使用nlp.管道（）使用具有空间感的预处理和预处理文本

Question

我正在尝试标记和解析文本，这些文本已经被拆分成句子，并且已经被标记化。例如：

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

处理批量文本的最快方法是.pipe()。但是，我不清楚如何将其用于预标记化和预分段文本。在这里，性能是关键。我尝试了下面的方法，但出现了一个错误

^{2}$

跟踪：

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'

Answer 1

只需将管道中的默认标记器替换为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_)

输出：

^{pr2}$