I want to create a text file that is essentially a dictionary, with each word being paired with its vector representation through word2vec. I'm assuming the process would be to first train word2vec and then look-up each word from my list and find its representation (and then save it in a new text file)?
I'm new to word2vec and I don't know how to go about doing this. I've read from several of the main sites, and several of the questions on Stack, and haven't found a good tutorial yet.
You can directly use author's code (in C++) to train and extract the vectors. It's simple 600-700 lines of optimized code. I might be able to help with exact arguments if you require it. code.google.com/p/word2vec is the original author's code.
After feeding the Word2Vec algorithm with our corpus, it will learn a vector representation for each word. This by itself, however, is still not enough to be used as features for text classification as each record in our data is a document not a word.
The direct access model[word]
is deprecated and will be removed in Gensim 4.0.0 in order to separate the training and the embedding. The command should be replaced with, simply, model.wv[word]
.
Using Gensim in Python, after vocabs are built and the model trained, you can find the word count and sampling information already mapped in model.wv.vocab
, where model
is the variable name of your Word2Vec
object.
Thus, to create a dictionary object, you may:
my_dict = dict({})
for idx, key in enumerate(model.wv.vocab):
my_dict[key] = model.wv[key]
# Or my_dict[key] = model.wv.get_vector(key)
# Or my_dict[key] = model.wv.word_vec(key, use_norm=False)
Now that you have your dictionary, you can write it to a file with whatever means you like. For example, you can use the pickle library. Alternatively, if you are using Jupyter Notebook, they have a convenient 'magic command' %store my_dict > filename.txt
. Your filename.txt will look like:
{'one': array([-0.06590105, 0.01573388, 0.00682817, 0.53970253, -0.20303348,
-0.24792041, 0.08682659, -0.45504045, 0.89248925, 0.0655603 ,
......
-0.8175681 , 0.27659689, 0.22305458, 0.39095637, 0.43375066,
0.36215973, 0.4040089 , -0.72396156, 0.3385369 , -0.600869 ],
dtype=float32),
'two': array([ 0.04694849, 0.13303463, -0.12208422, 0.02010536, 0.05969441,
-0.04734801, -0.08465996, 0.10344813, 0.03990637, 0.07126121,
......
0.31673026, 0.22282903, -0.18084198, -0.07555179, 0.22873943,
-0.72985399, -0.05103955, -0.10911274, -0.27275378, 0.01439812],
dtype=float32),
'three': array([-0.21048863, 0.4945509 , -0.15050395, -0.29089224, -0.29454648,
0.3420335 , -0.3419629 , 0.87303966, 0.21656844, -0.07530259,
......
-0.80034876, 0.02006451, 0.5299498 , -0.6286509 , -0.6182588 ,
-1.0569025 , 0.4557548 , 0.4697938 , 0.8928275 , -0.7877308 ],
dtype=float32),
'four': ......
}
You may also wish to look into the native save / load methods of Gensim's word2vec.
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