一、准备训练数据
主要的数据有两个：
1.小黄鸡的聊天语料：噪声很大
2.微博的标题和评论：质量相对较高
二、数据的处理和保存
由于数据中存到大量的噪声，可以对其进行基础的处理，然后分别把input和target使用两个文件保存，即input中的第n行尾问，target的第n行为答
后续可能会把单个字作为特征（存放在input_word.txt），也可能会把词语作为特征(input.txt)
2.1 小黄鸡的语料的处理
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def format_xiaohuangji_corpus(word=false): """处理小黄鸡的语料""" if word: corpus_path = "./chatbot/corpus/xiaohuangji50w_nofenci.conv" input_path = "./chatbot/corpus/input_word.txt" output_path = "./chatbot/corpus/output_word.txt" else: corpus_path = "./chatbot/corpus/xiaohuangji50w_nofenci.conv" input_path = "./chatbot/corpus/input.txt" output_path = "./chatbot/corpus/output.txt" f_input = open(input_path, "a") f_output = open(output_path, "a") pair = [] for line in tqdm(open(corpus_path), ascii=true): if line.strip() == "e": if not pair: continue else: assert len(pair) == 2, "长度必须是2" if len(pair[0].strip()) >= 1 and len(pair[1].strip()) >= 1: f_input.write(pair[0] + "\n") f_output.write(pair[1] + "\n") pair = [] elif line.startswith("m"): line = line[1:] if word: pair.append(" ".join(list(line.strip()))) else: pair.append(" ".join(jieba_cut(line.strip()))) |
2.2 微博语料的处理
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def format_weibo(word=false): """ 微博数据存在一些噪声，未处理 :return: """ if word: origin_input = "./chatbot/corpus/stc_weibo_train_post" input_path = "./chatbot/corpus/input_word.txt" origin_output = "./chatbot/corpus/stc_weibo_train_response" output_path = "./chatbot/corpus/output_word.txt" else: origin_input = "./chatbot/corpus/stc_weibo_train_post" input_path = "./chatbot/corpus/input.txt" origin_output = "./chatbot/corpus/stc_weibo_train_response" output_path = "./chatbot/corpus/output.txt" f_input = open(input_path, "a") f_output = open(output_path, "a") with open(origin_input) as in_o, open(origin_output) as out_o: for _in, _out in tqdm(zip(in_o, out_o), ascii=true): _in = _in.strip() _out = _out.strip() if _in.endswith("）") or _in.endswith("」") or _in.endswith(")"): _in = re.sub("（.*）|「.*?」|\(.*?\)", " ", _in) _in = re.sub("我在.*?alink|alink|（.*?\d+x\d+.*?）|#|】|【|-+|_+|via.*?：*.*", " ", _in) _in = re.sub("\s+", " ", _in) if len(_in) < 1 or len(_out) < 1: continue if word: _in = re.sub("\s+", "", _in) # 转化为一整行，不含空格 _out = re.sub("\s+", "", _out) if len(_in) >= 1 and len(_out) >= 1: f_input.write(" ".join(list(_in)) + "\n") f_output.write(" ".join(list(_out)) + "\n") else: if len(_in) >= 1 and len(_out) >= 1: f_input.write(_in.strip() + "\n") f_output.write(_out.strip() + "\n") f_input.close() f_output.close() |
2.3 处理后的结果
三、构造文本序列化和反序列化方法
和之前的操作相同，需要把文本能转化为数字，同时还需实现方法把数字转化为文本
示例代码：
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import configimport pickle class word2sequence(): unk_tag = "unk" pad_tag = "pad" sos_tag = "sos" eos_tag = "eos" unk = 0 pad = 1 sos = 2 eos = 3 def __init__(self): self.dict = { self.unk_tag: self.unk, self.pad_tag: self.pad, self.sos_tag: self.sos, self.eos_tag: self.eos } self.count = {} self.fited = false def to_index(self, word): """word -> index""" assert self.fited == true, "必须先进行fit操作" return self.dict.get(word, self.unk) def to_word(self, index): """index -> word""" assert self.fited, "必须先进行fit操作" if index in self.inversed_dict: return self.inversed_dict[index] return self.unk_tag def __len__(self): return len(self.dict) def fit(self, sentence): """ :param sentence:[word1,word2,word3] :param min_count: 最小出现的次数 :param max_count: 最大出现的次数 :param max_feature: 总词语的最大数量 :return: """ for a in sentence: if a not in self.count: self.count[a] = 0 self.count[a] += 1 self.fited = true def build_vocab(self, min_count=1, max_count=none, max_feature=none): # 比最小的数量大和比最大的数量小的需要 if min_count is not none: self.count = {k: v for k, v in self.count.items() if v >= min_count} if max_count is not none: self.count = {k: v for k, v in self.count.items() if v <= max_count} # 限制最大的数量 if isinstance(max_feature, int): count = sorted(list(self.count.items()), key=lambda x: x[1]) if max_feature is not none and len(count) > max_feature: count = count[-int(max_feature):] for w, _ in count: self.dict[w] = len(self.dict) else: for w in sorted(self.count.keys()): self.dict[w] = len(self.dict) # 准备一个index->word的字典 self.inversed_dict = dict(zip(self.dict.values(), self.dict.keys())) def transform(self, sentence, max_len=none, add_eos=false): """ 实现吧句子转化为数组（向量） :param sentence: :param max_len: :return: """ assert self.fited, "必须先进行fit操作" r = [self.to_index(i) for i in sentence] if max_len is not none: if max_len > len(sentence): if add_eos: r += [self.eos] + [self.pad for _ in range(max_len - len(sentence) - 1)] else: r += [self.pad for _ in range(max_len - len(sentence))] else: if add_eos: r = r[:max_len - 1] r += [self.eos] else: r = r[:max_len] else: if add_eos: r += [self.eos] # print(len(r),r) return r def inverse_transform(self, indices): """ 实现从数组 转化为 向量 :param indices: [1,2,3....] :return:[word1,word2.....] """ sentence = [] for i in indices: word = self.to_word(i) sentence.append(word) return sentence # 之后导入该word_sequence使用word_sequence = pickle.load(open("./pkl/ws.pkl", "rb")) if not config.use_word else pickle.load( open("./pkl/ws_word.pkl", "rb")) if __name__ == '__main__': from word_sequence import word2sequence from tqdm import tqdm import pickle word_sequence = word2sequence() # 词语级别 input_path = "../corpus/input.txt" target_path = "../corpus/output.txt" for line in tqdm(open(input_path).readlines()): word_sequence.fit(line.strip().split()) for line in tqdm(open(target_path).readlines()): word_sequence.fit(line.strip().split()) # 使用max_feature=5000个数据 word_sequence.build_vocab(min_count=5, max_count=none, max_feature=5000) print(len(word_sequence)) pickle.dump(word_sequence, open("./pkl/ws.pkl", "wb")) |
word_sequence.py:
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class wordsequence(object): pad_tag = 'pad' # 填充标记 unk_tag = 'unk' # 未知词标记 sos_tag = 'sos' # start of sequence eos_tag = 'eos' # end of sequence pad = 0 unk = 1 sos = 2 eos = 3 def __init__(self): self.dict = { self.pad_tag: self.pad, self.unk_tag: self.unk, self.sos_tag: self.sos, self.eos_tag: self.eos } self.count = {} # 保存词频词典 self.fited = false def to_index(self, word): """ word --> index :param word: :return: """ assert self.fited == true, "必须先进行fit操作" return self.dict.get(word, self.unk) def to_word(self, index): """ index -- > word :param index: :return: """ assert self.fited, '必须先进行fit操作' if index in self.inverse_dict: return self.inverse_dict[index] return self.unk_tag def fit(self, sentence): """ 传入句子，统计词频 :param sentence: :return: """ for word in sentence: # 对word出现的频率进行统计，当word不在sentence时，返回值是0，当word在sentence中时，返回+1，以此进行累计计数 # self.count[word] = self.dict.get(word, 0) + 1 if word not in self.count: self.count[word] = 0 self.count[word] += 1 self.fited = true def build_vocab(self, min_count=2, max_count=none, max_features=none): """ 构造词典 :param min_count:最小词频 :param max_count: 最大词频 :param max_features: 词典中词的数量 :return: """ # self.count.pop(key),和del self.count[key] 无法在遍历self.count的同时进行删除key.