class Processor(object):
def __init__(self, train_path, token_dict):
self.train_path = train_path
self.tokenizer = Tokenizer(token_dict)
def get_tags(self):
tags = set()
train_data = self.get_data(self.train_path)
for item in train_data:
for tag in item[1].split(" "):
tags.add(tag)
# PAD-tag用X
self.tag2id = {list(tags)[i]: i for i in range(len(tags))}
self.tag2id["X"] = len(self.tag2id)
self.id2tag = {self.tag2id[i]: i for i in self.tag2id}
return self.tag2id, self.id2tag
def get_data(self, path):
with codecs.open(path, "r", encoding="utf-8") as f:
data = json.load(f)
return data
def get_bert_inputs(self, path, max_len):
srcs = self.get_data(path)
src_data, src_tags = [], []
for item in srcs:
src_data.append(item[0])
src_tags.append(item[1])
tokens, segs, tags = [], [], []
for item in src_data:
res = self.tokenizer.encode(item, first_length=max_len)
tokens.append(np.array(res[0]))
segs.append(np.array(res[1]))
max_len -= 2
for item in src_tags:
len_item = len(item.split(" "))
if len_item >= max_len:
tags.append(["X"] + item.split(" ")[:max_len] + ["X"])
else:
tags.append(["X"] + item.split(" ") + ["X"] *
(max_len - len_item + 1))
tags = [[self.tag2id[item] for item in term[1:]] for term in tags]
tags = np.expand_dims(tags, axis=-1)
return tokens, segs, tags
# 测试代码可用性: 提取特征
from bert4keras.bert import load_pretrained_model
from bert4keras.utils import Tokenizer
from keras.models import load_model
import numpy as np
config_path = '/root/kg/bert/chinese_L-12_H-768_A-12/bert_config.json'
checkpoint_path = '/root/kg/bert/chinese_L-12_H-768_A-12/bert_model.ckpt'
dict_path = '/root/kg/bert/chinese_L-12_H-768_A-12/vocab.txt'
tokenizer = Tokenizer(dict_path) # 建立分词器
model = load_pretrained_model(config_path, checkpoint_path) # 建立模型，加载权重
# 编码测试
token_ids, segment_ids = tokenizer.encode(u'语言模型')
print('\n ===== predicting =====\n')
print(model.predict([np.array([token_ids]), np.array([segment_ids])]))
"""
输出：
[[[-0.63251007 0.2030236 0.07936534 ... 0.49122632 -0.20493352
0.2575253 ]
[-0.7588351 0.09651865 1.0718756 ... -0.6109694 0.04312154
0.03881441]
[ 0.5477043 -0.792117 0.44435206 ... 0.42449304 0.41105673
0.08222899]
[-0.2924238 0.6052722 0.49968526 ... 0.8604137 -0.6533166
0.5369075 ]
[-0.7473459 0.49431565 0.7185162 ... 0.3848612 -0.74090636
0.39056838]
#! -*- coding: utf-8 -*-
# 测试代码可用性: MLM
from bert4keras.bert import load_pretrained_model
from bert4keras.utils import Tokenizer, load_vocab
import numpy as np
config_path = '../../kg/bert/chinese_L-12_H-768_A-12/bert_config.json'
checkpoint_path = '../../kg/bert/chinese_L-12_H-768_A-12/bert_model.ckpt'
dict_path = '../../kg/bert/chinese_L-12_H-768_A-12/vocab.txt'
token_dict = load_vocab(dict_path) # 读取词典
tokenizer = Tokenizer(token_dict) # 建立分词器
model = load_pretrained_model(config_path, checkpoint_path,
with_mlm=True) # 建立模型，加载权重
token_ids, segment_ids = tokenizer.encode(u'科学技术是第一生产力')
# mask掉“技术”
token_ids[3] = token_ids[4] = token_dict['[MASK]']
# 用mlm模型预测被mask掉的部分
probas = model.predict([np.array([token_ids]), np.array([segment_ids])])[0]
print(tokenizer.decode(probas[3:5].argmax(axis=1))) # 结果正是“技术”
dict_path = 'albert_tiny_zh_google/vocab.txt'
tokenizer = Tokenizer(dict_path)
# 加载预训练模型
bert = build_bert_model(
config_path=config_path,
checkpoint_path=checkpoint_path,
with_pool=True,
albert=True,
return_keras_model=False,
)
model = Model(bert.model.input, bert.model.output)
token_ids1, segment_ids1 = tokenizer.encode(u'我想去北京')
token_ids2, segment_ids2 = tokenizer.encode(u'我想去香港')
token_ids3, segment_ids3 = tokenizer.encode(u'目前的局势，止暴制乱，刻不容缓')
sentence_vec1 = model.predict(
[np.array([token_ids1]), np.array([segment_ids1])])[0]
sentence_vec2 = model.predict(
[np.array([token_ids2]), np.array([segment_ids2])])[0]
sentence_vec3 = model.predict(
[np.array([token_ids3]), np.array([segment_ids3])])[0]
print("《我想去北京》和《我想去香港》的余弦距离为%f" %
similarity_count(sentence_vec1, sentence_vec2))
print("《我想去北京》和《我想去香港》的欧式距离为%f" %
similarity_count(sentence_vec1, sentence_vec2, mode='eu'))
dict_path = 'albert_tiny_zh_google/vocab.txt'
tokenizer = Tokenizer(dict_path)
# 加载预训练模型
bert = build_bert_model(
config_path=config_path,
checkpoint_path=checkpoint_path,
with_pool=True,
albert=True,
return_keras_model=False,
)
model = Model(bert.model.input, bert.model.output)
token_ids1, segment_ids1 = tokenizer.encode(u'我想去北京逛一逛天安门')
token_ids2, segment_ids2 = tokenizer.encode(u'我想去香港')
token_ids3, segment_ids3 = tokenizer.encode(u'我想到天安门广场走一走')
sentence_vec1 = model.predict(
[np.array([token_ids1]), np.array([segment_ids1])])[0]
sentence_vec2 = model.predict(
[np.array([token_ids2]), np.array([segment_ids2])])[0]
sentence_vec3 = model.predict(
[np.array([token_ids3]), np.array([segment_ids3])])[0]
print("《我想去北京》和《我想去香港》的余弦距离为%f" %
similarity_count(sentence_vec1, sentence_vec2))
print("《我想去北京》和《我想去香港》的欧式距离为%f" %
similarity_count(sentence_vec1, sentence_vec2, mode='eu'))
