def read_dialog_file(file_path):
\n dialogs = []
\n current_dialog = []
\n with open(file_path, 'r', encoding='utf-8') as file:
\n for line in file:
\n line = line.strip()
\n if not line:
\n continue
\n prefix = line[0]
\n content = line[2:]
\n if prefix == 'E': # \u5bf9\u8bdd\u7ed3\u675f\u6807\u8bb0
\n if current_dialog:
\n dialogs.append(current_dialog)
\n current_dialog = []
\n elif prefix == 'M': # \u5bf9\u8bdd\u5185\u5bb9\u6807\u8bb0
\n if not current_dialog:
\n current_dialog.append(('user', content))
\n else:
\n current_dialog.append(('machine', content))
\n if current_dialog:
\n dialogs.append(current_dialog)
\n return dialogs<\/p>\n
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250810003852-6897ea1c888ef.png\")
<\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u8bcd\u6c47\u8868\u6784\u5efa：build_vocab\u51fd\u6570\u4ece\u5bf9\u8bdd\u6570\u636e\u4e2d\u63d0\u53d6\u6240\u6709\u51fa\u73b0\u7684\u5b57\u7b26，\u6784\u5efa\u8bcd\u6c47\u8868\u5e76\u4e3a\u6bcf\u4e2a\u5b57\u7b26\u5206\u914d\u552f\u4e00\u7d22\u5f15，\u540c\u65f6\u52a0\u5165\u7279\u6b8a\u6807\u8bb0<pad>（\u586b\u5145）\u3001<sos>（\u5e8f\u5217\u5f00\u59cb）\u548c<eos>（\u5e8f\u5217\u7ed3\u675f）<\/p>\n
def read_dialog_file(file_path):
\n dialogs = []
\n current_dialog = []<\/p>\n
with open(file_path, 'r', encoding='utf-8') as file:
\n for line in file:
\n line = line.strip()
\n if not line:
\n continue<\/p>\n
# \u5206\u5272\u6bcf\u884c\u7684\u524d\u7f00\u548c\u5185\u5bb9
\n prefix = line[0]
\n content = line[2:]<\/p>\n
if prefix == 'E':
\n # \u5982\u679c\u5f53\u524d\u5bf9\u8bdd\u4e0d\u4e3a\u7a7a，\u4fdd\u5b58\u5e76\u91cd\u7f6e
\n if current_dialog:
\n dialogs.append(current_dialog)
\n current_dialog = []
\n elif prefix == 'M':
\n if not current_dialog:
\n current_dialog.append(('user', content))
\n else:
\n current_dialog.append(('machine', content))<\/p>\n
# \u4fdd\u5b58\u6700\u540e\u4e00\u4e2a\u5bf9\u8bdd
\n if current_dialog:
\n dialogs.append(current_dialog)<\/p>\n
return dialogs<\/p>\n
def preprocess_dialogs(dialogs):
\n preprocessed_dialogs = []
\n for dialog in dialogs:
\n preprocessed_dialog = []
\n for turn in dialog:
\n # \u53bb\u9664\u659c\u6760\u5e76\u5408\u5e76\u5206\u8bcd\u540e\u7684\u8bcd
\n text = turn[1].replace('\/', '')
\n preprocessed_dialog.append((turn[0], text))
\n preprocessed_dialogs.append(preprocessed_dialog)
\n return preprocessed_dialogs<\/p>\n
def build_vocab(dialogs):
\n vocab = set()
\n for dialog in dialogs:
\n for turn in dialog:
\n for char in turn[1]:
\n vocab.add(char)
\n return sorted(vocab)<\/p>\n
def save_vocab(vocab, file_path):
\n with open(file_path, 'w', encoding='utf-8') as file:
\n for word in vocab:
\n file.write(word + '\\\\n')<\/p>\n
