Instructions to use ivanenclonar/typhoon-t5-finetuned3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivanenclonar/typhoon-t5-finetuned3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ivanenclonar/typhoon-t5-finetuned3") model = AutoModelForSeq2SeqLM.from_pretrained("ivanenclonar/typhoon-t5-finetuned3") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: ivanenclonar/typhoon-t5-pretrained | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: typhoon-t5-finetuned3 | |
| 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. --> | |
| # typhoon-t5-finetuned3 | |
| This model is a fine-tuned version of [ivanenclonar/typhoon-t5-pretrained](https://huggingface.co/ivanenclonar/typhoon-t5-pretrained) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4705 | |
| ## 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-05 | |
| - train_batch_size: 64 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 15 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 12 | 1.7729 | | |
| | No log | 2.0 | 24 | 0.8577 | | |
| | No log | 3.0 | 36 | 0.6314 | | |
| | No log | 4.0 | 48 | 0.5748 | | |
| | No log | 5.0 | 60 | 0.5426 | | |
| | No log | 6.0 | 72 | 0.5223 | | |
| | No log | 7.0 | 84 | 0.5081 | | |
| | No log | 8.0 | 96 | 0.4975 | | |
| | No log | 9.0 | 108 | 0.4893 | | |
| | No log | 10.0 | 120 | 0.4828 | | |
| | No log | 11.0 | 132 | 0.4783 | | |
| | No log | 12.0 | 144 | 0.4747 | | |
| | No log | 13.0 | 156 | 0.4725 | | |
| | No log | 14.0 | 168 | 0.4709 | | |
| | No log | 15.0 | 180 | 0.4705 | | |
| ### Framework versions | |
| - Transformers 4.56.0 | |
| - Pytorch 2.8.0+cu129 | |
| - Datasets 4.4.2 | |
| - Tokenizers 0.22.0 | |