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---
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
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