Code Review Sentiment Classifier
A lightweight sklearn-based classifier for code review comments. Classifies review feedback as positive, neutral, or negative.
Model Details
- Type: TF-IDF + Logistic Regression pipeline
- Task: 3-class text classification
- Framework: scikit-learn
- Labels: negative (0), neutral (1), positive (2)
Usage
import pickle
with open("model.pkl", "rb") as f:
model = pickle.load(f)
review = "Great implementation, clean code!"
label = model.predict([review])[0] # 0=negative, 1=neutral, 2=positive
proba = model.predict_proba([review])[0]
Training Data
30 code review comments (10 per class) covering:
- Positive: Praise, LGTM, good patterns
- Neutral: Suggestions, minor nits, questions
- Negative: Bugs, security issues, performance problems
Limitations
- Small training set
- English only
- Focused on software engineering domain
License
MIT
- Downloads last month
- -
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("zachz/code-review-sentiment", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html