做百度推广的网站吗西安百度网站快速优化

最近刚学习NNLM。NNLM网络在训练过程中为每个单词生成了一个词向量，但从表示每个单词语义信息的的角度来说，生成质量不佳，于是有了后来的word2vec，转么学习高质量的词向量表示。

先给出CBOW的简单实现代码：

import torch
import torch.nn as nn
import torch.optim as optim
from collections import Counter
import random# 1. 准备语料和词汇表
corpus = "we are learning natural language processing using word2vec cbow model".split()
vocab = list(set(corpus))
word2idx = {w: i for i, w in enumerate(vocab)}
idx2word = {i: w for w, i in word2idx.items()}# 2. 构造训练样本 (上下文词 → 中心词)
window_size = 2
def generate_context_target(corpus, window_size):dataset = []for i in range(window_size, len(corpus) - window_size):context = [corpus[j] for j in range(i - window_size, i + window_size + 1) if j != i]target = corpus[i]dataset.append((context, target))return datasettraining_data = generate_context_target(corpus, window_size)# 3. 定义 CBOW 模型
class CBOW(nn.Module):def __init__(self, vocab_size, embedding_dim):super(CBOW, self).__init__()self.embeddings = nn.Embedding(vocab_size, embedding_dim)self.linear = nn.Linear(embedding_dim, vocab_size)def forward(self, context_idxs):# context_idxs shape: (batch_size, context_size)embeds = self.embeddings(context_idxs)  # (batch_size, context_size, embed_dim)avg_embeds = embeds.mean(dim=1)         # (batch_size, embed_dim)out = self.linear(avg_embeds)           # (batch_size, vocab_size)return out# 4. 超参数和模型初始化
embedding_dim = 50
model = CBOW(vocab_size=len(vocab), embedding_dim=embedding_dim)
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)# 5. 训练模型
for epoch in range(100):total_loss = 0for context, target in training_data:context_idxs = torch.tensor([word2idx[w] for w in context], dtype=torch.long).unsqueeze(0)  # shape (1, context_size)target_idx = torch.tensor([word2idx[target]], dtype=torch.long)output = model(context_idxs)loss = loss_fn(output, target_idx)optimizer.zero_grad()loss.backward()optimizer.step()total_loss += loss.item()if epoch % 10 == 0:print(f"Epoch {epoch}, Loss: {total_loss:.4f}")# 6. 查看词向量
word_vec = model.embeddings.weight.data
print("Embedding for 'cbow':", word_vec[word2idx["cbow"]])

再给出skip gram的简单实现代码：

import torch
import torch.nn as nn
import torch.optim as optim
from collections import defaultdict# 1. 语料与词表准备
corpus = "we are learning natural language processing using word2vec skipgram model".split()
vocab = list(set(corpus))
word2idx = {w: i for i, w in enumerate(vocab)}
idx2word = {i: w for w, i in word2idx.items()}# 2. 构造训练数据（中心词 → 上下文词）
window_size = 2
def generate_skipgram_data(corpus, window_size):pairs = []for i in range(window_size, len(corpus) - window_size):center = corpus[i]context = [corpus[j] for j in range(i - window_size, i + window_size + 1) if j != i]for ctx_word in context:pairs.append((center, ctx_word))return pairstraining_pairs = generate_skipgram_data(corpus, window_size)# 3. 定义 Skip-gram 模型
class SkipGram(nn.Module):def __init__(self, vocab_size, embedding_dim):super(SkipGram, self).__init__()self.input_embeddings = nn.Embedding(vocab_size, embedding_dim)self.output_embeddings = nn.Linear(embedding_dim, vocab_size)def forward(self, center_word_idx):embed = self.input_embeddings(center_word_idx)  # (batch_size, embedding_dim)out = self.output_embeddings(embed)             # (batch_size, vocab_size)return out# 4. 初始化
embedding_dim = 50
model = SkipGram(len(vocab), embedding_dim)
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)# 5. 训练模型
for epoch in range(100):total_loss = 0for center, context in training_pairs:center_idx = torch.tensor([word2idx[center]], dtype=torch.long)context_idx = torch.tensor([word2idx[context]], dtype=torch.long)output = model(center_idx)loss = loss_fn(output, context_idx)optimizer.zero_grad()loss.backward()optimizer.step()total_loss += loss.item()if epoch % 10 == 0:print(f"Epoch {epoch}, Loss: {total_loss:.4f}")# 6. 查看词向量
word_vec = model.input_embeddings.weight.data
print("Embedding for 'skipgram':", word_vec[word2idx["skipgram"]])

可以发现，word2vec网络更简洁了。

再之后

ELMo将word level embedding变成了token level embedding，更多地利用了上下文信息来生成token的embedding。那么，为什么transformer不使用ELMo的embedding，而只是简单地使用了nn.Embedding模块呢？

答案：Transformer的上下文相关信息，是通过后面的多层Self-Attention和Feed-forward层逐步编码出来的，而不是直接在Embedding层就完成的。