Instructions to use ttqdunggg/multi_task_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ttqdunggg/multi_task_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("ttqdunggg/multi_task_model") model = PhoBERTMultiTask.from_pretrained("ttqdunggg/multi_task_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
multi_task_model
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: 0.6289
- Accuracy: 0.8416
- F1: 0.8040
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 |
|---|---|---|---|---|---|
| 1.0828 | 1.0 | 330 | 0.6567 | 0.7862 | 0.4760 |
| 0.5446 | 2.0 | 660 | 0.5265 | 0.8247 | 0.6807 |
| 0.3744 | 3.0 | 990 | 0.4952 | 0.8452 | 0.8029 |
| 0.271 | 4.0 | 1320 | 0.5000 | 0.8509 | 0.8141 |
| 0.2007 | 5.0 | 1650 | 0.5475 | 0.8412 | 0.7975 |
| 0.1487 | 6.0 | 1980 | 0.6289 | 0.8416 | 0.8040 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.22.1
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