python用transformer库代码

可以通过安装transformers库来使用transformer模型，下面是一个例子：

from transformers import pipeline

translator = pipeline('translation_en_to_fr')
result = translator('Hello world!')
print(result)

这段代码使用transformers库中的pipeline函数创建了一个翻译模型，然后使用该模型将英语文本"Hello world!"翻译成法语。

相关问题

手把手教你用python代码transformer

好的，我可以为您提供一些关于使用Python代码实现Transformer的指导。

首先，您需要安装PyTorch和torchtext库。然后，您可以按照以下步骤实现Transformer模型：

1. 定义输入和输出的词汇表，并使用torchtext库加载数据集。
2. 定义Transformer模型的编码器和解码器部分。
3. 实现自注意力机制（self-attention）和前向神经网络（feed-forward network）。
4. 实现残差连接（residual connection）和层归一化（layer normalization）。
5. 定义Transformer模型的训练和评估过程。

下面是一个简单的示例代码，用于实现一个基本的Transformer模型：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchtext.datasets import TranslationDataset, Multi30k
from torchtext.data import Field, BucketIterator

# 定义输入和输出的词汇表
SRC = Field(tokenize='spacy', tokenizer_language='de', init_token='<sos>', eos_token='<eos>', lower=True)
TRG = Field(tokenize='spacy', tokenizer_language='en', init_token='<sos>', eos_token='<eos>', lower=True)

# 加载数据集
train_data, valid_data, test_data = Multi30k.splits(exts=('.de', '.en'), fields=(SRC, TRG))
SRC.build_vocab(train_data, min_freq=2)
TRG.build_vocab(train_data, min_freq=2)

# 定义Transformer模型的编码器和解码器部分
class Encoder(nn.Module):
    def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device):
        super().__init__()
        self.device = device
        self.tok_embedding = nn.Embedding(input_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)
        self.layers = nn.ModuleList([EncoderLayer(hid_dim, n_heads, pf_dim, dropout, device) for _ in range(n_layers)])
        self.dropout = nn.Dropout(dropout)
        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

    def forward(self, src, src_mask):
        # src: [batch_size, src_len]
        # src_mask: [batch_size, 1, 1, src_len]
        batch_size = src.shape[0]
        src_len = src.shape[1]
        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)
        # pos: [batch_size, src_len]
        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))
        for layer in self.layers:
            src = layer(src, src_mask)
        return src

class EncoderLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
        super().__init__()
        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src, src_mask):
        

python中的Transformer应用代码举例

下面是一个使用PyTorch实现Transformer模型进行文本分类的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import IMDB
from torchtext.data import Field, LabelField, BucketIterator

# 定义Field
text_field = Field(tokenize='spacy', lower=True)
label_field = LabelField(dtype=torch.float)

# 加载数据集
train_data, test_data = IMDB.splits(text_field, label_field)
text_field.build_vocab(train_data, max_size=10000, vectors='glove.6B.100d')
label_field.build_vocab(train_data)

# 定义模型
class TransformerClassifier(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers, num_heads, dropout):
        super(TransformerClassifier, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(embedding_dim, num_heads, hidden_dim, dropout), num_layers)
        self.fc = nn.Linear(embedding_dim, 1)
        self.dropout = nn.Dropout(dropout)
    
    def forward(self, text):
        embedded = self.embedding(text)
        embedded = embedded.permute(1, 0, 2)
        encoded = self.transformer(embedded)
        pooled = encoded.mean(dim=0)
        output = self.fc(pooled)
        return self.dropout(output)

# 定义超参数
vocab_size = len(text_field.vocab)
embedding_dim = 100
hidden_dim = 256
num_layers = 6
num_heads = 8
dropout = 0.2
batch_size = 32

# 初始化模型和优化器
model = TransformerClassifier(vocab_size, embedding_dim, hidden_dim, num_layers, num_heads, dropout)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
criterion = nn.BCEWithLogitsLoss()

# 定义迭代器
train_iter, test_iter = BucketIterator.splits((train_data, test_data), batch_size=batch_size)

# 训练模型
for epoch in range(10):
    for batch in train_iter:
        text, label = batch.text, batch.label
        optimizer.zero_grad()
        output = model(text).squeeze(1)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()
    print(f'Epoch: {epoch+1}, Loss: {loss.item():.4f}')

# 评估模型
with torch.no_grad():
    correct = 0
    total = 0
    for batch in test_iter:
        text, label = batch.text, batch.label
        output = model(text).squeeze(1)
        predicted = torch.round(torch.sigmoid(output))
        total += label.size(0)
        correct += (predicted == label).sum().item()
    print(f'Accuracy: {100*correct/total:.2f}%')

在这个代码中，我们使用了torchtext库加载IMDB数据集，并使用Field和LabelField定义了text和label的处理方法。然后，我们定义了一个TransformerClassifier类，使用nn.TransformerEncoder和nn.Linear搭建了Transformer模型，并定义了forward方法用于进行前向传播。在训练时，我们使用Adam优化器和BCEWithLogitsLoss损失函数，并使用BucketIterator.splits定义了训练和测试数据的迭代器。最后，我们进行了10次epoch的训练，并在测试集上进行了模型评估，输出了准确率的结果。