  We present a new corpus of Twitter data annotated for codeswitching and
borrowing between Spanish and English. The corpus contains 9,500 tweets
annotated at the token level with codeswitches, borrowings, and named entities.
This corpus differs from prior corpora of codeswitching in that we attempt to
clearly define and annotate the boundary between codeswitching and borrowing
and do not treat common "internet-speak" ('lol', etc.) as codeswitching when
used in an otherwise monolingual context. The result is a corpus that enables
the study and modeling of Spanish-English borrowing and codeswitching on
Twitter in one dataset. We present baseline scores for modeling the labels of
this corpus using Transformer-based language models. The annotation itself is
released with a CC BY 4.0 license, while the text it applies to is distributed
in compliance with the Twitter terms of service.
