def predict(sentence):
# 预测语句
text = sentence
text = text.replace("\n", "").replace("\r", "").replace("\t", "")
labels = []
bert_model = BertVector(pooling_strategy="NONE", max_seq_len=200)
# 将句子转换成向量
vec = bert_model.encode([text])["encodes"][0]
x_train = np.array([vec])
# 模型预测
predicted = load_model.predict(x_train)[0]
indices = [i for i in range(len(predicted)) if predicted[i] > 0.5]
with open(
"/Users/xuzhang/Documents/STUDY/Github/IntentRec/utils/event_type.json",
"r",
encoding="utf-8") as g:
movie_genres = json.loads(g.read())
#print("预测语句: %s" % text)
#print("意图分析: %s" % "|".join([movie_genres[index] for index in indices]))
return "|".join([movie_genres[index] for index in indices])
from keras_contrib.metrics import crf_accuracy, crf_viterbi_accuracy
from keras.models import load_model
from collections import defaultdict
from pprint import pprint
from utils import MAX_SEQ_LEN, event_type
from albert_zh.extract_feature import BertVector
# 读取label2id字典
with open("%s_label2id.json" % event_type, "r", encoding="utf-8") as h:
label_id_dict = json.loads(h.read())
id_label_dict = {v: k for k, v in label_id_dict.items()}
# 利用ALBERT提取文本特征
bert_model = BertVector(pooling_strategy="NONE", max_seq_len=MAX_SEQ_LEN)
f = lambda text: bert_model.encode([text])["encodes"][0]
# 载入模型
custom_objects = {
'CRF': CRF,
'crf_loss': crf_loss,
'crf_viterbi_accuracy': crf_viterbi_accuracy
}
ner_model = load_model("%s_ner.h5" % event_type, custom_objects=custom_objects)
# 从预测的标签列表中获取实体
def get_entity(sent, tags_list):
entity_dict = defaultdict(list)
'联合国秘书长潘基文８日访问了日本福岛县，与当地灾民交流并访问了一所高中。',
'国务院总理温家宝当地时间23日下午乘专机抵达布宜诺斯艾利斯，开始对阿根廷进行正式访问。',
'正在中国访问的巴巴多斯总理斯图尔特１５日在陕西西安参观访问。',
'据外媒报道,当地时间10日,美国白宫发声明称,美国总统特朗普将于2月底访问印度,与印度总理莫迪进行战略对话。',
'2月28日，唐山曹妃甸蓝色海洋科技有限公司董事长赵力军等一行5人到黄海水产研究所交流访问。黄海水产研究所副所长辛福言及相关部门负责人、专家等参加了会议。',
'2018年7月2日，莫斯科孔子文化促进会会长姜彦彬，常务副会长陈国建，在中国著名留俄油画大师牟克教授的陪同下，访问了莫斯科国立苏里科夫美术学院，受到第一副校长伊戈尔·戈尔巴秋克先生接待。'
'据外媒报道，当地时间26日晚，阿尔及利亚总统特本抵达沙特阿拉伯，进行为期三天的访问。两国领导人预计将就国家间合作和地区发展进行磋商。',
'与标准Mozy一样，Stash文件夹为用户提供了对其备份文件的基于云的访问，但是它们还使他们可以随时，跨多个设备(包括所有计算机，智能手机和平板电脑)访问它们。换句话说，使用浏览器的任何人都可以同时查看文件(如果需要)。操作系统和设备品牌无关。',
'研究表明，每个网页的平均预期寿命为44至100天。当用户通过浏览器访问已消失的网页时，就会看到「Page Not Found」的错误信息。对于这种情况，相信大多数人也只能不了了之。不过有责任心的组织——互联网档案馆为了提供更可靠的Web服务，它联手Brave浏览器专门针对此类网页提供了一键加载存档页面的功能。',
'据外媒报道，土耳其总统府于当地时间2日表示，土耳其总统埃尔多安计划于5日对俄罗斯进行为期一天的访问。',
'3日，根据三星电子的消息，李在镕副会长这天访问了位于韩国庆尚北道龟尾市的三星电子工厂。'
] * 10
labels = []
bert_model = BertVector(pooling_strategy="REDUCE_MEAN", max_seq_len=100)
init_time = time.time()
# 对上述句子进行预测
for text in texts:
# 将句子转换成向量
vec = bert_model.encode([text])["encodes"][0]
x_train = np.array([vec])
# 模型预测
predicted = load_model.predict(x_train)
y = np.argmax(predicted[0])
label = 'Y' if y else 'N'
labels.append(label)
# @File : model_evaluate.py
# @Place : Yangpu, Shanghai
# 模型评估脚本,利用hamming_loss作为多标签分类的评估指标，该值越小模型效果越好
import json
import numpy as np
import pandas as pd
from keras.models import load_model
from sklearn.metrics import hamming_loss, classification_report
from att import Attention
from albert_zh.extract_feature import BertVector
# 加载训练好的模型
model = load_model("event_type.h5", custom_objects={"Attention": Attention})
with open("event_type.json", "r", encoding="utf-8") as f:
event_type_list = json.loads(f.read())
bert_model = BertVector(pooling_strategy="NONE", max_seq_len=200)
# 对单句话进行预测
def predict_single_text(text):
# 将句子转换成向量
vec = bert_model.encode([text])["encodes"][0]
x_train = np.array([vec])
# 模型预测
predicted = model.predict(x_train)[0]
indices = [i for i in range(len(predicted)) if predicted[i] > 0.5]
one_hot = [0] * len(event_type_list)
for index in indices:
one_hot[index] = 1
# author: Jclian91
# place: Pudong Shanghai
# time: 2020/5/15 3:44 下午
import numpy as np
from sklearn.linear_model import LogisticRegression as LR
