论文标题
Tevatron：高效且灵活的工具包
Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval
论文作者
论文摘要
在深度训练的语言模型和大型数据集的介绍中，最近的快速进步为基于嵌入的密集检索提供了动力。尽管已经出现了几篇好的研究论文，但其中许多带有自己的软件堆栈。这些堆栈通常用于某些特定的研究目标，而不是效率或代码结构。在本文中，我们提出了Tevatron，这是一种优化的效率，灵活性和代码简单性的密集检索工具包。 Tevatron提供了一条标准化的管道，用于致密检索，包括文本处理，模型培训，语料库/查询编码和搜索。本文介绍了Tevatron的概述，并展示了其在几个IR和QA数据集中的有效性和效率。我们还展示了Tevatron的灵活设计如何在数据集，模型体系结构和加速器平台（GPU/TPU）之间轻松概括。我们认为，Tevatron可以作为密集检索系统研究（包括设计，建模和优化）的有效软件基础。
Recent rapid advancements in deep pre-trained language models and the introductions of large datasets have powered research in embedding-based dense retrieval. While several good research papers have emerged, many of them come with their own software stacks. These stacks are typically optimized for some particular research goals instead of efficiency or code structure. In this paper, we present Tevatron, a dense retrieval toolkit optimized for efficiency, flexibility, and code simplicity. Tevatron provides a standardized pipeline for dense retrieval including text processing, model training, corpus/query encoding, and search. This paper presents an overview of Tevatron and demonstrates its effectiveness and efficiency across several IR and QA data sets. We also show how Tevatron's flexible design enables easy generalization across datasets, model architectures, and accelerator platforms(GPU/TPU). We believe Tevatron can serve as an effective software foundation for dense retrieval system research including design, modeling, and optimization.
