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.
google-cloud-http-load-balancer
redhat-sso
arangodb-java
footer
marquee
sbt-avro
floating
cloudflare-kv
ionic3
gradle-plugin
r-tree
r-grid
decoupling
arrange-act-assert
flask-httpauth
godoc
nsnetservice
gradient-exploding
controlsfx
factoring
aws-code-deploy
s3transfermanager
less
clflush
media-player
google-url-shortener
nbconvert
bluetoothlescanner
rabbitmqctl
ndk-stack