单位建设网站的请示,国内最好的网站建设公司,wordpress网站维护教程,长沙优化排名训练自己的中文word2vec#xff08;词向量#xff09;–skip-gram方法
什么是词向量
将单词映射/嵌入#xff08;Embedding#xff09;到一个新的空间#xff0c;形成词向量#xff0c;以此来表示词的语义信息#xff0c;在这个新的空间中#xff0c;语义相同的单…训练自己的中文word2vec词向量–skip-gram方法
什么是词向量
将单词映射/嵌入Embedding到一个新的空间形成词向量以此来表示词的语义信息在这个新的空间中语义相同的单词距离很近。
Skip-Gram方法(本次使用方法)
以某个词为中心分别计算该中心词前后可能出现其他词的各个概率即给定input word来预测上下文。 CBOWContinous Bags Of WordsCBOW
CBOW根据某个词前面的n个词、或者前后各n个连续的词来计算某个词出现的概率即给定上下文来预测input word。相比Skip-GramCBOW更快一些。
本次使用 Skip-Gram方法和三国演义第一章作为数据训练32维中文词向量。
数据代码下载链接见文末
导入库
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset,DataLoader
import re
import collections
import numpy as np
import jieba#指定设备
device torch.device(cuda:0 if torch.cuda.is_available() else cpu)
print(device)导入数据
因为算力问题我在此出只截取三国演义的第一章作为示例数据。
training_file /home/mw/input/sanguo5529/三国演义.txt#读取text文件并选择第一章作为输入文本
def get_ch_lable(txt_file): labels with open(txt_file, rb) as f:for label in f:labels labelslabel.decode(utf-8)text re.findall(第1章.*?第2章, labels,re.S)return text[0]
training_data get_ch_lable(training_file)
# print(training_data)
print(总字数,len(training_data))总字数 4945
分词
#jieba分词
def fenci(training_data):seg_list jieba.cut(training_data) # 默认是精确模式 training_ci .join(seg_list)training_ci training_ci.split()#以空格将字符串分开training_ci np.array(training_ci)training_ci np.reshape(training_ci, [-1, ])return training_ci
training_ci fenci(training_data)
print(总词数,len(training_ci))总词数 3053
构建词表
def build_dataset(words, n_words):count [[UNK, -1]]count.extend(collections.Counter(words).most_common(n_words - 1))dictionary dict()for word, _ in count:dictionary[word] len(dictionary)data list()unk_count 0for word in words:if word in dictionary:index dictionary[word]else:index 0 # dictionary[UNK]unk_count 1data.append(index)count[0][1] unk_countreversed_dictionary dict(zip(dictionary.values(), dictionary.keys()))return data, count, dictionary, reversed_dictionarytraining_label, count, dictionary, words build_dataset(training_ci, 3053)#计算词频
word_count np.array([freq for _,freq in count], dtypenp.float32)
word_freq word_count / np.sum(word_count)#计算每个词的词频
word_freq word_freq ** (3. / 4.)#词频变换
words_size len(dictionary)
print(字典词数,words_size)
print(Sample data, training_label[:10], [words[i] for i in training_label[:10]])字典词数 1456 Sample data [100, 305, 140, 306, 67, 101, 307, 308, 46, 27] [‘第’, ‘1’, ‘章’, ‘宴’, ‘桃园’, ‘豪杰’, ‘三’, ‘结义’, ‘斩’, ‘黄巾’] 制作数据集
C 3
num_sampled 64 # 负采样个数
BATCH_SIZE 32
EMBEDDING_SIZE 32 #想要的词向量长度class SkipGramDataset(Dataset):def __init__(self, training_label, word_to_idx, idx_to_word, word_freqs):super(SkipGramDataset, self).__init__()self.text_encoded torch.Tensor(training_label).long()self.word_to_idx word_to_idxself.idx_to_word idx_to_wordself.word_freqs torch.Tensor(word_freqs)def __len__(self):return len(self.text_encoded)def __getitem__(self, idx):idx min( max(idx,C),len(self.text_encoded)-2-C)#防止越界center_word self.text_encoded[idx]pos_indices list(range(idx-C, idx)) list(range(idx1, idx1C))pos_words self.text_encoded[pos_indices] #多项式分布采样取出指定个数的高频词neg_words torch.multinomial(self.word_freqs, num_sampled2*C, False)#True)#去掉正向标签neg_words torch.Tensor(np.setdiff1d(neg_words.numpy(),pos_words.numpy())[:num_sampled]).long()return center_word, pos_words, neg_wordsprint(制作数据集...)
