|
--- |
|
license: mit |
|
base_model: kavg/LiLT-SER-ZH |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- xfun |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: LiLT-SER-ZH-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: 0.7417061611374408 |
|
- name: Recall |
|
type: recall |
|
value: 0.770935960591133 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7560386473429951 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.8558002524898303 |
|
--- |
|
|
|
<!-- 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-SIN |
|
|
|
This model is a fine-tuned version of [kavg/LiLT-SER-ZH](https://huggingface.co/kavg/LiLT-SER-ZH) on the xfun dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.2037 |
|
- Precision: 0.7417 |
|
- Recall: 0.7709 |
|
- F1: 0.7560 |
|
- Accuracy: 0.8558 |
|
|
|
## 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 | |
|
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0013 | 21.74 | 500 | 0.9018 | 0.6843 | 0.7475 | 0.7145 | 0.8599 | |
|
| 0.012 | 43.48 | 1000 | 1.0791 | 0.7115 | 0.7623 | 0.7360 | 0.8561 | |
|
| 0.0002 | 65.22 | 1500 | 1.0060 | 0.7360 | 0.7623 | 0.7489 | 0.8565 | |
|
| 0.03 | 86.96 | 2000 | 1.1521 | 0.7282 | 0.6700 | 0.6979 | 0.8313 | |
|
| 0.0013 | 108.7 | 2500 | 1.1517 | 0.7240 | 0.7463 | 0.7350 | 0.8579 | |
|
| 0.0016 | 130.43 | 3000 | 0.9393 | 0.7319 | 0.7697 | 0.7503 | 0.8732 | |
|
| 0.0021 | 152.17 | 3500 | 0.9972 | 0.7249 | 0.7562 | 0.7402 | 0.8635 | |
|
| 0.0001 | 173.91 | 4000 | 1.0485 | 0.7049 | 0.7796 | 0.7404 | 0.8583 | |
|
| 0.0002 | 195.65 | 4500 | 1.0827 | 0.7055 | 0.7315 | 0.7183 | 0.8433 | |
|
| 0.0 | 217.39 | 5000 | 1.0528 | 0.7354 | 0.7599 | 0.7474 | 0.8586 | |
|
| 0.0001 | 239.13 | 5500 | 1.1183 | 0.7001 | 0.7131 | 0.7065 | 0.8465 | |
|
| 0.0002 | 260.87 | 6000 | 1.1749 | 0.7231 | 0.7685 | 0.7451 | 0.8520 | |
|
| 0.0 | 282.61 | 6500 | 1.1206 | 0.7315 | 0.7685 | 0.7495 | 0.8611 | |
|
| 0.0 | 304.35 | 7000 | 1.2037 | 0.7417 | 0.7709 | 0.7560 | 0.8558 | |
|
| 0.0 | 326.09 | 7500 | 1.3737 | 0.7391 | 0.75 | 0.7445 | 0.8513 | |
|
| 0.0 | 347.83 | 8000 | 1.2926 | 0.7221 | 0.7648 | 0.7428 | 0.8475 | |
|
| 0.0 | 369.57 | 8500 | 1.4108 | 0.6966 | 0.7549 | 0.7246 | 0.8293 | |
|
| 0.0 | 391.3 | 9000 | 1.4346 | 0.7222 | 0.7586 | 0.7399 | 0.8303 | |
|
| 0.0 | 413.04 | 9500 | 1.4146 | 0.7225 | 0.7599 | 0.7407 | 0.8363 | |
|
| 0.0 | 434.78 | 10000 | 1.4097 | 0.7121 | 0.7586 | 0.7346 | 0.8346 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.1 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.1 |
|
|
