导言
在笔者的上一篇文章大白话讲懂word2vec原理和如何使用中提到了如何将词语转变成计算机能够识别的语言，即将文本数据转换成计算机能够运算的数字或者向量这个概念，并详细阐述了word2vec这个模型的原理，如何在gensim框架下使用word2vec将单词转变成一个能够表达单词特征的向量。但是在gensim框架下使用word2vec模型提取特征向量之前，需要准备一些场景中的语料，同事也需要对语料进行分词操作，然后再输入到模型中进行训练，最后才可以用训练好的模型进行特征提取。整个流程还是较为繁琐，同时模型效果取决于语料覆盖度是否全面和训练超参数的选择。其实呢，nlp也有一些现成的文本特征提取的模型，比如前些年比较火爆的google推出的bert模型。本篇文章就简单的介绍一下bert模型，以及新手如何快速上手使用bert提取文本特征，以便去做后续的nlp任务。
bert模型和Transformers框架
bert是由Google的雅各布·德夫林和同事在2018年创建的，并发表了论文Pre-training of Deep Bidirectional Transformers for Language Understanding，bert是”Bidirectional Encoder Representations from Transformers”的首字母缩， 直译就是基于变换器的双向编码器表示技术。
最初的英语bert发布时提供两种类型的预训练模型：（1）BERTBASE模型，一个12层，768维，12个自注意头（self attention head），110M参数的神经网络结构；（2）BERTLARGE模型，一个24层，1024维，16个自注意头，340M参数的神经网络结构。两者的训练语料都是BooksCorpus以及英语维基百科语料，单词量分别是8亿以及25亿。
当然，国内也有公司，比如像腾讯，百度等，也都开源了基于中文语料训练了的bert中文模型。这篇文章使用的是大佬开源库pycorrector提供的用中文文本fine-tuned3轮后的预训练BERT MLM模型，下载链接在该开源库的bert文件夹中的readme中。这里为什么要使用这个bert预训练模型呢，因为这个模型是基于transformers开发的，和transformers完美兼容，使用起来也很方面。当然了，bert还有其他各种框架的开源预训练模型，读者们可以自行挑选自己喜欢的。
这里也简单介绍一下transformers框架。Transformers是huggingface开源的一个机器学习框架。Transformers 提供了数以千计的预训练模型，支持 100 多种语言的文本分类、信息抽取、问答、摘要、翻译、文本生成。它的宗旨让最先进的 NLP 技术人人易用。
Transformers 提供了便于快速下载和使用的API，让你可以把预训练模型用在给定文本、在你的数据集上微调然后通过 model hub 与社区共享。同时，每个定义的 Python 模块均完全独立，方便修改和快速研究实验。
Transformers 支持三个最热门的深度学习库： Jax, PyTorch and TensorFlow — 并与之无缝整合。你可以直接使用一个框架训练你的模型然后用另一个加载和推理。
bert模型提取中文词向量
首先需要安装所需要的依赖库
pip install transformers
pip install numpy
pip install torch
安装完成之后，需要将下载的bert中文模型放在home路径下，如果是windows系统就是"C:\Users\用户名/"目录。最后就是直接提取中文词向量
import os
import numpy as np
import torch
from transformers import AutoModel, AutoTokenizer
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
model_dir = os.path.expanduser('~/.pycorrector/datasets/bert_models/chinese_finetuned_lm')
#print(model_dir)
tokenizer = AutoTokenizer.from_pretrained(model_dir)
print("token ok")
model = AutoModel.from_pretrained(model_dir)
print("model ok")
# tensor([[ 101, 3217, 4697, 679, 6230, 3236, 102]])
inputs = tokenizer('春眠不觉晓', return_tensors='pt')
outputs = model(**inputs) # shape (1, 7, 768)
print(outputs)
v = torch.mean(outputs[0], dim=1) # shape (1, 768)
print(v)
print(v.shape)
这是打印结果
token ok
model ok
BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=tensor([[[ 0.0462, 0.5329, 0.1521, ..., 0.1440, -0.4105, 0.2640],
[ 0.3368, 0.5288, -0.5288, ..., 0.0241, -0.0017, 0.6836],
[ 0.8783, -0.7624, -0.3651, ..., -0.1129, -0.1835, 0.4492],
...,
[ 1.1596, 0.7187, -0.4550, ..., 0.4255, -1.0546, 0.4000],
[ 0.3089, 0.1456, -0.7718, ..., -0.3547, -1.1788, -0.1022],
[-0.1397, 0.1891, 0.0370, ..., -0.4032, 0.1067, 0.8418]]],
grad_fn=<NativeLayerNormBackward>), pooler_output=tensor([[ 0.9999, 1.0000, 1.0000, 0.9994, 0.9917, 0.9673, -0.9951, -0.9998,
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grad_fn=<TanhBackward>), hidden_states=None, past_key_values=None, attentions=None, cross_attentions=None)
tensor([[ 4.2115e-01, 2.3145e-01, -2.0698e-01, -9.7974e-02, 4.9489e-01,
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torch.Size([1, 768])
需要说明的是，这里的bert中文提取是直接将一行中文分割成单字再做one-hot编码，比如这里的input是 “春眠不觉晓” ，tokenizer这个函数则会将其转变成tensor([[ 101, 3217, 4697, 679, 6230, 3236, 102]])， 其中 101， 102分别对应的是"[CLS]" "[SEP]"这两个字符，中间的五个数字才是输入中文的one-hot编码。
整个句子经过bert特征提取之后，输出的是17768维的矩阵，其实就是每个字的特征为768，最后v = torch.mean(outputs[0], dim=1) # shape (1, 768)这行代码是将整个句子所有字的特征做一个加和求平均，得到的是整个句子的特征向量