Re: Bits from /me: A humble draft policy on "deep learning v.s. freedom"
Hi Ben,
Good catch! I'm quite sure that the 3 categories are not overlapping
with each other. And I've fixed the language to make it logically
correct:
A **ToxicCandy Model** refers to an explicitly free software licensed
model, trained from unknown or non-free dataset.
A model is **Non-free Model** as long as any of the following
conditions is
satisfied: (1) trained from unknown/non-free data and released WITHOUT
explicit free software license declaration; ...
Category 2 is a special but common case: the warm hearted upstream
wants to share the training results freely, but actually the results
are trained from non-free data and free software community could
never reproduce that with purely free stuff.
Cat.3 is easier and more obvious to identify compared to cat.2.
Fixed in the git repo.
On 2019-05-21 21:43, Ben Hutchings wrote:
> Thanks for this. Something I don't quite understand is the division
> into 3 categories. You write:
>
>> 2. A ToxicCandy Model refers to a free software licensed model,
>> trained from unknown or non-free dataset [...]
>
>> 3. A model is Non-free Model as long as any of the following
>> conditions is satisfied: (1) trained from unknown/non-free data [...]
>
> Is category 2 intended to be a subset of category 3, or am I missing
> some distinction?
>
> Ben.
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