Different output from the Readme

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When I type

kw_model = KeyBERT()
kw_model.extract_keywords(doc, keyphrase_ngram_range=(1, 1), stop_words=None)

I get

[('supervised', 0.6676), ('labeled', 0.4896), ('learning', 0.4813), ('training', 0.4134), ('labels', 0.3947)]

which is different than the output from Readme

[('learning', 0.4604),
('algorithm', 0.4556),
('training', 0.4487),
('class', 0.4086),
('mapping', 0.3700)]

I get

[('supervised learning', 0.6779), ('supervised', 0.6676), ('signal supervised', 0.6152), ('in supervised', 0.6124), ('labeled training', 0.6013)]

which is different and less diverse than the output from the Readme, which is
[('learning algorithm', 0.6978),
('machine learning', 0.6305),
('supervised learning', 0.5985),
('algorithm analyzes', 0.5860),
('learning function', 0.5850)]

How to reproduce exactly same output as the one provided in Readme file?

Start with the README example and the two extract_keywords calls shown in the issue, comparing their inputs and reported outputs. Investigate which conditions affect the differing keyword rankings; done means identifying the missing reproducibility conditions or documenting a corrected README result.

Written by the indexing model from the issue text.