Hi Marcel,

There were actually several papers based on Transformers for Metamath. You
can probably check these two and the reference therein and it will give you
a good idea of what was done and what can be achieved (up to last year) :)

https://arxiv.org/abs/2009.03393

https://arxiv.org/abs/2205.11491

For transformers learning more or less complicated specific maths notions
(ODE, stability of ODEs, equilibrium in graphs, linear algebra, etc.) you
can check for instance https://arxiv.org/abs/1912.01412 (learning to solve
ODEs), https://arxiv.org/abs/2006.06462 (learning to predict advance
properties such as stability, controllability, etc.),
https://arxiv.org/abs/2112.03588 (learning to find equilibrium in graphs),
https://arxiv.org/abs/2112.01898 (learning linear algebra). There are
probably many other references but these can give you a good start and
their models and dataset are public.

Best,


Le dim. 20 août 2023 à 06:30, 'Marcel Richter' via Metamath <
[email protected]> a écrit :

> I had a lot of fun training transformers on some toy data, and now i am
> thinking what to do next. I was surprised how fast even small transformers
> lern long multiplication.
>
> Anyone know if someone trained a GTP on set.mm yet.
>
> set.mm is only ~40mb, i guess that is not enough to avoid overfitting
> really fast. Anyone produced some procedural generated metamath code?
>
> I belive a well trained model could be of great help as some kind of
> methamath copilote.
>
> Have a nice day :D
>
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