File size: 7,287 Bytes
15f6380 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 |
#! /usr/bin/python3 -i
# coding=utf-8
import os
PACKAGE_DIR=os.path.abspath(os.path.dirname(__file__))
DOWNLOAD_DIR=os.path.join(PACKAGE_DIR,"models")
from transformers.file_utils import hf_bucket_url
MODEL_URL=hf_bucket_url("KoichiYasuoka/SuPar-Kanbun","suparkanbun/models/")
import numpy
from spacy.language import Language
from spacy.symbols import LANG,NORM,LEMMA,POS,TAG,DEP,HEAD
from spacy.tokens import Doc,Span,Token
from spacy.util import get_lang_class
class SuParKanbunLanguage(Language):
lang="lzh"
max_length=10**6
def __init__(self,BERT,Danku):
self.Defaults.lex_attr_getters[LANG]=lambda _text:"lzh"
try:
self.vocab=self.Defaults.create_vocab()
self.pipeline=[]
except:
from spacy.vocab import create_vocab
self.vocab=create_vocab("lzh",self.Defaults)
self._components=[]
self._disabled=set()
self.tokenizer=SuParKanbunTokenizer(BERT,Danku,self.vocab)
self._meta={
"author":"Koichi Yasuoka",
"description":"derived from SuParKanbun",
"lang":"SuParKanbun_lzh",
"license":"MIT",
"name":"SuParKanbun_lzh",
"parent_package":"suparkanbun",
"pipeline":"Tokenizer, POS-Tagger, Parser",
"spacy_version":">=2.1.0"
}
self._path=None
class SuParKanbunTokenizer(object):
to_disk=lambda self,*args,**kwargs:None
from_disk=lambda self,*args,**kwargs:None
to_bytes=lambda self,*args,**kwargs:None
from_bytes=lambda self,*args,**kwargs:None
def __init__(self,bert,danku,vocab):
from supar import Parser
self.bert=bert
self.vocab=vocab
self.simplify={}
if bert.startswith("guwenbert"):
from suparkanbun.simplify import simplify
self.simplify=simplify
d=os.path.join(DOWNLOAD_DIR,bert+".pos")
self.tagger=AutoModelTagger(d)
f=os.path.join(d,bert+".supar")
self.supar=Parser.load(f)
if danku:
d=os.path.join(DOWNLOAD_DIR,bert+".danku")
self.danku=AutoModelTagger(d,["B","E","E2","E3","M","S"])
else:
self.danku=None
self.gloss=MakeGloss()
def __call__(self,text):
from suparkanbun.tradify import tradify
t=""
for c in text:
if c in self.simplify:
t+=self.simplify[c]
else:
t+=c
if self.danku!=None:
u=t.replace("\n","")
t=""
while len(u)>500:
s=self.danku(u[0:500])
r=""
for c,p in s:
r+=c
if p=="S" or p=="E":
r+="\n"
r="\n".join(r.split("\n")[0:-2])+"\n"
t+=r
u=u[len(r.replace("\n","")):]
s=self.danku(u)
for c,p in s:
t+=c
if p=="S" or p=="E":
t+="\n"
if len(t)<500:
p=self.tagger(t.replace("\n",""))
else:
p=[]
u=""
for s in t.strip().split("\n"):
u+=s
if len(u)>400:
p+=self.tagger(u)
u=""
if len(u)>0:
p+=self.tagger(u)
u=self.supar.predict([[c for c in s] for s in t.strip().split("\n")],lang=None)
t=text.replace("\n","")
i=0
w=[]
for s in u.sentences:
v=[]
for h,d in zip(s.values[6],s.values[7]):
j=t[i]
k=tradify[j] if j in tradify else j
v.append({"form":j,"lemma":k,"pos":p[i][1],"head":h,"deprel":d})
i+=1
for j in reversed(range(0,len(v)-1)):
if v[j]["deprel"]=="compound" and v[j]["head"]==j+2 and v[j]["pos"]==v[j+1]["pos"]:
k=v.pop(j)
v[j]["form"]=k["form"]+v[j]["form"]
v[j]["lemma"]=k["lemma"]+v[j]["lemma"]
for k in range(0,len(v)):
