Advanced RAG pipeline using Re-Ranking after initial retrieval
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Updated
May 11, 2024 - Python
Advanced RAG pipeline using Re-Ranking after initial retrieval
predicting a movie list with Two-sided Fairness-aware Recommendation Model (accotding to TFROM_A article) dataset : https://grouplens.org/datasets/movielens/100k/
Exploring search relevance techniques.
Assignment on Information Retrieval course, implementing ranking algorithms with Lucene.
A Java implementation of the classical Information Retrieval models in the TREC-COVID Challenge with the CORD19 Dataset
Frontend for comic book semantic search engine. Renders explanations along with search results
Multi-stage Retrieval using SPLADE or RM3 and T5.
Information Retrieval using KoSentence-BERT
This repository showcases a comprehensive approach to information retrieval, document re-ranking, and language model integration. It incorporates techniques such as document chunking, embedding projection, and automatic query expansion to enhance the effectiveness of information retrieval systems.
Training a customized dataset on fast-reid, evaluation and visualization
임베딩(SentenceTransformer) 및 재순위화(Re-Ranking)
Work in progress. An llm util to work as an evaluation step in RAG applications
Smart Untact Meeting / 전문가추천시스템 APP
Implementation of Probabilistic Retrieval Query expansion and Relevance Model based Language Modelling aimed at improving the precision of results using pseudo-relevance feedback in Information Retrieval.
This is an official implementation for "Robust Graph Structure Learning over Images via Multiple Statistical Tests" accepted at NeurIPS 2022.
Python-based toolkit for building and evaluating a transformer-based FAQ retrieval system
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