Add trained model and model card

README.md

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+---
+language: en
+license: mit
+library_name: sklearn
+tags:
+  - text-classification
+  - sentiment-analysis
+  - code-review
+  - sklearn
+pipeline_tag: text-classification
+---
+
+# 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
+
+```python
+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

config.json

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+{
+  "model_type": "sklearn-pipeline",
+  "task": "text-classification",
+  "labels": {
+    "0": "negative",
+    "1": "neutral",
+    "2": "positive"
+  },
+  "features": "tfidf",
+  "accuracy": 0.3666666666666667
+}