# Path: deepnlp/pipeline.py
# class Pipeline(object):
#   def __init__(self, name):
#   def analyze(self, string):
#   def segment(self, string):
#   def tag_pos(self, words):
#   def tag_ner(self, words):
#   def parse(self, words):
# def _concat_tuples(tagging):
# def load_model(lang = 'zh'):
#   TOKEN_BLANK = " "


import sys,os
import codecs
import deepnlp
from deepnlp import pipeline

#coding:utf-8
from __future__ import unicode_literals # compatible with python3 unicode


deepnlp.download('segment')   # download all the required pretrained models from github if installed from pip
deepnlp.download('pos')       
deepnlp.download('ner')


# Path: deepnlp/pipeline.py
# class Pipeline(object):
#   def __init__(self, name):
#   def analyze(self, string):
#   def segment(self, string):
#   def tag_pos(self, words):
#   def tag_ner(self, words):
#   def parse(self, words):
# def _concat_tuples(tagging):
# def load_model(lang = 'zh'):
#   TOKEN_BLANK = " "


# Path: test/test_pipeline.py
import sys,os
import codecs
import deepnlp
from deepnlp import pipeline

#coding:utf-8
from __future__ import unicode_literals # compatible with python3 unicode


deepnlp.download('segment')   # download all the required pretrained models from github if installed from pip
deepnlp.download('pos')       
deepnlp.download('ner')


p = pipeline.load_model('zh')
