Instructions to use ivanenclonar/typhoon-t5-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivanenclonar/typhoon-t5-finetuned with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ivanenclonar/typhoon-t5-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("ivanenclonar/typhoon-t5-finetuned") - 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-finetuned | |
| 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-finetuned | |
| 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: 0.3899 | |
| ## 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 | 35 | 0.7777 | | |
| | No log | 2.0 | 70 | 0.5711 | | |
| | No log | 3.0 | 105 | 0.5039 | | |
| | No log | 4.0 | 140 | 0.4666 | | |
| | No log | 5.0 | 175 | 0.4435 | | |
| | No log | 6.0 | 210 | 0.4294 | | |
| | No log | 7.0 | 245 | 0.4184 | | |
| | No log | 8.0 | 280 | 0.4104 | | |
| | No log | 9.0 | 315 | 0.4042 | | |
| | No log | 10.0 | 350 | 0.3996 | | |
| | No log | 11.0 | 385 | 0.3959 | | |
| | No log | 12.0 | 420 | 0.3932 | | |
| | No log | 13.0 | 455 | 0.3913 | | |
| | No log | 14.0 | 490 | 0.3903 | | |
| | 0.6133 | 15.0 | 525 | 0.3899 | | |
| ### Framework versions | |
| - Transformers 4.56.0 | |
| - Pytorch 2.8.0+cu129 | |
| - Datasets 4.4.2 | |
| - Tokenizers 0.22.0 | |