Open nh2 opened 5 months ago
Some other examples I'm collecting that SwiftKey does well and FUTO doesn't (all typing, no swiping):
I'm not sure bit
-> I'm not sure but
Autocorrect Threshold T = 0.7
does not help hereI'm not sure id
-> I'm not sure if
The dog doesn't like the cst
-> The dog doesn't like the cat
Autocorrect Threshold T = 0.7
FUTO corrects it to cats
instead of cat
; not sure why, cat
seems like a much better suggestion.I would like to eat some pizza with my friends and drunk some xoke
T = 0.7
FUTO corrects xoke
to comedy
here; code
is shown as a suggestion but not the default one. drunk some comedy
makes no sense.Tyoinf
-> Typing
T = 0.7
corrects this to Thing
Ibwisg
-> I wish
Obstinent
-- makes no senseI can nitbswe
-> I can not see
I can bother
I don't like what is being done here because it is inoeofrssinal
-> I don't like what is being done here because it is unprofessional
inoeofrssinal
to `impressiveunprofessional
is the first suggest but not applied when pressing space.
If duxks
-> If ducks
If sucks
-- that's not English
Hi,
for a long time I'm searching for a good open-source alternative to SwiftKey. FUTO looks quite good but there are still many inputs where SwiftKey does much better suggestions out-of-the-box.
I'm not sure bit
; SwiftKey reliably corrects that toI'm not sure but
and FUTO doesn't."I'm not sure bit" is just a very unlikely sentence that doesn't make sense in English. I suspect any Markov chain model should easily correct that.
Is it documented somehow in high-level terms how exactly FUTO's engine works? I see in the settings that there's a slider to weigh dictionary-based and neural network based suggestions, but couldn't find any details.
What does "dictionary" mean here? Does that use a Markov chain?
If not, I'd like to propose this to the project (perhaps as another model to combine in from). Markov chains