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([231, 450])
>>> trigger_label_ffn.layers.1.bias: torch.Size([231])
>>> 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([116, 450])
>>> event_type_ffn.layers.1.bias: torch.Size([116])
>>> 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([233, 233])
>>> mention_crf.transition: torch.Size([13, 13])
n_trainable_params: 564977528, 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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2023-01-23 05:23:58.936030: step: 3088/1545, loss: 3.7859914302825928
2023-01-23 05:23:59.844651: step: 3090/1545, loss: 3.6111247539520264
==================================================
Loss: 3.441
--------------------
Dev Chinese: {'event': {'p': 0.5910577971646674, 'r': 0.7217043941411452, 'f1': 0.6498800959232615}, 'argument': {'p': 0.5394602050331777, 'r': 0.5487473193413837, 'f1': 0.5440641325714943}, 'combined': 0.35357645066396876, 'epoch': 0}
Test Chinese: {'event': {'p': 0.628068303094984, 'r': 0.7525575447570333, 'f1': 0.6847004072134962}, 'argument': {'p': 0.5481112704378934, 'r': 0.5596042631462748, 'f1': 0.5537981445888243}, 'combined': 0.37918581511404664, 'epoch': 0}
Dev Korean: {'event': {'p': 0.5929978118161926, 'r': 0.7217043941411452, 'f1': 0.6510510510510511}, 'argument': {'p': 0.5387353965925397, 'r': 0.5399496762242283, 'f1': 0.5393418529489291}, 'combined': 0.35113908023822177, 'epoch': 0}
Test Korean: {'event': {'p': 0.6137566137566137, 'r': 0.7416879795396419, 'f1': 0.6716850028951941}, 'argument': {'p': 0.5403725277710205, 'r': 0.5546145844617005, 'f1': 0.5474009357665343}, 'combined': 0.3676809991251765, 'epoch': 0}
Dev Russian: {'event': {'p': 0.5590551181102362, 'r': 0.7563249001331558, 'f1': 0.6428975664968875}, 'argument': {'p': 0.5366216502105567, 'r': 0.551602497997394, 'f1': 0.5440089584908998}, 'combined': 0.34974203556630573, 'epoch': 0}
Test Russian: {'event': {'p': 0.6103637269556552, 'r': 0.7832480818414322, 'f1': 0.6860823298795855}, 'argument': {'p': 0.5456215226774778, 'r': 0.5625827909854293, 'f1': 0.5539723591853573}, 'combined': 0.3800706468787805, 'epoch': 0}
Chinese: {'event': {'p': 0.5769230769230769, 'r': 0.8333333333333334, 'f1': 0.6818181818181818}, 'argument': {'p': 0.5105152208584702, 'r': 0.4852004165183808, 'f1': 0.4975360203281702}, 'combined': 0.3392291047692069, 'epoch': 0}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5396825396825397, 'f1': 0.5964912280701754}, 'argument': {'p': 0.4049995892895167, 'r': 0.20797276206758963, 'f1': 0.27482114987502915}, 'combined': 0.16392840518861387, 'epoch': 0}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.3675100588780262, 'r': 0.4200114958606013, 'f1': 0.39201072946989457}, 'combined': 0.21833508983133368, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5910577971646674, 'r': 0.7217043941411452, 'f1': 0.6498800959232615}, 'argument': {'p': 0.5394602050331777, 'r': 0.5487473193413837, 'f1': 0.5440641325714943}, 'combined': 0.35357645066396876, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.628068303094984, 'r': 0.7525575447570333, 'f1': 0.6847004072134962}, 'argument': {'p': 0.5481112704378934, 'r': 0.5596042631462748, 'f1': 0.5537981445888243}, 'combined': 0.37918581511404664, 'epoch': 0}
Chinese: {'event': {'p': 0.5769230769230769, 'r': 0.8333333333333334, 'f1': 0.6818181818181818}, 'argument': {'p': 0.5105152208584702, 'r': 0.4852004165183808, 'f1': 0.4975360203281702}, 'combined': 0.3392291047692069, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.5929978118161926, 'r': 0.7217043941411452, 'f1': 0.6510510510510511}, 'argument': {'p': 0.5387353965925397, 'r': 0.5399496762242283, 'f1': 0.5393418529489291}, 'combined': 0.35113908023822177, 'epoch': 0}
Test for Korean: {'event': {'p': 0.6137566137566137, 'r': 0.7416879795396419, 'f1': 0.6716850028951941}, 'argument': {'p': 0.5403725277710205, 'r': 0.5546145844617005, 'f1': 0.5474009357665343}, 'combined': 0.3676809991251765, 'epoch': 0}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5396825396825397, 'f1': 0.5964912280701754}, 'argument': {'p': 0.4049995892895167, 'r': 0.20797276206758963, 'f1': 0.27482114987502915}, 'combined': 0.16392840518861387, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.5590551181102362, 'r': 0.7563249001331558, 'f1': 0.6428975664968875}, 'argument': {'p': 0.5366216502105567, 'r': 0.551602497997394, 'f1': 0.5440089584908998}, 'combined': 0.34974203556630573, 'epoch': 0}
Test for Russian: {'event': {'p': 0.6103637269556552, 'r': 0.7832480818414322, 'f1': 0.6860823298795855}, 'argument': {'p': 0.5456215226774778, 'r': 0.5625827909854293, 'f1': 0.5539723591853573}, 'combined': 0.3800706468787805, 'epoch': 0}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.3675100588780262, 'r': 0.4200114958606013, 'f1': 0.39201072946989457}, 'combined': 0.21833508983133368, '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
2023-01-23 05:29:26.126777: step: 2/1545, loss: 3.1149637699127197
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2023-01-23 05:52:10.838282: step: 3090/1545, loss: 3.1110260486602783
==================================================
Loss: 1.769
--------------------
Dev Chinese: {'event': {'p': 0.5976008724100327, 'r': 0.729693741677763, 'f1': 0.657074340527578}, 'argument': {'p': 0.6028173133182873, 'r': 0.6596273219032567, 'f1': 0.6299440924176101}, 'combined': 0.41392009909454475, 'epoch': 1}
Test Chinese: {'event': {'p': 0.6350053361792957, 'r': 0.7608695652173914, 'f1': 0.692262943571844}, 'argument': {'p': 0.6367310808893076, 'r': 0.7166870066956367, 'f1': 0.6743472643356324}, 'combined': 0.4668256221986053, 'epoch': 1}
Dev Korean: {'event': {'p': 0.6061269146608315, 'r': 0.7376830892143809, 'f1': 0.6654654654654655}, 'argument': {'p': 0.6008600156798031, 'r': 0.6690267041603817, 'f1': 0.6331137882953913}, 'combined': 0.42131536182059676, 'epoch': 1}
