<p>As far as I understand by reading the <a href="https://arxiv.org/pdf/1411.4166.pdf" rel="nofollow noreferrer">paper</a> and browsing the <a href="https://github.com/mfaruqui/retrofitting" rel="nofollow noreferrer">repository</a>, <strong>the proposed methodology only allows to improve the quality of the vectors</strong> (.vec) given in input.</p>
<p>As you can read <a href="https://stackoverflow.com/questions/47118678/difference-between-fasttext-vec-and-bin-file">here</a>, fastText's ability to represent out-of-vocabulary words is inherent in the .bin model (which contains the vectors for all the n-grams).</p>
<p><strong>As you too may have understood, there is no out-of-the-box way to retrofit a fastText model, using the proposed methodology.</strong></p>
How to retrofit a fasttext model?
<p>I have read various research papers that one can retrofitting a fasttext model to improve its accuracy (<a href="https://github.com/mfaruqui/retrofitting" rel="nofollow noreferrer">https://github.com/mfaruqui/retrofitting</a>). However I am having trouble on how to implement it.</p>
<p>The github link above, will take one vector file and retrofitting it, output another vector file. I can load it using gensim library. However, since it is a vector file, it is no longer a model and it will not predict OOV (out-of-vocabulary) words. This makes it pointless. Is there a way to retrain the model somehow so it has better accuracy?</p>