# -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2019-12-21 15:39
import tensorflow as tf
from hanlp.components.tok import NgramConvTokenizer
from hanlp.datasets.cws.sighan2005.msr import SIGHAN2005_MSR_TRAIN, SIGHAN2005_MSR_VALID, SIGHAN2005_MSR_TEST
from hanlp.pretrained.word2vec import CONVSEG_W2V_NEWS_TENSITE_CHAR
from tests import cdroot
cdroot()
tokenizer = NgramConvTokenizer()
save_dir = 'data/model/cws/convseg-msr-nocrf-noembed'
tokenizer.fit(SIGHAN2005_MSR_TRAIN,
SIGHAN2005_MSR_VALID,
save_dir,
word_embed={
'class_name': 'HanLP>Word2VecEmbedding',
'config': {
'trainable': True,
'filepath': CONVSEG_W2V_NEWS_TENSITE_CHAR,
'expand_vocab': False,
'lowercase': False,
}
},
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001,
epsilon=1e-8,
clipnorm=5),
epochs=100,
window_size=0,
metrics='f1',
# -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2019-12-29 21:58
import tensorflow as tf
from hanlp.components.tok import NgramConvTokenizer
from hanlp.datasets.cws.ctb import CTB6_CWS_TRAIN, CTB6_CWS_VALID, CTB6_CWS_TEST
from hanlp.pretrained.word2vec import CONVSEG_W2V_NEWS_TENSITE_CHAR
from tests import cdroot
cdroot()
tokenizer = NgramConvTokenizer()
save_dir = 'data/model/cws/ctb6_cws'
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001,
epsilon=1e-8, clipnorm=5)
tokenizer.fit(CTB6_CWS_TRAIN,
CTB6_CWS_VALID,
save_dir,
word_embed={'class_name': 'HanLP>Word2VecEmbedding',
'config': {
'trainable': True,
'filepath': CONVSEG_W2V_NEWS_TENSITE_CHAR,
'expand_vocab': False,
'lowercase': False,
}},
optimizer=optimizer,
window_size=0,
weight_norm=True)
tokenizer.evaluate(CTB6_CWS_TEST, save_dir=save_dir, output=False)
print(tokenizer.tokenize(['中央民族乐团离开北京前往维也纳', '商品和服务']))
# -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2019-12-21 15:39
import tensorflow as tf
from hanlp.components.tok import NgramConvTokenizer
from hanlp.pretrained.word2vec import RADICAL_CHAR_EMBEDDING_100
from tests import cdroot
cdroot()
tokenizer = NgramConvTokenizer()
save_dir = 'data/model/cws/pku98_6m_conv_ngram'
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001,
epsilon=1e-8,
clipnorm=5)
tokenizer.fit('data/cws/pku98/199801-06-seg.txt',
'data/cws/pku98/test_pku98_name_merged.txt',
save_dir,
word_embed={
'class_name': 'HanLP>Word2VecEmbedding',
'config': {
'trainable': False,
'filepath': RADICAL_CHAR_EMBEDDING_100,
'expand_vocab': True,
'lowercase': False,
}
},
optimizer=optimizer,
window_size=0,
weight_norm=True)