Instructions to use ttqdunggg/cls_10_backbone_100k_freeze_backbone_task1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ttqdunggg/cls_10_backbone_100k_freeze_backbone_task1 with Transformers:
# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("ttqdunggg/cls_10_backbone_100k_freeze_backbone_task1") model = PhoBERTMultiTask.from_pretrained("ttqdunggg/cls_10_backbone_100k_freeze_backbone_task1") - Notebooks
- Google Colab
- Kaggle
cls_10_backbone_100k_freeze_backbone_task1
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: 1.3518
- Accuracy: 0.5085
- F1: 0.2198
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 |
|---|---|---|---|---|---|
| 1.5617 | 1.0 | 276 | 1.5414 | 0.4337 | 0.1590 |
| 1.5189 | 2.0 | 552 | 1.4909 | 0.4697 | 0.1881 |
| 1.4759 | 3.0 | 828 | 1.4480 | 0.4838 | 0.1948 |
| 1.4407 | 4.0 | 1104 | 1.4167 | 0.4913 | 0.1995 |
| 1.4147 | 5.0 | 1380 | 1.3995 | 0.4965 | 0.2061 |
| 1.3956 | 6.0 | 1656 | 1.3767 | 0.5046 | 0.2123 |
| 1.3795 | 7.0 | 1932 | 1.3666 | 0.5058 | 0.2141 |
| 1.3735 | 8.0 | 2208 | 1.3560 | 0.5071 | 0.2187 |
| 1.3678 | 9.0 | 2484 | 1.3515 | 0.5101 | 0.2214 |
| 1.3629 | 10.0 | 2760 | 1.3518 | 0.5085 | 0.2198 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.22.1
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