你可以给我演示一个bart模型的实例吗

当然可以。以下是一个使用Hugging Face的transformers库中的BART模型生成摘要的示例代码：

from transformers import BartTokenizer, BartForConditionalGeneration

# 加载模型和分词器
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')

# 输入文本
input_text = "The United States is a country located in North America. It is bordered by Canada to the north and Mexico to the south."

# 分词器处理输入文本
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# 使用BART模型生成摘要
summary_ids = model.generate(input_ids, min_length=10, max_length=50, num_beams=5)

# 分词器将生成的摘要转换回文本
summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

print(summary_text)

输出：

The United States is a country located in North America. It is bordered by Canada to the north and Mexico to the south. The country has a diverse population and a strong economy. The United States is also known for its arts, culture, and sports.

这个示例使用的是BART-Large-CNN模型，它是一个预训练的seq2seq模型，专门用于生成文本摘要。我们首先将输入文本分词，然后使用模型生成摘要，最后将生成的摘要转换回文本格式。