Create README.md

README.md

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+# SBERT + FAISS Semantic Search
+
+This Hugging Face Space hosts a **semantic search system** built with:
+
+- [Sentence-BERT (SBERT)](https://www.sbert.net/) for embeddings  
+- [FAISS](https://faiss.ai/) for fast vector search  
+- [MS MARCO v1.1 dataset](https://microsoft.github.io/msmarco/) (10,000 passages subset)  
+- [Gradio](https://gradio.app/) for the interactive interface  
+
+---
+
+## 🔹 Features
+- Enter a **query** to retrieve the **Top-10 most similar passages**.  
+- Optionally provide **ground truth relevant passages** (one per line) to compute **IR metrics**:  
+  - Precision@10  
+  - Recall@10  
+  - F1-score  
+  - Mean Reciprocal Rank (MRR)  
+  - Normalized Discounted Cumulative Gain (nDCG@10)  
+
 ---
-title: SBERT Semantic Search System
-emoji: 🏆
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 5.44.1
-app_file: app.py
-pinned: false
-license: cc-by-nc-2.0
-short_description: SBERT Semantic Search System
+
+## 🔹 How to Use
+1. Type a query into the input box.  
+2. (Optional) Paste one or more relevant passages into the second box, each on a new line.  
+3. Press **Submit**.  
+4. View:  
+   - **Top-10 retrieved passages** with FAISS similarity scores  
+   - **Evaluation metrics** if ground truth passages were provided  
+
+---
+
+## 🔹 Tech Stack
+- **Embeddings:** `sentence-transformers/all-mpnet-base-v2`  
+- **Indexing:** FAISS (L2 similarity)  
+- **Dataset:** MS MARCO v1.1 (first 10,000 passages)  
+- **Interface:** Gradio  
+
+---
+
+## 🔹 Citation
+If you use this system in research, please cite:
+
+- [Sentence-BERT](https://arxiv.org/abs/1908.10084)  
+- [MS MARCO](https://microsoft.github.io/msmarco/)  
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 🔹 Author
+Built for a research project on **user-centered evaluation of semantic search systems**.