Test Korean: {'event': {'p': 0.6232804232804233, 'r': 0.7531969309462916, 'f1': 0.6821077012159815}, 'argument': {'p': 0.6315981785010799, 'r': 0.7147159189006099, 'f1': 0.6705913180208881}, 'combined': 0.4574155023906232, 'epoch': 1}
Dev Russian: {'event': {'p': 0.5698818897637795, 'r': 0.7709720372836218, 'f1': 0.6553480475382004}, 'argument': {'p': 0.5917286542565424, 'r': 0.6709364268735599, 'f1': 0.6288481639331521}, 'combined': 0.4121144164315734, 'epoch': 1}
Test Russian: {'event': {'p': 0.6178375685102142, 'r': 0.7928388746803069, 'f1': 0.69448333800056}, 'argument': {'p': 0.6356813558492974, 'r': 0.7266631010476607, 'f1': 0.6781342016420302}, 'combined': 0.47095290396870193, 'epoch': 1}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.5602212602212602, 'r': 0.6805993822522747, 'f1': 0.614571083974069}, 'combined': 0.42833742216374515, 'epoch': 1}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.5115322670045308, 'r': 0.26287074832177276, 'f1': 0.34727878677371815}, 'combined': 0.21324136029965152, 'epoch': 1}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.499025657742763, 'r': 0.5703150374203005, 'f1': 0.5322940349256139}, 'combined': 0.2829917654034909, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5976008724100327, 'r': 0.729693741677763, 'f1': 0.657074340527578}, 'argument': {'p': 0.6028173133182873, 'r': 0.6596273219032567, 'f1': 0.6299440924176101}, 'combined': 0.41392009909454475, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.6350053361792957, 'r': 0.7608695652173914, 'f1': 0.692262943571844}, 'argument': {'p': 0.6367310808893076, 'r': 0.7166870066956367, 'f1': 0.6743472643356324}, 'combined': 0.4668256221986053, 'epoch': 1}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.5602212602212602, 'r': 0.6805993822522747, 'f1': 0.614571083974069}, 'combined': 0.42833742216374515, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.6061269146608315, 'r': 0.7376830892143809, 'f1': 0.6654654654654655}, 'argument': {'p': 0.6008600156798031, 'r': 0.6690267041603817, 'f1': 0.6331137882953913}, 'combined': 0.42131536182059676, 'epoch': 1}
Test for Korean: {'event': {'p': 0.6232804232804233, 'r': 0.7531969309462916, 'f1': 0.6821077012159815}, 'argument': {'p': 0.6315981785010799, 'r': 0.7147159189006099, 'f1': 0.6705913180208881}, 'combined': 0.4574155023906232, 'epoch': 1}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.5115322670045308, 'r': 0.26287074832177276, 'f1': 0.34727878677371815}, 'combined': 0.21324136029965152, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.5698818897637795, 'r': 0.7709720372836218, 'f1': 0.6553480475382004}, 'argument': {'p': 0.5917286542565424, 'r': 0.6709364268735599, 'f1': 0.6288481639331521}, 'combined': 0.4121144164315734, 'epoch': 1}
Test for Russian: {'event': {'p': 0.6178375685102142, 'r': 0.7928388746803069, 'f1': 0.69448333800056}, 'argument': {'p': 0.6356813558492974, 'r': 0.7266631010476607, 'f1': 0.6781342016420302}, 'combined': 0.47095290396870193, 'epoch': 1}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.499025657742763, 'r': 0.5703150374203005, 'f1': 0.5322940349256139}, 'combined': 0.2829917654034909, '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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2023-01-23 06:20:29.312208: step: 3090/1545, loss: 3.235130786895752
==================================================
Loss: 1.410
--------------------
Dev Chinese: {'event': {'p': 0.6052344601962922, 'r': 0.7390146471371505, 'f1': 0.6654676258992807}, 'argument': {'p': 0.587981304984902, 'r': 0.6864912618410113, 'f1': 0.6334291353218994}, 'combined': 0.4215265828580986, 'epoch': 2}
Test Chinese: {'event': {'p': 0.6360725720384205, 'r': 0.7621483375959079, 'f1': 0.6934264107038975}, 'argument': {'p': 0.628516619423252, 'r': 0.7394313169685317, 'f1': 0.6794774264035156}, 'combined': 0.4711675929453115, 'epoch': 2}
Dev Korean: {'event': {'p': 0.611597374179431, 'r': 0.7443408788282291, 'f1': 0.6714714714714715}, 'argument': {'p': 0.592062414933287, 'r': 0.6894791458652102, 'f1': 0.6370682007265497}, 'combined': 0.42777312216953906, 'epoch': 2}
Test Korean: {'event': {'p': 0.6264550264550265, 'r': 0.7570332480818415, 'f1': 0.6855819339895773}, 'argument': {'p': 0.6242069589433683, 'r': 0.7378293346439585, 'f1': 0.6762789029421492}, 'combined': 0.4636445981954283, 'epoch': 2}
Dev Russian: {'event': {'p': 0.5708661417322834, 'r': 0.7723035952063915, 'f1': 0.656479909451047}, 'argument': {'p': 0.581316540030282, 'r': 0.6982456364859079, 'f1': 0.6344384742574395}, 'combined': 0.41649611213278426, 'epoch': 2}
Test Russian: {'event': {'p': 0.6223218734429496, 'r': 0.7985933503836317, 'f1': 0.6995239428731448}, 'argument': {'p': 0.6284616341238207, 'r': 0.7497726317151879, 'f1': 0.6837782879562884}, 'combined': 0.4783192840422314, 'epoch': 2}
Chinese: {'event': {'p': 0.5769230769230769, 'r': 0.8333333333333334, 'f1': 0.6818181818181818}, 'argument': {'p': 0.5654826409793297, 'r': 0.7056849486601552, 'f1': 0.6278520499108733}, 'combined': 0.42808094312104994, 'epoch': 2}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.5058144469909176, 'r': 0.3582852332852333, 'f1': 0.41945588287051705}, 'combined': 0.2575606298327736, 'epoch': 2}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5047448393420144, 'r': 0.621917748474982, 'f1': 0.557238302633584}, 'combined': 0.310360573618705, 'epoch': 2}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6052344601962922, 'r': 0.7390146471371505, 'f1': 0.6654676258992807}, 'argument': {'p': 0.587981304984902, 'r': 0.6864912618410113, 'f1': 0.6334291353218994}, 'combined': 0.4215265828580986, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.6360725720384205, 'r': 0.7621483375959079, 'f1': 0.6934264107038975}, 'argument': {'p': 0.628516619423252, 'r': 0.7394313169685317, 'f1': 0.6794774264035156}, 'combined': 0.4711675929453115, 'epoch': 2}
Chinese: {'event': {'p': 0.5769230769230769, 'r': 0.8333333333333334, 'f1': 0.6818181818181818}, 'argument': {'p': 0.5654826409793297, 'r': 0.7056849486601552, 'f1': 0.6278520499108733}, 'combined': 0.42808094312104994, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.611597374179431, 'r': 0.7443408788282291, 'f1': 0.6714714714714715}, 'argument': {'p': 0.592062414933287, 'r': 0.6894791458652102, 'f1': 0.6370682007265497}, 'combined': 0.42777312216953906, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6264550264550265, 'r': 0.7570332480818415, 'f1': 0.6855819339895773}, 'argument': {'p': 0.6242069589433683, 'r': 0.7378293346439585, 'f1': 0.6762789029421492}, 'combined': 0.4636445981954283, 'epoch': 2}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.5058144469909176, 'r': 0.3582852332852333, 'f1': 0.41945588287051705}, 'combined': 0.2575606298327736, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.5708661417322834, 'r': 0.7723035952063915, 'f1': 0.656479909451047}, 'argument': {'p': 0.581316540030282, 'r': 0.6982456364859079, 'f1': 0.6344384742574395}, 'combined': 0.41649611213278426, 'epoch': 2}
Test for Russian: {'event': {'p': 0.6223218734429496, 'r': 0.7985933503836317, 'f1': 0.6995239428731448}, 'argument': {'p': 0.6284616341238207, 'r': 0.7497726317151879, 'f1': 0.6837782879562884}, 'combined': 0.4783192840422314, 'epoch': 2}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5047448393420144, 'r': 0.621917748474982, 'f1': 0.557238302633584}, 'combined': 0.310360573618705, '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
2023-01-23 06:26:18.375305: step: 2/1545, loss: 0.7754907608032227
2023-01-23 06:26:19.290455: step: 4/1545, loss: 3.829986810684204
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2023-01-23 06:26:21.100412: step: 8/1545, loss: 0.48395588994026184
2023-01-23 06:26:22.067785: step: 10/1545, loss: 0.6997342705726624
2023-01-23 06:26:22.972211: step: 12/1545, loss: 1.1014710664749146
2023-01-23 06:26:23.841688: step: 14/1545, loss: 2.8528974056243896
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2023-01-23 06:49:04.401121: step: 3090/1545, loss: 6.213527679443359
==================================================
Loss: 1.047
--------------------
Dev Chinese: {'event': {'p': 0.604143947655398, 'r': 0.7376830892143809, 'f1': 0.6642685851318945}, 'argument': {'p': 0.6551612787541361, 'r': 0.6306417698187268, 'f1': 0.6426677379693508}, 'combined': 0.4269039890108158, 'epoch': 3}
Test Chinese: {'event': {'p': 0.6435432230522946, 'r': 0.7710997442455243, 'f1': 0.7015706806282722}, 'argument': {'p': 0.7100295259176541, 'r': 0.7032621838864146, 'f1': 0.7066296526849514}, 'combined': 0.49575064638630095, 'epoch': 3}
Dev Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6621925205794895, 'r': 0.6288590127817241, 'f1': 0.6450954497205971}, 'combined': 0.43161341200525233, 'epoch': 3}
Test Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7041948357813179, 'r': 0.7012269648259177, 'f1': 0.7027077666329988}, 'combined': 0.48827407061933914, 'epoch': 3}
Dev Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6951841648811345, 'r': 0.5687870439936555, 'f1': 0.625665748393021}, 'combined': 0.4360700670618026, 'epoch': 3}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6058186000046465, 'r': 0.3568520520575315, 'f1': 0.44914137586551384}, 'combined': 0.28366823738874564, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.604143947655398, 'r': 0.7376830892143809, 'f1': 0.6642685851318945}, 'argument': {'p': 0.6551612787541361, 'r': 0.6306417698187268, 'f1': 0.6426677379693508}, 'combined': 0.4269039890108158, 'epoch': 3}
Test for Chinese: {'event': {'p': 0.6435432230522946, 'r': 0.7710997442455243, 'f1': 0.7015706806282722}, 'argument': {'p': 0.7100295259176541, 'r': 0.7032621838864146, 'f1': 0.7066296526849514}, 'combined': 0.49575064638630095, 'epoch': 3}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6951841648811345, 'r': 0.5687870439936555, 'f1': 0.625665748393021}, 'combined': 0.4360700670618026, 'epoch': 3}
--------------------
Dev for Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6621925205794895, 'r': 0.6288590127817241, 'f1': 0.6450954497205971}, 'combined': 0.43161341200525233, 'epoch': 3}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7041948357813179, 'r': 0.7012269648259177, 'f1': 0.7027077666329988}, 'combined': 0.48827407061933914, 'epoch': 3}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6058186000046465, 'r': 0.3568520520575315, 'f1': 0.44914137586551384}, 'combined': 0.28366823738874564, 'epoch': 3}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
2023-01-23 06:54:32.200741: step: 2/1545, loss: 0.2932772934436798
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2023-01-23 07:17:16.487352: step: 3090/1545, loss: 0.38108181953430176
==================================================
Loss: 0.826
--------------------
Dev Chinese: {'event': {'p': 0.6030534351145038, 'r': 0.7363515312916112, 'f1': 0.6630695443645084}, 'argument': {'p': 0.639051346299566, 'r': 0.6801878850583704, 'f1': 0.6589782555750419}, 'combined': 0.43694841167026155, 'epoch': 4}
Test Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6895525757392267, 'r': 0.7216860418355543, 'f1': 0.7052534742536323}, 'combined': 0.4906824637622712, 'epoch': 4}
Dev Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6433246520356244, 'r': 0.6844393854363974, 'f1': 0.6632454518254343}, 'combined': 0.4437570170171374, 'epoch': 4}
Test Korean: {'event': {'p': 0.6264550264550265, 'r': 0.7570332480818415, 'f1': 0.6855819339895773}, 'argument': {'p': 0.6873315855909078, 'r': 0.7186217381101572, 'f1': 0.7026284733196001}, 'combined': 0.4817093876145956, 'epoch': 4}
Dev Russian: {'event': {'p': 0.5698818897637795, 'r': 0.7709720372836218, 'f1': 0.6553480475382004}, 'argument': {'p': 0.6428217111128143, 'r': 0.6888691116223282, 'f1': 0.6650492944846267}, 'combined': 0.43583875665715777, 'epoch': 4}
Test Russian: {'event': {'p': 0.6193323368211261, 'r': 0.7947570332480819, 'f1': 0.6961635396247551}, 'argument': {'p': 0.6924442431098461, 'r': 0.7305533047860525, 'f1': 0.7109884777130794}, 'combined': 0.49496425527715365, 'epoch': 4}
Chinese: {'event': {'p': 0.5641025641025641, 'r': 0.8148148148148148, 'f1': 0.6666666666666667}, 'argument': {'p': 0.6782739677476518, 'r': 0.6390349778779529, 'f1': 0.6580700623253815}, 'combined': 0.4387133748835877, 'epoch': 4}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.5904022242695012, 'r': 0.3073326646882335, 'f1': 0.40423936076109995}, 'combined': 0.24821715134453506, 'epoch': 4}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.5603976466214615, 'r': 0.5904189491190398, 'f1': 0.5750167156637606}, 'combined': 0.3057050893402271, 'epoch': 4}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6030534351145038, 'r': 0.7363515312916112, 'f1': 0.6630695443645084}, 'argument': {'p': 0.639051346299566, 'r': 0.6801878850583704, 'f1': 0.6589782555750419}, 'combined': 0.43694841167026155, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6895525757392267, 'r': 0.7216860418355543, 'f1': 0.7052534742536323}, 'combined': 0.4906824637622712, 'epoch': 4}
Chinese: {'event': {'p': 0.5641025641025641, 'r': 0.8148148148148148, 'f1': 0.6666666666666667}, 'argument': {'p': 0.6782739677476518, 'r': 0.6390349778779529, 'f1': 0.6580700623253815}, 'combined': 0.4387133748835877, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6621925205794895, 'r': 0.6288590127817241, 'f1': 0.6450954497205971}, 'combined': 0.43161341200525233, 'epoch': 3}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7041948357813179, 'r': 0.7012269648259177, 'f1': 0.7027077666329988}, 'combined': 0.48827407061933914, 'epoch': 3}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6058186000046465, 'r': 0.3568520520575315, 'f1': 0.44914137586551384}, 'combined': 0.28366823738874564, 'epoch': 3}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
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2023-01-23 07:45:28.346529: step: 3088/1545, loss: 4.839550971984863
2023-01-23 07:45:29.232255: step: 3090/1545, loss: 1.6920313835144043
==================================================
Loss: 0.716
--------------------
Dev Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6964114198955085, 'r': 0.6234341752956798, 'f1': 0.657905259237779}, 'combined': 0.43308151957019264, 'epoch': 5}
Test Chinese: {'event': {'p': 0.6350053361792957, 'r': 0.7608695652173914, 'f1': 0.692262943571844}, 'argument': {'p': 0.7272924330941487, 'r': 0.6900381570870325, 'f1': 0.708175684166213}, 'combined': 0.4902437836869072, 'epoch': 5}
Dev Korean: {'event': {'p': 0.6105032822757112, 'r': 0.7430093209054593, 'f1': 0.6702702702702702}, 'argument': {'p': 0.6914323890031635, 'r': 0.6157583794121126, 'f1': 0.651404978696081}, 'combined': 0.4366173911260218, 'epoch': 5}
Test Korean: {'event': {'p': 0.6253968253968254, 'r': 0.7557544757033248, 'f1': 0.6844238563983787}, 'argument': {'p': 0.7217273465797566, 'r': 0.6830584493271099, 'f1': 0.7018606874131883}, 'combined': 0.48037019833375133, 'epoch': 5}
Dev Russian: {'event': {'p': 0.5669291338582677, 'r': 0.7669773635153129, 'f1': 0.6519524617996605}, 'argument': {'p': 0.693675105554045, 'r': 0.6305685594183976, 'f1': 0.6606181680661036}, 'combined': 0.4306916409802781, 'epoch': 5}
Test Russian: {'event': {'p': 0.6233183856502242, 'r': 0.7998721227621484, 'f1': 0.7006440772892747}, 'argument': {'p': 0.7281978207557468, 'r': 0.6927557703258528, 'f1': 0.7100347899233893}, 'combined': 0.4974816702291571, 'epoch': 5}
Chinese: {'event': {'p': 0.6153846153846154, 'r': 0.8888888888888888, 'f1': 0.7272727272727274}, 'argument': {'p': 0.6753413767302655, 'r': 0.6027840387344519, 'f1': 0.6370032199726521}, 'combined': 0.4632750690710198, 'epoch': 5}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6556681954409226, 'r': 0.3951972684849397, 'f1': 0.49315214699830073}, 'combined': 0.31146451389366364, 'epoch': 5}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6320465795835254, 'r': 0.5530407571355848, 'f1': 0.5899101409446238}, 'combined': 0.3285575468552335, 'epoch': 5}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6964114198955085, 'r': 0.6234341752956798, 'f1': 0.657905259237779}, 'combined': 0.43308151957019264, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6350053361792957, 'r': 0.7608695652173914, 'f1': 0.692262943571844}, 'argument': {'p': 0.7272924330941487, 'r': 0.6900381570870325, 'f1': 0.708175684166213}, 'combined': 0.4902437836869072, 'epoch': 5}
Chinese: {'event': {'p': 0.6153846153846154, 'r': 0.8888888888888888, 'f1': 0.7272727272727274}, 'argument': {'p': 0.6753413767302655, 'r': 0.6027840387344519, 'f1': 0.6370032199726521}, 'combined': 0.4632750690710198, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.6105032822757112, 'r': 0.7430093209054593, 'f1': 0.6702702702702702}, 'argument': {'p': 0.6914323890031635, 'r': 0.6157583794121126, 'f1': 0.651404978696081}, 'combined': 0.4366173911260218, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6253968253968254, 'r': 0.7557544757033248, 'f1': 0.6844238563983787}, 'argument': {'p': 0.7217273465797566, 'r': 0.6830584493271099, 'f1': 0.7018606874131883}, 'combined': 0.48037019833375133, 'epoch': 5}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6556681954409226, 'r': 0.3951972684849397, 'f1': 0.49315214699830073}, 'combined': 0.31146451389366364, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
2023-01-23 07:50:57.834277: step: 2/1545, loss: 0.42893439531326294
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2023-01-23 08:13:43.147333: step: 3090/1545, loss: 0.2105856090784073
==================================================
Loss: 0.615
