This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. It appears in our research paper "Ordinal Quantification Through Regularization", which we have published at ECML-PKDD 2022. The data is extracted from the McAuley data set of product reviews in Amazon, where the goal is to predict the 5-star rating of each textual review. We have sampled this data according to two protocols that are suited for quantification research. The goal of quantification is not to predict the star rating of each individual instance, but the distribution of ratings in sets of textual reviews. More generally speaking, quantification aims at estimating the distribution of labels in unlabeled samples of data. The first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification, where classes are ordered and a similarity of neighboring classes can be assumed. 5-star ratings of product reviews lie on an ordinal scale and, hence, pose such an ordinal quantification task. This data set comprises two representations of the McAuley data. The first representation consists of TF-IDF features. The second representation is a RoBERTa embedding. This second representation is dense, while the first is sparse. In our experience, logistic regression classifiers work well with both representations. RoBERTa embeddings yield more accurate predictors than the TF-IDF features. You can extract our data sets yourself, for instance, if you require a raw textual representation. The original McAuley data set is public already and we provide all of our extraction scripts.