Command that produces this log: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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> trainable params:
>>> pad_vector: torch.Size([1, 1, 1024])
>>> 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])
>>> upos_embedding.weight: torch.Size([19, 50])
>>> dist_embedding.weight: torch.Size([104, 50])
>>> deprel_embedding.weight: torch.Size([38, 50])
>>> gcn_layer.W.0.weight: torch.Size([450, 50])
>>> gcn_layer.W.0.bias: torch.Size([450])
>>> gcn_layer.W.1.weight: torch.Size([450, 450])
>>> gcn_layer.W.1.bias: torch.Size([450])
>>> trans_rep.weight: torch.Size([1024, 2924])
>>> trans_rep.bias: torch.Size([1024])
>>> mention_label_ffn.layers.0.weight: torch.Size([350, 1024])
>>> mention_label_ffn.layers.0.bias: torch.Size([350])
>>> mention_label_ffn.layers.1.weight: torch.Size([11, 350])
>>> mention_label_ffn.layers.1.bias: torch.Size([11])
>>> trigger_label_ffn.layers.0.weight: torch.Size([450, 1024])
>>> trigger_label_ffn.layers.0.bias: torch.Size([450])
>>> trigger_label_ffn.layers.1.weight: torch.Size([233, 450])
>>> trigger_label_ffn.layers.1.bias: torch.Size([233])
>>> event_type_ffn.layers.0.weight: torch.Size([450, 1024])
>>> event_type_ffn.layers.0.bias: torch.Size([450])
>>> event_type_ffn.layers.1.weight: torch.Size([117, 450])
>>> event_type_ffn.layers.1.bias: torch.Size([117])
>>> role_type_ffn.layers.0.weight: torch.Size([350, 1024])
>>> role_type_ffn.layers.0.bias: torch.Size([350])
>>> role_type_ffn.layers.1.weight: torch.Size([6, 350])
>>> role_type_ffn.layers.1.bias: torch.Size([6])
>>> trigger_crf.transition: torch.Size([235, 235])
>>> mention_crf.transition: torch.Size([13, 13])
n_trainable_params: 564979817, n_nontrainable_params: 0
----------------------------------------------------------------------------------------------------
******************************
Epoch: 0
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:09:11.387411: step: 3068/1533, loss: 2.0479488372802734
==================================================
Loss: 4.297
--------------------
Dev Chinese: {'event': {'p': 0.5967741935483871, 'r': 0.7390146471371505, 'f1': 0.6603212373587151}, 'argument': {'p': 0.5970078513670788, 'r': 0.4803562429647447, 'f1': 0.5323668201157237}, 'combined': 0.3515331173875391, 'epoch': 0}
Test Chinese: {'event': {'p': 0.6081651806663538, 'r': 0.7405714285714285, 'f1': 0.667869105900541}, 'argument': {'p': 0.6018255924352599, 'r': 0.5015213270293832, 'f1': 0.5471141749411453}, 'combined': 0.36540065484345496, 'epoch': 0}
Dev Korean: {'event': {'p': 0.6042780748663101, 'r': 0.7523302263648469, 'f1': 0.6702253855278767}, 'argument': {'p': 0.6013828683590768, 'r': 0.4761311388822589, 'f1': 0.5314772858483213}, 'combined': 0.35620956880700066, 'epoch': 0}
Test Korean: {'event': {'p': 0.6107159791370318, 'r': 0.736, 'f1': 0.667530448302669}, 'argument': {'p': 0.6034124847529527, 'r': 0.49579355705990036, 'f1': 0.5443347485547293}, 'combined': 0.3633600187294591, 'epoch': 0}
Dev Russian: {'event': {'p': 0.5341018251681076, 'r': 0.7403462050599201, 'f1': 0.6205357142857142}, 'argument': {'p': 0.5967447802525905, 'r': 0.47669736652218636, 'f1': 0.5300083774183235}, 'combined': 0.3288891270586918, 'epoch': 0}
Test Russian: {'event': {'p': 0.5976168652612283, 'r': 0.7451428571428571, 'f1': 0.6632756866734486}, 'argument': {'p': 0.6059251879718366, 'r': 0.5033521868680717, 'f1': 0.5498963115299964}, 'combined': 0.364732853629255, 'epoch': 0}
Chinese: {'event': {'p': 0.6052631578947368, 'r': 0.8518518518518519, 'f1': 0.7076923076923076}, 'argument': {'p': 0.654380707012286, 'r': 0.4962387028176502, 'f1': 0.564442126427659}, 'combined': 0.39945135101034324, 'epoch': 0}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.3954154980555843, 'r': 0.1557697416582605, 'f1': 0.2234957162922868}, 'combined': 0.13371538581589806, 'epoch': 0}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.4247383661857346, 'r': 0.31462101198943304, 'f1': 0.3614794605836039}, 'combined': 0.21775871119494214, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5967741935483871, 'r': 0.7390146471371505, 'f1': 0.6603212373587151}, 'argument': {'p': 0.5970078513670788, 'r': 0.4803562429647447, 'f1': 0.5323668201157237}, 'combined': 0.3515331173875391, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.6081651806663538, 'r': 0.7405714285714285, 'f1': 0.667869105900541}, 'argument': {'p': 0.6018255924352599, 'r': 0.5015213270293832, 'f1': 0.5471141749411453}, 'combined': 0.36540065484345496, 'epoch': 0}
Chinese: {'event': {'p': 0.6052631578947368, 'r': 0.8518518518518519, 'f1': 0.7076923076923076}, 'argument': {'p': 0.654380707012286, 'r': 0.4962387028176502, 'f1': 0.564442126427659}, 'combined': 0.39945135101034324, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.6042780748663101, 'r': 0.7523302263648469, 'f1': 0.6702253855278767}, 'argument': {'p': 0.6013828683590768, 'r': 0.4761311388822589, 'f1': 0.5314772858483213}, 'combined': 0.35620956880700066, 'epoch': 0}
Test for Korean: {'event': {'p': 0.6107159791370318, 'r': 0.736, 'f1': 0.667530448302669}, 'argument': {'p': 0.6034124847529527, 'r': 0.49579355705990036, 'f1': 0.5443347485547293}, 'combined': 0.3633600187294591, 'epoch': 0}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.3954154980555843, 'r': 0.1557697416582605, 'f1': 0.2234957162922868}, 'combined': 0.13371538581589806, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.5341018251681076, 'r': 0.7403462050599201, 'f1': 0.6205357142857142}, 'argument': {'p': 0.5967447802525905, 'r': 0.47669736652218636, 'f1': 0.5300083774183235}, 'combined': 0.3288891270586918, 'epoch': 0}
Test for Russian: {'event': {'p': 0.5976168652612283, 'r': 0.7451428571428571, 'f1': 0.6632756866734486}, 'argument': {'p': 0.6059251879718366, 'r': 0.5033521868680717, 'f1': 0.5498963115299964}, 'combined': 0.364732853629255, 'epoch': 0}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.4247383661857346, 'r': 0.31462101198943304, 'f1': 0.3614794605836039}, 'combined': 0.21775871119494214, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:35:52.303362: step: 3068/1533, loss: 1.5208008289337158
==================================================
Loss: 1.762
--------------------
Dev Chinese: {'event': {'p': 0.6086021505376344, 'r': 0.7536617842876165, 'f1': 0.6734086853063653}, 'argument': {'p': 0.6251780858840966, 'r': 0.6404715453608758, 'f1': 0.6327324164714914}, 'combined': 0.42608750472678664, 'epoch': 1}
Test Chinese: {'event': {'p': 0.6213045518535899, 'r': 0.7565714285714286, 'f1': 0.6822983767070343}, 'argument': {'p': 0.6666016142332752, 'r': 0.6509302304574333, 'f1': 0.6586727207007642}, 'combined': 0.44941132811533724, 'epoch': 1}
Dev Korean: {'event': {'p': 0.6106951871657754, 'r': 0.7603195739014648, 'f1': 0.6773428232502966}, 'argument': {'p': 0.6149695422535391, 'r': 0.6283578486886668, 'f1': 0.6215916119834766}, 'combined': 0.4210306173695909, 'epoch': 1}
Test Korean: {'event': {'p': 0.6220957799905168, 'r': 0.7497142857142857, 'f1': 0.6799689038611039}, 'argument': {'p': 0.6662276917694496, 'r': 0.6444705137982919, 'f1': 0.6551685216282479}, 'combined': 0.44549422149585965, 'epoch': 1}
Dev Russian: {'event': {'p': 0.5417867435158501, 'r': 0.7509986684420772, 'f1': 0.6294642857142857}, 'argument': {'p': 0.6211451071454264, 'r': 0.6311342828451335, 'f1': 0.6260998541929969}, 'combined': 0.3941074975054132, 'epoch': 1}
Test Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.676236966605047, 'r': 0.6522993748668152, 'f1': 0.6640525167563074}, 'combined': 0.44889409703414673, 'epoch': 1}
Chinese: {'event': {'p': 0.5789473684210527, 'r': 0.8148148148148148, 'f1': 0.6769230769230768}, 'argument': {'p': 0.5678503787878788, 'r': 0.6057070707070707, 'f1': 0.5861681329423266}, 'combined': 0.3967907361455749, 'epoch': 1}
Korean: {'event': {'p': 0.6851851851851852, 'r': 0.5873015873015873, 'f1': 0.6324786324786325}, 'argument': {'p': 0.4802004415606706, 'r': 0.23651663539555418, 'f1': 0.3169322914300426}, 'combined': 0.20045290227199275, 'epoch': 1}
Russian: {'event': {'p': 0.48936170212765956, 'r': 0.6388888888888888, 'f1': 0.5542168674698795}, 'argument': {'p': 0.4409799479266625, 'r': 0.563474377906291, 'f1': 0.4947579903567433}, 'combined': 0.27420322357120713, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6086021505376344, 'r': 0.7536617842876165, 'f1': 0.6734086853063653}, 'argument': {'p': 0.6251780858840966, 'r': 0.6404715453608758, 'f1': 0.6327324164714914}, 'combined': 0.42608750472678664, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.6213045518535899, 'r': 0.7565714285714286, 'f1': 0.6822983767070343}, 'argument': {'p': 0.6666016142332752, 'r': 0.6509302304574333, 'f1': 0.6586727207007642}, 'combined': 0.44941132811533724, 'epoch': 1}
Chinese: {'event': {'p': 0.5789473684210527, 'r': 0.8148148148148148, 'f1': 0.6769230769230768}, 'argument': {'p': 0.5678503787878788, 'r': 0.6057070707070707, 'f1': 0.5861681329423266}, 'combined': 0.3967907361455749, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.6106951871657754, 'r': 0.7603195739014648, 'f1': 0.6773428232502966}, 'argument': {'p': 0.6149695422535391, 'r': 0.6283578486886668, 'f1': 0.6215916119834766}, 'combined': 0.4210306173695909, 'epoch': 1}
Test for Korean: {'event': {'p': 0.6220957799905168, 'r': 0.7497142857142857, 'f1': 0.6799689038611039}, 'argument': {'p': 0.6662276917694496, 'r': 0.6444705137982919, 'f1': 0.6551685216282479}, 'combined': 0.44549422149585965, 'epoch': 1}
Korean: {'event': {'p': 0.6851851851851852, 'r': 0.5873015873015873, 'f1': 0.6324786324786325}, 'argument': {'p': 0.4802004415606706, 'r': 0.23651663539555418, 'f1': 0.3169322914300426}, 'combined': 0.20045290227199275, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.5417867435158501, 'r': 0.7509986684420772, 'f1': 0.6294642857142857}, 'argument': {'p': 0.6211451071454264, 'r': 0.6311342828451335, 'f1': 0.6260998541929969}, 'combined': 0.3941074975054132, 'epoch': 1}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.676236966605047, 'r': 0.6522993748668152, 'f1': 0.6640525167563074}, 'combined': 0.44889409703414673, 'epoch': 1}
Russian: {'event': {'p': 0.48936170212765956, 'r': 0.6388888888888888, 'f1': 0.5542168674698795}, 'argument': {'p': 0.4409799479266625, 'r': 0.563474377906291, 'f1': 0.4947579903567433}, 'combined': 0.27420322357120713, 'epoch': 1}
******************************
Epoch: 2
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 15:02:43.800526: step: 3068/1533, loss: 0.5172680616378784
==================================================
Loss: 1.473
--------------------
Dev Chinese: {'event': {'p': 0.6021505376344086, 'r': 0.7456724367509987, 'f1': 0.6662700773349197}, 'argument': {'p': 0.6770982023402332, 'r': 0.6094885445621774, 'f1': 0.6415169421938994}, 'combined': 0.42742354268719057, 'epoch': 2}
Test Chinese: {'event': {'p': 0.6198967620835288, 'r': 0.7548571428571429, 'f1': 0.6807523834063385}, 'argument': {'p': 0.6943640514686402, 'r': 0.6484361189814544, 'f1': 0.6706146463232038}, 'combined': 0.4565225188317197, 'epoch': 2}
Dev Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6760213474062498, 'r': 0.6030306367080387, 'f1': 0.6374433386879306}, 'combined': 0.4347923128654331, 'epoch': 2}
Test Korean: {'event': {'p': 0.6249407302038881, 'r': 0.7531428571428571, 'f1': 0.6830785177507126}, 'argument': {'p': 0.692088493439061, 'r': 0.6430447267901286, 'f1': 0.666665841932562}, 'combined': 0.45538511514232527, 'epoch': 2}
Dev Russian: {'event': {'p': 0.5456292026897214, 'r': 0.7563249001331558, 'f1': 0.6339285714285714}, 'argument': {'p': 0.6794982199713187, 'r': 0.6030608745545304, 'f1': 0.6390018090287556}, 'combined': 0.40508150393787185, 'epoch': 2}
Test Russian: {'event': {'p': 0.6109074243813015, 'r': 0.7617142857142857, 'f1': 0.678026449643947}, 'argument': {'p': 0.7007026273868728, 'r': 0.6556267946516383, 'f1': 0.6774156928737518}, 'combined': 0.45930575717228433, 'epoch': 2}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.633147804576376, 'r': 0.6278715728715729, 'f1': 0.6304986505823326}, 'combined': 0.4655990035069533, 'epoch': 2}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5587865126326665, 'r': 0.31583585496628974, 'f1': 0.4035680369013703}, 'combined': 0.24834956117007398, 'epoch': 2}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.6064646642449847, 'r': 0.5166180473198018, 'f1': 0.557947491105386}, 'combined': 0.3226684285910666, 'epoch': 2}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6021505376344086, 'r': 0.7456724367509987, 'f1': 0.6662700773349197}, 'argument': {'p': 0.6770982023402332, 'r': 0.6094885445621774, 'f1': 0.6415169421938994}, 'combined': 0.42742354268719057, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.6198967620835288, 'r': 0.7548571428571429, 'f1': 0.6807523834063385}, 'argument': {'p': 0.6943640514686402, 'r': 0.6484361189814544, 'f1': 0.6706146463232038}, 'combined': 0.4565225188317197, 'epoch': 2}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.633147804576376, 'r': 0.6278715728715729, 'f1': 0.6304986505823326}, 'combined': 0.4655990035069533, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6760213474062498, 'r': 0.6030306367080387, 'f1': 0.6374433386879306}, 'combined': 0.4347923128654331, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6249407302038881, 'r': 0.7531428571428571, 'f1': 0.6830785177507126}, 'argument': {'p': 0.692088493439061, 'r': 0.6430447267901286, 'f1': 0.666665841932562}, 'combined': 0.45538511514232527, 'epoch': 2}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5587865126326665, 'r': 0.31583585496628974, 'f1': 0.4035680369013703}, 'combined': 0.24834956117007398, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.5456292026897214, 'r': 0.7563249001331558, 'f1': 0.6339285714285714}, 'argument': {'p': 0.6794982199713187, 'r': 0.6030608745545304, 'f1': 0.6390018090287556}, 'combined': 0.40508150393787185, 'epoch': 2}
Test for Russian: {'event': {'p': 0.6109074243813015, 'r': 0.7617142857142857, 'f1': 0.678026449643947}, 'argument': {'p': 0.7007026273868728, 'r': 0.6556267946516383, 'f1': 0.6774156928737518}, 'combined': 0.45930575717228433, 'epoch': 2}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.6064646642449847, 'r': 0.5166180473198018, 'f1': 0.557947491105386}, 'combined': 0.3226684285910666, 'epoch': 2}
******************************
Epoch: 3
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 15:29:30.688557: step: 3068/1533, loss: 0.773865282535553
==================================================
Loss: 0.966
--------------------
Dev Chinese: {'event': {'p': 0.6053763440860215, 'r': 0.7496671105193076, 'f1': 0.6698393813206425}, 'argument': {'p': 0.6563982576507957, 'r': 0.5834651179118184, 'f1': 0.6177865954360431}, 'combined': 0.41381779087506515, 'epoch': 3}
Test Chinese: {'event': {'p': 0.6180197090567808, 'r': 0.7525714285714286, 'f1': 0.6786910590054109}, 'argument': {'p': 0.6603074627664621, 'r': 0.6387170280166051, 'f1': 0.6493328234961054}, 'combined': 0.44069638162554536, 'epoch': 3}
Dev Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6546307399411151, 'r': 0.5771399551841059, 'f1': 0.6134478720868497}, 'combined': 0.4184252982798797, 'epoch': 3}
Test Korean: {'event': {'p': 0.6206733048838312, 'r': 0.748, 'f1': 0.6784140969162995}, 'argument': {'p': 0.6631079045544331, 'r': 0.6382442094748207, 'f1': 0.6504385335471705}, 'combined': 0.44126667033596584, 'epoch': 3}
Dev Russian: {'event': {'p': 0.5417867435158501, 'r': 0.7509986684420772, 'f1': 0.6294642857142857}, 'argument': {'p': 0.6567927505065052, 'r': 0.5722930983945572, 'f1': 0.6116382489091831}, 'combined': 0.3850044334651554, 'epoch': 3}
