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
