---
tags:
- nlp
- regression
- tfidf
- ridge
- summaries
- kaggle
---

# 🧠 CommonLit Summary Scoring Model

This model was trained using the **CommonLit Evaluate Student Summaries** dataset on Kaggle.  
It predicts two scores for student-written summaries:

- `content` → Idea coverage quality  
- `wording` → Clarity and phrasing quality

Built with:
- TF-IDF vectorizer
- Ridge Regression (scikit-learn)
- MultiOutputRegressor wrapper

Example usage:
```python
from joblib import load

model = load("ridge_model.pkl")
tfidf = load("tfidf_vectorizer.pkl")
summary = "This text discusses..."
X = tfidf.transform([summary])
pred = model.predict(X)