如何在我的模型中使用预训练的 bert 模型作为嵌入层？

如何解决如何在我的模型中使用预训练的 bert 模型作为嵌入层？

我在我的功能模型中使用了这个由 bert-for-tf2 预先训练的模型，如下所示：

def build_model(model_dir,batch_size,max_seq_num,max_seq_len):
    bert_params = bert.params_from_pretrained_ckpt(model_dir)
    l_bert = bert.BertModelLayer.from_params(bert_params,name="bert",trainable=False)

    input_ids = tf.keras.layers.Input(shape=(max_seq_num,max_seq_len,),dtype='int32',name='input_ids')
    reshaped_input_ids = tf.reshape(input_ids,(batch_size * max_seq_num,max_seq_len))

    token_type_ids = tf.keras.layers.Input(shape=(max_seq_num,name='token_type')
    reshaped_token_type_ids = tf.reshape(token_type_ids,max_seq_len))

    mask_ids = tf.keras.layers.Input(shape=(max_seq_num,name='mask_ids')
    reshaped_mask_ids = tf.reshape(mask_ids,max_seq_len))

    # provide a custom token_type/segment id as a layer input
    bert_embedd = l_bert([reshaped_input_ids,reshaped_token_type_ids],mask=reshaped_mask_ids)  # [batch_size*max_seq_num,hidden_size]
    model = tf.keras.models.Model(inputs=[input_ids,token_type_ids,mask_ids],outputs=bert_embedd)
    model.build(input_shape=[(batch_size,max_seq_len),(batch_size,max_seq_len)])
    bert.load_bert_weights(l_bert,os.path.join(model_dir,"bert_model.ckpt"))  # should be called after model.build()
    model.summary()
    tf.keras.utils.plot_model(model,show_shapes=True)
    learning_rate = 1e-2
    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),loss=tf.keras.losses.MeanSquaredError(),metrics=['mse'])
    return model

我可以成功构建模型。但是当我将数据输入模型时：

model = build_model(path,16,16)
x_input = np.random.randint(0,10000,size=[16,16])
x_token_type = [[[i] * 16 for i in range(16)] for _ in range(16)]
x_mask = np.ones(shape=[16,16])
y_predict = model(x_input,x_token_type,x_mask)

出现错误：

ValueError: Layer model expects 2 input(s),but it received 1 input tensors. Inputs received: ...