Instructions to use RonTon05/step3_semisup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RonTon05/step3_semisup with Transformers:
# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("RonTon05/step3_semisup") model = PhoBERTMultiTask.from_pretrained("RonTon05/step3_semisup") - 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: step3_semisup | |
| 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. --> | |
| # step3_semisup | |
| 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. | |
| ## 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: 5e-06 | |
| - train_batch_size: 64 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 128 | |
| - 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 | |
| - lr_scheduler_warmup_steps: 1150 | |
| - num_epochs: 15 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 5.12.1 | |
| - Pytorch 2.7.1+cu118 | |
| - Datasets 5.0.0 | |
| - Tokenizers 0.22.2 | |