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from sklearn.externals import joblib
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from load_data import train_df, test_df
from albert_zh.extract_feature import BertVector
# 读取文件并进行转换
bert_model = BertVector(pooling_strategy="REDUCE_MEAN", max_seq_len=200)
print('begin encoding')
f = lambda text: bert_model.encode([text])["encodes"][0]
train_df['x'] = train_df['text'].apply(f)
test_df['x'] = test_df['text'].apply(f)
print('end encoding')
x_train = np.array([vec for vec in train_df['x']])
x_test = np.array([vec for vec in test_df['x']])
y_train = np.array([vec for vec in train_df['label']])
y_test = np.array([vec for vec in test_df['label']])
print('x_train: ', x_train.shape)
# Logistic Regression
lr = LR(random_state=123)
lr.fit(x_train, y_train)
import json
import numpy as np
from keras.models import load_model
from att import Attention
from albert_zh.extract_feature import BertVector
load_model = load_model("event_type.h5", custom_objects={"Attention": Attention})
# 预测语句
text = "昨天18：30，陕西宁强县胡家坝镇向家沟村三组发生山体坍塌，5人被埋。当晚，3人被救出，其中1人在医院抢救无效死亡，2人在送医途中死亡。今天凌晨，另外2人被发现，已无生命迹象。"
text = text.replace("\n", "").replace("\r", "").replace("\t", "")
labels = []
bert_model = BertVector(pooling_strategy="NONE", max_seq_len=200)
# 将句子转换成向量
vec = bert_model.encode([text])["encodes"][0]
x_train = np.array([vec])
# 模型预测
predicted = load_model.predict(x_train)[0]
indices = [i for i in range(len(predicted)) if predicted[i] > 0.5]
with open("event_type.json", "r", encoding="utf-8") as g:
movie_genres = json.loads(g.read())
print("预测语句: %s" % text)
print("预测事件类型: %s" % "|".join([movie_genres[index] for index in indices]))
y_train = []
y_test = []
for line in train_content:
genres = line.split('\t', maxsplit=1)[0].split('|')
y_train.append(mlb.transform([genres])[0])
for line in test_content:
genres = line.split('\t', maxsplit=1)[0].split('|')
y_test.append(mlb.transform([genres])[0])
y_train = np.array(y_train)
y_test = np.array(y_test)
# 利用ALBERT对x值进行编码
bert_model = BertVector(pooling_strategy="NONE", max_seq_len=200)
print('begin encoding')
f = lambda text: bert_model.encode([text])['encodes'][0]
x_train = []
x_test = []
process_bar = tqdm(train_content)
for ch, line in zip(process_bar, train_content):
movie_intro = line.split('\t', maxsplit=1)[1]
x_train.append(f(movie_intro))
process_bar = tqdm(test_content)
for ch, line in zip(process_bar, test_content):
movie_intro = line.split('\t', maxsplit=1)[1]
from albert_zh.extract_feature import BertVector
from tqdm import tqdm
MAX_SEQ_LEN = 200
import numpy as np
bert_model = BertVector(pooling_strategy="NONE", max_seq_len=MAX_SEQ_LEN)
def cos_sim(vector_a, vector_b):
"""
计算两个向量之间的余弦相似度
:param vector_a: 向量 a
:param vector_b: 向量 b
:return: sim
"""
vector_a = np.mat(vector_a)
vector_b = np.mat(vector_b)
num = float(vector_a * vector_b.T)
denom = np.linalg.norm(vector_a) * np.linalg.norm(vector_b)
cos = num / denom
sim = 0.5 + 0.5 * cos
return sim
def sim_text(s1, s2):
sent_vec1 = bert_model.encode([s1])["encodes"][0].mean(axis=1)
sent_vec2 = bert_model.encode([s2])["encodes"][0].mean(axis=1)
return cos_sim(sent_vec1, sent_vec2)
import tornado.ioloop
import tornado.options
import tornado.web
from tornado.options import define, options
import json
import numpy as np
from albert_zh.extract_feature import BertVector
from keras.models import load_model
# 定义端口为10008
define("port", default=10008, help="run on the given port", type=int)
# 加载ALBERT
bert_model = BertVector(pooling_strategy="REDUCE_MEAN", max_seq_len=100)
# 加载已经训练好的模型
load_model = load_model("visit_classify.h5")
# 对句子进行预测
class PredictHandler(tornado.web.RequestHandler):
def post(self):
text = self.get_argument("text")
# 将句子转换成向量
vec = bert_model.encode([text])["encodes"][0]
x_train = np.array([vec])
