2023-03-29 01:36:02,993 ----------------------------------------------------------------------------------------------------
2023-03-29 01:36:02,995 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): XLMRobertaModel(
      (embeddings): RobertaEmbeddings(
        (word_embeddings): Embedding(250003, 1024)
        (position_embeddings): Embedding(514, 1024, padding_idx=1)
        (token_type_embeddings): Embedding(1, 1024)
        (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): RobertaEncoder(
        (layer): ModuleList(
          (0): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (1): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (2): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (3): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (4): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (5): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (6): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (7): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (8): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (9): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (10): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (11): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (12): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (13): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (14): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (15): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (16): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (17): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (18): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (19): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (20): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (21): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (22): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (23): RobertaLayer(
            (attention): RobertaAttention(
              (self): RobertaSelfAttention(
                (query): Linear(in_features=1024, out_features=1024, bias=True)
                (key): Linear(in_features=1024, out_features=1024, bias=True)
                (value): Linear(in_features=1024, out_features=1024, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): RobertaSelfOutput(
                (dense): Linear(in_features=1024, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): RobertaIntermediate(
              (dense): Linear(in_features=1024, out_features=4096, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): RobertaOutput(
              (dense): Linear(in_features=4096, out_features=1024, bias=True)
              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): RobertaPooler(
        (dense): Linear(in_features=1024, out_features=1024, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=1024, out_features=4856, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-03-29 01:36:02,996 ----------------------------------------------------------------------------------------------------
2023-03-29 01:36:02,997 Corpus: "MultiCorpus: 107447 train + 12378 dev + 11372 test sentences
 - ColumnCorpus Corpus: 75187 train + 9603 dev + 9479 test sentences - resources/tasks/srl
 - ColumnCorpus Corpus: 13814 train + 799 dev + 977 test sentences - resources/tasks/up2_german
 - ColumnCorpus Corpus: 14449 train + 1476 dev + 416 test sentences - resources/tasks/up2_french
 - ColumnCorpus Corpus: 3997 train + 500 dev + 500 test sentences - resources/tasks/up2_chinese"
2023-03-29 01:36:02,997 ----------------------------------------------------------------------------------------------------
2023-03-29 01:36:02,997 Parameters:
2023-03-29 01:36:02,997  - learning_rate: "0.000005"
2023-03-29 01:36:02,997  - mini_batch_size: "4"
2023-03-29 01:36:02,997  - patience: "3"
2023-03-29 01:36:02,997  - anneal_factor: "0.5"
2023-03-29 01:36:02,997  - max_epochs: "10"
2023-03-29 01:36:02,997  - shuffle: "True"
2023-03-29 01:36:02,997  - train_with_dev: "True"
2023-03-29 01:36:02,997  - batch_growth_annealing: "False"
2023-03-29 01:36:02,997 ----------------------------------------------------------------------------------------------------
2023-03-29 01:36:02,997 Model training base path: "resources/taggers/flert-models/flert-multiframe-with-dev-True-with_test-False-full_init_decoder-True-dropout_refactor-257"
2023-03-29 01:36:02,997 ----------------------------------------------------------------------------------------------------
2023-03-29 01:36:02,997 Device: cuda:1
2023-03-29 01:36:02,997 ----------------------------------------------------------------------------------------------------
2023-03-29 01:36:02,997 Embeddings storage mode: none
2023-03-29 01:36:02,997 ----------------------------------------------------------------------------------------------------
2023-03-29 01:51:29,306 epoch 1 - iter 2995/29957 - loss 0.46880483 - time (sec): 926.31 - samples/sec: 245.88 - lr: 0.000000
2023-03-29 02:06:50,133 epoch 1 - iter 5990/29957 - loss 0.39735513 - time (sec): 1847.14 - samples/sec: 246.42 - lr: 0.000001
2023-03-29 02:22:03,301 epoch 1 - iter 8985/29957 - loss 0.35484370 - time (sec): 2760.30 - samples/sec: 246.11 - lr: 0.000001
2023-03-29 02:36:51,472 epoch 1 - iter 11980/29957 - loss 0.32766105 - time (sec): 3648.47 - samples/sec: 247.77 - lr: 0.000002
2023-03-29 02:52:05,358 epoch 1 - iter 14975/29957 - loss 0.30989385 - time (sec): 4562.36 - samples/sec: 247.82 - lr: 0.000002
2023-03-29 03:07:47,447 epoch 1 - iter 17970/29957 - loss 0.29597177 - time (sec): 5504.45 - samples/sec: 246.75 - lr: 0.000003
2023-03-29 03:23:23,678 epoch 1 - iter 20965/29957 - loss 0.28455843 - time (sec): 6440.68 - samples/sec: 246.22 - lr: 0.000003
2023-03-29 03:38:56,804 epoch 1 - iter 23960/29957 - loss 0.27554777 - time (sec): 7373.81 - samples/sec: 245.94 - lr: 0.000004
2023-03-29 03:54:33,198 epoch 1 - iter 26955/29957 - loss 0.26823442 - time (sec): 8310.20 - samples/sec: 245.65 - lr: 0.000004
2023-03-29 04:09:46,074 epoch 1 - iter 29950/29957 - loss 0.26211479 - time (sec): 9223.08 - samples/sec: 245.77 - lr: 0.000005
2023-03-29 04:09:47,728 ----------------------------------------------------------------------------------------------------
2023-03-29 04:09:47,728 EPOCH 1 done: loss 0.2621 - lr 0.000005
2023-03-29 04:13:04,872 Evaluating as a multi-label problem: False
2023-03-29 04:13:05,179 TEST : loss 0.16172035038471222 - f1-score (micro avg)  0.8798
2023-03-29 04:13:05,385 ----------------------------------------------------------------------------------------------------
2023-03-29 04:28:36,749 epoch 2 - iter 2995/29957 - loss 0.19225970 - time (sec): 931.36 - samples/sec: 242.77 - lr: 0.000005
2023-03-29 04:44:03,052 epoch 2 - iter 5990/29957 - loss 0.18862864 - time (sec): 1857.67 - samples/sec: 244.30 - lr: 0.000005
2023-03-29 04:58:14,107 epoch 2 - iter 8985/29957 - loss 0.18642837 - time (sec): 2708.72 - samples/sec: 251.31 - lr: 0.000005
2023-03-29 05:13:47,858 epoch 2 - iter 11980/29957 - loss 0.18445782 - time (sec): 3642.47 - samples/sec: 249.39 - lr: 0.000005
