AI大语言模型与知识图谱融合：引领未来的技术革命

    from transformers import BertForMaskedLM, BertTokenizer, LineByLineTextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments
    
    # 初始化模型和分词器
    model = BertForMaskedLM.from_pretrained("bert-base-uncased")
    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
    
    # 准备数据集
    dataset = LineByLineTextDataset(tokenizer=tokenizer, file_path="train.txt", block_size=128)
    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15)
    
    # 设置训练参数
    training_args = TrainingArguments(output_dir="output", overwrite_output_dir=True, num_train_epochs=1, per_device_train_batch_size=8, 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=dataset)
    trainer.train()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationDataset, Trainer, TrainingArguments
    
    # 初始化模型和分词器
    model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
    
    # 准备数据集
    train_dataset = TextClassificationDataset(tokenizer=tokenizer, file_path="train.tsv", block_size=128)
    eval_dataset = TextClassificationDataset(tokenizer=tokenizer, file_path="eval.tsv", block_size=128)
    
    # 设置训练参数
    training_args = TrainingArguments(output_dir="output", overwrite_output_dir=True, num_train_epochs=1, per_device_train_batch_size=8, per_device_eval_batch_size=8, save_steps=10_000, save_total_limit=2, evaluation_strategy="epoch")
    
    # 训练模型
    trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset)
    trainer.train()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    from openke.config import Trainer, Tester
    from openke.module.loss import SigmoidLoss
    from openke.module.strategy import NegativeSampling
    from openke.data import TrainDataLoader, TestDataLoader
    from openke.module.model import TransE
    
    # 准备数据集
    train_dataloader = TrainDataLoader(in_path="./benchmarks/FB15K237/", batch_size=1000, threads=8, sampling_mode="normal", bern_flag=1, filter_flag=1, neg_ent=1, neg_rel=0)
    test_dataloader = TestDataLoader("./benchmarks/FB15K237/", "link")
    
    # 初始化模型
    transe = TransE(ent_tot=train_dataloader.get_ent_tot(), rel_tot=train_dataloader.get_rel_tot(), dim=100, p_norm=1, norm_flag=True)
    
    # 设置训练参数
    model = NegativeSampling(model=transe, loss=SigmoidLoss(adv_temperature=2.0), batch_size=train_dataloader.get_batch_size(), regul_rate=1.0, sample_strategy=train_dataloader.sampling_mode)
    trainer = Trainer(model=model, data_loader=train_dataloader, train_times=1000, alpha=1.0, use_gpu=True, opt_method="adam", save_steps=200, checkpoint_dir="./checkpoint", test_use_gpu=True)
    
    # 训练模型
    trainer.run()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    from openke.config import Tester
    
    # 初始化测试器
    tester = Tester(model=transe, data_loader=test_dataloader, use_gpu=True)
    
    # 计算实体和关系的嵌入
    entity_embeddings = tester.get_entity_embeddings()
    relation_embeddings = tester.get_relation_embeddings()
    
    # 进行链接预测和三元组分类
    tester.run_link_prediction(type_constrain=False)
    tester.run_triple_classification()
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import paddle
    from paddlenlp.transformers import ErnieModel, ErnieTokenizer
    from paddlenlp.transformers import ErnieForPretraining, ErniePretrainingCriterion
    from paddlenlp.transformers import ErnieForSequenceClassification
    from paddlenlp.data import Stack, Tuple, Pad
    
    # 初始化模型和分词器
    ernie = ErnieModel.from_pretrained("ernie-1.0")
    tokenizer = ErnieTokenizer.from_pretrained("ernie-1.0")
    
    # 准备数据集
    train_dataset = ...
    eval_dataset = ...
    
    # 设置训练参数
    optimizer = paddle.optimizer.AdamW(learning_rate=2e-5, parameters=ernie.parameters())
    criterion = ErniePretrainingCriterion(ernie)
    
    # 训练模型
    for epoch in range(epochs):
    for step, batch in enumerate(train_dataloader):
        input_ids, token_type_ids, masked_positions, masked_lm_labels = batch
        prediction_scores = ernie(input_ids=input_ids, token_type_ids=token_type_ids, masked_positions=masked_positions)
        loss = criterion(prediction_scores, masked_lm_labels)
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    from paddlenlp.transformers import ErnieModel, ErnieTokenizer
    from paddlenlp.transformers import ErnieForSequenceClassification
    from paddlenlp.data import Stack, Tuple, Pad
    
    # 初始化模型和分词器
    ernie = ErnieModel.from_pretrained("ernie-1.0")
    tokenizer = ErnieTokenizer.from_pretrained("ernie-1.0")
    
    # 准备数据集
    train_dataset = ...
    eval_dataset = ...
    
    # 设置训练参数
    optimizer = paddle.optimizer.AdamW(learning_rate=2e-5, parameters=ernie.parameters())
    criterion = paddle.nn.CrossEntropyLoss()
    
    # 训练模型
    for epoch in range(epochs):
    for step, batch in enumerate(train_dataloader):
        input_ids, token_type_ids, labels = batch
        logits = ernie(input_ids=input_ids, token_type_ids=token_type_ids)
        loss = criterion(logits, labels)
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()
    
    # 预测结果
    predictions = ernie(input_ids=input_ids, token_type_ids=token_type_ids)
    
    # 进行实体链接和关系链接
    entity_linking(predictions)
    relation_linking(predictions)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    from paddlenlp.transformers import ErnieModel, ErnieTokenizer
    from paddlenlp.transformers import ErnieForSequenceClassification
    from paddlenlp.data import Stack, Tuple, Pad
    
    # 初始化模型和分词器
    ernie = ErnieModel.from_pretrained("ernie-1.0")
    tokenizer = ErnieTokenizer.from_pretrained("ernie-1.0")
    
    # 准备数据集
    train_dataset = ...
    eval_dataset = ...
    
    # 设置训练参数
    optimizer = paddle.optimizer.AdamW(learning_rate=2e-5, parameters=ernie.parameters())
    criterion = paddle.nn.CrossEntropyLoss()
    
    # 训练模型
    for epoch in range(epochs):
    for step, batch in enumerate(train_dataloader):
        input_ids, token_type_ids, labels = batch
        logits = ernie(input_ids=input_ids, token_type_ids=token_type_ids)
        loss = criterion(logits, labels)
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()
    
    # 预测结果
    predictions = ernie(input_ids=input_ids, token_type_ids=token_type_ids)
    
    # 进行实体抽取、关系抽取和属性抽取
    entity_extraction(predictions)
    relation_extraction(predictions)
    attribute_extraction(predictions)
    
    # 进行知识补全和知识修复
    knowledge_completion(predictions)
    knowledge_repair(predictions)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读