if v[k]["head"]>j+1:
v[k]["head"]-=1
w.append(list(v))
vs=self.vocab.strings
r=vs.add("ROOT")
words=[]
lemmas=[]
pos=[]
tags=[]
feats=[]
heads=[]
deps=[]
spaces=[]
norms=[]
for s in w:
for i,t in enumerate(s):
form=t["form"]
words.append(form)
lemmas.append(vs.add(t["lemma"]))
p=t["pos"].split(",")
xpos=",".join(p[0:4])
pos.append(vs.add(p[4]))
tags.append(vs.add(xpos))
feats.append(p[5])
if t["deprel"]=="root":
heads.append(0)
deps.append(r)
else:
heads.append(t["head"]-i-1)
deps.append(vs.add(t["deprel"]))
spaces.append(False)
g=self.gloss(form,xpos)
if g!=None:
norms.append(vs.add(g))
else:
norms.append(vs.add(form))
doc=Doc(self.vocab,words=words,spaces=spaces)
a=numpy.array(list(zip(lemmas,pos,tags,deps,heads,norms)),dtype="uint64")
doc.from_array([LEMMA,POS,TAG,DEP,HEAD,NORM],a)
try:
doc.is_tagged=True
doc.is_parsed=True
except:
for i,j in enumerate(feats):
if j!="_" and j!="":
doc[i].set_morph(j)
return doc
class AutoModelTagger(object):
def __init__(self,dir,label=None):
from suparkanbun.download import checkdownload
from transformers import AutoModelForTokenClassification,AutoTokenizer
checkdownload(MODEL_URL+os.path.basename(dir)+"/",dir)
self.model=AutoModelForTokenClassification.from_pretrained(dir)
self.tokenizer=AutoTokenizer.from_pretrained(dir)
self.label=label if label else self.model.config.id2label
def __call__(self,text):
import torch
input=self.tokenizer.encode(text,return_tensors="pt")
output=self.model(input)
predict=torch.argmax(output[0],dim=2)
return [(t,self.label[p]) for t,p in zip(text,predict[0].tolist()[1:])]
class MakeGloss(object):
def __init__(self,file=None):
if file==None:
file=os.path.join(DOWNLOAD_DIR,"gloss.orig.txt")
with open(file,"r",encoding="utf-8") as f:
r=f.read()
self.gloss={}
for s in r.split("\n"):
t=s.split()
if len(t)==4:
self.gloss[(t[0],t[2])]=t[3]
elif len(t)==5:
self.gloss[(t[0],t[3])]=t[4]
self.extra={
"n,名詞,人,姓氏":"[surname]",
"n,名詞,人,名":"[given-name]",
"n,名詞,主体,書物":"[book-name]",
"n,名詞,主体,国名":"[country-name]",
"n,名詞,固定物,地名":"[place-name]"
}
def __call__(self,form,xpos):
if (form,xpos) in self.gloss:
return self.gloss[(form,xpos)]
if xpos in self.extra:
return self.extra[xpos]
if xpos=="n,名詞,時,*":
if len(form)>1:
return "[era-name]"
return None
def load(BERT="roberta-classical-chinese-base-char",Danku=False):
return SuParKanbunLanguage(BERT,Danku)
def to_conllu(item,offset=1):
if type(item)==Doc:
return "".join(to_conllu(s)+"\n" for s in item.sents)
elif type(item)==Span:
return "# text = "+str(item)+"\n"+"".join(to_conllu(t,1-item.start)+"\n" for t in item)
elif type(item)==Token:
m="_" if item.whitespace_ else "SpaceAfter=No"
if item.norm_!="":
if item.norm_!=item.orth_:
m="Gloss="+item.norm_+"|"+m
m=m.replace("|_","")
l=item.lemma_
if l=="":
l="_"
t=item.tag_
if t=="":
t="_"
try:
f=str(item.morph)
if f.startswith("<spacy") or f=="":
f="_"
except:
f="_"
return "\t".join([str(item.i+offset),item.orth_,l,item.pos_,t,f,str(0 if item.head==item else item.head.i+offset),item.dep_.lower(),"_",m])
return "".join(to_conllu(s)+"\n" for s in item)
|