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.

本文介绍了 Tevatron，一个用于密集检索的工具包，其优化了效率、灵活性和代码的简明性。Tevatron 为密集检索提供了标准化的流程，包括文本处理、模型训练、语料库 / 查询编码和搜索等。作者展示了 Tevatron 在多个 IR 和 QA 数据集上的效果和效率，并证明了其灵活的设计可以轻松地在数据集、模型架构和加速器平台（GPU/TPU）之间进行推广。作者认为 Tevatron 可以作为密集检索系统研究的有效软件基础，包括设计、建模和优化。