pnsahoo/10-20-macro-embedding

该数据集包含多个字段，主要涉及新闻报道的相关信息，如文章内容、作者、日期、报纸元数据、主题分类（如反垄断、民权、犯罪等）、命名实体识别（NER）信息、地理位置信息、提及的人物信息等。数据集还包含嵌入向量和年份信息，适用于文本分析、主题分类、实体识别等自然语言处理任务。

This dataset includes multiple fields related to news articles, such as article content, byline, dates, newspaper metadata, topic classifications (e.g., antitrust, civil rights, crime), named entity recognition (NER) information, geographic location information, mentioned people information, etc. The dataset also contains embedding vectors and year information, making it suitable for natural language processing tasks such as text analysis, topic classification, and entity recognition.