能不能写一个sentence-transformers的神经网络代码

当然可以，以下是一个使用sentence-transformers的神经网络代码：

from sentence_transformers import SentenceTransformer, SentencesDataset, losses, InputExample
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch

# 加载预训练模型
model = SentenceTransformer('bert-base-nli-mean-tokens')

# 定义训练集和测试集
train_sentences = ['The cat is on the mat.', 'Dogs are running in the park.']
train_labels = [0, 1]
train_examples = [InputExample(texts=[s], label=l) for s, l in zip(train_sentences, train_labels)]

test_sentences = ['The sun is shining.', 'The sky is blue.']
test_labels = [0, 0]
test_examples = [InputExample(texts=[s], label=l) for s, l in zip(test_sentences, test_labels)]

# 定义数据集
train_dataset = SentencesDataset(train_examples, model)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=16)
train_loss = losses.CosineSimilarityLoss(model=model)

test_dataset = SentencesDataset(test_examples, model)
test_dataloader = DataLoader(test_dataset, shuffle=False, batch_size=16)
evaluator = losses.CosineSimilarityEvaluator(test_dataloader)

# 定义训练参数
num_epochs = 10
warmup_steps = int(len(train_dataset) * num_epochs * 0.1)
model_save_path = 'my_model'

# 定义优化器和学习率调度器
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)
scheduler = torch.optim.lr_scheduler.LinearLR(optimizer, warmup_steps=warmup_steps, total_steps=len(train_dataset) * num_epochs)

# 定义TensorBoard输出
writer = SummaryWriter()

# 训练模型
model.train()
for epoch in range(num_epochs):
    for step, batch in enumerate(train_dataloader):
        loss = train_loss(batch)
        writer.add_scalar('Train loss', loss, global_step=epoch * len(train_dataloader) + step)
        loss.backward()
        optimizer.step()
        scheduler.step()
        model.zero_grad()

    # 在测试集上评估模型
    model.eval()
    result = evaluator(model, output_path=model_save_path)
    writer.add_scalar('Test loss', result['cosine_similarity_loss'], global_step=epoch)
    writer.add_scalar('Test accuracy', result['accuracy'], global_step=epoch)
    model.train()

# 保存模型
model.save(model_save_path)

此代码实现了一个简单的训练过程，使用了 `bert-base-nli-mean-tokens` 模型进行训练，并使用了交叉熵损失函数和余弦相似度评估函数。此外，代码还使用了 PyTorch 内置的优化器和学习率调度器，并使用了 TensorBoard 进行训练过程的可视化。