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import re
from nltk import word_tokenize
from rag.nlp import stemmer, huqie
def callback__(progress, msg, func):
if not func :return
func(progress, msg)
BULLET_PATTERN = [[
r"第[零一二三四五六七八九十百]+编",
r"第[零一二三四五六七八九十百]+章",
r"第[零一二三四五六七八九十百]+节",
r"第[零一二三四五六七八九十百]+条",
r"[\(（][零一二三四五六七八九十百]+[\)）]",
], [
r"[0-9]{,3}[\. 、]",
r"[0-9]{,2}\.[0-9]{,2}",
r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
], [
r"[零一二三四五六七八九十百]+[ 、]",
r"[\(（][零一二三四五六七八九十百]+[\)）]",
r"[\(（][0-9]{,2}[\)）]",
] ,[
r"PART (ONE|TWO|THREE|FOUR|FIVE|SIX|SEVEN|EIGHT|NINE|TEN)",
r"Chapter (I+V?|VI*|XI|IX|X)",
r"Section [0-9]+",
r"Article [0-9]+"
]
]
def bullets_category(sections):
global BULLET_PATTERN
hits = [0] * len(BULLET_PATTERN)
for i, pro in enumerate(BULLET_PATTERN):
for sec in sections:
for p in pro:
if re.match(p, sec):
hits[i] += 1
break
maxium = 0
res = -1
for i,h in enumerate(hits):
if h <= maxium:continue
res = i
maxium = h
return res
def is_english(texts):
eng = 0
for t in texts:
if re.match(r"[a-zA-Z]", t.strip()):
eng += 1
if eng / len(texts) > 0.8:
return True
return False
def tokenize(d, t, eng):
d["content_with_weight"] = t
if eng:
t = re.sub(r"([a-z])-([a-z])", r"\1\2", t)
d["content_ltks"] = " ".join([stemmer.stem(w) for w in word_tokenize(t)])
else:
d["content_ltks"] = huqie.qie(t)
d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"]) |