Python 搭建 NLP 模型的详细步骤与代码

四、特征工程

在深度学习之前，我们可以使用 TF-IDF 或 Word2Vec 提取文本特征。

1. TF-IDF

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(max_features=5000)
X_train = vectorizer.fit_transform(train_texts)
X_test = vectorizer.transform(test_texts)

2. Word2Vec

使用 gensim 训练 Word2Vec 词向量。

from gensim.models import Word2Vec

sentences = [text.split() for text in train_texts]
word2vec_model = Word2Vec(sentences, vector_size=100, window=5, min_count=5, workers=4)
word2vec_model.save("word2vec.model")

五、搭建 NLP 模型

1. 逻辑回归

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

model = LogisticRegression()
model.fit(X_train, train_labels)

# 预测
preds = model.predict(X_test)
print("Logistic Regression Accuracy:", accuracy_score(test_labels, preds))

2. LSTM 深度学习模型

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

# 定义 LSTM 模型
class LSTMModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim):
        super(LSTMModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        embedded = self.embedding(x)
        _, (hidden, _) = self.lstm(embedded)
        return self.fc(hidden.squeeze(0))

# 超参数
VOCAB_SIZE = 5000
EMBEDDING_DIM = 100
HIDDEN_DIM = 128
OUTPUT_DIM = 1

model = LSTMModel(VOCAB_SIZE, EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM)

# 训练模型
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(5):
    model.train()
    optimizer.zero_grad()
    outputs = model(torch.randint(0, VOCAB_SIZE, (len(train_labels), 50)))
    loss = criterion(outputs.squeeze(), torch.tensor(train_labels.values, dtype=torch.float))
    loss.backward()
    optimizer.step()
    print(f"Epoch {epoch+1}, Loss: {loss.item()}")

六、使用预训练的 BERT 模型

from transformers import BertTokenizer, BertForSequenceClassification
from torch.utils.data import DataLoader

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# Tokenize 数据
train_encodings = tokenizer(list(train_texts), truncation=True, padding=True, max_length=512, return_tensors="pt")
test_encodings = tokenizer(list(test_texts), truncation=True, padding=True, max_length=512, return_tensors="pt")

# 转换为 PyTorch Dataset
class IMDbDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __len__(self):
        return len(self.labels)

    def __getitem__(self, idx):
        item = {key: val[idx] for key, val in self.encodings.items()}
        item["labels"] = torch.tensor(self.labels[idx])
        return item

train_dataset = IMDbDataset(train_encodings, list(train_labels))
test_dataset = IMDbDataset(test_encodings, list(test_labels))

# 训练 BERT
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)

model.train()
for epoch in range(3):
    for batch in train_loader:
        optimizer.zero_grad()
        outputs = model(**batch)
        loss = outputs.loss
        loss.backward()
        optimizer.step()
    print(f"Epoch {epoch+1}, Loss: {loss.item()}")

七、模型评估

from sklearn.metrics import classification_report

model.eval()
preds = []
with torch.no_grad():
    for batch in test_dataset:
        output = model(**batch)
        preds.append(torch.argmax(output.logits, axis=1).numpy())

print(classification_report(test_labels, preds))

八、部署模型

可以使用 FastAPI 部署 NLP 模型：

from fastapi import FastAPI
import torch

app = FastAPI()

@app.post("/predict/")
def predict(text: str):
    encoding = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        output = model(**encoding)
        pred = torch.argmax(output.logits, axis=1).item()
    return {"sentiment": "positive" if pred == 1 else "negative"}

运行：

uvicorn main:app --reload

总结

本文介绍了 NLP 模型的完整实现流程：

1. 数据预处理
2. 特征工程
3. 机器学习模型
4. 深度学习 LSTM
5. BERT 预训练模型
6. 模型部署

可以根据业务需求选择合适的 NLP 方案。