如何建立语言模型？请给出详细步骤和相关代码

建立语言模型的步骤大致如下：

1. 收集语料库：搜索相关数据集或创建自己的语料库。
2. 预处理：对语料进行清洗、分词和标记。
3. 构建词汇表：建立词汇表和每个词对应的 ID。
4. 训练模型：使用具有特定架构的神经网络对语料进行训练。
5. 应用模型：使用模型生成新的文本。

相关 Python 代码：

1. 收集语料库：

import urllib.request 

url = "https://url-to-corpus.com/corpus.txt"
urllib.request.urlretrieve(url, "corpus.txt")

2. 预处理：

import re 

def clean_text(text): 
    text = re.sub(r'[^\w\s]','',text) 
    text = re.sub(r'\d+','',text)
    text = text.lower() 
    text = text.strip()
    return text

def tokenize_text(text): 
    tokens = text.split() 
    return tokens

def prepare_corpus(filepath): 
    with open(filepath, 'r', encoding='utf-8') as f: 
        text = f.read() 
        text = clean_text(text) 
        tokens = tokenize_text(text) 
    return tokens

3. 构建词汇表：

import collections 

def build_vocab(tokens): 
    word_counts = collections.Counter(tokens) 
    vocabulary_inv = [x[0] for x in word_counts.most_common()] 
    vocabulary = {x: i for i, x in enumerate(vocabulary_inv)} 
    return [vocabulary, vocabulary_inv]

4. 训练模型：

import tensorflow as tf 

def build_model(vocab_size, embedding_dim, rnn_units): 
    model = tf.keras.Sequential([ 
        tf.keras.layers.Embedding(vocab_size, embedding_dim), 
        tf.keras.layers.LSTM(rnn_units, return_sequences=True), 
        tf.keras.layers.Dense(vocab_size) 
    ]) 
    return model

def compile_model(model, learning_rate): 
    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), 
                  loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True)) 

def train_model(model, input_data, epochs, batch_size, checkpoint_dir): 
    checkpoint_callback = tf.keras.callbacks.ModelCheckpoint( 
        filepath=os.path.join(checkpoint_dir,'ckpt_{epoch}'),save_weights_only=True) 
    model.fit(input_data, epochs=epochs, callbacks=[checkpoint_callback], batch_size=batch_size) 

5. 应用模型：

def generate_text(model, start_string, vocabulary_inv, num_generate): 
    input_eval = [vocabulary[word] for word in start_string.split()] 
    input_eval = tf.expand_dims(input_eval, 0) 
    text_generated = [] 
    temperature = 0.5 
    model.reset_states() 
    for i in range(num_generate): 
        predictions = model(input_eval) 
        predictions = tf.squeeze(predictions, 0) 
        predictions = predictions / temperature 
        predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy() 
        input_eval = tf.expand_dims([predicted_id], 0) 
        text_generated.append(vocabulary_inv[predicted_id])
    return (start_string + ' '.join(text_generated))