本文共 2745 字，大约阅读时间需要 9 分钟。

CONTEXT_SIZE = 2      # 2 words to the left, 2 to the rightEMBEDDING_DIM = 100raw_text = """We are about to study the idea of a computational process.Computational processes are abstract beings that inhabit computers.As they evolve, processes manipulate other abstract things called data.The evolution of a process is directed by a pattern of rulescalled a program. People create programs to direct processes. In effect,we conjure the spirits of the computer with our spells.""".split()# By deriving a set from `raw_text`, we deduplicate the arrayvocab = set(raw_text)vocab_size = len(vocab)word_to_ix = {word:i for i, word in enumerate(vocab)}data = []for i in range(2, len(raw_text)-2):    context = [raw_text[i-2], raw_text[i-1],               raw_text[i+1], raw_text[i+2]]    target = raw_text[i]    data.append((context, target))print(data[:5])

测试打印 data[:5] 为：

[(['We', 'are', 'to', 'study'], 'about'), (['are', 'about', 'study', 'the'], 'to'), (['about', 'to', 'the', 'idea'], 'study'), (['to', 'study', 'idea', 'of'], 'the'), (['study', 'the', 'of', 'a'], 'idea')]

构建模型：

class CBOW(nn.Module):    def __init__(self, vocab_size, n_dim, context_size):        super(CBOW, self).__init__()        self.embeddings = nn.Embedding(vocab_size, n_dim)        self.linear1 = nn.Linear(2*context_size * n_dim, 128)        self.linear2 = nn.Linear(128, vocab_size)            def forward(self, inputs):        embeds = self.embeddings(inputs).view(1, -1)        out = F.relu(self.linear1(embeds))        out = self.linear2(out)        log_probs = F.log_softmax(out, dim=1)        return log_probs

# create your model and train.  here are some functions to help you make# the data ready for use by your moduledef make_context_vector(context, word_to_ix):    idxs = [word_to_ix[w] for w in context]    return torch.tensor(idxs, dtype=torch.long)print(make_context_vector(data[0][0], word_to_ix))  # examplemodel = CBOW(len(vocab), EMBEDDING_DIM, CONTEXT_SIZE)if torch.cuda.is_available():    model = model.cuda()losses = []loss_function = nn.NLLLoss()optimizer = optim.SGD(model.parameters(), lr=0.001)for epoch in range(200):    total_loss = 0    context_one_hots = []    for context, target in data:        context_vector = make_context_vector(context, word_to_ix)        target = torch.tensor([word_to_ix[target]], dtype=torch.long)        if torch.cuda.is_available():            context_vector = context_vector.cuda()            target = target.cuda()                optimizer.zero_grad()                log_probs = model(context_vector)        loss = loss_function(log_probs, target)        loss.backward()        optimizer.step()                total_loss += loss.item()    print("epoch", epoch, " -->", total_loss)    losses.append(total_loss)

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