我想使用stanza来标记、标记和解析我拥有的一些文本，但它一直给我这个错误。我试着改变了我对它的称呼，但什么也没发生。有什么想法吗？
我的代码(这里迭代文本列表，并将节应用于每个文本(
t = time()
data_stanza = []
for text in data:
stz = apply_stanza(text[0])
data_stanza.append(stz)
print('Time to run: {} mins'.format(round((time() - t) / 60, 2)))
这是我用来apply_stanza到每个文本的函数：
nlp = stanza.Pipeline('pt')
def apply_stanza(text):
doc = nlp(text)
All = []
for sent in doc.sentences:
for word in sent.words:
All.append((word.id,word.text,word.lemma,word.upos,word.feats,word.head,word.deprel))
return All
错误：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-17-7ac303eec8e8> in <module>
3 data_staza = []
4 for text in data:
----> 5 stz = apply_stanza(text[0])
6 data_stanza.append(stz)
7
<ipython-input-16-364c3ac30f32> in apply_stanza(text)
2
3 def apply_stanza(text):
----> 4 doc = nlp(text)
5 All = []
6 for sent in doc.sentences:
~anaconda3libsite-packagesstanzapipelinecore.py in __call__(self, doc)
174 assert any([isinstance(doc, str), isinstance(doc, list),
175 isinstance(doc, Document)]), 'input should be either str, list or Document'
--> 176 doc = self.process(doc)
177 return doc
178
~anaconda3libsite-packagesstanzapipelinecore.py in process(self, doc)
168 for processor_name in PIPELINE_NAMES:
169 if self.processors.get(processor_name):
--> 170 doc = self.processors[processor_name].process(doc)
171 return doc
172
~anaconda3libsite-packagesstanzapipelinemwt_processor.py in process(self, document)
31 preds = []
32 for i, b in enumerate(batch):
---> 33 preds += self.trainer.predict(b)
34
35 if self.config.get('ensemble_dict', False):
~anaconda3libsite-packagesstanzamodelsmwttrainer.py in predict(self, batch, unsort)
77 self.model.eval()
78 batch_size = src.size(0)
---> 79 preds, _ = self.model.predict(src, src_mask, self.args['beam_size'])
80 pred_seqs = [self.vocab.unmap(ids) for ids in preds] # unmap to tokens
81 pred_seqs = utils.prune_decoded_seqs(pred_seqs)
~anaconda3libsite-packagesstanzamodelscommonseq2seq_model.py in predict(self, src, src_mask, pos, beam_size)
259 done = []
260 for b in range(batch_size):
--> 261 is_done = beam[b].advance(log_probs.data[b])
262 if is_done:
263 done += [b]
~anaconda3libsite-packagesstanzamodelscommonbeam.py in advance(self, wordLk, copy_indices)
82 # bestScoresId is flattened beam x word array, so calculate which
83 # word and beam each score came from
---> 84 prevK = bestScoresId / numWords
85 self.prevKs.append(prevK)
86 self.nextYs.append(bestScoresId - prevK * numWords)
RuntimeError: Integer division of tensors using div or / is no longer supported, and in a future release div will perform
true division as in Python 3. Use true_divide or floor_divide (// in Python) instead.
ATT：毕竟它是和错误的节管道的mwt模块，所以我只是指定不使用它。
使用//而不是/进行除法。
尝试按如下方式编辑代码：
print('Time to run: {} mins'.format(round((time() - t) // 60, 2)))
使用floor除法(//(将结果降为尽可能大的整数。
使用torc.true_divide(股息，除数(或numpy.true_divide。
例如：3/4=torch.true_divide(3，4(
https://pytorch.org/docs/stable/generated/torch.true_divide.htmlhttps://numpy.org/doc/stable/reference/generated/numpy.true_divide.html