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vinaykarman/
robertabase

anasnadeem/github-issues

apache-2.0

text-embeddings-inference

endpoints_compatible

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Model Card for the Automatic Issue Classifier (AIC)

This model card is for a RoBERTa-based model fine-tuned to classify GitHub issue reports into three categories: Bug, Enhancement, and Question.

Model Details

The Automatic Issue Classifier (AIC) is a transformer-based model designed for the multi-label classification of GitHub issue reports. It addresses the challenge that issue submitters can tag a single issue with more than one label.

The model is a fine-tuned version of roberta-base , which is an optimized variant of BERT. It was developed to overcome the limitations of traditional keyword-based approaches, which often fail to capture the contextual relationships between words in issue reports.

The model and an associated industry tool were developed to automatically assign labels to newly reported issues, helping to streamline software maintenance workflows.

Intended Use

This model is intended to be used for automatically labeling new issue reports in GitHub repositories. Its primary benefits include:

Helping development teams to effectively track and prioritize issues.
Routing issues to the correct developer or team member.
Assisting project managers in optimizing resource allocation.

python

from transformers import pipeline

# Load the pipeline

classifier = pipeline("text-classification", model="vinaykarman/robertabase", return_all_scores=True)

# Example issue text (concatenated title and body)
issue_text = """
Title: USBhost: additional functions for keyboard appreciated
Body: for USBhost it would be fine to have additional functions to read the USB keyboard: kbhit() getch() getche() getchar() gets() scanf()
"""

# Get predictions
predictions = classifier(issue_text)
print(predictions)

# Expected output would be a list of scores for each label:
# [[{'label': 'bug', 'score': 0.8}, {'label': 'enhancement', 'score': 0.75}, {'label': 'question', 'score': 0.05}]]