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This repository uses code and model by Twitter Word2vec model by Frederic Godin.

This zip contains a word2vec model trained on Twitter data as described in:

Godin, F., Vandersmissen, B., De Neve, W., & Van de Walle, R. (2015). Multimedia Lab @ ACL W-NUT NER shared task: Named entity recognition for Twitter microposts using distributed word representations. Workshop on Noisy User-generated Text, ACL 2015.



Please cite the paper if you use the model.

This zip contains 2 additional files to read the word2vec model with Python. The code for this was extracted from the Gensim Library which can be found here: The only difference is that it does not use a strict encoding to read the model from the file. (One can easily integrate, inherit or extend the library or the Python files)

How to install

pip install virtualenv
virtualenv .env
. .env/bin/activate
pip install -r requirements.txt 

Download the Word2vec model

You can download the Word2Vec 400M Twitter model from here

How to run

Loading the model, this can take some time...
The vocabulary size is: 3039345

Running with REPL (Interactive Shell)

>>> execfile('')
Loading the model, this can take some time...
The vocabulary size is: 3039345
>>> model
<word2vecReader.Word2Vec instance at 0x1053a4830>

How to use

Load the model into a python script or using REPL, then you can get the most similar words for a term like

>>> similar=model.most_similar('cat')
>>> words=list((w[0] for w in similar))
>>> words
[u'dog', u'Cat', u'kitten', u'cats', u'chihuahua', u'kitty', u'pug', u'ferret', u'puppy', u'dogs']

or obtain meaningful results like king – man + women = queen by adding or subtracting vectors.

>>> similar=model.most_similar(positive=['king', 'woman'], negative=['man'], topn=1)
>>> words=list((w[0] for w in similar))
>>> words

If you want to retrieve the distance as well you can add the 2nd dimension in the array

>>> similar=model.most_similar('healthcare')
>>> words=list(((w[0],w[1]) for w in similar))
>>> words
[(u'health-care', 0.7003277540206909), (u'#healthcare', 0.6571943759918213), (u'heathcare', 0.6552523374557495), (u'welfare', 0.621768593788147), (u'medicaid', 0.6213265657424927), (u'higher-ed', 0.6198979616165161), (u'health', 0.6175448894500732), (u'#healthinsurance', 0.6174722909927368), (u'#HIX', 0.6140642166137695), (u'ACA', 0.6094388961791992)]

so that you can evaluate the rule Term 'X' is to term 'Y' as Term 'W' is to term 'Z like

>>> similar=model.most_similar(positive=['boy', 'sister'], negative=['brother'], topn=1)
>>> words=list(((w[0],w[1]) for w in similar))
>>> words
[(u'girl', 0.6565942764282227)]

For more information please see Word2Vec


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