Instructions to use ivanenclonar/typhoon-t5-finetuned2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivanenclonar/typhoon-t5-finetuned2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ivanenclonar/typhoon-t5-finetuned2") model = AutoModelForSeq2SeqLM.from_pretrained("ivanenclonar/typhoon-t5-finetuned2") - Notebooks
- Google Colab
- Kaggle
typhoon-t5-finetuned2
This model is a fine-tuned version of ivanenclonar/typhoon-t5-pretrained on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4566
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 | 25 | 1.0733 |
| No log | 2.0 | 50 | 0.6809 |
| No log | 3.0 | 75 | 0.5968 |
| No log | 4.0 | 100 | 0.5535 |
| No log | 5.0 | 125 | 0.5249 |
| No log | 6.0 | 150 | 0.5048 |
| No log | 7.0 | 175 | 0.4909 |
| No log | 8.0 | 200 | 0.4807 |
| No log | 9.0 | 225 | 0.4731 |
| No log | 10.0 | 250 | 0.4673 |
| No log | 11.0 | 275 | 0.4631 |
| No log | 12.0 | 300 | 0.4602 |
| No log | 13.0 | 325 | 0.4581 |
| No log | 14.0 | 350 | 0.4570 |
| No log | 15.0 | 375 | 0.4566 |
Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu129
- Datasets 4.4.2
- Tokenizers 0.22.0
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