This corpus has been collected from free or free for research sources at the Internet: - A collection of 425 SMS spam messages was manually extracted from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages. - A subset of 3,375 SMS randomly chosen ham messages of the NUS SMS Corpus (NSC), which is a dataset of about 10,000 legitimate messages collected for research at the Department of Computer Science at the National University of Singapore. The messages largely originate from Singaporeans and mostly from students attending the University. These messages were collected from volunteers who were made aware that their contributions were going to be made publicly available. - A list of 450 SMS ham messages collected from Caroline Tag's PhD Thesis. - the SMS Spam Corpus v.0.1 Big. It has 1,002 SMS ham messages and 322 spam messages. Source: SMS Spam Collection Data Set
Wild-Time is a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. Source: Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time Image Source: https://arxiv.org/pdf/2211.14238v1.pdf
The model forecasts for the sub-seasonal forecasting application considered in the Online Learning under Optimism and Delay paper experiments. This dataset consists of a single ZIP archive (919MB) that contains 1) a "models" folder that contains, for each model the forecasts for the Precip. 3-4w, Precip. 5-6w, Temp. 3-4w, Temp. 5-6w tasks on the western United States geography, and 2) a "data" folder that contains supporting geographic data. The data should be used to reproduce the PoolD experiments in https://github.com/geflaspohler/poold as described in the README. (2021-06-10)