🔎 A full-fledged search engine that crawls, indexes, perform analysis, and searches through the papaers listed on research gate website. It comes with a Flask webserver and a light-weight UI as well. It was a course project.
Results of work for Information Retrieval class in Computer Science Universitas Gadjah Mada
This is the project for the course CS410 (Text information System)
Implementation of the IR models using Russian Wikipedia articles
Projects for COMP 479: Information Retrieval And Web Search.
Documents and queries are represented as vectors. Each dimension corresponds to a separate term. If a term occurs in the document, its value in the vector is non-zero. Several different ways of computing these values, also known as (term) weights, have been developed. One of the best known schemes is tf-idf weighting (see the example below). The…
RATE: An LLM-Powered Retrieval Augmented Generation Technology-Extraction Pipeline
LLM News & Research Radar : Self-Improving RAG Engine
Clinical + Genomic **RAG evaluation (pro)** with hybrid retrieval (BM25 + embeddings), stronger faithfulness, YAML configs, and HTML dashboards. Python **3.10+**.
Working prototype by Team FIONA at the NASA Space Apps Hackathon Challenge 2022
Laboratory 10 - Retrieval Information
Search Engine w/ BM25 & PageRank scoring based on Cosine Similarities
paper: Are We Consuming the Same News? An Empirical Analysis of Information Similarity for Danish Facebook Users
Various python code scripts (Security, NLP, Naive Bayes, IR)
Text Technologies for Data Science
Coursework for CS6200, Information Retrieval.
A search engine trained from a corpus of wikipedia articles to provide efficient query results.
Information retrieval from basic concepts (tokenization, stop word removal, stemming, TF-IDF, etc.)
Coursework for CS 5154 - Information Retrieval. Dual level (grad & undergrad) course introducing information storage and retrieval with unstructured data. Includes concepts such as tf-idf, cosine similarity, relevance-based evaluation, text classification, clustering etc.
STaRK: Agentic AI benchmark, which is designed to evaluate how well LLMs and retrieval systems work with semi-structured knowledge bases.
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