写一个大型语言模型,给出代码
时间: 2023-02-06 19:24:59 浏览: 96
我们可以使用 TensorFlow 或 PyTorch 等框架来构建一个大型语言模型。这里是一个使用 TensorFlow 2.0 和 TensorFlow Datasets 构建 Transformer 模型的例子:
```python
import tensorflow as tf
import tensorflow_datasets as tfds
# 下载并准备数据集
dataset, info = torchds.load('wiki_multilingual/wiki_text_103', with_info=True)
train_dataset = dataset['train']
# 创建数据集迭代器
batch_size = 32
train_dataset = train_dataset.shuffle(10000).batch(batch_size)
# 定义模型
class Transformer(tf.keras.Model):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
target_vocab_size, rate=0.1):
super(Transformer, self).__init__()
self.encoder = Encoder(num_layers, d_model, num_heads, dff,
input_vocab_size, rate)
self.decoder = Decoder(num_layers, d_model, num_heads, dff,
target_vocab_size, rate)
self.final_layer = tf.keras.layers.Dense(target_vocab_size)
def call(self, inp, tar, training, enc_padding_mask,
look_ahead_mask, dec_padding_mask):
enc_output = self.encoder(inp, training, enc_padding_mask) # (batch_size, inp_seq_len, d_model)
# dec_output.shape == (batch_size, tar_seq_len, d_model)
dec_output, attention_weights = self.decoder(
tar, enc_output, training, look_ahead_mask, dec_padding_mask)
final_output = self.final_layer(dec_output) # (batch_size, tar_seq_len, target_vocab_size)
return final_output, attention_weights
# 初始化模型并进行训练
transformer = Transformer(num_layers, d_model, num_heads, dff,
input_vocab_size, target_vocab_size)
# 使用 Adam 优化器和交叉熵损失函数
optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98,
epsilon=