无法重新创建用于训练FastText的Gensim文档.类型错误:必须提供corpus_file或corpus_iter

Question

我正在尝试制作自己的Fasttext嵌入，所以我查阅了Gensim的官方文档，并在下面用4.0版本实现了这个确切的代码。

from gensim.models import FastText
from gensim.test.utils import common_texts
model = FastText(vector_size=4, window=3, min_count=1)  # instantiate
model.build_vocab(sentences=common_texts)
model.train(sentences=common_texts, total_examples=len(common_texts), epochs=10)

令我惊讶的是，它给了我错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-6b2d1de02d90> in <module>
1 model = FastText(vector_size=4, window=3, min_count=1)  # instantiate
----> 2 model.build_vocab(sentences=common_texts)
3 model.train(sentences=common_texts, total_examples=len(common_texts), epochs=10)
~/anaconda3/lib/python3.8/site-packages/gensim/models/word2vec.py in build_vocab(self, corpus_iterable, corpus_file, update, progress_per, keep_raw_vocab, trim_rule, **kwargs)
477 
478         """
--> 479         self._check_corpus_sanity(corpus_iterable=corpus_iterable, corpus_file=corpus_file, passes=1)
480         total_words, corpus_count = self.scan_vocab(
481             corpus_iterable=corpus_iterable, corpus_file=corpus_file, progress_per=progress_per, trim_rule=trim_rule)
~/anaconda3/lib/python3.8/site-packages/gensim/models/word2vec.py in _check_corpus_sanity(self, corpus_iterable, corpus_file, passes)
1484         """Checks whether the corpus parameters make sense."""
1485         if corpus_file is None and corpus_iterable is None:
-> 1486             raise TypeError("Either one of corpus_file or corpus_iterable value must be provided")
1487         if corpus_file is not None and corpus_iterable is not None:
1488             raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time")
TypeError: Either one of corpus_file or corpus_iterable value must be provided

有人能帮忙吗？

Answer 1

所以我找到了答案。他们在以下两种情况下都有论点sentence的问题：

model.build_vocab(sentences=common_texts)
model.train(sentences=common_texts, total_examples=len(common_texts), epochs=10)

您所要做的就是删除参数名称或简单地传递第一个参数corpus_iterable

model.build_vocab(common_texts)
model.train(common_texts, total_examples=len(common_texts), epochs=10)

或

model.build_vocab(corpus_iterable=common_texts)
model.train(corpus_iterable=common_texts, total_examples=len(common_texts), epochs=10)