帮我写基于transformer的二分类模型代码

好的，下面是一个基于Transformer的二分类模型的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping

from transformers import TFAutoModel, AutoTokenizer

# 加载预训练模型和分词器
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
transformer_model = TFAutoModel.from_pretrained(model_name)

# 构建模型
def build_model(max_len):
    # 输入层
    input_layer = Input(shape=(max_len,), dtype=tf.int32, name="input_layer")
    
    # 获取Transformer的输出
    transformer_output = transformer_model(input_layer)[0]
    
    # 进行平均池化
    avg_pool = tf.keras.layers.GlobalAveragePooling1D()(transformer_output)
    
    # 加入全连接层和Dropout层
    dense_layer = Dense(64, activation="relu")(avg_pool)
    dropout_layer = Dropout(0.3)(dense_layer)
    
    # 输出层
    output_layer = Dense(1, activation="sigmoid")(dropout_layer)
    
    # 定义模型
    model = Model(inputs=input_layer, outputs=output_layer)
    
    # 编译模型
    optimizer = Adam(learning_rate=1e-5)
    model.compile(optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"])
    
    return model

# 训练模型
def train_model(X_train, y_train, X_val, y_val, epochs, batch_size):
    # 获取最大长度
    max_len = max([len(text.split()) for text in X_train])
    
    # 对训练集和验证集进行编码
    X_train_encoded = tokenizer.batch_encode_plus(X_train, max_length=max_len, padding=True, truncation=True, return_tensors="tf")["input_ids"]
    X_val_encoded = tokenizer.batch_encode_plus(X_val, max_length=max_len, padding=True, truncation=True, return_tensors="tf")["input_ids"]
    
    # 构建模型
    model = build_model(max_len)
    
    # 设置EarlyStopping
    early_stopping = EarlyStopping(monitor="val_loss", patience=2, restore_best_weights=True)
    
    # 训练模型
    model.fit(X_train_encoded, y_train, validation_data=(X_val_encoded, y_val), epochs=epochs, batch_size=batch_size, callbacks=[early_stopping])
    
    return model

这个模型使用了Hugging Face的Transformers库，加载了预训练模型和分词器，使用了DistilBERT模型。在构建模型时，首先定义了输入层，然后获取了Transformer的输出，并进行了平均池化。接着加入了全连接层和Dropout层，最终输出层为一个sigmoid激活函数的Dense层。

在训练模型时，首先获取了训练集和验证集的最大长度，然后使用分词器对训练集和验证集进行编码。接着构建模型，并使用EarlyStopping设置了早停策略。最后使用fit方法进行训练。