|
--- |
|
license: mit |
|
base_model: kavg/LiLT-SER-PT |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- xfun |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: LiLT-SER-PT-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: 0.7639225181598063 |
|
- name: Recall |
|
type: recall |
|
value: 0.7770935960591133 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7704517704517705 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.8626735867583111 |
|
--- |
|
|
|
<!-- 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-PT-SIN |
|
|
|
This model is a fine-tuned version of [kavg/LiLT-SER-PT](https://huggingface.co/kavg/LiLT-SER-PT) on the xfun dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.2074 |
|
- Precision: 0.7639 |
|
- Recall: 0.7771 |
|
- F1: 0.7705 |
|
- Accuracy: 0.8627 |
|
|
|
## 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 | |
|
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:| |
|
| 0.0124 | 21.74 | 500 | 0.8590 | 0.7403 | 0.8082 | 0.7381 | 0.7426 | |
|
| 0.0017 | 43.48 | 1000 | 0.8301 | 0.7272 | 1.2608 | 0.75 | 0.7057 | |
|
| 0.0004 | 65.22 | 1500 | 0.8694 | 0.7323 | 0.8843 | 0.7098 | 0.7562 | |
|
| 0.0 | 86.96 | 2000 | 0.8617 | 0.7532 | 1.0638 | 0.7419 | 0.7648 | |
|
| 0.0001 | 108.7 | 2500 | 0.8580 | 0.7674 | 1.1504 | 0.7689 | 0.7660 | |
|
| 0.0006 | 130.43 | 3000 | 0.8677 | 0.7479 | 0.9865 | 0.7230 | 0.7746 | |
|
| 0.0 | 152.17 | 3500 | 0.8617 | 0.7558 | 1.1492 | 0.7494 | 0.7623 | |
|
| 0.0001 | 173.91 | 4000 | 0.8385 | 0.7590 | 1.3124 | 0.7485 | 0.7697 | |
|
| 0.0055 | 195.65 | 4500 | 1.1331 | 0.7295 | 0.7869 | 0.7571 | 0.8479 | |
|
| 0.0 | 217.39 | 5000 | 1.2061 | 0.7392 | 0.7611 | 0.7500 | 0.8500 | |
|
| 0.0001 | 239.13 | 5500 | 1.2572 | 0.7253 | 0.7672 | 0.7457 | 0.8482 | |
|
| 0.0 | 260.87 | 6000 | 1.3558 | 0.7494 | 0.7734 | 0.7612 | 0.8569 | |
|
| 0.0 | 282.61 | 6500 | 1.4382 | 0.7598 | 0.7672 | 0.7635 | 0.8589 | |
|
| 0.0 | 304.35 | 7000 | 1.4720 | 0.7537 | 0.7574 | 0.7555 | 0.8533 | |
|
| 0.0 | 326.09 | 7500 | 1.3835 | 0.7524 | 0.7783 | 0.7651 | 0.8579 | |
|
| 0.0 | 347.83 | 8000 | 1.2693 | 0.7534 | 0.7599 | 0.7566 | 0.8599 | |
|
| 0.0 | 369.57 | 8500 | 1.2005 | 0.7417 | 0.7709 | 0.7560 | 0.8600 | |
|
| 0.0 | 391.3 | 9000 | 1.2175 | 0.7560 | 0.7820 | 0.7688 | 0.8601 | |
|
| 0.0 | 413.04 | 9500 | 1.2339 | 0.7556 | 0.7845 | 0.7698 | 0.8601 | |
|
| 0.0 | 434.78 | 10000 | 1.2074 | 0.7639 | 0.7771 | 0.7705 | 0.8627 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.1 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.1 |
|
|
