testtesttest#!/usr/bin/python
# -*- coding:utf-8 -*-
from __future__ import unicode_literals # compatible with python3 unicode
import deepnlp
deepnlp.download('ner') # download the NER pretrained models from github if installed from pip
from deepnlp import ner_tagger
tagger = ner_tagger.load_model(name = 'zh') # Base LSTM Based Model
tagger.load_dict("zh_o2o") # Change to other dict
text = "北京 望京 最好吃 的 黑椒 牛排 在哪里"
words = text.split(" ")
# Use the prefix dict and merging function to combine separated words
tagging = tagger._predict_ner_tags_dict(words, merge = True)
print ("DEBUG: NER tagger zh_o2o dictionary")
for (w,t) in tagging:
pair = w + "/" + t
print (pair)
#北京/city
#望京/area
#最好吃/nt
#的/nt
#黑椒牛排/dish
#在哪里/nt
# Word Sense Disambuguition
#coding:utf-8
from __future__ import unicode_literals
import deepnlp
deepnlp.download(
'pos'
) # download the POS pretrained models from github if installed from pip
from deepnlp import pos_tagger
tagger = pos_tagger.load_model(
lang='en') # Loading English model, lang code 'en'
#Segmentation
text = "I want to see a funny movie"
words = text.split(" ")
print(" ".join(words).encode('utf-8'))
#POS Tagging
tagging = tagger.predict(words)
for (w, t) in tagging:
str = w + "/" + t
print(str.encode('utf-8'))
#Results
#I/nn
#want/vb
#to/to
#see/vb
#a/at
#funny/jj
#movie/nn
#coding:utf-8
from __future__ import unicode_literals # compatible with python3 unicode
import sys,os
import codecs
import deepnlp
deepnlp.download('segment') # download all the required pretrained models from github if installed from pip
deepnlp.download('pos')
deepnlp.download('ner')
from deepnlp import pipeline
p = pipeline.load_model('zh')
# concatenate tuples into one string "w1/t1 w2/t2 ..."
def _concat_tuples(tagging):
TOKEN_BLANK = " "
wl = [] # wordlist
for (x, y) in tagging:
wl.append(x + "/" + y) # unicode
concat_str = TOKEN_BLANK.join(wl)
return concat_str
# input file
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
docs = []
file = codecs.open(os.path.join(BASE_DIR, 'docs_test.txt'), 'r', encoding='utf-8')
for line in file:
line = line.replace("\n", "").replace("\r", "")
docs.append(line)
#coding=utf-8
from __future__ import unicode_literals
import tensorflow as tf
import deepnlp
# Download module and domain-specific model
deepnlp.download(module='segment', name='zh_entertainment')
deepnlp.download(module='pos', name='en')
deepnlp.download(module='ner', name='zh_o2o')
# Download module
deepnlp.download('segment')
deepnlp.download('pos')
deepnlp.download('ner')
deepnlp.download('parse')
# deepnlp.download()
## 测试 load model
from deepnlp import segmenter
try:
tokenizer = segmenter.load_model(name='zh')
tokenizer = segmenter.load_model(name='zh_o2o')
tokenizer = segmenter.load_model(name='zh_entertainment')
except Exception as e:
print("DEBUG: ERROR Found...")
print(e)
## pos
from deepnlp import pos_tagger
import deepnlp
deepnlp.download('ner')
from deepnlp import segmenter
from deepnlp import ner_tagger
tokenizer = segmenter.load_model(name='zh')
tagger = ner_tagger.load_model(name='zh')
file_r = open('./data/person_resume.txt', 'r')
file_w = open('./data/person_resume02.txt', 'w')
for line in file_r:
text = line.rstrip()
words = tokenizer.seg(text)
tagging = tagger.predict(words)
for (w, t) in tagging:
pair = w + ' ' + t
file_w.write(pair)
file_w.close()
file_r.close()
.::::.
.::::::::.
:::::::::::
..:::::::::::'
'::::::::::::'
.::::::::::
'::::::::::::::..
..::::::::::::.
``::::::::::::::::
::::``:::::::::' .:::.
::::' ':::::' .::::::::.
.::::' :::: .:::::::'::::.
.:::' ::::: .:::::::::' ':::::.
.::' :::::.:::::::::' ':::::.
.::' ::::::::::::::' ``::::.
...::: ::::::::::::' ``::.
```` ':. ':::::::::' ::::..
'.:::::' ':'````..
"""
#coding=utf-8
import deepnlp
# Download all the modules
deepnlp.download()
#coding=utf-8
from deepnlp import segmenter
tokenizer = segmenter.load_model(name = 'zh_entertainment')
text = "我刚刚在浙江卫视看了电视剧老九门，觉得陈伟霆很帅"
segList = tokenizer.seg(text)
text_seg = " ".join(segList)
#coding:utf-8
from __future__ import unicode_literals
import deepnlp
deepnlp.download(module='pos',name='en') # download the POS pretrained models from github if installed from pip
from deepnlp import pos_tagger
tagger = pos_tagger.load_model(name = 'en') # Loading English model, lang code 'en'
#Segmentation
text = "I want to see a funny movie"
words = text.split(" ")
print (" ".join(words))
#POS Tagging
tagging = tagger.predict(words)
for (w,t) in tagging:
pair = w + "/" + t
print (pair)
#Results
#I/nn
#want/vb
#to/to
#see/vb
#a/at
#funny/jj
#movie/nn
#!/usr/bin/python
# -*- coding:utf-8 -*-
import deepnlp
from deepnlp import segmenter
try:
deepnlp.download("segment", "zh_finance")
except Exception as e:
print (e)
deepnlp.register_model("segment", "zh_finance")
deepnlp.download("segment", "zh_finance")
try:
seg_tagger = segmenter.load_model("zh_finance")
except Exception as e:
print (e)
from deepnlp import pos_tagger
try:
deepnlp.download("pos", "zh_finance")
except Exception as e:
print (e)
deepnlp.register_model("pos", "zh_finance")
deepnlp.download("pos", "zh_finance")
try:
pos_tagger.load_model("zh_finance")
except Exception as e:
print (e)