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Exploring search relevance techniques.

License: MIT License

Jupyter Notebook 97.27% Python 2.73%
artificial-intelligence machine-learning search-relevance semantic-search reranking retrieval-systems

learn-search-relevance's Introduction

Search Relevance Repository ๐Ÿ”

I created this repository to explore various search relevance techniques by developing tutorials ๐Ÿ—๏ธ, creating Hugging Face ๐Ÿค— spaces and demos, and compiling useful resources ๐Ÿ“š.

List of tutorials ๐Ÿ“–

  • Quora Similar Questions Finder: Hugging Face ๐Ÿค— Space. A demonstration of constructing a vector index database with Qdrant using questions from Quora. It then employs semantic search to identify the top K questions most similar to the user's input.
  • Semantic Search on Simple Wikipedia Through Retrieval and Reranking: Hugging Face ๐Ÿค— Space. Based on the user's input question or query, the algorithm searches through ~250k passages to find the top K relevant passages to the query, then reranks the results and outputs the top article with the relevant passage highlighted. Qdrant is used as a vector store, and SentenceTransformers are used for generating embeddings and reranking through cross-encoders. Finally, the demo is powered by Gradio.

Contributing ๐Ÿ’ก

Feel free to contribute by sharing your insights, improvements, or any useful resources related to search relevance.

Stay Connected ๐Ÿค

Star or watch this repository for updates on new tutorials and resources. Your support helps this project grow.

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