Instructions to use Jaykl2910/phobert-vietnamese-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jaykl2910/phobert-vietnamese-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jaykl2910/phobert-vietnamese-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jaykl2910/phobert-vietnamese-sentiment") model = AutoModelForSequenceClassification.from_pretrained("Jaykl2910/phobert-vietnamese-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
phobert-vietnamese-sentiment
This model is a fine-tuned version of vinai/phobert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2211
- Accuracy: 0.9378
- F1 Macro: 0.8704
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: 16
- 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: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.2055 | 1.0 | 644 | 0.2008 | 0.9238 | 0.8400 |
| 0.1199 | 2.0 | 1288 | 0.1873 | 0.9409 | 0.8689 |
| 0.0876 | 3.0 | 1932 | 0.2211 | 0.9378 | 0.8704 |
Framework versions
- Transformers 5.14.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for Jaykl2910/phobert-vietnamese-sentiment
Base model
vinai/phobert-base-v2