9个特征的csv文件怎么使用tranformer模型进行三分类python代码

要使用 transformer 模型对具有 9 个特征的 CSV 文件进行三分类，你可以按照以下步骤编写 Python 代码：

1. 导入所需的库和模块：

import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from transformers import BertTokenizer, BertForSequenceClassification, AdamW

2. 加载数据：

# 读取 CSV 文件
data = pd.read_csv('your_file.csv')

# 获取特征列和标签列
features = data['your_features_column'].values
labels = data['your_labels_column'].values

# 将标签转换为整数（0、1、2）
labels = labels.astype(int)

3. 数据预处理：

# 划分训练集和测试集
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.2, random_state=42)

# 初始化 tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)

# 编码文本数据
train_encodings = tokenizer(list(train_features), truncation=True, padding=True)
test_encodings = tokenizer(list(test_features), truncation=True, padding=True)

# 转换为 PyTorch tensors
train_dataset = torch.utils.data.TensorDataset(torch.tensor(train_encodings['input_ids']),
                                               torch.tensor(train_encodings['attention_mask']),
                                               torch.tensor(train_labels))
test_dataset = torch.utils.data.TensorDataset(torch.tensor(test_encodings['input_ids']),
                                              torch.tensor(test_encodings['attention_mask']),
                                              torch.tensor(test_labels))

4. 初始化并训练模型：

# 初始化分类模型
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=3)

# 设置训练参数
optimizer = AdamW(model.parameters(), lr=1e-5)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)

# 训练模型
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
model.train()

for epoch in range(10):
    for batch in train_loader:
        input_ids, attention_mask, labels = batch
        input_ids = input_ids.to(device)
        attention_mask = attention_mask.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()
        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        loss.backward()
        optimizer.step()

# 保存模型
model.save_pretrained('saved_model')

5. 测试模型：

# 加载保存的模型
model = BertForSequenceClassification.from_pretrained('saved_model')

# 预测测试集
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)
model.eval()

predictions = []
with torch.no_grad():
    for batch in test_loader:
        input_ids, attention_mask, labels = batch
        input_ids = input_ids.to(device)
        attention_mask = attention_mask.to(device)

        outputs = model(input_ids, attention_mask=attention_mask)
        logits = outputs.logits
        _, preds = torch.max(logits, dim=1)
        predictions.extend(preds.tolist())

# 输出分类报告
target_names = ['class_0', 'class_1', 'class_2']
print(classification_report(test_labels, predictions, target_names=target_names))

请确保替换代码中的以下内容：
- 'your_file.csv'：你的 CSV 文件路径
- 'your_features_column'：包含特征的列名称
- 'your_labels_column'：包含标签的列名称

还需要根据你的具体需求调整模型参数、训练参数和保存模型的路径。这个示例使用了预训练的 BERT 模型，你可以根据需要选择其他预训练模型。