# \u8bfb\u53d6\u6587\u4ef6
\nfile_path = '..\/data\/xiaohuangji50w_nofenci.conv' # \u66ff\u6362\u4e3a\u4f60\u7684\u6587\u4ef6\u8def\u5f84
\ndialogs = read_dialog_file(file_path)<\/p>\n
# \u9884\u5904\u7406\u5bf9\u8bdd
\npreprocessed_dialogs = preprocess_dialogs(dialogs)<\/p>\n
# \u6784\u5efa\u8bcd\u6c47\u8868
\nvocab = build_vocab(preprocessed_dialogs)<\/p>\n
# \u4fdd\u5b58\u8bcd\u6c47\u8868
\nvocab_file_path = '..\/data\/xhj_vocab.txt' # \u66ff\u6362\u4e3a\u4f60\u5e0c\u671b\u4fdd\u5b58\u7684\u8bcd\u6c47\u8868\u6587\u4ef6\u8def\u5f84
\nsave_vocab(vocab, vocab_file_path)<\/p>\n
print(f"\u8bcd\u6c47\u8868\u5df2\u4fdd\u5b58\u5230 {vocab_file_path}") <\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u6587\u672c\u4e0e\u5411\u91cf\u8f6c\u6362：convert\u51fd\u6570\u5b9e\u73b0\u6587\u672c\u4e0e\u6574\u6570\u5411\u91cf\u4e4b\u95f4\u7684\u53cc\u5411\u8f6c\u6362，\u662f\u81ea\u7136\u8bed\u8a00\u4e0e\u6a21\u578b\u8f93\u5165\u8f93\u51fa\u4e4b\u95f4\u7684\u6865\u6881\u3002\u5728\u4e4b\u540e\u4ee3\u7801\u4e2d\u4f1a\u7528\u5230\u3002<\/p>\n
# \u5b57\u7b26\u5411\u91cf\u7684\u8f6c\u6362
\ndef convert(char_list, mode, vocab):
\n con = []
\n if mode == "word2vec":
\n conver = dict((x, y) for x, y in vocab.items())
\n if isinstance(char_list, str):
\n char_list = list(char_list)
\n con = [vocab["<sos>"]] + [conver.get(char, vocab["<pad>"]) for char in char_list] + [vocab["<eos>"]]
\n elif mode == "vec2word":
\n conver = dict((y, x) for x, y in vocab.items())
\n for char in char_list:
\n con.append(conver[char])
\n return con <\/p>\n
2. \u6a21\u578b\u67b6\u6784<\/h4>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u5b9e\u73b0\u804a\u5929\u673a\u5668\u4eba\u7684\u6838\u5fc3\u6a21\u578b，\u91c7\u7528\u4e86\u5e26\u6ce8\u610f\u529b\u673a\u5236\u7684 Encoder-Decoder \u67b6\u6784：<\/p>\n
- \n
- \u7f16\u7801\u5668（Encoder）： \u63a5\u6536\u8f93\u5165\u5e8f\u5217（\u7528\u6237\u8bdd\u8bed），\u901a\u8fc7\u5d4c\u5165\u5c42\u5c06\u8bcd\u7d22\u5f15\u8f6c\u6362\u4e3a\u8bcd\u5411\u91cf，\u518d\u7ecf\u53cc\u5411 GRU \u5904\u7406，\u8f93\u51fa\u6240\u6709\u65f6\u95f4\u6b65\u7684\u9690\u85cf\u72b6\u6001\u548c\u6700\u7ec8\u7684\u4e0a\u4e0b\u6587\u5411\u91cf<\/li>\n<\/ul>\n
class Encoder(nn.Module):
\n def __init__(self, input_dim, emb_dim, enc_hid_dim, dec_hid_dim, dropout):
\n super().__init__()
\n self.embedding = nn.Embedding(input_dim, emb_dim)
\n self.rnn = nn.GRU(emb_dim, enc_hid_dim, bidirectional=True, num_layers=2)
\n self.fc = nn.Linear(enc_hid_dim * 2, dec_hid_dim)
\n self.dropout = nn.Dropout(dropout)<\/p>\ndef forward(self, src):
\n embedded = self.dropout(self.embedding(src))
\n outputs, hidden = self.rnn(embedded)
\n # \u878d\u5408\u53cc\u5411GRU\u7684\u9690\u85cf\u72b6\u6001
