Hey小伙伴们！👋 今天要和大家分享一个超酷的项目：用PyTorch和Transformer来处理文本分类任务！如果你对自然语言处理感兴趣，或者正在寻找一个实践项目来提升你的技能，那么这篇教程绝对不容错过！👩💻✨
📚 项目背景
Transformers是近年来自然语言处理领域的一大突破，它们能够有效地处理长序列数据，并且在很多任务上取得了非常好的效果。在这个项目中，我们将使用一个简单的Transformer模型来对文本进行情感分析！
💻 环境准备
首先，确保你安装了PyTorch和其他必需的库。可以使用pip来安装：
pip install torch transformers
📝 代码详解
接下来，我们一步步来实现这个项目吧！👇
1. 导入所需库
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
2. 数据预处理
这里我们使用一个简单的文本数据集，你可以替换为你自己的数据集。
# 假设我们有一些文本数据和对应的标签
texts = ["I love this movie!", "This is terrible.", "Absolutely amazing!", "What a waste of time."]
labels = [1, 0, 1, 0] # 1代表正面评价，0代表负面评价# 划分训练集和测试集
train_texts, test_texts, train_labels, test_labels = train_test_split(texts, labels, test_size=0.2, random_state=42)# 加载预训练的BERT模型和分词器
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)# 定义数据集类
class TextDataset(Dataset):def __init__(self, texts, labels, tokenizer, max_len):self.texts = textsself.labels = labelsself.tokenizer = tokenizerself.max_len = max_lendef __len__(self):return len(self.texts)def __getitem__(self, item):text = str(self.texts[item])label = self.labels[item]encoding = self.tokenizer.encode_plus(text,add_special_tokens=True,max_length=self.max_len,return_token_type_ids=False,pad_to_max_length=True,return_attention_mask=True,return_tensors='pt',)return {'text': text,'input_ids': encoding['input_ids'].flatten(),'attention_mask': encoding['attention_mask'].flatten(),'labels': torch.tensor(label, dtype=torch.long)}# 创建数据加载器
def create_data_loader(texts, labels, tokenizer, max_len, batch_size):ds = TextDataset(texts, labels, tokenizer, max_len)return DataLoader(ds, batch_size=batch_size, num_workers=2)# 定义参数
MAX_LEN = 128
BATCH_SIZE = 8
train_data_loader = create_data_loader(train_texts, train_labels, tokenizer, MAX_LEN, BATCH_SIZE)
test_data_loader = create_data_loader(test_texts, test_labels, tokenizer, MAX_LEN, BATCH_SIZE)
3. 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)# 定义损失函数和优化器
loss_fn = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)# 训练循环
def train_epoch(model, data_loader, loss_fn, optimizer, device):model = model.train()losses = []correct_predictions = 0for d in data_loader:input_ids = d["input_ids"].to(device)attention_mask = d["attention_mask"].to(device)targets = d["labels"].to(device)outputs = model(input_ids=input_ids, attention_mask=attention_mask)_, preds = torch.max(outputs.logits, dim=1)loss = loss_fn(outputs.logits, targets)correct_predictions += torch.sum(preds == targets)losses.append(loss.item())loss.backward()nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)optimizer.step()optimizer.zero_grad()return correct_predictions.double() / len(data_loader.dataset), np.mean(losses)# 测试模型
def eval_model(model, data_loader, loss_fn, device):model = model.eval()losses = []correct_predictions = 0with torch.no_grad():for d in data_loader:input_ids = d["input_ids"].to(device)attention_mask = d["attention_mask"].to(device)targets = d["labels"].to(device)outputs = model(input_ids=input_ids, attention_mask=attention_mask)_, preds = torch.max(outputs.logits, dim=1)loss = loss_fn(outputs.logits, targets)correct_predictions += torch.sum(preds == targets)losses.append(loss.item())return correct_predictions.double() / len(data_loader.dataset), np.mean(losses)# 训练和测试
EPOCHS = 5
for epoch in range(EPOCHS):train_acc, train_loss = train_epoch(model, train_data_loader, loss_fn, optimizer, device)print(f'Epoch {epoch + 1}/{EPOCHS}')print(f'Train loss {train_loss} accuracy {train_acc}')test_acc, test_loss = eval_model(model, test_data_loader, loss_fn, device)print(f'Test loss {test_loss} accuracy {test_acc}')print('-' * 50)
完整代码如下：
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np# 假设我们有一些文本数据和对应的标签
texts = ["I love this movie!", "This is terrible.", "Absolutely amazing!", "What a waste of time."]
