ai-content-maker/.venv/Lib/site-packages/spacy/lang/en/lex_attrs.py

47 lines
1.7 KiB
Python
Raw Normal View History

2024-05-03 04:18:51 +03:00
from ...attrs import LIKE_NUM
# fmt: off
_num_words = [
"zero", "one", "two", "three", "four", "five", "six", "seven", "eight",
"nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen",
"sixteen", "seventeen", "eighteen", "nineteen", "twenty", "thirty", "forty",
"fifty", "sixty", "seventy", "eighty", "ninety", "hundred", "thousand",
"million", "billion", "trillion", "quadrillion", "quintillion", "sextillion",
"septillion", "octillion", "nonillion", "decillion", "gajillion", "bazillion"
]
_ordinal_words = [
"first", "second", "third", "fourth", "fifth", "sixth", "seventh", "eighth",
"ninth", "tenth", "eleventh", "twelfth", "thirteenth", "fourteenth",
"fifteenth", "sixteenth", "seventeenth", "eighteenth", "nineteenth",
"twentieth", "thirtieth", "fortieth", "fiftieth", "sixtieth", "seventieth",
"eightieth", "ninetieth", "hundredth", "thousandth", "millionth", "billionth",
"trillionth", "quadrillionth", "quintillionth", "sextillionth", "septillionth",
"octillionth", "nonillionth", "decillionth", "gajillionth", "bazillionth"
]
# fmt: on
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
text_lower = text.lower()
if text_lower in _num_words:
return True
# Check ordinal number
if text_lower in _ordinal_words:
return True
if text_lower.endswith(("st", "nd", "rd", "th")):
if text_lower[:-2].isdigit():
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}