Use hyper_parameters

[tintinrevient]

Changes

Currently, the CML pipeline fails because I rename from hyper_params to hyper_parameters for the model card in proteingym-base.

Checklist

• I broke the PR down so that it contains a reasonable amount of changes for an effective review
• I performed a self-review of my code. Amongst other things, I have commented my code in hard-to-understand areas.
• I made corresponding changes to the documentation
• I added tests that prove my fix is effective or that my feature works
• I accounted for dependent changes to be merged and published in downstream modules

[tintinrevient]

✅ Supervised models have all passed validation.

metric_name,metric_value
Overall ACC,0.0
Overall RACCU,0.0030303030303030303
Overall RACC,0.0
Kappa,0.0
Gwet AC1,-0.0030395136778115636
Bennett S,-0.00303951367781155
Kappa Standard Error,0.0
Kappa Unbiased,-0.00303951367781155
Scott PI,-0.00303951367781155
Kappa No Prevalence,-1.0
Kappa 95% CI,"(0.0, 0.0)"
Standard Error,0.0
95% CI,"(0.0, 0.0)"
Chi-Squared,None
Phi-Squared,None
Cramer V,None
Response Entropy,7.366322214245807
Reference Entropy,7.366322214245807
Cross Entropy,0
Joint Entropy,7.366322214245807
Conditional Entropy,-0.0
Mutual Information,7.366322214245807
KL Divergence,None
Lambda B,1.0
Lambda A,1.0
Chi-Squared DF,108241
Overall J,"(0.0, 0.0)"
Hamming Loss,1.0
Zero-one Loss,165
NIR,0.006060606060606061
P-Value,1
Overall CEN,0.0
Overall MCEN,0.0
Overall MCC,0.0
RR,0.5
CBA,0.0
AUNU,None
AUNP,None
RCI,1.0
Pearson C,None
TPR Micro,0.0
TPR Macro,None
CSI,None
ARI,None
TNR Micro,0.9969604863221885
TNR Macro,0.996969696969697
Bangdiwala B,None
Krippendorff Alpha,0.0
SOA1(Landis & Koch),Slight
SOA2(Fleiss),Poor
SOA3(Altman),Poor
SOA4(Cicchetti),Poor
SOA5(Cramer),None
SOA6(Matthews),Negligible
SOA7(Lambda A),Perfect
SOA8(Lambda B),Perfect
SOA9(Krippendorff Alpha),Low
SOA10(Pearson C),None
FPR Macro,0.00303030303030305
FNR Macro,None
PPV Macro,None
NPV Macro,0.996969696969697
ACC Macro,0.9939393939393939
F1 Macro,0.0
FPR Micro,0.003039513677811523
FNR Micro,1.0
PPV Micro,0.0
F1 Micro,0.0
NPV Micro,0.9969604863221885

✅ Zero-shot models have all passed validation.

metric_name,metric_value
Overall ACC,0.0
Overall RACCU,0.00010350554262465215
Overall RACC,0.0
Kappa,0.0
Gwet AC1,-0.00010027040554341335
Bennett S,-0.0001002707309736288
Kappa Standard Error,0.0
Kappa Unbiased,-0.00010351625713101698
Scott PI,-0.00010351625713101698
Kappa No Prevalence,-1.0
Kappa 95% CI,"(0.0, 0.0)"
Standard Error,0.0
95% CI,"(0.0, 0.0)"
Chi-Squared,None
Phi-Squared,None
Cramer V,None
Response Entropy,12.286557761608659
Reference Entropy,12.270402713018697
Cross Entropy,0
Joint Entropy,12.286557761608659
Conditional Entropy,0.0161550485899576
Mutual Information,12.2704027130187
KL Divergence,None
Lambda B,0.9963963963963964
Lambda A,1.0
Chi-Squared DF,99460729
Overall J,"(0.0, 0.0)"
Hamming Loss,0.9999999999999999
Zero-one Loss,4996
NIR,0.0038030424339471577
P-Value,1
Overall CEN,0.0005655020094343343
Overall MCEN,0.0005655020094343343
Overall MCC,0.0
RR,0.5009023460998596
CBA,0.0
AUNU,None
AUNP,None
RCI,1.0000000000000002
Pearson C,None
TPR Micro,0.0
TPR Macro,None
CSI,None
ARI,0.0
TNR Micro,0.9998997292690264
TNR Macro,0.9998997393222379
Bangdiwala B,None
Krippendorff Alpha,-3.425833166131969e-06
SOA1(Landis & Koch),Slight
SOA2(Fleiss),Poor
SOA3(Altman),Poor
SOA4(Cicchetti),Poor
SOA5(Cramer),None
SOA6(Matthews),Negligible
SOA7(Lambda A),Perfect
SOA8(Lambda B),Very Strong
SOA9(Krippendorff Alpha),Low
SOA10(Pearson C),None
FPR Macro,0.00010026067776214287
FNR Macro,None
PPV Macro,None
NPV Macro,0.9998997393222379
ACC Macro,0.9997994786444756
F1 Macro,0.0
FPR Micro,0.00010027073097362837
FNR Micro,1.0
PPV Micro,0.0
F1 Micro,0.0
NPV Micro,0.9998997292690264