  Foundation models are trained on increasingly immense and opaque datasets.
Even while these models are now key in AI system building, it can be difficult
to answer the straightforward question: has the model already encountered a
given example during training? We therefore propose a widespread adoption of
Data Portraits: artifacts that record training data and allow for downstream
inspection. First we outline the properties of such an artifact and discuss how
existing solutions can be used to increase transparency. We then propose and
implement a solution based on data sketching, stressing fast and space
efficient querying. Using our tools, we document a popular language modeling
corpus (The Pile) and a recently released code modeling dataset (The Stack). We
show that our solution enables answering questions about test set leakage and
model plagiarism. Our tool is lightweight and fast, costing only 3% of the
dataset size in overhead. We release a live interface of our tools at
https://dataportraits.org/ and call on dataset and model creators to release
Data Portraits as a complement to current documentation practices.
