Question

文件GoogleNews-vectors-negative300.bin包含3亿个字向量。我认为（不确定）在写入以下行时会加载此文件：

from gensim.models.keyedvectors import KeyedVectors

我想在名为words的列表中下载我在外部提供的单词的向量。这是我的代码：

import math
import sys
import gensim
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')

from gensim.models.keyedvectors import KeyedVectors

words = ['access', 'aeroway', 'airport', 'amenity', 'area', 'atm', 'barrier', 'bay', 'bench', 'boundary', 'bridge', 'building', 'bus', 'cafe', 'car', 'coast', 'continue', 'created', 'defibrillator', 'drinking', 'ele', 'embankment', 'entrance', 'ferry', 'foot', 'fountain', 'fuel', 'gate', 'golf', 'gps', 'grave', 'highway', 'horse', 'hospital', 'house', 'landuse', 'layer', 'leisure', 'man', 'manmade', 'market', 'marketplace', 'maxheight', 'name', 'natural', 'noexit', 'oneway', 'park', 'parking', 'pgs', 'place', 'worship', 'playground', 'police', 'police station', '', 'post', 'post box or mail', 'power', 'powerstation', 'private', 'public', 'railway', 'ref', 'residential', 'restaurant', 'road', 'route', 'school', 'shelter', 'shop', 'source', 'sport', 'toilet', 'toilets', 'tourism', 'unknown', 'vehicle', 'vending', 'vending machine', 'village', 'wall', 'waste', 'water', 'waterway', 'worship'];

model = gensim.models.KeyedVectors.load_word2vec_format(words, binary=True)

M = len(words)
count = 0
for i in range(1,M):
    wi = id2word[words[i]]
    if wi in word2vec.vocab:
        vector[:,count] = model[:,i]
        count = count+1

f = open('word_vectors.csv', 'w')
print(vector, file=f)
f.close()

但是当我运行代码时，它只会冻结我的系统。是因为它在搜索words中的单词之前加载了整个二进制文件吗？如果是，我该如何解决这个问题？我想到了这一点，因为我收到了以下警告，这就是为什么我使用warning包来抑制它：

c:\Python35\lib\site-packages\gensim\utils.py:860: UserWarning: detected Windows; aliasing chunkize to chunkize_serial
  warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")

它给出的错误是：

Traceback (most recent call last):
  File "word2vec.py", line 18, in <module>
    model = gensim.models.KeyedVectors.load_word2vec_format(topic, binary=True) 
  File "c:\Python35\lib\site-packages\gensim\models\keyedvectors.py", line 196, in load_word2vec_format
    with utils.smart_open(fname) as fin:
  File "c:\Python35\lib\site-packages\smart_open\smart_open_lib.py", line 208, in smart_open
    raise TypeError('don\'t know how to handle uri %s' % repr(uri))
TypeError: don't know how to handle uri [['access'], ['aeroway'], ['airport'], ['amenity'], ['area'], ['atm'], ['barrier'], ['bay'], ['bench'], ['boundary'], ['bridge'], ['building'], ['bus'], ['cafe'], ['car'], ['coast'], ['continue'], ['created'], ['defibrillator'], ['drinking'], ['ele'], ['embankment'], ['entrance'], ['ferry'], ['foot'], ['fountain'], ['fuel'], ['gate'], ['golf'], ['gps'], ['grave'], ['highway'], ['horse'], ['hospital'], ['house'], ['landuse'], ['layer'], ['leisure'], ['man'], ['manmade'], ['market'], ['marketplace'], ['maxheight'], ['name'], ['natural'], ['noexit'], ['oneway'], ['park'], ['parking'], ['pgs'], ['place'], ['worship'], ['playground'], ['police'], ['police station'], [''], ['post'], ['post box or mail'], ['power'], ['powerstation'], ['private'], ['public'], ['railway'], ['ref'], ['residential'], ['restaurant'], ['road'], ['route'], ['school'], ['shelter'], ['shop'], ['source'], ['sport'], ['toilet'], ['toilets'], ['tourism'], ['unknown'], ['vehicle'], ['vending'], ['vending machine'], ['village'], ['wall'], ['waste'], ['water'], ['waterway'], ['worship']]

我想这意味着程序无法搜索二进制文件中的单词。那么，如何解决呢？

Answer 1

使用以下代码从经过Google培训的word2vec模型中提取单词向量：

import math
import sys
import gensim
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')

# this line doesn't load the trained model 
from gensim.models.keyedvectors import KeyedVectors

words = ['access', 'aeroway', 'airport']

# this is how you load the model
model = KeyedVectors.load_word2vec_format(path_to_model, binary=True)

# to extract word vector
print(model[words[0]])  #access

结果向量：

[ -8.74023438e-02  -1.86523438e-01 .. ]

由于模型尺寸较大，您的系统正在冻结。尝试使用具有更多内存的系统，或者可以限制要加载的模型的大小。

加载时限制模型尺寸

model = KeyedVectors.load_word2vec_format(path_to_model, binary=True, limit=20000)

如何从Google预训练的word2vec模型中提取单词向量？

1 个答案: