Command that produces this log: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
Initialized slot model with checkpoint at logs/slot/slot-model.mdl.lang-chinese
----------------------------------------------------------------------------------------------------
> trainable params:
>>> xlmr.embeddings.word_embeddings.weight: torch.Size([250002, 1024])
>>> xlmr.embeddings.position_embeddings.weight: torch.Size([514, 1024])
>>> xlmr.embeddings.token_type_embeddings.weight: torch.Size([1, 1024])
>>> xlmr.embeddings.LayerNorm.weight: torch.Size([1024])
>>> xlmr.embeddings.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.0.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.0.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.0.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.1.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.1.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.1.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.2.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.2.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.2.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.3.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.3.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.3.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.4.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.4.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.4.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.5.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.5.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.5.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.6.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.6.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.6.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.7.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.7.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.7.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.8.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.8.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.8.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.9.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.9.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.9.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.10.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.10.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.10.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.11.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.11.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.11.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.12.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.12.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.12.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.13.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.13.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.13.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.14.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.14.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.14.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.15.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.15.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.15.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.16.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.16.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.16.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.17.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.17.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.17.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.18.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.18.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.18.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.19.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.19.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.19.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.20.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.20.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.20.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.21.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.21.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.21.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.22.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.22.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.22.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.23.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.23.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.23.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.pooler.dense.weight: torch.Size([1024, 1024])
>>> xlmr.pooler.dense.bias: torch.Size([1024])
>>> basic_gcn.T_T.0.weight: torch.Size([1024, 1024])
>>> basic_gcn.T_T.0.bias: torch.Size([1024])
>>> basic_gcn.T_T.1.weight: torch.Size([1024, 1024])
>>> basic_gcn.T_T.1.bias: torch.Size([1024])
>>> basic_gcn.T_T.2.weight: torch.Size([1024, 1024])
>>> basic_gcn.T_T.2.bias: torch.Size([1024])
>>> basic_gcn.T_E.0.weight: torch.Size([1024, 1024])
>>> basic_gcn.T_E.0.bias: torch.Size([1024])
>>> basic_gcn.T_E.1.weight: torch.Size([1024, 1024])
>>> basic_gcn.T_E.1.bias: torch.Size([1024])
>>> basic_gcn.T_E.2.weight: torch.Size([1024, 1024])
>>> basic_gcn.T_E.2.bias: torch.Size([1024])
>>> basic_gcn.E_T.0.weight: torch.Size([1024, 1024])
>>> basic_gcn.E_T.0.bias: torch.Size([1024])
>>> basic_gcn.E_T.1.weight: torch.Size([1024, 1024])
>>> basic_gcn.E_T.1.bias: torch.Size([1024])
>>> basic_gcn.E_T.2.weight: torch.Size([1024, 1024])
>>> basic_gcn.E_T.2.bias: torch.Size([1024])
>>> basic_gcn.E_E.0.weight: torch.Size([1024, 1024])
>>> basic_gcn.E_E.0.bias: torch.Size([1024])
>>> basic_gcn.E_E.1.weight: torch.Size([1024, 1024])
>>> basic_gcn.E_E.1.bias: torch.Size([1024])
>>> basic_gcn.E_E.2.weight: torch.Size([1024, 1024])
>>> basic_gcn.E_E.2.bias: torch.Size([1024])
>>> basic_gcn.f_t.0.weight: torch.Size([1024, 2048])
>>> basic_gcn.f_t.0.bias: torch.Size([1024])
>>> basic_gcn.f_e.0.weight: torch.Size([1024, 2048])
>>> basic_gcn.f_e.0.bias: torch.Size([1024])
>>> name2classifier.occupy-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.occupy-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.occupy-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.occupy-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.outcome-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.outcome-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.outcome-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.outcome-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.protest-event-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.protest-event-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.protest-event-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.protest-event-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.when-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.when-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.when-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.when-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.where-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.where-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.where-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.where-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.who-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.who-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.who-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.who-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.protest-against-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.protest-against-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.protest-against-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.protest-against-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.protest-for-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.protest-for-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.protest-for-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.protest-for-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.organizer-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.organizer-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.organizer-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.organizer-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.wounded-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.wounded-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.wounded-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.wounded-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.arrested-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.arrested-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.arrested-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.arrested-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.killed-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.killed-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.killed-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.killed-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.imprisoned-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.imprisoned-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.imprisoned-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.imprisoned-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.corrupt-event-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.corrupt-event-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.corrupt-event-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.corrupt-event-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.judicial-actions-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.judicial-actions-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.judicial-actions-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.judicial-actions-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.charged-with-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.charged-with-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.charged-with-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.charged-with-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.prison-term-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.prison-term-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.prison-term-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.prison-term-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.fine-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.fine-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.fine-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.fine-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.npi-events-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.npi-events-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.npi-events-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.npi-events-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.disease-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.disease-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.disease-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.disease-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.infected-cumulative-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.infected-cumulative-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.infected-cumulative-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.infected-cumulative-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.killed-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.killed-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.killed-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.killed-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.killed-cumulative-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.killed-cumulative-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.killed-cumulative-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.killed-cumulative-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.outbreak-event-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.outbreak-event-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.outbreak-event-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.outbreak-event-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.killed-individuals-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.killed-individuals-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.killed-individuals-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.killed-individuals-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.hospitalized-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.hospitalized-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.hospitalized-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.hospitalized-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.hospitalized-individuals-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.hospitalized-individuals-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.hospitalized-individuals-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.hospitalized-individuals-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.infected-individuals-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.infected-individuals-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.infected-individuals-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.infected-individuals-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.tested-individuals-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.tested-individuals-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.tested-individuals-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.tested-individuals-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.infected-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.infected-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.infected-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.infected-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.vaccinated-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.vaccinated-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.vaccinated-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.vaccinated-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.exposed-individuals-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.exposed-individuals-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.exposed-individuals-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.exposed-individuals-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.recovered-cumulative-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.recovered-cumulative-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.recovered-cumulative-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.recovered-cumulative-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.tested-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.tested-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.tested-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.tested-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.tested-cumulative-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.tested-cumulative-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.tested-cumulative-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.tested-cumulative-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.recovered-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.recovered-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.recovered-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.recovered-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.exposed-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.exposed-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.exposed-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.exposed-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.vaccinated-cumulative-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.vaccinated-cumulative-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.vaccinated-cumulative-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.vaccinated-cumulative-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.vaccinated-individuals-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.vaccinated-individuals-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.vaccinated-individuals-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.vaccinated-individuals-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.exposed-cumulative-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.exposed-cumulative-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.exposed-cumulative-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.exposed-cumulative-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.hospitalized-cumulative-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.hospitalized-cumulative-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.hospitalized-cumulative-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.hospitalized-cumulative-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.recovered-individuals-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.recovered-individuals-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.recovered-individuals-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.recovered-individuals-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.blamed-by-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.blamed-by-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.blamed-by-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.blamed-by-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.claimed-by-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.claimed-by-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.claimed-by-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.claimed-by-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.terror-event-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.terror-event-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.terror-event-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.terror-event-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.kidnapped-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.kidnapped-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.kidnapped-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.kidnapped-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.named-perp-org-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.named-perp-org-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.named-perp-org-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.named-perp-org-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.target-physical-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.target-physical-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.target-physical-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.target-physical-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.named-perp-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.named-perp-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.named-perp-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.named-perp-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.perp-killed-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.perp-killed-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.perp-killed-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.perp-killed-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.target-human-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.target-human-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.target-human-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.target-human-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.perp-captured-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.perp-captured-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.perp-captured-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.perp-captured-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.perp-objective-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.perp-objective-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.perp-objective-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.perp-objective-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.weapon-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.weapon-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.weapon-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.weapon-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.named-organizer-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.named-organizer-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.named-organizer-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.named-organizer-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.affected-cumulative-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.affected-cumulative-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.affected-cumulative-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.affected-cumulative-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.damage-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.damage-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.damage-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.damage-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.human-displacement-events-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.human-displacement-events-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.human-displacement-events-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.human-displacement-events-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.major-disaster-event-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.major-disaster-event-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.major-disaster-event-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.major-disaster-event-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.related-natural-phenomena-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.related-natural-phenomena-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.related-natural-phenomena-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.related-natural-phenomena-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.responders-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.responders-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.responders-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.responders-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.assistance-provided-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.assistance-provided-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.assistance-provided-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.assistance-provided-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.rescue-events-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.rescue-events-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.rescue-events-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.rescue-events-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.individuals-affected-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.individuals-affected-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.individuals-affected-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.individuals-affected-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.missing-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.missing-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.missing-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.missing-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.injured-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.injured-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.injured-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.injured-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.assistance-needed-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.assistance-needed-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.assistance-needed-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.assistance-needed-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.rescued-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.rescued-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.rescued-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.rescued-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.repair-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.repair-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.repair-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.repair-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.declare-emergency-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.declare-emergency-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.declare-emergency-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.declare-emergency-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.announce-disaster-warnings-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.announce-disaster-warnings-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.announce-disaster-warnings-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.announce-disaster-warnings-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.disease-outbreak-events-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.disease-outbreak-events-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.disease-outbreak-events-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.disease-outbreak-events-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.current-location-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.current-location-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.current-location-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.current-location-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.event-or-soa-at-origin-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.event-or-soa-at-origin-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.event-or-soa-at-origin-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.event-or-soa-at-origin-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.group-identity-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.group-identity-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.group-identity-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.group-identity-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.human-displacement-event-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.human-displacement-event-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.human-displacement-event-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.human-displacement-event-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.origin-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.origin-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.origin-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.origin-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.total-displaced-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.total-displaced-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.total-displaced-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.total-displaced-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.transitory-events-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.transitory-events-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.transitory-events-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.transitory-events-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.destination-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.destination-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.destination-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.destination-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.transiting-location-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.transiting-location-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.transiting-location-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.transiting-location-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.detained-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.detained-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.detained-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.detained-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.blocked-migration-count-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.blocked-migration-count-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.blocked-migration-count-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.blocked-migration-count-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.settlement-status-event-or-soa-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.settlement-status-event-or-soa-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.settlement-status-event-or-soa-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.settlement-status-event-or-soa-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.cybercrime-event-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.cybercrime-event-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.cybercrime-event-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.cybercrime-event-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.perpetrator-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.perpetrator-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.perpetrator-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.perpetrator-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.victim-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.victim-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.victim-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.victim-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.response-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.response-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.response-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.response-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.information-stolen-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.information-stolen-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.information-stolen-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.information-stolen-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.related-crimes-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.related-crimes-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.related-crimes-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.related-crimes-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.victim-impact-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.victim-impact-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.victim-impact