srvmishra832/github_issues-dataset-distilbert-base-uncased

github_issues-dataset-distilbert-base-uncased

This model is a fine-tuned version of 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

Intended uses & limitations

Multi Label Classification on GitHub repository issues.

Training and evaluation data

GitHub issues dataset taken from 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
• —trainbatchsize: 2
• —evalbatchsize: 2
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 5

Training results

Framework versions

• —Transformers 4.49.0
• —Pytorch 2.6.0+cu124
• —Datasets 3.4.1
• —Tokenizers 0.21.1