Import GoogleNews-vectors-negative300.bin

user_7297511 0
Viewed 58162

I am working on code using the gensim and having a tough time troubleshooting a ValueError within my code. I finally was able to zip GoogleNews-vectors-negative300.bin.gz file so I could implement it in my model. I also tried gzip which the results were unsuccessful. The error in the code occurs in the last line. I would like to know what can be done to fix the error. Is there any workarounds? Finally, is there a website that I could reference?

Thank you respectfully for your assistance! 

import gensim
from keras import backend
from keras.layers import Dense, Input, Lambda, LSTM, TimeDistributed
from keras.layers.merge import concatenate
from keras.layers.embeddings import Embedding
from keras.models import Mode

pretrained_embeddings_path = "GoogleNews-vectors-negative300.bin"
word2vec = 
gensim.models.KeyedVectors.load_word2vec_format(pretrained_embeddings_path, 
binary=True)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-23bd96c1d6ab> in <module>()
  1 pretrained_embeddings_path = "GoogleNews-vectors-negative300.bin"
----> 2 word2vec = 
gensim.models.KeyedVectors.load_word2vec_format(pretrained_embeddings_path, 
binary=True)

C:\Users\green\Anaconda3\envs\py35\lib\site-
packages\gensim\models\keyedvectors.py in load_word2vec_format(cls, fname, 
fvocab, binary, encoding, unicode_errors, limit, datatype)
244                             word.append(ch)
245                     word = utils.to_unicode(b''.join(word), 
encoding=encoding, errors=unicode_errors)
--> 246                     weights = fromstring(fin.read(binary_len), 
dtype=REAL)
247                     add_word(word, weights)
248             else:

ValueError: string size must be a multiple of element size
gensim python
4 Answers

The below commands work.

brew install wget

wget -c "https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz"

This downloads the GZIP compressed file that you can uncompress using:

gzip -d GoogleNews-vectors-negative300.bin.gz

You can then use the below command to get wordVector.

from gensim import models

w = models.KeyedVectors.load_word2vec_format(
    '../GoogleNews-vectors-negative300.bin', binary=True)
user_50271680
user_85957900

Here is what worked for me. I loaded a part of the model and not the entire model as it's huge.

!pip install wget

import wget
url = 'https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz'
filename = wget.download(url)

f_in = gzip.open('GoogleNews-vectors-negative300.bin.gz', 'rb')
f_out = open('GoogleNews-vectors-negative300.bin', 'wb')
f_out.writelines(f_in)

import gensim
from gensim.models import Word2Vec, KeyedVectors
from sklearn.decomposition import PCA

model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True, limit=100000)
user_79171300
Related