-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.victim-impact-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.contract-amount-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.contract-amount-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.contract-amount-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.contract-amount-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.etip-event-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.etip-event-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.etip-event-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.etip-event-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.project-location-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.project-location-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.project-location-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.project-location-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.project-name-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.project-name-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.project-name-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.project-name-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.signatories-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.signatories-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.signatories-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.signatories-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.contract-awardee-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.contract-awardee-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.contract-awardee-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.contract-awardee-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.overall-project-value-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.overall-project-value-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.overall-project-value-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.overall-project-value-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.funding-amount-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.funding-amount-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.funding-amount-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.funding-amount-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.funding-recipient-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.funding-recipient-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.funding-recipient-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.funding-recipient-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.funding-source-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.funding-source-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.funding-source-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.funding-source-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.contract-awarder-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.contract-awarder-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.contract-awarder-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.contract-awarder-ffn.layers.1.bias: torch.Size([2])
>>> name2classifier.agreement-length-ffn.layers.0.weight: torch.Size([350, 1024])
>>> name2classifier.agreement-length-ffn.layers.0.bias: torch.Size([350])
>>> name2classifier.agreement-length-ffn.layers.1.weight: torch.Size([2, 350])
>>> name2classifier.agreement-length-ffn.layers.1.bias: torch.Size([2])
>>> irrealis_classifier.layers.0.weight: torch.Size([350, 1128])
>>> irrealis_classifier.layers.0.bias: torch.Size([350])
>>> irrealis_classifier.layers.1.weight: torch.Size([7, 350])
>>> irrealis_classifier.layers.1.bias: torch.Size([7])
n_trainable_params: 614103147, n_nontrainable_params: 0
----------------------------------------------------------------------------------------------------
******************************
Epoch: 0
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
2023-01-22 14:27:15.355141: step: 2/463, loss: 0.7110377550125122
2023-01-22 14:27:15.989436: step: 4/463, loss: 1.9634547233581543
2023-01-22 14:27:16.636335: step: 6/463, loss: 1.5215129852294922
2023-01-22 14:27:17.223242: step: 8/463, loss: 0.5526178479194641
2023-01-22 14:27:17.842902: step: 10/463, loss: 1.2928082942962646
2023-01-22 14:27:18.447579: step: 12/463, loss: 0.5128095149993896
2023-01-22 14:27:19.121427: step: 14/463, loss: 0.3281662166118622
2023-01-22 14:27:19.810715: step: 16/463, loss: 0.4295336902141571
2023-01-22 14:27:20.399208: step: 18/463, loss: 0.30979084968566895
2023-01-22 14:27:21.122020: step: 20/463, loss: 0.4251997768878937
2023-01-22 14:27:21.716456: step: 22/463, loss: 0.26424384117126465
2023-01-22 14:27:22.334817: step: 24/463, loss: 0.5383137464523315
2023-01-22 14:27:22.945994: step: 26/463, loss: 0.32456859946250916
2023-01-22 14:27:23.611610: step: 28/463, loss: 2.2164814472198486
2023-01-22 14:27:24.231932: step: 30/463, loss: 0.5305370092391968
2023-01-22 14:27:24.846273: step: 32/463, loss: 0.5779165029525757
2023-01-22 14:27:25.420754: step: 34/463, loss: 0.24728155136108398
2023-01-22 14:27:26.034734: step: 36/463, loss: 0.17951759696006775
2023-01-22 14:27:26.625656: step: 38/463, loss: 1.298595666885376
2023-01-22 14:27:27.284742: step: 40/463, loss: 1.1850732564926147
2023-01-22 14:27:27.847737: step: 42/463, loss: 0.45125308632850647
2023-01-22 14:27:28.449211: step: 44/463, loss: 1.7101916074752808
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==================================================
Loss: 0.744
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28520174708818635, 'r': 0.3252490512333966, 'f1': 0.3039117907801418}, 'combined': 0.22393500373273606, 'epoch': 0}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.37191344751505184, 'r': 0.2850904475725136, 'f1': 0.32276512210379255}, 'combined': 0.22707094017352242, 'epoch': 0}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2903846153846154, 'r': 0.3223434535104364, 'f1': 0.30553057553956836}, 'combined': 0.22512779250283982, 'epoch': 0}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36121212786452517, 'r': 0.27562876655864466, 'f1': 0.31266978657047834}, 'combined': 0.2219955484650396, 'epoch': 0}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.301291961130742, 'r': 0.3235887096774194, 'f1': 0.3120425434583715}, 'combined': 0.2299260846535369, 'epoch': 0}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.38111410877910484, 'r': 0.27421624899959984, 'f1': 0.318946559120102}, 'combined': 0.2264520569752724, 'epoch': 0}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.22598870056497172, 'r': 0.38095238095238093, 'f1': 0.2836879432624113}, 'combined': 0.1891252955082742, 'epoch': 0}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.31666666666666665, 'r': 0.41304347826086957, 'f1': 0.3584905660377358}, 'combined': 0.1792452830188679, 'epoch': 0}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3888888888888889, 'r': 0.2413793103448276, 'f1': 0.2978723404255319}, 'combined': 0.19858156028368792, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
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Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28520174708818635, 'r': 0.3252490512333966, 'f1': 0.3039117907801418}, 'combined': 0.22393500373273606, 'epoch': 0}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.37191344751505184, 'r': 0.2850904475725136, 'f1': 0.32276512210379255}, 'combined': 0.22707094017352242, 'epoch': 0}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.22598870056497172, 'r': 0.38095238095238093, 'f1': 0.2836879432624113}, 'combined': 0.1891252955082742, 'epoch': 0}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2903846153846154, 'r': 0.3223434535104364, 'f1': 0.30553057553956836}, 'combined': 0.22512779250283982, 'epoch': 0}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36121212786452517, 'r': 0.27562876655864466, 'f1': 0.31266978657047834}, 'combined': 0.2219955484650396, 'epoch': 0}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.31666666666666665, 'r': 0.41304347826086957, 'f1': 0.3584905660377358}, 'combined': 0.1792452830188679, 'epoch': 0}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.301291961130742, 'r': 0.3235887096774194, 'f1': 0.3120425434583715}, 'combined': 0.2299260846535369, 'epoch': 0}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.38111410877910484, 'r': 0.27421624899959984, 'f1': 0.318946559120102}, 'combined': 0.2264520569752724, 'epoch': 0}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3888888888888889, 'r': 0.2413793103448276, 'f1': 0.2978723404255319}, 'combined': 0.19858156028368792, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.541
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.32028864503816795, 'r': 0.31846537001897535, 'f1': 0.3193744053282588}, 'combined': 0.2353285091892433, 'epoch': 1}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3783160839747361, 'r': 0.2851412929870718, 'f1': 0.3251860363248976}, 'combined': 0.2308820857906773, 'epoch': 1}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4, 'r': 0.20689655172413793, 'f1': 0.2727272727272727}, 'combined': 0.1818181818181818, 'epoch': 1}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.301291961130742, 'r': 0.3235887096774194, 'f1': 0.3120425434583715}, 'combined': 0.2299260846535369, 'epoch': 0}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.38111410877910484, 'r': 0.27421624899959984, 'f1': 0.318946559120102}, 'combined': 0.2264520569752724, 'epoch': 0}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3888888888888889, 'r': 0.2413793103448276, 'f1': 0.2978723404255319}, 'combined': 0.19858156028368792, 'epoch': 0}
******************************
Epoch: 2
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.449
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2586267407906559, 'r': 0.31211879913255625, 'f1': 0.2828660483970028}, 'combined': 0.20842761460831785, 'epoch': 2}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.31496860059607057, 'r': 0.3080975578256502, 'f1': 0.3114951930023777}, 'combined': 0.21914234683584363, 'epoch': 2}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.26608296792060926, 'r': 0.31253388452155056, 'f1': 0.287443904263276}, 'combined': 0.21180077156241386, 'epoch': 2}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.31941779034226536, 'r': 0.30854755140391604, 'f1': 0.31388858758001575}, 'combined': 0.22286089718181118, 'epoch': 2}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.283145285477375, 'r': 0.3239783816752507, 'f1': 0.3021886852085967}, 'combined': 0.2226653469958081, 'epoch': 2}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3221816396918299, 'r': 0.2918718687103686, 'f1': 0.3062787068368401}, 'combined': 0.21745788185415646, 'epoch': 2}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2333333333333333, 'r': 0.36666666666666664, 'f1': 0.28518518518518515}, 'combined': 0.19012345679012344, 'epoch': 2}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2875, 'r': 0.5, 'f1': 0.36507936507936506}, 'combined': 0.18253968253968253, 'epoch': 2}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.28703703703703703, 'r': 0.2672413793103448, 'f1': 0.27678571428571425}, 'combined': 0.18452380952380948, 'epoch': 2}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.301291961130742, 'r': 0.3235887096774194, 'f1': 0.3120425434583715}, 'combined': 0.2299260846535369, 'epoch': 0}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.38111410877910484, 'r': 0.27421624899959984, 'f1': 0.318946559120102}, 'combined': 0.2264520569752724, 'epoch': 0}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3888888888888889, 'r': 0.2413793103448276, 'f1': 0.2978723404255319}, 'combined': 0.19858156028368792, 'epoch': 0}
******************************
Epoch: 3
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 14:54:28.953264: step: 926/463, loss: 0.14364628493785858
==================================================
Loss: 0.417
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28833017367906066, 'r': 0.31951579018704257, 'f1': 0.30312299087051564}, 'combined': 0.22335378274669573, 'epoch': 3}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.337231890926726, 'r': 0.3031264860901957, 'f1': 0.3192709637699307}, 'combined': 0.22461273833060452, 'epoch': 3}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2894932844932845, 'r': 0.3213540254811602, 'f1': 0.3045927543679342}, 'combined': 0.22443676637637255, 'epoch': 3}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3399848515292938, 'r': 0.3020440834423282, 'f1': 0.3198934106263624}, 'combined': 0.2271243215447173, 'epoch': 3}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3029068676507381, 'r': 0.328197004608295, 'f1': 0.3150452120739007}, 'combined': 0.232138577317611, 'epoch': 3}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.34829808414918945, 'r': 0.29207195363644944, 'f1': 0.31771661971273946}, 'combined': 0.225578799996045, 'epoch': 3}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2783687943262411, 'r': 0.37380952380952376, 'f1': 0.31910569105691056}, 'combined': 0.2127371273712737, 'epoch': 3}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2833333333333333, 'r': 0.3695652173913043, 'f1': 0.32075471698113206}, 'combined': 0.16037735849056603, 'epoch': 3}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3125, 'r': 0.1724137931034483, 'f1': 0.22222222222222224}, 'combined': 0.14814814814814814, 'epoch': 3}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.301291961130742, 'r': 0.3235887096774194, 'f1': 0.3120425434583715}, 'combined': 0.2299260846535369, 'epoch': 0}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.38111410877910484, 'r': 0.27421624899959984, 'f1': 0.318946559120102}, 'combined': 0.2264520569752724, 'epoch': 0}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3888888888888889, 'r': 0.2413793103448276, 'f1': 0.2978723404255319}, 'combined': 0.19858156028368792, 'epoch': 0}
******************************
Epoch: 4
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
2023-01-22 14:57:03.459217: step: 2/463, loss: 0.28018978238105774
2023-01-22 14:57:04.137382: step: 4/463, loss: 0.08867963403463364
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==================================================
Loss: 0.407
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.257823732718894, 'r': 0.3791525481160206, 'f1': 0.3069330151415405}, 'combined': 0.2261611690516614, 'epoch': 4}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.2976644688385023, 'r': 0.3374397210064419, 'f1': 0.3163065743367794}, 'combined': 0.22252723822687998, 'epoch': 4}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2682373892953408, 'r': 0.3776701002981838, 'f1': 0.313683440279185}, 'combined': 0.2311351665215047, 'epoch': 4}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.29962244208288746, 'r': 0.3380892534245333, 'f1': 0.31769568746088683}, 'combined': 0.22556393809722963, 'epoch': 4}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2697430845364313, 'r': 0.38081376640437364, 'f1': 0.31579678189630983}, 'combined': 0.23269236560780723, 'epoch': 4}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3126555591905385, 'r': 0.32412414738792067, 'f1': 0.3182865769804195}, 'combined': 0.22598346965609784, 'epoch': 4}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.21162280701754385, 'r': 0.4595238095238095, 'f1': 0.2897897897897898}, 'combined': 0.1931931931931932, 'epoch': 4}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.24468085106382978, 'r': 0.5, 'f1': 0.32857142857142857}, 'combined': 0.16428571428571428, 'epoch': 4}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3235294117647059, 'r': 0.1896551724137931, 'f1': 0.2391304347826087}, 'combined': 0.15942028985507245, 'epoch': 4}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.301291961130742, 'r': 0.3235887096774194, 'f1': 0.3120425434583715}, 'combined': 0.2299260846535369, 'epoch': 0}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.38111410877910484, 'r': 0.27421624899959984, 'f1': 0.318946559120102}, 'combined': 0.2264520569752724, 'epoch': 0}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3888888888888889, 'r': 0.2413793103448276, 'f1': 0.2978723404255319}, 'combined': 0.19858156028368792, 'epoch': 0}
