MongoDB/subset_arxiv_papers_with_embeddings

This dataset is a curated subset of the original arXiv dataset, each entry enriched with a 256-dimensional embedding vector. The embeddings are generated using OpenAI's "text-embedding-3-small" model. For each data point, the embedding is created by concatenating the text of the title, author(s), and abstract into a single string, which is then processed by the embedding model. This approach captures the semantic essence of each document, facilitating tasks such as similarity search, clustering, and recommendation systems based on content relevance. The dataset is designed for use in advanced machine learning applications that require an understanding of document content at a granular level.

原始信息汇总

数据集概述

数据集来源与构成

• 本数据集是原始arXiv数据集的一个精选子集。
• 每个数据条目均附带一个256维的嵌入向量。

嵌入向量生成

• 嵌入向量通过OpenAI的"text-embedding-3-small"模型生成。
• 嵌入过程涉及将标题、作者和摘要文本合并为一个字符串，再通过嵌入模型处理。

数据集用途

• 该数据集适用于需要对文档内容进行细致理解的先进机器学习应用。
• 可用于相似性搜索、聚类分析和基于内容相关性的推荐系统。