How does predict_proba work exactly ?

Hi everyone !

Thanks for this amazing package first ! it is more than useful for a project at my work currently ! and the 0.6.0 was much needed on my side !

BUT i'd like to have some clarifications on how the function predict_proba works because I have a hard understanding.

This table :

score | predicted | pred_proba_0 | pred_proba_1
-- | -- | -- | --
1 | 1 | 0.866082 | 0.133918
1 | 1 | 0.762696 | 0.237304
1 | 1 | 0.730971 | 0.269029
1 | 1 | 0.871808 | 0.128192
1 | 1 | 0.671637 | 0.328363
1 | 1 | 0.780433 | 0.219567
1 | 1 | 0.652668 | 0.347332
1 | 0 | 0.767050 | 0.232950

The score column is the true outcome, predicted is what the predict method gives me when I'm doing inference.
pred_proba_0 and pred_proba_1 are given from this code :
validate_dataset['pred_proba_0'] = trainer.model.predict_proba(validate_dataset['Fulltext_clean_translated+metadata_clean_translated'].to_list(),as_numpy=True)[:,0]
validate_dataset['pred_proba_1'] = trainer.model.predict_proba(validate_dataset['Fulltext_clean_translated+metadata_clean_translated'].to_list(),as_numpy=True)[:,1]

Also when I use this code :
model.predict_proba(validate_dataset['Fulltext_clean_translated+metadata_clean_translated'].to_list(),as_numpy=True)
I have this output :
array([[9.1999289e-07, 9.9999905e-01],
[7.2725675e-07, 9.9999928e-01],
[8.1967613e-07, 9.9999917e-01],
...,
[9.4037086e-06, 9.9999058e-01],
[9.1749916e-07, 9.9999905e-01],
[1.2628381e-06, 9.9999869e-01]], dtype=float32)

my question is , i'd like to know if the predict_proba output gives (probability to predict 0 , probability to predict 1) ?
It doesn't seem like it because of this line :

229 | 0 | 1 | 0.694485 | 0.305515
-- | -- | -- | -- | --

something is strange also train.model.predict_proba doesn't give the same result as model.predict_proba... can someone please explain to help me understand ?

Thank you very much !