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In the following example the text:

'The quick brown fox jumped over the lazy dog.'

gets the encodings [5, 9, 8, 7, 9, 1, 5, 3, 8].

My code:

enter image description here

In this way the words 'brown' and 'dog' get both the encodings of 8 and 'quick' and 'jumped' 9.

What is the point of encoding if unicity is not guaranteed?

https://machinelearningmastery.com/prepare-text-data-deep-learning-keras/ https://keras.io/preprocessing/text/

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    $\begingroup$ Just a note - formatting code as markdown as opposed to png is always easier for people to read/answer on this site. $\endgroup$ – Ethan Sep 23 at 14:48
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It seems to be bad choice of function names since the one_hot function actually is using hashing and is equivalent to hashing_trick (with standard settings).

See this issue: https://github.com/keras-team/keras/pull/6887

When I do:

from keras.preprocessing.text import one_hot
from keras.preprocessing.text import hashing_trick

text = 'The quick brown fox jumped over the lazy dog.'
print(one_hot(text, n=9))
print(hashing_trick(text, n=9))

I get identical results:

[5, 6, 2, 6, 5, 8, 5, 4, 1]
[5, 6, 2, 6, 5, 8, 5, 4, 1]

So in case you want to have "real" (unique) one-hot encoded results, you need to resort to some other solution (there is no Keras built-in solution for this in the moment as I believe).

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You could think of it as dimensionality reduction, though it would be important to know how and why it put certain things into buckets. I remember reading about Vowpal Wabbit doing a sophisticated version of this ("feature hashing") in order to handle a "terafeature".

https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Feature-Hashing-and-Extraction

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