Test Russian: {'event': {'p': 0.6099908340971586, 'r': 0.7605714285714286, 'f1': 0.6770091556459817}, 'argument': {'p': 0.6712345687088728, 'r': 0.6422291960562607, 'f1': 0.6564116178784039}, 'combined': 0.444396675176071, 'epoch': 3}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6102488297610249, 'r': 0.6255050505050506, 'f1': 0.6177827659309141}, 'combined': 0.44670446151927623, 'epoch': 3}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5833333333333334, 'r': 0.3804347826086957, 'f1': 0.46052631578947373}, 'combined': 0.2834008097165992, 'epoch': 3}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.559499491352209, 'r': 0.6113049998107469, 'f1': 0.5842561060138112}, 'combined': 0.3378830492609992, 'epoch': 3}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6021505376344086, 'r': 0.7456724367509987, 'f1': 0.6662700773349197}, 'argument': {'p': 0.6770982023402332, 'r': 0.6094885445621774, 'f1': 0.6415169421938994}, 'combined': 0.42742354268719057, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.6198967620835288, 'r': 0.7548571428571429, 'f1': 0.6807523834063385}, 'argument': {'p': 0.6943640514686402, 'r': 0.6484361189814544, 'f1': 0.6706146463232038}, 'combined': 0.4565225188317197, 'epoch': 2}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.633147804576376, 'r': 0.6278715728715729, 'f1': 0.6304986505823326}, 'combined': 0.4655990035069533, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6546307399411151, 'r': 0.5771399551841059, 'f1': 0.6134478720868497}, 'combined': 0.4184252982798797, 'epoch': 3}
Test for Korean: {'event': {'p': 0.6206733048838312, 'r': 0.748, 'f1': 0.6784140969162995}, 'argument': {'p': 0.6631079045544331, 'r': 0.6382442094748207, 'f1': 0.6504385335471705}, 'combined': 0.44126667033596584, 'epoch': 3}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5833333333333334, 'r': 0.3804347826086957, 'f1': 0.46052631578947373}, 'combined': 0.2834008097165992, 'epoch': 3}
--------------------
Dev for Russian: {'event': {'p': 0.5456292026897214, 'r': 0.7563249001331558, 'f1': 0.6339285714285714}, 'argument': {'p': 0.6794982199713187, 'r': 0.6030608745545304, 'f1': 0.6390018090287556}, 'combined': 0.40508150393787185, 'epoch': 2}
Test for Russian: {'event': {'p': 0.6109074243813015, 'r': 0.7617142857142857, 'f1': 0.678026449643947}, 'argument': {'p': 0.7007026273868728, 'r': 0.6556267946516383, 'f1': 0.6774156928737518}, 'combined': 0.45930575717228433, 'epoch': 2}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.6064646642449847, 'r': 0.5166180473198018, 'f1': 0.557947491105386}, 'combined': 0.3226684285910666, 'epoch': 2}
******************************
Epoch: 4
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 15:35:03.442897: step: 2/1533, loss: 0.7874150276184082
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2023-01-21 15:56:10.008751: step: 3068/1533, loss: 0.7526330947875977
==================================================
Loss: 0.854
--------------------
Dev Chinese: {'event': {'p': 0.6064516129032258, 'r': 0.7509986684420772, 'f1': 0.6710291493158833}, 'argument': {'p': 0.6790273957968875, 'r': 0.6508394534954601, 'f1': 0.6646346879374533}, 'combined': 0.44598924925249683, 'epoch': 4}
Test Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6585873610846393, 'r': 0.6801362317949752, 'f1': 0.6691883648847945}, 'combined': 0.4538273064614221, 'epoch': 4}
Dev Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6737026968157246, 'r': 0.6384508115172274, 'f1': 0.6556032213788842}, 'combined': 0.44640124445015356, 'epoch': 4}
Test Korean: {'event': {'p': 0.6230440967283073, 'r': 0.7508571428571429, 'f1': 0.6810054418243068}, 'argument': {'p': 0.6616486952115583, 'r': 0.6734923207368598, 'f1': 0.6675179773935782}, 'combined': 0.45458337512058133, 'epoch': 4}
Dev Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6740295271196981, 'r': 0.6405496090450893, 'f1': 0.6568632320469866}, 'combined': 0.4120057326009, 'epoch': 4}
Test Russian: {'event': {'p': 0.6095325389550871, 'r': 0.76, 'f1': 0.6765005086469988}, 'argument': {'p': 0.6677602010733893, 'r': 0.6866311907530254, 'f1': 0.677064229391182}, 'combined': 0.45803429556982295, 'epoch': 4}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6188844971453668, 'r': 0.5930976430976431, 'f1': 0.6057167418869547}, 'combined': 0.4472985170857512, 'epoch': 4}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5486666666666666, 'r': 0.20472636815920398, 'f1': 0.29818840579710143}, 'combined': 0.18350055741360086, 'epoch': 4}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5634597971598879, 'r': 0.58432867853618, 'f1': 0.5737045207446131}, 'combined': 0.33178092765953526, 'epoch': 4}
New best chinese model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6064516129032258, 'r': 0.7509986684420772, 'f1': 0.6710291493158833}, 'argument': {'p': 0.6790273957968875, 'r': 0.6508394534954601, 'f1': 0.6646346879374533}, 'combined': 0.44598924925249683, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6585873610846393, 'r': 0.6801362317949752, 'f1': 0.6691883648847945}, 'combined': 0.4538273064614221, 'epoch': 4}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6188844971453668, 'r': 0.5930976430976431, 'f1': 0.6057167418869547}, 'combined': 0.4472985170857512, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6546307399411151, 'r': 0.5771399551841059, 'f1': 0.6134478720868497}, 'combined': 0.4184252982798797, 'epoch': 3}
Test for Korean: {'event': {'p': 0.6206733048838312, 'r': 0.748, 'f1': 0.6784140969162995}, 'argument': {'p': 0.6631079045544331, 'r': 0.6382442094748207, 'f1': 0.6504385335471705}, 'combined': 0.44126667033596584, 'epoch': 3}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5833333333333334, 'r': 0.3804347826086957, 'f1': 0.46052631578947373}, 'combined': 0.2834008097165992, 'epoch': 3}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6740295271196981, 'r': 0.6405496090450893, 'f1': 0.6568632320469866}, 'combined': 0.4120057326009, 'epoch': 4}
Test for Russian: {'event': {'p': 0.6095325389550871, 'r': 0.76, 'f1': 0.6765005086469988}, 'argument': {'p': 0.6677602010733893, 'r': 0.6866311907530254, 'f1': 0.677064229391182}, 'combined': 0.45803429556982295, 'epoch': 4}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5634597971598879, 'r': 0.58432867853618, 'f1': 0.5737045207446131}, 'combined': 0.33178092765953526, 'epoch': 4}
******************************
Epoch: 5
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 16:01:48.310951: step: 2/1533, loss: 1.1769095659255981
2023-01-21 16:01:49.138164: step: 4/1533, loss: 1.1286969184875488
2023-01-21 16:01:49.945522: step: 6/1533, loss: 0.3312980830669403
2023-01-21 16:01:50.763465: step: 8/1533, loss: 0.3321256637573242
2023-01-21 16:01:51.576277: step: 10/1533, loss: 0.4750522971153259
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2023-01-21 16:01:53.236026: step: 14/1533, loss: 0.517540693283081
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==================================================
Loss: 0.691
--------------------
Dev Chinese: {'event': {'p': 0.6, 'r': 0.7430093209054593, 'f1': 0.6638905413444378}, 'argument': {'p': 0.6878621171794077, 'r': 0.6521687530559395, 'f1': 0.66954006687402}, 'combined': 0.44450131744878424, 'epoch': 5}
Test Chinese: {'event': {'p': 0.6180197090567808, 'r': 0.7525714285714286, 'f1': 0.6786910590054109}, 'argument': {'p': 0.673965269660018, 'r': 0.6691446733852018, 'f1': 0.6715463206491036}, 'combined': 0.45577248353252736, 'epoch': 5}
Dev Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6871220875833564, 'r': 0.6432420123240419, 'f1': 0.6644583905416265}, 'combined': 0.4524307427887231, 'epoch': 5}
Test Korean: {'event': {'p': 0.6178283546704599, 'r': 0.7445714285714286, 'f1': 0.6753044830266908}, 'argument': {'p': 0.6759424651299736, 'r': 0.6580320832252908, 'f1': 0.6668670388326748}, 'combined': 0.4503383009064396, 'epoch': 5}
Dev Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6837641720326711, 'r': 0.6378136213920534, 'f1': 0.6599900587193659}, 'combined': 0.41175719065192584, 'epoch': 5}
Test Russian: {'event': {'p': 0.6095325389550871, 'r': 0.76, 'f1': 0.6765005086469988}, 'argument': {'p': 0.683444799596825, 'r': 0.6718017876786508, 'f1': 0.677573280699945}, 'combined': 0.45837866903912855, 'epoch': 5}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.5982823219665324, 'r': 0.5683682058682058, 'f1': 0.5829417496084162}, 'combined': 0.43048006124929195, 'epoch': 5}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6815221815221815, 'r': 0.38520818955601566, 'f1': 0.4922104644326866}, 'combined': 0.29448489325032534, 'epoch': 5}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.538432849097669, 'r': 0.5683457851586505, 'f1': 0.5529850882624708}, 'combined': 0.3197986052602241, 'epoch': 5}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6064516129032258, 'r': 0.7509986684420772, 'f1': 0.6710291493158833}, 'argument': {'p': 0.6790273957968875, 'r': 0.6508394534954601, 'f1': 0.6646346879374533}, 'combined': 0.44598924925249683, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6585873610846393, 'r': 0.6801362317949752, 'f1': 0.6691883648847945}, 'combined': 0.4538273064614221, 'epoch': 4}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6188844971453668, 'r': 0.5930976430976431, 'f1': 0.6057167418869547}, 'combined': 0.4472985170857512, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6871220875833564, 'r': 0.6432420123240419, 'f1': 0.6644583905416265}, 'combined': 0.4524307427887231, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6178283546704599, 'r': 0.7445714285714286, 'f1': 0.6753044830266908}, 'argument': {'p': 0.6759424651299736, 'r': 0.6580320832252908, 'f1': 0.6668670388326748}, 'combined': 0.4503383009064396, 'epoch': 5}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6815221815221815, 'r': 0.38520818955601566, 'f1': 0.4922104644326866}, 'combined': 0.29448489325032534, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6740295271196981, 'r': 0.6405496090450893, 'f1': 0.6568632320469866}, 'combined': 0.4120057326009, 'epoch': 4}
Test for Russian: {'event': {'p': 0.6095325389550871, 'r': 0.76, 'f1': 0.6765005086469988}, 'argument': {'p': 0.6677602010733893, 'r': 0.6866311907530254, 'f1': 0.677064229391182}, 'combined': 0.45803429556982295, 'epoch': 4}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5634597971598879, 'r': 0.58432867853618, 'f1': 0.5737045207446131}, 'combined': 0.33178092765953526, 'epoch': 4}
******************************
Epoch: 6
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.588
--------------------
Dev Chinese: {'event': {'p': 0.6064516129032258, 'r': 0.7509986684420772, 'f1': 0.6710291493158833}, 'argument': {'p': 0.6587444228770803, 'r': 0.6543527933912332, 'f1': 0.6565412642721403}, 'combined': 0.44055832605530887, 'epoch': 6}
Test Chinese: {'event': {'p': 0.6198967620835288, 'r': 0.7548571428571429, 'f1': 0.6807523834063385}, 'argument': {'p': 0.673501863075834, 'r': 0.6845202984635778, 'f1': 0.6789663812346644}, 'combined': 0.4622079822782744, 'epoch': 6}
Dev Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6622000388596759, 'r': 0.649705698503833, 'f1': 0.6558933718229172}, 'combined': 0.44659880833494, 'epoch': 6}
Test Korean: {'event': {'p': 0.6235182550972025, 'r': 0.7514285714285714, 'f1': 0.6815237108059082}, 'argument': {'p': 0.6762080595195828, 'r': 0.679466893541364, 'f1': 0.6778335596626588}, 'combined': 0.46195964289007324, 'epoch': 6}
Dev Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6343475073313782, 'r': 0.6554924242424243, 'f1': 0.6447466467958272}, 'combined': 0.46620142152929034, 'epoch': 6}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.5916389811738649, 'r': 0.3533399470899471, 'f1': 0.44244306418219465}, 'combined': 0.26470952557909083, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
New best chinese model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6064516129032258, 'r': 0.7509986684420772, 'f1': 0.6710291493158833}, 'argument': {'p': 0.6587444228770803, 'r': 0.6543527933912332, 'f1': 0.6565412642721403}, 'combined': 0.44055832605530887, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.6198967620835288, 'r': 0.7548571428571429, 'f1': 0.6807523834063385}, 'argument': {'p': 0.673501863075834, 'r': 0.6845202984635778, 'f1': 0.6789663812346644}, 'combined': 0.4622079822782744, 'epoch': 6}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6343475073313782, 'r': 0.6554924242424243, 'f1': 0.6447466467958272}, 'combined': 0.46620142152929034, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6871220875833564, 'r': 0.6432420123240419, 'f1': 0.6644583905416265}, 'combined': 0.4524307427887231, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6178283546704599, 'r': 0.7445714285714286, 'f1': 0.6753044830266908}, 'argument': {'p': 0.6759424651299736, 'r': 0.6580320832252908, 'f1': 0.6668670388326748}, 'combined': 0.4503383009064396, 'epoch': 5}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6815221815221815, 'r': 0.38520818955601566, 'f1': 0.4922104644326866}, 'combined': 0.29448489325032534, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 7
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 17:16:14.913887: step: 3062/1533, loss: 0.9078800678253174
2023-01-21 17:16:15.754369: step: 3064/1533, loss: 0.2908715605735779
2023-01-21 17:16:16.550833: step: 3066/1533, loss: 0.3948438763618469
2023-01-21 17:16:17.330750: step: 3068/1533, loss: 0.6961497664451599
==================================================
Loss: 0.451
--------------------
Dev Chinese: {'event': {'p': 0.6010752688172043, 'r': 0.7443408788282291, 'f1': 0.6650803093396788}, 'argument': {'p': 0.6576971712279236, 'r': 0.6586700960374323, 'f1': 0.6581832740891416}, 'combined': 0.43774473553340887, 'epoch': 7}
Test Chinese: {'event': {'p': 0.6166119192867199, 'r': 0.7508571428571429, 'f1': 0.6771450657047153}, 'argument': {'p': 0.6555937391291313, 'r': 0.6911339384204821, 'f1': 0.6728948851078089}, 'combined': 0.45564745118869415, 'epoch': 7}
Dev Korean: {'event': {'p': 0.611764705882353, 'r': 0.7616511318242344, 'f1': 0.6785290628707}, 'argument': {'p': 0.6641886532084442, 'r': 0.6487648238160856, 'f1': 0.6563861433322804}, 'combined': 0.44537707471656524, 'epoch': 7}
Test Korean: {'event': {'p': 0.622569938359412, 'r': 0.7502857142857143, 'f1': 0.6804871728427053}, 'argument': {'p': 0.6552451708097886, 'r': 0.6843521360420417, 'f1': 0.6694824332376921}, 'combined': 0.4555742082617723, 'epoch': 7}
Dev Russian: {'event': {'p': 0.5389048991354467, 'r': 0.7470039946737683, 'f1': 0.6261160714285714}, 'argument': {'p': 0.6566160996610402, 'r': 0.6542214384660692, 'f1': 0.6554165817588461}, 'combined': 0.4103668553199918, 'epoch': 7}
