update readme

README.md

@@ -76,28 +76,28 @@ Once the model is loaded, you can use it as shown:
 
 ```python
 model.wv.most_similar("bendo")
-[('binks', 0.8920747637748718),
- ('bando', 0.8460732698440552),
- ('hood', 0.8299438953399658),
- ('tieks', 0.8264378309249878),
- ('hall', 0.817583441734314),
- ('secteur', 0.8145656585693359),
- ('barrio', 0.809047281742096),
- ('block', 0.793493390083313),
- ('bâtiment', 0.7826434969902039),
- ('bloc', 0.7753982543945312)]
+[('binks', 0.7833775877952576),
+ ('bando', 0.7511972188949585),
+ ('tieks', 0.7123318910598755),
+ ('ghetto', 0.6887569427490234),
+ ('hall', 0.679759681224823),
+ ('barrio', 0.6694452166557312),
+ ('hood', 0.6490002274513245),
+ ('block', 0.6299082040786743),
+ ('bloc', 0.627208411693573),
+ ('secteur', 0.6225507855415344)]
 
 model.wv.most_similar("kichta")
-[('liasse', 0.878665566444397),
- ('sse-lia', 0.8552991151809692),
- ('kishta', 0.8535938262939453),
- ('kich', 0.7646669149398804),
- ('skalape', 0.7576569318771362),
- ('moula', 0.7466527223587036),
- ('valise', 0.7429592609405518),
- ('sacoche', 0.7324921488761902),
- ('mallette', 0.7247079014778137),
- ('re-pai', 0.7060815095901489)]
+[('liasse', 0.7877408266067505),
+ ('sse-lia', 0.7605615854263306),
+ ('kishta', 0.7043415904045105),
+ ('kich', 0.663270890712738),
+ ('sacoche', 0.6381840705871582),
+ ('moula', 0.6318666338920593),
+ ('valise', 0.5628494024276733),
+ ('bonbonne', 0.55326247215271),
+ ('skalape', 0.5523083806037903),
+ ('kichtas', 0.5385912656784058)]
 ```
 
 2. **To find the word that doesn't match in a list of words**
@@ -114,10 +114,10 @@ model.wv.doesnt_match(["Zidane","Mbappé","Ronaldo","Messi","Jordan"])
 
 ```python
 model.wv.similarity("kichta", "moula")
-0.7466528
+0.63186663
 
 model.wv.similarity("bonheur", "moula")
-0.16985293
+0.14551902
 ```
 
 4. **Or even get the vector representation of a word**