phobert-intent-classifier
This model is a fine-tuned version of vinai/phobert-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
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: 0.0001
- train_batch_size: 96
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1863 | 1.0 | 375 | 0.0010 | 1.0 |
| 0.0043 | 2.0 | 750 | 0.0004 | 1.0 |
| 0.0013 | 3.0 | 1125 | 0.0002 | 1.0 |
| 0.0002 | 4.0 | 1500 | 0.0001 | 1.0 |
| 0.0002 | 5.0 | 1875 | 0.0001 | 1.0 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for TVNPeter/phobert-intent-classifier
Base model
vinai/phobert-base-v2