def __init__(self, name):
print("Starting new Tensorflow session...")
self.session = tf.Session()
print("Loading pipeline modules...")
self.tokenizer = segmenter.load_model(name)
self.tagger_pos = pos_tagger.load_model(name) # class tagger_pos
self.tagger_ner = ner_tagger.load_model(name) # class tagger_ner
#coding:utf-8
from __future__ import unicode_literals # compatible with python3 unicode
from deepnlp import segmenter
from deepnlp import pos_tagger
tagger = pos_tagger.load_model(lang='zh')
#Segmentation
text = "我爱吃北京烤鸭" # unicode coding, py2 and py3 compatible
words = segmenter.seg(text)
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
#我/r
#爱/v
#吃/v
#北京/ns
#烤鸭/n
for pair in nextWords:
print str(pair[0]) + "\t" + str(pair[1])
from deepnlp import pos_tagger
if __name__ == "__main__":
import argparse
import dill
parser = argparse.ArgumentParser(description='Predictive typing')
parser.add_argument('-b', '--build', action="store_true")
args = parser.parse_args()
filePath = "models/brownCorpus.p"
#corpus = brown.tagged_words()[0:1000]
corpus = brown.tagged_words()[0:100000]
tagger = pos_tagger.load_model(lang='en')
def tagger_function(words):
return [(x[0], x[1].upper()) for x in tagger.predict(words)]
trained = model(corpus, tagger_function)
if (args.build):
trained.build()
with open(filePath, "wb") as saveFile:
trained.save(saveFile)
else:
data = dill.load(open(filePath, "rb"))
trained.build(data)
#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
# 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
try:
tagger = pos_tagger.load_model(
name='en') # Loading English model, lang code 'en'
tagger = pos_tagger.load_model(
name='zh') # Loading English model, lang code 'en'
except Exception as e:
print("DEBUG: ERROR Found...")
print(e)
## ner
from deepnlp import ner_tagger
try:
my_tagger = ner_tagger.load_model(name='zh')
my_tagger = ner_tagger.load_model(name='zh_o2o')
my_tagger = ner_tagger.load_model(name='zh_entertainment')
except Exception as e:
print("DEBUG: ERROR Found...")
print(e)
#coding:utf-8
from __future__ import unicode_literals # compatible with python3 unicode
from deepnlp import segmenter
from deepnlp import pos_tagger
# Load Model
tokenizer = segmenter.load_model(name='zh')
tagger = pos_tagger.load_model(name='zh')
#Segmentation
text = "我爱吃北京烤鸭" # unicode coding, py2 and py3 compatible
words = tokenizer.seg(text)
print(" ".join(words))
#POS Tagging
tagging = tagger.predict(words)
for (w, t) in tagging:
pair = w + "/" + t
print(pair)
#Results
#我/r
#爱/v
#吃/v
#北京/ns
#烤鸭/n
def __init__(self, lang):
print("Starting new Tensorflow session...")
self.session = tf.Session()
print("Loading pipeline modules...")
self.tagger_pos = pos_tagger.load_model(lang) # class tagger_pos
self.tagger_ner = ner_tagger.load_model(lang) # class tagger_ner
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)
from deepnlp import ner_tagger
try:
deepnlp.download("ner", "zh_finance")
except Exception as e:
print (e)
deepnlp.register_model("ner", "zh_finance")
deepnlp.download("ner", "zh_finance")
try:
ner_tagger.load_model("zh_finance")
except Exception as e:
print (e)