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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: 0.1675
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 |
|---|---|---|---|
| 0.239 | 1.0 | 291 | 0.2306 |
| 0.1865 | 2.0 | 582 | 0.1971 |
| 0.169 | 3.0 | 873 | 0.1918 |
| 0.1603 | 4.0 | 1164 | 0.1875 |
| 0.1536 | 5.0 | 1455 | 0.1567 |
| 0.1461 | 6.0 | 1746 | 0.1755 |
| 0.1411 | 7.0 | 2037 | 0.1719 |
| 0.1374 | 8.0 | 2328 | 0.1658 |
| 0.1341 | 9.0 | 2619 | 0.1594 |
| 0.1302 | 10.0 | 2910 | 0.1666 |
| 0.1284 | 11.0 | 3201 | 0.1634 |
| 0.1264 | 12.0 | 3492 | 0.1588 |
| 0.1238 | 13.0 | 3783 | 0.1690 |
| 0.1237 | 14.0 | 4074 | 0.1558 |
| 0.1218 | 15.0 | 4365 | 0.1523 |
| 0.1213 | 16.0 | 4656 | 0.1675 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.0
- Tokenizers 0.13.3
