训练自定义 SpaCy NER 模型会出现训练错误

Question

我想用 SpaCy 训练我自己的自定义 NER 来识别地址。

这是我的数据：

training_data = [('send to: Aargauerstrasse 8005', {'entities': [(9, 28, 'ADDRESS')]}), 
                ('send to: Abeggweg 8057', {'entities': [(9, 21, 'ADDRESS')]}), 
                ('send to: Abendweg 8038', {'entities': [(9, 21, 'ADDRESS')]}), 
                ('send to: Ackermannstrasse 8044', {'entities': [(9, 29, 'ADDRESS')]}), 
                ('send to: Aehrenweg 8050', {'entities': [(9, 22, 'ADDRESS')]}), 
                ('send to: Aemmerliweg 8050', {'entities': [(9, 24, 'ADDRESS')]}), 
                ('send to: Albisgütliweg 8045', {'entities': [(9, 26, 'ADDRESS')]}), 
                ('send to: Albisstrasse 8038', {'entities': [(9, 25, 'ADDRESS')]}), 
                ('send to: Albulastrasse 8048', {'entities': [(9, 26, 'ADDRESS')]}), 
                ('send to: Alderstrasse 8008', {'entities': [(9, 25, 'ADDRESS')]})]

我已经按照本教程（官方教程... 20 分 30 秒）： https://www.youtube.com/watch?v=IqOJU1-_Fi0&t=1328s

这些是我的功能：

# CREATING BLANK MODEL
def create_blank_nlp(train_data):

    nlp = spacy.blank("en") # prazan model
    nlp.add_pipe("transformer")
    nlp.add_pipe("parser")
    
    ner = nlp.create_pipe("ner") # ubaci custom ner
    nlp.add_pipe("ner", last = True)
    ner = nlp.get_pipe("ner")
        
    for _, data in train_data:
        for ent in data.get("entities"):
            ner.add_label(ent[2])

    return nlp

nlp = create_blank_nlp(train_data)
optimizer = nlp.begin_training()

# TRAINING 
for i in range(5):
    
    random.shuffle(train_data)
    
    losses = {}

    sizes = compounding(1.0, 5.0, 150.0)
    batches = minibatch(train_data, size = sizes)
    for batch in batches:
        for text, annotations in batch:
            
            doc = nlp.make_doc(text)
            example = Example.from_dict(doc, annotations)
            
            nlp.update([example], drop = 0.2, sgd = optimizer, losses = losses)

        
    print("Lossess at iteration", i, losses)

我该怎么办？