--------------------
Dev Chinese: {'event': {'p': 0.5954198473282443, 'r': 0.7270306258322237, 'f1': 0.6546762589928057}, 'argument': {'p': 0.6799690518533097, 'r': 0.6413171087412293, 'f1': 0.6600777323995562}, 'combined': 0.43213722049179576, 'epoch': 6}
Test Chinese: {'event': {'p': 0.6408751334044824, 'r': 0.7679028132992327, 'f1': 0.6986620127981384}, 'argument': {'p': 0.7187725442920168, 'r': 0.6932491206115442, 'f1': 0.7057801542785751}, 'combined': 0.49310178318125, 'epoch': 6}
Dev Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6762623994878489, 'r': 0.6457543213154647, 'f1': 0.6606563441150525}, 'combined': 0.4420247251316327, 'epoch': 6}
Test Korean: {'event': {'p': 0.6322751322751323, 'r': 0.7640664961636828, 'f1': 0.6919513607411696}, 'argument': {'p': 0.7171651136399282, 'r': 0.6930310677507069, 'f1': 0.7048915761058339}, 'combined': 0.48775068526141946, 'epoch': 6}
Dev Russian: {'event': {'p': 0.562992125984252, 'r': 0.7616511318242344, 'f1': 0.6474250141482739}, 'argument': {'p': 0.6801431975961602, 'r': 0.6485199444890426, 'f1': 0.6639552415933222}, 'combined': 0.4298612316823773, 'epoch': 6}
Test Russian: {'event': {'p': 0.6268061783756851, 'r': 0.8043478260869565, 'f1': 0.7045645477457295}, 'argument': {'p': 0.723251864631154, 'r': 0.6999648334225133, 'f1': 0.7114178348826229}, 'combined': 0.5012397850923213, 'epoch': 6}
Chinese: {'event': {'p': 0.5512820512820513, 'r': 0.7962962962962963, 'f1': 0.6515151515151515}, 'argument': {'p': 0.6279018252702463, 'r': 0.5915769262876701, 'f1': 0.6091983666451751}, 'combined': 0.3969019661476141, 'epoch': 6}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6901455026455027, 'r': 0.34507275132275134, 'f1': 0.46009700176366847}, 'combined': 0.2825157028373403, 'epoch': 6}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5399046555296555, 'r': 0.462775419025419, 'f1': 0.49837352818122044}, 'combined': 0.2775751296199202, 'epoch': 6}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6964114198955085, 'r': 0.6234341752956798, 'f1': 0.657905259237779}, 'combined': 0.43308151957019264, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6350053361792957, 'r': 0.7608695652173914, 'f1': 0.692262943571844}, 'argument': {'p': 0.7272924330941487, 'r': 0.6900381570870325, 'f1': 0.708175684166213}, 'combined': 0.4902437836869072, 'epoch': 5}
Chinese: {'event': {'p': 0.6153846153846154, 'r': 0.8888888888888888, 'f1': 0.7272727272727274}, 'argument': {'p': 0.6753413767302655, 'r': 0.6027840387344519, 'f1': 0.6370032199726521}, 'combined': 0.4632750690710198, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.6105032822757112, 'r': 0.7430093209054593, 'f1': 0.6702702702702702}, 'argument': {'p': 0.6914323890031635, 'r': 0.6157583794121126, 'f1': 0.651404978696081}, 'combined': 0.4366173911260218, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6253968253968254, 'r': 0.7557544757033248, 'f1': 0.6844238563983787}, 'argument': {'p': 0.7217273465797566, 'r': 0.6830584493271099, 'f1': 0.7018606874131883}, 'combined': 0.48037019833375133, 'epoch': 5}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6556681954409226, 'r': 0.3951972684849397, 'f1': 0.49315214699830073}, 'combined': 0.31146451389366364, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
2023-01-23 08:18:59.092089: step: 2/1545, loss: 0.4041239619255066
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2023-01-23 08:41:56.320427: step: 3090/1545, loss: 0.25390711426734924
==================================================
Loss: 0.502
--------------------
Dev Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Dev Korean: {'event': {'p': 0.612691466083151, 'r': 0.7456724367509987, 'f1': 0.6726726726726727}, 'argument': {'p': 0.662984814123997, 'r': 0.6580037110877536, 'f1': 0.6604848713859729}, 'combined': 0.4442901236950689, 'epoch': 7}
Test Korean: {'event': {'p': 0.6317460317460317, 'r': 0.7634271099744245, 'f1': 0.6913723219455704}, 'argument': {'p': 0.697622599900428, 'r': 0.7136353604184179, 'f1': 0.7055381362079215}, 'combined': 0.48778953945122083, 'epoch': 7}
Dev Russian: {'event': {'p': 0.5708661417322834, 'r': 0.7723035952063915, 'f1': 0.656479909451047}, 'argument': {'p': 0.6611753521292184, 'r': 0.6607024083866366, 'f1': 0.6609387956526069}, 'combined': 0.4338930407227074, 'epoch': 7}
Test Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7036865752196788, 'r': 0.7218649255342268, 'f1': 0.7126598470160488}, 'combined': 0.5029131376310397, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6358730158730159, 'r': 0.43552946292672323, 'f1': 0.5169699316040779}, 'combined': 0.3174376773007496, 'epoch': 7}
Russian: {'event': {'p': 0.46511627906976744, 'r': 0.5555555555555556, 'f1': 0.5063291139240507}, 'argument': {'p': 0.5833514236099365, 'r': 0.5521004544879756, 'f1': 0.5672958798408557}, 'combined': 0.2872384201725852, 'epoch': 7}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.612691466083151, 'r': 0.7456724367509987, 'f1': 0.6726726726726727}, 'argument': {'p': 0.662984814123997, 'r': 0.6580037110877536, 'f1': 0.6604848713859729}, 'combined': 0.4442901236950689, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6317460317460317, 'r': 0.7634271099744245, 'f1': 0.6913723219455704}, 'argument': {'p': 0.697622599900428, 'r': 0.7136353604184179, 'f1': 0.7055381362079215}, 'combined': 0.48778953945122083, 'epoch': 7}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6358730158730159, 'r': 0.43552946292672323, 'f1': 0.5169699316040779}, 'combined': 0.3174376773007496, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
2023-01-23 08:47:30.455464: step: 2/1545, loss: 0.12301982939243317
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2023-01-23 09:10:21.850549: step: 3082/1545, loss: 0.2333299219608307
2023-01-23 09:10:22.705620: step: 3084/1545, loss: 0.16710446774959564
2023-01-23 09:10:23.616878: step: 3086/1545, loss: 0.5646357536315918
