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bert-base-uncased-issues-128
This model is a fine-tuned version of bert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 2.3196
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.6586 | 1.0 | 500 | 2.4203 |
| 2.4655 | 2.0 | 1000 | 2.3889 |
| 2.3769 | 3.0 | 1500 | 2.3279 |
| 2.3024 | 4.0 | 2000 | 2.3623 |
| 2.2178 | 5.0 | 2500 | 2.4500 |
| 2.1352 | 6.0 | 3000 | 2.3042 |
| 2.0793 | 7.0 | 3500 | 2.3053 |
| 2.0462 | 8.0 | 4000 | 2.2402 |
| 1.9886 | 9.0 | 4500 | 2.3407 |
| 1.9393 | 10.0 | 5000 | 2.2826 |
| 1.904 | 11.0 | 5500 | 2.3401 |
| 1.8742 | 12.0 | 6000 | 2.3276 |
| 1.8441 | 13.0 | 6500 | 2.2815 |
| 1.8082 | 14.0 | 7000 | 2.2739 |
| 1.8058 | 15.0 | 7500 | 2.3225 |
| 1.7951 | 16.0 | 8000 | 2.2933 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
