CHINCHUANLIM/amazon_polarity

Amazon极性分类数据集是一个用于文本分类任务的大规模英语数据集，特别专注于情感分析。它包含亚马逊产品评论，每条评论被标注为正面或负面极性，用于情感分类、情感评分和仇恨言论检测等任务。数据集分为训练集（约360万个样本）和测试集（约40万个样本），所有文本均为英文书面评论，平均文本长度约431个字符。该数据集是MTEB（大规模文本嵌入基准）的一部分，适用于评估嵌入模型在情感极性分类上的性能。

Amazon Polarity Classification Dataset is a large-scale English dataset for text classification tasks, specifically focused on sentiment analysis. It contains Amazon product reviews, each labeled as positive or negative polarity, and is used for tasks such as sentiment classification, sentiment scoring, and hate speech detection. The dataset is split into a training set (approximately 3.6 million examples) and a test set (approximately 400,000 examples), with all texts being written reviews in English and an average text length of about 431 characters. It is part of the MTEB (Massive Text Embedding Benchmark) and is suitable for evaluating embedding models on sentiment polarity classification.