Python 空间数组枚举上标量变量的索引无效

我试图在本地运行以下句子压缩：

所以我复制了这些文件，并用conda安装了所有依赖项。我做了一些小修改，比如从url而不是本地磁盘读取数据，并将他的并行_data_gen.py捆绑在我的单个py文件中

但是，当我运行它时，我得到：

Spacy库无法将句子解析为树。请忽略这对句子

----------------59-------------------
reducing sentence: This year the Venezuelan government plans to continue its pace of land expropriations in order to move towards what it terms ``agrarian socialism''.
reducing headline: Venezuelan government to continue pace of land expropriations for ``agrarian socialism''
Traceback (most recent call last):
  File "/home/user/dev/projects/python-snippets/zhaohengyang/sentence-compression.py", line 701, in <module>
    reduce_sentence(sample)
  File "/home/user/dev/projects/python-snippets/zhaohengyang/sentence-compression.py", line 641, in reduce_sentence
    sentence_info = parse_info(sentence)
  File "/home/user/dev/projects/python-snippets/zhaohengyang/sentence-compression.py", line 616, in parse_info
    heads = [index + item[0] for index, item in enumerate(doc.to_array([HEAD]))]
IndexError: invalid index to scalar variable.

现在我加载了

nlp

：

import spacy
print('if you didnt run: python -m spacy download en')
import spacy.lang.en
nlp = spacy.load('en')

有关我的环境的更多信息：

/home/user/home/user/dev/anaconda3/envs/pymachine/bin/python --version
Python 2.7.15 :: Anaconda, Inc.

---

请注意，我正在python 3.6上运行spaCy 2.0，但只是在一个示例语句上运行了一个快速测试：

nlp = spacy.load('en_core_web_lg')

doc = nlp("Here is a test sentence for me to use.")

我在运行代码时遇到一些错误，这两个错误都在您指定的行中：

heads = [(index, item) for index, item in enumerate(doc.to_array([HEAD]))]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'HEAD' is not defined

解决了这个问题。您还将注意到，

枚举

返回的

项

是

int

或

标量

类型，因此它没有索引属性。摆脱

索引[0]

，这将解决您的问题

您的方法没有错误：

def parse_info(sentence):
    doc = nlp(sentence)

    heads = [index + item for index, item in enumerate(doc.to_array(['HEAD']))]

# Note that HEAD is now a string, rather than a variable
heads = [(index, item) for index, item in enumerate(doc.to_array(['HEAD']))]
heads
[(0, 3), (1, 1), (2, 1), (3, 0), (4, 18446744073709551615), (5, 1), (6, 18446744073709551614), (7, 18446744073709551612)]

def parse_info(sentence):
    doc = nlp(sentence)

    heads = [index + item for index, item in enumerate(doc.to_array(['HEAD']))]