5500 | 0.8181 | 0.8832 | 1.5232 | 0.8509 | 0.9181 |
| 0.0 | 127.66 | 6000 | 0.8069 | 0.8808 | 1.6845 | 0.8532 | 0.9102 |
| 0.0179 | 138.3 | 6500 | 0.8192 | 0.8793 | 1.6293 | 0.8398 | 0.9227 |
| 0.0 | 148.94 | 7000 | 0.8081 | 0.8815 | 1.8209 | 0.8381 | 0.9296 |
| 0.0 | 159.57 | 7500 | 1.8224 | 0.8443 | 0.9184 | 0.8798 | 0.8070 |
| 0.0 | 170.21 | 8000 | 1.7810 | 0.8450 | 0.9305 | 0.8857 | 0.8127 |
| 0.0 | 180.85 | 8500 | 1.7531 | 0.8454 | 0.9230 | 0.8825 | 0.8088 |
| 0.0 | 191.49 | 9000 | 1.8757 | 0.8394 | 0.9302 | 0.8825 | 0.8070 |
| 0.0 | 202.13 | 9500 | 1.8757 | 0.8417 | 0.9338 | 0.8854 | 0.8123 |
| 0.0 | 212.77 | 10000 | 1.8792 | 0.8408 | 0.9348 | 0.8853 | 0.8117 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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