Instructions to use ttqdunggg/3adapter_ronbackbone_2_task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ttqdunggg/3adapter_ronbackbone_2_task with Transformers:
# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("ttqdunggg/3adapter_ronbackbone_2_task") model = PhoBERTMultiTask.from_pretrained("ttqdunggg/3adapter_ronbackbone_2_task") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: agpl-3.0 | |
| base_model: RonTon05/model_content_V2_test | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: 3adapter_ronbackbone_2_task | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # 3adapter_ronbackbone_2_task | |
| This model is a fine-tuned version of [RonTon05/model_content_V2_test](https://huggingface.co/RonTon05/model_content_V2_test) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Acc Classification: 0.8229 | |
| - F1 Classification: 0.7802 | |
| - Acc Content: 0.9692 | |
| - F1 Content: 0.9474 | |
| - Loss: 0.3420 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 64 | |
| - 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 | Acc Classification | F1 Classification | Acc Content | F1 Content | Validation Loss | | |
| |:-------------:|:-----:|:----:|:------------------:|:-----------------:|:-----------:|:----------:|:---------------:| | |
| | No log | 1.0 | 276 | 0.7814 | 0.6988 | 0.9612 | 0.9345 | 0.4392 | | |
| | 0.5301 | 2.0 | 552 | 0.8061 | 0.7603 | 0.9626 | 0.9367 | 0.3629 | | |
| | 0.5301 | 3.0 | 828 | 0.8145 | 0.7695 | 0.9671 | 0.9439 | 0.3474 | | |
| | 0.2514 | 4.0 | 1104 | 0.8231 | 0.7821 | 0.9689 | 0.9470 | 0.3383 | | |
| | 0.2514 | 5.0 | 1380 | 0.8229 | 0.7802 | 0.9692 | 0.9474 | 0.3420 | | |
| ### Framework versions | |
| - Transformers 4.57.1 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 4.4.1 | |
| - Tokenizers 0.22.1 | |