******************************
Epoch: 5
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.563
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.27948282747603836, 'r': 0.3313565340909091, 'f1': 0.3032170710571924}, 'combined': 0.2234231049895102, 'epoch': 5}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3307446947006414, 'r': 0.31456853495982395, 'f1': 0.322453869766337}, 'combined': 0.2268519686798351, 'epoch': 5}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2825206611570248, 'r': 0.3237215909090909, 'f1': 0.30172109443954104}, 'combined': 0.2223208064291355, 'epoch': 5}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3279665758218992, 'r': 0.31078055438145036, 'f1': 0.3191423630195162}, 'combined': 0.2265910777438565, 'epoch': 5}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2882374138952349, 'r': 0.339006503842691, 'f1': 0.31156733512435314}, 'combined': 0.2295759311442602, 'epoch': 5}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.34057803379390106, 'r': 0.3004225450933101, 'f1': 0.31924251891586086}, 'combined': 0.2266621884302612, 'epoch': 5}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.21450617283950615, 'r': 0.33095238095238094, 'f1': 0.26029962546816476}, 'combined': 0.17353308364544318, 'epoch': 5}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.25, 'r': 0.44565217391304346, 'f1': 0.3203125}, 'combined': 0.16015625, 'epoch': 5}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2857142857142857, 'r': 0.27586206896551724, 'f1': 0.28070175438596495}, 'combined': 0.18713450292397663, 'epoch': 5}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.301291961130742, 'r': 0.3235887096774194, 'f1': 0.3120425434583715}, 'combined': 0.2299260846535369, 'epoch': 0}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.38111410877910484, 'r': 0.27421624899959984, 'f1': 0.318946559120102}, 'combined': 0.2264520569752724, 'epoch': 0}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3888888888888889, 'r': 0.2413793103448276, 'f1': 0.2978723404255319}, 'combined': 0.19858156028368792, 'epoch': 0}
******************************
Epoch: 6
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.506
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.26815476190476195, 'r': 0.328197004608295, 'f1': 0.2951532788883472}, 'combined': 0.21748136339141372, 'epoch': 6}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3009323115341943, 'r': 0.31168926425686383, 'f1': 0.30621634783950563}, 'combined': 0.2154285864197527, 'epoch': 6}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.270383555351401, 'r': 0.3191244239631337, 'f1': 0.29273902772597293}, 'combined': 0.21570244148229584, 'epoch': 6}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3014546817272049, 'r': 0.3040851763321543, 'f1': 0.30276421553140653}, 'combined': 0.21496259302729862, 'epoch': 6}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2893626329100397, 'r': 0.33877940133869927, 'f1': 0.3121271757089065}, 'combined': 0.22998844525919426, 'epoch': 6}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3200163294971871, 'r': 0.2985143597142173, 'f1': 0.30889160833633683}, 'combined': 0.21931304191879913, 'epoch': 6}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.26666666666666666, 'r': 0.38095238095238093, 'f1': 0.3137254901960784}, 'combined': 0.2091503267973856, 'epoch': 6}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.23026315789473684, 'r': 0.3804347826086957, 'f1': 0.28688524590163933}, 'combined': 0.14344262295081966, 'epoch': 6}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3977272727272727, 'r': 0.3017241379310345, 'f1': 0.34313725490196073}, 'combined': 0.2287581699346405, 'epoch': 6}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2893626329100397, 'r': 0.33877940133869927, 'f1': 0.3121271757089065}, 'combined': 0.22998844525919426, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3200163294971871, 'r': 0.2985143597142173, 'f1': 0.30889160833633683}, 'combined': 0.21931304191879913, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3977272727272727, 'r': 0.3017241379310345, 'f1': 0.34313725490196073}, 'combined': 0.2287581699346405, 'epoch': 6}
******************************
Epoch: 7
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 15:23:57.431873: step: 926/463, loss: 0.1550433337688446
==================================================
Loss: 0.417
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28595472440944886, 'r': 0.34455645161290327, 'f1': 0.3125322719449226}, 'combined': 0.23028693722257454, 'epoch': 7}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.34012196615338974, 'r': 0.2928910555696439, 'f1': 0.31474449425362955}, 'combined': 0.22142828741461376, 'epoch': 7}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28585209003215434, 'r': 0.3373814041745731, 'f1': 0.3094865100087032}, 'combined': 0.22804269158536022, 'epoch': 7}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3450865569819719, 'r': 0.2968648546962815, 'f1': 0.3191645620913073}, 'combined': 0.22660683908482815, 'epoch': 7}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2970570388349515, 'r': 0.3483515180265655, 'f1': 0.32066593886462885}, 'combined': 0.2362801654792002, 'epoch': 7}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3573347532314924, 'r': 0.2886765473704196, 'f1': 0.3193571466078555}, 'combined': 0.22674357409157742, 'epoch': 7}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.26190476190476186, 'r': 0.36666666666666664, 'f1': 0.30555555555555547}, 'combined': 0.20370370370370364, 'epoch': 7}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.25, 'r': 0.41304347826086957, 'f1': 0.31147540983606553}, 'combined': 0.15573770491803277, 'epoch': 7}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.35526315789473684, 'r': 0.23275862068965517, 'f1': 0.28125}, 'combined': 0.1875, 'epoch': 7}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2893626329100397, 'r': 0.33877940133869927, 'f1': 0.3121271757089065}, 'combined': 0.22998844525919426, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3200163294971871, 'r': 0.2985143597142173, 'f1': 0.30889160833633683}, 'combined': 0.21931304191879913, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3977272727272727, 'r': 0.3017241379310345, 'f1': 0.34313725490196073}, 'combined': 0.2287581699346405, 'epoch': 6}
******************************
Epoch: 8
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
2023-01-22 15:26:31.895758: step: 2/463, loss: 0.16487164795398712
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==================================================
Loss: 0.370
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2782685185185185, 'r': 0.35641603415559775, 'f1': 0.3125311980033278}, 'combined': 0.23028614589718888, 'epoch': 8}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.32812936617648, 'r': 0.31409940200314007, 'f1': 0.3209611365988395}, 'combined': 0.22580180464239968, 'epoch': 8}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28882978723404257, 'r': 0.3606261859582543, 'f1': 0.3207594936708861}, 'combined': 0.23634910059960026, 'epoch': 8}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.32266427723983016, 'r': 0.30972394877162424, 'f1': 0.3160617164066308}, 'combined': 0.22440381864870784, 'epoch': 8}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2960370879120879, 'r': 0.3651311330984006, 'f1': 0.3269738439130962}, 'combined': 0.24092809551491298, 'epoch': 8}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3432383458787202, 'r': 0.300071252597903, 'f1': 0.3202065090629999}, 'combined': 0.22734662143472992, 'epoch': 8}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.22005208333333331, 'r': 0.40238095238095234, 'f1': 0.2845117845117845}, 'combined': 0.1896745230078563, 'epoch': 8}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.22023809523809523, 'r': 0.40217391304347827, 'f1': 0.2846153846153846}, 'combined': 0.1423076923076923, 'epoch': 8}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2708333333333333, 'r': 0.22413793103448276, 'f1': 0.24528301886792453}, 'combined': 0.16352201257861634, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2893626329100397, 'r': 0.33877940133869927, 'f1': 0.3121271757089065}, 'combined': 0.22998844525919426, 'epoch': 6}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3200163294971871, 'r': 0.2985143597142173, 'f1': 0.30889160833633683}, 'combined': 0.21931304191879913, 'epoch': 6}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3977272727272727, 'r': 0.3017241379310345, 'f1': 0.34313725490196073}, 'combined': 0.2287581699346405, 'epoch': 6}
******************************
Epoch: 9
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.284
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.26878457814661133, 'r': 0.36875, 'f1': 0.31093}, 'combined': 0.22910631578947366, 'epoch': 9}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.33020162504944583, 'r': 0.31032037537369384, 'f1': 0.31995245180229703}, 'combined': 0.22509217714734467, 'epoch': 9}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.27314685314685316, 'r': 0.37058823529411766, 'f1': 0.3144927536231884}, 'combined': 0.23173150266971776, 'epoch': 9}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3217223191886182, 'r': 0.3031938086594308, 'f1': 0.31218338250108507}, 'combined': 0.2216502015757704, 'epoch': 9}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2792885164051355, 'r': 0.37150142314990514, 'f1': 0.3188619706840391}, 'combined': 0.23495092576718668, 'epoch': 9}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3400664122883469, 'r': 0.29521922603896666, 'f1': 0.3160598539641111}, 'combined': 0.2244024963145189, 'epoch': 9}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.23391812865497075, 'r': 0.38095238095238093, 'f1': 0.2898550724637681}, 'combined': 0.1932367149758454, 'epoch': 9}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.25, 'r': 0.41304347826086957, 'f1': 0.31147540983606553}, 'combined': 0.15573770491803277, 'epoch': 9}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.40476190476190477, 'r': 0.29310344827586204, 'f1': 0.34}, 'combined': 0.22666666666666668, 'epoch': 9}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2792885164051355, 'r': 0.37150142314990514, 'f1': 0.3188619706840391}, 'combined': 0.23495092576718668, 'epoch': 9}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3400664122883469, 'r': 0.29521922603896666, 'f1': 0.3160598539641111}, 'combined': 0.2244024963145189, 'epoch': 9}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.40476190476190477, 'r': 0.29310344827586204, 'f1': 0.34}, 'combined': 0.22666666666666668, 'epoch': 9}
******************************
Epoch: 10
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.300
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2763234157650695, 'r': 0.3392433586337761, 'f1': 0.30456771720613285}, 'combined': 0.22441831794136102, 'epoch': 10}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3522054392206747, 'r': 0.3331340529921316, 'f1': 0.3424043901938875}, 'combined': 0.2408875106891671, 'epoch': 10}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.26967229199372056, 'r': 0.3259606261859583, 'f1': 0.2951567869415807}, 'combined': 0.21748394827274367, 'epoch': 10}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.345705756367181, 'r': 0.3263824651815918, 'f1': 0.33576632761268876}, 'combined': 0.23839409260500902, 'epoch': 10}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28493110236220476, 'r': 0.343323055028463, 'f1': 0.31141351118760763}, 'combined': 0.22946258719086876, 'epoch': 10}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36354871148677315, 'r': 0.31270273785225944, 'f1': 0.3362142219013015}, 'combined': 0.23871209754992406, 'epoch': 10}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2876984126984127, 'r': 0.3452380952380952, 'f1': 0.3138528138528138}, 'combined': 0.20923520923520916, 'epoch': 10}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.24285714285714285, 'r': 0.3695652173913043, 'f1': 0.29310344827586204}, 'combined': 0.14655172413793102, 'epoch': 10}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.25, 'r': 0.1724137931034483, 'f1': 0.20408163265306123}, 'combined': 0.13605442176870747, 'epoch': 10}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3095149595559295, 'r': 0.3253724622276754, 'f1': 0.31724567547453275}, 'combined': 0.23375997140228727, 'epoch': 1}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.36402697976295945, 'r': 0.29452226435922085, 'f1': 0.32560678286267597}, 'combined': 0.22907009849635496, 'epoch': 1}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29409479409479405, 'r': 0.32770562770562767, 'f1': 0.3099918099918099}, 'combined': 0.2066612066612066, 'epoch': 1}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2792885164051355, 'r': 0.37150142314990514, 'f1': 0.3188619706840391}, 'combined': 0.23495092576718668, 'epoch': 9}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3400664122883469, 'r': 0.29521922603896666, 'f1': 0.3160598539641111}, 'combined': 0.2244024963145189, 'epoch': 9}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.40476190476190477, 'r': 0.29310344827586204, 'f1': 0.34}, 'combined': 0.22666666666666668, 'epoch': 9}
******************************
Epoch: 11
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.260
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.286031314699793, 'r': 0.2996001626457035, 'f1': 0.29265854627300414}, 'combined': 0.21564313935905566, 'epoch': 11}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3466959902350088, 'r': 0.3012773015579334, 'f1': 0.32239486942414375}, 'combined': 0.2268104609014077, 'epoch': 11}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2857077084977638, 'r': 0.2943819463269179, 'f1': 0.28997997329773034}, 'combined': 0.21366945400885393, 'epoch': 11}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.33830651938339856, 'r': 0.28900705277168165, 'f1': 0.31171960703656204}, 'combined': 0.22132092099595904, 'epoch': 11}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29229077825159916, 'r': 0.29728246137164543, 'f1': 0.29476548850960893}, 'combined': 0.21719562311234342, 'epoch': 11}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36189253213247025, 'r': 0.2879982454666779, 'f1': 0.32074440165676665}, 'combined': 0.22772852517630432, 'epoch': 11}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.32215447154471544, 'r': 0.3773809523809524, 'f1': 0.3475877192982456}, 'combined': 0.2317251461988304, 'epoch': 11}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3106060606060606, 'r': 0.44565217391304346, 'f1': 0.3660714285714286}, 'combined': 0.1830357142857143, 'epoch': 11}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4364035087719298, 'r': 0.18058076225045372, 'f1': 0.2554557124518614}, 'combined': 0.17030380830124092, 'epoch': 11}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.286031314699793, 'r': 0.2996001626457035, 'f1': 0.29265854627300414}, 'combined': 0.21564313935905566, 'epoch': 11}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3466959902350088, 'r': 0.3012773015579334, 'f1': 0.32239486942414375}, 'combined': 0.2268104609014077, 'epoch': 11}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.32215447154471544, 'r': 0.3773809523809524, 'f1': 0.3475877192982456}, 'combined': 0.2317251461988304, 'epoch': 11}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2792885164051355, 'r': 0.37150142314990514, 'f1': 0.3188619706840391}, 'combined': 0.23495092576718668, 'epoch': 9}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3400664122883469, 'r': 0.29521922603896666, 'f1': 0.3160598539641111}, 'combined': 0.2244024963145189, 'epoch': 9}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.40476190476190477, 'r': 0.29310344827586204, 'f1': 0.34}, 'combined': 0.22666666666666668, 'epoch': 9}
