| --- |
| library_name: transformers |
| license: mit |
| base_model: FacebookAI/roberta-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: roberta-large-touche-rawplusctx-binary |
| 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-large-touche-rawplusctx-binary |
|
|
| This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7313 |
| - Accuracy: 0.64 |
| - Macro F1: 0.6400 |
| - Fallacy F1: 0.6364 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 8 |
| - 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: 0.1 |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Fallacy F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:| |
| | 1.3741 | 1.0 | 93 | 0.7383 | 0.5 | 0.3333 | 0.6667 | |
| | 1.2926 | 2.0 | 186 | 0.6534 | 0.605 | 0.6021 | 0.6359 | |
| | 0.8819 | 3.0 | 279 | 0.7313 | 0.64 | 0.6400 | 0.6364 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.9.0 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
| |
