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xlnet embedding and similarity

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This commit is contained in:
yongzhuo 2019-08-28 01:51:17 +08:00
parent be89b4ab22
commit 30536dfffe
7 changed files with 290 additions and 4 deletions
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2
Data/corpus/ner/__init__.py → Data/chinese_xlnet_mid_L-24_H-768_A-12/__init__.py
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# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time :2019/5/21 15:23
# @time :2019/8/28 1:49
# @author :Mo
# @function :

5
FeatureProject/xlnet/__init__.py Normal file
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# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time :2019/8/27 22:26
# @author :Mo
# @function :

33
FeatureProject/xlnet/args.py Normal file
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# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time :2019/8/27 23:03
# @author :Mo
# @function :
import pathlib
import sys
import os
# base dir
projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent.parent)
sys.path.append(projectdir)
# path of BERT model
model_dir = os.path.join(projectdir, 'Data', 'chinese_xlnet_mid_L-24_H-768_A-12')
config_name = os.path.join(model_dir, 'xlnet_config.json')
ckpt_name = os.path.join(model_dir, 'xlnet_model.ckpt')
spiece_model = os.path.join(model_dir, 'spiece.model')
attention_type = 'bi' # or 'uni'
# 批处理尺寸
batch_size = 1
# 历史序列长度
memory_len=0
# 当前目标序列长度
target_len=32
# 默认取倒数第二层的输出值作为句向量
layer_indexes = [0, 23] # 可填 0, 1, 2, 3, 4, 5, 6, 7..., 24,其中0为embedding层
# gpu使用率
gpu_memory_fraction = 0.64

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FeatureProject/xlnet/extract_keras_xlnet_feature.py Normal file
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# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time :2019/8/27 22:27
# @author :Mo
# @function :
from keras_xlnet import Tokenizer, ATTENTION_TYPE_BI, ATTENTION_TYPE_UNI
from keras_xlnet import load_trained_model_from_checkpoint
from FeatureProject.bert.layers_keras import NonMaskingLayer
import keras.backend.tensorflow_backend as ktf_keras
from keras.models import Model
from keras.layers import Add
import tensorflow as tf
import numpy as np
import codecs
import os
from FeatureProject.xlnet import args
# 全局使用，使其可以django、flask、tornado等调用
graph = None
model = None
# gpu配置与使用率设置
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = args.gpu_memory_fraction
sess = tf.Session(config=config)
ktf_keras.set_session(sess)
class KerasXlnetVector():
def __init__(self):
self.attention_type = ATTENTION_TYPE_BI if args.attention_type[0] == 'bi' else ATTENTION_TYPE_UNI
self.memory_len, self.target_len, self.batch_size = args.memory_len, args.target_len, args.batch_size
self.checkpoint_path, self.config_path = args.ckpt_name, args.config_name
self.layer_indexes, self.in_train_phase = args.layer_indexes, False
print("load KerasXlnetEmbedding start! ")
# 全局使用，使其可以django、flask、tornado等调用
global graph
graph = tf.get_default_graph()
global model
# 模型加载
model = load_trained_model_from_checkpoint(checkpoint_path=self.checkpoint_path,
attention_type=self.attention_type,
in_train_phase=self.in_train_phase,
config_path=self.config_path,
memory_len=self.memory_len,
target_len=self.target_len,
batch_size=self.batch_size,
mask_index=0)
# 字典加载
self.tokenizer = Tokenizer(args.spiece_model)
# debug时候查看layers
self.model_layers = model.layers
len_layers = self.model_layers.__len__()
print(len_layers)
len_couche = int((len_layers - 6) / 10)
# 一共246个layer
# 每层10个layer（MultiHeadAttention,Dropout,Add,LayerNormalization）,第一是9个layer的输入和embedding层
# 一共24层
layer_dict = [5]
layer_0 = 6
for i in range(len_couche):
layer_0 = layer_0 + 10
layer_dict.append(layer_0-2)
# 输出它本身
if len(self.layer_indexes) == 0:
encoder_layer = model.output
# 分类如果只有一层，取得不正确的话就取倒数第二层
elif len(self.layer_indexes) == 1:
if self.layer_indexes[0] in [i + 1 for i in range(len_couche + 1)]:
encoder_layer = model.get_layer(index=layer_dict[self.layer_indexes[0]]).output
else:
encoder_layer = model.get_layer(index=layer_dict[-2]).output
# 否则遍历需要取的层，把所有层的weight取出来并加起来shape:768*层数
else:
# layer_indexes must be [0, 1, 2,3,......24]
all_layers = [model.get_layer(index=layer_dict[lay]).output
if lay in [i + 1 for i in range(len_couche + 1)]
else model.get_layer(index=layer_dict[-2]).output # 如果给出不正确，就默认输出倒数第二层
for lay in self.layer_indexes]
print(self.layer_indexes)
print(all_layers)
all_layers_select = []
for all_layers_one in all_layers:
all_layers_select.append(all_layers_one)
encoder_layer = Add()(all_layers_select)
print(encoder_layer.shape)
output_layer = NonMaskingLayer()(encoder_layer)
model = Model(model.inputs, output_layer)
print("load KerasXlnetEmbedding end")
model.summary(132)
def xlnet_encode(self, texts):
# 相当于pool，采用的是https://github.com/terrifyzhao/bert-utils/blob/master/graph.py
mul_mask = lambda x, m: x * np.expand_dims(m, axis=-1)
masked_reduce_mean = lambda x, m: np.sum(mul_mask(x, m), axis=1) / (np.sum(m, axis=1, keepdims=True) + 1e-9)
# 文本预处理
predicts = []
for text in texts:
# print(text)
tokens = self.tokenizer.encode(text)
tokens = tokens + [0]*(self.target_len-len(tokens)) if len(tokens) < self.target_len else tokens[0:self.target_len]
token_input = np.expand_dims(np.array(tokens), axis=0)
mask_input = np.array([0 if ids == 0 else 1 for ids in tokens])
segment_input = np.zeros_like(token_input)
memory_length_input = np.zeros((1, 1))
# 全局使用，使其可以django、flask、tornado等调用
with graph.as_default():
predict = model.predict([token_input, segment_input, memory_length_input], batch_size=1)
# print(predict)
prob = predict[0]
pooled = masked_reduce_mean(prob, [mask_input])
pooled = pooled.tolist()
predicts.append(pooled[0])
return predicts
if __name__ == "__main__":
xlnet_vector = KerasXlnetVector()
pooled = xlnet_vector.xlnet_encode(['你是谁呀', '小老弟'])
print(pooled)
while True:
print("input:")
ques = input()
print(ques)
print(xlnet_vector.xlnet_encode([ques]))

