Instructions to use RonTon05/multi_task_model_content_test_22K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RonTon05/multi_task_model_content_test_22K with Transformers:
# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("RonTon05/multi_task_model_content_test_22K") model = PhoBERTMultiTask.from_pretrained("RonTon05/multi_task_model_content_test_22K") - Notebooks
- Google Colab
- Kaggle
multi_task_model_content_test_22K
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: 0.9429
- Accuracy: 0.7771
- F1: 0.7808
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 |
|---|---|---|---|---|---|
| 1.5127 | 1.0 | 292 | 1.0712 | 0.6152 | 0.3225 |
| 0.9098 | 2.0 | 584 | 0.8403 | 0.7041 | 0.5276 |
| 0.701 | 3.0 | 876 | 0.7457 | 0.7508 | 0.7122 |
| 0.5416 | 4.0 | 1168 | 0.7378 | 0.7608 | 0.7700 |
| 0.4139 | 5.0 | 1460 | 0.7585 | 0.7723 | 0.7802 |
| 0.3201 | 6.0 | 1752 | 0.8053 | 0.7708 | 0.7791 |
| 0.2549 | 7.0 | 2044 | 0.8727 | 0.7714 | 0.7810 |
| 0.2028 | 8.0 | 2336 | 0.9825 | 0.7583 | 0.7741 |
| 0.1688 | 9.0 | 2628 | 0.9506 | 0.7756 | 0.7811 |
| 0.1449 | 10.0 | 2920 | 0.9429 | 0.7771 | 0.7808 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.22.1
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