Instructions to use dtran612/CafeBERT-vinli-h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtran612/CafeBERT-vinli-h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dtran612/CafeBERT-vinli-h")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dtran612/CafeBERT-vinli-h") model = AutoModelForSequenceClassification.from_pretrained("dtran612/CafeBERT-vinli-h") - Notebooks
- Google Colab
- Kaggle
CafeBERT-vinli-h
This model is a fine-tuned version of uitnlp/CafeBERT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0152
- Accuracy: 0.9965
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 |
|---|---|---|---|---|
| 1.0687 | 1.0 | 142 | 0.9872 | 0.5495 |
| 0.9797 | 2.0 | 284 | 0.8184 | 0.6736 |
| 0.7276 | 3.0 | 426 | 0.5078 | 0.8295 |
| 0.5854 | 4.0 | 568 | 0.3243 | 0.8980 |
| 0.4555 | 5.0 | 710 | 0.1880 | 0.9426 |
| 0.3342 | 6.0 | 852 | 0.0872 | 0.9788 |
| 0.2635 | 7.0 | 994 | 0.0621 | 0.9806 |
| 0.1458 | 8.0 | 1136 | 0.0418 | 0.9872 |
| 0.0817 | 9.0 | 1278 | 0.0191 | 0.9943 |
| 0.0276 | 10.0 | 1420 | 0.0152 | 0.9965 |
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-vinli-h
Base model
uitnlp/CafeBERT