HuggingFace变压器如何在添加额外的keras层后冻结变压器

from transformers import TFAutoModel, AutoTokenizer
import tensorflow as tf

model_name = "bert-base-uncased"  # 替换为所需的变压器模型名称
tokenizer = AutoTokenizer.from_pretrained(model_name)
transformer_model = TFAutoModel.from_pretrained(model_name)

input_ids = tf.keras.Input(shape=(max_length,), dtype=tf.int32)
attention_mask = tf.keras.Input(shape=(max_length,), dtype=tf.int32)

# 变压器部分
transformer_output = transformer_model(input_ids, attention_mask)[0]

# 添加额外的Keras层
additional_layer = tf.keras.layers.Dense(units=256, activation="relu")(transformer_output)

# 构建整体模型
model = tf.keras.Model(inputs=[input_ids, attention_mask], outputs=additional_layer)

for layer in transformer_model.layers:
    layer.trainable = False

model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
model.fit(train_dataset, epochs=num_epochs, validation_data=val_dataset)