Datasets:

ines-besrour
/

unarxive_E5

dataset: unarxive_E5
title: UnarXive 2024 - Dense Vector Index (e5)
license: mit
tags:
  - dense-retrieval
  - e5
  - rag
  - scholarly-nlp
  - scientific-papers
  - vector-index
  - unarxive
pretty_name: UnarXive E5 Index
size_categories:
  - 1M<n<10M
source_datasets:
  - unarxive_2024
dataset_type: embeddings

Dataset Card for UnarXive E5 Index

This repository contains a dense vector index built using the e5-large-v2 model on the abstracts of over 2.3 million arXiv papers in the UnarXive 2024 dataset.

It is designed for neural retrieval, especially in retrieval-augmented generation (RAG) pipelines, scientific QA systems, and dense ranking models.

What's Inside

Dense embeddings of paper abstracts (e5-large-v2)
Index format: FAISS (flat or HNSW depending on version)
Each vector is associated with paper_id, title, and abstract

Use Cases

Dense retrieval in scholarly QA or chatbots
Scientific search engines with semantic understanding
RAG systems over structured academic corpora
Paper similarity or clustering