******************************
Epoch: 12
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 16:00:42.459278: step: 926/463, loss: 0.2490263134241104
==================================================
Loss: 0.244
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2871675531914894, 'r': 0.35855075901328276, 'f1': 0.31891350210970465}, 'combined': 0.2349888962913613, 'epoch': 12}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.33691268922160644, 'r': 0.3230951531016976, 'f1': 0.32985928325571984}, 'combined': 0.23206180731558182, 'epoch': 12}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.295820189274448, 'r': 0.3558823529411765, 'f1': 0.3230835486649441}, 'combined': 0.23806156217416932, 'epoch': 12}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3364794936903662, 'r': 0.3179819299011053, 'f1': 0.3269693061163451}, 'combined': 0.23214820734260502, 'epoch': 12}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3031662306642402, 'r': 0.3612682976416373, 'f1': 0.3296768707482994}, 'combined': 0.2429197994987469, 'epoch': 12}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3502538951337507, 'r': 0.30685419783096485, 'f1': 0.32712084717607975}, 'combined': 0.23225580149501662, 'epoch': 12}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.22557471264367815, 'r': 0.37380952380952376, 'f1': 0.28136200716845877}, 'combined': 0.18757467144563916, 'epoch': 12}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2714285714285714, 'r': 0.41304347826086957, 'f1': 0.3275862068965517}, 'combined': 0.16379310344827586, 'epoch': 12}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.39705882352941174, 'r': 0.23275862068965517, 'f1': 0.2934782608695652}, 'combined': 0.19565217391304346, 'epoch': 12}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.286031314699793, 'r': 0.2996001626457035, 'f1': 0.29265854627300414}, 'combined': 0.21564313935905566, 'epoch': 11}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3466959902350088, 'r': 0.3012773015579334, 'f1': 0.32239486942414375}, 'combined': 0.2268104609014077, 'epoch': 11}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.32215447154471544, 'r': 0.3773809523809524, 'f1': 0.3475877192982456}, 'combined': 0.2317251461988304, 'epoch': 11}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2792885164051355, 'r': 0.37150142314990514, 'f1': 0.3188619706840391}, 'combined': 0.23495092576718668, 'epoch': 9}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3400664122883469, 'r': 0.29521922603896666, 'f1': 0.3160598539641111}, 'combined': 0.2244024963145189, 'epoch': 9}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.40476190476190477, 'r': 0.29310344827586204, 'f1': 0.34}, 'combined': 0.22666666666666668, 'epoch': 9}
******************************
Epoch: 13
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 16:08:00.715157: step: 926/463, loss: 0.17905595898628235
==================================================
Loss: 0.194
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2942608615611193, 'r': 0.324971198156682, 'f1': 0.30885450212546695}, 'combined': 0.22757700156613353, 'epoch': 13}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3307754597605348, 'r': 0.30275343391182574, 'f1': 0.31614471667035154}, 'combined': 0.22241336851180513, 'epoch': 13}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.297223624432105, 'r': 0.3192193006234752, 'f1': 0.3078290419552999}, 'combined': 0.22682139933548415, 'epoch': 13}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.33226810168539556, 'r': 0.30092752964869446, 'f1': 0.31582220114368026}, 'combined': 0.22423376281201296, 'epoch': 13}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.30099634384879564, 'r': 0.326126968382661, 'f1': 0.31305812811960354}, 'combined': 0.23067441019339208, 'epoch': 13}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3543294617812976, 'r': 0.29646080732443936, 'f1': 0.32282227711505757}, 'combined': 0.22920381675169085, 'epoch': 13}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2540849673202614, 'r': 0.3702380952380952, 'f1': 0.3013565891472868}, 'combined': 0.20090439276485786, 'epoch': 13}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2361111111111111, 'r': 0.3695652173913043, 'f1': 0.28813559322033905}, 'combined': 0.14406779661016952, 'epoch': 13}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.359375, 'r': 0.19827586206896552, 'f1': 0.2555555555555556}, 'combined': 0.1703703703703704, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.286031314699793, 'r': 0.2996001626457035, 'f1': 0.29265854627300414}, 'combined': 0.21564313935905566, 'epoch': 11}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3466959902350088, 'r': 0.3012773015579334, 'f1': 0.32239486942414375}, 'combined': 0.2268104609014077, 'epoch': 11}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.32215447154471544, 'r': 0.3773809523809524, 'f1': 0.3475877192982456}, 'combined': 0.2317251461988304, 'epoch': 11}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2792885164051355, 'r': 0.37150142314990514, 'f1': 0.3188619706840391}, 'combined': 0.23495092576718668, 'epoch': 9}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3400664122883469, 'r': 0.29521922603896666, 'f1': 0.3160598539641111}, 'combined': 0.2244024963145189, 'epoch': 9}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.40476190476190477, 'r': 0.29310344827586204, 'f1': 0.34}, 'combined': 0.22666666666666668, 'epoch': 9}
******************************
Epoch: 14
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.169
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2612685149093744, 'r': 0.36339624559501227, 'f1': 0.30398384353741503}, 'combined': 0.22398809523809526, 'epoch': 14}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3321868704633461, 'r': 0.3304476721886689, 'f1': 0.33131498891357347}, 'combined': 0.23308592184874516, 'epoch': 14}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2712763798701299, 'r': 0.3623881810788832, 'f1': 0.3102820006962981}, 'combined': 0.2286288426183249, 'epoch': 14}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.32746578598366227, 'r': 0.32060786376410916, 'f1': 0.3240005395711367}, 'combined': 0.23004038309550706, 'epoch': 14}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2695473997373954, 'r': 0.3728653783843667, 'f1': 0.3128981758098109}, 'combined': 0.23055655059670274, 'epoch': 14}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.34422073764876104, 'r': 0.3111803527086531, 'f1': 0.32686772154364474}, 'combined': 0.23207608229598775, 'epoch': 14}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.22628205128205126, 'r': 0.4202380952380952, 'f1': 0.29416666666666663}, 'combined': 0.19611111111111107, 'epoch': 14}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.22282608695652173, 'r': 0.44565217391304346, 'f1': 0.2971014492753623}, 'combined': 0.14855072463768115, 'epoch': 14}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2916666666666667, 'r': 0.2413793103448276, 'f1': 0.26415094339622647}, 'combined': 0.17610062893081763, 'epoch': 14}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.286031314699793, 'r': 0.2996001626457035, 'f1': 0.29265854627300414}, 'combined': 0.21564313935905566, 'epoch': 11}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3466959902350088, 'r': 0.3012773015579334, 'f1': 0.32239486942414375}, 'combined': 0.2268104609014077, 'epoch': 11}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.32215447154471544, 'r': 0.3773809523809524, 'f1': 0.3475877192982456}, 'combined': 0.2317251461988304, 'epoch': 11}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2792885164051355, 'r': 0.37150142314990514, 'f1': 0.3188619706840391}, 'combined': 0.23495092576718668, 'epoch': 9}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3400664122883469, 'r': 0.29521922603896666, 'f1': 0.3160598539641111}, 'combined': 0.2244024963145189, 'epoch': 9}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.40476190476190477, 'r': 0.29310344827586204, 'f1': 0.34}, 'combined': 0.22666666666666668, 'epoch': 9}
******************************
Epoch: 15
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 16:22:37.633710: step: 926/463, loss: 0.5286181569099426
==================================================
Loss: 0.240
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2781136035372145, 'r': 0.32983112373958073, 'f1': 0.3017725732825678}, 'combined': 0.22235873820820784, 'epoch': 15}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3272835825429386, 'r': 0.3161359321331791, 'f1': 0.32161318728786326}, 'combined': 0.22626053377035607, 'epoch': 15}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28039443897014577, 'r': 0.3250872907225029, 'f1': 0.30109139228604404}, 'combined': 0.221856815368664, 'epoch': 15}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3248514328907363, 'r': 0.30839607646483, 'f1': 0.31640995300379066}, 'combined': 0.22465106663269135, 'epoch': 15}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29419352931628273, 'r': 0.339969372587507, 'f1': 0.3154293298479158}, 'combined': 0.23242161146688528, 'epoch': 15}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.343089588698682, 'r': 0.29459001333289975, 'f1': 0.31699545096666953}, 'combined': 0.22506677018633536, 'epoch': 15}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.23809523809523808, 'r': 0.38095238095238093, 'f1': 0.293040293040293}, 'combined': 0.19536019536019533, 'epoch': 15}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.30303030303030304, 'r': 0.43478260869565216, 'f1': 0.35714285714285715}, 'combined': 0.17857142857142858, 'epoch': 15}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.484375, 'r': 0.2672413793103448, 'f1': 0.34444444444444444}, 'combined': 0.22962962962962963, 'epoch': 15}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.286031314699793, 'r': 0.2996001626457035, 'f1': 0.29265854627300414}, 'combined': 0.21564313935905566, 'epoch': 11}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3466959902350088, 'r': 0.3012773015579334, 'f1': 0.32239486942414375}, 'combined': 0.2268104609014077, 'epoch': 11}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.32215447154471544, 'r': 0.3773809523809524, 'f1': 0.3475877192982456}, 'combined': 0.2317251461988304, 'epoch': 11}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29419352931628273, 'r': 0.339969372587507, 'f1': 0.3154293298479158}, 'combined': 0.23242161146688528, 'epoch': 15}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.343089588698682, 'r': 0.29459001333289975, 'f1': 0.31699545096666953}, 'combined': 0.22506677018633536, 'epoch': 15}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.484375, 'r': 0.2672413793103448, 'f1': 0.34444444444444444}, 'combined': 0.22962962962962963, 'epoch': 15}
******************************
Epoch: 16
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
2023-01-22 16:25:21.887729: step: 2/463, loss: 0.14428521692752838
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==================================================
Loss: 0.147
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2899444861617679, 'r': 0.3477133116778696, 'f1': 0.3162121057018763}, 'combined': 0.23299839367506672, 'epoch': 16}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3425991168576647, 'r': 0.310013336982023, 'f1': 0.32549270195272406}, 'combined': 0.23109981838643406, 'epoch': 16}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2987150015373381, 'r': 0.357097629921296, 'f1': 0.32530760755146587}, 'combined': 0.23970034240634325, 'epoch': 16}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.35595372138424786, 'r': 0.29626541177221677, 'f1': 0.32337835698683337}, 'combined': 0.22959863346065168, 'epoch': 16}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.25675675675675674, 'r': 0.41304347826086957, 'f1': 0.31666666666666665}, 'combined': 0.15833333333333333, 'epoch': 16}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3068181818181818, 'r': 0.23275862068965517, 'f1': 0.2647058823529411}, 'combined': 0.17647058823529407, 'epoch': 16}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31359210182048525, 'r': 0.32489807892596767, 'f1': 0.3191449908555172}, 'combined': 0.23515946694617054, 'epoch': 1}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3653684903443556, 'r': 0.29076445304891124, 'f1': 0.32382513429937054}, 'combined': 0.22991584535255308, 'epoch': 1}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.296875, 'r': 0.41304347826086957, 'f1': 0.3454545454545454}, 'combined': 0.1727272727272727, 'epoch': 1}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29419352931628273, 'r': 0.339969372587507, 'f1': 0.3154293298479158}, 'combined': 0.23242161146688528, 'epoch': 15}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.343089588698682, 'r': 0.29459001333289975, 'f1': 0.31699545096666953}, 'combined': 0.22506677018633536, 'epoch': 15}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.484375, 'r': 0.2672413793103448, 'f1': 0.34444444444444444}, 'combined': 0.22962962962962963, 'epoch': 15}
******************************
Epoch: 17
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 16:37:33.245829: step: 926/463, loss: 0.07033561915159225
==================================================
Loss: 0.131
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.27950921379164684, 'r': 0.34050268549191137, 'f1': 0.3070058430354787}, 'combined': 0.2262148317103527, 'epoch': 17}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.34796317690927714, 'r': 0.3264240439009435, 'f1': 0.33684964314384364}, 'combined': 0.2369796484429051, 'epoch': 17}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2910401858939311, 'r': 0.3424002186987425, 'f1': 0.31463803880424984}, 'combined': 0.2318385549083946, 'epoch': 17}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36911606832749205, 'r': 0.31307226737724453, 'f1': 0.33879208537707484}, 'combined': 0.24054238061772312, 'epoch': 17}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.22549019607843135, 'r': 0.43809523809523804, 'f1': 0.2977346278317152}, 'combined': 0.19848975188781015, 'epoch': 17}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.32142857142857145, 'r': 0.23275862068965517, 'f1': 0.26999999999999996}, 'combined': 0.17999999999999997, 'epoch': 17}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29419352931628273, 'r': 0.339969372587507, 'f1': 0.3154293298479158}, 'combined': 0.23242161146688528, 'epoch': 15}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.343089588698682, 'r': 0.29459001333289975, 'f1': 0.31699545096666953}, 'combined': 0.22506677018633536, 'epoch': 15}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.484375, 'r': 0.2672413793103448, 'f1': 0.34444444444444444}, 'combined': 0.22962962962962963, 'epoch': 15}
