使用Pytorch深度学习框架实现seq2seq机器翻译模型，数据集为中文与英文语句对，共10000条语句对，输出为该模型的BLEU评分值

使用PyTorch实现Seq2Seq机器翻译模型，首先你需要准备一个包含10000条中文-英文句子对的数据集。这个过程通常包括数据预处理，如分词、编码成数字序列以及创建词汇表。接下来，可以按照以下步骤操作：

1. 导入所需的库：

import torch
from torchtext.data import Field, BucketIterator
from torchtext.datasets import Multi30k
from transformers import EncoderDecoderModel, BertTokenizer

2. 定义数据字段并加载数据集：

src_field = Field(tokenize='spacy', tokenizer_language='zh', init_token='<sos>', eos_token='<eos>')
trg_field = Field(tokenize='spacy', tokenizer_language='en', init_token='<sos>', eos_token='<eos>')

train_data, valid_data, test_data = Multi30k.splits(exts=('.de', '.en'), fields=(src_field, trg_field))

3. 对数据进行预处理和构建迭代器：

src_field.build_vocab(train_data, min_freq=2)
trg_field.build_vocab(train_data, min_freq=2)

train_iterator, valid_iterator, test_iterator = BucketIterator.splits((train_data, valid_data, test_data), batch_size=16, device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'))

4. 初始化BertEncoder和自定义解码器，并训练模型：

model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-chinese', 'bert-base-multilingual-cased')
model.train()

optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss(ignore_index=trg_field.vocab.stoi['<pad>'])

# 训练和评估循环
for epoch in range(num_epochs):
    for batch in train_iterator:
        src = batch.src
        trg = batch.trg

        optimizer.zero_grad()
        output = model(src, trg[:, :-1])
        loss = criterion(output[1:].view(-1, output.shape[-1]), trg[:, 1:].contiguous().view(-1))
        loss.backward()
        optimizer.step()

5. 计算测试集的BLEU评分：

def evaluate(model, iterator, field):
    model.eval()
    with torch.no_grad():
        scores = []
        for batch in iterator:
            src = batch.src
            trg = batch.trg

            output = model.generate(src)
            output = [field.vocab.itos[i] for i in output]
            reference = [[field.vocab.itos[i] for i in trg[batch_idx]] for batch_idx in range(trg.shape[0])]

            # BLEU calculation
            bleu = sacrebleu.corpus_bleu(output, reference)
            scores.append(bleu.score)
        
        return sum(scores) / len(scores)

test_bleu = evaluate(model, test_iterator, trg_field)
print(f"Test BLEU Score: {test_bleu}")