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creat_BERT_embedding.py
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creat_BERT_embedding.py
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# -*- coding: utf-8 -*-
'''
# @Author : plzhao
# @Software: PyCharm
'''
from bert_embedding import BertEmbedding
import numpy as np
import time
def load_data_and_labels(positive_data_file):
"""
Loads MR polarity data from files, splits the data into words and generates labels.
Returns split sentences and labels.
"""
# Load data from files
examples = list(open(positive_data_file, "r").readlines())
examples = [s.strip() for s in examples]
# find the input examples
input = []
target = []
for index,i in enumerate(examples):
if index%3 == 0:
i_target =examples[index + 1].strip()
i = i.replace("$T$", i_target)
input.append(i)
target.append(i_target)
x_text = input
# Generate labels
lable=[]
for index,i in enumerate(examples):
if index%3 == 2:
if i[0:1]=='1':
lable.append([1,0,0])
if i[0:1]=='0':
lable.append([0,1,0])
if i[0:1]=='-':
lable.append([0,0,1])
y = np.array(lable)
return [x_text,target, y]
def create_bert_embedding(input,max_len):
print("creating BERT embedding ")
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
result = bert_embedding(input)
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
print("finish BERT embedding ")
# padding
sentence_BERT = []
for i in result:
embedding_i = i[1] #句子长度的list，每一个元素都是一个词向量。
pad = [np.zeros(768)]
sentence_BERT_i = embedding_i + pad * (max_len - len(i[0]))
sentence_BERT.append(sentence_BERT_i)
return np.array(sentence_BERT)
def save_BERT_embeddinf(save_file,bert_embedding):
np.save(save_file, bert_embedding)
print("Finish save BERT embedding in: ", save_file, "\n")
print()
train_file = "data_res/bert_embedding/Restaurants_Train_bert.txt"
test_file = "data_res/bert_embedding/Restaurants_Test_bert.txt"
train_save_file = "data_res/bert_embedding/Res_Train_Embedding.npy"
test_save_file = "data_res/bert_embedding/Res_Test_Embedding.npy"
train_target_save_file = "data_res/bert_embedding/Res_Train_target_Embedding.npy"
test_target_save_file = "data_res/bert_embedding/Res_Test_target_Embedding.npy"
bert_embedding = BertEmbedding(model='bert_12_768_12', dataset_name='book_corpus_wiki_en_uncased',max_seq_length=100)
# result2 = bert_embedding(["eat apple","apple tree"])
print("loading data:")
train_x_str, train_target_str, train_y = load_data_and_labels(train_file)
test_x_str, test_target_str, test_y = load_data_and_labels(test_file)
max_sentence_length = max([len(x.split(" ")) for x in (train_x_str + test_x_str)])
max_target_length = max([len(x.split(" ")) for x in (train_target_str + test_target_str)])
#create_bert_embedding
train_BERT_em = create_bert_embedding(train_x_str, max_sentence_length)
test_BERT_em = create_bert_embedding(test_x_str, max_sentence_length)
train_target_BERT_em = create_bert_embedding(train_target_str, max_target_length)
test_target_BERT_em = create_bert_embedding(test_target_str, max_target_length)
#save_BERT_embeddinf
save_BERT_embeddinf(train_save_file,train_BERT_em)
save_BERT_embeddinf(test_save_file,test_BERT_em)
save_BERT_embeddinf(train_target_save_file,train_target_BERT_em)
save_BERT_embeddinf(test_target_save_file,test_target_BERT_em)
train_file = "data_lap/bert_embedding/Laptops_Train_bert.txt"
test_file = "data_lap/bert_embedding/Laptops_Test_bert.txt"
train_save_file = "data_lap/bert_embedding/Lap_Train_Embedding.npy"
test_save_file = "data_lap/bert_embedding/Lap_Test_Embedding.npy"
train_target_save_file = "data_lap/bert_embedding/Lap_Train_target_Embedding.npy"
test_target_save_file = "data_lap/bert_embedding/Lap_Test_target_Embedding.npy"
print("loading data:")
train_x_str, train_target_str, train_y = load_data_and_labels(train_file)
test_x_str, test_target_str, test_y = load_data_and_labels(test_file)
max_sentence_length = max([len(x.split(" ")) for x in (train_x_str + test_x_str)])
max_target_length = max([len(x.split(" ")) for x in (train_target_str + test_target_str)])
#create_bert_embedding
train_BERT_em = create_bert_embedding(train_x_str, max_sentence_length)
test_BERT_em = create_bert_embedding(test_x_str, max_sentence_length)
train_target_BERT_em = create_bert_embedding(train_target_str, max_target_length)
test_target_BERT_em = create_bert_embedding(test_target_str, max_target_length)
#save_BERT_embeddinf
save_BERT_embeddinf(train_save_file,train_BERT_em)
save_BERT_embeddinf(test_save_file,test_BERT_em)
save_BERT_embeddinf(train_target_save_file,train_target_BERT_em)
save_BERT_embeddinf(test_target_save_file,test_target_BERT_em)