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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: 1.1663
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 |
|---|---|---|---|
| 2.1124 | 1.0 | 291 | 1.6330 |
| 1.6254 | 2.0 | 582 | 1.5264 |
| 1.4981 | 3.0 | 873 | 1.4292 |
| 1.4031 | 4.0 | 1164 | 1.3636 |
| 1.3311 | 5.0 | 1455 | 1.2945 |
| 1.2789 | 6.0 | 1746 | 1.2382 |
| 1.2376 | 7.0 | 2037 | 1.2670 |
| 1.2049 | 8.0 | 2328 | 1.2065 |
| 1.1789 | 9.0 | 2619 | 1.1641 |
| 1.1466 | 10.0 | 2910 | 1.1732 |
| 1.1331 | 11.0 | 3201 | 1.1338 |
| 1.088 | 12.0 | 3492 | 1.2339 |
| 1.0916 | 13.0 | 3783 | 1.1853 |
| 1.0755 | 14.0 | 4074 | 1.1018 |
| 1.058 | 15.0 | 4365 | 1.2024 |
| 1.0536 | 16.0 | 4656 | 1.1663 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
