lgalke/vec4ir
Word Embeddings for Information Retrieval
This tool helps researchers and data scientists evaluate different information retrieval models. You provide a collection of documents and a set of queries, and it helps you test how well various retrieval methods, especially those using word embeddings, find the most relevant documents for each query. This is for anyone researching or implementing search systems and needing to compare how different approaches perform.
226 stars. No commits in the last 6 months.
Use this if you are developing or evaluating information retrieval systems and want to compare how different search algorithms perform when using word embeddings to understand document and query meaning.
Not ideal if you are looking for a ready-to-use search engine for an application rather than a framework for evaluating and comparing retrieval models.
Stars
226
Forks
41
Language
Python
License
MIT
Category
Last pushed
Oct 04, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/lgalke/vec4ir"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
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