我应该如何训练布朗语料库中的gensim

时间:2014-12-24 06:17:48

标签: python gensim

我正在尝试使用gensim word2vec。我无法训练基于布朗语料库的模型。这是我的代码。

from gensim import models

model = models.Word2Vec([sentence for sentence in models.word2vec.BrownCorpus("E:\\nltk_data\\")],workers=4)
model.save("E:\\data.bin")

我使用nltk.download()下载了nltk_data。我收到以下错误。

C:\Python27\lib\site-packages\gensim-0.10.1-py2.7.egg\gensim\models\word2vec.py:401: UserWarning: Cython compilation failed, training will be slow. Do you have Cython installed? `pip install cython`
  warnings.warn("Cython compilation failed, training will be slow. Do you have Cython installed? `pip install cython`")
Traceback (most recent call last):
  File "E:\eclipse_workspace\Python_files\Test\Test.py", line 8, in <module>
    model = models.Word2Vec([sentence for sentence in models.word2vec.BrownCorpus("E:\\nltk_data\\")],workers=4)
  File "C:\Python27\lib\site-packages\gensim-0.10.1-py2.7.egg\gensim\models\word2vec.py", line 276, in __init__
    self.train(sentences)
  File "C:\Python27\lib\site-packages\gensim-0.10.1-py2.7.egg\gensim\models\word2vec.py", line 407, in train
    raise RuntimeError("you must first build vocabulary before training the model")
RuntimeError: you must first build vocabulary before training the model

我做错了什么?

2 个答案:

答案 0 :(得分:10)

也许你以错误的方式创造句子 试试这个,它对我有用。

import gensim
import logging
from nltk.corpus import brown    

logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentences = brown.sents()
model = gensim.models.Word2Vec(sentences, min_count=1)
model.save('/tmp/brown_model')

日志部分不是必需的,您可以根据需要更改Word2Vec()中的参数。

答案 1 :(得分:2)

您需要完整的目录路径,而不仅仅是nltk_data目录。在我的系统上它将是:

from os.path import expanduser, join
from gensim.models.word2vec import BrownCorpus, Word2Vec

dirname = expanduser(join('~', 'nltk_data', 'corpora', 'brown'))
model = Word2Vec(BrownCorpus(dirname))

model.similar_by_word('house/nn')

给出:

[(u'room/nn', 0.9538693428039551), (u'door/nn', 0.9475813508033752), ...

请注意,NLTK中的Brown Corpus附带POS标签。 Gensim BrownCorpus类忽略非字母标记,但保留POS标记。使用nltk.corpus.brown.sents(),您可以获得没有POS标签的句子。