******************************
Epoch: 18
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.109
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2885878489326765, 'r': 0.33349146110056926, 'f1': 0.30941901408450706}, 'combined': 0.22799295774647887, 'epoch': 18}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.33908642004896467, 'r': 0.3171907873407418, 'f1': 0.32777334742334546}, 'combined': 0.2305943147701928, 'epoch': 18}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2914572864321608, 'r': 0.3301707779886148, 'f1': 0.3096085409252669}, 'combined': 0.22813260910282823, 'epoch': 18}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3417843009829022, 'r': 0.3149426019528313, 'f1': 0.327814915384146}, 'combined': 0.23274858992274364, 'epoch': 18}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29920624677336083, 'r': 0.3383812582104802, 'f1': 0.3175902459072539}, 'combined': 0.23401386540534494, 'epoch': 18}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36355641796658256, 'r': 0.3042326394676725, 'f1': 0.331259482023708}, 'combined': 0.23519423223683264, 'epoch': 18}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2580409356725146, 'r': 0.4202380952380952, 'f1': 0.3197463768115942}, 'combined': 0.21316425120772944, 'epoch': 18}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.25, 'r': 0.40217391304347827, 'f1': 0.30833333333333335}, 'combined': 0.15416666666666667, 'epoch': 18}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3875, 'r': 0.2672413793103448, 'f1': 0.3163265306122449}, 'combined': 0.2108843537414966, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29419352931628273, 'r': 0.339969372587507, 'f1': 0.3154293298479158}, 'combined': 0.23242161146688528, 'epoch': 15}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.343089588698682, 'r': 0.29459001333289975, 'f1': 0.31699545096666953}, 'combined': 0.22506677018633536, 'epoch': 15}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.484375, 'r': 0.2672413793103448, 'f1': 0.34444444444444444}, 'combined': 0.22962962962962963, 'epoch': 15}
******************************
Epoch: 19
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 16:52:34.195451: step: 926/463, loss: 0.04851355403661728
==================================================
Loss: 0.101
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28830040353281555, 'r': 0.3265945747800586, 'f1': 0.30625505499838235}, 'combined': 0.22566161947249225, 'epoch': 19}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3387477477506408, 'r': 0.3210122635752146, 'f1': 0.32964162549927944}, 'combined': 0.2319086812557745, 'epoch': 19}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2866866438356165, 'r': 0.3176944971537002, 'f1': 0.3013951395139514}, 'combined': 0.22208062911554313, 'epoch': 19}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.339292351677531, 'r': 0.3167912882154958, 'f1': 0.32765597138534136}, 'combined': 0.23263573968359236, 'epoch': 19}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29497819175687323, 'r': 0.3268828538634041, 'f1': 0.3101120863834635}, 'combined': 0.2285036425983415, 'epoch': 19}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3561835053267278, 'r': 0.303035704793682, 'f1': 0.32746715482655314}, 'combined': 0.23250167992685272, 'epoch': 19}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2535919540229885, 'r': 0.4202380952380952, 'f1': 0.3163082437275986}, 'combined': 0.2108721624850657, 'epoch': 19}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.29285714285714287, 'r': 0.44565217391304346, 'f1': 0.35344827586206895}, 'combined': 0.17672413793103448, 'epoch': 19}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4166666666666667, 'r': 0.3017241379310345, 'f1': 0.35}, 'combined': 0.2333333333333333, 'epoch': 19}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29497819175687323, 'r': 0.3268828538634041, 'f1': 0.3101120863834635}, 'combined': 0.2285036425983415, 'epoch': 19}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3561835053267278, 'r': 0.303035704793682, 'f1': 0.32746715482655314}, 'combined': 0.23250167992685272, 'epoch': 19}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4166666666666667, 'r': 0.3017241379310345, 'f1': 0.35}, 'combined': 0.2333333333333333, 'epoch': 19}
******************************
Epoch: 20
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
2023-01-22 16:55:24.579413: step: 2/463, loss: 0.08323995769023895
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2023-01-22 17:00:14.913386: step: 926/463, loss: 0.5193667411804199
==================================================
Loss: 0.102
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28174586776859506, 'r': 0.32344639468690706, 'f1': 0.3011594522968198}, 'combined': 0.22190696485028824, 'epoch': 20}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35800929520358865, 'r': 0.3148982282419, 'f1': 0.3350727665415203}, 'combined': 0.23572958450157208, 'epoch': 20}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2811266447368421, 'r': 0.3243358633776091, 'f1': 0.3011894273127753}, 'combined': 0.22192905170415023, 'epoch': 20}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.35088108535058565, 'r': 0.3079785945653612, 'f1': 0.328033014676594}, 'combined': 0.23290344042038175, 'epoch': 20}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2404970760233918, 'r': 0.3916666666666666, 'f1': 0.2980072463768116}, 'combined': 0.19867149758454106, 'epoch': 20}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.21794871794871795, 'r': 0.3695652173913043, 'f1': 0.27419354838709675}, 'combined': 0.13709677419354838, 'epoch': 20}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
******************************
Epoch: 21
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 17:07:55.628795: step: 926/463, loss: 0.4857058525085449
==================================================
Loss: 0.069
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28191302652106087, 'r': 0.342896110056926, 'f1': 0.3094285102739726}, 'combined': 0.22799995493871664, 'epoch': 21}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3384979966952492, 'r': 0.32696837060693945, 'f1': 0.332633304615678}, 'combined': 0.2340133801316328, 'epoch': 21}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.291068515497553, 'r': 0.3385673624288425, 'f1': 0.3130263157894737}, 'combined': 0.23065096952908587, 'epoch': 21}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.34050941074485314, 'r': 0.3235582872405242, 'f1': 0.33181750012575034}, 'combined': 0.23559042508928274, 'epoch': 21}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.291850884244373, 'r': 0.3444615749525617, 'f1': 0.3159812880765884}, 'combined': 0.23282831753011773, 'epoch': 21}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3538062394812509, 'r': 0.30838308035134354, 'f1': 0.3295367494188412}, 'combined': 0.23397109208737724, 'epoch': 21}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2626488095238095, 'r': 0.4202380952380952, 'f1': 0.3232600732600732}, 'combined': 0.21550671550671546, 'epoch': 21}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2847222222222222, 'r': 0.44565217391304346, 'f1': 0.3474576271186441}, 'combined': 0.17372881355932204, 'epoch': 21}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.42105263157894735, 'r': 0.27586206896551724, 'f1': 0.3333333333333333}, 'combined': 0.2222222222222222, 'epoch': 21}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
******************************
Epoch: 22
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.071
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2758798984034833, 'r': 0.36068548387096777, 'f1': 0.3126336348684211}, 'combined': 0.2303616256925208, 'epoch': 22}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.33520418603042307, 'r': 0.33900667679691127, 'f1': 0.3370947085546727}, 'combined': 0.23715205626961902, 'epoch': 22}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28127749266862173, 'r': 0.3640061669829222, 'f1': 0.3173387096774194}, 'combined': 0.23382852292020376, 'epoch': 22}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3309096821235833, 'r': 0.33408595306892314, 'f1': 0.33249023205990963}, 'combined': 0.23606806476253583, 'epoch': 22}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28504931643799986, 'r': 0.3656420074233167, 'f1': 0.3203546764955742}, 'combined': 0.23605081425989677, 'epoch': 22}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.34833035469302703, 'r': 0.32401409956611416, 'f1': 0.33573251184698627}, 'combined': 0.23837008341136023, 'epoch': 22}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.23996913580246912, 'r': 0.3702380952380952, 'f1': 0.29119850187265917}, 'combined': 0.19413233458177276, 'epoch': 22}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2616279069767442, 'r': 0.4891304347826087, 'f1': 0.34090909090909094}, 'combined': 0.17045454545454547, 'epoch': 22}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3804347826086957, 'r': 0.3017241379310345, 'f1': 0.3365384615384615}, 'combined': 0.22435897435897434, 'epoch': 22}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
******************************
Epoch: 23
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 17:22:54.828181: step: 926/463, loss: 0.05778537318110466
==================================================
Loss: 0.097
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2884159482758621, 'r': 0.31742172675521824, 'f1': 0.3022244805781391}, 'combined': 0.22269172253126038, 'epoch': 23}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3485597377789355, 'r': 0.31844855624305884, 'f1': 0.33282448285868255}, 'combined': 0.2341478773880179, 'epoch': 23}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2880791083916084, 'r': 0.3126778937381404, 'f1': 0.2998748862602366}, 'combined': 0.22096044250754276, 'epoch': 23}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3501879299562226, 'r': 0.3132134626571799, 'f1': 0.3306703161723889}, 'combined': 0.23477592448239612, 'epoch': 23}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2996737339783267, 'r': 0.3281057770502742, 'f1': 0.31324591395922924}, 'combined': 0.23081277870680048, 'epoch': 23}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36324738983017096, 'r': 0.30514050841503, 'f1': 0.3316681630658378}, 'combined': 0.23548439577674482, 'epoch': 23}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2796296296296296, 'r': 0.35952380952380947, 'f1': 0.3145833333333333}, 'combined': 0.2097222222222222, 'epoch': 23}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3125, 'r': 0.43478260869565216, 'f1': 0.36363636363636365}, 'combined': 0.18181818181818182, 'epoch': 23}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3194444444444444, 'r': 0.19827586206896552, 'f1': 0.24468085106382978}, 'combined': 0.1631205673758865, 'epoch': 23}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
******************************
Epoch: 24
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
2023-01-22 17:25:34.710742: step: 2/463, loss: 0.02154787816107273
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==================================================
Loss: 0.070
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29892015706806285, 'r': 0.3250118595825427, 'f1': 0.3114204545454546}, 'combined': 0.22946770334928232, 'epoch': 24}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35556081469012174, 'r': 0.3139856408956398, 'f1': 0.3334824323136267}, 'combined': 0.2346107564015464, 'epoch': 24}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2982700892857143, 'r': 0.31694734345351044, 'f1': 0.30732520699172033}, 'combined': 0.22645015252021497, 'epoch': 24}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3611765258057013, 'r': 0.31012015828342937, 'f1': 0.33370676187118314}, 'combined': 0.23693180092854002, 'epoch': 24}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.30846788015905663, 'r': 0.3283690337177054, 'f1': 0.31810750141402716}, 'combined': 0.23439500104191474, 'epoch': 24}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36871348141567106, 'r': 0.2994790722415494, 'f1': 0.33050943394368587}, 'combined': 0.23466169810001697, 'epoch': 24}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.26215277777777773, 'r': 0.35952380952380947, 'f1': 0.3032128514056224}, 'combined': 0.20214190093708156, 'epoch': 24}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.27702702702702703, 'r': 0.44565217391304346, 'f1': 0.3416666666666667}, 'combined': 0.17083333333333334, 'epoch': 24}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.40789473684210525, 'r': 0.2672413793103448, 'f1': 0.3229166666666667}, 'combined': 0.2152777777777778, 'epoch': 24}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
******************************
Epoch: 25
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 17:37:50.057964: step: 926/463, loss: 0.04482921585440636
==================================================
Loss: 0.058
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2866532456356037, 'r': 0.31602568446733537, 'f1': 0.300623710675606}, 'combined': 0.221512207866236, 'epoch': 25}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3694899052560343, 'r': 0.29958640966705485, 'f1': 0.3308864823188367}, 'combined': 0.23278445992279972, 'epoch': 25}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2964895546659995, 'r': 0.3212439007861209, 'f1': 0.30837073900598494}, 'combined': 0.22722054453072574, 'epoch': 25}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3696017850411415, 'r': 0.29291022022876867, 'f1': 0.32681714260933625}, 'combined': 0.23204017125262874, 'epoch': 25}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2982403288950908, 'r': 0.3208771660028776, 'f1': 0.30914491130441774}, 'combined': 0.22779098727693936, 'epoch': 25}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3877496750283486, 'r': 0.2926731840309961, 'f1': 0.3335688402779926}, 'combined': 0.23683387659737473, 'epoch': 25}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2976190476190476, 'r': 0.41666666666666663, 'f1': 0.34722222222222227}, 'combined': 0.2314814814814815, 'epoch': 25}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2777777777777778, 'r': 0.43478260869565216, 'f1': 0.3389830508474576}, 'combined': 0.1694915254237288, 'epoch': 25}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.39705882352941174, 'r': 0.23275862068965517, 'f1': 0.2934782608695652}, 'combined': 0.19565217391304346, 'epoch': 25}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
******************************
Epoch: 26
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.075
