This link has my code and the example output
https://files.fm/u/vpncrxvcz
My output is :
barely -> FOUNDcarats -> FOUNDconfabs -> FOUNDcorepulent -> FOUND 1: corpulentcostumye -> FOUND 1: costumeypamek -> NOT FOUNDdarjed -> FOUND 3: dared darked darneddoitd -> FOUND 2: doit doitsdovrtailing -> FOUND 1: dovetailingflavoonls -> FOUND 1: flavonolsoutstpend -> FOUND 1: outspendhut -> FOUND 2: shut jutpashas -> FOUNDpoolry -> FOUND 1: poorlyquixmasters -> FOUND 1: quizmastersrelocatiing -> FOUND 1: relocatingsorceresd -> FOUND 1: sorceressturbulences -> FOUNDsut -> FOUND 1: shutinteters -> FOUND 1: integersmarteatment -> NOT FOUNDagew -> FOUND 3: anew ages ageeshu -> FOUND 3: shut shy shh
but the target output is :
barely -> FOUNDcarats -> FOUNDconfabs -> FOUNDcorepulent -> FOUND 1: corpulentcostumye -> FOUND 1: costumeypamek -> NOT FOUNDdarjed -> FOUND 3: darned dared darkeddoitd -> FOUND 2: doit doitsdovrtailing -> FOUND 1: dovetailingflavoonls -> FOUND 1: flavonolsoutstpend -> FOUND 1: outspendhut -> FOUND 2: shut jutpashas -> FOUNDpoolry -> FOUND 1: poorlyquixmasters -> FOUND 1: quizmastersrelocatiing -> FOUND 1: relocatingsorceresd -> FOUND 1: sorceressturbulences -> FOUNDsut -> FOUND 1: shutinteters -> FOUND 1: integersmarteatment -> NOT FOUNDagew -> FOUND 3: ages agee anewshu -> FOUND 3: shut shy shh
And the reference in this probably help :
After going through all of the above, if there are multiple potential matches, sort them by their potential frequency from the English dictionary and return the top 3 values that are in most frequent usage as the most likely corrections in order.
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