Instructions to use phunganhsang/multi_task_model_content_unfreeze_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phunganhsang/multi_task_model_content_unfreeze_new with Transformers:
# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("phunganhsang/multi_task_model_content_unfreeze_new") model = PhoBERTMultiTask.from_pretrained("phunganhsang/multi_task_model_content_unfreeze_new") - Notebooks
- Google Colab
- Kaggle
multi_task_model_content_unfreeze_new
This model is a fine-tuned version of RonTon05/model_content_V2_test on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6763
- Accuracy: 0.8388
- F1: 0.8031
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.9482 | 1.0 | 330 | 0.6122 | 0.7932 | 0.5319 |
| 0.4989 | 2.0 | 660 | 0.4864 | 0.8338 | 0.7163 |
| 0.3434 | 3.0 | 990 | 0.4715 | 0.8490 | 0.7988 |
| 0.2514 | 4.0 | 1320 | 0.4979 | 0.8534 | 0.8136 |
| 0.1852 | 5.0 | 1650 | 0.5475 | 0.8479 | 0.8003 |
| 0.1398 | 6.0 | 1980 | 0.7072 | 0.8255 | 0.8037 |
| 0.1155 | 7.0 | 2310 | 0.6763 | 0.8388 | 0.8031 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.22.1
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