Python 3.x SPACY自定义NER未返回任何实体

我试图训练一个Spacy模型来识别一些自定义NER，下面给出了训练数据，主要与识别一些服务器模型、FY格式的日期和硬盘类型有关：

TRAIN_DATA = [('Send me the number of units shipped in FY21 for A566TY server', {'entities': [(39, 42, 'DateParse'),(48,53,'server')]}),
            ('Send me the number of units shipped in FY-21 for A5890Y server', {'entities': [(39, 43, 'DateParse'),(49,53,'server')]}),              
          ('How many systems sold with 3.5 inch drives in FY20-Q2 for F567?', {'entities': [(46, 52, 'DateParse'),(58,61,'server'),(27,29,'HDD')]}),              
          ('Total revenue in FY20Q2 for 3.5 HDD', {'entities': [(17, 22, 'DateParse'),(28,30,'HDD')]}),
          ('How many systems sold with 3.5 inch drives in FY20-Q2 for F567?', {'entities': [(46, 52, 'DateParse'),(58,61,'server'),(27,29,'HDD')]}),

          ('Total units shipped in FY2017-FY2021', {'entities': [(23, 28, 'DateParse'),(30,35,'DateParse')]}),
          ('Total units shipped in FY 18', {'entities': [(23, 27, 'DateParse')]}),
          ('Total units shipped between FY16 and FY2021', {'entities': [(28, 31, 'DateParse'),(37,42,'DateParse')]})
         ]
def train_spacy(data,iterations):
TRAIN_DATA = data
nlp = spacy.blank('en')  # create blank Language class
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if 'ner' not in nlp.pipe_names:
    ner = nlp.create_pipe('ner')
    nlp.add_pipe(ner, last=True)


# add labels
for _, annotations in TRAIN_DATA:
     for ent in annotations.get('entities'):
        ner.add_label(ent[2])

# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
with nlp.disable_pipes(*other_pipes):  # only train NER
    optimizer = nlp.begin_training()
    for itn in range(iterations):
        print("Statring iteration " + str(itn))
        random.shuffle(TRAIN_DATA)
        losses = {}
         # batch up the examples using spaCy's minibatch
        batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
        for batch in batches:
            texts, annotations = zip(*batch)
            nlp.update(
                texts,  # batch of texts
                annotations,  # batch of annotations
                drop=0.2,  # dropout - make it harder to memorise data
                losses=losses,
            )
        print("Losses", losses)
return nlp

但是，即使在训练数据上运行代码，也不会返回任何实体

prdnlp = train_spacy(TRAIN_DATA, 100)
for text, _ in TRAIN_DATA:
    doc = prdnlp(text)
    print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
    print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc])

输出如下所示：

---

Spacy当前只能从与令牌边界对齐的实体注释进行训练。主要问题是您的跨度结束字符太短了一个字符。字符的开始/结束值应与文本的字符串片段相同：

text = "Send me the number of units shipped in FY21 for A566TY server"
# (39, 42, 'DateParse')
assert text[39:42] == "FY2"

你应该用39，43来代替“DateParse”

第二个问题是，您可能还需要针对FY2017-FY2021这样的情况调整标记器，因为默认的英语标记器将其视为一个标记，因此在培训期间将忽略注释[23,28，‘DateParse'，30,35，'DateParse']

请参见此处的更详细说明：