>>> from numerizer import numerize
>>> numerize('forty two')
'42'
>>> numerize('forty-two')
'42'
>>> numerize('four hundred and sixty two')
'462'
>>> numerize('one fifty')
'150'
>>> numerize('twelve hundred')
'1200'
>>> numerize('twenty one thousand four hundred and seventy three')
'21473'
>>> numerize('one million two hundred and fifty thousand and seven')
'1250007'
>>> numerize('one billion and one')
'1000000001'
>>> numerize('nine and three quarters')
'9.75'
>>> numerize('platform nine and three quarters')
'platform 9.75'

>>> from spacy import load
>>> nlp = load('en_core_web_sm')  # or load any other spaCy model
>>> doc = nlp('The projected revenue for the next quarter is over two million dollars.')
>>> doc._.numerize()
{the next quarter: 'the next 1/4', over two million dollars: 'over 2000000 dollars'}

>>> doc._.numerize(labels=['MONEY'])  # only numerize entities of type 'MONEY'
{over two million dollars: 'over 2000000 dollars'}

>>> two_million = doc[-4:-2]  # span corresponding to "two million"
>>> two_million._.numerize()
'2000000'
>>> quarter = doc[6]  # token corresponding to "quarter"
>>> quarter._.numerized
'1/4'