31
FeatureProject/xlnet/layers_keras.py Normal file
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# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time :2019/5/10 10:49
# @author :Mo
# @function :create model of keras-bert for get [-2] layers
from keras.engine import Layer
class NonMaskingLayer(Layer):
"""
fix convolutional 1D can't receive masked input, detail: https://github.com/keras-team/keras/issues/4978
thanks for https://github.com/jacoxu
"""
def __init__(self, **kwargs):
self.supports_masking = True
super(NonMaskingLayer, self).__init__(**kwargs)
def build(self, input_shape):
pass
def compute_mask(self, input, input_mask=None):
# do not pass the mask to the next layers
return None
def call(self, x, mask=None):
return x
def get_output_shape_for(self, input_shape):
return input_shape

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FeatureProject/xlnet/tet_xlnet_keras_sim.py Normal file
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# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time :2019/5/7 20:27
# @author :Mo
# @function :test sentence of xlnet encode and cosin sim of two question
def calculate_count():
"""
统计一下1000条测试数据的平均耗时
:return:
"""
from FeatureProject.xlnet.extract_keras_xlnet_feature import KerasXlnetVector
import time
xlnet_vector = KerasXlnetVector()
print("xlnet start ok!")
time_start = time.time()
for i in range(1000):
vector = xlnet_vector.xlnet_encode(["yx，你知道吗，我很喜欢你呀，在一起在一起在一起，哈哈哈哈"])
time_end = time.time()
time_avg = (time_end-time_start)/1000
print(vector)
print(time_avg)
# 0.12605296468734742 win10 gpu avg
# 0.01629048466682434 linux cpu avg
def sim_two_question():
"""测试一下两个问题的相似句子"""
from FeatureProject.xlnet.extract_keras_xlnet_feature import KerasXlnetVector
from sklearn import preprocessing
from math import pi
import numpy as np
import time
import math
def cosine_distance(v1, v2): # 余弦距离
if v1.all() and v2.all():
return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
else:
return 0
def scale_zoom(rate): # sig 缩放
zoom = (1 + np.exp(-float(rate))) / 2
return zoom
def scale_triangle(rate): # sin 缩放
triangle = math.sin(rate/1*pi/2 - pi/2)
return triangle
xlnet_vector = KerasXlnetVector()
print("xlnet start ok!")
while True:
print("input ques-1: ")
ques_1 = input()
print("input ques_2: ")
ques_2 = input()
vector_1 = xlnet_vector.xlnet_encode([ques_1])
vector_2 = xlnet_vector.xlnet_encode([ques_2])
sim = cosine_distance(vector_1[0], vector_2[0])
# sim_list = [sim, 0, 0.2, 0.4, 0.6, 0.8, 1.0]
# sim = preprocessing.scale(sim_list)[0]
# sim = preprocessing.MinMaxScaler(feature_range=(0, 1)).fit_transform(sim_list)[0]
# sim_1 = preprocessing.normalize(sim_list, norm='l1')[0]
# sim_2 = preprocessing.normalize(sim_list, norm='l2')[0]
# sim = scale_zoom(sim)
# sim = scale_triangle(sim)
# print(sim_1)
# print(sim_2)
print(sim)
if __name__=="__main__":
calculate_count()
sim_two_question()

5
readme.md
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@ -34,6 +34,9 @@
- bert句向量、文本相似度
- bert/extract_keras_bert_feature.py:提取bert句向量特征
- bert/tet_bert_keras_sim.py:测试bert句向量cosin相似度
- xlnet句向量、文本相似度
- xlnet/extract_keras_xlnet_feature.py:提取bert句向量特征
- xlnet/tet_xlnet_keras_sim.py:测试bert句向量cosin相似度
- normalization_util指的是数据归一化
- 0-1归一化处理
- 均值归一化
@ -83,6 +86,8 @@
- chinese_L-12_H-768_A-12（谷歌预训练好的模型）
github项目中只是上传部分数据，需要的前往链接: https://pan.baidu.com/s/1I3vydhmFEQ9nuPG2fDou8Q 提取码: rket
解压后就可以啦
- chinese_xlnet_mid_L-24_H-768_A-12(哈工大训练的中文xlnet, mid, 24层, wiki语料+通用语料)
- 下载地址[https://github.com/ymcui/Chinese-PreTrained-XLNet](https://github.com/ymcui/Chinese-PreTrained-XLNet)
- chinese_vector
github项目中只是上传部分数据，需要的前往链接: https://pan.baidu.com/s/1I3vydhmFEQ9nuPG2fDou8Q 提取码: rket
- 截取的部分word2vec训练词向量（自己需要下载全效果才会好）