train_dataset SkipGramDataset(training_label, dictionary, words, word_freq)
dataloader torch.utils.data.DataLoader(train_dataset, batch_sizeBATCH_SIZE,drop_lastTrue, shuffleTrue)制作数据集…
#将数据集转化成迭代器
sample iter(dataloader)
#从迭代器中取出一批次样本
center_word, pos_words, neg_words sample.next()
print(center_word[0],words[np.compat.long(center_word[0])],[words[i] for i in pos_words[0].numpy()])模型构建
class Model(nn.Module):def __init__(self, vocab_size, embed_size):super(Model, self).__init__()self.vocab_size vocab_sizeself.embed_size embed_sizeinitrange 0.5 / self.embed_sizeself.in_embed nn.Embedding(self.vocab_size, self.embed_size, sparseFalse)self.in_embed.weight.data.uniform_(-initrange, initrange)def forward(self, input_labels, pos_labels, neg_labels):input_embedding self.in_embed(input_labels)pos_embedding self.in_embed(pos_labels)neg_embedding self.in_embed(neg_labels)log_pos torch.bmm(pos_embedding, input_embedding.unsqueeze(2)).squeeze()log_neg torch.bmm(neg_embedding, -input_embedding.unsqueeze(2)).squeeze()log_pos F.logsigmoid(log_pos).sum(1)log_neg F.logsigmoid(log_neg).sum(1)loss log_pos log_negreturn -loss
model Model(words_size, EMBEDDING_SIZE).to(device)
model.train()valid_size 32
valid_window words_size/2 # 取样数据的分布范围.
valid_examples np.random.choice(int(valid_window), valid_size, replaceFalse)optimizer torch.optim.Adam(model.parameters(), lr1e-3)
NUM_EPOCHS 10开始训练
for e in range(NUM_EPOCHS):for ei, (input_labels, pos_labels, neg_labels) in enumerate(dataloader):input_labels input_labels.to(device)pos_labels pos_labels.to(device)neg_labels neg_labels.to(device)optimizer.zero_grad()loss model(input_labels, pos_labels, neg_labels).mean()loss.backward()optimizer.step()if ei % 20 0:print(epoch: {}, iter: {}, loss: {}.format(e, ei, loss.item()))if e %40 0: norm torch.sum(model.in_embed.weight.data.pow(2),-1).sqrt().unsqueeze(1)normalized_embeddings model.in_embed.weight.data / normvalid_embeddings normalized_embeddings[valid_examples]similarity torch.mm(valid_embeddings, normalized_embeddings.T)for i in range(valid_size):valid_word words[valid_examples[i]]top_k 8 # 取最近的排名前8的词nearest (-similarity[i, :]).argsort()[1:top_k 1] #argsort函数返回的是数组值从小到大的索引值log_str Nearest to %s: % valid_word for k in range(top_k):close_word words[nearest[k].cpu().item()]log_str %s,%s % (log_str, close_word)print(log_str)epoch: 0, iter: 0, loss: 48.52019500732422
epoch: 0, iter: 20, loss: 48.51792526245117
epoch: 0, iter: 40, loss: 48.50772476196289
epoch: 0, iter: 60, loss: 48.50897979736328
epoch: 0, iter: 80, loss: 48.45783996582031
Nearest to 伏山:,王,送,话,遥望,操忽心生,江渚上,接,提刀
Nearest to 与:,”,将次,盖地,,下文,。,妖术,玄德
Nearest to 必获:,听调,直取,各处,一端,奏帝,必出,遥望,入帐
Nearest to 郭胜十人:,南华,因为,把,贼战,告变,处,擒,玄德遂
Nearest to 官军:,秀才,统兵,说起,军齐出,四散,放荡,一彪,云长
Nearest to 碧眼:,名备,刘焉然,大汉,卷入,大胜,老人,重枣,左有
Nearest to 天书:,泛溢,约期,而进,龚景,因起,车盖,遂解,震
Nearest to 转头:,近因,直取,接,七尺,王,备下,大汉,齐声
Nearest to 张宝:,侯览,惊告嵩,誓毕,帝惊,呼风唤雨,狂风,大将军,曾
Nearest to 玄德幼:,直取,近闻,刘焉令,几度,崩,临江仙,右有翼德,左右两
Nearest to 不祥:,无数,调兵,项,刘备,玄德谢,原来,八尺,共
Nearest to 靖:,操忽心生,此病,赵忠,刘焉然,崩,庄田,剑,传至
Nearest to 五千:,岂可,丹凤眼,北行,听罢,”,性命,卓,之囚
Nearest to 日非:,赵忠,矣,闻得,蜺,破贼,早丧,书报,忽起
Nearest to 徐:,震怒,我气,卢植,”,结为,、,燕颔虎须,。
Nearest to 五百余:,帝惊,普,本部,三,神功,桓帝,滚滚,左右两
Nearest to 而:,转头,卷入,祭,近因,大商,章,人公,天子
Nearest to 去:,封谞,夏恽,周末,必,嵩信,广宗,人氏,民心