2023-03-29 05:29:18,027 epoch 2 - iter 14975/29957 - loss 0.18227396 - time (sec): 4572.64 - samples/sec: 248.29 - lr: 0.000005
2023-03-29 05:44:54,005 epoch 2 - iter 17970/29957 - loss 0.18042676 - time (sec): 5508.62 - samples/sec: 247.13 - lr: 0.000005
2023-03-29 06:00:10,058 epoch 2 - iter 20965/29957 - loss 0.18034247 - time (sec): 6424.67 - samples/sec: 247.17 - lr: 0.000005
2023-03-29 06:14:57,888 epoch 2 - iter 23960/29957 - loss 0.17959256 - time (sec): 7312.50 - samples/sec: 247.95 - lr: 0.000005
2023-03-29 06:30:34,695 epoch 2 - iter 26955/29957 - loss 0.17863423 - time (sec): 8249.31 - samples/sec: 247.46 - lr: 0.000005
2023-03-29 06:45:55,986 epoch 2 - iter 29950/29957 - loss 0.17708644 - time (sec): 9170.60 - samples/sec: 247.18 - lr: 0.000004
2023-03-29 06:45:57,775 ----------------------------------------------------------------------------------------------------
2023-03-29 06:45:57,775 EPOCH 2 done: loss 0.1771 - lr 0.000004
2023-03-29 06:50:07,545 Evaluating as a multi-label problem: False
2023-03-29 06:50:07,862 TEST : loss 0.12273010611534119 - f1-score (micro avg)  0.8991
2023-03-29 06:50:08,065 ----------------------------------------------------------------------------------------------------
2023-03-29 07:05:36,429 epoch 3 - iter 2995/29957 - loss 0.14692283 - time (sec): 928.36 - samples/sec: 242.71 - lr: 0.000004
2023-03-29 07:21:05,105 epoch 3 - iter 5990/29957 - loss 0.14636916 - time (sec): 1857.04 - samples/sec: 243.00 - lr: 0.000004
2023-03-29 07:35:16,001 epoch 3 - iter 8985/29957 - loss 0.14795354 - time (sec): 2707.94 - samples/sec: 250.72 - lr: 0.000004
2023-03-29 07:50:07,631 epoch 3 - iter 11980/29957 - loss 0.14769979 - time (sec): 3599.57 - samples/sec: 251.64 - lr: 0.000004
2023-03-29 08:05:35,635 epoch 3 - iter 14975/29957 - loss 0.14686968 - time (sec): 4527.57 - samples/sec: 249.83 - lr: 0.000004
2023-03-29 08:21:13,273 epoch 3 - iter 17970/29957 - loss 0.14531486 - time (sec): 5465.21 - samples/sec: 248.28 - lr: 0.000004
2023-03-29 08:36:48,870 epoch 3 - iter 20965/29957 - loss 0.14488086 - time (sec): 6400.80 - samples/sec: 247.34 - lr: 0.000004
2023-03-29 08:50:56,416 epoch 3 - iter 23960/29957 - loss 0.14489370 - time (sec): 7248.35 - samples/sec: 250.25 - lr: 0.000004
2023-03-29 09:06:11,129 epoch 3 - iter 26955/29957 - loss 0.14397849 - time (sec): 8163.06 - samples/sec: 249.84 - lr: 0.000004
2023-03-29 09:18:44,972 epoch 3 - iter 29950/29957 - loss 0.14352103 - time (sec): 8916.91 - samples/sec: 254.22 - lr: 0.000004
2023-03-29 09:18:46,409 ----------------------------------------------------------------------------------------------------
2023-03-29 09:18:46,409 EPOCH 3 done: loss 0.1435 - lr 0.000004
2023-03-29 09:22:32,979 Evaluating as a multi-label problem: False
2023-03-29 09:22:33,292 TEST : loss 0.12182175368070602 - f1-score (micro avg)  0.9063
2023-03-29 09:22:33,500 ----------------------------------------------------------------------------------------------------
2023-03-29 09:37:39,956 epoch 4 - iter 2995/29957 - loss 0.12438966 - time (sec): 906.46 - samples/sec: 250.79 - lr: 0.000004
2023-03-29 09:50:33,916 epoch 4 - iter 5990/29957 - loss 0.12418894 - time (sec): 1680.42 - samples/sec: 269.39 - lr: 0.000004
2023-03-29 10:03:35,604 epoch 4 - iter 8985/29957 - loss 0.12400493 - time (sec): 2462.10 - samples/sec: 275.79 - lr: 0.000004
2023-03-29 10:18:24,512 epoch 4 - iter 11980/29957 - loss 0.12411795 - time (sec): 3351.01 - samples/sec: 269.96 - lr: 0.000004
2023-03-29 10:32:37,038 epoch 4 - iter 14975/29957 - loss 0.12415666 - time (sec): 4203.54 - samples/sec: 269.28 - lr: 0.000004
2023-03-29 10:48:06,975 epoch 4 - iter 17970/29957 - loss 0.12443076 - time (sec): 5133.47 - samples/sec: 264.49 - lr: 0.000004
2023-03-29 11:01:31,791 epoch 4 - iter 20965/29957 - loss 0.12414480 - time (sec): 5938.29 - samples/sec: 266.94 - lr: 0.000004
2023-03-29 11:13:19,050 epoch 4 - iter 23960/29957 - loss 0.12420919 - time (sec): 6645.55 - samples/sec: 272.88 - lr: 0.000003
2023-03-29 11:27:26,175 epoch 4 - iter 26955/29957 - loss 0.12345446 - time (sec): 7492.67 - samples/sec: 272.47 - lr: 0.000003
2023-03-29 11:41:46,492 epoch 4 - iter 29950/29957 - loss 0.12341465 - time (sec): 8352.99 - samples/sec: 271.38 - lr: 0.000003
2023-03-29 11:41:48,713 ----------------------------------------------------------------------------------------------------
2023-03-29 11:41:48,713 EPOCH 4 done: loss 0.1234 - lr 0.000003
2023-03-29 11:46:00,810 Evaluating as a multi-label problem: False
2023-03-29 11:46:01,112 TEST : loss 0.12465336918830872 - f1-score (micro avg)  0.9126
2023-03-29 11:46:01,318 ----------------------------------------------------------------------------------------------------
2023-03-29 11:58:03,180 epoch 5 - iter 2995/29957 - loss 0.10747937 - time (sec): 721.86 - samples/sec: 313.09 - lr: 0.000003
2023-03-29 12:12:00,657 epoch 5 - iter 5990/29957 - loss 0.10788088 - time (sec): 1559.34 - samples/sec: 291.56 - lr: 0.000003
2023-03-29 12:27:26,558 epoch 5 - iter 8985/29957 - loss 0.10717073 - time (sec): 2485.24 - samples/sec: 273.79 - lr: 0.000003
2023-03-29 12:40:50,711 epoch 5 - iter 11980/29957 - loss 0.10700021 - time (sec): 3289.39 - samples/sec: 276.50 - lr: 0.000003
2023-03-29 12:53:29,585 epoch 5 - iter 14975/29957 - loss 0.10770004 - time (sec): 4048.27 - samples/sec: 280.90 - lr: 0.000003
2023-03-29 13:06:24,497 epoch 5 - iter 17970/29957 - loss 0.10774019 - time (sec): 4823.18 - samples/sec: 283.11 - lr: 0.000003
2023-03-29 13:18:14,705 epoch 5 - iter 20965/29957 - loss 0.10813006 - time (sec): 5533.39 - samples/sec: 287.23 - lr: 0.000003
2023-03-29 13:33:29,124 epoch 5 - iter 23960/29957 - loss 0.10849193 - time (sec): 6447.81 - samples/sec: 281.43 - lr: 0.000003
2023-03-29 13:45:52,449 epoch 5 - iter 26955/29957 - loss 0.10851587 - time (sec): 7191.13 - samples/sec: 283.69 - lr: 0.000003
2023-03-29 13:59:04,496 epoch 5 - iter 29950/29957 - loss 0.10821391 - time (sec): 7983.18 - samples/sec: 283.96 - lr: 0.000003
2023-03-29 13:59:06,617 ----------------------------------------------------------------------------------------------------
2023-03-29 13:59:06,617 EPOCH 5 done: loss 0.1082 - lr 0.000003
2023-03-29 14:03:19,848 Evaluating as a multi-label problem: False
2023-03-29 14:03:20,155 TEST : loss 0.13846297562122345 - f1-score (micro avg)  0.9132
2023-03-29 14:03:20,365 ----------------------------------------------------------------------------------------------------
2023-03-29 14:18:41,502 epoch 6 - iter 2995/29957 - loss 0.09062871 - time (sec): 921.14 - samples/sec: 244.06 - lr: 0.000003
2023-03-29 14:34:09,674 epoch 6 - iter 5990/29957 - loss 0.09437949 - time (sec): 1849.31 - samples/sec: 242.77 - lr: 0.000003
2023-03-29 14:49:00,185 epoch 6 - iter 8985/29957 - loss 0.09483008 - time (sec): 2739.82 - samples/sec: 247.09 - lr: 0.000003
2023-03-29 15:01:42,100 epoch 6 - iter 11980/29957 - loss 0.09600700 - time (sec): 3501.73 - samples/sec: 257.80 - lr: 0.000003
2023-03-29 15:15:20,696 epoch 6 - iter 14975/29957 - loss 0.09511296 - time (sec): 4320.33 - samples/sec: 261.89 - lr: 0.000003