Test Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6632952275907948, 'r': 0.6915638615658543, 'f1': 0.6771346372801371}, 'combined': 0.45773750404135416, 'epoch': 7}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6072804314329738, 'r': 0.5971590909090909, 'f1': 0.6021772345301757}, 'combined': 0.43542046189105005, 'epoch': 7}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6531531531531531, 'r': 0.36069651741293535, 'f1': 0.4647435897435898}, 'combined': 0.2859960552268245, 'epoch': 7}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5554601722730377, 'r': 0.5554601722730377, 'f1': 0.5554601722730377}, 'combined': 0.3212299791458531, 'epoch': 7}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6064516129032258, 'r': 0.7509986684420772, 'f1': 0.6710291493158833}, 'argument': {'p': 0.6587444228770803, 'r': 0.6543527933912332, 'f1': 0.6565412642721403}, 'combined': 0.44055832605530887, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.6198967620835288, 'r': 0.7548571428571429, 'f1': 0.6807523834063385}, 'argument': {'p': 0.673501863075834, 'r': 0.6845202984635778, 'f1': 0.6789663812346644}, 'combined': 0.4622079822782744, 'epoch': 6}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6343475073313782, 'r': 0.6554924242424243, 'f1': 0.6447466467958272}, 'combined': 0.46620142152929034, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6871220875833564, 'r': 0.6432420123240419, 'f1': 0.6644583905416265}, 'combined': 0.4524307427887231, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6178283546704599, 'r': 0.7445714285714286, 'f1': 0.6753044830266908}, 'argument': {'p': 0.6759424651299736, 'r': 0.6580320832252908, 'f1': 0.6668670388326748}, 'combined': 0.4503383009064396, 'epoch': 5}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6815221815221815, 'r': 0.38520818955601566, 'f1': 0.4922104644326866}, 'combined': 0.29448489325032534, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 8
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 17:21:42.348685: step: 2/1533, loss: 0.289215087890625
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2023-01-21 17:42:49.157338: step: 3068/1533, loss: 0.5315219163894653
==================================================
Loss: 0.367
--------------------
Dev Chinese: {'event': {'p': 0.6010752688172043, 'r': 0.7443408788282291, 'f1': 0.6650803093396788}, 'argument': {'p': 0.6544656695270359, 'r': 0.6665674755463967, 'f1': 0.6604611410324135}, 'combined': 0.4392596999846748, 'epoch': 8}
Test Chinese: {'event': {'p': 0.6166119192867199, 'r': 0.7508571428571429, 'f1': 0.6771450657047153}, 'argument': {'p': 0.6484223496203766, 'r': 0.7078113901542143, 'f1': 0.6768165563675492}, 'combined': 0.4583029915315433, 'epoch': 8}
Dev Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6530067450694772, 'r': 0.6643798668994245, 'f1': 0.6586442133722352}, 'combined': 0.44847186058797506, 'epoch': 8}
Test Korean: {'event': {'p': 0.6197249881460408, 'r': 0.7468571428571429, 'f1': 0.6773775589530968}, 'argument': {'p': 0.6498506073614089, 'r': 0.7041724847569446, 'f1': 0.6759218798707075}, 'combined': 0.4578543130298082, 'epoch': 8}
Dev Russian: {'event': {'p': 0.5321805955811719, 'r': 0.7376830892143809, 'f1': 0.6183035714285714}, 'argument': {'p': 0.6560509686632596, 'r': 0.6651494857469107, 'f1': 0.660568898494576}, 'combined': 0.4084321091138338, 'epoch': 8}
Test Russian: {'event': {'p': 0.6040329972502292, 'r': 0.7531428571428571, 'f1': 0.6703967446592065}, 'argument': {'p': 0.6548905528171692, 'r': 0.713187426720744, 'f1': 0.6827969094351743}, 'combined': 0.4577448253487079, 'epoch': 8}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6325860228299253, 'r': 0.6484006734006733, 'f1': 0.64039572681548}, 'combined': 0.46305537169734695, 'epoch': 8}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6235495209206029, 'r': 0.3943033735233224, 'f1': 0.4831104396321788}, 'combined': 0.2972987320813408, 'epoch': 8}
Russian: {'event': {'p': 0.48936170212765956, 'r': 0.6388888888888888, 'f1': 0.5542168674698795}, 'argument': {'p': 0.5303751267684332, 'r': 0.5794839348025475, 'f1': 0.5538430527316383}, 'combined': 0.30694916175488385, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6064516129032258, 'r': 0.7509986684420772, 'f1': 0.6710291493158833}, 'argument': {'p': 0.6587444228770803, 'r': 0.6543527933912332, 'f1': 0.6565412642721403}, 'combined': 0.44055832605530887, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.6198967620835288, 'r': 0.7548571428571429, 'f1': 0.6807523834063385}, 'argument': {'p': 0.673501863075834, 'r': 0.6845202984635778, 'f1': 0.6789663812346644}, 'combined': 0.4622079822782744, 'epoch': 6}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6343475073313782, 'r': 0.6554924242424243, 'f1': 0.6447466467958272}, 'combined': 0.46620142152929034, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6871220875833564, 'r': 0.6432420123240419, 'f1': 0.6644583905416265}, 'combined': 0.4524307427887231, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6178283546704599, 'r': 0.7445714285714286, 'f1': 0.6753044830266908}, 'argument': {'p': 0.6759424651299736, 'r': 0.6580320832252908, 'f1': 0.6668670388326748}, 'combined': 0.4503383009064396, 'epoch': 5}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6815221815221815, 'r': 0.38520818955601566, 'f1': 0.4922104644326866}, 'combined': 0.29448489325032534, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 9
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.315
--------------------
Dev Chinese: {'event': {'p': 0.5956989247311828, 'r': 0.7376830892143809, 'f1': 0.6591314693634741}, 'argument': {'p': 0.6314276821803267, 'r': 0.6898932083081347, 'f1': 0.6593669601529075}, 'combined': 0.4346095132953132, 'epoch': 9}
Test Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6403050964778503, 'r': 0.7131711619714114, 'f1': 0.6747767119233051}, 'combined': 0.4576171877820508, 'epoch': 9}
Dev Korean: {'event': {'p': 0.6064171122994653, 'r': 0.7549933422103862, 'f1': 0.6725978647686833}, 'argument': {'p': 0.6267334069838706, 'r': 0.6817658759570551, 'f1': 0.6530923719630323}, 'combined': 0.4392685348790502, 'epoch': 9}
Test Korean: {'event': {'p': 0.6183025130393551, 'r': 0.7451428571428571, 'f1': 0.6758227520082923}, 'argument': {'p': 0.639159792982824, 'r': 0.7071461530625806, 'f1': 0.6714363702065032}, 'combined': 0.45377197551141757, 'epoch': 9}
Dev Russian: {'event': {'p': 0.5302593659942363, 'r': 0.7350199733688415, 'f1': 0.6160714285714286}, 'argument': {'p': 0.6309496342227828, 'r': 0.6847943513665213, 'f1': 0.6567702383515219}, 'combined': 0.4046173789844198, 'epoch': 9}
Test Russian: {'event': {'p': 0.6054078826764436, 'r': 0.7548571428571429, 'f1': 0.6719226856561547}, 'argument': {'p': 0.646131936823122, 'r': 0.716751055556599, 'f1': 0.679611896745676}, 'combined': 0.4566466508652279, 'epoch': 9}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6727372244613623, 'r': 0.6503126503126502, 'f1': 0.6613348986230341}, 'combined': 0.47819600361973225, 'epoch': 9}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6051598135670895, 'r': 0.42361186949696267, 'f1': 0.49836690529054434}, 'combined': 0.3066873263326426, 'epoch': 9}
Russian: {'event': {'p': 0.48936170212765956, 'r': 0.6388888888888888, 'f1': 0.5542168674698795}, 'argument': {'p': 0.5524526104858514, 'r': 0.5831444221795098, 'f1': 0.567383762120604}, 'combined': 0.3144536512957565, 'epoch': 9}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5956989247311828, 'r': 0.7376830892143809, 'f1': 0.6591314693634741}, 'argument': {'p': 0.6314276821803267, 'r': 0.6898932083081347, 'f1': 0.6593669601529075}, 'combined': 0.4346095132953132, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6403050964778503, 'r': 0.7131711619714114, 'f1': 0.6747767119233051}, 'combined': 0.4576171877820508, 'epoch': 9}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6727372244613623, 'r': 0.6503126503126502, 'f1': 0.6613348986230341}, 'combined': 0.47819600361973225, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6871220875833564, 'r': 0.6432420123240419, 'f1': 0.6644583905416265}, 'combined': 0.4524307427887231, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6178283546704599, 'r': 0.7445714285714286, 'f1': 0.6753044830266908}, 'argument': {'p': 0.6759424651299736, 'r': 0.6580320832252908, 'f1': 0.6668670388326748}, 'combined': 0.4503383009064396, 'epoch': 5}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6815221815221815, 'r': 0.38520818955601566, 'f1': 0.4922104644326866}, 'combined': 0.29448489325032534, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 10
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 18:36:06.870688: step: 3068/1533, loss: 0.429300993680954
==================================================
Loss: 0.272
--------------------
Dev Chinese: {'event': {'p': 0.6075268817204301, 'r': 0.7523302263648469, 'f1': 0.6722189173111244}, 'argument': {'p': 0.6386413652188669, 'r': 0.6670254258952609, 'f1': 0.6525248731584073}, 'combined': 0.43863956375312335, 'epoch': 10}
Test Chinese: {'event': {'p': 0.6180197090567808, 'r': 0.7525714285714286, 'f1': 0.6786910590054109}, 'argument': {'p': 0.6527210921863241, 'r': 0.6916397263030555, 'f1': 0.6716170708683102}, 'combined': 0.4558205010737256, 'epoch': 10}
Dev Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6429548809093955, 'r': 0.6602310832286747, 'f1': 0.6514784676502222}, 'combined': 0.4451383124158102, 'epoch': 10}
Test Korean: {'event': {'p': 0.620199146514936, 'r': 0.7474285714285714, 'f1': 0.6778958279346982}, 'argument': {'p': 0.6507387250291925, 'r': 0.6870028025849989, 'f1': 0.6683792326354057}, 'combined': 0.45309149328173653, 'epoch': 10}
Dev Russian: {'event': {'p': 0.5437079731027857, 'r': 0.7536617842876165, 'f1': 0.6316964285714286}, 'argument': {'p': 0.6408941075849283, 'r': 0.6605563081098128, 'f1': 0.6505766804333336}, 'combined': 0.41096696554159245, 'epoch': 10}
Test Russian: {'event': {'p': 0.6063244729605867, 'r': 0.756, 'f1': 0.67293997965412}, 'argument': {'p': 0.6629103857680427, 'r': 0.6902492258082381, 'f1': 0.6763036328339858}, 'combined': 0.45511175291930983, 'epoch': 10}
Chinese: {'event': {'p': 0.5921052631578947, 'r': 0.8333333333333334, 'f1': 0.6923076923076922}, 'argument': {'p': 0.6871788055998582, 'r': 0.6528198653198652, 'f1': 0.6695588362255028}, 'combined': 0.4635407327715019, 'epoch': 10}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6360930735930735, 'r': 0.3998299319727891, 'f1': 0.4910192147034252}, 'combined': 0.29377217973709197, 'epoch': 10}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.4950200520527199, 'r': 0.5316882040566251, 'f1': 0.5126993396260313}, 'combined': 0.30885502387110325, 'epoch': 10}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5956989247311828, 'r': 0.7376830892143809, 'f1': 0.6591314693634741}, 'argument': {'p': 0.6314276821803267, 'r': 0.6898932083081347, 'f1': 0.6593669601529075}, 'combined': 0.4346095132953132, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6403050964778503, 'r': 0.7131711619714114, 'f1': 0.6747767119233051}, 'combined': 0.4576171877820508, 'epoch': 9}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6727372244613623, 'r': 0.6503126503126502, 'f1': 0.6613348986230341}, 'combined': 0.47819600361973225, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6871220875833564, 'r': 0.6432420123240419, 'f1': 0.6644583905416265}, 'combined': 0.4524307427887231, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6178283546704599, 'r': 0.7445714285714286, 'f1': 0.6753044830266908}, 'argument': {'p': 0.6759424651299736, 'r': 0.6580320832252908, 'f1': 0.6668670388326748}, 'combined': 0.4503383009064396, 'epoch': 5}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6815221815221815, 'r': 0.38520818955601566, 'f1': 0.4922104644326866}, 'combined': 0.29448489325032534, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 11
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 18:41:34.559760: step: 2/1533, loss: 0.02060120739042759
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2023-01-21 19:02:41.496316: step: 3068/1533, loss: 0.22685977816581726
==================================================
Loss: 0.221
--------------------
Dev Chinese: {'event': {'p': 0.6053763440860215, 'r': 0.7496671105193076, 'f1': 0.6698393813206425}, 'argument': {'p': 0.6518105707153332, 'r': 0.6532601123848262, 'f1': 0.6525345365473014}, 'combined': 0.43709333025119657, 'epoch': 11}
Test Chinese: {'event': {'p': 0.6161426560300328, 'r': 0.7502857142857143, 'f1': 0.6766297346044834}, 'argument': {'p': 0.6599061609486649, 'r': 0.6861677326598873, 'f1': 0.6727807683908851}, 'combined': 0.455223472763325, 'epoch': 11}
Dev Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Dev Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6509792346406577, 'r': 0.6528812923021351, 'f1': 0.6519288761208045}, 'combined': 0.408910745959701, 'epoch': 11}
Test Russian: {'event': {'p': 0.6040329972502292, 'r': 0.7531428571428571, 'f1': 0.6703967446592065}, 'argument': {'p': 0.671860802736944, 'r': 0.689691746123634, 'f1': 0.6806595170776898}, 'combined': 0.4563119244701908, 'epoch': 11}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6096988713267782, 'r': 0.6554262866762867, 'f1': 0.631736179928951}, 'combined': 0.46651287133214847, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.535592445241568, 'r': 0.6248578527818294, 'f1': 0.576791864106304}, 'combined': 0.3335663792421999, 'epoch': 11}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5956989247311828, 'r': 0.7376830892143809, 'f1': 0.6591314693634741}, 'argument': {'p': 0.6314276821803267, 'r': 0.6898932083081347, 'f1': 0.6593669601529075}, 'combined': 0.4346095132953132, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6403050964778503, 'r': 0.7131711619714114, 'f1': 0.6747767119233051}, 'combined': 0.4576171877820508, 'epoch': 9}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6727372244613623, 'r': 0.6503126503126502, 'f1': 0.6613348986230341}, 'combined': 0.47819600361973225, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 12
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 19:08:12.471182: step: 2/1533, loss: 0.14835840463638306
2023-01-21 19:08:13.302168: step: 4/1533, loss: 0.17057912051677704
2023-01-21 19:08:14.151130: step: 6/1533, loss: 0.0337742455303669
2023-01-21 19:08:14.942935: step: 8/1533, loss: 0.15440413355827332
2023-01-21 19:08:15.764275: step: 10/1533, loss: 0.4773857891559601
2023-01-21 19:08:16.638043: step: 12/1533, loss: 0.29906803369522095
2023-01-21 19:08:17.485596: step: 14/1533, loss: 0.061883118003606796
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2023-01-21 19:29:19.581388: step: 3068/1533, loss: 0.43131476640701294
==================================================
Loss: 0.207
--------------------
Dev Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6700863609011254, 'r': 0.6532101118117637, 'f1': 0.661540623365252}, 'combined': 0.44233888201222077, 'epoch': 12}
Test Chinese: {'event': {'p': 0.6194274988268419, 'r': 0.7542857142857143, 'f1': 0.6802370523061068}, 'argument': {'p': 0.6736855268351913, 'r': 0.691824980352567, 'f1': 0.6826347712643119}, 'combined': 0.46435346460648896, 'epoch': 12}
Dev Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6690962174745387, 'r': 0.6554808874677893, 'f1': 0.6622185764725977}, 'combined': 0.45090564993507837, 'epoch': 12}
Test Korean: {'event': {'p': 0.622569938359412, 'r': 0.7502857142857143, 'f1': 0.6804871728427053}, 'argument': {'p': 0.6697033129579469, 'r': 0.6853742782990139, 'f1': 0.6774481812030388}, 'combined': 0.46099479757428863, 'epoch': 12}
Dev Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6725828471596386, 'r': 0.6509658674116298, 'f1': 0.6615978266418122}, 'combined': 0.41571381294569226, 'epoch': 12}
Test Russian: {'event': {'p': 0.608615948670944, 'r': 0.7588571428571429, 'f1': 0.6754832146490336}, 'argument': {'p': 0.6795022984376552, 'r': 0.6919957012689911, 'f1': 0.6856920967027756}, 'combined': 0.4631735017402269, 'epoch': 12}
Chinese: {'event': {'p': 0.6052631578947368, 'r': 0.8518518518518519, 'f1': 0.7076923076923076}, 'argument': {'p': 0.6325017415534656, 'r': 0.6114183501683501, 'f1': 0.6217813730525594}, 'combined': 0.4400298947756574, 'epoch': 12}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6450216450216449, 'r': 0.4113181504485852, 'f1': 0.5023177412557944}, 'combined': 0.3005319819479112, 'epoch': 12}
