AG News-数据集

496,835 条来自 AG 新闻语料库 4 大类别超过 2000 个新闻源的新闻文章，数据集仅仅援用了标题和描述字段。 每个类别分别拥有 30,000 个训练样本及 1900 个测试样本。

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.

译：

本文对字符级卷积网络（ConvNets）在文本分类中的应用进行了实证研究。我们构建了几个大规模的数据集，以证明字符级卷积网络可以达到最先进或最具竞争力的结果。比较了传统模型，如单词包、n-grams及其TFIDF变体，以及基于单词的ConvNets和递归神经网络等深度学习模型。

大家可以到官网地址下载数据集，我自己也在百度网盘分享了一份。可关注本人公众号，回复“2020082104”获取下载链接。

只要自己有时间，都尽量写写文章，与大家交流分享。

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CSDN博客地址：https://blog.csdn.net/ispeasant