因此浅拷贝temp后对temp遍历并删除self.count temp = self.count.copy() for key in temp: cur_count = self.count.get(key, 0) # 当前词频 if min_count is not none: if cur_count < min_count: del self.count[key] if max_count is not none: if cur_count > max_count: del self.count[key] if max_features is not none: self.count = dict(sorted(list(self.count.items()), key=lambda x: x[1], reversed=true)[:max_features]) for key in self.count: self.dict[key] = len(self.dict) # 准备一个index-->word的字典 self.inverse_dict = dict(zip(self.dict.values(), self.dict.keys())) def transforms(self, sentence, max_len=10, add_eos=false): """ 把sentence转化为序列 :param sentence: 传入的句子 :param max_len: 句子的最大长度 :param add_eos: 是否添加结束符 add_eos : true时，输出句子长度为max_len + 1 add_eos : false时，输出句子长度为max_len :return: """ assert self.fited, '必须先进行fit操作！' if len(sentence) > max_len: sentence = sentence[:max_len] # 提前计算句子长度，实现ass_eos后，句子长度统一 sentence_len = len(sentence) # sentence[1,3,4,5,unk,eos,pad,....] if add_eos: sentence += [self.eos_tag] if sentence_len < max_len: # 句子长度不够，用pad来填充 sentence += (max_len - sentence_len) * [self.pad_tag] # 对于新出现的词采用特殊标记 result = [self.dict.get(i, self.unk) for i in sentence] return result def invert_transform(self, indices): """ 序列转化为sentence :param indices: :return: """ # return [self.inverse_dict.get(i, self.unk_tag) for i in indices] result = [] for i in indices: if self.inverse_dict[i] == self.eos_tag: break result.append(self.inverse_dict.get(i, self.unk_tag)) return result def __len__(self): return len(self.dict) if __name__ == '__main__': num_sequence = wordsequence() print(num_sequence.dict) str1 = ['中国', '您好', '我爱你', '中国', '我爱你', '北京'] num_sequence.fit(str1) num_sequence.build_vocab() print(num_sequence.transforms(str1)) print(num_sequence.dict) print(num_sequence.inverse_dict) print(num_sequence.invert_transform([5, 4])) # 这儿要传列表 |
运行结果：
四、构建dataset和dataloader
创建dataset.py 文件，准备数据集
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import configimport torchfrom torch.utils.data import dataset, dataloaderfrom word_sequence import wordsequence class chatdataset(dataset): def __init__(self): self.input_path = config.chatbot_input_path self.target_path = config.chatbot_target_path self.input_lines = open(self.input_path, encoding='utf-8').readlines() self.target_lines = open(self.target_path, encoding='utf-8').readlines() assert len(self.input_lines) == len(self.target_lines), 'input和target长度不一致' def __getitem__(self, item): input = self.input_lines[item].strip().split() target = self.target_lines[item].strip().split() if len(input) == 0 or len(target) == 0: input = self.input_lines[item + 1].strip().split() target = self.target_lines[item + 1].strip().split() # 此处句子的长度如果大于max_len，那么应该返回max_len input_length = min(len(input), config.max_len) target_length = min(len(target), config.max_len) return input, target, input_length, target_length def __len__(self): return len(self.input_lines) def collate_fn(batch): # 1.排序 batch = sorted(batch, key=lambda x: x[2], reversed=true) input, target, input_length, target_length = zip(*batch) # 2.进行padding的操作 input = torch.longtensor([wordsequence.transform(i, max_len=config.max_len) for i in input]) target = torch.longtensor([wordsequence.transforms(i, max_len=config.max_len, add_eos=true) for i in target]) input_length = torch.longtensor(input_length) target_length = torch.longtensor(target_length) return input, target, input_length, target_length data_loader = dataloader(dataset=chatdataset(), batch_size=config.batch_size, shuffle=true, collate_fn=collate_fn, drop_last=true) if __name__ == '__main__': print(len(data_loader)) for idx, (input, target, input_length, target_length) in enumerate(data_loader): print(idx) print(input) print(target) print(input_length) print(target_length) |
