NLP（三十三）：中文BERT字字量

中文字、词Bert向量生成
利用Bert预训练模型生成中文的字、词向量，字向量是直接截取Bert的输出结果；词向量则是把词语中的每个字向量进行累计求平均（毕竟原生Bert是基于字符训练的），Bert预训练模型采用的是科大讯飞的chinese_wwm_ext_pytorch，网盘下载地址：

链接：https://pan.baidu.com/s/1Tnewi3mbKN2x1XsX5IQl6g
提取码：9qv7

，希望对各位有帮助！！！这里输出的词向量和字向量还是静态的，因为没有进行fine tuning微调，如果想使用动态词向量，直接加载Bert预训练模型后进行fine tuning，可以在Bert后连接其他网络进行微调。

# coding = utf-8
import jieba
import logging
import numpy as np
from transformers import BertModel, BertTokenizer

jieba.setLogLevel(logging.INFO)

bert_path = "../chinese_wwm_ext_pytorch"
bert = BertModel.from_pretrained(bert_path)
token = BertTokenizer.from_pretrained(bert_path)


# Bert 字向量生成
def get_data(path, char):
    words = []
    with open(path, "r", encoding="utf-8") as f:
        sentences = f.readlines()
        if char:
            for sent in sentences:
                words.extend([word.strip() for word in sent.strip().replace(" ", "") if word not in words])
        else:
            for sentence in sentences:
                cut_word = jieba.lcut(sentence.strip().replace(" ", ""))
                words.extend([w for w in cut_word if w not in words])
    return words


def get_bert_embed(path, char=False):
    words = get_data(path, char)
    file_word = open("word_embed.txt", "a+", encoding="utf-8")
    file_word.write(str(len(words)) + " " + "768" + "\n")
    # 字向量
    if char:
        file_char = open("char_embed.txt", "a+", encoding="utf-8")
        file_char.write(str(len(words)) + " " + "768" + "\n")
        for word in words:
            inputs = token.encode_plus(word, padding="max_length", truncation=True, max_length=10,
                                       add_special_tokens=True,
                                       return_tensors="pt")
            out = bert(**inputs)
            out = out[0].detach().numpy().tolist()
            out_str = " ".join("%s" % embed for embed in out[0][1])
            embed_out = word + " " + out_str + "\n"
            file_char.write(embed_out)
        file_char.close()

    # 词向量 (采用字向量累加求均值)
    for word in words:
        words_embed = np.zeros(768)  # bert tensor shape is 768
        inputs = token.encode_plus(word, padding="max_length", truncation=True, max_length=50, add_special_tokens=True,
                                   return_tensors="pt")
        out = bert(**inputs)
        word_len = len(word)
        out_ = out[0].detach().numpy()
        for i in range(1, word_len + 1):
            out_str = out_[0][i]
            words_embed += out_str
        words_embed = words_embed / word_len
        words_embedding = words_embed.tolist()
        result = word + " " + " ".join("%s" % embed for embed in words_embedding) + "\n"
        file_word.write(result)

    file_word.close()


# char 为False时执行的是词向量生成， 为True则执行字向量生成
get_bert_embed("text.txt", char=False)
print("Generate Finished!!!")