  Machine learning methods and in particular Graph Neural Networks (GNNs) have
revolutionized many tasks within the high energy physics community. We report
on the novel use of GNNs and a domain-adversarial training method to identify
$\Lambda$ hyperon events with the CLAS12 experiment at Jefferson Lab. The GNN
method we have developed increases the purity of the $\Lambda$ yield by a
factor of $1.95$ and by $1.82$ using the domain-adversarial training. This work
also provides a good benchmark for developing event tagging machine learning
methods for the $\Lambda$ and other channels at CLAS12 and other experiments,
such as the planned Electron Ion Collider.
