google bert 论文阅读 1.原文解读 Bidirectional Encoder Representations from Transformers,bert,根据字面意思,训练的其实还是一个embedding,而且还是一个字级别的word embedding,通过这个embedding,有两种用法,一种是将其作为特征,训练特定的任务时候,不学习这些参数,通过设计特定的架构
Bert系列:论文阅读Learning-to-rank with bert in tf-ranking 背景: 首次使用bert做query和documents的 排序工作。 做排序的通用方法: 构建一个分类模型,目标是该doc是否与query相关,预测的结果用于排序,也就是说分类模型得到的是排序文档的候选集。但相比于pairwiese和listwise,这种方式做排序没有考虑doc之间的相关...
《Learning-to-Rank with BERT in TF-Ranking》S Han, X Wang, M Bendersky, M Najork [Google Research] (2020) http://t.cn/A6Ahzypi view:http://t.cn/A6AhzypJ
In this work, we empirically investigate a few aspects of this domain. Firstly, in addition to traditional features like TF-IDF, BM25 etc., we introduce a BERT-based feature that captures the semantic similarity between the question and answer. Secondly, most of the existing research works ...
We provide a demo, with no installation required, to get started on using TF-Ranking. This demo runs on acolaboratory notebook, an interactive Python environment. Using sparse features and embeddings in TF-Ranking . This demo demonstrates how to: ...