|
--- |
|
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-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: 0.7058139534883721 |
|
- name: Recall |
|
type: recall |
|
value: 0.7475369458128078 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7260765550239234 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.8621124982465984 |
|
--- |
|
|
|
<!-- 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-SIN |
|
|
|
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: 1.1967 |
|
- Precision: 0.7058 |
|
- Recall: 0.7475 |
|
- F1: 0.7261 |
|
- Accuracy: 0.8621 |
|
|
|
## 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 | |
|
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:| |
|
| 0.0739 | 21.74 | 500 | 0.8268 | 0.5620 | 0.7143 | 0.5 | 0.6416 | |
|
| 0.0509 | 43.48 | 1000 | 0.8324 | 0.5839 | 0.8499 | 0.5348 | 0.6429 | |
|
| 0.0004 | 65.22 | 1500 | 0.8398 | 0.6521 | 0.9889 | 0.6256 | 0.6810 | |
|
| 0.0004 | 86.96 | 2000 | 0.8461 | 0.6678 | 1.0577 | 0.6251 | 0.7167 | |
|
| 0.003 | 108.7 | 2500 | 0.8561 | 0.6929 | 1.0734 | 0.6532 | 0.7377 | |
|
| 0.0006 | 130.43 | 3000 | 0.8569 | 0.6924 | 1.1114 | 0.6686 | 0.7180 | |
|
| 0.0022 | 152.17 | 3500 | 0.8245 | 0.6749 | 1.4184 | 0.6774 | 0.6724 | |
|
| 0.0001 | 173.91 | 4000 | 0.8502 | 0.6937 | 1.0524 | 0.6546 | 0.7377 | |
|
| 0.001 | 195.65 | 4500 | 0.8493 | 0.6900 | 1.1949 | 0.6663 | 0.7155 | |
|
| 0.0001 | 217.39 | 5000 | 0.8460 | 0.6885 | 1.1462 | 0.6790 | 0.6983 | |
|
| 0.0001 | 239.13 | 5500 | 0.8641 | 0.6970 | 1.1296 | 0.6697 | 0.7266 | |
|
| 0.0 | 260.87 | 6000 | 0.8529 | 0.7046 | 1.2585 | 0.6929 | 0.7167 | |
|
| 0.0037 | 282.61 | 6500 | 0.8634 | 0.7139 | 1.2292 | 0.6917 | 0.7377 | |
|
| 0.0 | 304.35 | 7000 | 0.8621 | 0.7261 | 1.1967 | 0.7058 | 0.7475 | |
|
| 0.0 | 326.09 | 7500 | 0.8585 | 0.7230 | 1.2144 | 0.7089 | 0.7377 | |
|
| 0.0 | 347.83 | 8000 | 0.8609 | 0.7180 | 1.2117 | 0.6918 | 0.7463 | |
|
| 0.0 | 369.57 | 8500 | 0.8628 | 0.7135 | 1.1961 | 0.6755 | 0.7562 | |
|
| 0.0 | 391.3 | 9000 | 0.8624 | 0.7220 | 1.2292 | 0.7059 | 0.7389 | |
|
| 0.0 | 413.04 | 9500 | 0.8611 | 0.7262 | 1.2278 | 0.7071 | 0.7463 | |
|
| 0.0 | 434.78 | 10000 | 0.8609 | 0.7242 | 1.2317 | 0.7056 | 0.7438 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