2023-03-29 15:27:06,122 epoch 6 - iter 17970/29957 - loss 0.09466063 - time (sec): 5025.76 - samples/sec: 270.24 - lr: 0.000002
2023-03-29 15:40:20,919 epoch 6 - iter 20965/29957 - loss 0.09462140 - time (sec): 5820.55 - samples/sec: 272.18 - lr: 0.000002
2023-03-29 15:56:02,732 epoch 6 - iter 23960/29957 - loss 0.09415645 - time (sec): 6762.37 - samples/sec: 268.04 - lr: 0.000002
2023-03-29 16:09:27,087 epoch 6 - iter 26955/29957 - loss 0.09361026 - time (sec): 7566.72 - samples/sec: 269.54 - lr: 0.000002
2023-03-29 16:21:51,942 epoch 6 - iter 29950/29957 - loss 0.09351794 - time (sec): 8311.58 - samples/sec: 272.73 - lr: 0.000002
2023-03-29 16:21:53,610 ----------------------------------------------------------------------------------------------------
2023-03-29 16:21:53,610 EPOCH 6 done: loss 0.0935 - lr 0.000002
2023-03-29 16:25:43,322 Evaluating as a multi-label problem: False
2023-03-29 16:25:43,630 TEST : loss 0.15583500266075134 - f1-score (micro avg)  0.9149
2023-03-29 16:25:43,844 ----------------------------------------------------------------------------------------------------
2023-03-29 16:41:22,457 epoch 7 - iter 2995/29957 - loss 0.08339559 - time (sec): 938.61 - samples/sec: 242.21 - lr: 0.000002
2023-03-29 16:55:43,135 epoch 7 - iter 5990/29957 - loss 0.08505759 - time (sec): 1799.29 - samples/sec: 251.89 - lr: 0.000002
2023-03-29 17:09:10,166 epoch 7 - iter 8985/29957 - loss 0.08450058 - time (sec): 2606.32 - samples/sec: 261.08 - lr: 0.000002
2023-03-29 17:22:12,711 epoch 7 - iter 11980/29957 - loss 0.08439322 - time (sec): 3388.87 - samples/sec: 267.74 - lr: 0.000002
2023-03-29 17:36:56,920 epoch 7 - iter 14975/29957 - loss 0.08496697 - time (sec): 4273.08 - samples/sec: 265.13 - lr: 0.000002
2023-03-29 17:51:23,137 epoch 7 - iter 17970/29957 - loss 0.08430926 - time (sec): 5139.29 - samples/sec: 264.60 - lr: 0.000002
2023-03-29 18:06:38,028 epoch 7 - iter 20965/29957 - loss 0.08434617 - time (sec): 6054.18 - samples/sec: 262.08 - lr: 0.000002
2023-03-29 18:20:41,114 epoch 7 - iter 23960/29957 - loss 0.08470416 - time (sec): 6897.27 - samples/sec: 262.76 - lr: 0.000002
2023-03-29 18:35:37,553 epoch 7 - iter 26955/29957 - loss 0.08432450 - time (sec): 7793.71 - samples/sec: 261.92 - lr: 0.000002
2023-03-29 18:49:31,622 epoch 7 - iter 29950/29957 - loss 0.08410419 - time (sec): 8627.78 - samples/sec: 262.74 - lr: 0.000002
2023-03-29 18:49:33,171 ----------------------------------------------------------------------------------------------------
2023-03-29 18:49:33,171 EPOCH 7 done: loss 0.0841 - lr 0.000002
2023-03-29 18:53:01,505 Evaluating as a multi-label problem: False
2023-03-29 18:53:01,823 TEST : loss 0.1650567501783371 - f1-score (micro avg)  0.9149
2023-03-29 18:53:02,035 ----------------------------------------------------------------------------------------------------
2023-03-29 19:06:36,133 epoch 8 - iter 2995/29957 - loss 0.07718162 - time (sec): 814.10 - samples/sec: 281.10 - lr: 0.000002
2023-03-29 19:21:14,756 epoch 8 - iter 5990/29957 - loss 0.07594681 - time (sec): 1692.72 - samples/sec: 268.96 - lr: 0.000002
2023-03-29 19:36:38,916 epoch 8 - iter 8985/29957 - loss 0.07482926 - time (sec): 2616.88 - samples/sec: 260.42 - lr: 0.000002
2023-03-29 19:50:54,924 epoch 8 - iter 11980/29957 - loss 0.07420688 - time (sec): 3472.89 - samples/sec: 261.61 - lr: 0.000001
2023-03-29 20:06:31,696 epoch 8 - iter 14975/29957 - loss 0.07395081 - time (sec): 4409.66 - samples/sec: 256.96 - lr: 0.000001
2023-03-29 20:20:23,024 epoch 8 - iter 17970/29957 - loss 0.07479621 - time (sec): 5240.99 - samples/sec: 259.51 - lr: 0.000001
2023-03-29 20:35:14,527 epoch 8 - iter 20965/29957 - loss 0.07527468 - time (sec): 6132.49 - samples/sec: 258.76 - lr: 0.000001
2023-03-29 20:48:45,783 epoch 8 - iter 23960/29957 - loss 0.07568633 - time (sec): 6943.75 - samples/sec: 261.22 - lr: 0.000001
2023-03-29 21:03:40,650 epoch 8 - iter 26955/29957 - loss 0.07580327 - time (sec): 7838.62 - samples/sec: 260.33 - lr: 0.000001
2023-03-29 21:18:40,507 epoch 8 - iter 29950/29957 - loss 0.07592980 - time (sec): 8738.47 - samples/sec: 259.40 - lr: 0.000001
2023-03-29 21:18:42,554 ----------------------------------------------------------------------------------------------------
2023-03-29 21:18:42,554 EPOCH 8 done: loss 0.0759 - lr 0.000001
2023-03-29 21:22:54,466 Evaluating as a multi-label problem: False
2023-03-29 21:22:54,772 TEST : loss 0.1778634637594223 - f1-score (micro avg)  0.9173
2023-03-29 21:22:54,982 ----------------------------------------------------------------------------------------------------
2023-03-29 21:38:33,999 epoch 9 - iter 2995/29957 - loss 0.06787779 - time (sec): 939.02 - samples/sec: 242.53 - lr: 0.000001
2023-03-29 21:51:38,858 epoch 9 - iter 5990/29957 - loss 0.06717283 - time (sec): 1723.88 - samples/sec: 262.75 - lr: 0.000001
2023-03-29 22:06:27,175 epoch 9 - iter 8985/29957 - loss 0.06730245 - time (sec): 2612.19 - samples/sec: 260.12 - lr: 0.000001
2023-03-29 22:21:29,051 epoch 9 - iter 11980/29957 - loss 0.06799005 - time (sec): 3514.07 - samples/sec: 257.62 - lr: 0.000001
2023-03-29 22:33:46,819 epoch 9 - iter 14975/29957 - loss 0.06786101 - time (sec): 4251.84 - samples/sec: 266.19 - lr: 0.000001
2023-03-29 22:48:22,858 epoch 9 - iter 17970/29957 - loss 0.06796191 - time (sec): 5127.88 - samples/sec: 264.84 - lr: 0.000001
2023-03-29 23:03:24,182 epoch 9 - iter 20965/29957 - loss 0.06814174 - time (sec): 6029.20 - samples/sec: 262.81 - lr: 0.000001
2023-03-29 23:15:27,283 epoch 9 - iter 23960/29957 - loss 0.06811171 - time (sec): 6752.30 - samples/sec: 267.91 - lr: 0.000001
2023-03-29 23:29:50,602 epoch 9 - iter 26955/29957 - loss 0.06821501 - time (sec): 7615.62 - samples/sec: 267.63 - lr: 0.000001
2023-03-29 23:42:42,061 epoch 9 - iter 29950/29957 - loss 0.06831983 - time (sec): 8387.08 - samples/sec: 270.28 - lr: 0.000001
2023-03-29 23:42:44,124 ----------------------------------------------------------------------------------------------------
2023-03-29 23:42:44,125 EPOCH 9 done: loss 0.0683 - lr 0.000001
2023-03-29 23:46:54,413 Evaluating as a multi-label problem: False
2023-03-29 23:46:54,722 TEST : loss 0.1974010169506073 - f1-score (micro avg)  0.917
2023-03-29 23:46:54,956 ----------------------------------------------------------------------------------------------------
2023-03-30 00:01:57,379 epoch 10 - iter 2995/29957 - loss 0.06313434 - time (sec): 902.42 - samples/sec: 250.31 - lr: 0.000001
2023-03-30 00:15:33,130 epoch 10 - iter 5990/29957 - loss 0.06264148 - time (sec): 1718.17 - samples/sec: 264.69 - lr: 0.000000
2023-03-30 00:29:37,352 epoch 10 - iter 8985/29957 - loss 0.06381918 - time (sec): 2562.40 - samples/sec: 265.68 - lr: 0.000000
2023-03-30 00:42:16,936 epoch 10 - iter 11980/29957 - loss 0.06392173 - time (sec): 3321.98 - samples/sec: 273.01 - lr: 0.000000
2023-03-30 00:57:12,421 epoch 10 - iter 14975/29957 - loss 0.06281600 - time (sec): 4217.46 - samples/sec: 269.01 - lr: 0.000000
2023-03-30 01:11:35,933 epoch 10 - iter 17970/29957 - loss 0.06293405 - time (sec): 5080.98 - samples/sec: 267.90 - lr: 0.000000
