python脚本，给出利用CBOW，进行文本分类 示例

好的，关于利用CBOW进行文本分类的示例，我为您提供以下Python脚本代码作为参考：

import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Embedding, Lambda
from keras.preprocessing.text import Tokenizer
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split

# 设置超参数
vocab_size = 5000
embedding_dim = 50
max_len = 100
batch_size = 32
epochs = 10

# 生成样本数据
texts = ['This is an example of text classification using CBOW.',
         'We will use Keras to build a neural network.',
         'The neural network will have one embedding layer, one dense layer, and one output layer.',
         'We will use the cross-entropy loss function and Adam optimizer.',
         'The neural network will be trained on a small dataset.']
labels = ['example', 'example', 'example', 'example', 'example']
num_labels = len(set(labels))

# 对标签进行编码
le = LabelEncoder()
labels = le.fit_transform(labels)

# 对文本进行分词处理
tokenizer = Tokenizer(num_words=vocab_size)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
word_index = tokenizer.word_index

# 对序列进行填充处理
data = keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_len)

# 构建CBOW神经网络模型
model = Sequential()
model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_len))
model.add(Lambda(lambda x: keras.backend.mean(x, axis=1)))
model.add(Dense(256, activation='relu'))
model.add(Dense(num_labels, activation='softmax'))

# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 拆分训练和测试数据集
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2)

# 训练模型
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test))

希望这个CBOW文本分类的示例对您有帮助。