使用transformers包给我一个文本分类的模型

好的，以下是使用 transformers 包构建文本分类模型的示例代码：

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
import torch

# 加载预训练模型tokenizer和模型
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 定义文本分类任务的数据集
train_texts = ["This is a positive example.", "This is a negative example."]
train_labels = [1, 0]
train_encodings = tokenizer(train_texts, truncation=True, padding=True)

class TextClassificationDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels
        
    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item
    
    def __len__(self):
        return len(self.labels)

train_dataset = TextClassificationDataset(train_encodings, train_labels)

# 定义训练参数
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=1,
    per_device_train_batch_size=2,
    per_device_eval_batch_size=2,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir='./logs',
    logging_steps=10,
)

# 训练模型
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)
trainer.train()

这个示例代码使用了 DistilBERT 模型，可以根据实际情况选择不同的预训练模型。训练数据集中包含两个样本，分别为正例和反例。你需要根据自己的数据集修改训练数据集的内容。训练参数可以根据实际情况进行调整，比如训练轮数、批次大小等。