Qdrant/arxiv-abstracts-instructorxl-embeddings

该数据集包含使用InstructorXL模型从arXiv论文摘要生成的768维嵌入向量。每个向量都包含用于生成它的摘要和DOI（数字对象标识符）。数据集是通过Alexandria Index暴露的预计算嵌入创建的。嵌入生成过程使用了特定的指令，并提供了生成嵌入的代码示例。

原始信息汇总

arxiv-abstracts-instructorxl-embeddings

概述

该数据集包含从arxiv论文摘要生成的768维嵌入向量，使用InstructorXL模型。每个向量对应一个摘要和DOI（数字对象标识符）。数据集是通过Alexandria Index暴露的预计算嵌入创建的。

生成过程

嵌入向量的生成遵循以下指令： text Represent the Research Paper abstract for retrieval; Input:

代码示例

以下代码片段展示了如何使用InstructorXL模型生成嵌入： python from InstructorEmbedding import INSTRUCTOR

model = INSTRUCTOR(hkunlp/instructor-xl) sentence = "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train." instruction = "Represent the Research Paper abstract for retrieval; Input:" embeddings = model.encode([[instruction, sentence]])