natquest-t5-base
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0311
- Accuracy: 0.9890
- Macro F1: 0.9888
- Micro F1: 0.9890
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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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: 100
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Micro F1 |
|---|---|---|---|---|---|---|
| No log | 1.0 | 422 | 0.1761 | 0.9322 | 0.9304 | 0.9322 |
| 0.3279 | 2.0 | 844 | 0.0609 | 0.9783 | 0.9777 | 0.9783 |
| 0.1456 | 3.0 | 1266 | 0.0311 | 0.9890 | 0.9888 | 0.9890 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.11.0+cu130
- Datasets 3.6.0
- Tokenizers 0.21.4