基于text2vec进行文本向量化、聚类

介绍

文本向量表征工具，把文本转化为向量矩阵，是文本进行计算机处理的第一步。

text2vec实现了Word2Vec、RankBM25、BERT、Sentence-BERT、CoSENT等多种文本表征、文本相似度计算模型，并在文本语义匹配（相似度计算）任务上比较了各模型的效果。

安装

安装text2vec库

pip install  text2vec

安装transformers库

pip install transformers

模型下载

默认情况下模型会下载到cache的目录下，不方便直接调用

需要手动下载以下三个文件，新建bert_chinese文件夹，把这三个文件放进去。

https://huggingface.co/bert-base-chinese/tree/main

在这里插入图片描述

文本向量化

使用text2vec

from text2vec import SentenceModel
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
model_path = "bert_chinese"
model = SentenceModel(model_path)
embeddings = model.encode(sentences)
print(embeddings)

使用transformers

from transformers import BertTokenizer, BertModel
import torch

# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Load model from local
model_path = "bert_chinese"
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertModel.from_pretrained(model_path)
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)

文本聚类

训练流程：

加载新闻数据
基于text2vec利用bert模型进行文本向量化
基于KMeans对向量化的模型进行聚类
基于三种评估指标查看模型好坏
利用joblib保存模型

训练代码

from transformers import BertTokenizer, BertModel
import torch
from sklearn.cluster import KMeans
from sklearn import metrics
from sklearn.metrics import silhouette_score
from sklearn.metrics import  davies_bouldin_score
import joblib
import os

#get txt file
file_path = "data\THUCNews"
files = os.listdir(file_path)
contents = []
for file in files:
    file_p = os.path.join(file_path,file)
    with open(file_p, 'r',encoding='utf-8') as f:
        a = f.read()[:200]
        contents.append(a)


# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

model_path = "bert_chinese"
# Load model from HuggingFace Hub
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertModel.from_pretrained(model_path)
# sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡','明天下午会下雨','周二下午可能是阴天','星期六不是晴天']
# Tokenize sentences
encoded_input = tokenizer(contents, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings.shape)

X = sentence_embeddings
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
joblib.dump(kmeans, 'kmeans.joblib')
#kmeans = joblib.load('kmeans.joblib')

labels = kmeans.predict(X)
print(labels)
score = silhouette_score(X, labels)
ch_score = metrics.calinski_harabasz_score(X, kmeans.labels_)
davies_bouldin_score = davies_bouldin_score(X, kmeans.labels_)

print("Calinski-Harabasz指数：", ch_score)
print("轮廓系数评分为：", score)
print("Davies-Bouldin指数评分：", davies_bouldin_score)

推理流程

输入文本
基于text2vec利用bert模型进行文本向量化
加载训练好的聚类模型
对向量化的文本进行预测类别
类别映射

推理代码

import joblib
from transformers import BertTokenizer, BertModel
import torch

map_labels = ["娱乐","星座",'体育']
contents = '双鱼综合症患者的自述(图)新浪网友：比雅   星座真心话征稿启事双鱼座是眼泪泡大的星座，双鱼座是多愁善感的星座，双鱼座是多情的星座，双鱼座是爱幻想的星座。'
kmeans = joblib.load('kmeans.joblib')

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

model_path = "bert_chinese"
# Load model from HuggingFace Hub
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertModel.from_pretrained(model_path)
# Tokenize sentences
encoded_input = tokenizer(contents, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings.shape)

X = sentence_embeddings
labels = kmeans.predict(X)
print(map_labels[labels[0]])