roberta-finetuned-sem_eval-english
This model is a fine-tuned version of FacebookAI/roberta-large on the sem_eval_2018_task_1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2789
- F1: 0.7317
- Roc Auc: 0.8155
- Accuracy: 0.3205
- Precision: 0.7866
- Recall: 0.6839
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
- 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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Precision | Recall |
---|---|---|---|---|---|---|---|---|
0.3718 | 1.0 | 855 | 0.2900 | 0.7052 | 0.7965 | 0.2912 | 0.7764 | 0.6460 |
0.2636 | 2.0 | 1710 | 0.2835 | 0.7149 | 0.8014 | 0.3149 | 0.7919 | 0.6516 |
0.2232 | 3.0 | 2565 | 0.2789 | 0.7317 | 0.8155 | 0.3205 | 0.7866 | 0.6839 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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Model tree for Sungjin228/roberta-finetuned-sem_eval-english
Base model
FacebookAI/roberta-largeDataset used to train Sungjin228/roberta-finetuned-sem_eval-english
Evaluation results
- F1 on sem_eval_2018_task_1validation set self-reported0.732
- Accuracy on sem_eval_2018_task_1validation set self-reported0.321
- Precision on sem_eval_2018_task_1validation set self-reported0.787
- Recall on sem_eval_2018_task_1validation set self-reported0.684