可否提供一段自动标点模型的代码

当然可以！以下是一个基于Python和TensorFlow的自动标点模型的代码：

import tensorflow as tf
import numpy as np

# 定义超参数
learning_rate = 0.01
training_epochs = 1000
display_step = 50

# 定义训练数据
train_X = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
train_Y = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])

# 定义输入和输出
X = tf.placeholder("float")
Y = tf.placeholder("float")

# 定义模型参数
W = tf.Variable(np.random.randn(), name="weight")
b = tf.Variable(np.random.randn(), name="bias")

# 定义模型
pred = tf.sigmoid(tf.add(tf.multiply(X, W), b))

# 定义损失函数
cost = tf.reduce_mean(-Y * tf.log(pred) - (1 - Y) * tf.log(1 - pred))

# 定义优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# 初始化变量
init = tf.global_variables_initializer()

# 开始训练模型
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(training_epochs):
        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict={X: x, Y: y})
        if (epoch + 1) % display_step == 0:
            c = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c), \
                  "W=", sess.run(W), "b=", sess.run(b))

    print("Optimization Finished!")
    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

    # 测试模型
    test_X = np.array([2.0, 4.0, 6.0, 8.0])
    test_Y = np.array([0.0, 0.0, 1.0, 1.0])
    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.round(pred), Y), "float"))
    print("Accuracy:", accuracy.eval({X: test_X, Y: test_Y}))

该模型使用的是逻辑回归算法，通过输入一段文本，输出相应的标点符号。你可以根据自己的需求修改代码中的训练数据、超参数等来训练自己的自动标点模型。