Open hoosierEE opened 1 year ago
Normally our training examples are tokenized like this:
<S>
b
o
b
<E>
<S>
j
a
k
e
<E>
But I was thinking you could append special "label" tokens:
<S>
b
o
b
<E>
<is_palindrome=1>
<S>
j
a
k
e
<E>
<is_palindrome=0>
Maybe this is a silly idea, but I'm going to give it a try and see if it works. At least it won't require changing the model architecture very much.
Normally our training examples are tokenized like this:
<S>
b
o
b
<E>
<S>
j
a
k
e
<E>
But I was thinking you could append special "label" tokens:
<S>
b
o
b
<E>
<is_palindrome=1>
<S>
j
a
k
e
<E>
<is_palindrome=0>
Maybe this is a silly idea, but I'm going to give it a try and see if it works. At least it won't require changing the model architecture very much.
Did you have any luck with this?
Haven't tried it yet but this is a good reminder that I should.
If we had labels for these names, such as:
Can
makemore
-style generative models be modified to perform classification so I can feed in a new name likeasdf
and get a prediction for itsh_index
?While a suggestion like "add this layer here" would absolutely be helpful, I'm secretly hoping someone will share a general, intuitive way to think about repurposing machine learning models for new tasks...