Russian: {'event': {'p': 0.46808510638297873, 'r': 0.6111111111111112, 'f1': 0.5301204819277109}, 'argument': {'p': 0.5064708474482911, 'r': 0.45957539861048635, 'f1': 0.48188488397992746}, 'combined': 0.2554570469291182, 'epoch': 12}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5956989247311828, 'r': 0.7376830892143809, 'f1': 0.6591314693634741}, 'argument': {'p': 0.6314276821803267, 'r': 0.6898932083081347, 'f1': 0.6593669601529075}, 'combined': 0.4346095132953132, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6403050964778503, 'r': 0.7131711619714114, 'f1': 0.6747767119233051}, 'combined': 0.4576171877820508, 'epoch': 9}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6727372244613623, 'r': 0.6503126503126502, 'f1': 0.6613348986230341}, 'combined': 0.47819600361973225, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 13
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 19:55:43.292339: step: 3052/1533, loss: 0.45214372873306274
2023-01-21 19:55:44.097867: step: 3054/1533, loss: 0.09053904563188553
2023-01-21 19:55:44.881833: step: 3056/1533, loss: 0.008733508177101612
2023-01-21 19:55:45.689775: step: 3058/1533, loss: 0.06481976062059402
2023-01-21 19:55:46.516828: step: 3060/1533, loss: 0.18082524836063385
2023-01-21 19:55:47.344857: step: 3062/1533, loss: 0.012996898032724857
2023-01-21 19:55:48.160901: step: 3064/1533, loss: 0.06638330966234207
2023-01-21 19:55:48.965786: step: 3066/1533, loss: 0.27807363867759705
2023-01-21 19:55:49.751692: step: 3068/1533, loss: 0.00396750820800662
==================================================
Loss: 0.177
--------------------
Dev Chinese: {'event': {'p': 0.5989247311827957, 'r': 0.7416777629826897, 'f1': 0.6627007733491969}, 'argument': {'p': 0.6711659390942798, 'r': 0.6334097500704713, 'f1': 0.6517414869346586}, 'combined': 0.43190958741535374, 'epoch': 13}
Test Chinese: {'event': {'p': 0.6147348662599719, 'r': 0.7485714285714286, 'f1': 0.6750837413037878}, 'argument': {'p': 0.6732376404340135, 'r': 0.6679636398447825, 'f1': 0.6705902706733736}, 'combined': 0.45270458880810077, 'epoch': 13}
Dev Korean: {'event': {'p': 0.6106951871657754, 'r': 0.7603195739014648, 'f1': 0.6773428232502966}, 'argument': {'p': 0.6676491699168648, 'r': 0.6332492489704952, 'f1': 0.6499943873976479}, 'combined': 0.4402690334567699, 'epoch': 13}
Test Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6705096277348398, 'r': 0.6612834890663178, 'f1': 0.6658646008714187}, 'combined': 0.45207702883729395, 'epoch': 13}
Dev Russian: {'event': {'p': 0.5437079731027857, 'r': 0.7536617842876165, 'f1': 0.6316964285714286}, 'argument': {'p': 0.6640325113870275, 'r': 0.6286755651206138, 'f1': 0.645870513136277}, 'combined': 0.4079940964677821, 'epoch': 13}
Test Russian: {'event': {'p': 0.6031164069660861, 'r': 0.752, 'f1': 0.669379450661241}, 'argument': {'p': 0.6855185379967357, 'r': 0.6680367663715214, 'f1': 0.6766647597377801}, 'combined': 0.45294548515509586, 'epoch': 13}
Chinese: {'event': {'p': 0.6052631578947368, 'r': 0.8518518518518519, 'f1': 0.7076923076923076}, 'argument': {'p': 0.6623627580149319, 'r': 0.6347643097643098, 'f1': 0.6482699333763163}, 'combined': 0.45877564515862385, 'epoch': 13}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6111816927034318, 'r': 0.3897390504195797, 'f1': 0.4759645040522301}, 'combined': 0.2929012332629108, 'epoch': 13}
Russian: {'event': {'p': 0.48936170212765956, 'r': 0.6388888888888888, 'f1': 0.5542168674698795}, 'argument': {'p': 0.5214374514374515, 'r': 0.531093700538145, 'f1': 0.5262212812671528}, 'combined': 0.29164071009986786, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5956989247311828, 'r': 0.7376830892143809, 'f1': 0.6591314693634741}, 'argument': {'p': 0.6314276821803267, 'r': 0.6898932083081347, 'f1': 0.6593669601529075}, 'combined': 0.4346095132953132, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6403050964778503, 'r': 0.7131711619714114, 'f1': 0.6747767119233051}, 'combined': 0.4576171877820508, 'epoch': 9}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6727372244613623, 'r': 0.6503126503126502, 'f1': 0.6613348986230341}, 'combined': 0.47819600361973225, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 14
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 20:01:13.636845: step: 2/1533, loss: 0.11982876062393188
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2023-01-21 20:22:20.969505: step: 3068/1533, loss: 0.09135694056749344
==================================================
Loss: 0.162
--------------------
Dev Chinese: {'event': {'p': 0.6021505376344086, 'r': 0.7456724367509987, 'f1': 0.6662700773349197}, 'argument': {'p': 0.6634479432442683, 'r': 0.6516620434383005, 'f1': 0.6575021812435728}, 'combined': 0.4380740291450337, 'epoch': 14}
Test Chinese: {'event': {'p': 0.6166119192867199, 'r': 0.7508571428571429, 'f1': 0.6771450657047153}, 'argument': {'p': 0.6688583460737291, 'r': 0.6820669889311606, 'f1': 0.6753980938944094}, 'combined': 0.45734248666696936, 'epoch': 14}
Dev Korean: {'event': {'p': 0.611764705882353, 'r': 0.7616511318242344, 'f1': 0.6785290628707}, 'argument': {'p': 0.664967402473465, 'r': 0.6461202159208014, 'f1': 0.6554083428728512}, 'combined': 0.4447136086871542, 'epoch': 14}
Test Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6680551031839843, 'r': 0.6740342538493143, 'f1': 0.6710313596409383}, 'combined': 0.45558490859270756, 'epoch': 14}
Dev Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.66106525286707, 'r': 0.6388689013109493, 'f1': 0.6497775759435789}, 'combined': 0.4053857867772998, 'epoch': 14}
Test Russian: {'event': {'p': 0.6031164069660861, 'r': 0.752, 'f1': 0.669379450661241}, 'argument': {'p': 0.6792572494172058, 'r': 0.6813380238367956, 'f1': 0.6802960455505541}, 'combined': 0.45537619325764456, 'epoch': 14}
Chinese: {'event': {'p': 0.6052631578947368, 'r': 0.8518518518518519, 'f1': 0.7076923076923076}, 'argument': {'p': 0.6443926443926445, 'r': 0.6282828282828283, 'f1': 0.6362357754762819}, 'combined': 0.4502591641832148, 'epoch': 14}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6219047619047618, 'r': 0.44421768707482995, 'f1': 0.5182539682539683}, 'combined': 0.3189255189255189, 'epoch': 14}
Russian: {'event': {'p': 0.48936170212765956, 'r': 0.6388888888888888, 'f1': 0.5542168674698795}, 'argument': {'p': 0.5506002488289938, 'r': 0.5302076470205125, 'f1': 0.5402115648888242}, 'combined': 0.2993943612636857, 'epoch': 14}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5956989247311828, 'r': 0.7376830892143809, 'f1': 0.6591314693634741}, 'argument': {'p': 0.6314276821803267, 'r': 0.6898932083081347, 'f1': 0.6593669601529075}, 'combined': 0.4346095132953132, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6403050964778503, 'r': 0.7131711619714114, 'f1': 0.6747767119233051}, 'combined': 0.4576171877820508, 'epoch': 9}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6727372244613623, 'r': 0.6503126503126502, 'f1': 0.6613348986230341}, 'combined': 0.47819600361973225, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 15
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 20:48:52.029514: step: 3068/1533, loss: 0.05074726790189743
==================================================
Loss: 0.164
--------------------
Dev Chinese: {'event': {'p': 0.6010752688172043, 'r': 0.7443408788282291, 'f1': 0.6650803093396788}, 'argument': {'p': 0.6563099365355741, 'r': 0.6572793751419783, 'f1': 0.6567942981123531}, 'combined': 0.436820954961101, 'epoch': 15}
Test Chinese: {'event': {'p': 0.6203660253402159, 'r': 0.7554285714285714, 'f1': 0.6812677145065705}, 'argument': {'p': 0.6617031021873252, 'r': 0.6923333342831306, 'f1': 0.6766717685042707}, 'combined': 0.46099462920002365, 'epoch': 15}
Dev Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6561990778919954, 'r': 0.6533418975818707, 'f1': 0.6547673708129583}, 'combined': 0.44738553450565127, 'epoch': 15}
Test Korean: {'event': {'p': 0.6235182550972025, 'r': 0.7514285714285714, 'f1': 0.6815237108059082}, 'argument': {'p': 0.6597513011258145, 'r': 0.6892652960144435, 'f1': 0.674185442685282}, 'combined': 0.45947336467019734, 'epoch': 15}
Dev Russian: {'event': {'p': 0.5417867435158501, 'r': 0.7509986684420772, 'f1': 0.6294642857142857}, 'argument': {'p': 0.6551672717454684, 'r': 0.6489594185875303, 'f1': 0.6520485699759001}, 'combined': 0.4104412873509014, 'epoch': 15}
Test Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6674195326522901, 'r': 0.6946796905550386, 'f1': 0.6807768281690139}, 'combined': 0.46019959544080336, 'epoch': 15}
Chinese: {'event': {'p': 0.6052631578947368, 'r': 0.8518518518518519, 'f1': 0.7076923076923076}, 'argument': {'p': 0.6321604474497037, 'r': 0.6374284511784511, 'f1': 0.6347835198457606}, 'combined': 0.4492314140446921, 'epoch': 15}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6170634920634921, 'r': 0.4231292517006803, 'f1': 0.5020177562550445}, 'combined': 0.3089340038492581, 'epoch': 15}
Russian: {'event': {'p': 0.48936170212765956, 'r': 0.6388888888888888, 'f1': 0.5542168674698795}, 'argument': {'p': 0.5216485890648569, 'r': 0.5313087481216135, 'f1': 0.5264343559370116}, 'combined': 0.29175879967593416, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5956989247311828, 'r': 0.7376830892143809, 'f1': 0.6591314693634741}, 'argument': {'p': 0.6314276821803267, 'r': 0.6898932083081347, 'f1': 0.6593669601529075}, 'combined': 0.4346095132953132, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6175504458000939, 'r': 0.752, 'f1': 0.6781757279051791}, 'argument': {'p': 0.6403050964778503, 'r': 0.7131711619714114, 'f1': 0.6747767119233051}, 'combined': 0.4576171877820508, 'epoch': 9}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6727372244613623, 'r': 0.6503126503126502, 'f1': 0.6613348986230341}, 'combined': 0.47819600361973225, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 16
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 20:54:16.528222: step: 2/1533, loss: 0.1262107491493225
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2023-01-21 21:15:22.346705: step: 3068/1533, loss: 0.08756706863641739
==================================================
Loss: 0.141
--------------------
Dev Chinese: {'event': {'p': 0.5978494623655914, 'r': 0.7403462050599201, 'f1': 0.661511005353956}, 'argument': {'p': 0.6567488151332839, 'r': 0.661117432528849, 'f1': 0.6589258830287533}, 'combined': 0.43588672333609385, 'epoch': 16}
Test Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6577116286274475, 'r': 0.6984548268610061, 'f1': 0.6774712054531647}, 'combined': 0.4601427718563624, 'epoch': 16}
Dev Korean: {'event': {'p': 0.6096256684491979, 'r': 0.758988015978695, 'f1': 0.6761565836298933}, 'argument': {'p': 0.6597383850903354, 'r': 0.6520892443936359, 'f1': 0.655891514040246}, 'combined': 0.44348536536529093, 'epoch': 16}
Test Korean: {'event': {'p': 0.6220957799905168, 'r': 0.7497142857142857, 'f1': 0.6799689038611039}, 'argument': {'p': 0.6577680785568247, 'r': 0.691470775841889, 'f1': 0.67419849661298}, 'combined': 0.4584340127267322, 'epoch': 16}
Dev Russian: {'event': {'p': 0.5379442843419788, 'r': 0.7456724367509987, 'f1': 0.6249999999999999}, 'argument': {'p': 0.6525467089912199, 'r': 0.6506428528016029, 'f1': 0.6515933902052575}, 'combined': 0.4072458688782859, 'epoch': 16}
Test Russian: {'event': {'p': 0.6104491292392301, 'r': 0.7611428571428571, 'f1': 0.6775178026449644}, 'argument': {'p': 0.662501712155675, 'r': 0.7015520686132327, 'f1': 0.6814679203640039}, 'combined': 0.46170664797805355, 'epoch': 16}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6788270637408569, 'r': 0.656199494949495, 'f1': 0.667321520287622}, 'combined': 0.48252479159258815, 'epoch': 16}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6131790160322769, 'r': 0.42046561099356133, 'f1': 0.4988575045686321}, 'combined': 0.30698923358069663, 'epoch': 16}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5301905785458416, 'r': 0.549827266640132, 'f1': 0.5398304072466751}, 'combined': 0.3121910788896434, 'epoch': 16}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5978494623655914, 'r': 0.7403462050599201, 'f1': 0.661511005353956}, 'argument': {'p': 0.6567488151332839, 'r': 0.661117432528849, 'f1': 0.6589258830287533}, 'combined': 0.43588672333609385, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6577116286274475, 'r': 0.6984548268610061, 'f1': 0.6774712054531647}, 'combined': 0.4601427718563624, 'epoch': 16}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6788270637408569, 'r': 0.656199494949495, 'f1': 0.667321520287622}, 'combined': 0.48252479159258815, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 17
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 21:42:00.648385: step: 3068/1533, loss: 0.37377098202705383
==================================================
Loss: 0.114
--------------------
Dev Chinese: {'event': {'p': 0.6010752688172043, 'r': 0.7443408788282291, 'f1': 0.6650803093396788}, 'argument': {'p': 0.6692118996910628, 'r': 0.6429585831228716, 'f1': 0.6558226091347674}, 'combined': 0.43617470375530637, 'epoch': 17}
Test Chinese: {'event': {'p': 0.6208352885969028, 'r': 0.756, 'f1': 0.6817830456068024}, 'argument': {'p': 0.6795778982877142, 'r': 0.6675499708843918, 'f1': 0.6735102384815739}, 'combined': 0.45918786163933123, 'epoch': 17}
Dev Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6642096320348982, 'r': 0.6387186034339934, 'f1': 0.651214759204389}, 'combined': 0.44495812728556117, 'epoch': 17}
Test Korean: {'event': {'p': 0.6235182550972025, 'r': 0.7514285714285714, 'f1': 0.6815237108059082}, 'argument': {'p': 0.6774463873067829, 'r': 0.6629979263459914, 'f1': 0.6701442878608475}, 'combined': 0.45671922183830754, 'epoch': 17}
Dev Russian: {'event': {'p': 0.5417867435158501, 'r': 0.7509986684420772, 'f1': 0.6294642857142857}, 'argument': {'p': 0.6696713307576208, 'r': 0.6389211165901789, 'f1': 0.6539349287293738}, 'combined': 0.4116286828162576, 'epoch': 17}
Test Russian: {'event': {'p': 0.608615948670944, 'r': 0.7588571428571429, 'f1': 0.6754832146490336}, 'argument': {'p': 0.6885363195071657, 'r': 0.673751797309159, 'f1': 0.6810638323187158}, 'combined': 0.46004718683583656, 'epoch': 17}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6501302369946439, 'r': 0.6392947330447332, 'f1': 0.6446669576921679}, 'combined': 0.4661438001774136, 'epoch': 17}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6175432800317167, 'r': 0.41463620230700976, 'f1': 0.4961458831024048}, 'combined': 0.3053205434476337, 'epoch': 17}
Russian: {'event': {'p': 0.46808510638297873, 'r': 0.6111111111111112, 'f1': 0.5301204819277109}, 'argument': {'p': 0.5676953372442094, 'r': 0.5151309541660418, 'f1': 0.5401373111643933}, 'combined': 0.28633785170160614, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5978494623655914, 'r': 0.7403462050599201, 'f1': 0.661511005353956}, 'argument': {'p': 0.6567488151332839, 'r': 0.661117432528849, 'f1': 0.6589258830287533}, 'combined': 0.43588672333609385, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6577116286274475, 'r': 0.6984548268610061, 'f1': 0.6774712054531647}, 'combined': 0.4601427718563624, 'epoch': 16}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6788270637408569, 'r': 0.656199494949495, 'f1': 0.667321520287622}, 'combined': 0.48252479159258815, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 18
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 22:08:27.337483: step: 3064/1533, loss: 0.004524730611592531
2023-01-21 22:08:28.150686: step: 3066/1533, loss: 0.1761685460805893
2023-01-21 22:08:28.949189: step: 3068/1533, loss: 0.014753306284546852
==================================================
Loss: 0.131
--------------------
Dev Chinese: {'event': {'p': 0.603225806451613, 'r': 0.7470039946737683, 'f1': 0.6674598453301607}, 'argument': {'p': 0.6471683390550375, 'r': 0.6524376593582243, 'f1': 0.6497923168396321}, 'combined': 0.4337102792945076, 'epoch': 18}
