|
--- |
|
language: |
|
- en |
|
license: mit |
|
base_model: xlm-roberta-base |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- tmnam20/VieGLUE |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: xlm-roberta-base-qqp-100 |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: tmnam20/VieGLUE/QQP |
|
type: tmnam20/VieGLUE |
|
config: qqp |
|
split: validation |
|
args: qqp |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.8946326984912194 |
|
- name: F1 |
|
type: f1 |
|
value: 0.858697094334616 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# xlm-roberta-base-qqp-100 |
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tmnam20/VieGLUE/QQP dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2785 |
|
- Accuracy: 0.8946 |
|
- F1: 0.8587 |
|
- Combined Score: 0.8767 |
|
|
|
## 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: 2e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 16 |
|
- seed: 100 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| |
|
| 0.3304 | 0.44 | 5000 | 0.3286 | 0.8591 | 0.8046 | 0.8318 | |
|
| 0.2856 | 0.88 | 10000 | 0.2910 | 0.8744 | 0.8273 | 0.8509 | |
|
| 0.2795 | 1.32 | 15000 | 0.2818 | 0.8808 | 0.8413 | 0.8610 | |
|
| 0.2492 | 1.76 | 20000 | 0.2750 | 0.8863 | 0.8484 | 0.8674 | |
|
| 0.2093 | 2.2 | 25000 | 0.2791 | 0.8919 | 0.8542 | 0.8730 | |
|
| 0.2022 | 2.64 | 30000 | 0.2926 | 0.8928 | 0.8566 | 0.8747 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.2.0.dev20231203+cu121 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
|
|
