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-10
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: 0.2671
- Accuracy: 0.9010
- F1: 0.8682
- Combined Score: 0.8846
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: 10
- 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 |
|---|---|---|---|---|---|---|
| 0.2894 | 0.88 | 10000 | 0.2821 | 0.8794 | 0.8402 | 0.8598 |
| 0.2352 | 1.76 | 20000 | 0.2630 | 0.8931 | 0.8566 | 0.8748 |
| 0.1732 | 2.64 | 30000 | 0.2666 | 0.8995 | 0.8656 | 0.8826 |
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-10
Collection including tmnam20/xlm-roberta-large-qqp-10
Evaluation results
- Accuracy on tmnam20/VieGLUE/QQPvalidation set self-reported0.901
- F1 on tmnam20/VieGLUE/QQPvalidation set self-reported0.868