Test Chinese: {'event': {'p': 0.6208352885969028, 'r': 0.756, 'f1': 0.6817830456068024}, 'argument': {'p': 0.6599635481828144, 'r': 0.694367099416876, 'f1': 0.6767283535741428}, 'combined': 0.4613819179482561, 'epoch': 18}
Dev Korean: {'event': {'p': 0.6128342245989304, 'r': 0.762982689747004, 'f1': 0.6797153024911031}, 'argument': {'p': 0.6435618321540345, 'r': 0.647755935332446, 'f1': 0.6456520726770899}, 'combined': 0.43885959388371587, 'epoch': 18}
Test Korean: {'event': {'p': 0.6249407302038881, 'r': 0.7531428571428571, 'f1': 0.6830785177507126}, 'argument': {'p': 0.658648035067943, 'r': 0.6879465166278619, 'f1': 0.6729785455290267}, 'combined': 0.459697187357998, 'epoch': 18}
Dev Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6498778008713699, 'r': 0.6494041290048457, 'f1': 0.6496408785961707}, 'combined': 0.4082006859929064, 'epoch': 18}
Test Russian: {'event': {'p': 0.6109074243813015, 'r': 0.7617142857142857, 'f1': 0.678026449643947}, 'argument': {'p': 0.6663133634813425, 'r': 0.6930747579302188, 'f1': 0.6794306435765708}, 'combined': 0.46067194704352427, 'epoch': 18}
Chinese: {'event': {'p': 0.5921052631578947, 'r': 0.8333333333333334, 'f1': 0.6923076923076922}, 'argument': {'p': 0.670152855993564, 'r': 0.6310606060606061, 'f1': 0.6500195083886071}, 'combined': 0.45001350580749716, 'epoch': 18}
Korean: {'event': {'p': 0.6296296296296297, 'r': 0.5396825396825397, 'f1': 0.5811965811965812}, 'argument': {'p': 0.6388888888888888, 'r': 0.4791666666666667, 'f1': 0.5476190476190476}, 'combined': 0.3182743182743183, 'epoch': 18}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.533694978658681, 'r': 0.5732279400408055, 'f1': 0.5527555136107767}, 'combined': 0.3329852491631185, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5978494623655914, 'r': 0.7403462050599201, 'f1': 0.661511005353956}, 'argument': {'p': 0.6567488151332839, 'r': 0.661117432528849, 'f1': 0.6589258830287533}, 'combined': 0.43588672333609385, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6577116286274475, 'r': 0.6984548268610061, 'f1': 0.6774712054531647}, 'combined': 0.4601427718563624, 'epoch': 16}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6788270637408569, 'r': 0.656199494949495, 'f1': 0.667321520287622}, 'combined': 0.48252479159258815, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 19
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 22:13:54.487031: step: 2/1533, loss: 0.046989403665065765
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2023-01-21 22:35:01.519200: step: 3068/1533, loss: 0.2293049693107605
==================================================
Loss: 0.110
--------------------
Dev Chinese: {'event': {'p': 0.6021505376344086, 'r': 0.7456724367509987, 'f1': 0.6662700773349197}, 'argument': {'p': 0.6492579311907826, 'r': 0.667039236486163, 'f1': 0.6580284837479417}, 'combined': 0.43842468875532103, 'epoch': 19}
Test Chinese: {'event': {'p': 0.6203660253402159, 'r': 0.7554285714285714, 'f1': 0.6812677145065705}, 'argument': {'p': 0.6481947444747747, 'r': 0.6905402330556378, 'f1': 0.6686977742835319}, 'combined': 0.45556220438177236, 'epoch': 19}
Dev Korean: {'event': {'p': 0.611764705882353, 'r': 0.7616511318242344, 'f1': 0.6785290628707}, 'argument': {'p': 0.6456719598495823, 'r': 0.6620595730437341, 'f1': 0.6537630871659931}, 'combined': 0.44359725487419704, 'epoch': 19}
Test Korean: {'event': {'p': 0.6249407302038881, 'r': 0.7531428571428571, 'f1': 0.6830785177507126}, 'argument': {'p': 0.6486802570271454, 'r': 0.6814486411450115, 'f1': 0.6646608164008659}, 'combined': 0.454015525274082, 'epoch': 19}
Dev Russian: {'event': {'p': 0.5389048991354467, 'r': 0.7470039946737683, 'f1': 0.6261160714285714}, 'argument': {'p': 0.6464391753703754, 'r': 0.6596318116024239, 'f1': 0.6529688640104803}, 'combined': 0.408834299899419, 'epoch': 19}
Test Russian: {'event': {'p': 0.6104491292392301, 'r': 0.7611428571428571, 'f1': 0.6775178026449644}, 'argument': {'p': 0.6583427763432304, 'r': 0.6935350868253165, 'f1': 0.6754808655300795}, 'combined': 0.45765031174265813, 'epoch': 19}
Chinese: {'event': {'p': 0.6052631578947368, 'r': 0.8518518518518519, 'f1': 0.7076923076923076}, 'argument': {'p': 0.649906321277289, 'r': 0.6715698653198653, 'f1': 0.6605605232654412}, 'combined': 0.46747360108015834, 'epoch': 19}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6478174603174603, 'r': 0.43187830687830686, 'f1': 0.5182539682539683}, 'combined': 0.3100664767331434, 'epoch': 19}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5267411957230765, 'r': 0.5950224618353273, 'f1': 0.5588037032888291}, 'combined': 0.33662873692098144, 'epoch': 19}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5978494623655914, 'r': 0.7403462050599201, 'f1': 0.661511005353956}, 'argument': {'p': 0.6567488151332839, 'r': 0.661117432528849, 'f1': 0.6589258830287533}, 'combined': 0.43588672333609385, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6577116286274475, 'r': 0.6984548268610061, 'f1': 0.6774712054531647}, 'combined': 0.4601427718563624, 'epoch': 16}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6788270637408569, 'r': 0.656199494949495, 'f1': 0.667321520287622}, 'combined': 0.48252479159258815, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 20
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 22:40:27.934649: step: 2/1533, loss: 0.00040090468246489763
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2023-01-21 23:01:34.079347: step: 3068/1533, loss: 0.003862471552565694
==================================================
Loss: 0.106
--------------------
Dev Chinese: {'event': {'p': 0.6, 'r': 0.7430093209054593, 'f1': 0.6638905413444378}, 'argument': {'p': 0.6700333230445048, 'r': 0.653654730703417, 'f1': 0.6617426970000846}, 'combined': 0.4393247173421144, 'epoch': 20}
Test Chinese: {'event': {'p': 0.6156733927733459, 'r': 0.7497142857142857, 'f1': 0.6761144035042514}, 'argument': {'p': 0.6746968368227044, 'r': 0.6744671136913382, 'f1': 0.6745819556994587}, 'combined': 0.4560945765924709, 'epoch': 20}
Dev Korean: {'event': {'p': 0.6096256684491979, 'r': 0.758988015978695, 'f1': 0.6761565836298933}, 'argument': {'p': 0.671819187462962, 'r': 0.6508856910420147, 'f1': 0.6611867895752522}, 'combined': 0.4470658007804197, 'epoch': 20}
Test Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6735112600168435, 'r': 0.6702687711446853, 'f1': 0.671886103581091}, 'combined': 0.4565134396448875, 'epoch': 20}
Dev Russian: {'event': {'p': 0.5379442843419788, 'r': 0.7456724367509987, 'f1': 0.6249999999999999}, 'argument': {'p': 0.6734828159647535, 'r': 0.6489389815637057, 'f1': 0.6609831348963654}, 'combined': 0.4131144593102283, 'epoch': 20}
Test Russian: {'event': {'p': 0.6058661778185152, 'r': 0.7554285714285714, 'f1': 0.6724313326551373}, 'argument': {'p': 0.6815106549193253, 'r': 0.6782642618710536, 'f1': 0.6798835830965301}, 'combined': 0.4571750238319495, 'epoch': 20}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6591842065979998, 'r': 0.6372113997113998, 'f1': 0.6480115929268474}, 'combined': 0.478531637853672, 'epoch': 20}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6183538170124664, 'r': 0.42739160881744004, 'f1': 0.505437033036277}, 'combined': 0.3110381741761704, 'epoch': 20}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5202002310554942, 'r': 0.5394669062797718, 'f1': 0.529658417074685}, 'combined': 0.31907133558715967, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5978494623655914, 'r': 0.7403462050599201, 'f1': 0.661511005353956}, 'argument': {'p': 0.6567488151332839, 'r': 0.661117432528849, 'f1': 0.6589258830287533}, 'combined': 0.43588672333609385, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6577116286274475, 'r': 0.6984548268610061, 'f1': 0.6774712054531647}, 'combined': 0.4601427718563624, 'epoch': 16}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6788270637408569, 'r': 0.656199494949495, 'f1': 0.667321520287622}, 'combined': 0.48252479159258815, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5369836695485111, 'r': 0.7443408788282291, 'f1': 0.6238839285714286}, 'argument': {'p': 0.6639299706304141, 'r': 0.6489067377183391, 'f1': 0.6563323962156695}, 'combined': 0.40947523379973133, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6090742438130156, 'r': 0.7594285714285715, 'f1': 0.6759918616480163}, 'argument': {'p': 0.6826200582367344, 'r': 0.6912342949880166, 'f1': 0.686900170447336}, 'combined': 0.46433892498703433, 'epoch': 6}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5827834811387442, 'r': 0.6043680545142534, 'f1': 0.593379544432176}, 'combined': 0.34315925461137886, 'epoch': 6}
******************************
Epoch: 21
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 23:28:02.398066: step: 3066/1533, loss: 0.00023088489251676947
2023-01-21 23:28:03.180269: step: 3068/1533, loss: 0.0009361315751448274
==================================================
Loss: 0.100
--------------------
Dev Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Dev Korean: {'event': {'p': 0.611764705882353, 'r': 0.7616511318242344, 'f1': 0.6785290628707}, 'argument': {'p': 0.6583599689756857, 'r': 0.6469019274561669, 'f1': 0.6525806568851896}, 'combined': 0.4427949415638536, 'epoch': 21}
Test Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6630720751965286, 'r': 0.6655793948346042, 'f1': 0.6643233692138835}, 'combined': 0.4513749349776633, 'epoch': 21}
Dev Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6612016742645831, 'r': 0.6472054344443839, 'f1': 0.6541286943480679}, 'combined': 0.4110205970066543, 'epoch': 21}
Test Russian: {'event': {'p': 0.6081576535288726, 'r': 0.7582857142857143, 'f1': 0.6749745676500508}, 'argument': {'p': 0.672523893661531, 'r': 0.6729817043447587, 'f1': 0.6727527211176935}, 'combined': 0.45409097707181034, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.5996112730806608, 'r': 0.408068783068783, 'f1': 0.4856355765446673}, 'combined': 0.2905511996421086, 'epoch': 21}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.529933333639663, 'r': 0.6280691361655265, 'f1': 0.574842938185397}, 'combined': 0.34629092661770905, 'epoch': 21}
New best chinese model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6149732620320856, 'r': 0.7656458055925432, 'f1': 0.6820877817319099}, 'argument': {'p': 0.6466511554638583, 'r': 0.6475903728283665, 'f1': 0.6471204233560672}, 'combined': 0.4413929340803543, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6603233108502582, 'r': 0.6805326556458817, 'f1': 0.6702756858994832}, 'combined': 0.45541924032869785, 'epoch': 11}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6924603174603176, 'r': 0.47482993197278917, 'f1': 0.563357546408394}, 'combined': 0.3370515234922015, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6612016742645831, 'r': 0.6472054344443839, 'f1': 0.6541286943480679}, 'combined': 0.4110205970066543, 'epoch': 21}
Test for Russian: {'event': {'p': 0.6081576535288726, 'r': 0.7582857142857143, 'f1': 0.6749745676500508}, 'argument': {'p': 0.672523893661531, 'r': 0.6729817043447587, 'f1': 0.6727527211176935}, 'combined': 0.45409097707181034, 'epoch': 21}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.529933333639663, 'r': 0.6280691361655265, 'f1': 0.574842938185397}, 'combined': 0.34629092661770905, 'epoch': 21}
******************************
Epoch: 22
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 23:33:45.228503: step: 2/1533, loss: 0.00390715291723609
2023-01-21 23:33:46.062902: step: 4/1533, loss: 0.17578601837158203
2023-01-21 23:33:46.907039: step: 6/1533, loss: 0.0007702896255068481
2023-01-21 23:33:47.786297: step: 8/1533, loss: 0.2770765721797943
2023-01-21 23:33:48.583241: step: 10/1533, loss: 0.0019606053829193115
2023-01-21 23:33:49.412230: step: 12/1533, loss: 0.0009340230608358979
2023-01-21 23:33:50.270123: step: 14/1533, loss: 0.0025879524182528257
2023-01-21 23:33:51.098907: step: 16/1533, loss: 0.0017531878547742963
2023-01-21 23:33:51.889615: step: 18/1533, loss: 0.06865618377923965
2023-01-21 23:33:52.678060: step: 20/1533, loss: 0.000806847121566534
2023-01-21 23:33:53.461271: step: 22/1533, loss: 0.0031326792668551207
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2023-01-21 23:33:57.554459: step: 32/1533, loss: 0.2928197383880615
2023-01-21 23:33:58.375448: step: 34/1533, loss: 0.0005177968414500356
2023-01-21 23:33:59.212893: step: 36/1533, loss: 0.05665164813399315
2023-01-21 23:34:00.047980: step: 38/1533, loss: 0.02290467359125614
2023-01-21 23:34:00.860318: step: 40/1533, loss: 0.003497519064694643
2023-01-21 23:34:01.704679: step: 42/1533, loss: 0.05086753889918327
2023-01-21 23:34:02.532807: step: 44/1533, loss: 0.00018806717707775533
2023-01-21 23:34:03.347342: step: 46/1533, loss: 0.02204699069261551
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2023-01-21 23:54:51.820580: step: 3068/1533, loss: 0.0005582728772424161
==================================================
Loss: 0.083
--------------------
Dev Chinese: {'event': {'p': 0.603225806451613, 'r': 0.7470039946737683, 'f1': 0.6674598453301607}, 'argument': {'p': 0.6806869576222285, 'r': 0.6368529344444833, 'f1': 0.6580407758581238}, 'combined': 0.43921579447520226, 'epoch': 22}
Test Chinese: {'event': {'p': 0.6194274988268419, 'r': 0.7542857142857143, 'f1': 0.6802370523061068}, 'argument': {'p': 0.6776710240950952, 'r': 0.6711927759985903, 'f1': 0.6744163433618768}, 'combined': 0.4587629854355463, 'epoch': 22}
Dev Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6825195717298118, 'r': 0.6329538802461653, 'f1': 0.6568029337595853}, 'combined': 0.44721813046026326, 'epoch': 22}
Test Korean: {'event': {'p': 0.620199146514936, 'r': 0.7474285714285714, 'f1': 0.6778958279346982}, 'argument': {'p': 0.6821697905229096, 'r': 0.6673706525053672, 'f1': 0.6746890774153477}, 'combined': 0.45736891073297475, 'epoch': 22}
Dev Russian: {'event': {'p': 0.5437079731027857, 'r': 0.7536617842876165, 'f1': 0.6316964285714286}, 'argument': {'p': 0.6860950451493055, 'r': 0.632008720422207, 'f1': 0.6579422089501699}, 'combined': 0.4156197436002189, 'epoch': 22}
Test Russian: {'event': {'p': 0.6054078826764436, 'r': 0.7548571428571429, 'f1': 0.6719226856561547}, 'argument': {'p': 0.6895538680603477, 'r': 0.6749526591055179, 'f1': 0.6821751418229053}, 'combined': 0.45836895338151473, 'epoch': 22}
Chinese: {'event': {'p': 0.6052631578947368, 'r': 0.8518518518518519, 'f1': 0.7076923076923076}, 'argument': {'p': 0.6475943646996278, 'r': 0.6152146464646465, 'f1': 0.6309893809893811}, 'combined': 0.44654633116171577, 'epoch': 22}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.7090450310559007, 'r': 0.4379395780051151, 'f1': 0.5414525691699605}, 'combined': 0.333201581027668, 'epoch': 22}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5427266633148986, 'r': 0.5125751820196265, 'f1': 0.5272201872201873}, 'combined': 0.3176025224217996, 'epoch': 22}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6825195717298118, 'r': 0.6329538802461653, 'f1': 0.6568029337595853}, 'combined': 0.44721813046026326, 'epoch': 22}
Test for Korean: {'event': {'p': 0.620199146514936, 'r': 0.7474285714285714, 'f1': 0.6778958279346982}, 'argument': {'p': 0.6821697905229096, 'r': 0.6673706525053672, 'f1': 0.6746890774153477}, 'combined': 0.45736891073297475, 'epoch': 22}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.7090450310559007, 'r': 0.4379395780051151, 'f1': 0.5414525691699605}, 'combined': 0.333201581027668, 'epoch': 22}
--------------------