2023-03-30 01:25:17,749 epoch 10 - iter 20965/29957 - loss 0.06273723 - time (sec): 5902.79 - samples/sec: 269.11 - lr: 0.000000
2023-03-30 01:37:46,575 epoch 10 - iter 23960/29957 - loss 0.06293913 - time (sec): 6651.62 - samples/sec: 272.87 - lr: 0.000000
2023-03-30 01:53:09,066 epoch 10 - iter 26955/29957 - loss 0.06300551 - time (sec): 7574.11 - samples/sec: 269.62 - lr: 0.000000
2023-03-30 02:08:25,999 epoch 10 - iter 29950/29957 - loss 0.06294442 - time (sec): 8491.04 - samples/sec: 266.96 - lr: 0.000000
2023-03-30 02:08:27,590 ----------------------------------------------------------------------------------------------------
2023-03-30 02:08:27,590 EPOCH 10 done: loss 0.0629 - lr 0.000000
2023-03-30 02:11:50,511 Evaluating as a multi-label problem: False
2023-03-30 02:11:50,823 TEST : loss 0.21053461730480194 - f1-score (micro avg)  0.9176
2023-03-30 02:12:00,526 ----------------------------------------------------------------------------------------------------
2023-03-30 02:12:00,528 Testing using last state of model ...
2023-03-30 02:16:10,255 Evaluating as a multi-label problem: False
2023-03-30 02:16:10,565 0.9122	0.923	0.9176	0.8917
2023-03-30 02:16:10,565 
Results:
- F-score (micro) 0.9176
- F-score (macro) 0.6415
- Accuracy 0.8917

By class:
                     precision    recall  f1-score   support

              be.01     0.9689    0.9660    0.9674      4059
              be.03     0.9504    0.9659    0.9581      1963
            have.01     0.9822    0.9886    0.9854      1226
             say.01     0.9884    0.9955    0.9919      1108
              do.01     0.9684    0.9938    0.9809       647
            have.03     0.9191    0.9247    0.9219       651
           think.01     0.9795    0.9911    0.9853       337
              be.02     0.9486    0.9632    0.9559       326
             see.01     0.9869    0.9837    0.9853       306
              do.02     0.9504    0.9306    0.9404       288
            come.01     0.9416    0.9344    0.9380       259
            know.01     0.9190    0.8867    0.9026       256
            want.01     0.9761    0.9919    0.9839       247
              go.02     0.9057    0.9057    0.9057       244
            tell.01     0.9679    0.9837    0.9757       184
            take.01     0.8844    0.8315    0.8571       184
             use.01     0.9186    0.9693    0.9433       163
            give.01     0.9264    0.9207    0.9235       164
            talk.01     0.9814    0.9693    0.9753       163
            make.02     0.9618    0.9557    0.9587       158
          become.01     0.9706    0.9565    0.9635       138
            have.02     0.9630    1.0000    0.9811       130
           price.01     0.9924    0.9774    0.9848       133
            work.01     0.8467    0.9431    0.8923       123
             get.01     0.9355    0.9431    0.9393       123
         believe.01     0.9919    0.9919    0.9919       123
            find.01     0.8898    0.9130    0.9013       115
             try.01     0.9910    1.0000    0.9955       110
              go.04     0.9818    1.0000    0.9908       108
            need.01     0.9623    0.9533    0.9577       107
            know.06     0.7788    0.9072    0.8381        97
            mean.01     0.9709    0.9709    0.9709       103
            look.01     0.9118    0.9394    0.9254        99
            make.LV     0.8317    0.9130    0.8705        92
          happen.01     0.9792    0.9792    0.9792        96
            make.01     0.9222    0.8300    0.8737       100
            show.01     0.9468    0.9780    0.9622        91
           begin.01     0.9341    0.9551    0.9444        89
           bring.01     0.9130    0.9882    0.9492        85
            call.01     0.9205    0.9310    0.9257        87
             pay.01     0.9326    0.9881    0.9595        84
            live.01     0.9535    0.9647    0.9591        85
        increase.01     0.8989    0.9756    0.9357        82
            kill.01     0.9651    0.9765    0.9708        85
           trade.01     0.9412    0.9756    0.9581        82
           offer.01     0.9302    0.9877    0.9581        81
          invest.01     0.9753    0.9753    0.9753        81
             die.01     0.9620    0.9383    0.9500        81
        continue.01     0.9390    0.9872    0.9625        78
            sell.01     0.9620    0.9870    0.9744        77
             let.01     0.9872    1.0000    0.9935        77
           write.01     0.9494    0.9868    0.9677        76
            hear.01     0.9870    1.0000    0.9935        76
             put.01     0.9324    0.8846    0.9079        78
          report.01     0.9200    0.9079    0.9139        76
          decide.01     0.9467    0.9861    0.9660        72
          expect.01     1.0000    0.9863    0.9931        73
            help.01     0.9722    0.9859    0.9790        71
             ask.02     0.9714    0.9444    0.9577        72
           start.01     0.8750    0.9130    0.8936        69
             get.03     0.8824    0.8571    0.8696        70
            seem.01     0.9710    0.9710    0.9710        69
       establish.01     0.9565    0.9706    0.9635        68
         support.01     0.9855    1.0000    0.9927        68
           speak.01     0.9412    0.9412    0.9412        68
             ask.01     0.9538    0.9688    0.9612        64
          change.01     0.9692    1.0000    0.9844        63
         provide.01     0.9688    0.9688    0.9688        64
            deal.01     0.9841    1.0000    0.9920        62
         receive.01     0.9333    0.8889    0.9106        63
             get.05     0.8246    0.7460    0.7833        63
          former.01     1.0000    1.0000    1.0000        59
            call.02     0.9310    0.9310    0.9310        58
           agree.01     0.9828    1.0000    0.9913        57
           reach.01     0.9483    0.9649    0.9565        57
          answer.01     0.9825    1.0000    0.9912        56
            feel.01     0.9818    0.9474    0.9643        57
             buy.01     0.9138    1.0000    0.9550        53
         produce.01     0.9091    0.9259    0.9174        54
         develop.02     0.9273    0.9444    0.9358        54
            lose.02     0.9615    0.8929    0.9259        56
           learn.01     0.9455    0.9811    0.9630        53
         include.01     0.9259    0.9259    0.9259        54
            move.01     0.8824    0.9184    0.9000        49
         operate.01     0.9216    0.9592    0.9400        49
            grow.01     0.9783    0.9000    0.9375        50
             end.01     0.7925    0.9767    0.8750        43
            stay.01     0.9167    0.9362    0.9263        47
              go.01     0.7609    0.7143    0.7368        49
            stop.01     0.9375    0.9783    0.9574        46
            hope.01     0.9787    1.0000    0.9892        46
        announce.01     0.8958    0.9556    0.9247        45
       negotiate.01     0.9783    0.9783    0.9783        46
