README.md

metadata

title: SBERT + FAISS Semantic Search
emoji: 🔍
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.45.0
app_file: app.py
pinned: false

SBERT + FAISS Semantic Search + Evaluation Metrics

This Hugging Face Space hosts a semantic search system built with:

• Sentence-BERT (SBERT) for embeddings
• FAISS for fast vector search
• MS MARCO v1.1 dataset (10,000 passages subset)
• Gradio for the interactive interface

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🔹 Features

• Enter a query to retrieve the Top-10 most similar passages.
• Computes true IR metrics when the query matches one in MS MARCO validation set:
  • Precision@10
  • Recall@10
  • F1-score
  • Mean Reciprocal Rank (MRR)
  • Normalized Discounted Cumulative Gain (nDCG@10)

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🔹 How to Use

1. Type a query into the input box.
2. Press Submit.
3. View:
   • Top-10 retrieved passages with similarity scores
   • Evaluation metrics if the query exists in the validation set

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🔹 Tech Stack

• Embeddings: sentence-transformers/all-mpnet-base-v2
• Indexing: FAISS (L2 similarity)
• Dataset: MS MARCO v1.1 (first 10,000 passages)
• Interface: Gradio

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🔹 Citation

If you use this system in research, please cite:

• Sentence-BERT
• MS MARCO

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🔹 Author

Built for a research project on user-centered evaluation of semantic search systems.