logun-base
This model is a fine-tuned version of Itau-Unibanco/NorBERTo-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4919
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: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5470 | 0.0860 | 500 | 0.5439 |
| 0.5278 | 0.1719 | 1000 | 0.5265 |
| 0.5182 | 0.2579 | 1500 | 0.5230 |
| 0.5087 | 0.3439 | 2000 | 0.5111 |
| 0.5099 | 0.4299 | 2500 | 0.5067 |
| 0.5029 | 0.5158 | 3000 | 0.5020 |
| 0.4995 | 0.6018 | 3500 | 0.5057 |
| 0.4999 | 0.6878 | 4000 | 0.5016 |
| 0.4924 | 0.7737 | 4500 | 0.4992 |
| 0.4986 | 0.8597 | 5000 | 0.4918 |
| 0.4951 | 0.9457 | 5500 | 0.4952 |
| 0.4841 | 1.0316 | 6000 | 0.4954 |
| 0.5001 | 1.1176 | 6500 | 0.4933 |
| 0.4961 | 1.2036 | 7000 | 0.4953 |
| 0.4884 | 1.2895 | 7500 | 0.4918 |
| 0.4946 | 1.3755 | 8000 | 0.4919 |
| 0.4763 | 1.4615 | 8500 | 0.4874 |
| 0.4814 | 1.5000 | 8724 | 0.4919 |
Framework versions
- PEFT 0.20.0
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.23.1