Example (from TfidfTransformer)

if isinstance(docs[0], tuple):
    docs = [docs]
return [self.gensim_model[doc] for doc in docs]

This method expects a list of tuples, instead of an iterable. This means that the entire corpus has to be stored as a lis

This is an awesome library, thanks @ddbourgin!!

Users might not know the best way to install this package and try it out. (I didn't, so I eventually just copied the source files.)
Neither the readme nor readthedocs have install instructions.

I couldn't find it on PyPi or Anaconda, and there doesn't appear to be a pyproject.toml, setup.cfg, setup.py, or conda recipe.

Moreover, the t

Hi there,

I think there might be a mistake in the documentation. The Understanding Scaled F-Score section says

The F-Score of these two values is defined as:

$$ \mathcal{F}_\beta(\mbox{prec}, \mbox{freq}) = (1 + \beta^2) \frac{\mbox{prec} \cdot \mbox{freq}}{\beta^2 \cdot \mbox{prec} + \mbox{freq}}. $$

$\beta \in \mathcal{R}^+$ is a scaling factor where frequency is favored if $\beta

I would like to know what all the abbreviations mean? Some I can guess, like "PUNCT", but no idea what "X" might be. I want to retain contractions, but hard to choose options without documentation.

Thanks. Great performance code!

大佬您好，我参考了您得assignment1中得word2vec.py得实现。但是在运行过程中梯度检测报错了。
==== Gradient check for skip-gram ====
Gradient check failed.
First gradient error found at index (0, 0)
Your gradient: -0.087147 Numerical gradient: 1254.567123
我是用py3实现的，之前的所有代码几乎一致，也都正确通过了，唯独这里通过不了。之后我将您的代码直接全部拷贝下来运行，同样报以上错误，请问您知道怎么回事嘛，您当时运行通过了吗？

def get_all_words(self): """ Return all words tokenized, in lowercase and without punctuation """ return [w.lower() for w in word_tokenize(self.text) if w not in string.punctuation]
I found that in this function, only punctuation of the text was removed. But there are other types of words that have not been removed.
eg:
`from nltk.corpus import stopwords