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cross-encoder-reranking

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First-place official system for JOKER 2026 (CLEF) Task 1 English: a three-stage humor retrieval pipeline pairing hybrid sparse-dense retrieval and cross-encoder reranking with a rationale-distilled LLM judge ensemble. 0.6347 MAP. Team VANGUARD.

  • Updated Jun 20, 2026
  • Python

A production-grade RAG system and neural voice assistant for low-resource Kannada literature ('Heli Hogu Kaarana'). It features BM25 + ChromaDB hybrid search with page-level routing, Gemini/Groq model fallbacks, and Sarvam AI TTS for streaming audio. Validated using RAGAS with 0.92 Faithfulness and 0.88 Answer Relevancy.

  • Updated Aug 22, 2026
  • Python

Hybrid RAG pipeline (Dense + BM25 + Cross-Encoder Reranker) with conversational memory and systematic evaluation. 5 retrieval experiments, 92.3% Hit Rate, 5.0/5.0 LLM-as-a-Judge. Built with LangChain LCEL & ChromaDB.

  • Updated Aug 28, 2026
  • Python

Multimodal RAG over PDFs. Three parallel pipelines (LLM summaries, raw atomic content, CLIP visual) with HyDE expansion and cross encoder reranking, answers cited inline.

  • Updated Jul 22, 2026
  • Jupyter Notebook

Successfully developed a multi-domain RAG pipeline that retrieves and answers questions grounded in research papers on lung cancer detection, NSCLC microbiome classification, and Bhutan land cover, with a full safety evaluation suite covering toxicity, information leakage, and scope adherence using DeepEval.

  • Updated Sep 4, 2026
  • Python

Built an advanced Retrieval-Augmented Generation (RAG) system utililzing text datasets chunked dynamically. Engineered a dual-stage retrieval pipeline using all-MiniLM-L6-v2 embeddings to pull 20 FAISS candidates, refined via Cross-Encoders to feed the top 5 most relevant chunks to the LLM Synthesized context with a fine-tuned Qwen2.5-1.5B-Instruct

  • Updated Sep 6, 2026
  • Python

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