peler1nl1kelt0s/github-issues-classification-final

results

This model is a fine-tuned version of bert-base-uncased on an unknown dataset.

Model description

This model is fine-tuned for classifying GitHub issues into four categories: New Feature, Improvement, Bug, and Task. The base model used is bert-large-uncased, and it has been trained on an open-source dataset of GitHub issues containing titles and descriptions. This model can efficiently predict the type of issue based on the input of the issue’s title and description.

Fine-Tuning Details

Base Model: bert-large-uncased Fine-Tuning Dataset: GitHub Issues with labels mapped to four categories:

• New Feature
• Improvement
• Bug
• Task

Training Framework: Hugging Face Transformers, PyTorch Training Setup: The model was fine-tuned using a batch size of 64 for a few epochs, with a learning rate of 6e-5.

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: 6e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 4

Training results

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1