ATAT (Astronomical Transformer for time series And Tabular data) implements Transformer-based models for classifying astronomical sources from light-curve time series and tabular metadata. It provides scripts to download, process, and partition survey data, extracting and normalizing light curves and assembling metadata into training, validation, and test sets. Specialized layers for time modulation and multi-head attention encode light curves and tabular features, while training drivers construct models, run optimization and validation loops, and save trained configurations. ATAT also provides utilities to compute classification metrics at multiple observation durations and generate visualizations such as performance curves and confusion matrices.​

ASTROMER is a Transformer-based model trained on millions of stars for the representation of light curves. Pretrained models can be directly used or finetuned on specific datasets. ASTROMER is useful in downstream tasks in which data are limited to train deep learning models.