五、完成encoder编码器逻辑
encode.py:
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import torch.nn as nnimport configfrom torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence class encoder(nn.module): def __init__(self): super(encoder, self).__init__() # torch.nn.embedding(num_embeddings词典大小即不重复词数,embedding_dim单个词用多长向量表示) self.embedding = nn.embedding( num_embeddings=len(config.word_sequence.dict), embedding_dim=config.embedding_dim, padding_idx=config.word_sequence.pad ) self.gru = nn.gru( input_size=config.embedding_dim, num_layers=config.num_layer, hidden_size=config.hidden_size, bidirectional=false, batch_first=true ) def forward(self, input, input_length): """ :param input: [batch_size, max_len] :return: """ embedded = self.embedding(input) # embedded [batch_size, max_len, embedding_dim] # 加速循环过程 embedded = pack_padded_sequence(embedded, input_length, batch_first=true) # 打包 out, hidden = self.gru(embedded) out, out_length = pad_packed_sequence(out, batch_first=true, padding_value=config.num_sequence.pad) # 解包 # hidden即h_n [num_layer*[1/2],batchsize, hidden_size] # out : [batch_size, seq_len/max_len, hidden_size] return out, hidden, out_length if __name__ == '__main__': from dataset import data_loader encoder = encoder() print(encoder) for input, target, input_length, target_length in data_loader: out, hidden, out_length = encoder(input, input_length) print(input.size()) print(out.size()) print(hidden.size()) print(out_length) break |
六、完成decoder解码器的逻辑
decode.py:
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import torchimport torch.nn as nnimport configimport torch.nn.functional as ffrom word_sequence import wordsequence class decode(nn.module): def __init__(self): super().__init__() self.max_seq_len = config.max_len self.vocab_size = len(wordsequence) self.embedding_dim = config.embedding_dim self.dropout = config.dropout self.embedding = nn.embedding(num_embeddings=self.vocab_size, embedding_dim=self.embedding_dim, padding_idx=wordsequence.pad) self.gru = nn.gru(input_size=self.embedding_dim, hidden_size=config.hidden_size, num_layers=1, batch_first=true, dropout=self.dropout) self.log_softmax = nn.logsoftmax() self.fc = nn.linear(config.hidden_size, self.vocab_size) def forward(self, encoder_hidden, target, target_length): # encoder_hidden [batch_size,hidden_size] # target [batch_size,seq-len] decoder_input = torch.longtensor([[wordsequence.sos]] * config.batch_size).to(config.device) decoder_outputs = torch.zeros(config.batch_size, config.max_len, self.vocab_size).to( config.device) # [batch_size,seq_len,14] decoder_hidden = encoder_hidden # [batch_size,hidden_size] for t in range(config.max_len): decoder_output_t, decoder_hidden = self.forward_step(decoder_input, decoder_hidden) decoder_outputs[:, t, :] = decoder_output_t value, index = torch.topk(decoder_output_t, 1) # index [batch_size,1] decoder_input = index return decoder_outputs, decoder_hidden def forward_step(self, decoder_input, decoder_hidden): """ :param decoder_input:[batch_size,1] :param decoder_hidden:[1,batch_size,hidden_size] :return:[batch_size,vocab_size],decoder_hidden:[1,batch_size,didden_size] """ embeded = self.embedding(decoder_input) # embeded: [batch_size,1 , embedding_dim] out, decoder_hidden = self.gru(embeded, decoder_hidden) # out [1, batch_size, hidden_size] out = out.squeeze(0) out = f.log_softmax(self.fc(out), dim=1) # [batch_size, vocab_size] out = out.squeeze(0) # print("out size:",out.size(),decoder_hidden.size()) return out, decoder_hidden |