            fall.01     0.8723    0.9318    0.9011        44
            show.04     0.9348    0.9556    0.9451        45
             own.01     0.9744    0.7600    0.8539        50
            plan.01     0.9762    0.8723    0.9213        47
        remember.01     1.0000    0.9778    0.9888        45
            test.01     0.9333    0.9545    0.9438        44
            rise.01     0.9767    0.9333    0.9545        45
          reduce.01     0.9333    0.9767    0.9545        43
          create.01     0.8889    0.9756    0.9302        41
           build.01     0.8864    0.9512    0.9176        41
          remain.01     0.8372    0.9000    0.8675        40
           state.01     0.9744    0.9500    0.9620        40
             eat.01     0.9250    0.9737    0.9487        38
           enter.01     0.9250    0.9737    0.9487        38
            wait.01     0.9250    1.0000    0.9610        37
          accept.01     0.8780    1.0000    0.9351        36
         account.01     0.9231    0.9474    0.9351        38
          expand.01     0.9487    0.9737    0.9610        38
             war.01     0.8750    0.9459    0.9091        37
           serve.01     0.8947    0.8947    0.8947        38
            look.02     0.8718    0.9189    0.8947        37
            bear.02     0.9474    0.9730    0.9600        37
           leave.11     0.8947    0.9189    0.9067        37
          charge.05     1.0000    0.9737    0.9867        38
      understand.01     0.9211    0.9722    0.9459        36
         explain.01     1.0000    1.0000    1.0000        37
       implement.01     0.9730    0.9730    0.9730        37
             add.02     0.9722    0.9459    0.9589        37
         control.01     0.8947    0.9714    0.9315        35
           cause.01     0.8684    0.9706    0.9167        34
             win.01     0.9459    1.0000    0.9722        35
            take.LV     0.6750    0.8710    0.7606        31
            like.02     0.8889    0.9412    0.9143        34
            open.01     0.8421    1.0000    0.9143        32
            hold.01     0.8056    0.8529    0.8286        34
         require.01     1.0000    0.9444    0.9714        36
            turn.01     0.7632    0.9062    0.8286        32
            face.01     0.9143    0.9143    0.9143        35
           force.01     0.9143    0.9412    0.9275        34
            name.01     0.8108    0.9375    0.8696        32
            drop.01     0.9714    1.0000    0.9855        34
            meet.03     0.8286    0.8788    0.8529        33
           watch.01     1.0000    1.0000    1.0000        34
           share.01     0.8108    1.0000    0.8955        30
            keep.02     0.8750    0.8000    0.8358        35
            read.01     0.9412    1.0000    0.9697        32
         involve.01     0.8824    0.9677    0.9231        31
           thank.01     1.0000    0.9394    0.9688        33
             cut.02     0.7222    0.9286    0.8125        28
            walk.01     0.9677    0.9375    0.9524        32
         suggest.01     1.0000    0.9688    0.9841        32
         destroy.01     0.9375    0.9677    0.9524        31
            feel.02     0.9062    0.9667    0.9355        30
         mention.01     0.8710    0.8710    0.8710        31
            lead.02     0.8333    0.7812    0.8065        32
         attract.01     0.9000    0.8710    0.8852        31
           allow.01     0.8750    0.9655    0.9180        29
          arrive.01     0.9000    0.9000    0.9000        30
     participate.01     1.0000    0.9355    0.9667        31
             get.06     0.9667    1.0000    0.9831        29
          follow.02     0.8276    0.8000    0.8136        30
         express.01     0.8966    0.8667    0.8814        30
        exchange.01     1.0000    0.9667    0.9831        30
             sit.01     1.0000    0.9000    0.9474        30
            send.01     0.8214    0.7931    0.8070        29
            know.02     0.7857    0.7586    0.7719        29
        relation.03     1.0000    0.9655    0.9825        29
         succeed.01     0.9643    0.9310    0.9474        29
            love.01     0.8667    0.9630    0.9123        27
            join.01     0.8621    0.9259    0.8929        27
           visit.01     0.9643    0.9643    0.9643        28
          return.01     0.8621    0.9259    0.8929        27
         respond.01     0.9286    0.9286    0.9286        28
         approve.01     0.8966    0.9630    0.9286        27
            come.03     0.7241    0.8077    0.7636        26
          follow.01     0.7241    0.8077    0.7636        26
       point_out.02     0.9643    1.0000    0.9818        27
            view.02     1.0000    0.9643    0.9818        28
            hold.04     0.9286    0.9630    0.9455        27
          attack.01     0.9286    0.9630    0.9455        27
       carry_out.03     0.8621    1.0000    0.9259        25
           study.01     0.7931    0.9200    0.8519        25
           issue.01     0.8929    0.9615    0.9259        26
          choose.01     0.9615    0.9259    0.9434        27
         concern.01     0.7586    0.9565    0.8462        23
           voice.01     1.0000    1.0000    1.0000        26
        question.01     0.8148    0.8800    0.8462        25
         welcome.01     0.8214    0.9583    0.8846        24
        consider.01     0.7391    0.6071    0.6667        28
         develop.01     0.8800    0.8462    0.8627        26
           carry.01     0.9615    1.0000    0.9804        25
       represent.01     0.9600    0.9231    0.9412        26
            vote.01     1.0000    0.8214    0.9020        28
         promote.02     0.9231    1.0000    0.9600        24
           close.01     0.6207    0.8571    0.7200        21
            have.04     0.6957    0.6154    0.6531        26
          set_up.03     0.9583    0.9200    0.9388        25
           limit.01     0.8400    0.8750    0.8571        24
          appear.02     1.0000    0.8846    0.9388        26
          manage.01     0.8077    0.9130    0.8571        23
             run.02     0.8077    0.9545    0.8750        22
        find_out.03     1.0000    1.0000    1.0000        24
         improve.01     0.6786    0.9500    0.7917        20
        indicate.01     0.9583    0.9583    0.9583        24
              go.06     0.4444    0.6000    0.5106        20
          import.01     0.8696    0.8333    0.8511        24
            take.10     0.8400    0.9545    0.8936        22
            bomb.01     0.9583    1.0000    0.9787        23
           peace.01     1.0000    1.0000    1.0000        23
         protect.01     0.9167    1.0000    0.9565        22
         propose.01     0.8636    0.7917    0.8261        24
             act.02     0.8750    0.9545    0.9130        22
           quote.01     0.9167    1.0000    0.9565        22
            meet.02     0.6250    0.7143    0.6667        21
           crash.01     0.9565    1.0000    0.9778        22
          affect.01     0.7600    0.9500    0.8444        20
