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: 1.2432
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 |
|---|---|---|---|
| 2.0987 | 1.0 | 291 | 1.6066 |
| 1.631 | 2.0 | 582 | 1.4775 |
| 1.4933 | 3.0 | 873 | 1.4646 |
| 1.3984 | 4.0 | 1164 | 1.3314 |
| 1.3377 | 5.0 | 1455 | 1.3122 |
| 1.274 | 6.0 | 1746 | 1.2062 |
| 1.2538 | 7.0 | 2037 | 1.2626 |
| 1.192 | 8.0 | 2328 | 1.1832 |
| 1.1612 | 9.0 | 2619 | 1.2055 |
| 1.1489 | 10.0 | 2910 | 1.1605 |
| 1.1262 | 11.0 | 3201 | 1.1925 |
| 1.1022 | 12.0 | 3492 | 1.1309 |
| 1.0892 | 13.0 | 3783 | 1.1692 |
| 1.0812 | 14.0 | 4074 | 1.2384 |
| 1.0666 | 15.0 | 4365 | 1.0822 |
| 1.0533 | 16.0 | 4656 | 1.2432 |
Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0
- Datasets 2.2.2
- Tokenizers 0.10.3
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