Modelpublic

loke007/bugflow-severity-classifier

sourceHugging Faceapache-2.0updated 10mo agoView on Hugging Face

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BugFlow Severity Classifier

Fine-tuned CodeBERT model for classifying bug report severity levels.

Labels

—Low: Minor issues, cosmetic changes
—Medium: Standard bugs affecting some functionality
—High: Important bugs affecting major functionality
—Critical: System crashes, data loss, security issues

Usage

python

from transformers import RobertaTokenizer, RobertaForSequenceClassification
import torch

model = RobertaForSequenceClassification.from_pretrained("YOUR_USERNAME/bugflow-severity-classifier")
tokenizer = RobertaTokenizer.from_pretrained("YOUR_USERNAME/bugflow-severity-classifier")

text = "Application crashes when clicking login button"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)
labels = ['low', 'medium', 'high', 'critical']
predicted = labels[torch.argmax(probs).item()]
print(f"Severity: {predicted}")

Training

—Base model: microsoft/codebert-base
—Dataset: Custom GitHub issues dataset + domain-specific bugs
—Fine-tuned using Hugging Face Transformers