\n hidden = torch.tanh(self.fc(torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)))
\n return outputs, hidden<\/p>\n- \n
- \u6ce8\u610f\u529b\u673a\u5236（Attention）： \u89e3\u51b3\u4f20\u7edf Encoder-Decoder \u67b6\u6784\u4e2d\u4e0a\u4e0b\u6587\u5411\u91cf\u96be\u4ee5\u5904\u7406\u957f\u5e8f\u5217\u7684\u95ee\u9898，\u4f7f\u89e3\u7801\u5668\u5728\u751f\u6210\u6bcf\u4e2a\u8bcd\u65f6\u80fd\u5173\u6ce8\u8f93\u5165\u5e8f\u5217\u7684\u4e0d\u540c\u90e8\u5206<\/li>\n<\/ul>\n
class Attention(nn.Module):
\n def __init__(self, enc_hid_dim, dec_hid_dim):
\n super().__init__()
\n self.attn = nn.Linear((enc_hid_dim * 2) + dec_hid_dim, dec_hid_dim)
\n self.v = nn.Parameter(torch.rand(dec_hid_dim))<\/p>\ndef forward(self, hidden, encoder_outputs):
\n # \u8ba1\u7b97\u6ce8\u610f\u529b\u6743\u91cd\u5e76\u8fd4\u56de\u6ce8\u610f\u529b\u5206\u5e03
\n batch_size = encoder_outputs.shape[1]
\n src_len = encoder_outputs.shape[0]
\n hidden = hidden.unsqueeze(1).repeat(1, src_len, 1)
\n encoder_outputs = encoder_outputs.permute(1, 0, 2)
\n energy = torch.tanh(self.attn(torch.cat((hidden, encoder_outputs), dim=2)))
\n energy = energy.permute(0, 2, 1)
\n v = self.v.repeat(batch_size, 1).unsqueeze(1)
\n attention = torch.bmm(v, energy).squeeze(1)
\n return F.softmax(attention, dim=1)<\/p>\n<\/p>\n![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250810003852-6897ea1cedc01.png\")
<\/p>\n- \n
- \n
\u89e3\u7801\u5668（Decoder）： \u4ee5\u7f16\u7801\u5668\u8f93\u51fa\u548c\u4e0a\u4e00\u65f6\u95f4\u6b65\u7684\u8f93\u51fa\u4e3a\u8f93\u5165，\u901a\u8fc7 GRU \u548c\u6ce8\u610f\u529b\u673a\u5236\u751f\u6210\u5f53\u524d\u65f6\u95f4\u6b65\u7684\u8f93\u51fa<\/p>\n<\/li>\n
- \n
Seq2Seq \u6a21\u578b： \u6574\u5408\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668，\u5b9e\u73b0\u7aef\u5230\u7aef\u7684\u5e8f\u5217\u8f6c\u6362\u529f\u80fd，\u652f\u6301\u6559\u5e08\u5f3a\u5236（teacher forcing）\u673a\u5236\u52a0\u901f\u8bad\u7ec3<\/p>\n<\/li>\n<\/ul>\n
class Seq2Seq(nn.Module):
\n def __init__(self, encoder, decoder, device="CPU"):
\n super().__init__()
\n self.encoder = encoder
\n self.decoder = decoder
\n self.device = device<\/p>\ndef forward(self, src, trg, teacher_forcing_ratio=0.5,max_length=30):
\n batch_size = src.shape[1]
\n trg_len = trg.shape[0]
\n trg_vocab_size = self.decoder.output_dim
\n outputs = torch.zeros(trg_len, batch_size, trg_vocab_size).to(self.device)
\n encoder_outputs, hidden = self.encoder(src)
\n input = trg[0, :]
\n for t in range(1, trg_len):
\n output, hidden = self.decoder(input, hidden, encoder_outputs) # output:torch.Size([batch_size, vocab_size])
\n outputs[t] = output
\n teacher_force = random.random() < teacher_forcing_ratio
\n top1 = output.argmax(1)