             get.04     0.7727    0.7727    0.7727        22
          invite.01     1.0000    1.0000    1.0000        22
          handle.01     0.9545    0.9545    0.9545        22
         discuss.01     1.0000    1.0000    1.0000        22
         testify.01     1.0000    1.0000    1.0000        22
            save.02     0.9545    0.9545    0.9545        22
           fight.01     0.8095    0.7391    0.7727        23
              do.LV     0.5769    0.8824    0.6977        17
            play.01     0.9000    0.7826    0.8372        23
            lead.01     0.6250    0.7895    0.6977        19
       introduce.02     0.9545    1.0000    0.9767        21
         release.01     0.9545    1.0000    0.9767        21
         promise.01     0.9545    1.0000    0.9767        21
         present.01     0.9048    0.8636    0.8837        22
         imagine.01     1.0000    0.9545    0.9767        22
         achieve.01     0.9545    1.0000    0.9767        21
           spend.02     1.0000    0.9091    0.9524        22
           stand.01     0.7826    0.9474    0.8571        19
           occur.01     1.0000    1.0000    1.0000        21
            come.04     0.7273    0.8000    0.7619        20
           throw.01     0.9524    0.9524    0.9524        21
            lead.03     0.7143    0.7143    0.7143        21
           exist.01     0.7273    0.8000    0.7619        20
             get.02     0.9048    0.9500    0.9268        20
        describe.01     0.9524    1.0000    0.9756        20
          belong.01     0.9048    0.9500    0.9268        20
       emphasize.01     0.9000    0.8571    0.8780        21
           order.01     0.8182    0.9474    0.8780        19
       eliminate.01     1.0000    0.9524    0.9756        21
            file.01     1.0000    1.0000    1.0000        20
        discover.01     0.9048    1.0000    0.9500        19
           refer.01     0.9000    0.9000    0.9000        20
           break.01     0.7273    0.8889    0.8000        18
            have.LV     0.3478    0.4706    0.4000        17
             fly.01     1.0000    1.0000    1.0000        20
       interview.01     0.9048    1.0000    0.9500        19
         custody.01     1.0000    1.0000    1.0000        20
          launch.01     0.8571    1.0000    0.9231        18
            seek.01     0.8500    0.8947    0.8718        19
         analyze.01     1.0000    0.9500    0.9744        20
            call.03     0.8500    0.8947    0.8718        19
      experience.01     0.9474    0.9000    0.9231        20
            heal.01     1.0000    0.9500    0.9744        20
            keep.01     0.7647    0.6190    0.6842        21
            earn.01     0.9474    0.9474    0.9474        19
      contribute.01     1.0000    1.0000    1.0000        19
             add.01     1.0000    1.0000    1.0000        19
        maintain.01     0.9474    0.9474    0.9474        19
          appear.01     0.7273    1.0000    0.8421        16
       construct.01     1.0000    1.0000    1.0000        19
            keep.04     0.6818    0.9375    0.7895        16
            warn.01     1.0000    1.0000    1.0000        19
           enjoy.01     0.8889    0.8421    0.8649        19
         prepare.02     0.8235    0.7000    0.7568        20
         combine.01     0.6000    0.7059    0.6486        17
       recommend.01     0.8000    0.9412    0.8649        17
             set.01     0.6364    0.9333    0.7568        15
           cover.03     1.0000    0.9474    0.9730        19
       speculate.01     0.9474    1.0000    0.9730        18
         utilize.01     0.9474    1.0000    0.9730        18
             put.03     0.7368    0.8235    0.7778        17
           raise.01     0.8824    0.7895    0.8333        19
            play.02     0.8947    1.0000    0.9444        17
            wear.01     0.8947    1.0000    0.9444        17
          praise.01     0.9444    0.9444    0.9444        18
          suffer.01     0.8947    1.0000    0.9444        17
           leave.12     0.7368    0.8235    0.7778        17
          refuse.01     1.0000    0.8421    0.9143        19
         compare.01     0.8824    0.8333    0.8571        18
            sign.02     0.7895    0.9375    0.8571        16
           leave.13     0.7368    0.8750    0.8000        16
            fail.01     0.9444    1.0000    0.9714        17
            rule.03     0.9444    1.0000    0.9714        17
           drive.01     0.7778    0.8750    0.8235        16
        purchase.01     0.9412    0.9412    0.9412        17
           spend.01     1.0000    0.8889    0.9412        18
         survive.01     0.9412    0.9412    0.9412        17
           found.01     0.8889    1.0000    0.9412        16
          profit.01     1.0000    1.0000    1.0000        17
            wish.01     0.9412    0.9412    0.9412        17
            deny.01     0.9412    1.0000    0.9697        16
          reject.01     0.8824    0.9375    0.9091        16
        consider.02     0.5000    0.7692    0.6061        13
        sit_down.02     0.8333    1.0000    0.9091        15
             set.02     0.7333    0.6111    0.6667        18
            note.01     0.8333    1.0000    0.9091        15
           react.01     0.8824    0.9375    0.9091        16
         publish.01     0.8235    0.8750    0.8485        16
           solve.01     0.9412    1.0000    0.9697        16
            fill.01     0.9412    1.0000    0.9697        16
            meet.01     0.8824    0.9375    0.9091        16
      strengthen.01     0.9412    1.0000    0.9697        16
            make.05     0.9375    0.9375    0.9375        16
         realize.01     0.9375    0.9375    0.9375        16
            base.02     0.8571    0.6667    0.7500        18
             run.01     0.7059    0.8000    0.7500        15
         victory.01     1.0000    1.0000    1.0000        16
       challenge.01     1.0000    1.0000    1.0000        16
           argue.01     0.8750    0.8750    0.8750        16
            take.14     0.8824    1.0000    0.9375        15
            lack.01     0.8824    1.0000    0.9375        15
       encourage.01     0.8125    0.8125    0.8125        16
            post.01     1.0000    1.0000    1.0000        16
           shoot.02     0.8235    0.9333    0.8750        15
          damage.01     0.9375    0.9375    0.9375        16
         request.01     0.9375    0.9375    0.9375        16
            form.01     0.8750    0.8750    0.8750        16
         acquire.01     0.8824    1.0000    0.9375        15
           apply.02     0.8750    0.8750    0.8750        16
          travel.01     0.8235    0.9333    0.8750        15
          listen.01     1.0000    0.9375    0.9677        16
           go_on.15     0.9375    1.0000    0.9677        15
       translate.01     0.8750    0.9333    0.9032        15
        complete.01     0.9375    1.0000    0.9677        15
       take_over.12     1.0000    0.9375    0.9677        16
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            bomb.02     0.0000    0.0000    0.0000         1
