  Despite recent advances in natural language processing, many statistical
models for processing text perform extremely poorly under domain shift.
Processing biomedical and clinical text is a critically important application
area of natural language processing, for which there are few robust, practical,
publicly available models. This paper describes scispaCy, a new tool for
practical biomedical/scientific text processing, which heavily leverages the
spaCy library. We detail the performance of two packages of models released in
scispaCy and demonstrate their robustness on several tasks and datasets. Models
and code are available at https://allenai.github.io/scispacy/
