bert-base-uncased-issues-128
This model is a fine-tuned version of bert-base-uncased on the GitHub issues dataset. The model is used in Chapter 9: Dealing with Few to No Labels in the NLP with Transformers book. You can find the full code in the accompanying Github repository.
It achieves the following results on the evaluation set:
- Loss: 1.2520
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 |
---|---|---|---|
2.0949 | 1.0 | 291 | 1.7072 |
1.649 | 2.0 | 582 | 1.4409 |
1.4835 | 3.0 | 873 | 1.4099 |
1.3938 | 4.0 | 1164 | 1.3858 |
1.3326 | 5.0 | 1455 | 1.2004 |
1.2949 | 6.0 | 1746 | 1.2955 |
1.2451 | 7.0 | 2037 | 1.2682 |
1.1992 | 8.0 | 2328 | 1.1938 |
1.1784 | 9.0 | 2619 | 1.1686 |
1.1397 | 10.0 | 2910 | 1.2050 |
1.1293 | 11.0 | 3201 | 1.2058 |
1.1006 | 12.0 | 3492 | 1.1680 |
1.0835 | 13.0 | 3783 | 1.2414 |
1.0757 | 14.0 | 4074 | 1.1522 |
1.062 | 15.0 | 4365 | 1.1176 |
1.0535 | 16.0 | 4656 | 1.2520 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.0
- Tokenizers 0.10.3
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