Amir-Entezari/IR-document-ranking

An information retrieval system for document ranking. Implementation and evaluation of Okapi BM25, Cosine Similarity, and Language Models for document ranking and retrieval. Includes precision-recall evaluation metrics and a detailed project report.

12
/ 100
Experimental

This project helps information architects, researchers, or anyone managing large text collections to effectively find relevant documents. You input a collection of text documents and specific queries, and it outputs a ranked list of documents most relevant to each query. This is designed for practitioners who need to optimize document search and retrieval within their systems.

No commits in the last 6 months.

Use this if you need to build or evaluate a system that retrieves the most relevant text documents in response to a user's query.

Not ideal if you are looking for a pre-built search engine with a user interface, or if your data is not primarily text-based.

information-retrieval document-search text-analytics content-management library-science
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 8 / 25
Community 0 / 25

How are scores calculated?

Stars

5

Forks

Language

Jupyter Notebook

License

Last pushed

Jun 27, 2024

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/Amir-Entezari/IR-document-ranking"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.