Fit/Transform does not give top n matches

I was using get_matches() to get top 5 matches. Now, since moving to production thought of using Fit/Predict but seems it returns only top first matches for each item. Is there any other way to get top 5 matches in Fit/Predict

I am matching current text notes (non-semantic long text) with historical ones. Historical data will be large in lakhs. So, to make code more efficient planning to pass historical text notes in fit and current text notes in transform. Planning to retrain it monthly.

Sample Current/Input Data:

<style> </style>

ID	Alloc_No	Text_Notes
2354657	78	RHJ…..//32456hjfg//vkcmEGHJJJYMM
4354657	35	TFHGDVASFHC4636587//5748UJKNM
345676	889	WUSERHIFKDJVN//23475//IUOSJDFGKV
34747586	57	YWEIHFDSK//2435467//WEKSFDHLV
465768	3777	324TYVHBJN//435465//HUJNKHJKN

Sample Reference/Historical Data:

<style> </style>

ID	Alloc_No	Text_Notes
2354657	78	RHJ…..//32456hjfg//vkcmEGHJJJYMM
4354657	35	TFHGDVASFHC4636587//5748UJKNM
345676	889	WUSERHIFKDJVN//23475//IUOSJDFGKV
34747586	57	YWEIHFDSK//2435467//WEKSFDHLV
465768	3777	324TYVHBJN//435465//HUJNKHJKN
2354657	78	RHJ…..//32456hjfg//vkcmEGHJJJYMM
4354657	35	TFHGDVASFHC4636587//5748UJKNM
345676	889	WUSERHIFKDJVN//23475//IUOSJDFGKV
34747586	57	YWEIHFDSK//2435467//WEKSFDHLV
465768	3777	324TYVHBJN//435465//HUJNKHJKN

Sample Code:

Old Code using get_matches():

Passing reference historical text notes to "to_list"

to_list = Ref.Text_Notes.to_list()

for i in range(0,Input.shape[0]):

# Passing the new text notes one by one to get similarity score for all reference items and then get top 5 from it

from_list=[]
from_list.append(Input.Text_Notes[i])
#print(to_list)
model = PolyFuzz("TF-IDF").match(from_list, to_list)
matches=model.get_matches().sort_values(by='Similarity',ascending=False)
matches1=pd.merge(matches,Ref,left_index=True, right_index=True)
dict1=matches1[['ID','Similarity','Alloc_No','From','To']].to_dict('index')
list1=list(dict1.items())[:5]
dict2.update({Input['ID'][i]: list1})

dict2

New Changed Code for Production using Fit/Transform:

Fit the reference historical text notes

Frequency - monthly

from_list = Ref.Text_Notes.to_list()
model = PolyFuzz("TF-IDF")
model.fit(from_list)
model.save("TF-IDF")

Match the new text notes

Frequency - Daily

dict2 ={}
for i in range(0,Input.shape[0]):
to_list=[]
to_list.append(Input.Text_Notes[i])
model = PolyFuzz.load("TF-IDF")
matches=model.transform(to_list)
print(matches)
dict2

Now the issue is in transform i don't get similarity score for all reference rather only top 1 match whereas I need top 5

Start by comparing the PolyFuzz("TF-IDF") get_matches(), fit(), and transform() entry points using the sample inputs in this issue. Trace how transform selects matches and verify the behavior against the existing get_matches() results. Done means transform can provide the top five reference matches and their similarity scores for each input note.