Create README.md

README.md

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+---
+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](https://huggingface.co/intfloat/e5-large-v2) model on the **abstracts** of over 2.3 million arXiv papers in the [UnarXive 2024](https://huggingface.co/datasets/ines-besrour/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