| --- |
| library_name: transformers |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: roberta-base-ECB-dapt |
| 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. --> |
|
|
| # roberta-base-ECB-dapt |
|
|
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.9623 |
|
|
| ## 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: 3e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 16 |
| - total_train_batch_size: 128 |
| - 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 |
| - lr_scheduler_warmup_steps: 100 |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:----:|:---------------:| |
| | 40.2248 | 0.2690 | 150 | 2.3196 | |
| | 38.1977 | 0.5381 | 300 | 2.1706 | |
| | 36.8653 | 0.8071 | 450 | 2.1671 | |
| | 35.4280 | 1.0753 | 600 | 2.1277 | |
| | 35.2623 | 1.3444 | 750 | 2.0359 | |
| | 35.0369 | 1.6134 | 900 | 2.0045 | |
| | 34.3773 | 1.8824 | 1050 | 2.0329 | |
| | 34.1305 | 2.1507 | 1200 | 1.9814 | |
| | 33.9732 | 2.4197 | 1350 | 1.9703 | |
| | 33.9225 | 2.6887 | 1500 | 1.9592 | |
| | 33.9132 | 2.9577 | 1650 | 1.9181 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 5.0.0 |
| - Tokenizers 0.22.2 |
| |
