大语言模型与知识图谱的融合在金融领域的应用

    pip install transformers

    from transformers import GPT3LMHeadModel, GPT3Tokenizer, GPT3Config
    from transformers import TextDataset, DataCollatorForLanguageModeling
    from transformers import Trainer, TrainingArguments
    
    # 加载预训练模型和分词器
    model = GPT3LMHeadModel.from_pretrained("gpt3")
    tokenizer = GPT3Tokenizer.from_pretrained("gpt3")
    
    # 准备训练数据
    train_dataset = TextDataset(
    tokenizer=tokenizer,
    file_path="train.txt",
    block_size=128
    )
    data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer, mlm=False
    )
    
    # 设置训练参数
    training_args = TrainingArguments(
    output_dir="./gpt3_finetuned",
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=4,
    save_steps=10_000,
    save_total_limit=2,
    )
    
    # 训练模型
    trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_dataset,
    )
    
    trainer.train()

    from transformers import pipeline
    
    # 加载微调后的模型
    generator = pipeline("text-generation", model="./gpt3_finetuned")
    
    # 生成文本
    generated_text = generator("金融领域的应用", max_length=50, do_sample=True, temperature=0.7)
    print(generated_text[0]["generated_text"])

    from rdflib import Graph, Literal, Namespace, URIRef
    
    # 创建知识图谱
    g = Graph()
    
    # 定义命名空间
    n = Namespace("http://example.org/")
    
    # 添加实体、属性和关系
    g.add((n.Alice, n.age, Literal(30)))
    g.add((n.Alice, n.gender, Literal("female")))
    g.add((n.Alice, n.worksAt, n.CompanyA))
    g.add((n.CompanyA, n.location, n.CityB))
    
    # 保存知识图谱
    g.serialize("knowledge_graph.rdf", format="xml")

    from rdflib import RDF, RDFS, OWL
    
    # 加载知识图谱
    g = Graph()
    g.parse("knowledge_graph.rdf", format="xml")
    
    # 查询Alice的年龄
    qres = g.query(
    """
    SELECT ?age
    WHERE {
        <http://example.org/Alice> <http://example.org/age> ?age .
    }
    """
    )
    
    for row in qres:
    print("Alice's age:", row[0])
    
    # 查询Alice工作的公司所在的城市
    qres = g.query(
    """
    SELECT ?city
    WHERE {
        <http://example.org/Alice> <http://example.org/worksAt> ?company .
        ?company <http://example.org/location> ?city .
    }
    """
    )
    
    for row in qres:
    print("Alice's company is located in:", row[0])

    def knowledge_distillation(g):
    text = ""
    for s, p, o in g:
        if p == n.age:
            text += f"{s}的年龄是{o}。\n"
        elif p == n.gender:
            text += f"{s}的性别是{o}。\n"
        elif p == n.worksAt:
            text += f"{s}在{o}工作。\n"
        elif p == n.location:
            text += f"{o}位于{o}。\n"
    return text
    
    # 将知识图谱转化为文本
    knowledge_text = knowledge_distillation(g)
    
    # 将文本保存为训练数据
    with open("train.txt", "w") as f:
    f.write(knowledge_text)