帮我写一个spacy进行实体识别和关系抽取的pipline模型，包含模型训练和模型预测两部分代码

好的，以下是一个基本的Spacy实体识别和关系抽取pipeline模型的训练和预测代码示例：

import spacy
from spacy.matcher import Matcher
from spacy.tokens import Span
import random

# 加载英文语言模型
nlp = spacy.load("en_core_web_sm")

# 定义实体类型
LABEL_1 = "PERSON"
LABEL_2 = "ORG"

# 添加实体类型到语言模型
ner_pipe = nlp.create_pipe("ner")
ner_pipe.add_label(LABEL_1)
ner_pipe.add_label(LABEL_2)
nlp.add_pipe(ner_pipe)

# 准备训练数据
TRAIN_DATA = [
    ("Bill Gates is the founder of Microsoft.", {"entities": [(0, 10, LABEL_1), (27, 36, LABEL_2)]}),
    ("Steve Jobs was the CEO of Apple.", {"entities": [(0, 10, LABEL_1), (29, 34, LABEL_2)]}),
    ("Mark Zuckerberg is the founder of Facebook.", {"entities": [(0, 15, LABEL_1), (27, 35, LABEL_2)]}),
    ("Jeff Bezos is the founder of Amazon.", {"entities": [(0, 9, LABEL_1), (26, 32, LABEL_2)]}),
]

# 配置Spacy的训练流程
n_iter = 20
optimizer = nlp.begin_training()

# 开始训练模型
for i in range(n_iter):
    random.shuffle(TRAIN_DATA)
    losses = {}
    for text, annotations in TRAIN_DATA:
        # 基于text创建一个Doc对象
        doc = nlp.make_doc(text)
        # 更新该Doc对象的实体标注
        example = Example.from_dict(doc, annotations)
        nlp.update([example], sgd=optimizer, losses=losses)
    print(f"Epoch {i} Losses: {losses}")

# 定义自定义的关系抽取函数
def extract_relations(doc):
    matcher = Matcher(nlp.vocab)
    pattern = [{"LOWER": "founder"}, {"IS_PUNCT": True, "OP": "?"}, {"LOWER": "of"}]
    matcher.add("FOUNDER_OF", None, pattern)
    matches = matcher(doc)
    relations = []
    for match_id, start, end in matches:
        if doc[start-1].ent_type_ == LABEL_1 and doc[end+1].ent_type_ == LABEL_2:
            relations.append((doc[start-1], doc[end+1], "FOUNDER_OF"))
    return relations

# 测试模型预测
text = "Bill Gates is the founder of Microsoft and Jeff Bezos is the founder of Amazon."
doc = nlp(text)
relations = extract_relations(doc)
for r1, r2, rel in relations:
    print(f"{r1.text} {rel} {r2.text}")

这个示例代码包含两个部分：

- 实体识别 (NER)：使用Spacy自带的Named Entity Recognition管道来训练模型，添加自定义的实体类型，然后用训练数据训练模型。
- 关系抽取：定义一个自定义的函数来提取文本中的关系。在这个例子中，我们使用了Spacy的匹配器来找到"founder of"这个关键词序列，然后用实体识别的结果来确定关系中的实体。

请注意，这只是一个基本的示例代码，可以根据实际应用需求进行修改。