Dev for Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6612016742645831, 'r': 0.6472054344443839, 'f1': 0.6541286943480679}, 'combined': 0.4110205970066543, 'epoch': 21}
Test for Russian: {'event': {'p': 0.6081576535288726, 'r': 0.7582857142857143, 'f1': 0.6749745676500508}, 'argument': {'p': 0.672523893661531, 'r': 0.6729817043447587, 'f1': 0.6727527211176935}, 'combined': 0.45409097707181034, 'epoch': 21}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.529933333639663, 'r': 0.6280691361655265, 'f1': 0.574842938185397}, 'combined': 0.34629092661770905, 'epoch': 21}
******************************
Epoch: 23
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 00:21:16.527520: step: 3042/1533, loss: 0.004135443828999996
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2023-01-22 00:21:18.199988: step: 3046/1533, loss: 0.0023168923798948526
2023-01-22 00:21:19.041306: step: 3048/1533, loss: 0.08767040073871613
2023-01-22 00:21:19.916165: step: 3050/1533, loss: 0.015178813599050045
2023-01-22 00:21:20.733496: step: 3052/1533, loss: 0.0024117506109178066
2023-01-22 00:21:21.578546: step: 3054/1533, loss: 0.025252671912312508
2023-01-22 00:21:22.432569: step: 3056/1533, loss: 0.06462466716766357
2023-01-22 00:21:23.253439: step: 3058/1533, loss: 0.0014498032396659255
2023-01-22 00:21:24.079717: step: 3060/1533, loss: 0.008508885279297829
2023-01-22 00:21:24.876399: step: 3062/1533, loss: 0.0009257936617359519
2023-01-22 00:21:25.690497: step: 3064/1533, loss: 0.18340222537517548
2023-01-22 00:21:26.542049: step: 3066/1533, loss: 0.005096838343888521
2023-01-22 00:21:27.348865: step: 3068/1533, loss: 0.09196627140045166
==================================================
Loss: 0.104
--------------------
Dev Chinese: {'event': {'p': 0.603225806451613, 'r': 0.7470039946737683, 'f1': 0.6674598453301607}, 'argument': {'p': 0.6290226074202311, 'r': 0.6750826947978958, 'f1': 0.6512392460697505}, 'combined': 0.4346760464546461, 'epoch': 23}
Test Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6420135986677074, 'r': 0.7132270839231251, 'f1': 0.6757493229045984}, 'combined': 0.4589732582263647, 'epoch': 23}
Dev Korean: {'event': {'p': 0.6106951871657754, 'r': 0.7603195739014648, 'f1': 0.6773428232502966}, 'argument': {'p': 0.6295403633987168, 'r': 0.6692287776129837, 'f1': 0.6487781616479925}, 'combined': 0.43944523167378857, 'epoch': 23}
Test Korean: {'event': {'p': 0.6178283546704599, 'r': 0.7445714285714286, 'f1': 0.6753044830266908}, 'argument': {'p': 0.6443928830346869, 'r': 0.7051094064017792, 'f1': 0.6733852721892853}, 'combined': 0.4547400931135728, 'epoch': 23}
Dev Russian: {'event': {'p': 0.5360230547550432, 'r': 0.7430093209054593, 'f1': 0.622767857142857}, 'argument': {'p': 0.6287646307160675, 'r': 0.6704684072431536, 'f1': 0.6489471991094227}, 'combined': 0.4041434565882341, 'epoch': 23}
Test Russian: {'event': {'p': 0.6067827681026581, 'r': 0.7565714285714286, 'f1': 0.6734486266531028}, 'argument': {'p': 0.651442264986535, 'r': 0.7135689115111364, 'f1': 0.681091782898796}, 'combined': 0.4586803258179074, 'epoch': 23}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.616931406301485, 'r': 0.6529190716690716, 'f1': 0.6344152923100291}, 'combined': 0.4684912927827908, 'epoch': 23}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6286790078350181, 'r': 0.4580375628512275, 'f1': 0.5299608165220814}, 'combined': 0.32612973324435773, 'epoch': 23}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5225120413565664, 'r': 0.6095973815826609, 'f1': 0.5627052753070715}, 'combined': 0.33897908151028405, 'epoch': 23}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6825195717298118, 'r': 0.6329538802461653, 'f1': 0.6568029337595853}, 'combined': 0.44721813046026326, 'epoch': 22}
Test for Korean: {'event': {'p': 0.620199146514936, 'r': 0.7474285714285714, 'f1': 0.6778958279346982}, 'argument': {'p': 0.6821697905229096, 'r': 0.6673706525053672, 'f1': 0.6746890774153477}, 'combined': 0.45736891073297475, 'epoch': 22}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.7090450310559007, 'r': 0.4379395780051151, 'f1': 0.5414525691699605}, 'combined': 0.333201581027668, 'epoch': 22}
--------------------
Dev for Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6612016742645831, 'r': 0.6472054344443839, 'f1': 0.6541286943480679}, 'combined': 0.4110205970066543, 'epoch': 21}
Test for Russian: {'event': {'p': 0.6081576535288726, 'r': 0.7582857142857143, 'f1': 0.6749745676500508}, 'argument': {'p': 0.672523893661531, 'r': 0.6729817043447587, 'f1': 0.6727527211176935}, 'combined': 0.45409097707181034, 'epoch': 21}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.529933333639663, 'r': 0.6280691361655265, 'f1': 0.574842938185397}, 'combined': 0.34629092661770905, 'epoch': 21}
******************************
Epoch: 24
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 00:26:56.328303: step: 2/1533, loss: 0.009389325976371765
2023-01-22 00:26:57.151365: step: 4/1533, loss: 0.0004578688822221011
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2023-01-22 00:26:59.634263: step: 10/1533, loss: 0.01938479393720627
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2023-01-22 00:27:01.347043: step: 14/1533, loss: 0.0011046979343518615
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2023-01-22 00:48:01.231224: step: 3068/1533, loss: 0.0008088054601103067
==================================================
Loss: 0.085
--------------------
Dev Chinese: {'event': {'p': 0.6021505376344086, 'r': 0.7456724367509987, 'f1': 0.6662700773349197}, 'argument': {'p': 0.6729393539831982, 'r': 0.6390682392009203, 'f1': 0.6555665840246342}, 'combined': 0.43678439863628216, 'epoch': 24}
Test Chinese: {'event': {'p': 0.6189582355701548, 'r': 0.7537142857142857, 'f1': 0.6797217212058747}, 'argument': {'p': 0.6811339534780846, 'r': 0.6681422976896743, 'f1': 0.6745755797857235}, 'combined': 0.4585236741754028, 'epoch': 24}
Dev Korean: {'event': {'p': 0.6085561497326203, 'r': 0.7576564580559254, 'f1': 0.6749703440094899}, 'argument': {'p': 0.6748064980421882, 'r': 0.6302761199987951, 'f1': 0.6517816044082028}, 'combined': 0.43993325374646186, 'epoch': 24}
Test Korean: {'event': {'p': 0.620199146514936, 'r': 0.7474285714285714, 'f1': 0.6778958279346982}, 'argument': {'p': 0.683748840389412, 'r': 0.6637493044523507, 'f1': 0.6736006560986664}, 'combined': 0.4566310744633613, 'epoch': 24}
Dev Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6690726675964788, 'r': 0.6353994261200695, 'f1': 0.6518014315081296}, 'combined': 0.4095582655570056, 'epoch': 24}
Test Russian: {'event': {'p': 0.611365719523373, 'r': 0.7622857142857142, 'f1': 0.6785350966429298}, 'argument': {'p': 0.6913378351464231, 'r': 0.6682539823885528, 'f1': 0.6795999439744984}, 'combined': 0.46113241366326596, 'epoch': 24}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.647599851728292, 'r': 0.5882365319865319, 'f1': 0.6164924352697279}, 'combined': 0.4552559521991837, 'epoch': 24}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6269047619047619, 'r': 0.4477891156462585, 'f1': 0.522420634920635}, 'combined': 0.3125593542260209, 'epoch': 24}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.562687462446049, 'r': 0.5522673242526035, 'f1': 0.5574287011147775}, 'combined': 0.3358004223582997, 'epoch': 24}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6825195717298118, 'r': 0.6329538802461653, 'f1': 0.6568029337595853}, 'combined': 0.44721813046026326, 'epoch': 22}
Test for Korean: {'event': {'p': 0.620199146514936, 'r': 0.7474285714285714, 'f1': 0.6778958279346982}, 'argument': {'p': 0.6821697905229096, 'r': 0.6673706525053672, 'f1': 0.6746890774153477}, 'combined': 0.45736891073297475, 'epoch': 22}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.7090450310559007, 'r': 0.4379395780051151, 'f1': 0.5414525691699605}, 'combined': 0.333201581027668, 'epoch': 22}
--------------------
Dev for Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6612016742645831, 'r': 0.6472054344443839, 'f1': 0.6541286943480679}, 'combined': 0.4110205970066543, 'epoch': 21}
Test for Russian: {'event': {'p': 0.6081576535288726, 'r': 0.7582857142857143, 'f1': 0.6749745676500508}, 'argument': {'p': 0.672523893661531, 'r': 0.6729817043447587, 'f1': 0.6727527211176935}, 'combined': 0.45409097707181034, 'epoch': 21}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.529933333639663, 'r': 0.6280691361655265, 'f1': 0.574842938185397}, 'combined': 0.34629092661770905, 'epoch': 21}
******************************
Epoch: 25
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 01:14:28.014461: step: 3068/1533, loss: 0.06393463909626007
==================================================
Loss: 0.080
--------------------
Dev Chinese: {'event': {'p': 0.5978494623655914, 'r': 0.7403462050599201, 'f1': 0.661511005353956}, 'argument': {'p': 0.6793886433857529, 'r': 0.6436842809280264, 'f1': 0.661054704320611}, 'combined': 0.4372949620490895, 'epoch': 25}
Test Chinese: {'event': {'p': 0.6217738151102769, 'r': 0.7571428571428571, 'f1': 0.6828137078072662}, 'argument': {'p': 0.6638372308523515, 'r': 0.6907801471113658, 'f1': 0.677040745890197}, 'combined': 0.4622927020378826, 'epoch': 25}
Dev Korean: {'event': {'p': 0.6, 'r': 0.7470039946737683, 'f1': 0.6654804270462634}, 'argument': {'p': 0.6794213871505754, 'r': 0.6449079640006913, 'f1': 0.6617149474258768}, 'combined': 0.44035834579586824, 'epoch': 25}
Test Korean: {'event': {'p': 0.622569938359412, 'r': 0.7502857142857143, 'f1': 0.6804871728427053}, 'argument': {'p': 0.6658170209766436, 'r': 0.6809336096881877, 'f1': 0.6732904773345447}, 'combined': 0.4581655334232999, 'epoch': 25}
Dev Russian: {'event': {'p': 0.5292987512007685, 'r': 0.7336884154460719, 'f1': 0.6149553571428571}, 'argument': {'p': 0.6802092681469034, 'r': 0.6429443338072265, 'f1': 0.6610520412933482}, 'combined': 0.40651749414356564, 'epoch': 25}
Test Russian: {'event': {'p': 0.608615948670944, 'r': 0.7588571428571429, 'f1': 0.6754832146490336}, 'argument': {'p': 0.6757093056617579, 'r': 0.6883889412166527, 'f1': 0.6819901932418486}, 'combined': 0.46067292809011956, 'epoch': 25}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6511396163035507, 'r': 0.6619919432419432, 'f1': 0.6565209354465553}, 'combined': 0.48481546002207165, 'epoch': 25}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6123809523809524, 'r': 0.4252645502645503, 'f1': 0.5019516003122559}, 'combined': 0.30031292326374287, 'epoch': 25}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5403081827213807, 'r': 0.6103481323334116, 'f1': 0.57319650688703}, 'combined': 0.3452991005343555, 'epoch': 25}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6825195717298118, 'r': 0.6329538802461653, 'f1': 0.6568029337595853}, 'combined': 0.44721813046026326, 'epoch': 22}
Test for Korean: {'event': {'p': 0.620199146514936, 'r': 0.7474285714285714, 'f1': 0.6778958279346982}, 'argument': {'p': 0.6821697905229096, 'r': 0.6673706525053672, 'f1': 0.6746890774153477}, 'combined': 0.45736891073297475, 'epoch': 22}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.7090450310559007, 'r': 0.4379395780051151, 'f1': 0.5414525691699605}, 'combined': 0.333201581027668, 'epoch': 22}
--------------------
Dev for Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6612016742645831, 'r': 0.6472054344443839, 'f1': 0.6541286943480679}, 'combined': 0.4110205970066543, 'epoch': 21}
Test for Russian: {'event': {'p': 0.6081576535288726, 'r': 0.7582857142857143, 'f1': 0.6749745676500508}, 'argument': {'p': 0.672523893661531, 'r': 0.6729817043447587, 'f1': 0.6727527211176935}, 'combined': 0.45409097707181034, 'epoch': 21}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.529933333639663, 'r': 0.6280691361655265, 'f1': 0.574842938185397}, 'combined': 0.34629092661770905, 'epoch': 21}
******************************
Epoch: 26
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 01:19:51.912817: step: 2/1533, loss: 0.2470976710319519
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2023-01-22 01:40:58.224851: step: 3068/1533, loss: 0.0008936162339523435
==================================================
Loss: 0.071
--------------------
Dev Chinese: {'event': {'p': 0.6021505376344086, 'r': 0.7456724367509987, 'f1': 0.6662700773349197}, 'argument': {'p': 0.6558033983013307, 'r': 0.649969713512883, 'f1': 0.6528735245933576}, 'combined': 0.43499009372073794, 'epoch': 26}
Test Chinese: {'event': {'p': 0.6194274988268419, 'r': 0.7542857142857143, 'f1': 0.6802370523061068}, 'argument': {'p': 0.6679655694432808, 'r': 0.6936740535533252, 'f1': 0.6805771165355264}, 'combined': 0.4629537716191162, 'epoch': 26}
Dev Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Dev Russian: {'event': {'p': 0.5437079731027857, 'r': 0.7536617842876165, 'f1': 0.6316964285714286}, 'argument': {'p': 0.64809335596597, 'r': 0.6471472342784284, 'f1': 0.6476199495700853}, 'combined': 0.40909920921503157, 'epoch': 26}
Test Russian: {'event': {'p': 0.6081576535288726, 'r': 0.7582857142857143, 'f1': 0.6749745676500508}, 'argument': {'p': 0.6755733614946742, 'r': 0.6970018480632296, 'f1': 0.6861203352430383}, 'combined': 0.4631137766365777, 'epoch': 26}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6627401524095738, 'r': 0.668262987012987, 'f1': 0.6654901115482028}, 'combined': 0.48120054219639274, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5281243610190978, 'r': 0.5672446840575495, 'f1': 0.5469859453412085}, 'combined': 0.31632922140214464, 'epoch': 26}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6612016742645831, 'r': 0.6472054344443839, 'f1': 0.6541286943480679}, 'combined': 0.4110205970066543, 'epoch': 21}
Test for Russian: {'event': {'p': 0.6081576535288726, 'r': 0.7582857142857143, 'f1': 0.6749745676500508}, 'argument': {'p': 0.672523893661531, 'r': 0.6729817043447587, 'f1': 0.6727527211176935}, 'combined': 0.45409097707181034, 'epoch': 21}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.529933333639663, 'r': 0.6280691361655265, 'f1': 0.574842938185397}, 'combined': 0.34629092661770905, 'epoch': 21}
******************************
Epoch: 27
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.079
--------------------
Dev Chinese: {'event': {'p': 0.5967741935483871, 'r': 0.7390146471371505, 'f1': 0.6603212373587151}, 'argument': {'p': 0.6570676537027399, 'r': 0.6478200348728496, 'f1': 0.6524110757764617}, 'combined': 0.43080088882324363, 'epoch': 27}
Test Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.674086598268944, 'r': 0.6901472047627581, 'f1': 0.6820223638052417}, 'combined': 0.4632339476914757, 'epoch': 27}
Dev Korean: {'event': {'p': 0.6064171122994653, 'r': 0.7549933422103862, 'f1': 0.6725978647686833}, 'argument': {'p': 0.6572656766000843, 'r': 0.6410604314917427, 'f1': 0.6490619199905384}, 'combined': 0.4365576614882981, 'epoch': 27}
Test Korean: {'event': {'p': 0.6230440967283073, 'r': 0.7508571428571429, 'f1': 0.6810054418243068}, 'argument': {'p': 0.6719924335733339, 'r': 0.6842398885181025, 'f1': 0.678060860729474}, 'combined': 0.4617631360448452, 'epoch': 27}
Dev Russian: {'event': {'p': 0.5379442843419788, 'r': 0.7456724367509987, 'f1': 0.6249999999999999}, 'argument': {'p': 0.6555176352148946, 'r': 0.6454768836908152, 'f1': 0.6504585134436661}, 'combined': 0.4065365709022912, 'epoch': 27}
Test Russian: {'event': {'p': 0.608615948670944, 'r': 0.7588571428571429, 'f1': 0.6754832146490336}, 'argument': {'p': 0.6799196747742511, 'r': 0.6940508843692185, 'f1': 0.6869126102254783}, 'combined': 0.4639979381380647, 'epoch': 27}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6322843822843822, 'r': 0.6164772727272727, 'f1': 0.6242807825086305}, 'combined': 0.4610073470832964, 'epoch': 27}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6129785247432307, 'r': 0.4341931216931217, 'f1': 0.5083236546651181}, 'combined': 0.30412526347485697, 'epoch': 27}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5456928418929327, 'r': 0.5659036878889672, 'f1': 0.5556145299273496}, 'combined': 0.32131924622304553, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6612016742645831, 'r': 0.6472054344443839, 'f1': 0.6541286943480679}, 'combined': 0.4110205970066543, 'epoch': 21}