Nearest to 上:,灵,文,陷邕,四年,关羽,直赶,九尺,伏山
Nearest to :,“,曰,飞,是,兄弟,喜,来代,我答
Nearest to 后:,必获,阁下,字,手起,祭礼,侍奉,各处,奏帝
Nearest to 因起:,帝览奏,青,,汝可引,夺路,一把,是非成败,卷入
Nearest to 骤起:,挟恨,张宝称,明公宜,议,一统天下,又,擒,玄德请
Nearest to 汉室:,六月,临江仙,今汉运,手起,威力,抹额,讹言,提刀
Nearest to 云游四方:,背义忘恩,复,渔樵,地公,扬鞭,若,故冒姓,截住
Nearest to 桓:,崩,赵忠,刘焉然,左有,刘备,名备,二帝,游荡
Nearest to 二字于:,操故,分,白土,左右两,张角本,赏劳,当时,梁上
Nearest to 人出:,多,五十匹,肩,奏帝,梁上,九尺,六月,大汉
Nearest to 大浪:,卷入,临江仙,听调,汉武时,左有,束草,围城,及
Nearest to 青:,夺路,肩,贩马,师事,围城,卷入,大胜,客人
Nearest to 郎蔡邕:,浊酒,近闻,六月,角战于,中郎将,崩,转头,众大溃
Nearest to 二月:,马舞刀,国谯郡,只见,内外,郎蔡邕,豪,落到,汝得
epoch: 1, iter: 0, loss: 48.46757888793945
epoch: 1, iter: 20, loss: 48.42853546142578
epoch: 1, iter: 40, loss: 48.35804748535156
epoch: 1, iter: 60, loss: 48.083805084228516
epoch: 1, iter: 80, loss: 48.1635856628418
epoch: 2, iter: 0, loss: 47.89817428588867
epoch: 2, iter: 20, loss: 48.067501068115234
epoch: 2, iter: 40, loss: 48.6464729309082
epoch: 2, iter: 60, loss: 47.825260162353516
epoch: 2, iter: 80, loss: 48.07224655151367
epoch: 3, iter: 0, loss: 48.15058898925781
epoch: 3, iter: 20, loss: 47.26418685913086
epoch: 3, iter: 40, loss: 47.87504577636719
epoch: 3, iter: 60, loss: 48.74541473388672
epoch: 3, iter: 80, loss: 48.01288986206055
epoch: 4, iter: 0, loss: 47.257896423339844
epoch: 4, iter: 20, loss: 48.337745666503906
epoch: 4, iter: 40, loss: 47.70765686035156
epoch: 4, iter: 60, loss: 48.57493591308594
epoch: 4, iter: 80, loss: 48.206268310546875
epoch: 5, iter: 0, loss: 47.139137268066406
epoch: 5, iter: 20, loss: 48.70667266845703
epoch: 5, iter: 40, loss: 47.97750473022461
epoch: 5, iter: 60, loss: 48.098899841308594
epoch: 5, iter: 80, loss: 47.778892517089844
epoch: 6, iter: 0, loss: 47.86349105834961
epoch: 6, iter: 20, loss: 47.77979278564453
epoch: 6, iter: 40, loss: 48.67324447631836
epoch: 6, iter: 60, loss: 48.117042541503906
epoch: 6, iter: 80, loss: 48.69907760620117
epoch: 7, iter: 0, loss: 47.63265609741211
epoch: 7, iter: 20, loss: 47.82151794433594
epoch: 7, iter: 40, loss: 48.54405212402344
epoch: 7, iter: 60, loss: 48.06487274169922
epoch: 7, iter: 80, loss: 48.67494583129883
epoch: 8, iter: 0, loss: 48.053466796875
epoch: 8, iter: 20, loss: 47.872459411621094
epoch: 8, iter: 40, loss: 47.462432861328125
epoch: 8, iter: 60, loss: 48.10865783691406
epoch: 8, iter: 80, loss: 46.380184173583984
epoch: 9, iter: 0, loss: 47.2872314453125
epoch: 9, iter: 20, loss: 48.553428649902344
epoch: 9, iter: 40, loss: 47.00652313232422
epoch: 9, iter: 60, loss: 47.970741271972656
epoch: 9, iter: 80, loss: 48.159828186035156
查看训练好的词向量
final_embeddings normalized_embeddings
labels words[10]
print(labels)
print(final_embeddings[10])玄德 tensor([-0.2620, 0.0660, 0.0464, 0.2948, -0.1974, 0.2471, -0.0893, 0.1720, -0.1488, 0.0283, -0.1165, 0.2156, -0.1642, -0.2376, -0.0356, -0.0607, 0.1985, -0.2166, 0.2222, 0.2453, -0.1414, -0.0526, 0.1153, -0.1325, -0.2964, 0.2775, -0.0637, -0.0716, 0.2672, 0.0539, 0.1697, 0.0489]) with open(skip-gram-sanguo.txt, a) as f: for i in range(len(words)):f.write(words[i] str(list(final_embeddings.numpy()[i])) \n)
f.close()
print(word vectors have written done.)word vectors have written done.
按照路径/home/mw/project/skip-gram-sanguo.txt查看保存的文件不一定要保存为txt我们平常加载的词向量更多是vec格式 数据代码下载链接
数据及代码右上角fork后可以免费获取
