自然语言处理（NLP）技术在AIGC中的突破

pip install openai

import openai

# 设置API密钥
openai.api_key = 'YOUR_API_KEY'

def generate_text(prompt, max_tokens=100):
    response = openai.Completion.create(
        engine="text-davinci-003",  # 使用GPT-3的davinci引擎
        prompt=prompt,
        max_tokens=max_tokens,
        n=1,
        stop=None,
        temperature=0.7
    )
    return response.choices[0].text.strip()

# 示例使用
prompt = "写一段关于人工智能在医疗领域应用的文章"
generated_text = generate_text(prompt)
print(generated_text)

# 使用混合专家模型的示例代码（伪代码）
import openai

def generate_text_with_experts(prompt, experts, max_tokens=100):
    # 根据任务选择合适的专家
    selected_expert = select_expert(prompt, experts)
    response = selected_expert.generate(
        prompt=prompt,
        max_tokens=max_tokens
    )
    return response.text.strip()

def select_expert(prompt, experts):
    # 简单示例：根据关键词选择专家
    if "医疗" in prompt:
        return experts['medical']
    elif "金融" in prompt:
        return experts['financial']
    else:
        return experts['general']

# 初始化专家模型
experts = {
    'medical': openai.Engine(id='davinci-medical'),
    'financial': openai.Engine(id='davinci-financial'),
    'general': openai.Engine(id='davinci-general')
}

# 示例使用
prompt = "写一段关于人工智能在医疗领域应用的文章"
generated_text = generate_text_with_experts(prompt, experts)
print(generated_text)

# 差分隐私的简单示例（伪代码）
from diffprivlib.models import LogisticRegression

# 训练带有差分隐私保护的模型
def train_with_privacy(X_train, y_train):
    model = LogisticRegression(epsilon=1.0)  # 设置隐私参数epsilon
    model.fit(X_train, y_train)
    return model

# 示例使用
X_train, y_train = load_data()  # 加载训练数据
model = train_with_privacy(X_train, y_train)

# 多语言预训练模型的使用示例（伪代码）
from transformers import MBartForConditionalGeneration, MBartTokenizer

model_name = 'facebook/mbart-large-50'
tokenizer = MBartTokenizer.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)

def translate(text, src_lang, tgt_lang):
    tokenizer.src_lang = src_lang
    encoded = tokenizer(text, return_tensors='pt')
    generated_tokens = model.generate(**encoded, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang])
    return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]

# 示例使用
text = "人工智能在医疗领域有广泛的应用"
translated_text = translate(text, src_lang='zh_CN', tgt_lang='en_XX')
print(translated_text)