| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: github_issues-dataset-distilbert-base-uncased |
| results: [] |
| datasets: |
| - lewtun/github-issues |
| language: |
| - en |
| --- |
| |
| # github_issues-dataset-distilbert-base-uncased |
| |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a GitHub issues dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1495 |
| - Accuracy: 0.9580 |
| - F1: 0.6067 |
| - Precision: 0.7297 |
| - Recall: 0.5192 |
| |
| ## Model description |
| |
| [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) |
| |
| ## Intended uses & limitations |
| |
| Multi Label Classification on GitHub repository issues. |
| |
| ## Training and evaluation data |
| |
| GitHub issues dataset taken from [GitHub issues](https://huggingface.co/datasets/lewtun/github-issues). |
| |
| Split the dataset into 80-20 train-test splits. Filtered out the pull requests and issues with no labels. |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 2 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.3962 | 1.0 | 114 | 0.2513 | 0.9208 | 0.34 | 0.3542 | 0.3269 | |
| | 0.2008 | 2.0 | 228 | 0.1847 | 0.9436 | 0.4198 | 0.5862 | 0.3269 | |
| | 0.1633 | 3.0 | 342 | 0.1608 | 0.9544 | 0.5581 | 0.7059 | 0.4615 | |
| | 0.1468 | 4.0 | 456 | 0.1519 | 0.9580 | 0.6067 | 0.7297 | 0.5192 | |
| | 0.1385 | 5.0 | 570 | 0.1495 | 0.9580 | 0.6067 | 0.7297 | 0.5192 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.49.0 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.4.1 |
| - Tokenizers 0.21.1 |