2023-01-23 09:10:24.506827: step: 3088/1545, loss: 0.186584934592247
2023-01-23 09:10:25.396913: step: 3090/1545, loss: 0.3787005841732025
==================================================
Loss: 0.403
--------------------
Dev Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.658083880362888, 'r': 0.6492241123400518, 'f1': 0.6536239745471757}, 'combined': 0.4310469856126458, 'epoch': 8}
Test Chinese: {'event': {'p': 0.6387406616862327, 'r': 0.7653452685421995, 'f1': 0.6963350785340314}, 'argument': {'p': 0.7105799845148731, 'r': 0.7122097551215586, 'f1': 0.7113939363871696}, 'combined': 0.49536855256279355, 'epoch': 8}
Dev Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6577124920673522, 'r': 0.6517871543009796, 'f1': 0.654736417442613}, 'combined': 0.43806388530394647, 'epoch': 8}
Test Korean: {'event': {'p': 0.6280423280423281, 'r': 0.7589514066496164, 'f1': 0.6873190503763752}, 'argument': {'p': 0.707702090430235, 'r': 0.7112229963527735, 'f1': 0.7094581750218732}, 'combined': 0.48762411913779014, 'epoch': 8}
Dev Russian: {'event': {'p': 0.5679133858267716, 'r': 0.7683089214380826, 'f1': 0.653084323712507}, 'argument': {'p': 0.6501582658436547, 'r': 0.6589944654509718, 'f1': 0.6545465454354946}, 'combined': 0.42747408796409775, 'epoch': 8}
Test Russian: {'event': {'p': 0.6238166417538615, 'r': 0.8005115089514067, 'f1': 0.7012041444973396}, 'argument': {'p': 0.7119859522076413, 'r': 0.7226484149887367, 'f1': 0.7172775609197011}, 'combined': 0.5029579984718374, 'epoch': 8}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.68475665748393, 'r': 0.6225060522581182, 'f1': 0.6521491976037429}, 'combined': 0.4545282286329118, 'epoch': 8}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5396825396825397, 'f1': 0.5964912280701754}, 'argument': {'p': 0.6637820512820513, 'r': 0.36876780626780625, 'f1': 0.4741300366300366}, 'combined': 0.2828144078144078, 'epoch': 8}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5913205745636086, 'r': 0.5385240946918579, 'f1': 0.5636887720139073}, 'combined': 0.31395324010901166, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.612691466083151, 'r': 0.7456724367509987, 'f1': 0.6726726726726727}, 'argument': {'p': 0.662984814123997, 'r': 0.6580037110877536, 'f1': 0.6604848713859729}, 'combined': 0.4442901236950689, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6317460317460317, 'r': 0.7634271099744245, 'f1': 0.6913723219455704}, 'argument': {'p': 0.697622599900428, 'r': 0.7136353604184179, 'f1': 0.7055381362079215}, 'combined': 0.48778953945122083, 'epoch': 7}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6358730158730159, 'r': 0.43552946292672323, 'f1': 0.5169699316040779}, 'combined': 0.3174376773007496, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
2023-01-23 09:15:38.440906: step: 2/1545, loss: 0.18555378913879395
2023-01-23 09:15:39.338845: step: 4/1545, loss: 0.16183839738368988
2023-01-23 09:15:40.264457: step: 6/1545, loss: 0.12861491739749908
2023-01-23 09:15:41.233327: step: 8/1545, loss: 0.12324485182762146
2023-01-23 09:15:42.132741: step: 10/1545, loss: 0.17686064541339874
2023-01-23 09:15:43.037115: step: 12/1545, loss: 0.08496680110692978
2023-01-23 09:15:43.903185: step: 14/1545, loss: 0.2579628527164459
2023-01-23 09:15:44.762312: step: 16/1545, loss: 0.13385805487632751
2023-01-23 09:15:45.634392: step: 18/1545, loss: 0.20973585546016693
2023-01-23 09:15:46.483824: step: 20/1545, loss: 0.510250449180603
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2023-01-23 09:15:48.198192: step: 24/1545, loss: 0.8993829488754272
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2023-01-23 09:15:49.979496: step: 28/1545, loss: 0.37467867136001587
2023-01-23 09:15:50.864472: step: 30/1545, loss: 1.4405430555343628
2023-01-23 09:15:51.739348: step: 32/1545, loss: 0.11764224618673325
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2023-01-23 09:38:30.892004: step: 3090/1545, loss: 0.10561300814151764
==================================================
Loss: 0.330
--------------------
Dev Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.651816810178987, 'r': 0.645970919504736, 'f1': 0.648880698421424}, 'combined': 0.42791892581748586, 'epoch': 9}
Test Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.7159741031102995, 'r': 0.7088554313394132, 'f1': 0.71239698422466}, 'combined': 0.497310285671665, 'epoch': 9}
Dev Korean: {'event': {'p': 0.612691466083151, 'r': 0.7456724367509987, 'f1': 0.6726726726726727}, 'argument': {'p': 0.6540410788569386, 'r': 0.6466702177127959, 'f1': 0.6503347637141906}, 'combined': 0.4374624236395757, 'epoch': 9}
Test Korean: {'event': {'p': 0.6354497354497355, 'r': 0.7679028132992327, 'f1': 0.6954255935147655}, 'argument': {'p': 0.7132062904851311, 'r': 0.7077515578619179, 'f1': 0.7104684544372034}, 'combined': 0.4940779466005103, 'epoch': 9}
Dev Russian: {'event': {'p': 0.5708661417322834, 'r': 0.7723035952063915, 'f1': 0.656479909451047}, 'argument': {'p': 0.6537343970311752, 'r': 0.6579429875700025, 'f1': 0.6558319405510614}, 'combined': 0.43054049294806523, 'epoch': 9}
Test Russian: {'event': {'p': 0.6273044344793224, 'r': 0.8049872122762148, 'f1': 0.7051246149537944}, 'argument': {'p': 0.7177230819877092, 'r': 0.7201423732753082, 'f1': 0.7189306923331735}, 'combined': 0.5069357276098938, 'epoch': 9}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6557547956630525, 'r': 0.5907212622088654, 'f1': 0.6215415019762845}, 'combined': 0.43319559228650145, 'epoch': 9}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6633986928104574, 'r': 0.46990740740740733, 'f1': 0.5501355013550133}, 'combined': 0.3474540008557979, 'epoch': 9}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5830037357738188, 'r': 0.5934145167697799, 'f1': 0.5881630608691624}, 'combined': 0.32758448959801445, 'epoch': 9}
New best korean model...
