bert-base-uncased-issues-128
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5503
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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.6214 | 1.0 | 291 | 2.2471 |
| 2.0594 | 2.0 | 582 | 1.9293 |
| 1.8563 | 3.0 | 873 | 1.7961 |
| 1.7442 | 4.0 | 1164 | 1.7518 |
| 1.657 | 5.0 | 1455 | 1.7390 |
| 1.577 | 6.0 | 1746 | 1.7173 |
| 1.5071 | 7.0 | 2037 | 1.6223 |
| 1.4661 | 8.0 | 2328 | 1.5691 |
| 1.4365 | 9.0 | 2619 | 1.6280 |
| 1.3827 | 10.0 | 2910 | 1.4641 |
| 1.3517 | 11.0 | 3201 | 1.6498 |
| 1.3294 | 12.0 | 3492 | 1.3006 |
| 1.2836 | 13.0 | 3783 | 1.6520 |
| 1.2867 | 14.0 | 4074 | 1.6064 |
| 1.2819 | 15.0 | 4365 | 1.4131 |
| 1.2611 | 16.0 | 4656 | 1.5503 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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