Test for Russian: {'event': {'p': 0.6081576535288726, 'r': 0.7582857142857143, 'f1': 0.6749745676500508}, 'argument': {'p': 0.672523893661531, 'r': 0.6729817043447587, 'f1': 0.6727527211176935}, 'combined': 0.45409097707181034, 'epoch': 21}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.529933333639663, 'r': 0.6280691361655265, 'f1': 0.574842938185397}, 'combined': 0.34629092661770905, 'epoch': 21}
******************************
Epoch: 28
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 02:34:01.917136: step: 3068/1533, loss: 0.0002052343770628795
==================================================
Loss: 0.069
--------------------
Dev Chinese: {'event': {'p': 0.603225806451613, 'r': 0.7470039946737683, 'f1': 0.6674598453301607}, 'argument': {'p': 0.6750738139901993, 'r': 0.6480708614305913, 'f1': 0.6612967973781545}, 'combined': 0.4413890580953536, 'epoch': 28}
Test Chinese: {'event': {'p': 0.6203660253402159, 'r': 0.7554285714285714, 'f1': 0.6812677145065705}, 'argument': {'p': 0.6819980084846061, 'r': 0.6845549199001926, 'f1': 0.6832740721167426}, 'combined': 0.4654925654925709, 'epoch': 28}
Dev Korean: {'event': {'p': 0.6096256684491979, 'r': 0.758988015978695, 'f1': 0.6761565836298933}, 'argument': {'p': 0.6813704012298404, 'r': 0.6487358246832733, 'f1': 0.6646527631327513}, 'combined': 0.44940934162000984, 'epoch': 28}
Test Korean: {'event': {'p': 0.6244665718349929, 'r': 0.7525714285714286, 'f1': 0.6825602487691113}, 'argument': {'p': 0.6767745607760635, 'r': 0.6746800040198789, 'f1': 0.6757256592697806}, 'combined': 0.4612234740908531, 'epoch': 28}
Dev Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6224051289840763, 'r': 0.5912848725348725, 'f1': 0.6064460231126898}, 'combined': 0.44783706322167866, 'epoch': 28}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6550522648083624, 'r': 0.3782696177062374, 'f1': 0.4795918367346939}, 'combined': 0.28693528693528697, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 29
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 03:00:37.059977: step: 3068/1533, loss: 0.00031555836903862655
==================================================
Loss: 0.069
--------------------
Dev Chinese: {'event': {'p': 0.5978494623655914, 'r': 0.7403462050599201, 'f1': 0.661511005353956}, 'argument': {'p': 0.6522712117383678, 'r': 0.6575703131631255, 'f1': 0.654910043415132}, 'combined': 0.43323020123594697, 'epoch': 29}
Test Chinese: {'event': {'p': 0.6170811825434068, 'r': 0.7514285714285714, 'f1': 0.6776603968049472}, 'argument': {'p': 0.6596077516784656, 'r': 0.6941939259237784, 'f1': 0.6764590446045579}, 'combined': 0.45840950458902013, 'epoch': 29}
Dev Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.647710878057186, 'r': 0.6566351033531153, 'f1': 0.652142461306407}, 'combined': 0.44559200203142396, 'epoch': 29}
Test Korean: {'event': {'p': 0.6187766714082503, 'r': 0.7457142857142857, 'f1': 0.6763410209898938}, 'argument': {'p': 0.6614234702336402, 'r': 0.6861984387063093, 'f1': 0.6735832203188935}, 'combined': 0.45557196295214103, 'epoch': 29}
Dev Russian: {'event': {'p': 0.5417867435158501, 'r': 0.7509986684420772, 'f1': 0.6294642857142857}, 'argument': {'p': 0.6518621096267452, 'r': 0.6537625822495636, 'f1': 0.6528109627703067}, 'combined': 0.41092118638666625, 'epoch': 29}
Test Russian: {'event': {'p': 0.6049495875343721, 'r': 0.7542857142857143, 'f1': 0.671414038657172}, 'argument': {'p': 0.671200893090649, 'r': 0.695417126810053, 'f1': 0.6830944562246526}, 'combined': 0.4586392076381188, 'epoch': 29}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6222643097643098, 'r': 0.6222643097643098, 'f1': 0.6222643097643098}, 'combined': 0.4499449624449624, 'epoch': 29}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.6138593847623948, 'r': 0.4433428889950629, 'f1': 0.5148498065749116}, 'combined': 0.3080297988055027, 'epoch': 29}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5583409696066266, 'r': 0.6307185027037819, 'f1': 0.5923269416696387}, 'combined': 0.3425505204836465, 'epoch': 29}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 30
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 03:27:08.103775: step: 3068/1533, loss: 0.00016238633543252945
==================================================
Loss: 0.067
--------------------
Dev Chinese: {'event': {'p': 0.5978494623655914, 'r': 0.7403462050599201, 'f1': 0.661511005353956}, 'argument': {'p': 0.683717075581211, 'r': 0.643766151786155, 'f1': 0.6631404474018145}, 'combined': 0.43867470405164655, 'epoch': 30}
Test Chinese: {'event': {'p': 0.6189582355701548, 'r': 0.7537142857142857, 'f1': 0.6797217212058747}, 'argument': {'p': 0.6715616037999732, 'r': 0.6825408125814442, 'f1': 0.6770066978848378}, 'combined': 0.4601761579541876, 'epoch': 30}
Dev Korean: {'event': {'p': 0.6096256684491979, 'r': 0.758988015978695, 'f1': 0.6761565836298933}, 'argument': {'p': 0.6807095239782841, 'r': 0.6397088777589826, 'f1': 0.6595726401435674}, 'combined': 0.4459743830152236, 'epoch': 30}
Test Korean: {'event': {'p': 0.622569938359412, 'r': 0.7502857142857143, 'f1': 0.6804871728427053}, 'argument': {'p': 0.6720538233750704, 'r': 0.676913716725923, 'f1': 0.6744750157392626}, 'combined': 0.45897159661345, 'epoch': 30}
Dev Russian: {'event': {'p': 0.5341018251681076, 'r': 0.7403462050599201, 'f1': 0.6205357142857142}, 'argument': {'p': 0.6797888546893911, 'r': 0.6376121739239908, 'f1': 0.6580253696993352}, 'combined': 0.40832824280449814, 'epoch': 30}
Test Russian: {'event': {'p': 0.611365719523373, 'r': 0.7622857142857142, 'f1': 0.6785350966429298}, 'argument': {'p': 0.6783280922499612, 'r': 0.6820284841613145, 'f1': 0.6801732553706683}, 'combined': 0.4615214255668726, 'epoch': 30}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6674285963079067, 'r': 0.6451809764309765, 'f1': 0.6561162472179423}, 'combined': 0.47442251721912737, 'epoch': 30}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5804168657429527, 'r': 0.44384819145049326, 'f1': 0.503027950310559}, 'combined': 0.30955566172957477, 'epoch': 30}
Russian: {'event': {'p': 0.48936170212765956, 'r': 0.6388888888888888, 'f1': 0.5542168674698795}, 'argument': {'p': 0.5521679912030789, 'r': 0.5521679912030789, 'f1': 0.5521679912030789}, 'combined': 0.30602081440170636, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 31
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 03:53:23.718085: step: 3042/1533, loss: 0.04743805155158043
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2023-01-22 03:53:25.349569: step: 3046/1533, loss: 0.0001806908694561571
2023-01-22 03:53:26.177975: step: 3048/1533, loss: 0.0006082704057916999
2023-01-22 03:53:27.004116: step: 3050/1533, loss: 0.0003991306002717465
2023-01-22 03:53:27.803981: step: 3052/1533, loss: 0.00026149858604185283
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2023-01-22 03:53:29.436502: step: 3056/1533, loss: 0.00011849332804558799
2023-01-22 03:53:30.279025: step: 3058/1533, loss: 0.0231695007532835
2023-01-22 03:53:31.115686: step: 3060/1533, loss: 0.05364210158586502
2023-01-22 03:53:31.951989: step: 3062/1533, loss: 0.00029243959579616785
2023-01-22 03:53:32.780481: step: 3064/1533, loss: 0.00045437813969329
2023-01-22 03:53:33.609711: step: 3066/1533, loss: 0.05333754047751427
2023-01-22 03:53:34.376917: step: 3068/1533, loss: 6.949716043891385e-05
==================================================
Loss: 0.058
--------------------
Dev Chinese: {'event': {'p': 0.6053763440860215, 'r': 0.7496671105193076, 'f1': 0.6698393813206425}, 'argument': {'p': 0.6565650114167862, 'r': 0.6638655860433628, 'f1': 0.6601951165296693}, 'combined': 0.4422246884071432, 'epoch': 31}
Test Chinese: {'event': {'p': 0.6180197090567808, 'r': 0.7525714285714286, 'f1': 0.6786910590054109}, 'argument': {'p': 0.6605902656320337, 'r': 0.6925506805280299, 'f1': 0.6761930297238083}, 'combined': 0.4589261634353288, 'epoch': 31}
Dev Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6514825176375191, 'r': 0.6595196749668644, 'f1': 0.6554764602267142}, 'combined': 0.44631493258616123, 'epoch': 31}
Test Korean: {'event': {'p': 0.6192508297771455, 'r': 0.7462857142857143, 'f1': 0.6768592899714952}, 'argument': {'p': 0.6571955959980419, 'r': 0.6829502532492543, 'f1': 0.6698254506750103}, 'combined': 0.45337757894872427, 'epoch': 31}
Dev Russian: {'event': {'p': 0.542747358309318, 'r': 0.7523302263648469, 'f1': 0.6305803571428572}, 'argument': {'p': 0.6552841963450029, 'r': 0.6591106734039518, 'f1': 0.6571918650388748}, 'combined': 0.4144122809675941, 'epoch': 31}
Test Russian: {'event': {'p': 0.6076993583868011, 'r': 0.7577142857142857, 'f1': 0.6744659206510681}, 'argument': {'p': 0.6641229110751573, 'r': 0.6901270427651958, 'f1': 0.6768753129408516}, 'combined': 0.4565293311086313, 'epoch': 31}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6348615464994777, 'r': 0.613699494949495, 'f1': 0.6241011813045713}, 'combined': 0.4512731618663822, 'epoch': 31}
Korean: {'event': {'p': 0.6296296296296297, 'r': 0.5396825396825397, 'f1': 0.5811965811965812}, 'argument': {'p': 0.6082612392783201, 'r': 0.5009210205821459, 'f1': 0.549397248380418}, 'combined': 0.31930780247750795, 'epoch': 31}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5444985042852556, 'r': 0.6049983380947284, 'f1': 0.573156320300269}, 'combined': 0.33146389607726395, 'epoch': 31}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 32
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 03:59:00.183497: step: 2/1533, loss: 0.05716961249709129
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2023-01-22 04:20:02.522422: step: 3068/1533, loss: 0.001666960772126913
==================================================
Loss: 0.062
--------------------
Dev Chinese: {'event': {'p': 0.6, 'r': 0.7430093209054593, 'f1': 0.6638905413444378}, 'argument': {'p': 0.6836560283708544, 'r': 0.6437676781187883, 'f1': 0.6631125417823045}, 'combined': 0.4402341443361403, 'epoch': 32}
Test Chinese: {'event': {'p': 0.6189582355701548, 'r': 0.7537142857142857, 'f1': 0.6797217212058747}, 'argument': {'p': 0.6748950955658504, 'r': 0.6801856842495636, 'f1': 0.6775300619888709}, 'combined': 0.4605318999037983, 'epoch': 32}
Dev Korean: {'event': {'p': 0.6074866310160428, 'r': 0.7563249001331558, 'f1': 0.6737841043890866}, 'argument': {'p': 0.6844958274339598, 'r': 0.6418697295633439, 'f1': 0.6624978299902675}, 'combined': 0.4463805070397058, 'epoch': 32}
Test Korean: {'event': {'p': 0.6220957799905168, 'r': 0.7497142857142857, 'f1': 0.6799689038611039}, 'argument': {'p': 0.671785003764278, 'r': 0.6736331055628042, 'f1': 0.6727077853628554}, 'combined': 0.4574203754320115, 'epoch': 32}
Dev Russian: {'event': {'p': 0.5379442843419788, 'r': 0.7456724367509987, 'f1': 0.6249999999999999}, 'argument': {'p': 0.6886545762567373, 'r': 0.639931385959944, 'f1': 0.6633995691123503}, 'combined': 0.4146247306952189, 'epoch': 32}
Test Russian: {'event': {'p': 0.6076993583868011, 'r': 0.7577142857142857, 'f1': 0.6744659206510681}, 'argument': {'p': 0.6815939758749296, 'r': 0.6813615086253134, 'f1': 0.6814777224251813}, 'combined': 0.459633499458693, 'epoch': 32}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.689324926281448, 'r': 0.6606030543530543, 'f1': 0.6746584384882257}, 'combined': 0.4878299478299477, 'epoch': 32}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.677249856960002, 'r': 0.4232811606000012, 'f1': 0.5209614284307708}, 'combined': 0.3116863247021705, 'epoch': 32}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5439683382811579, 'r': 0.5540418260271053, 'f1': 0.5489588734947465}, 'combined': 0.31747019190057624, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 33
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 04:46:19.691193: step: 3046/1533, loss: -7.080586783558829e-06
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2023-01-22 04:46:27.909962: step: 3066/1533, loss: 0.11433787643909454
2023-01-22 04:46:28.679580: step: 3068/1533, loss: 2.6471954697626643e-05
==================================================
Loss: 0.059
--------------------
Dev Chinese: {'event': {'p': 0.6053763440860215, 'r': 0.7496671105193076, 'f1': 0.6698393813206425}, 'argument': {'p': 0.664381515931596, 'r': 0.6461590429223528, 'f1': 0.6551435918092852}, 'combined': 0.4388409782137151, 'epoch': 33}
Test Chinese: {'event': {'p': 0.6189582355701548, 'r': 0.7537142857142857, 'f1': 0.6797217212058747}, 'argument': {'p': 0.6672386552472974, 'r': 0.680647061466504, 'f1': 0.6738761667393882}, 'combined': 0.45804826793571396, 'epoch': 33}
Dev Korean: {'event': {'p': 0.6096256684491979, 'r': 0.758988015978695, 'f1': 0.6761565836298933}, 'argument': {'p': 0.6652777699977589, 'r': 0.6406378525904345, 'f1': 0.6527253592430842}, 'combined': 0.44134454895439856, 'epoch': 33}
Test Korean: {'event': {'p': 0.6235182550972025, 'r': 0.7514285714285714, 'f1': 0.6815237108059082}, 'argument': {'p': 0.6646036441853201, 'r': 0.6746733963699463, 'f1': 0.6696006640664128}, 'combined': 0.45634872933264203, 'epoch': 33}
Dev Russian: {'event': {'p': 0.5417867435158501, 'r': 0.7509986684420772, 'f1': 0.6294642857142857}, 'argument': {'p': 0.6688839429787038, 'r': 0.6439292384491399, 'f1': 0.6561694140588701}, 'combined': 0.41303521152812805, 'epoch': 33}
Test Russian: {'event': {'p': 0.6104491292392301, 'r': 0.7611428571428571, 'f1': 0.6775178026449644}, 'argument': {'p': 0.6761906712210675, 'r': 0.6805725343701807, 'f1': 0.6783745268649907}, 'combined': 0.45961081881188587, 'epoch': 33}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6423834619486793, 'r': 0.6156174843674844, 'f1': 0.6287157287157288}, 'combined': 0.46428238428238433, 'epoch': 33}
Korean: {'event': {'p': 0.6296296296296297, 'r': 0.5396825396825397, 'f1': 0.5811965811965812}, 'argument': {'p': 0.5750925403099316, 'r': 0.43930680162564223, 'f1': 0.49811164908734235}, 'combined': 0.2895007875037546, 'epoch': 33}
Russian: {'event': {'p': 0.44680851063829785, 'r': 0.5833333333333334, 'f1': 0.5060240963855422}, 'argument': {'p': 0.5768392543800892, 'r': 0.5768392543800892, 'f1': 0.5768392543800892}, 'combined': 0.2918945624573946, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 34
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 04:51:52.675810: step: 2/1533, loss: 0.013775968924164772
2023-01-22 04:51:53.512413: step: 4/1533, loss: 0.00028794139507226646
2023-01-22 04:51:54.327167: step: 6/1533, loss: 0.0005560716963373125
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2023-01-22 04:51:55.979204: step: 10/1533, loss: 0.00309372297488153
2023-01-22 04:51:56.793360: step: 12/1533, loss: 1.9313330994918942e-05
2023-01-22 04:51:57.623440: step: 14/1533, loss: 0.007168816402554512
2023-01-22 04:51:58.433276: step: 16/1533, loss: 0.003730912460014224
2023-01-22 04:51:59.230872: step: 18/1533, loss: 0.0010280224960297346
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2023-01-22 05:12:55.555144: step: 3068/1533, loss: 0.24459168314933777
==================================================
Loss: 0.051
--------------------
Dev Chinese: {'event': {'p': 0.6021505376344086, 'r': 0.7456724367509987, 'f1': 0.6662700773349197}, 'argument': {'p': 0.6592806798014241, 'r': 0.657331586882573, 'f1': 0.6583046906381356}, 'combined': 0.438608717141411, 'epoch': 34}