==================================================
Current best result:
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Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
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Dev for Korean: {'event': {'p': 0.612691466083151, 'r': 0.7456724367509987, 'f1': 0.6726726726726727}, 'argument': {'p': 0.6540410788569386, 'r': 0.6466702177127959, 'f1': 0.6503347637141906}, 'combined': 0.4374624236395757, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6354497354497355, 'r': 0.7679028132992327, 'f1': 0.6954255935147655}, 'argument': {'p': 0.7132062904851311, 'r': 0.7077515578619179, 'f1': 0.7104684544372034}, 'combined': 0.4940779466005103, 'epoch': 9}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6633986928104574, 'r': 0.46990740740740733, 'f1': 0.5501355013550133}, 'combined': 0.3474540008557979, 'epoch': 9}
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Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
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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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2023-01-23 10:06:33.937620: step: 3068/1545, loss: 0.015766138210892677
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2023-01-23 10:06:40.151092: step: 3082/1545, loss: 0.0819525346159935
2023-01-23 10:06:41.046892: step: 3084/1545, loss: 0.4032060503959656
2023-01-23 10:06:41.945045: step: 3086/1545, loss: 0.07920178025960922
2023-01-23 10:06:42.819209: step: 3088/1545, loss: 0.28814783692359924
2023-01-23 10:06:43.672149: step: 3090/1545, loss: 0.09629587084054947
==================================================
Loss: 0.297
--------------------
Dev Chinese: {'event': {'p': 0.5932388222464559, 'r': 0.7243675099866844, 'f1': 0.6522781774580336}, 'argument': {'p': 0.6456739634692049, 'r': 0.6703401598264555, 'f1': 0.6577759010424976}, 'combined': 0.4290528659078162, 'epoch': 10}
Test Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6888905035480135, 'r': 0.7304184316520144, 'f1': 0.7090469293925666}, 'combined': 0.4933217728641709, 'epoch': 10}
Dev Korean: {'event': {'p': 0.6017505470459519, 'r': 0.7323568575233023, 'f1': 0.6606606606606608}, 'argument': {'p': 0.6407581917570931, 'r': 0.6686800363265232, 'f1': 0.6544214179048546}, 'combined': 0.43235048630350764, 'epoch': 10}
Test Korean: {'event': {'p': 0.6312169312169312, 'r': 0.7627877237851662, 'f1': 0.6907932831499711}, 'argument': {'p': 0.6799729567320546, 'r': 0.7262048842715144, 'f1': 0.7023289202153028}, 'combined': 0.4851641006467031, 'epoch': 10}
Dev Russian: {'event': {'p': 0.562007874015748, 'r': 0.7603195739014648, 'f1': 0.6462931522354273}, 'argument': {'p': 0.6402602479328566, 'r': 0.6750669566902938, 'f1': 0.6572030678642276}, 'combined': 0.42474584238876506, 'epoch': 10}
Test Russian: {'event': {'p': 0.6233183856502242, 'r': 0.7998721227621484, 'f1': 0.7006440772892747}, 'argument': {'p': 0.6896732799106262, 'r': 0.7407984425546936, 'f1': 0.7143222527305366}, 'combined': 0.5004856556515829, 'epoch': 10}
Chinese: {'event': {'p': 0.5769230769230769, 'r': 0.8333333333333334, 'f1': 0.6818181818181818}, 'argument': {'p': 0.6304924242424242, 'r': 0.6252817430503381, 'f1': 0.6278762731044889}, 'combined': 0.42809745893487877, 'epoch': 10}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.5476190476190477, 'r': 0.4350945857795173, 'f1': 0.48491457651763004}, 'combined': 0.3062618378006085, 'epoch': 10}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5592347339448142, 'r': 0.5891937375490006, 'f1': 0.5738234661346789}, 'combined': 0.3195978798724794, 'epoch': 10}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.612691466083151, 'r': 0.7456724367509987, 'f1': 0.6726726726726727}, 'argument': {'p': 0.6540410788569386, 'r': 0.6466702177127959, 'f1': 0.6503347637141906}, 'combined': 0.4374624236395757, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6354497354497355, 'r': 0.7679028132992327, 'f1': 0.6954255935147655}, 'argument': {'p': 0.7132062904851311, 'r': 0.7077515578619179, 'f1': 0.7104684544372034}, 'combined': 0.4940779466005103, 'epoch': 9}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6633986928104574, 'r': 0.46990740740740733, 'f1': 0.5501355013550133}, 'combined': 0.3474540008557979, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
2023-01-23 10:12:03.216746: step: 2/1545, loss: 0.04014342650771141
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2023-01-23 10:34:55.044665: step: 3090/1545, loss: 0.07026205211877823
==================================================
Loss: 0.237
--------------------
Dev Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6646509869560432, 'r': 0.6397763242705625, 'f1': 0.6519764830095129}, 'combined': 0.4291787639954708, 'epoch': 11}
Test Chinese: {'event': {'p': 0.6414087513340448, 'r': 0.768542199488491, 'f1': 0.6992437463641652}, 'argument': {'p': 0.7169484890275156, 'r': 0.6862651757791084, 'f1': 0.7012713647067265}, 'combined': 0.4903596162754422, 'epoch': 11}
Dev Korean: {'event': {'p': 0.611597374179431, 'r': 0.7443408788282291, 'f1': 0.6714714714714715}, 'argument': {'p': 0.665808631000232, 'r': 0.6417975008063844, 'f1': 0.6535826117622782}, 'combined': 0.4388620780481844, 'epoch': 11}
Test Korean: {'event': {'p': 0.6312169312169312, 'r': 0.7627877237851662, 'f1': 0.6907932831499711}, 'argument': {'p': 0.7129914922238261, 'r': 0.6881324018819225, 'f1': 0.7003414189556707}, 'combined': 0.4837911481262972, 'epoch': 11}
Dev Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6645260284596569, 'r': 0.644547436337033, 'f1': 0.6543842794788046}, 'combined': 0.4303308051807419, 'epoch': 11}
Test Russian: {'event': {'p': 0.6292974588938715, 'r': 0.8075447570332481, 'f1': 0.7073648837860543}, 'argument': {'p': 0.7211792065906051, 'r': 0.6960688860316661, 'f1': 0.7084015982436961}, 'combined': 0.5010984142155073, 'epoch': 11}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6943722943722943, 'r': 0.6025544703230653, 'f1': 0.6452131938857603}, 'combined': 0.44969404422340875, 'epoch': 11}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5396825396825397, 'f1': 0.5964912280701754}, 'argument': {'p': 0.7074468085106383, 'r': 0.4618055555555556, 'f1': 0.5588235294117648}, 'combined': 0.33333333333333337, 'epoch': 11}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5857649368175684, 'r': 0.5753048486601118, 'f1': 0.5804877752246173}, 'combined': 0.3233096469605463, 'epoch': 11}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.612691466083151, 'r': 0.7456724367509987, 'f1': 0.6726726726726727}, 'argument': {'p': 0.6540410788569386, 'r': 0.6466702177127959, 'f1': 0.6503347637141906}, 'combined': 0.4374624236395757, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6354497354497355, 'r': 0.7679028132992327, 'f1': 0.6954255935147655}, 'argument': {'p': 0.7132062904851311, 'r': 0.7077515578619179, 'f1': 0.7104684544372034}, 'combined': 0.4940779466005103, 'epoch': 9}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6633986928104574, 'r': 0.46990740740740733, 'f1': 0.5501355013550133}, 'combined': 0.3474540008557979, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