关于 decoder_outputs[:,t,:] = decoder_output_t的演示
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decoder_outputs 形状 [batch_size, seq_len, vocab_size]decoder_output_t 形状[batch_size, vocab_size] |
示例代码：
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import torch a = torch.zeros((2, 3, 5))print(a.size())print(a) b = torch.randn((2, 5))print(b.size())print(b) a[:, 0, :] = bprint(a.size())print(a) |
运行结果：
关于torch.topk, torch.max(),torch.argmax()
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value, index = torch.topk(decoder_output_t , k = 1)decoder_output_t [batch_size, vocab_size] |
示例代码：
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import torch a = torch.randn((3, 5))print(a.size())print(a) values, index = torch.topk(a, k=1)print(values)print(index)print(index.size()) values, index = torch.max(a, dim=-1)print(values)print(index)print(index.size()) index = torch.argmax(a, dim=-1)print(index)print(index.size()) index = a.argmax(dim=-1)print(index)print(index.size()) |
运行结果：
若使用teacher forcing ,将采用下次真实值作为下个time step的输入
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# 注意unsqueeze 相当于浅拷贝,不会对原张量进行修改 decoder_input = target[:,t].unsqueeze(-1) target 形状 [batch_size, seq_len] decoder_input 要求形状[batch_size, 1] |
示例代码：
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import torch a = torch.randn((3, 5))print(a.size())print(a) b = a[:, 3]print(b.size())print(b)c = b.unsqueeze(-1)print(c.size())print(c) |
运行结果：
七、完成seq2seq的模型
seq2seq.py:
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import torchimport torch.nn as nn class seq2seq(nn.module): def __init__(self, encoder, decoder): super(seq2seq, self).__init__() self.encoder = encoder self.decoder = decoder def forward(self, input, target, input_length, target_length): encoder_outputs, encoder_hidden = self.encoder(input, input_length) decoder_outputs, decoder_hidden = self.decoder(encoder_hidden, target, target_length) return decoder_outputs, decoder_hidden def evaluation(self, inputs, input_length): encoder_outputs, encoder_hidden = self.encoder(inputs, input_length) decoded_sentence = self.decoder.evaluation(encoder_hidden) return decoded_sentence |
八、完成训练逻辑
为了加速训练，可以考虑在gpu上运行，那么在我们自顶一个所以的tensor和model都需要转化为cuda支持的类型。
当前的数据量为500多万条，在gtx1070（8g显存）上训练，大概需要90分一个epoch，耐心的等待吧
train.py:
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import torchimport configfrom torch import optimimport torch.nn as nnfrom encode import encoderfrom decode import decoderfrom seq2seq import seq2seqfrom dataset import data_loader as train_dataloaderfrom word_sequence import wordsequence encoder = encoder()decoder = decoder()model = seq2seq(encoder, decoder) # device在config文件中实现model.to(config.device) print(model) model.load_state_dict(torch.load("model/seq2seq_model.pkl"))optimizer = optim.adam(model.parameters())optimizer.load_state_dict(torch.load("model/seq2seq_optimizer.pkl"))criterion = nn.nllloss(ignore_index=wordsequence.pad, reduction="mean") def get_loss(decoder_outputs, target): target = target.view(-1) # [batch_size*max_len] decoder_outputs = decoder_outputs.view(config.batch_size * config.max_len, -1) return criterion(decoder_outputs, target) def train(epoch): for idx, (input, target, input_length, target_len) in enumerate(train_dataloader): input = input.to(config.device) target = target.to(config.device) input_length = input_length.to(config.device) target_len = target_len.to(config.device) optimizer.zero_grad() ##[seq_len,batch_size,vocab_size] [batch_size,seq_len] decoder_outputs, decoder_hidden = model(input, target, input_length, target_len) loss = get_loss(decoder_outputs, target) loss.backward() optimizer.step() print('train epoch: {} [{}/{} ({:.0f}%)]\tloss: {:.6f}'.format( epoch, idx * len(input), len(train_dataloader.dataset), 100. * idx / len(train_dataloader), loss.item())) torch.save(model.state_dict(), "model/seq2seq_model.pkl") torch.save(optimizer.state_dict(), 'model/seq2seq_optimizer.pkl') if __name__ == '__main__': for i in range(10): train(i) |
训练10个epoch之后的效果如下,可以看出损失依然很高：
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train epoch: 9 [2444544/4889919 (50%)] loss: 4.923604train epoch: 9 [2444800/4889919 (50%)] loss: 4.364594train epoch: 9 [2445056/4889919 (50%)] loss: 4.613254train epoch: 9 [2445312/4889919 (50%)] loss: 4.143538train epoch: 9 [2445568/4889919 (50%)] loss: 4.412729train epoch: 9 [2445824/4889919 (50%)] loss: 4.516526train epoch: 9 [2446080/4889919 (50%)] loss: 4.124945train epoch: 9 [2446336/4889919 (50%)] loss: 4.777015train epoch: 9 [2446592/4889919 (50%)] loss: 4.358538train epoch: 9 [2446848/4889919 (50%)] loss: 4.513412train epoch: 9 [2447104/4889919 (50%)] loss: 4.202757train epoch: 9 [2447360/4889919 (50%)] loss: 4.589584 |
九、评估逻辑
decoder 中添加评估方法
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def evaluate(self, encoder_hidden): """ 评估, 和fowward逻辑类似 :param encoder_hidden: encoder最后time step的隐藏状态 [1, batch_size, hidden_size] :return: """ batch_size = encoder_hidden.size(1) # 初始化一个[batch_size, 1]的sos张量,作为第一个time step的输出 decoder_input = torch.longtensor([[config.target_ws.sos]] * batch_size).to(config.device) # encoder_hidden 作为decoder第一个时间步的hidden [1, batch_size, hidden_size] decoder_hidden = encoder_hidden # 初始化[batch_size, seq_len, vocab_size]的outputs 拼接每个time step结果 decoder_outputs = torch.zeros((batch_size, config.chatbot_target_max_len, self.vocab_size)).to(config.device) # 初始化一个空列表,存储每次的预测序列 predict_result = [] # 对每个时间步进行更新 for t in range(config.chatbot_target_max_len): decoder_output_t, decoder_hidden = self.forward_step(decoder_input, decoder_hidden) # 拼接每个time step,decoder_output_t [batch_size, vocab_size] decoder_outputs[:, t, :] = decoder_output_t # 由于是评估,需要每次都获取预测值 index = torch.argmax(decoder_output_t, dim = -1) # 更新下一时间步的输入 decoder_input = index.unsqueeze(1) # 存储每个时间步的预测序列 predict_result.append(index.cpu().detach().numpy()) # [[batch], [batch]...] ->[seq_len, vocab_size] # 结果转换为ndarry,每行是一个预测结果即单个字对应的索引, 所有行为seq_len长度 predict_result = np.array(predict_result).transpose() # (batch_size, seq_len)的array return decoder_outputs, predict_result |
eval.py
