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ResearchFormerly WizWand

Passivization on Passivization (test)

100Full AccuracyTf+Nd+Nd
-3.58423.30850.277.092Nov 5, 2025
Updated 4mo ago

Evaluation results

MethodLinks
Tf+Nd+NdArchitecture=Stack Attention Transformer, Stack=Double2025.11100
T5Large pre-trained model=true2025.11100
mT5Large pre-trained model=true2025.11100
mBARTLarge pre-trained model=true, Overtrained=true2025.11100
TfTraining=Language Modeling Objective2025.11100
TfArchitecture=Transformer2025.1199.9
Tf+SupArchitecture=Transformer, Supervision=Yes2025.1199.8
Tf+Sup+SupArchitecture=Transformer, Supervision=Double2025.1199.8
Tf+NdArchitecture=Stack Attention Transformer2025.1198.8
BARTLarge pre-trained model=true2025.1195
RNNArchitecture=RNN2025.1192.6
RNN+SupArchitecture=RNN, Supervision=Yes2025.1184.8
RNN+NdArchitecture=Stack RNN2025.1174.7
RNN+Nd+RArchitecture=Stack RNN, Variant=R2025.1166.9
RNN+Sup+RArchitecture=RNN, Supervision=Yes, Variant=R2025.1153.7
LSTM+Nd+RArchitecture=Stack LSTM, Variant=R2025.1120.9
LSTM+Sup+RArchitecture=LSTM, Supervision=Yes, Variant=R2025.111.1
LSTM+SupArchitecture=LSTM, Supervision=Yes2025.110.9
LSTMArchitecture=LSTM2025.110.7
LSTM+NdArchitecture=Stack LSTM2025.110.4