Français

French être (to be)

>>> from verbecc import CompleteConjugator, LangCodeISO639_1 as Lang
>>> ccg = CompleteConjugator(Lang.fr) 
# If this is the first run, it will take a minute for the model to train, 
# but it should save the model .zip file and run fast subsequently
>>> cc = ccg.conjugate("être")
>>> print(cc.to_json())

(View Output JSON)

>>> print(cc.to_yaml())

(View Output YAML)

French se lever (to lift oneself)

This verb is conjugated with the auxiliary verb ềtre so it must be inflected for gender and number. That's why the output is nearly 1200 lines of JSON whereas être is just over 900 lines.

>>> from verbecc import CompleteConjugator, LangCodeISO639_1 as Lang
>>> ccg = CompleteConjugator(Lang.fr) 
# If this is the first run, it will take a minute for the model to train, 
# but it should save the model .zip file and run fast subsequently
>>> cc = ccg.conjugate("se lever")
>>> print(cc.to_json())

(View Output JSON)

>>> print(cc.to_yaml())

(View Output YAML)

ML Prediction French uberiser (to Uberize)

In this example, we will conjugate a verb that verbecc doesn't explicitly know. The conjugation will be predicted using a machine-learning model trained on verbecc's French verb conjugation data XML models.

>>> from verbecc import CompleteConjugator, LangCodeISO639_1 as Lang
>>> ccg = CompleteConjugator(Lang.fr)
>>> cc = ccg.conjugate('ubériser')
>>> print(cc.to_json())

(View Output JSON)

>>> print(cc.to_yaml())

(View Output YAML)