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import torchimport torch.nn as nnimport torch.nn.functional as ffrom dataset import get_dataloaderimport configimport numpy as npfrom seq2seq import seq2seqmodelimport osfrom tqdm import tqdm model = seq2seqmodel().to(config.device)if os.path.exists('./model/chatbot_model.pkl'): model.load_state_dict(torch.load('./model/chatbot_model.pkl')) def eval(): model.eval() loss_list = [] test_data_loader = get_dataloader(train = false) with torch.no_grad(): bar = tqdm(test_data_loader, desc = 'testing', total = len(test_data_loader)) for idx, (input, target, input_length, target_length) in enumerate(bar): input = input.to(config.device) target = target.to(config.device) input_length = input_length.to(config.device) target_length = target_length.to(config.device) # 获取模型的预测结果 decoder_outputs, predict_result = model.evaluation(input, input_length) # 计算损失 loss = f.nll_loss(decoder_outputs.view(-1, len(config.target_ws)), target.view(-1), ignore_index = config.target_ws.pad) loss_list.append(loss.item()) bar.set_description('idx{}:/{}, loss:{}'.format(idx, len(test_data_loader), np.mean(loss_list))) if __name__ == '__main__': eval() |
interface.py:
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from cut_sentence import cutimport torchimport configfrom seq2seq import seq2seqmodelimport os # 模拟聊天场景，对用户输入进来的话进行回答def interface(): # 加载训练集好的模型 model = seq2seqmodel().to(config.device) assert os.path.exists('./model/chatbot_model.pkl') , '请先对模型进行训练!' model.load_state_dict(torch.load('./model/chatbot_model.pkl')) model.eval() while true: # 输入进来的原始字符串,进行分词处理 input_string = input('me>>:') if input_string == 'q': print('下次再聊') break input_cuted = cut(input_string, by_word = true) # 进行序列转换和tensor封装 input_tensor = torch.longtensor([config.input_ws.transfrom(input_cuted, max_len = config.chatbot_input_max_len)]).to(config.device) input_length_tensor = torch.longtensor([len(input_cuted)]).to(config.device) # 获取预测结果 outputs, predict = model.evaluation(input_tensor, input_length_tensor) # 进行序列转换文本 result = config.target_ws.inverse_transform(predict[0]) print('chatbot>>:', result) if __name__ == '__main__': interface() |
config.py:
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import torchfrom word_sequence import wordsequence chatbot_input_path = './corpus/input.txt'chatbot_target_path = './corpus/target.txt' word_sequence = wordsequence()max_len = 9batch_size = 128embedding_dim = 100num_layer = 1hidden_size = 64dropout = 0.1model_save_path = './model.pkl'optimizer_save_path = './optimizer.pkl'device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') |
cut.py:
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"""分词"""import jiebaimport config1import stringimport jieba.posseg as psg # 返回词性from lib.stopwords import stopwords # 加载词典jieba.load_userdict(config1.user_dict_path)# 准备英文字符letters = string.ascii_lowercase + '+' def cut_sentence_by_word(sentence): """实现中英文分词""" temp = '' result = [] for word in sentence: if word.lower() in letters: # 如果是英文字符,则进行拼接空字符串 temp += word else: # 遇到汉字后,把英文先添加到结果中 if temp != '': result.append(temp.lower()) temp = '' result.append(word.strip()) if temp != '': # 若英文出现在最后 result.append(temp.lower()) return result def cut(sentence, by_word=false, use_stopwords=true, with_sg=false): """ :param sentence: 句子 :param by_word: t根据单个字分词或者f句子 :param use_stopwords: 是否使用停用词，默认false :param with_sg: 是否返回词性 :return: """ if by_word: result = cut_sentence_by_word(sentence) else: result = psg.lcut(sentence) # psg 源码返回i.word,i.flag 即词,定义的词性 result = [(i.word, i.flag) for i in result] # 是否返回词性 if not with_sg: result = [i[0] for i in result] # 是否使用停用词 if use_stopwords: result = [i for i in result if i not in stopwords] return result |
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原文链接：https://blog.csdn.net/weixin_44799217/article/details/115827085
