| --- |
| library_name: peft |
| license: cc-by-nc-sa-4.0 |
| base_model: Itau-Unibanco/NorBERTo-base |
| tags: |
| - base_model:adapter:Itau-Unibanco/NorBERTo-base |
| - lora |
| - transformers |
| - dapt |
| - sft |
| datasets: |
| - heitorrosa/cvm-corpus |
| - heitorrosa/financial-sentiment-pt |
| model-index: |
| - name: logun-base |
| 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. --> |
|
|
| # logun-base |
|
|
| This model is a fine-tuned version of [Itau-Unibanco/NorBERTo-base](https://huggingface.co/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 |
