kaggle中 Contradictory, My Dear Watson项目用pytorch怎么写，给出每个步骤的代码示例

Contradictory, My Dear Watson是一个文本分类任务，可以使用PyTorch实现。以下是使用PyTorch进行Contradictory, My Dear Watson的一些基本步骤和代码示例：

1. 导入必要的库和数据集

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from sklearn.model_selection import train_test_split

# 读取数据集
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')

2. 数据预处理

# 将标签转换为数字
label2id = {'contradiction': 0, 'entailment': 1, 'neutral': 2}
train_df['label'] = [label2id[x] for x in train_df['label']]

# 划分训练集和验证集
train_text, val_text, train_label, val_label = train_test_split(train_df['premise'].values, train_df['hypothesis'].values, train_df['label'].values, test_size=0.2)

# 构建词表
vocab = {}
for text in train_text:
    words = text.split()
    for word in words:
        if word not in vocab:
            vocab[word] = len(vocab)

# 将文本转换为数字序列
def text_to_seq(text, vocab):
    words = text.split()
    seq = [vocab[word] for word in words]
    return seq
train_text_seq = [text_to_seq(text, vocab) for text in train_text]
val_text_seq = [text_to_seq(text, vocab) for text in val_text]
test_text_seq = [text_to_seq(text, vocab) for text in test_df['premise'].values]

3. 定义模型

class TextClassifier(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_size, num_classes):
        super(TextClassifier, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        x = self.embedding(x)
        output, (h_n, c_n) = self.lstm(x)
        x = h_n[-1, :, :]
        x = self.fc(x)
        return x

4. 训练模型

# 定义超参数
vocab_size = len(vocab)
embedding_dim = 128
hidden_size = 128
num_classes = 3
lr = 0.001
batch_size = 64
num_epochs = 10

# 初始化模型和优化器
model = TextClassifier(vocab_size, embedding_dim, hidden_size, num_classes)
optimizer = optim.Adam(model.parameters(), lr=lr)

# 定义损失函数
criterion = nn.CrossEntropyLoss()

# 将数据转换为Tensor
train_text_seq = [torch.LongTensor(seq) for seq in train_text_seq]
train_label = torch.LongTensor(train_label)
val_text_seq = [torch.LongTensor(seq) for seq in val_text_seq]
val_label = torch.LongTensor(val_label)

# 定义训练函数和验证函数
def train(model, optimizer, criterion, train_text_seq, train_label, batch_size):
    model.train()
    train_loss = 0
    correct = 0
    total = 0
    for i in range(0, len(train_text_seq), batch_size):
        if i + batch_size > len(train_text_seq):
            end = len(train_text_seq)
        else:
            end = i + batch_size
        input_seq = train_text_seq[i:end]
        target = train_label[i:end]
        optimizer.zero_grad()
        output = model(input_seq)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * len(input_seq)
        _, predicted = torch.max(output.data, 1)
        total += target.size(0)
        correct += (predicted == target).sum().item()
    train_loss /= len(train_text_seq)
    train_acc = correct / total
    return train_loss, train_acc

def evaluate(model, criterion, val_text_seq, val_label, batch_size):
    model.eval()
    val_loss = 0
    correct = 0
    total = 0
    with torch.no_grad():
        for i in range(0, len(val_text_seq), batch_size):
            if i + batch_size > len(val_text_seq):
                end = len(val_text_seq)
            else:
                end = i + batch_size
            input_seq = val_text_seq[i:end]
            target = val_label[i:end]
            output = model(input_seq)
            loss = criterion(output, target)
            val_loss += loss.item() * len(input_seq)
            _, predicted = torch.max(output.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
    val_loss /= len(val_text_seq)
    val_acc = correct / total
    return val_loss, val_acc

# 开始训练
for epoch in range(num_epochs):
    train_loss, train_acc = train(model, optimizer, criterion, train_text_seq, train_label, batch_size)
    val_loss, val_acc = evaluate(model, criterion, val_text_seq, val_label, batch_size)
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'.format(epoch+1, num_epochs, train_loss, train_acc, val_loss, val_acc))

5. 模型预测和提交结果

# 将测试集转换为Tensor
test_text_seq = [torch.LongTensor(seq) for seq in test_text_seq]

# 对测试集进行预测
model.eval()
predictions = []
with torch.no_grad():
    for i in range(0, len(test_text_seq), batch_size):
        if i + batch_size > len(test_text_seq):
            end = len(test_text_seq)
        else:
            end = i + batch_size
        input_seq = test_text_seq[i:end]
        output = model(input_seq)
        _, predicted = torch.max(output.data, 1)
        predictions += predicted.tolist()

# 将结果保存为csv文件
submission_df = pd.DataFrame({'id': test_df['id'], 'prediction': predictions})
submission_df.to_csv('submission.csv', index=False)