Instructions to use phunganhsang/finetune_cls_backbone_2task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phunganhsang/finetune_cls_backbone_2task with Transformers:
# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("phunganhsang/finetune_cls_backbone_2task") model = PhoBERTMultiTask.from_pretrained("phunganhsang/finetune_cls_backbone_2task") - Notebooks
- Google Colab
- Kaggle
finetune_cls_backbone_2task
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: 0.9278
- F1 Task1: 0.9962
- F1 Task2: 0.6270
- Acc Task1: 0.9979
- Acc Task2: 0.7074
- F1: 0.8116
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Task1 | F1 Task2 | Acc Task1 | Acc Task2 | F1 |
|---|---|---|---|---|---|---|---|---|
| 1.7889 | 1.0 | 292 | 1.4094 | 0.9935 | 0.2110 | 0.9964 | 0.5064 | 0.6022 |
| 1.242 | 2.0 | 584 | 1.0919 | 0.9923 | 0.4151 | 0.9958 | 0.6568 | 0.7037 |
| 0.9872 | 3.0 | 876 | 1.0045 | 0.9929 | 0.4263 | 0.9961 | 0.6692 | 0.7096 |
| 0.8403 | 4.0 | 1168 | 0.9706 | 0.9951 | 0.5907 | 0.9973 | 0.6819 | 0.7929 |
| 0.7592 | 5.0 | 1460 | 0.9278 | 0.9962 | 0.6270 | 0.9979 | 0.7074 | 0.8116 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.22.1
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