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Readme.md

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-# 📚 CCSS Alignment with BM25 & SPLADE
-
-This project allows you to align input educational text (lesson plans, learning objectives) with Common Core State Standards (ELA) using two retrieval techniques:
-
-- **BM25** (sparse lexical search)
-- **SPLADE** (sparse transformer embeddings)
-
-## 🚀 How to Run the App
-
-Make sure you're in the project root folder, then run:
-
-```bash
-streamlit run app.py
-```
-
-You will be able to:
-- Select either BM25 or SPLADE
-- Input a query (e.g., "identify key ideas and details")
-- View top-matching CCSS standards
-- Compare accuracy between both retrieval models
-
-## 🧪 Sample Starter Code for app.py
-
-```python
-import streamlit as st
-from core.bm25_utility import bm25_utility
-from core.splade_utility import SpladeUtility
-
-query = st.text_input("Enter your query:")
-method = st.selectbox("Choose retrieval method", ["BM25", "SPLADE"])
-
-if st.button("Get Standards"):
-    if method == "BM25":
-        results = bm25_utility(query).retrieve_top_n_bm25()
-    else:
-        results = SpladeUtility(query).retrieve_top_n_splade()
-
-    for r in results:
-        st.write(f"**{r['ID']}** - {r['standard']} (Score: {r['score']})")
-```
-
-
-## 📝 Notes
-
-- Ensure that model weights for SPLADE are downloaded or cached.
-- Make sure you're using cleaned and preprocessed CCSS data for accurate matching.
-- Streamlit interface supports rapid switching between BM25 and SPLADE for testing.
-
----
-
-**Author**: Shivendra Gupta  
-**Purpose**: Educational NLP for aligning teaching content to learning standards.