以下是使用不同编程语言实现的文本生成基础代码框架，内含模型调用示例与扩展教程链接：
Python 示例 🐍
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForCausalLM.from_pretrained("bert-base-uncased")
input_text = "今天天气"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
JavaScript 示例 📜
const { AutoTokenizer, AutoModelForCausalLM } = require('@huggingface/transformers');
(async () => {
const tokenizer = await AutoTokenizer.from_pretrained('bert-base-uncased');
const model = await AutoModelForCausalLM.from_pretrained('bert-base-uncased');
const inputText = "今天天气";
const inputs = tokenizer(inputText, { return_tensors: 'pt' });
const outputs = await model.generate(inputs, { maxLength: 50 });
console.log(tokenizer.decode(outputs[0], { skipSpecialTokens: true }));
})();
Java 示例 🧠
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;
public class TextGeneration {
public static void main(String[] args) {
Session session = Session.create("bert-base-uncased");
SavedModelBundle model = SavedModelBundle.load("bert-base-uncased", "serve");
String inputText = "今天天气";
// 调用模型生成逻辑
System.out.println(generateText(session, model, inputText));
}
}