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.26506746626686656, 'r': 0.33548387096774196, 'f1': 0.2961474036850921}, 'combined': 0.21821387639954157, 'epoch': 26}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3307320270681909, 'r': 0.3246767763546495, 'f1': 0.3276764298097518}, 'combined': 0.23052613152444854, 'epoch': 26}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2749617151607963, 'r': 0.3407020872865275, 'f1': 0.3043220338983051}, 'combined': 0.2242372881355932, 'epoch': 26}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3324458667368832, 'r': 0.32519988010355966, 'f1': 0.3287829550275742}, 'combined': 0.23343589806957768, 'epoch': 26}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2833776393098427, 'r': 0.34897929395083094, 'f1': 0.3127756597144352}, 'combined': 0.23046627557905752, 'epoch': 26}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.35040652739500383, 'r': 0.3164666281447024, 'f1': 0.33257290770639975}, 'combined': 0.23612676447154382, 'epoch': 26}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.24513888888888888, 'r': 0.4202380952380952, 'f1': 0.3096491228070175}, 'combined': 0.20643274853801166, 'epoch': 26}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.26973684210526316, 'r': 0.44565217391304346, 'f1': 0.3360655737704918}, 'combined': 0.1680327868852459, 'epoch': 26}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3, 'r': 0.20689655172413793, 'f1': 0.24489795918367346}, 'combined': 0.16326530612244897, 'epoch': 26}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
******************************
Epoch: 27
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 17:52:45.413513: step: 926/463, loss: 0.05056929588317871
==================================================
Loss: 0.045
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2856879606229243, 'r': 0.34586132614312276, 'f1': 0.3129080152402158}, 'combined': 0.23056380070331686, 'epoch': 27}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.33892371933973614, 'r': 0.32975562571012723, 'f1': 0.3342768218167234}, 'combined': 0.23516962338864966, 'epoch': 27}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28507518750382604, 'r': 0.34295572842016264, 'f1': 0.3113482668000443}, 'combined': 0.22941451237898, 'epoch': 27}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3381720141345674, 'r': 0.32518809736151244, 'f1': 0.3315529889468801}, 'combined': 0.23540262215228488, 'epoch': 27}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3002336704567088, 'r': 0.35378578624974605, 'f1': 0.32481726368225816}, 'combined': 0.23933903639745338, 'epoch': 27}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.35346500388198765, 'r': 0.30870306015894117, 'f1': 0.32957109919066446}, 'combined': 0.23399548042537174, 'epoch': 27}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.23888888888888887, 'r': 0.4095238095238095, 'f1': 0.3017543859649123}, 'combined': 0.20116959064327483, 'epoch': 27}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.24404761904761904, 'r': 0.44565217391304346, 'f1': 0.3153846153846154}, 'combined': 0.1576923076923077, 'epoch': 27}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.31, 'r': 0.2672413793103448, 'f1': 0.287037037037037}, 'combined': 0.191358024691358, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
******************************
Epoch: 28
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
2023-01-22 17:55:26.442183: step: 2/463, loss: 0.03213844448328018
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2023-01-22 18:00:14.047525: step: 926/463, loss: 0.0033161970786750317
==================================================
Loss: 0.036
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28854839674563293, 'r': 0.32687550826782324, 'f1': 0.306518492628368}, 'combined': 0.2258557314103764, 'epoch': 28}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.34286741326306547, 'r': 0.33658450254882083, 'f1': 0.3396969087811084}, 'combined': 0.2389827498962572, 'epoch': 28}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2974273607748184, 'r': 0.3329831932773109, 'f1': 0.3142025834505691}, 'combined': 0.2315176930688404, 'epoch': 28}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.340478128507631, 'r': 0.3321592911618948, 'f1': 0.3362672682610702}, 'combined': 0.2387497604653598, 'epoch': 28}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.30627309113300494, 'r': 0.3370747492545405, 'f1': 0.3209365724609627}, 'combined': 0.23647957970807776, 'epoch': 28}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.35224466731550763, 'r': 0.31689725305609806, 'f1': 0.33363734680963564}, 'combined': 0.2368825162348413, 'epoch': 28}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2986111111111111, 'r': 0.4095238095238095, 'f1': 0.3453815261044177}, 'combined': 0.23025435073627845, 'epoch': 28}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.32142857142857145, 'r': 0.4891304347826087, 'f1': 0.3879310344827586}, 'combined': 0.1939655172413793, 'epoch': 28}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29411764705882354, 'r': 0.1724137931034483, 'f1': 0.2173913043478261}, 'combined': 0.14492753623188406, 'epoch': 28}
==================================================
Current best result:
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Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2887401938920082, 'r': 0.35393959251278423, 'f1': 0.3180326773303279}, 'combined': 0.2343398675065574, 'epoch': 16}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3374190197383612, 'r': 0.30885912016190303, 'f1': 0.32250801977725824}, 'combined': 0.22689006416490531, 'epoch': 16}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29006410256410253, 'r': 0.4309523809523809, 'f1': 0.34674329501915707}, 'combined': 0.23116219667943805, 'epoch': 16}
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Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
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Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
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Epoch: 29
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.136
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31716366158113735, 'r': 0.30994171862293307, 'f1': 0.31351110501782287}, 'combined': 0.23100818264471157, 'epoch': 29}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3483239962628753, 'r': 0.30516343128091344, 'f1': 0.3253184113934203}, 'combined': 0.2309760720893284, 'epoch': 29}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.33030674071525346, 'r': 0.32027845257209586, 'f1': 0.32521530733235937}, 'combined': 0.2396323317185806, 'epoch': 29}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3704599338642285, 'r': 0.2980489171228784, 'f1': 0.3303327456700374}, 'combined': 0.23453624942572654, 'epoch': 29}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.30357142857142855, 'r': 0.3695652173913043, 'f1': 0.3333333333333333}, 'combined': 0.16666666666666666, 'epoch': 29}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.39705882352941174, 'r': 0.23275862068965517, 'f1': 0.2934782608695652}, 'combined': 0.19565217391304346, 'epoch': 29}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29811343141907926, 'r': 0.34053944158308486, 'f1': 0.3179172466152094}, 'combined': 0.23425481329541745, 'epoch': 20}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36505995641065214, 'r': 0.2978456014345199, 'f1': 0.32804522752903387}, 'combined': 0.23291211154561403, 'epoch': 20}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 20}
******************************
Epoch: 30
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.050
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28104676999783224, 'r': 0.35144178639197615, 'f1': 0.31232684895205975}, 'combined': 0.230135572912044, 'epoch': 30}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.34333805844859144, 'r': 0.33674692643648935, 'f1': 0.3400105530363144}, 'combined': 0.23920340414615085, 'epoch': 30}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2891833931374748, 'r': 0.34954425318514504, 'f1': 0.31651172066764854}, 'combined': 0.2332191625972147, 'epoch': 30}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3382166806352807, 'r': 0.32935510821688907, 'f1': 0.33372707867994517}, 'combined': 0.23694622586276107, 'epoch': 30}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2919934913217624, 'r': 0.3557112361073462, 'f1': 0.3207182573628254}, 'combined': 0.23631871595155554, 'epoch': 30}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36122302043550025, 'r': 0.3233655859793779, 'f1': 0.34124755386763844}, 'combined': 0.24228576324602327, 'epoch': 30}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2513020833333333, 'r': 0.4595238095238095, 'f1': 0.3249158249158249}, 'combined': 0.21661054994388323, 'epoch': 30}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.27439024390243905, 'r': 0.4891304347826087, 'f1': 0.35156250000000006}, 'combined': 0.17578125000000003, 'epoch': 30}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 30}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2919934913217624, 'r': 0.3557112361073462, 'f1': 0.3207182573628254}, 'combined': 0.23631871595155554, 'epoch': 30}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36122302043550025, 'r': 0.3233655859793779, 'f1': 0.34124755386763844}, 'combined': 0.24228576324602327, 'epoch': 30}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 30}
******************************
Epoch: 31
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 18:22:57.204654: step: 926/463, loss: 0.004698766861110926
==================================================
Loss: 0.063
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2857067953020134, 'r': 0.3231143263757116, 'f1': 0.30326135351736416}, 'combined': 0.22345573417068937, 'epoch': 31}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3583955549733647, 'r': 0.3252744313228184, 'f1': 0.3410326990194449}, 'combined': 0.23992250182272506, 'epoch': 31}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29740827645051193, 'r': 0.33070445920303604, 'f1': 0.31317385444743934}, 'combined': 0.23075968222442897, 'epoch': 31}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.354957514911559, 'r': 0.32184465170708054, 'f1': 0.3375910521335358}, 'combined': 0.2396896470148104, 'epoch': 31}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29957053488679997, 'r': 0.3302665669245366, 'f1': 0.3141705429047487}, 'combined': 0.2314940842456043, 'epoch': 31}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36816481891513386, 'r': 0.3090012148274617, 'f1': 0.3359984719633843}, 'combined': 0.23855891509400282, 'epoch': 31}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.31292517006802717, 'r': 0.43809523809523804, 'f1': 0.365079365079365}, 'combined': 0.24338624338624332, 'epoch': 31}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2642857142857143, 'r': 0.40217391304347827, 'f1': 0.31896551724137934}, 'combined': 0.15948275862068967, 'epoch': 31}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.5, 'r': 0.27586206896551724, 'f1': 0.35555555555555557}, 'combined': 0.23703703703703705, 'epoch': 31}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2919934913217624, 'r': 0.3557112361073462, 'f1': 0.3207182573628254}, 'combined': 0.23631871595155554, 'epoch': 30}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36122302043550025, 'r': 0.3233655859793779, 'f1': 0.34124755386763844}, 'combined': 0.24228576324602327, 'epoch': 30}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 30}
******************************
Epoch: 32
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
2023-01-22 18:25:37.286258: step: 2/463, loss: 0.030614763498306274
2023-01-22 18:25:37.891466: step: 4/463, loss: 0.03874175623059273
2023-01-22 18:25:38.577474: step: 6/463, loss: 0.009867417626082897
2023-01-22 18:25:39.197442: step: 8/463, loss: 0.09041564166545868
2023-01-22 18:25:39.787801: step: 10/463, loss: 0.0008795055327937007
2023-01-22 18:25:40.409324: step: 12/463, loss: 0.0026088336016982794
2023-01-22 18:25:41.027206: step: 14/463, loss: 0.007183930370956659
2023-01-22 18:25:41.672904: step: 16/463, loss: 0.0006557835731655359
2023-01-22 18:25:42.302884: step: 18/463, loss: 0.004347142763435841
2023-01-22 18:25:42.884770: step: 20/463, loss: 0.0007631027256138623
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2023-01-22 18:25:44.191699: step: 24/463, loss: 0.008002565242350101
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2023-01-22 18:25:45.550624: step: 28/463, loss: 0.026644079014658928
2023-01-22 18:25:46.222979: step: 30/463, loss: 0.012972644530236721
2023-01-22 18:25:46.858778: step: 32/463, loss: 0.010772773995995522
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2023-01-22 18:25:48.860675: step: 38/463, loss: 0.0024651081766933203
2023-01-22 18:25:49.459154: step: 40/463, loss: 0.0024488207418471575
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==================================================
Loss: 0.049
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28321399582560297, 'r': 0.33104330441854163, 'f1': 0.305266529183852}, 'combined': 0.2249332320302067, 'epoch': 32}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3393303633905101, 'r': 0.32542580621583356, 'f1': 0.33223266553587993}, 'combined': 0.23373152349257886, 'epoch': 32}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28669905462184875, 'r': 0.33294083762537274, 'f1': 0.30809450645930014}, 'combined': 0.2270170047594843, 'epoch': 32}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3427604167660694, 'r': 0.3260112166448653, 'f1': 0.3341760771690659}, 'combined': 0.23726501479003678, 'epoch': 32}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.30493758958432876, 'r': 0.34602026294388727, 'f1': 0.3241825396825397}, 'combined': 0.23887134502923976, 'epoch': 32}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.35629862700228837, 'r': 0.3109063062847193, 'f1': 0.3320583662649472}, 'combined': 0.23576144004811253, 'epoch': 32}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3034420289855072, 'r': 0.3988095238095238, 'f1': 0.34465020576131683}, 'combined': 0.2297668038408779, 'epoch': 32}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2564102564102564, 'r': 0.43478260869565216, 'f1': 0.3225806451612903}, 'combined': 0.16129032258064516, 'epoch': 32}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.421875, 'r': 0.23275862068965517, 'f1': 0.3}, 'combined': 0.19999999999999998, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2919934913217624, 'r': 0.3557112361073462, 'f1': 0.3207182573628254}, 'combined': 0.23631871595155554, 'epoch': 30}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36122302043550025, 'r': 0.3233655859793779, 'f1': 0.34124755386763844}, 'combined': 0.24228576324602327, 'epoch': 30}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 30}
******************************
Epoch: 33
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.057
