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
时间: 2023-12-03 07:47:34 浏览: 196
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:
```python
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。
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