TensorFlow中的博尔特预训练代码

下面是使用TensorFlow实现BERT预训练的代码示例：

import tensorflow as tf
from transformers import BertTokenizer, TFBertModel

# 载入预训练的BERT模型和词汇表
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
model = TFBertModel.from_pretrained('bert-base-chinese')

# 定义数据集
class MyDataset(tf.keras.utils.Sequence):
    def __init__(self, texts, batch_size):
        self.texts = texts
        self.batch_size = batch_size
        
    def __len__(self):
        return len(self.texts) // self.batch_size
    
    def __getitem__(self, idx):
        text_batch = self.texts[idx * self.batch_size:(idx + 1) * self.batch_size]
        tokenized_text = tokenizer(text_batch, padding=True, truncation=True, return_tensors='tf')
        return tokenized_text
    
# 定义训练参数
batch_size = 32
num_epochs = 10
learning_rate = 1e-4

# 定义模型和优化器
class BertPretrain(tf.keras.Model):
    def __init__(self):
        super(BertPretrain, self).__init__()
        self.bert = model

    def call(self, inputs):
        input_ids = inputs['input_ids']
        attention_mask = inputs['attention_mask']
        outputs = self.bert(input_ids, attention_mask)
        return outputs.last_hidden_state
    
model = BertPretrain()
optimizer = tf.keras.optimizers.Adam(learning_rate)

# 载入数据集
dataset = MyDataset(texts, batch_size)
dataloader = tf.data.Dataset.from_generator(lambda: dataset,
                                            output_types=({'input_ids': tf.int32, 'attention_mask': tf.int32}),
                                            output_shapes=({'input_ids': (None, None), 'attention_mask': (None, None)}))

# 训练模型
for epoch in range(num_epochs):
    for step, batch in enumerate(dataloader):
        with tf.GradientTape() as tape:
            outputs = model(batch)
            loss = tf.reduce_mean(outputs)

        gradients = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))

        if step % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, step+1, len(dataloader), loss.numpy()))

在这个例子中，我们首先使用`BertTokenizer`和`TFBertModel`从预训练模型中载入BERT模型和词汇表，然后定义了一个数据集`MyDataset`和数据加载器`dataloader`，并将其用于训练模型。我们还定义了一个`BertPretrain`类，用于实例化BERT模型和定义前向传递。最后，我们使用Adam优化器和均方误差损失训练模型。