I'm collecting/creating a suite of tools for a newer/better version of @roborosewater (not affiliated with the people who run that account in any way) to showcase the progress that's been made since that time.
I'd be very interested in both the pre-trained model as well as the source code (if it's not too embarrasing / you're willing to open source it).
So would a standalone grading program help anyone? Changing 1 line at the end could easily append the new, correctly-formatted cards to either their own list or the existing card data.
It would certainly help me. I'm doing exploration in this space. There is a lot of information about MTG that informs whether cards are "correct" or not that's not written on the cards.
One of the things the network is pretty awful at is matching color pie. We have MaRo's article which tells us exactly which mechanics are in each color, https://magic.wizards.com/en/articles/archive/making-magic/mechanical-color-pie-2017-2017-06-05. So my first stab at this will be to try to classify cards as in-pie or out-of-pie (and if we want to feed the results back in to the network as an experiment, I'm all for experiments).
The following data sets would be useful for doing this.
1. For each card, tag it with all the abilities listed in Maro's article that are on that card.
We could use this to train a network that can tag newly-generated cards with their abilities in the same way.
2. For each card, provide some measure of whether it's in-pie or out-of-pie.
Even having one person read MaRo's article and give us a bunch of grades for random cards (doesn't have to be the whole corpus to start) could get us going here.
I'm collecting/creating a suite of tools for a newer/better version of @roborosewater (not affiliated with the people who run that account in any way) to showcase the progress that's been made since that time.
I'd be very interested in both the pre-trained model as well as the source code (if it's not too embarrasing / you're willing to open source it).
It would certainly help me. I'm doing exploration in this space. There is a lot of information about MTG that informs whether cards are "correct" or not that's not written on the cards.
One of the things the network is pretty awful at is matching color pie. We have MaRo's article which tells us exactly which mechanics are in each color, https://magic.wizards.com/en/articles/archive/making-magic/mechanical-color-pie-2017-2017-06-05. So my first stab at this will be to try to classify cards as in-pie or out-of-pie (and if we want to feed the results back in to the network as an experiment, I'm all for experiments).
The following data sets would be useful for doing this.
1. For each card, tag it with all the abilities listed in Maro's article that are on that card.
We could use this to train a network that can tag newly-generated cards with their abilities in the same way.
2. For each card, provide some measure of whether it's in-pie or out-of-pie.
Even having one person read MaRo's article and give us a bunch of grades for random cards (doesn't have to be the whole corpus to start) could get us going here.