Instructions to use funa21/phobert-finetuned-vihsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use funa21/phobert-finetuned-vihsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="funa21/phobert-finetuned-vihsd")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("funa21/phobert-finetuned-vihsd") model = AutoModelForSequenceClassification.from_pretrained("funa21/phobert-finetuned-vihsd") - Notebooks
- Google Colab
- Kaggle
phobert-finetuned-vihsd
This model is a fine-tuned version of vinai/phobert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5694
- Accuracy: 0.8694
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4256 | 1.0 | 376 | 0.3945 | 0.8638 |
| 0.3041 | 2.0 | 752 | 0.3730 | 0.8698 |
| 0.2207 | 3.0 | 1128 | 0.4149 | 0.8720 |
| 0.1577 | 4.0 | 1504 | 0.4563 | 0.8589 |
| 0.1122 | 5.0 | 1880 | 0.5156 | 0.8660 |
| 0.0787 | 6.0 | 2256 | 0.5694 | 0.8694 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.2
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Model tree for funa21/phobert-finetuned-vihsd
Base model
vinai/phobert-base