ViGLUE
Collection
A collection to store all the artifacts of the paper: ViGLUE: A Vietnamese General Language Understanding Benchmark and Analysis of Vietnamese LMs.
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145 items
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Updated
xlm-roberta-large-qqp-1
This model is a fine-tuned version of xlm-roberta-large on the tmnam20/VieGLUE/QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.2725
- Accuracy: 0.9048
- F1: 0.8722
- Combined Score: 0.8885
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: 1
- 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 |
|---|---|---|---|---|---|---|
| 0.3045 | 0.88 | 10000 | 0.2747 | 0.8808 | 0.8324 | 0.8566 |
| 0.2256 | 1.76 | 20000 | 0.2695 | 0.8957 | 0.8617 | 0.8787 |
| 0.1643 | 2.64 | 30000 | 0.2808 | 0.9019 | 0.8684 | 0.8851 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Base model
FacebookAI/xlm-roberta-largeDataset used to train tmnam20/xlm-roberta-large-qqp-1
Collection including tmnam20/xlm-roberta-large-qqp-1
Evaluation results
- Accuracy on tmnam20/VieGLUE/QQPvalidation set self-reported0.905
- F1 on tmnam20/VieGLUE/QQPvalidation set self-reported0.872