将 nlp.pipe() 与 spaCy 的预分段和预标记化文本结合使用

我正在尝试标记和解析已经分成句子并且已经被标记化的文本。举个例子：

sents = [['I', 'like', 'cookies', '.'], ['Do', 'you', '?']]

处理批量文本的最快方法是.pipe()。但是，我不清楚如何将其与预标记和预分段的文本一起使用。性能是这里的关键。我尝试了以下操作，但这引发了错误

docs = [nlp.tokenizer.tokens_from_list(sentence) for sentence in sents]
nlp.tagger(docs)
nlp.parser(docs)

Trace:

Traceback (most recent call last):
  File "C:\Python\Python37\Lib\multiprocessing\pool.py", line 121, in worker
    result = (True, func(*args, **kwds))
  File "C:\Python\projects\PreDicT\predicting-wte\build_id_dictionary.py", line 204, in process_batch
    self.nlp.tagger(docs)
  File "pipes.pyx", line 377, in spacy.pipeline.pipes.Tagger.__call__
  File "pipes.pyx", line 396, in spacy.pipeline.pipes.Tagger.predict
  File "C:\Users\bmvroy\.virtualenvs\predicting-wte-YKqW76ba\lib\site-packages\thinc\neural\_classes\model.py", line 169, in __call__
    return self.predict(x)
  File "C:\Users\bmvroy\.virtualenvs\predicting-wte-YKqW76ba\lib\site-packages\thinc\neural\_classes\feed_forward.py", line 40, in predict
    X = layer(X)
  File "C:\Users\bmvroy\.virtualenvs\predicting-wte-YKqW76ba\lib\site-packages\thinc\neural\_classes\model.py", line 169, in __call__
    return self.predict(x)
  File "C:\Users\bmvroy\.virtualenvs\predicting-wte-YKqW76ba\lib\site-packages\thinc\neural\_classes\model.py", line 133, in predict
    y, _ = self.begin_update(X, drop=None)
  File "C:\Users\bmvroy\.virtualenvs\predicting-wte-YKqW76ba\lib\site-packages\thinc\neural\_classes\feature_extracter.py", line 14, in begin_update
    features = [self._get_feats(doc) for doc in docs]
  File "C:\Users\bmvroy\.virtualenvs\predicting-wte-YKqW76ba\lib\site-packages\thinc\neural\_classes\feature_extracter.py", line 14, in <listcomp>
    features = [self._get_feats(doc) for doc in docs]
  File "C:\Users\bmvroy\.virtualenvs\predicting-wte-YKqW76ba\lib\site-packages\thinc\neural\_classes\feature_extracter.py", line 21, in _get_feats
    arr = doc.doc.to_array(self.attrs)[doc.start : doc.end]
AttributeError: 'list' object has no attribute 'doc'

只需将管道中的默认分词器替换为nlp.tokenizer.tokens_from_list而不是单独调用它：

import spacy
nlp = spacy.load('en')
nlp.tokenizer = nlp.tokenizer.tokens_from_list

for doc in nlp.pipe([['I', 'like', 'cookies', '.'], ['Do', 'you', '?']]):
    for token in doc:
        print(token, token.pos_)

Output:

I PRON
like VERB
cookies NOUN
. PUNCT
Do VERB
you PRON
? PUNCT