\n input = trg[t] if teacher_force else top1
\n if all(top1.item() == 2 for top1 in top1):
\n break
\n return outputs <\/p>\n3. \u6a21\u578b\u8bad\u7ec3<\/h4>\n
\u5b9e\u73b0\u6a21\u578b\u7684\u8bad\u7ec3\u6d41\u7a0b：<\/p>\n<\/p>\n
- \n
- \u6570\u636e\u52a0\u8f7d：GetDATA\u51fd\u6570\u6309\u6279\u6b21\u52a0\u8f7d\u5e76\u9884\u5904\u7406\u6570\u636e，\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u6a21\u578b\u53ef\u63a5\u53d7\u7684\u5f20\u91cf\u5f62\u5f0f<\/li>\n
- \u8bad\u7ec3\u5faa\u73af：train_model\u51fd\u6570\u5b9a\u4e49\u4e86\u5b8c\u6574\u7684\u8bad\u7ec3\u8fc7\u7a0b，\u5305\u62ec\u524d\u5411\u4f20\u64ad\u3001\u635f\u5931\u8ba1\u7b97\u3001\u53cd\u5411\u4f20\u64ad\u548c\u53c2\u6570\u66f4\u65b0<\/li>\n
- \u4f18\u5316\u7b56\u7565：\u4f7f\u7528 Adam \u4f18\u5316\u5668\u548c\u5b66\u4e60\u7387\u8c03\u5ea6\u5668，\u91c7\u7528\u4ea4\u53c9\u71b5\u635f\u5931\u51fd\u6570，\u901a\u8fc7\u4fdd\u5b58\u9a8c\u8bc1\u96c6\u8868\u73b0\u6700\u597d\u7684\u6a21\u578b\u6765\u9632\u6b62\u8fc7\u62df\u5408<\/li>\n<\/ul>\n
def train_model(model, batch_size, vocab, dialogs, optimizer, scheduler, criterion, device, epochs=10, teacher_forcing_ratio=0.5):
\n best_loss = float('inf')
\n for epoch in range(epochs):
\n model.train()
\n total_train_loss = 0
\n for batch in tqdm(range(int(train_sample \/ batch_size))):
\n src, trg = GetDATA(batch_size, vocab, dialogs, count=batch)
\n src = src.transpose(0, 1).to(device)
\n trg = trg.transpose(0, 1).to(device)
\n optimizer.zero_grad()
\n output = model(src, trg, teacher_forcing_ratio)
\n loss = criterion(output[1:].reshape(-1, output.shape[-1]), trg[1:].reshape(-1))
\n loss.backward()
\n optimizer.step()
\n total_train_loss += loss.item()
\n # \u4fdd\u5b58\u6700\u4f73\u6a21\u578b
\n if avg_train_loss < best_loss:
\n best_loss = avg_train_loss
\n torch.save(model.state_dict(), 'best_model.pth')<\/p>\n4. \u6a21\u578b\u63a8\u7406<\/h4>\n
ED\u4ee3\u7801\u6d41\u7a0b.py\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u8fdb\u884c\u5bf9\u8bdd\u751f\u6210：<\/p>\n<\/p>\n
- \n
- \u52a0\u8f7d\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u53c2\u6570<\/li>\n
- \u5c06\u7528\u6237\u8f93\u5165\u8f6c\u6362\u4e3a\u6a21\u578b\u53ef\u63a5\u53d7\u7684\u5411\u91cf\u5f62\u5f0f<\/li>\n
- \u5229\u7528\u89e3\u7801\u5668\u9010\u6b65\u751f\u6210\u56de\u590d，\u76f4\u5230\u751f\u6210<eos>\u6807\u8bb0\u6216\u8fbe\u5230\u6700\u5927\u957f\u5ea6<\/li>\n
- \u5c06\u751f\u6210\u7684\u5411\u91cf\u8f6c\u6362\u56de\u6587\u672c\u5f62\u5f0f，\u5f97\u5230\u6700\u7ec8\u56de\u590d<\/li>\n<\/ul>\n
# \u6a21\u578b\u63a8\u7406\u8fc7\u7a0b
\nbatch_size = src.shape[1]
\ntrg = torch.zeros((1, batch_size), dtype=torch.long).fill_(vocab["<sos>"]).to(device)
\nsrc = src.to(device)<\/p>\noutputs = []
