Instructions to use RonTon05/MTL_Frozen_backbone_binary_head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RonTon05/MTL_Frozen_backbone_binary_head with Transformers:
# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("RonTon05/MTL_Frozen_backbone_binary_head") model = PhoBERTMultiTask.from_pretrained("RonTon05/MTL_Frozen_backbone_binary_head") - Notebooks
- Google Colab
- Kaggle
Quick Links
MTL_Frozen_backbone_binary_head
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: 1.4753
- F1 Task1: 0.9972
- F1 Task2: 0.1840
- Acc Task1: 0.9984
- Acc Task2: 0.4232
- F1: 0.5906
- F1 Macro: 0.5906
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 | F1 Task1 | F1 Task2 | Acc Task1 | Acc Task2 | F1 | F1 Macro |
|---|---|---|---|---|---|---|---|---|---|
| 1.7773 | 1.0 | 275 | 1.6048 | 0.9972 | 0.1044 | 0.9984 | 0.3675 | 0.5508 | 0.5508 |
| 1.6021 | 2.0 | 550 | 1.5769 | 0.9972 | 0.1191 | 0.9984 | 0.3766 | 0.5581 | 0.5581 |
| 1.5781 | 3.0 | 825 | 1.5521 | 0.9972 | 0.1375 | 0.9984 | 0.3914 | 0.5673 | 0.5673 |
| 1.5592 | 4.0 | 1100 | 1.5308 | 0.9972 | 0.1654 | 0.9984 | 0.4089 | 0.5813 | 0.5813 |
| 1.5428 | 5.0 | 1375 | 1.5133 | 0.9972 | 0.1702 | 0.9984 | 0.4112 | 0.5837 | 0.5837 |
| 1.5241 | 6.0 | 1650 | 1.4985 | 0.9972 | 0.1821 | 0.9984 | 0.4255 | 0.5896 | 0.5896 |
| 1.5147 | 7.0 | 1925 | 1.4875 | 0.9972 | 0.1758 | 0.9984 | 0.4180 | 0.5865 | 0.5865 |
| 1.4986 | 8.0 | 2200 | 1.4837 | 0.9972 | 0.1775 | 0.9984 | 0.4196 | 0.5873 | 0.5873 |
| 1.4979 | 9.0 | 2475 | 1.4764 | 0.9972 | 0.1843 | 0.9984 | 0.4239 | 0.5907 | 0.5907 |
| 1.4914 | 10.0 | 2750 | 1.4753 | 0.9972 | 0.1840 | 0.9984 | 0.4232 | 0.5906 | 0.5906 |
Framework versions
- Transformers 5.10.1
- Pytorch 2.7.1+cu118
- Datasets 4.8.5
- Tokenizers 0.22.2
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# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("RonTon05/MTL_Frozen_backbone_binary_head") model = PhoBERTMultiTask.from_pretrained("RonTon05/MTL_Frozen_backbone_binary_head")