transformer实现对话生成
时间: 2023-12-10 09:35:10 浏览: 39
transformer是一种强大的神经网络模型,已经被广泛应用于自然语言处理领域,包括对话生成。下面是使用transformer实现对话生成的步骤:
1.准备数据集,包括对话数据和词汇表。
2.使用改造后的transformer对对话数据进行encoder和decoder处理,其中encoder将输入序列转换为一组向量,而decoder将这些向量转换为输出序列。
3.使用teacher forcing技术,将decoder的输入设置为目标输出序列中的前一个标记,以便在训练期间更好地指导模型。
4.使用beam search算法,从模型生成的候选回答中选择最佳回答。
下面是一个使用transformer实现对话生成的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import Multi30k
from torchtext.data import Field, BucketIterator
# 定义Field对象
SRC = Field(tokenize='spacy', tokenizer_language='en', init_token='<sos>', eos_token='<eos>', lower=True)
TRG = Field(tokenize='spacy', tokenizer_language='de', init_token='<sos>', eos_token='<eos>', lower=True)
# 加载数据集
train_data, valid_data, test_data = Multi30k.splits(exts=('.en', '.de'), fields=(SRC, TRG))
# 构建词汇表
SRC.build_vocab(train_data, min_freq=2)
TRG.build_vocab(train_data, min_freq=2)
# 定义模型
class Transformer(nn.Module):
def __init__(self, input_dim, output_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device):
super().__init__()
self.tok_embedding = nn.Embedding(input_dim, hid_dim)
self.pos_embedding = nn.Embedding(1000, hid_dim)
self.layers = nn.ModuleList([TransformerBlock(hid_dim, n_heads, pf_dim, dropout, device) for _ in range(n_layers)])
self.fc_out = nn.Linear(hid_dim, output_dim)
self.dropout = nn.Dropout(dropout)
self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)
def forward(self, src, trg, src_mask, trg_mask):
src_len = src.shape[0]
trg_len = trg.shape[0]
pos = torch.arange(0, src_len).unsqueeze(1).repeat(1, self.hid_dim).to(self.device)
src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))
pos = torch.arange(0, trg_len).unsqueeze(1).repeat(1, self.hid_dim).to(self.device)
trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))
for layer in self.layers:
trg, attention = layer(src, trg, src_mask, trg_mask)
output = self.fc_out(trg)
return output, attention
# 训练模型
INPUT_DIM = len(SRC.vocab)
OUTPUT_DIM = len(TRG.vocab)
HID_DIM = 256
N_LAYERS = 3
N_HEADS = 8
PF_DIM = 512
DROPOUT = 0.1
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Transformer(INPUT_DIM, OUTPUT_DIM, HID_DIM, N_LAYERS, N_HEADS, PF_DIM, DROPOUT, device).to(device)
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss(ignore_index=TRG.vocab.stoi['<pad>'])
train_iterator, valid_iterator, test_iterator = BucketIterator.splits((train_data, valid_data, test_data), batch_size=128, device=device)
for epoch in range(10):
for i, batch in enumerate(train_iterator):
src = batch.src
trg = batch.trg
src_mask = model.make_src_mask(src)
trg_mask = model.make_trg_mask(trg)
output, _ = model(src, trg[:, :-1], src_mask, trg_mask[:, :-1])
output_dim = output.shape[-1]
output = output.contiguous().view(-1, output_dim)
trg = trg[:, 1:].contiguous().view(-1)
loss = criterion(output, trg)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 使用模型生成回答
def generate_response(model, sentence, max_len=50):
model.eval()
tokens = [token.text.lower() for token in nlp(sentence)]
tokens = [SRC.init_token] + tokens + [SRC.eos_token]
src_indexes = [SRC.vocab.stoi[token] for token in tokens]
src_tensor = torch.LongTensor(src_indexes).unsqueeze(1).to(device)
trg_indexes = [TRG.vocab.stoi[TRG.init_token]]
for i in range(max_len):
trg_tensor = torch.LongTensor(trg_indexes).unsqueeze(1).to(device)
src_mask = model.make_src_mask(src_tensor)
trg_mask = model.make_trg_mask(trg_tensor)
output, attention = model(src_tensor, trg_tensor, src_mask, trg_mask)
pred_token = output.argmax(2)[-1, :].item()
trg_indexes.append(pred_token)
if pred_token == TRG.vocab.stoi[TRG.eos_token]:
break
trg_tokens = [TRG.vocab.itos[i] for i in trg_indexes]
return trg_tokens[1:], attention
# 示例
generate_response(model, "How are you?")
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