ForestLiuLZL/product_word_or_not_for_SEO_ForestLiu2309

#input_list：['hello','bag','hongkong','nike','dog'] #output_list：[0,1,0,0,0]

构建模型

class Classifier(nn.Module): def init(self, input_dim, output_dim): super(Classifier, self).init() self.fc1 = nn.Linear(input_dim, 300) self.relu1 = nn.ReLU() self.fc2 = nn.Linear(300, 300) self.relu1 = nn.ReLU() self.fc3 = nn.Linear(300, 300) self.relu1 = nn.ReLU() self.fc4 = nn.Linear(300, output_dim) self.softmax = nn.Softmax(dim=1)

def forward(self, x): x = self.fc1(x) x = self.relu1(x) x = self.fc2(x) x = self.relu1(x) x = self.fc3(x) x = self.relu1(x) x = self.fc4(x) x = self.softmax(x) return x

input_dim = 300 # 输入特征的维度（关键词向量的维度） output_dim = 2 # 二分类任务的类别数（符合要求为1，否则为0）

词向量生成需要加载预训练的Word2Vec模型

word2vec_model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300-SLIM.bin', binary=True)