CS221-bert-base-uncased-finetuned-semeval-NT-som
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2468
- F1: 0.5157
- Roc Auc: 0.7038
- Accuracy: 0.6003
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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.3098 | 1.0 | 340 | 0.2643 | 0.3195 | 0.5979 | 0.5634 |
| 0.2215 | 2.0 | 680 | 0.2357 | 0.4847 | 0.6787 | 0.6032 |
| 0.1457 | 3.0 | 1020 | 0.2468 | 0.5157 | 0.7038 | 0.6003 |
| 0.0879 | 4.0 | 1360 | 0.2827 | 0.4852 | 0.6824 | 0.6018 |
| 0.0285 | 5.0 | 1700 | 0.3326 | 0.4681 | 0.6919 | 0.5457 |
| 0.0292 | 6.0 | 2040 | 0.3682 | 0.4992 | 0.7087 | 0.5664 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0