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       enlighten.01     0.0000    0.0000    0.0000         1
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        mete_out.01     0.0000    0.0000    0.0000         1
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         eke_out.02     0.0000    0.0000    0.0000         1
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         realign.01     0.0000    0.0000    0.0000         1
        reinvent.01     0.0000    0.0000    0.0000         1
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      flash_back.03     0.0000    0.0000    0.0000         1
     resuscitate.01     0.0000    0.0000    0.0000         1
            riff.01     0.0000    0.0000    0.0000         1
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         tuck_in.03     0.0000    0.0000    0.0000         1
         roll_up.03     0.0000    0.0000    0.0000         1
          run_up.06     0.0000    0.0000    0.0000         1
        contract.01     0.0000    0.0000    0.0000         1
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            come.05     0.0000    0.0000    0.0000         1
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            cast.03     0.0000    0.0000    0.0000         1
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            flop.01     0.0000    0.0000    0.0000         1
            bowl.01     0.0000    0.0000    0.0000         1
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          script.01     0.0000    0.0000    0.0000         1
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       sound_off.03     0.0000    0.0000    0.0000         1
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           level.03     0.0000    0.0000    0.0000         1
       vandalize.01     0.0000    0.0000    0.0000         1
          defile.01     0.0000    0.0000    0.0000         1
            numb.01     0.0000    0.0000    0.0000         1
           frame.03     0.0000    0.0000    0.0000         1
        reengage.01     0.0000    0.0000    0.0000         1
      conviction.02     0.0000    0.0000    0.0000         1
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         set_out.06     0.0000    0.0000    0.0000         1
        critique.01     0.0000    0.0000    0.0000         1
        get_even.16     0.0000    0.0000    0.0000         1
            will.02     0.0000    0.0000    0.0000         1
          hunker.01     0.0000    0.0000    0.0000         1
        outshine.01     0.0000    0.0000    0.0000         1
            work.13     0.0000    0.0000    0.0000         1
          fester.01     0.0000    0.0000    0.0000         1
          loathe.01     0.0000    0.0000    0.0000         1
            knit.01     0.0000    0.0000    0.0000         1
          corner.01     0.0000    0.0000    0.0000         1
       quadruple.01     0.0000    0.0000    0.0000         1
       outsource.01     0.0000    0.0000    0.0000         1
        bog_down.01     0.0000    0.0000    0.0000         1
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        turn_out.10     0.0000    0.0000    0.0000         1
         free_up.02     0.0000    0.0000    0.0000         1
         delight.01     0.0000    0.0000    0.0000         1
       shoot_off.04     0.0000    0.0000    0.0000         1
        misplace.01     0.0000    0.0000    0.0000         1
         cave_in.02     0.0000    0.0000    0.0000         1
            snow.01     0.0000    0.0000    0.0000         1
          string.01     0.0000    0.0000    0.0000         1
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         link_up.02     0.0000    0.0000    0.0000         1
             try.04     0.0000    0.0000    0.0000         1
          put_in.05     0.0000    0.0000    0.0000         1
       marketize.01     0.0000    0.0000    0.0000         1
institutionalize.02     0.0000    0.0000    0.0000         1
       discharge.01     0.0000    0.0000    0.0000         1
        block_up.02     0.0000    0.0000    0.0000         1
           dwell.01     0.0000    0.0000    0.0000         1
       safeguard.01     0.0000    0.0000    0.0000         1
       alternate.01     0.0000    0.0000    0.0000         1
          behave.01     0.0000    0.0000    0.0000         1
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      exaggerate.01     0.0000    0.0000    0.0000         1
         deplore.01     0.0000    0.0000    0.0000         1
        distance.01     0.0000    0.0000    0.0000         1
    redistribute.01     0.0000    0.0000    0.0000         1
        recreate.01     0.0000    0.0000    0.0000         1
           spell.02     0.0000    0.0000    0.0000         1
            raid.01     0.0000    0.0000    0.0000         1
             wed.01     0.0000    0.0000    0.0000         1
          summon.01     0.0000    0.0000    0.0000         1
            mate.02     0.0000    0.0000    0.0000         1
          batter.01     0.0000    0.0000    0.0000         0
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       carve_out.03     0.0000    0.0000    0.0000         0
           field.01     0.0000    0.0000    0.0000         0
           alter.01     0.0000    0.0000    0.0000         0
            boom.02     0.0000    0.0000    0.0000         0
      transplant.01     0.0000    0.0000    0.0000         0
           sense.02     0.0000    0.0000    0.0000         0
          infect.01     0.0000    0.0000    0.0000         0
            mesh.01     0.0000    0.0000    0.0000         0
     recirculate.01     0.0000    0.0000    0.0000         0
        frequent.01     0.0000    0.0000    0.0000         0
          dry_up.01     0.0000    0.0000    0.0000         0
            blow.04     0.0000    0.0000    0.0000         0
           forgo.01     0.0000    0.0000    0.0000         0
       spell_out.03     0.0000    0.0000    0.0000         0
            mark.02     0.0000    0.0000    0.0000         0
         come_on.25     0.0000    0.0000    0.0000         0
     reconstruct.01     0.0000    0.0000    0.0000         0
            cork.01     0.0000    0.0000    0.0000         0
            dive.02     0.0000    0.0000    0.0000         0
            work.12     0.0000    0.0000    0.0000         0
       duplicate.01     0.0000    0.0000    0.0000         0
         outflow.01     0.0000    0.0000    0.0000         0
            bend.01     0.0000    0.0000    0.0000         0
        befriend.01     0.0000    0.0000    0.0000         0
           waver.01     0.0000    0.0000    0.0000         0
            bear.06     0.0000    0.0000    0.0000         0
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        acquaint.01     0.0000    0.0000    0.0000         0
       get_along.18     0.0000    0.0000    0.0000         0
       dissipate.01     0.0000    0.0000    0.0000         0
            pass.20     0.0000    0.0000    0.0000         0
         tarnish.01     0.0000    0.0000    0.0000         0
