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.
activity-stack
apple-cryptokit
half-precision-float
transform
cyclejs
filesystem-access
visa-api
bloomberg
everit
sealedsecret
gamekit
react-server-components
default-package
aws-storage-gateway
pdu
ofstream
rautomation
modx-template-varaiables
cozyroc
build.gradle
usb
nsslider
balloon
azure-backup-vault
gedmo-loggable
gdata-api
openargs
stackview
stardog
mapbox-api-vectortiles