一站式网上办事大厅

import pandas as pd

# 读取数据

df = pd.read_csv('user_forms.csv')

# 数据清洗：去除空值

df.dropna(inplace=True)

# 转换数据类型

df['submission_date'] = pd.to_datetime(df['submission_date'])

# 分类标签

df['category'] = df['form_type'].map({

'tax': '税务',

'visa': '签证',

'passport': '护照'

})

print(df.head())

import torch

import torch.nn as nn

from sklearn.model_selection import train_test_split

from sklearn.feature_extraction.text import TfidfVectorizer

# 假设我们有文本数据和对应的标签

texts = df['form_content'].tolist()

labels = df['category'].tolist()

# 特征提取

vectorizer = TfidfVectorizer(max_features=5000)

X = vectorizer.fit_transform(texts).toarray()

# 标签编码

label_to_index = {label: idx for idx, label in enumerate(set(labels))}

y = [label_to_index[label] for label in labels]

# 划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 构建神经网络模型

class TextClassifier(nn.Module):

def __init__(self, input_dim, output_dim):

super(TextClassifier, self).__init__()

self.fc1 = nn.Linear(input_dim, 128)

self.relu = nn.ReLU()

self.fc2 = nn.Linear(128, output_dim)

def forward(self, x):

x = self.fc1(x)

x = self.relu(x)

x = self.fc2(x)

return x

model = TextClassifier(input_dim=X.shape[1], output_dim=len(label_to_index))

# 定义损失函数和优化器

criterion = nn.CrossEntropyLoss()

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型

for epoch in range(10):

for i in range(len(X_train)):

inputs = torch.tensor(X_train[i]).float()

targets = torch.tensor(y_train[i]).long()

outputs = model(inputs)

loss = criterion(outputs, targets)

optimizer.zero_grad()

loss.backward()

optimizer.step()

print(f'Epoch {epoch+1}, Loss: {loss.item()}')

from transformers import BertTokenizer, BertForSequenceClassification

import torch

# 加载预训练的BERT模型和分词器

model_name = 'bert-base-uncased'

tokenizer = BertTokenizer.from_pretrained(model_name)

model = BertForSequenceClassification.from_pretrained(model_name, num_labels=len(label_to_index))

# 对文本进行编码

encoded_inputs = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors='pt')

# 模型预测

with torch.no_grad():

outputs = model(**encoded_inputs)

predictions = torch.argmax(outputs.logits, dim=1)

print(predictions)