RobertaForSequenceClassification( (roberta): RobertaModel( (embeddings): RobertaEmbeddings( (word_embeddings): Embedding(50265, 768, padding_idx=1) (position_embeddings): Embedding(514, 768, padding_idx=1) (token_type_embeddings): Embedding(1, 768) (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): RobertaEncoder( (layer): ModuleList( (0-11): 12 x RobertaLayer( (attention): RobertaAttention( (self): RobertaSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): RobertaSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): RobertaIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): RobertaOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) ) (classifier): RobertaClassificationHead( (dense): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) (out_proj): Linear(in_features=768, out_features=9, bias=True) ) )