\nmax_length = 50 # \u6700\u5927\u751f\u6210\u957f\u5ea6
\nwith torch.no_grad():
\n encoder_outputs, hidden = model.encoder(src)
\n for t in range(1, max_length):
\n output, hidden = model.decoder(trg[-1], hidden, encoder_outputs)
\n top1 = output.argmax(1) # \u8d2a\u5a6a\u89e3\u7801
\n outputs.append(top1)
\n trg = torch.cat((trg, top1.unsqueeze(0)), dim=0)
\n # \u5982\u679c\u6240\u6709\u5e8f\u5217\u90fd\u751f\u6210\u4e86<eos>\u6807\u8bb0，\u5219\u505c\u6b62\u751f\u6210
\n if all(top1.item() == vocab["<eos>"] for top1 in top1):
\n break<\/p>\n\u6700\u540e\u6548\u679c\u5c55\u793a：<\/h3>\n
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250810003853-6897ea1d4d769.png\")
<\/p>\n（\u56e0\u4e3a\u6211\u81ea\u5df1\u4f7f\u7528\u7684\u7b14\u8bb0\u672c\u7535\u8111\u7528CPU\u8bad\u7ec3\u51fa\u6765\u7684，\u53ea\u4f7f\u7528\u4e86\u524d12800\u6761\u6570\u636e\u96c6，\u6240\u4ee5\u6548\u679c\u53ea\u80fd\u8bf4\u8fd8\u884c……）<\/p>\n
\u529f\u80fd\u6269\u5c55\u4e0e\u4f18\u5316\u601d\u8def<\/h3>\n
\u4e0b\u9762\u63d0\u4f9b\u4e00\u4e9b\u53ef\u6269\u5c55\u7684\u529f\u80fd\u601d\u8def：<\/p>\n
- \u5bf9\u8bdd\u5386\u53f2\u8bb0\u5fc6：\u901a\u8fc7GetDATA_with_history\u51fd\u6570\u53ef\u4ee5\u5c06\u591a\u8f6e\u5bf9\u8bdd\u5386\u53f2\u4f5c\u4e3a\u4e0a\u4e0b\u6587\u8f93\u5165，\u589e\u5f3a\u6a21\u578b\u7684\u4e0a\u4e0b\u6587\u7406\u89e3\u80fd\u529b<\/li>\n
- \u591a\u6837\u5316\u56de\u590d\u751f\u6210：\u901a\u8fc7\u6e29\u5ea6\u53c2\u6570（Temperature）\u63a7\u5236\u8f93\u51fa\u6982\u7387\u5206\u5e03\u7684\u968f\u673a\u6027，\u7ed3\u5408\u968f\u673a\u91c7\u6837\u66ff\u4ee3\u8d2a\u5a6a\u9009\u62e9，\u751f\u6210\u66f4\u591a\u6837\u5316\u7684\u56de\u590d<\/li>\n
- \u56de\u590d\u8d28\u91cf\u63a7\u5236：\u901a\u8fc7\u53bb\u91cd\u3001\u8fc7\u6ee4\u65e0\u6548\u56de\u590d\u7b49\u7b56\u7565\u63d0\u9ad8\u751f\u6210\u56de\u590d\u7684\u8d28\u91cf<\/li>\n
\u5199\u5728\u6700\u540e<\/h3>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u6280\u672f\u7684\u8fdb\u6b65\u4ece\u6765\u4e0d\u662f\u4e00\u8e74\u800c\u5c31\u7684，\u6211\u73b0\u5728\u56de\u5934\u770b\u6700\u521d\u7684\u7248\u672c，\u4e5f\u4f1a\u89c9\u5f97\u7b28\u62d9\u3002\u4f46\u6b63\u662f\u8fd9\u4e9b\u4e0d\u5b8c\u7f8e\u7684\u5c1d\u8bd5，\u8ba9\u6211\u6162\u6162\u7406\u89e3\u4e86\u804a\u5929\u673a\u5668\u4eba\u7684\u5de5\u4f5c\u539f\u7406\u3002<\/p>\n
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- \u6ce8\u610f\u529b\u673a\u5236（Attention）： \u89e3\u51b3\u4f20\u7edf Encoder-Decoder \u67b6\u6784\u4e2d\u4e0a\u4e0b\u6587\u5411\u91cf\u96be\u4ee5\u5904\u7406\u957f\u5e8f\u5217\u7684\u95ee\u9898，\u4f7f\u89e3\u7801\u5668\u5728\u751f\u6210\u6bcf\u4e2a\u8bcd\u65f6\u80fd\u5173\u6ce8\u8f93\u5165\u5e8f\u5217\u7684\u4e0d\u540c\u90e8\u5206<\/li>\n<\/ul>\n