When using the scikit-learn library in Python, I can use the CountVectorizer to create ngrams of a desired length (e.g. 2 words) like so: from sklearn.metrics.
photo-upload
string-conversion
url-scheme
vue-strap
directorysearcher
courgette
wcm
react-beautiful-dnd
ng-content
libgit2
bitcount
recursive-type
mddialog
postman-newman
rsa-key-fingerprint
indexed-image
boost-histogram
razorpay
many-to-many
apache-kafka-security
nlua
metabase
mayanedms
google-chrome-theme
backbone-views
view
multiline
svgpanzoom
angular-ngfor
mouseenter