Instructions to use dtran612/CafeBERT-vianli-ph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtran612/CafeBERT-vianli-ph with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dtran612/CafeBERT-vianli-ph")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dtran612/CafeBERT-vianli-ph") model = AutoModelForSequenceClassification.from_pretrained("dtran612/CafeBERT-vianli-ph") - Notebooks
- Google Colab
- Kaggle
CafeBERT-vianli-ph
This model is a fine-tuned version of uitnlp/CafeBERT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0589
- Accuracy: 0.992
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1096 | 1.0 | 63 | 1.0760 | 0.416 |
| 1.0850 | 2.0 | 126 | 0.9988 | 0.556 |
| 0.9835 | 3.0 | 189 | 0.7536 | 0.74 |
| 0.8249 | 4.0 | 252 | 0.5729 | 0.791 |
| 0.6269 | 5.0 | 315 | 0.3356 | 0.923 |
| 0.4702 | 6.0 | 378 | 0.2245 | 0.951 |
| 0.2923 | 7.0 | 441 | 0.1405 | 0.974 |
| 0.2305 | 8.0 | 504 | 0.1006 | 0.986 |
| 0.1877 | 9.0 | 567 | 0.0677 | 0.992 |
| 0.1348 | 10.0 | 630 | 0.0589 | 0.992 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for dtran612/CafeBERT-vianli-ph
Base model
uitnlp/CafeBERT