pull_request_comments_model
Model description
This model is a fine-tuned version of distilbert-base-uncased on a pull request comments dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0791
- Train Accuracy: 0.9955
- Validation Loss: 0.5019
- Validation Accuracy: 0.8291
- Epoch: 12
Training and evaluation data
Training and evaluation data used for this model are the pull request comments of the tensorflow repository on GitHub. In particular, of all the pull request data (commit comments, review comments, events, exc.) only the rows with Type equal to PC (Pull request Comment) or RC (Review Comment) have been entered into the dataset. These comments has been classified into 4 categories:
- ML (Machine Learning), if the comment is about specific machine learning aspects, algorithms exc.
- Code, if the comment concerns either style and documentation in the code or maintainability issues or possible bugs exc.
- Management, if the comment is about management activities like checking an activity status, assign a review to someone, trigger Jenkins CI
- Other, if the comment doesn't belong to any of the above categories
Intended uses & limitations
One possible use of this model could be to label the pull request comments, clearly only on GitHub repositories that are about Machine Learning. In this way a developer, before reading a comment entirely, can have a preview of what that comment is about.
Training hyperparameters
The following hyperparameters were used during training:
- batch_size: 32
- num_epochs: 20
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 280, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 1.3102 | 0.4308 | 1.1851 | 0.4701 | 0 |
| 1.1436 | 0.4978 | 0.9891 | 0.6068 | 1 |
| 0.9590 | 0.6183 | 0.8287 | 0.6838 | 2 |
| 0.7801 | 0.6942 | 0.6916 | 0.7692 | 3 |
| 0.6074 | 0.7946 | 0.6212 | 0.8120 | 4 |
| 0.4755 | 0.8817 | 0.5471 | 0.8205 | 5 |
| 0.3503 | 0.9241 | 0.5244 | 0.8376 | 6 |
| 0.2594 | 0.9665 | 0.5171 | 0.8120 | 7 |
| 0.1711 | 0.9844 | 0.4832 | 0.8291 | 8 |
| 0.1474 | 0.9911 | 0.5000 | 0.8205 | 9 |
| 0.1082 | 0.9955 | 0.4875 | 0.8291 | 10 |
| 0.0981 | 0.9933 | 0.4928 | 0.8291 | 11 |
| 0.0791 | 0.9955 | 0.5019 | 0.8291 | 12 |
Framework versions
- Transformers 4.26.1
- TensorFlow 2.9.0
- Datasets 2.10.1
- Tokenizers 0.13.2
