在线文档中Transformers的Spacy 3.1示例代码似乎是错误的

pip install spacy
pip install transformers
pip install spacy-transformers

import spacy
from spacy.tokens import DocBin
from spacy.training import Example
from spacy_transformers import TransformersLanguage, TransformersWordPiecer, TransformersTok2Vec

# 加载预训练的Transformers模型
transformer_model = "bert-base-uncased"

# 创建一个新的Spacy语言对象
nlp = spacy.blank("en")

# 添加Transformers组件到管道中
nlp.add_pipe("transformer", config={"model": transformer_model})
nlp.add_pipe("ner")

# 准备训练数据
train_data = [
    ("Apple is looking at buying U.K. startup for $1 billion", {"entities": [(0, 5, "ORG"), (27, 31, "GPE"), (44, 54, "MONEY")]}),
    ("San Francisco considers banning sidewalk delivery robots", {"entities": [(0, 13, "GPE")]}),
]

# 创建DocBin对象来存储训练数据
db = DocBin()
for text, annotations in train_data:
    doc = nlp.make_doc(text)
    ents = []
    for start, end, label in annotations["entities"]:
        span = doc.char_span(start, end, label=label)
        if span is None:
            print(f"Skipping entity: {text[start:end]}")
        else:
            ents.append(span)
    doc.ents = ents
    db.add(doc)

# 保存训练数据到磁盘
db.to_disk("./train.spacy")

# 加载训练数据
train_docs = DocBin().from_disk("./train.spacy").get_docs(nlp.vocab)

# 准备训练示例
train_examples = []
for doc in train_docs:
    example = Example.from_dict(doc, {"entities": [(ent.start_char, ent.end_char, ent.label_) for ent in doc.ents]})
    train_examples.append(example)

# 开始训练
optimizer = nlp.begin_training()
for i in range(10):
    losses = {}
    nlp.update(train_examples, sgd=optimizer, losses=losses)
    print(f"Losses at iteration {i}: {losses}")

# 保存模型
nlp.to_disk("./model")

# 加载模型并测试
nlp = spacy.load("./model")
doc = nlp("Apple is looking at buying U.K. startup for $1 billion")
for ent in doc.ents:
    print(ent.text, ent.label_)