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29459852005870846, 'r': 0.3264621171049065, 'f1': 0.30971293557927226}, 'combined': 0.22820953147946377, 'epoch': 33}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3669334870862626, 'r': 0.33526268044150237, 'f1': 0.350383867395356}, 'combined': 0.24650121324296403, 'epoch': 33}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2977078201970444, 'r': 0.32764807535917595, 'f1': 0.31196122080268424}, 'combined': 0.2298661626967147, 'epoch': 33}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3700155375253328, 'r': 0.33320770918511644, 'f1': 0.35064833308185805}, 'combined': 0.2489603164881192, 'epoch': 33}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3028993919161733, 'r': 0.3339365212586275, 'f1': 0.31766163664854996}, 'combined': 0.23406646910945786, 'epoch': 33}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.38412633305988514, 'r': 0.32004936396430606, 'f1': 0.34917248379145344}, 'combined': 0.24791246349193194, 'epoch': 33}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3115530303030303, 'r': 0.3916666666666666, 'f1': 0.3470464135021097}, 'combined': 0.2313642756680731, 'epoch': 33}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.24342105263157895, 'r': 0.40217391304347827, 'f1': 0.30327868852459017}, 'combined': 0.15163934426229508, 'epoch': 33}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.39705882352941174, 'r': 0.23275862068965517, 'f1': 0.2934782608695652}, 'combined': 0.19565217391304346, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2919934913217624, 'r': 0.3557112361073462, 'f1': 0.3207182573628254}, 'combined': 0.23631871595155554, 'epoch': 30}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36122302043550025, 'r': 0.3233655859793779, 'f1': 0.34124755386763844}, 'combined': 0.24228576324602327, 'epoch': 30}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 30}
******************************
Epoch: 34
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.049
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.27846247453022416, 'r': 0.33341522092708054, 'f1': 0.30347119417715274}, 'combined': 0.2236103536042178, 'epoch': 34}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3495737848958868, 'r': 0.3276301820079148, 'f1': 0.33824646153292376}, 'combined': 0.23796233474678055, 'epoch': 34}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28687983814215345, 'r': 0.3315176877202494, 'f1': 0.3075877137826962}, 'combined': 0.22664357857672351, 'epoch': 34}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3512639634296152, 'r': 0.32429081440535157, 'f1': 0.33723890499866865}, 'combined': 0.23943962254905474, 'epoch': 34}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2979280374940674, 'r': 0.34032766332339387, 'f1': 0.3177195368847273}, 'combined': 0.23410913244137802, 'epoch': 34}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3662122419274383, 'r': 0.3169574949782457, 'f1': 0.3398092993914713}, 'combined': 0.24126460256794463, 'epoch': 34}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.27044025157232704, 'r': 0.4095238095238095, 'f1': 0.32575757575757575}, 'combined': 0.21717171717171715, 'epoch': 34}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.3125, 'r': 0.4891304347826087, 'f1': 0.38135593220338987}, 'combined': 0.19067796610169493, 'epoch': 34}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.32142857142857145, 'r': 0.23275862068965517, 'f1': 0.26999999999999996}, 'combined': 0.17999999999999997, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2919934913217624, 'r': 0.3557112361073462, 'f1': 0.3207182573628254}, 'combined': 0.23631871595155554, 'epoch': 30}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36122302043550025, 'r': 0.3233655859793779, 'f1': 0.34124755386763844}, 'combined': 0.24228576324602327, 'epoch': 30}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 30}
******************************
Epoch: 35
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.052
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.29174030960507075, 'r': 0.3244019381946327, 'f1': 0.30720542934154804}, 'combined': 0.22636189530429854, 'epoch': 35}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3656360313747181, 'r': 0.3280204675541281, 'f1': 0.34580834217333173}, 'combined': 0.24328225077520826, 'epoch': 35}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2969134852216749, 'r': 0.3267738547031716, 'f1': 0.3111288553361724}, 'combined': 0.22925284077402175, 'epoch': 35}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36736581810354946, 'r': 0.32376917651359943, 'f1': 0.34419246408588583}, 'combined': 0.24437664950097893, 'epoch': 35}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3093920510912699, 'r': 0.33815905394415835, 'f1': 0.3231365755731123}, 'combined': 0.23810063463281958, 'epoch': 35}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.38469814454868806, 'r': 0.3142037201549494, 'f1': 0.34589573803801343}, 'combined': 0.24558597400698953, 'epoch': 35}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.29450757575757575, 'r': 0.3702380952380952, 'f1': 0.3280590717299578}, 'combined': 0.21870604781997185, 'epoch': 35}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2962962962962963, 'r': 0.34782608695652173, 'f1': 0.31999999999999995}, 'combined': 0.15999999999999998, 'epoch': 35}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.359375, 'r': 0.19827586206896552, 'f1': 0.2555555555555556}, 'combined': 0.1703703703703704, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2919934913217624, 'r': 0.3557112361073462, 'f1': 0.3207182573628254}, 'combined': 0.23631871595155554, 'epoch': 30}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36122302043550025, 'r': 0.3233655859793779, 'f1': 0.34124755386763844}, 'combined': 0.24228576324602327, 'epoch': 30}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 30}
******************************
Epoch: 36
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 19:00:13.926459: step: 914/463, loss: 0.01665748655796051
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2023-01-22 19:00:17.068228: step: 924/463, loss: 0.0008791422587819397
2023-01-22 19:00:17.653358: step: 926/463, loss: 0.022510290145874023
==================================================
Loss: 0.032
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2788921225135624, 'r': 0.33445886419083765, 'f1': 0.304158449402194}, 'combined': 0.2241167521910903, 'epoch': 36}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3596750234403423, 'r': 0.33082576262385455, 'f1': 0.34464772909133623}, 'combined': 0.24246573905923152, 'epoch': 36}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2837778479559026, 'r': 0.3349332474925454, 'f1': 0.30724076837001113}, 'combined': 0.22638793458842923, 'epoch': 36}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.35788953989753547, 'r': 0.32853385337888946, 'f1': 0.34258398177634874}, 'combined': 0.2432346270612076, 'epoch': 36}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.30131371288286185, 'r': 0.3493409460558417, 'f1': 0.3235547953803666}, 'combined': 0.23840879659605957, 'epoch': 36}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3696345898496746, 'r': 0.31318951722865096, 'f1': 0.33907906163819934}, 'combined': 0.2407461337631215, 'epoch': 36}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2540849673202614, 'r': 0.3702380952380952, 'f1': 0.3013565891472868}, 'combined': 0.20090439276485786, 'epoch': 36}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.21428571428571427, 'r': 0.32608695652173914, 'f1': 0.2586206896551724}, 'combined': 0.1293103448275862, 'epoch': 36}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.40789473684210525, 'r': 0.2672413793103448, 'f1': 0.3229166666666667}, 'combined': 0.2152777777777778, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2919934913217624, 'r': 0.3557112361073462, 'f1': 0.3207182573628254}, 'combined': 0.23631871595155554, 'epoch': 30}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36122302043550025, 'r': 0.3233655859793779, 'f1': 0.34124755386763844}, 'combined': 0.24228576324602327, 'epoch': 30}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 30}
******************************
Epoch: 37
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.049
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2869111570247934, 'r': 0.3293761859582543, 'f1': 0.3066806537102474}, 'combined': 0.22597521852334015, 'epoch': 37}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.3410820934155749, 'r': 0.3241172772509259, 'f1': 0.3323833554626945}, 'combined': 0.23383753650641825, 'epoch': 37}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2930072156196944, 'r': 0.3274786527514232, 'f1': 0.30928539426523294}, 'combined': 0.22789450103754005, 'epoch': 37}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3393963800684782, 'r': 0.3177768898895961, 'f1': 0.3282310192099839}, 'combined': 0.23304402363908855, 'epoch': 37}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.30477045125482627, 'r': 0.3423607346164272, 'f1': 0.32247382867356056}, 'combined': 0.23761229481209725, 'epoch': 37}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3629923974553131, 'r': 0.3047108955951232, 'f1': 0.33130805156737303}, 'combined': 0.23522871661283484, 'epoch': 37}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.3049645390070922, 'r': 0.4095238095238095, 'f1': 0.34959349593495936}, 'combined': 0.23306233062330622, 'epoch': 37}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2569444444444444, 'r': 0.40217391304347827, 'f1': 0.31355932203389825}, 'combined': 0.15677966101694912, 'epoch': 37}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 37}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.30477045125482627, 'r': 0.3423607346164272, 'f1': 0.32247382867356056}, 'combined': 0.23761229481209725, 'epoch': 37}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3629923974553131, 'r': 0.3047108955951232, 'f1': 0.33130805156737303}, 'combined': 0.23522871661283484, 'epoch': 37}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 37}
******************************
Epoch: 38
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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==================================================
Loss: 0.043
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2906089193825043, 'r': 0.32148956356736247, 'f1': 0.3052702702702703}, 'combined': 0.22493598862019917, 'epoch': 38}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.34813207894015946, 'r': 0.30681797533120514, 'f1': 0.3261719849068284}, 'combined': 0.2294677280751557, 'epoch': 38}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.294271758436945, 'r': 0.31437381404174575, 'f1': 0.3039908256880734}, 'combined': 0.22399323998068565, 'epoch': 38}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.34885104213982376, 'r': 0.3065014396442469, 'f1': 0.326307901806288}, 'combined': 0.23167861028246448, 'epoch': 38}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3013433221389822, 'r': 0.31621035510978585, 'f1': 0.30859788359788365}, 'combined': 0.22738791423001953, 'epoch': 38}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.36284164059171536, 'r': 0.29122398926094883, 'f1': 0.3231118873098706}, 'combined': 0.22940943999000812, 'epoch': 38}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.2657407407407407, 'r': 0.3416666666666666, 'f1': 0.29895833333333327}, 'combined': 0.1993055555555555, 'epoch': 38}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.2661290322580645, 'r': 0.358695652173913, 'f1': 0.30555555555555547}, 'combined': 0.15277777777777773, 'epoch': 38}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.421875, 'r': 0.23275862068965517, 'f1': 0.3}, 'combined': 0.19999999999999998, 'epoch': 38}
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.30477045125482627, 'r': 0.3423607346164272, 'f1': 0.32247382867356056}, 'combined': 0.23761229481209725, 'epoch': 37}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3629923974553131, 'r': 0.3047108955951232, 'f1': 0.33130805156737303}, 'combined': 0.23522871661283484, 'epoch': 37}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4375, 'r': 0.3017241379310345, 'f1': 0.3571428571428571}, 'combined': 0.23809523809523805, 'epoch': 37}
******************************
Epoch: 39
command: python train.py --model_name slot --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --event_hidden_num 450 --role_hidden_num 350 --p1_data_weight 0.1 --learning_rate 9e-4
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2023-01-22 19:22:20.297984: step: 926/463, loss: 0.0014919567620381713
==================================================
Loss: 0.021
--------------------
Dev Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2996889880952381, 'r': 0.3412018839793982, 'f1': 0.3191009633667132}, 'combined': 0.23512702563863075, 'epoch': 39}
Test Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.34292218222869, 'r': 0.3165895888289302, 'f1': 0.3292301894718276}, 'combined': 0.2316192287741501, 'epoch': 39}
Dev Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.2991766376601402, 'r': 0.3355092843589048, 'f1': 0.31630302836698176}, 'combined': 0.2330653893230392, 'epoch': 39}
Test Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.33981542903675455, 'r': 0.310137225627431, 'f1': 0.3242987427793685}, 'combined': 0.2302521073733516, 'epoch': 39}
Dev Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3098214285714286, 'r': 0.34391942260775277, 'f1': 0.32598117934224047}, 'combined': 0.2401966584627035, 'epoch': 39}
Test Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.35270207665095765, 'r': 0.29576500494028823, 'f1': 0.3217339303859234}, 'combined': 0.2284310905740056, 'epoch': 39}
Sample Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.26996527777777773, 'r': 0.3702380952380952, 'f1': 0.3122489959839357}, 'combined': 0.20816599732262378, 'epoch': 39}
Sample Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.30833333333333335, 'r': 0.40217391304347827, 'f1': 0.34905660377358494}, 'combined': 0.17452830188679247, 'epoch': 39}
Sample Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4861111111111111, 'r': 0.3017241379310345, 'f1': 0.3723404255319149}, 'combined': 0.2482269503546099, 'epoch': 39}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.31154818059299194, 'r': 0.313321699647601, 'f1': 0.312432423300446}, 'combined': 0.23021336453717073, 'epoch': 29}
Test for Chinese: {'template': {'p': 0.9722222222222222, 'r': 0.5511811023622047, 'f1': 0.7035175879396985}, 'slot': {'p': 0.35841849662687986, 'r': 0.32120052010105204, 'f1': 0.33879042433116024}, 'combined': 0.23834502214252482, 'epoch': 29}
Chinese: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.34188034188034183, 'r': 0.38095238095238093, 'f1': 0.36036036036036034}, 'combined': 0.2402402402402402, 'epoch': 29}
--------------------
Dev for Korean: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.28642843540005986, 'r': 0.33860515228508026, 'f1': 0.3103389830508475}, 'combined': 0.22867082961641394, 'epoch': 17}
Test for Korean: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.3492453884409228, 'r': 0.3270179138496522, 'f1': 0.3377663639671779}, 'combined': 0.2398141184166963, 'epoch': 17}
Korean: {'template': {'p': 0.5, 'r': 0.5, 'f1': 0.5}, 'slot': {'p': 0.33088235294117646, 'r': 0.4891304347826087, 'f1': 0.39473684210526316}, 'combined': 0.19736842105263158, 'epoch': 17}
--------------------
Dev for Russian: {'template': {'p': 1.0, 'r': 0.5833333333333334, 'f1': 0.7368421052631579}, 'slot': {'p': 0.3098214285714286, 'r': 0.34391942260775277, 'f1': 0.32598117934224047}, 'combined': 0.2401966584627035, 'epoch': 39}
Test for Russian: {'template': {'p': 0.9726027397260274, 'r': 0.5590551181102362, 'f1': 0.71}, 'slot': {'p': 0.35270207665095765, 'r': 0.29576500494028823, 'f1': 0.3217339303859234}, 'combined': 0.2284310905740056, 'epoch': 39}
Russian: {'template': {'p': 1.0, 'r': 0.5, 'f1': 0.6666666666666666}, 'slot': {'p': 0.4861111111111111, 'r': 0.3017241379310345, 'f1': 0.3723404255319149}, 'combined': 0.2482269503546099, 'epoch': 39}