| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: adity12345/RoBerta_covi19_rumor |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: Roberta_feverous |
| | 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_feverous |
| | |
| | This model is a fine-tuned version of [adity12345/RoBerta_covi19_rumor](https://huggingface.co/adity12345/RoBerta_covi19_rumor) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.6168 |
| | - Accuracy: 0.674 |
| | - Auc: 0.67 |
| | - Precision: 0.677 |
| | - Recall: 0.897 |
| | - F1: 0.771 |
| | - F1-macro: 0.6 |
| | - F1-micro: 0.674 |
| | - F1-weighted: 0.639 |
| | |
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 32 |
| | - 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 |
| | - num_epochs: 2 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision | Recall | F1 | F1-macro | F1-micro | F1-weighted | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:-----:|:---------:|:------:|:-----:|:--------:|:--------:|:-----------:| |
| | | 0.6385 | 0.2896 | 500 | 0.6240 | 0.666 | 0.646 | 0.656 | 0.96 | 0.779 | 0.546 | 0.666 | 0.599 | |
| | | 0.6294 | 0.5793 | 1000 | 0.6270 | 0.665 | 0.652 | 0.673 | 0.885 | 0.764 | 0.593 | 0.665 | 0.632 | |
| | | 0.627 | 0.8689 | 1500 | 0.6192 | 0.669 | 0.658 | 0.674 | 0.891 | 0.768 | 0.595 | 0.669 | 0.634 | |
| | | 0.6126 | 1.1581 | 2000 | 0.6185 | 0.674 | 0.662 | 0.665 | 0.945 | 0.781 | 0.573 | 0.674 | 0.621 | |
| | | 0.6044 | 1.4478 | 2500 | 0.6155 | 0.673 | 0.665 | 0.669 | 0.927 | 0.777 | 0.582 | 0.673 | 0.627 | |
| | | 0.5942 | 1.7374 | 3000 | 0.6168 | 0.674 | 0.67 | 0.677 | 0.897 | 0.771 | 0.6 | 0.674 | 0.639 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.55.2 |
| | - Pytorch 2.8.0+cu126 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.21.4 |
| | |
