Instructions to use Minh64/PhoBERT_Language_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Minh64/PhoBERT_Language_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Minh64/PhoBERT_Language_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Minh64/PhoBERT_Language_classifier") model = AutoModelForSequenceClassification.from_pretrained("Minh64/PhoBERT_Language_classifier") - Notebooks
- Google Colab
- Kaggle
PhoBERT_Language_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: 0.0002
- Accuracy: 0.9999
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: 32
- seed: 42
- optimizer: Use 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0013 | 1.0 | 1700 | 0.0009 | 0.9999 |
| 0.0008 | 2.0 | 3400 | 0.0000 | 1.0 |
| 0.0001 | 3.0 | 5100 | 0.0002 | 0.9999 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.6.0
- Tokenizers 0.21.0
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Model tree for Minh64/PhoBERT_Language_classifier
Base model
vinai/phobert-base-v2