Import GoogleNews-vectors-negative300.bin

Question

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 

Answer 1

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)