| --- |
| library_name: transformers |
| license: mit |
| base_model: FacebookAI/roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: roberta-base-cpp |
| 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-cpp |
|
|
| This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0844 |
| - Accuracy: 0.9550 |
|
|
| ## 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: 2 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 4 |
| - 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 |
| - num_epochs: 1 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:| |
| | 0.1085 | 0.05 | 1000 | 0.1080 | 0.9183 | |
| | 0.0716 | 0.1 | 2000 | 0.2524 | 0.8473 | |
| | 0.0615 | 0.15 | 3000 | 0.1182 | 0.9299 | |
| | 0.0648 | 0.2 | 4000 | 0.0757 | 0.9498 | |
| | 0.0522 | 0.25 | 5000 | 0.1201 | 0.9273 | |
| | 0.0377 | 0.3 | 6000 | 0.0846 | 0.9555 | |
| | 0.0447 | 0.35 | 7000 | 0.1036 | 0.9323 | |
| | 0.0421 | 0.4 | 8000 | 0.1804 | 0.8914 | |
| | 0.0364 | 0.45 | 9000 | 0.0494 | 0.9628 | |
| | 0.0301 | 0.5 | 10000 | 0.0583 | 0.9689 | |
| | 0.0281 | 0.55 | 11000 | 0.0554 | 0.9689 | |
| | 0.0362 | 0.6 | 12000 | 0.0898 | 0.9428 | |
| | 0.022 | 0.65 | 13000 | 0.0772 | 0.9687 | |
| | 0.0221 | 0.7 | 14000 | 0.0706 | 0.9613 | |
| | 0.0256 | 0.75 | 15000 | 0.0487 | 0.9719 | |
| | 0.0215 | 0.8 | 16000 | 0.0427 | 0.9765 | |
| | 0.0162 | 0.85 | 17000 | 0.0437 | 0.9742 | |
| | 0.0186 | 0.9 | 18000 | 0.0613 | 0.9680 | |
| | 0.0211 | 0.95 | 19000 | 0.0950 | 0.9514 | |
| | 0.0136 | 1.0 | 20000 | 0.0844 | 0.9550 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.3 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.4.2 |
| - Tokenizers 0.22.1 |
| |