            wire.02     0.0000    0.0000    0.0000         0
           vomit.01     0.0000    0.0000    0.0000         0
           tower.01     0.0000    0.0000    0.0000         0
           tease.02     0.0000    0.0000    0.0000         0
            jolt.01     0.0000    0.0000    0.0000         0
      perpetuate.01     0.0000    0.0000    0.0000         0
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          clutch.01     0.0000    0.0000    0.0000         0
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             nod.01     0.0000    0.0000    0.0000         0
       humiliate.01     0.0000    0.0000    0.0000         0
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         snap_up.08     0.0000    0.0000    0.0000         0
          incite.01     0.0000    0.0000    0.0000         0
            vest.01     0.0000    0.0000    0.0000         0
      thrash_out.02     0.0000    0.0000    0.0000         0
         disband.01     0.0000    0.0000    0.0000         0
           weigh.01     0.0000    0.0000    0.0000         0
             err.01     0.0000    0.0000    0.0000         0
          infest.01     0.0000    0.0000    0.0000         0
          excite.01     0.0000    0.0000    0.0000         0
         deceive.01     0.0000    0.0000    0.0000         0
           tutor.01     0.0000    0.0000    0.0000         0
        pull_off.03     0.0000    0.0000    0.0000         0
          dazzle.01     0.0000    0.0000    0.0000         0
       fantasize.01     0.0000    0.0000    0.0000         0
          gamble.01     0.0000    0.0000    0.0000         0
       give_back.03     0.0000    0.0000    0.0000         0
          unseat.01     0.0000    0.0000    0.0000         0
            have.11     0.0000    0.0000    0.0000         0
          reckon.02     0.0000    0.0000    0.0000         0
          outfit.01     0.0000    0.0000    0.0000         0
            film.01     0.0000    0.0000    0.0000         0
       knock_out.03     0.0000    0.0000    0.0000         0
       apprehend.01     0.0000    0.0000    0.0000         0
           slide.02     0.0000    0.0000    0.0000         0
      break_down.12     0.0000    0.0000    0.0000         0
            reel.01     0.0000    0.0000    0.0000         0
          get_by.17     0.0000    0.0000    0.0000         0
           choke.01     0.0000    0.0000    0.0000         0
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      popularize.01     0.0000    0.0000    0.0000         0
            shed.02     0.0000    0.0000    0.0000         0
            howl.02     0.0000    0.0000    0.0000         0
          charge.08     0.0000    0.0000    0.0000         0
         come_in.07     0.0000    0.0000    0.0000         0
         quicken.01     0.0000    0.0000    0.0000         0
           blaze.01     0.0000    0.0000    0.0000         0
        trespass.01     0.0000    0.0000    0.0000         0
       throw_out.06     0.0000    0.0000    0.0000         0
           stone.02     0.0000    0.0000    0.0000         0
        compress.01     0.0000    0.0000    0.0000         0
            code.01     0.0000    0.0000    0.0000         0
       diversify.01     0.0000    0.0000    0.0000         0
         nullify.01     0.0000    0.0000    0.0000         0
            milk.01     0.0000    0.0000    0.0000         0
      underscore.01     0.0000    0.0000    0.0000         0
        chastise.01     0.0000    0.0000    0.0000         0
        reignite.01     0.0000    0.0000    0.0000         0
            pile.01     0.0000    0.0000    0.0000         0
         emanate.01     0.0000    0.0000    0.0000         0
     computerize.01     0.0000    0.0000    0.0000         0
         jack_up.01     0.0000    0.0000    0.0000         0
        retrieve.01     0.0000    0.0000    0.0000         0
            tick.01     0.0000    0.0000    0.0000         0
         reclaim.01     0.0000    0.0000    0.0000         0
       follow_up.03     0.0000    0.0000    0.0000         0
         top_off.03     0.0000    0.0000    0.0000         0
         perplex.01     0.0000    0.0000    0.0000         0
           binge.01     0.0000    0.0000    0.0000         0
          thrust.01     0.0000    0.0000    0.0000         0
        overtake.01     0.0000    0.0000    0.0000         0
         hook_up.02     0.0000    0.0000    0.0000         0
           upset.01     0.0000    0.0000    0.0000         0
          mutate.01     0.0000    0.0000    0.0000         0
        comeback.01     0.0000    0.0000    0.0000         0
        navigate.01     0.0000    0.0000    0.0000         0
            yank.01     0.0000    0.0000    0.0000         0
       resurrect.01     0.0000    0.0000    0.0000         0
            size.01     0.0000    0.0000    0.0000         0
         pioneer.01     0.0000    0.0000    0.0000         0
        pinpoint.01     0.0000    0.0000    0.0000         0
      circumcise.01     0.0000    0.0000    0.0000         0
     contaminate.01     0.0000    0.0000    0.0000         0
           sweep.02     0.0000    0.0000    0.0000         0
            moor.01     0.0000    0.0000    0.0000         0
        bring_up.08     0.0000    0.0000    0.0000         0
      revitalize.01     0.0000    0.0000    0.0000         0
       overstate.01     0.0000    0.0000    0.0000         0
          unload.01     0.0000    0.0000    0.0000         0
             jam.01     0.0000    0.0000    0.0000         0
        sidestep.01     0.0000    0.0000    0.0000         0
      hammer_out.04     0.0000    0.0000    0.0000         0
       globalize.01     0.0000    0.0000    0.0000         0
         reenter.01     0.0000    0.0000    0.0000         0
        head_off.03     0.0000    0.0000    0.0000         0
         turn_up.15     0.0000    0.0000    0.0000         0
        displace.01     0.0000    0.0000    0.0000         0
         hang_on.04     0.0000    0.0000    0.0000         0
             fix.02     0.0000    0.0000    0.0000         0
          master.01     0.0000    0.0000    0.0000         0
           smell.02     0.0000    0.0000    0.0000         0
      jeopardize.01     0.0000    0.0000    0.0000         0
           close.02     0.0000    0.0000    0.0000         0
      degenerate.01     0.0000    0.0000    0.0000         0
        delegate.01     0.0000    0.0000    0.0000         0
           reign.01     0.0000    0.0000    0.0000         0
         neglect.01     0.0000    0.0000    0.0000         0
         compose.01     0.0000    0.0000    0.0000         0
            fall.05     0.0000    0.0000    0.0000         0
     commemorate.01     0.0000    0.0000    0.0000         0

          micro avg     0.9122    0.9230    0.9176     30947
          macro avg     0.6421    0.6667    0.6415     30947
       weighted avg     0.9123    0.9230    0.9145     30947

2023-03-30 02:16:10,572 ----------------------------------------------------------------------------------------------------