<p>The <a href="https://arxiv.org/abs/1612.03651" rel="nofollow noreferrer">paper which introduced the FastText team's quantization strategy</a> only evaluated classification models, and used some pruning steps that might only make sense with labeled training documents. (Though, I don't see the arguments to <code>-quantize</code> as including the original training docs, so not sure the pruning technique as described in the paper is fully implmented.)</p>
<p>While some of the compression steps could be applied to the unsupervised dense vectors, I've not yet seen a library offering that functionality, but it could be a neat thing to implement/add.</p>
<p>However, it's possible that the kind of classification done by the FastText work is a &quot;sweet spot&quot; for these techniques, and applied to other word-vectors they'd have a much larger negative impact on downstream uses. So, extension of the technique should be accompanied by some experiments confirming its value.</p>
Fasttext Quantize Unsupervised model
<p>I am trying to quantize the unsupervised model in fasttext using this command.</p>
<pre><code>model.quantize(input=train_data, qnorm=True, retrain=True, cutoff=200000)
</code></pre>
<p>It's throwing an error that it is supported for only supervised models.</p>
<p><a href="https://i.stack.imgur.com/VvVad.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/VvVad.png" alt="enter image description here" /></a></p>
<p>Is there any alternate way to quantize the unsupervised models?</p>