| --- |
| library_name: transformers |
| license: mit |
| base_model: FacebookAI/roberta-base |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: roberta-v2 |
| 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-v2 |
|
|
| This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2181 |
| - F1 Micro: 0.9100 |
| - F1 Macro: 0.8958 |
| - Precision Micro: 0.9072 |
| - Recall Micro: 0.9129 |
|
|
| ## 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: 24 |
| - eval_batch_size: 24 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 48 |
| - 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: 2096 |
| - num_epochs: 4 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Precision Micro | Recall Micro | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:---------:|:----------------:|:-------------:| |
| | 0.4516 | 1.0 | 5240 | 0.2242 | 0.8697 | 0.8363 | 0.8975 | 0.8436 | |
| | 0.4487 | 2.0 | 10480 | 0.2218 | 0.8862 | 0.8683 | 0.8939 | 0.8786 | |
| | 0.4390 | 3.0 | 15720 | 0.2207 | 0.8997 | 0.8836 | 0.8985 | 0.9010 | |
| | 0.4409 | 4.0 | 20960 | 0.2200 | 0.9072 | 0.8929 | 0.9069 | 0.9075 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.11.0 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 5.0.0 |
| - Tokenizers 0.22.2 |
| |
