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.
aparapi
linker-flags
error-log
qqmlapplicationengine
engopen
qstandarditemmodel
ios-targets
imageicon
opaque-pointers
recharts
skproduct
socat
auth0
int64
compiledebugjavawithjavac
detachedcriteria
maven-site-plugin
gremlinjs
loopback-address
text-analysis
functional-dependencies
google-messages
vesa
lockbits
flutter-webrtc
.ico
self-extracting
gcc10
wtforms
multipart