labels = [1, 0, 1, 0] # 1代表正面评价，0代表负面评价# 划分训练集和测试集
train_texts, test_texts, train_labels, test_labels = train_test_split(texts, labels, test_size=0.2, random_state=42)# 加载预训练的BERT模型和分词器
bert_model_path = 'huggingfaceLib/bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(bert_model_path)
model = AutoModelForSequenceClassification.from_pretrained(bert_model_path, num_labels=2)# 定义数据集类
class TextDataset(Dataset):def __init__(self, texts, labels, tokenizer, max_len):self.texts = textsself.labels = labelsself.tokenizer = tokenizerself.max_len = max_lendef __len__(self):return len(self.texts)def __getitem__(self, item):text = str(self.texts[item])label = self.labels[item]encoding = self.tokenizer.encode_plus(text,add_special_tokens=True,max_length=self.max_len,return_token_type_ids=False,pad_to_max_length=True,return_attention_mask=True,return_tensors='pt',)return {'text': text,'input_ids': encoding['input_ids'].flatten(),'attention_mask': encoding['attention_mask'].flatten(),'labels': torch.tensor(label, dtype=torch.long)}# 创建数据加载器
def create_data_loader(texts, labels, tokenizer, max_len, batch_size):ds = TextDataset(texts, labels, tokenizer, max_len)return DataLoader(ds, batch_size=batch_size, num_workers=2)# 训练循环
def train_epoch(model, data_loader, loss_fn, optimizer, device):model = model.train()losses = []correct_predictions = 0for d in data_loader:input_ids = d["input_ids"].to(device)attention_mask = d["attention_mask"].to(device)targets = d["labels"].to(device)outputs = model(input_ids=input_ids, attention_mask=attention_mask)_, preds = torch.max(outputs.logits, dim=1)loss = loss_fn(outputs.logits, targets)correct_predictions += torch.sum(preds == targets)losses.append(loss.item())loss.backward()nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)optimizer.step()optimizer.zero_grad()return correct_predictions.double() / len(data_loader.dataset), np.mean(losses)# 测试模型
def eval_model(model, data_loader, loss_fn, device):model = model.eval()losses = []correct_predictions = 0with torch.no_grad():for d in data_loader:input_ids = d["input_ids"].to(device)attention_mask = d["attention_mask"].to(device)targets = d["labels"].to(device)outputs = model(input_ids=input_ids, attention_mask=attention_mask)_, preds = torch.max(outputs.logits, dim=1)loss = loss_fn(outputs.logits, targets)correct_predictions += torch.sum(preds == targets)losses.append(loss.item())return correct_predictions.double() / len(data_loader.dataset), np.mean(losses)if __name__ == '__main__':# 定义参数MAX_LEN = 128BATCH_SIZE = 8train_data_loader = create_data_loader(train_texts, train_labels, tokenizer, MAX_LEN, BATCH_SIZE)test_data_loader = create_data_loader(test_texts, test_labels, tokenizer, MAX_LEN, BATCH_SIZE)device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')model.to(device)# 定义损失函数和优化器loss_fn = nn.CrossEntropyLoss().to(device)optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)# 训练和测试EPOCHS = 5for epoch in range(EPOCHS):train_acc, train_loss = train_epoch(model, train_data_loader, loss_fn, optimizer, device)print(f'Epoch {epoch + 1}/{EPOCHS}')print(f'Train loss {train_loss} accuracy {train_acc}')test_acc, test_loss = eval_model(model, test_data_loader, loss_fn, device)print(f'Test loss {test_loss} accuracy {test_acc}')print('-' * 50)🏆 成果展示
通过训练，我们的模型能够在文本分类任务上取得不错的准确率！这不仅是一个很好的入门项目，也是进一步探索自然语言处理的好起点。如果你对这个项目有任何疑问，或者想要分享你的成果，欢迎留言讨论！喜欢我的请点赞，关注收藏我，我将带来更多人工智能相关知识👩💻✨
训练效果图：
#PyTorch #Transformer #自然语言处理 #情感分析