Test Chinese: {'event': {'p': 0.6198967620835288, 'r': 0.7548571428571429, 'f1': 0.6807523834063385}, 'argument': {'p': 0.6552957999677896, 'r': 0.6881052286446511, 'f1': 0.6712998675197527}, 'combined': 0.4569889847944309, 'epoch': 34}
Dev Korean: {'event': {'p': 0.611764705882353, 'r': 0.7616511318242344, 'f1': 0.6785290628707}, 'argument': {'p': 0.6552851074506139, 'r': 0.6472069009048123, 'f1': 0.6512209531906137}, 'combined': 0.44187234309019113, 'epoch': 34}
Test Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6531989884011378, 'r': 0.6826446413538387, 'f1': 0.6675972834508684}, 'combined': 0.45325340312030976, 'epoch': 34}
Dev Russian: {'event': {'p': 0.5389048991354467, 'r': 0.7470039946737683, 'f1': 0.6261160714285714}, 'argument': {'p': 0.6537749413435106, 'r': 0.6513923796039205, 'f1': 0.6525814858098422}, 'combined': 0.40859175618227844, 'epoch': 34}
Test Russian: {'event': {'p': 0.6054078826764436, 'r': 0.7548571428571429, 'f1': 0.6719226856561547}, 'argument': {'p': 0.665064288846769, 'r': 0.6874898217472561, 'f1': 0.6760911460894136}, 'combined': 0.45428097862874633, 'epoch': 34}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6835140456635783, 'r': 0.6094666907166907, 'f1': 0.6443700694802016}, 'combined': 0.47584251284691814, 'epoch': 34}
Korean: {'event': {'p': 0.6851851851851852, 'r': 0.5873015873015873, 'f1': 0.6324786324786325}, 'argument': {'p': 0.5844505982039256, 'r': 0.4211482251763582, 'f1': 0.48953981729901463}, 'combined': 0.30962347418912034, 'epoch': 34}
Russian: {'event': {'p': 0.46808510638297873, 'r': 0.6111111111111112, 'f1': 0.5301204819277109}, 'argument': {'p': 0.5324669704161537, 'r': 0.5423274698683046, 'f1': 0.5373519884933661}, 'combined': 0.284861295104917, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 35
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 05:39:23.689459: step: 3068/1533, loss: 0.000663469429127872
==================================================
Loss: 0.047
--------------------
Dev Chinese: {'event': {'p': 0.6010752688172043, 'r': 0.7443408788282291, 'f1': 0.6650803093396788}, 'argument': {'p': 0.680556214535246, 'r': 0.6508573856465037, 'f1': 0.6653755655153671}, 'combined': 0.44252818694002405, 'epoch': 35}
Test Chinese: {'event': {'p': 0.6198967620835288, 'r': 0.7548571428571429, 'f1': 0.6807523834063385}, 'argument': {'p': 0.6815960853712345, 'r': 0.673692174422182, 'f1': 0.6776210824719855}, 'combined': 0.4612921669391872, 'epoch': 35}
Dev Korean: {'event': {'p': 0.613903743315508, 'r': 0.7643142476697736, 'f1': 0.6809015421115064}, 'argument': {'p': 0.6801437789503932, 'r': 0.6451254988158914, 'f1': 0.6621719857589239}, 'combined': 0.45087392624628975, 'epoch': 35}
Test Korean: {'event': {'p': 0.6220957799905168, 'r': 0.7497142857142857, 'f1': 0.6799689038611039}, 'argument': {'p': 0.6833456779214186, 'r': 0.6708741486756076, 'f1': 0.6770524857528981}, 'combined': 0.46037463659383376, 'epoch': 35}
Dev Russian: {'event': {'p': 0.5360230547550432, 'r': 0.7430093209054593, 'f1': 0.622767857142857}, 'argument': {'p': 0.6781417721095763, 'r': 0.6465083366758934, 'f1': 0.66194734173084}, 'combined': 0.4122395275511257, 'epoch': 35}
Test Russian: {'event': {'p': 0.6076993583868011, 'r': 0.7577142857142857, 'f1': 0.6744659206510681}, 'argument': {'p': 0.690439131461008, 'r': 0.6786649307198585, 'f1': 0.6845014024322756}, 'combined': 0.4616728685784321, 'epoch': 35}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6717627033161013, 'r': 0.5765963203463204, 'f1': 0.6205520936462641}, 'combined': 0.45825385376954886, 'epoch': 35}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5783168613357292, 'r': 0.4507469654528478, 'f1': 0.5066246884428703}, 'combined': 0.31176903904176634, 'epoch': 35}
Russian: {'event': {'p': 0.48936170212765956, 'r': 0.6388888888888888, 'f1': 0.5542168674698795}, 'argument': {'p': 0.5410614076562352, 'r': 0.5611007190509105, 'f1': 0.5508988877954394}, 'combined': 0.30531745588662906, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 36
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 06:05:45.099984: step: 3062/1533, loss: 0.00048462674021720886
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2023-01-22 06:05:47.509799: step: 3068/1533, loss: 0.2722122371196747
==================================================
Loss: 0.049
--------------------
Dev Chinese: {'event': {'p': 0.603225806451613, 'r': 0.7470039946737683, 'f1': 0.6674598453301607}, 'argument': {'p': 0.6412918880602502, 'r': 0.6720776877086374, 'f1': 0.6563239734276921}, 'combined': 0.43806989779052385, 'epoch': 36}
Test Chinese: {'event': {'p': 0.6180197090567808, 'r': 0.7525714285714286, 'f1': 0.6786910590054109}, 'argument': {'p': 0.6562721290699831, 'r': 0.6971495285321365, 'f1': 0.6760935186754966}, 'combined': 0.45885862617656736, 'epoch': 36}
Dev Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6432784601902178, 'r': 0.6642549317181597, 'f1': 0.6535984354873872}, 'combined': 0.44658683136504745, 'epoch': 36}
Test Korean: {'event': {'p': 0.622569938359412, 'r': 0.7502857142857143, 'f1': 0.6804871728427053}, 'argument': {'p': 0.6513185674159374, 'r': 0.6880757061138775, 'f1': 0.6691927717426132}, 'combined': 0.45537709732990467, 'epoch': 36}
Dev Russian: {'event': {'p': 0.5360230547550432, 'r': 0.7430093209054593, 'f1': 0.622767857142857}, 'argument': {'p': 0.6469305177329244, 'r': 0.6615477524630415, 'f1': 0.6541574892823733}, 'combined': 0.407388257834335, 'epoch': 36}
Test Russian: {'event': {'p': 0.6058661778185152, 'r': 0.7554285714285714, 'f1': 0.6724313326551373}, 'argument': {'p': 0.6621812449037717, 'r': 0.697792081083076, 'f1': 0.6795204289764922}, 'combined': 0.45693082762305326, 'epoch': 36}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6315807848065913, 'r': 0.6526334776334777, 'f1': 0.6419345681640765}, 'combined': 0.47404398879808723, 'epoch': 36}
Korean: {'event': {'p': 0.6296296296296297, 'r': 0.5396825396825397, 'f1': 0.5811965811965812}, 'argument': {'p': 0.5886919985385458, 'r': 0.41699016563146996, 'f1': 0.48818360854416}, 'combined': 0.2837306442820759, 'epoch': 36}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.52995914069672, 'r': 0.6280997223072237, 'f1': 0.5748709322811878}, 'combined': 0.34630779053083605, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 37
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 06:11:14.605611: step: 2/1533, loss: 0.029730655252933502
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2023-01-22 06:11:16.200945: step: 6/1533, loss: 0.0018652169965207577
2023-01-22 06:11:17.114705: step: 8/1533, loss: 0.00030684046214446425
2023-01-22 06:11:17.938397: step: 10/1533, loss: 0.22024333477020264
2023-01-22 06:11:18.740744: step: 12/1533, loss: 9.093149128602818e-05
2023-01-22 06:11:19.539803: step: 14/1533, loss: 0.007758960127830505
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2023-01-22 06:32:17.475266: step: 3068/1533, loss: 0.007307238876819611
==================================================
Loss: 0.051
--------------------
Dev Chinese: {'event': {'p': 0.6096774193548387, 'r': 0.7549933422103862, 'f1': 0.6745984533016061}, 'argument': {'p': 0.6703142314326391, 'r': 0.648947654745016, 'f1': 0.6594579180798694}, 'combined': 0.44486929155417715, 'epoch': 37}
Test Chinese: {'event': {'p': 0.6208352885969028, 'r': 0.756, 'f1': 0.6817830456068024}, 'argument': {'p': 0.6741400499070487, 'r': 0.6766666395319132, 'f1': 0.6754009818147136}, 'combined': 0.46047693838746, 'epoch': 37}
Dev Korean: {'event': {'p': 0.6192513368983957, 'r': 0.7709720372836218, 'f1': 0.6868327402135231}, 'argument': {'p': 0.6668203751268919, 'r': 0.6430918359974672, 'f1': 0.6547411890340202}, 'combined': 0.4496976849948965, 'epoch': 37}
Test Korean: {'event': {'p': 0.6244665718349929, 'r': 0.7525714285714286, 'f1': 0.6825602487691113}, 'argument': {'p': 0.6714648355326959, 'r': 0.6707714122946242, 'f1': 0.6711179447962402}, 'combined': 0.4580784313535364, 'epoch': 37}
Dev Russian: {'event': {'p': 0.5408261287223823, 'r': 0.7496671105193076, 'f1': 0.6283482142857143}, 'argument': {'p': 0.6737297962322065, 'r': 0.6412490317681265, 'f1': 0.6570882668342553}, 'combined': 0.4128802390933992, 'epoch': 37}
Test Russian: {'event': {'p': 0.6095325389550871, 'r': 0.76, 'f1': 0.6765005086469988}, 'argument': {'p': 0.6813928418625841, 'r': 0.6806956459125201, 'f1': 0.6810440654548742}, 'combined': 0.46072665669124235, 'epoch': 37}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.6487514368870302, 'r': 0.6379389129389129, 'f1': 0.6432997441400804}, 'combined': 0.47505211874959785, 'epoch': 37}
Korean: {'event': {'p': 0.6851851851851852, 'r': 0.5873015873015873, 'f1': 0.6324786324786325}, 'argument': {'p': 0.5974043401579633, 'r': 0.4073211410167932, 'f1': 0.4843818974253756}, 'combined': 0.30636120008100676, 'epoch': 37}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.532543491243582, 'r': 0.5522673242526035, 'f1': 0.5422261001752834}, 'combined': 0.32664222902125506, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 38
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 06:58:41.532042: step: 3068/1533, loss: 0.06596145778894424
==================================================
Loss: 0.040
--------------------
Dev Chinese: {'event': {'p': 0.6086021505376344, 'r': 0.7536617842876165, 'f1': 0.6734086853063653}, 'argument': {'p': 0.6776304414806243, 'r': 0.6605768256328515, 'f1': 0.6689949710869432}, 'combined': 0.45050702395622827, 'epoch': 38}
Test Chinese: {'event': {'p': 0.6213045518535899, 'r': 0.7565714285714286, 'f1': 0.6822983767070343}, 'argument': {'p': 0.6706693637217607, 'r': 0.6864310074877013, 'f1': 0.6784586561130757}, 'combined': 0.46291123972878756, 'epoch': 38}
Dev Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6753199269349167, 'r': 0.6561653980982173, 'f1': 0.6656048855662476}, 'combined': 0.45479052679259624, 'epoch': 38}
Test Korean: {'event': {'p': 0.6258890469416786, 'r': 0.7542857142857143, 'f1': 0.6841150557139155}, 'argument': {'p': 0.6654765430760822, 'r': 0.6789968998396803, 'f1': 0.6721687394356336}, 'combined': 0.4598407546281608, 'epoch': 38}
Dev Russian: {'event': {'p': 0.5437079731027857, 'r': 0.7536617842876165, 'f1': 0.6316964285714286}, 'argument': {'p': 0.6817980282222198, 'r': 0.6524144755084791, 'f1': 0.66678269277425}, 'combined': 0.4212042456587338, 'epoch': 38}
Test Russian: {'event': {'p': 0.6109074243813015, 'r': 0.7617142857142857, 'f1': 0.678026449643947}, 'argument': {'p': 0.6777919028529201, 'r': 0.688888086801399, 'f1': 0.6832949494254743}, 'combined': 0.46329204861859463, 'epoch': 38}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.658954408954409, 'r': 0.6095328282828284, 'f1': 0.633280860553588}, 'combined': 0.45791077609259434, 'epoch': 38}
Korean: {'event': {'p': 0.6481481481481481, 'r': 0.5555555555555556, 'f1': 0.5982905982905983}, 'argument': {'p': 0.520988845988846, 'r': 0.3830800338153279, 'f1': 0.44151597117698815}, 'combined': 0.2641548545503348, 'epoch': 38}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5197615363563639, 'r': 0.5390119636288219, 'f1': 0.5292117461082979}, 'combined': 0.3060501664240759, 'epoch': 38}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}
******************************
Epoch: 39
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 07:25:09.748266: step: 3068/1533, loss: 0.0011596725089475513
==================================================
Loss: 0.045
--------------------
Dev Chinese: {'event': {'p': 0.603225806451613, 'r': 0.7470039946737683, 'f1': 0.6674598453301607}, 'argument': {'p': 0.6717981152767812, 'r': 0.6573988947719573, 'f1': 0.6645205114878284}, 'combined': 0.44354075781638524, 'epoch': 39}
Test Chinese: {'event': {'p': 0.6180197090567808, 'r': 0.7525714285714286, 'f1': 0.6786910590054109}, 'argument': {'p': 0.6750237627815701, 'r': 0.6851364783288595, 'f1': 0.6800425268171209}, 'combined': 0.4615387826942273, 'epoch': 39}
Dev Korean: {'event': {'p': 0.6128342245989304, 'r': 0.762982689747004, 'f1': 0.6797153024911031}, 'argument': {'p': 0.6679797097834539, 'r': 0.651498352648013, 'f1': 0.6596360984195165}, 'combined': 0.44836475017127275, 'epoch': 39}
Test Korean: {'event': {'p': 0.6216216216216216, 'r': 0.7491428571428571, 'f1': 0.6794506348795025}, 'argument': {'p': 0.6744599463056624, 'r': 0.6814179237434786, 'f1': 0.677921081849482}, 'combined': 0.4606139094608297, 'epoch': 39}
Dev Russian: {'event': {'p': 0.542747358309318, 'r': 0.7523302263648469, 'f1': 0.6305803571428572}, 'argument': {'p': 0.667500697403602, 'r': 0.6538782341912835, 'f1': 0.6606192469149051}, 'combined': 0.41657352065504627, 'epoch': 39}
Test Russian: {'event': {'p': 0.6095325389550871, 'r': 0.76, 'f1': 0.6765005086469988}, 'argument': {'p': 0.6807219993042771, 'r': 0.6909339897473008, 'f1': 0.6857899803481668}, 'combined': 0.4639372705305502, 'epoch': 39}
Chinese: {'event': {'p': 0.618421052631579, 'r': 0.8703703703703703, 'f1': 0.723076923076923}, 'argument': {'p': 0.6689237507821578, 'r': 0.6299031986531987, 'f1': 0.6488273290848398}, 'combined': 0.46915206872288406, 'epoch': 39}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6088975522370527, 'r': 0.4208556611050217, 'f1': 0.4977075644372431}, 'combined': 0.3062815781152265, 'epoch': 39}
Russian: {'event': {'p': 0.5106382978723404, 'r': 0.6666666666666666, 'f1': 0.5783132530120482}, 'argument': {'p': 0.5470580985616343, 'r': 0.587580920677311, 'f1': 0.5665958877959785}, 'combined': 0.32766991101454174, 'epoch': 39}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6043010752688172, 'r': 0.748335552596538, 'f1': 0.6686496133254014}, 'argument': {'p': 0.6592806805204416, 'r': 0.6490327942947353, 'f1': 0.6541166020829446}, 'combined': 0.4373748130524864, 'epoch': 21}
Test for Chinese: {'event': {'p': 0.6184889723134679, 'r': 0.7531428571428571, 'f1': 0.6792063901056429}, 'argument': {'p': 0.6659679214789787, 'r': 0.6711796473020534, 'f1': 0.6685636276633571}, 'combined': 0.4540926881011619, 'epoch': 21}
Chinese: {'event': {'p': 0.631578947368421, 'r': 0.8888888888888888, 'f1': 0.7384615384615385}, 'argument': {'p': 0.676293809768386, 'r': 0.6650222462722463, 'f1': 0.6706106685098281}, 'combined': 0.4952201859764885, 'epoch': 21}
--------------------
Dev for Korean: {'event': {'p': 0.6160427807486631, 'r': 0.7669773635153129, 'f1': 0.6832740213523132}, 'argument': {'p': 0.6492475939575457, 'r': 0.6445360729564025, 'f1': 0.6468832545767826}, 'combined': 0.44199852270015044, 'epoch': 26}
Test for Korean: {'event': {'p': 0.6211474632527264, 'r': 0.7485714285714286, 'f1': 0.6789323658979011}, 'argument': {'p': 0.6667166888757788, 'r': 0.691257000437426, 'f1': 0.6787651073371381}, 'combined': 0.46083560021334596, 'epoch': 26}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5714285714285714, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6685340802987861, 'r': 0.5014005602240896, 'f1': 0.5730292116846739}, 'combined': 0.3526333610367224, 'epoch': 26}
--------------------
Dev for Russian: {'event': {'p': 0.5398655139289145, 'r': 0.748335552596538, 'f1': 0.6272321428571428}, 'argument': {'p': 0.6734010990927325, 'r': 0.6508235112222764, 'f1': 0.6619198349866297}, 'combined': 0.4151773964983101, 'epoch': 28}
Test for Russian: {'event': {'p': 0.6136571952337305, 'r': 0.7651428571428571, 'f1': 0.6810783316378434}, 'argument': {'p': 0.6880171239673318, 'r': 0.6854376459974475, 'f1': 0.6867249627305826}, 'combined': 0.46771349191060535, 'epoch': 28}
Russian: {'event': {'p': 0.5319148936170213, 'r': 0.6944444444444444, 'f1': 0.6024096385542169}, 'argument': {'p': 0.5762488880972515, 'r': 0.6189339909192701, 'f1': 0.5968292055292961}, 'combined': 0.3595356659815037, 'epoch': 28}