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2023-01-23 11:02:49.774295: step: 3090/1545, loss: 0.06495800614356995
==================================================
Loss: 0.214
--------------------
Dev Chinese: {'event': {'p': 0.6019629225736096, 'r': 0.7350199733688415, 'f1': 0.6618705035971223}, 'argument': {'p': 0.6525858486705476, 'r': 0.6506319988242286, 'f1': 0.651607459092331}, 'combined': 0.4312797570970824, 'epoch': 12}
Test Chinese: {'event': {'p': 0.6398078975453575, 'r': 0.7666240409207161, 'f1': 0.6974985456660848}, 'argument': {'p': 0.6989886500288407, 'r': 0.7224571739519761, 'f1': 0.7105291755827877}, 'combined': 0.49559306662231667, 'epoch': 12}
Dev Korean: {'event': {'p': 0.6083150984682714, 'r': 0.7403462050599201, 'f1': 0.667867867867868}, 'argument': {'p': 0.6567437110319474, 'r': 0.6528022634038487, 'f1': 0.6547670557692479}, 'combined': 0.437297877486729, 'epoch': 12}
Test Korean: {'event': {'p': 0.6306878306878307, 'r': 0.7621483375959079, 'f1': 0.6902142443543717}, 'argument': {'p': 0.6938558610878542, 'r': 0.7238160109934436, 'f1': 0.708519356936541}, 'combined': 0.48903015255840004, 'epoch': 12}
Dev Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6513734211734282, 'r': 0.6555728200205349, 'f1': 0.6534663739726289}, 'combined': 0.43120644711493217, 'epoch': 12}
Test Russian: {'event': {'p': 0.6263079222720478, 'r': 0.8037084398976982, 'f1': 0.7040044805376645}, 'argument': {'p': 0.6984572049417093, 'r': 0.7308706381051718, 'f1': 0.714296395397593}, 'combined': 0.5028678627918087, 'epoch': 12}
Chinese: {'event': {'p': 0.6153846153846154, 'r': 0.8888888888888888, 'f1': 0.7272727272727274}, 'argument': {'p': 0.662396694214876, 'r': 0.6021788129226145, 'f1': 0.6308539944903582}, 'combined': 0.45880290508389693, 'epoch': 12}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6986772486772487, 'r': 0.4306914546640574, 'f1': 0.5328894269572236}, 'combined': 0.33656174334140443, 'epoch': 12}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5741057386463273, 'r': 0.6048614032166663, 'f1': 0.589082410089275}, 'combined': 0.3280965322016215, 'epoch': 12}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.612691466083151, 'r': 0.7456724367509987, 'f1': 0.6726726726726727}, 'argument': {'p': 0.6540410788569386, 'r': 0.6466702177127959, 'f1': 0.6503347637141906}, 'combined': 0.4374624236395757, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6354497354497355, 'r': 0.7679028132992327, 'f1': 0.6954255935147655}, 'argument': {'p': 0.7132062904851311, 'r': 0.7077515578619179, 'f1': 0.7104684544372034}, 'combined': 0.4940779466005103, 'epoch': 9}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6633986928104574, 'r': 0.46990740740740733, 'f1': 0.5501355013550133}, 'combined': 0.3474540008557979, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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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2023-01-23 11:30:51.665606: step: 3090/1545, loss: 0.14433418214321136
==================================================
Loss: 0.189
--------------------
Dev Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6712254768413644, 'r': 0.6536409770738136, 'f1': 0.6623165304289838}, 'combined': 0.43598534197303607, 'epoch': 13}
Test Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.7075100625542532, 'r': 0.7242590598350336, 'f1': 0.7157865953614917}, 'combined': 0.4996765063605527, 'epoch': 13}
Dev Korean: {'event': {'p': 0.6137855579868708, 'r': 0.7470039946737683, 'f1': 0.6738738738738739}, 'argument': {'p': 0.6690091520011272, 'r': 0.655950414799904, 'f1': 0.6624154302604034}, 'combined': 0.446384452103407, 'epoch': 13}
Test Korean: {'event': {'p': 0.6338624338624339, 'r': 0.7659846547314578, 'f1': 0.6936884771279677}, 'argument': {'p': 0.7011231647409775, 'r': 0.7214999756474074, 'f1': 0.7111656375114853}, 'combined': 0.4933274080710825, 'epoch': 13}
Dev Russian: {'event': {'p': 0.5748031496062992, 'r': 0.7776298268974701, 'f1': 0.6610073571024335}, 'argument': {'p': 0.6672747326469398, 'r': 0.6644088425997805, 'f1': 0.6658387038105403}, 'combined': 0.4401242818623153, 'epoch': 13}
Test Russian: {'event': {'p': 0.6258096661684106, 'r': 0.80306905370844, 'f1': 0.7034444133295996}, 'argument': {'p': 0.7109982329063265, 'r': 0.7357540680468313, 'f1': 0.7231643480232148}, 'combined': 0.5087059205360728, 'epoch': 13}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6697240259740259, 'r': 0.6199098422238918, 'f1': 0.643854857588763}, 'combined': 0.4585027016162403, 'epoch': 13}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.7052910052910053, 'r': 0.44080687830687837, 'f1': 0.5425315425315426}, 'combined': 0.33313340330884195, 'epoch': 13}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.6177016825623637, 'r': 0.5625497466192956, 'f1': 0.5888371179566457}, 'combined': 0.3130526449896091, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.612691466083151, 'r': 0.7456724367509987, 'f1': 0.6726726726726727}, 'argument': {'p': 0.6540410788569386, 'r': 0.6466702177127959, 'f1': 0.6503347637141906}, 'combined': 0.4374624236395757, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6354497354497355, 'r': 0.7679028132992327, 'f1': 0.6954255935147655}, 'argument': {'p': 0.7132062904851311, 'r': 0.7077515578619179, 'f1': 0.7104684544372034}, 'combined': 0.4940779466005103, 'epoch': 9}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6633986928104574, 'r': 0.46990740740740733, 'f1': 0.5501355013550133}, 'combined': 0.3474540008557979, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
2023-01-23 11:36:05.139001: step: 2/1545, loss: 0.03652255982160568
2023-01-23 11:36:06.030267: step: 4/1545, loss: 0.17792992293834686
2023-01-23 11:36:06.924602: step: 6/1545, loss: 0.04200435057282448
2023-01-23 11:36:07.808612: step: 8/1545, loss: 0.10593477636575699
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2023-01-23 11:58:54.514589: step: 3090/1545, loss: 0.0974901095032692
==================================================
Loss: 0.183
--------------------
Dev Chinese: {'event': {'p': 0.608505997818975, 'r': 0.7430093209054593, 'f1': 0.6690647482014388}, 'argument': {'p': 0.6648547066556879, 'r': 0.6419630026840101, 'f1': 0.6532083561202113}, 'combined': 0.43703868431064496, 'epoch': 14}
Test Chinese: {'event': {'p': 0.6451440768409819, 'r': 0.7730179028132992, 'f1': 0.7033158813263525}, 'argument': {'p': 0.7171363992606675, 'r': 0.7081003163375663, 'f1': 0.7125897131558447}, 'combined': 0.5011756621322957, 'epoch': 14}
Dev Korean: {'event': {'p': 0.6192560175054704, 'r': 0.7536617842876165, 'f1': 0.6798798798798799}, 'argument': {'p': 0.6632184815074122, 'r': 0.6433020105912738, 'f1': 0.6531084436795553}, 'combined': 0.44403529023739136, 'epoch': 14}
Test Korean: {'event': {'p': 0.6343915343915344, 'r': 0.7666240409207161, 'f1': 0.694267515923567}, 'argument': {'p': 0.7134384328444346, 'r': 0.7077112894615091, 'f1': 0.7105633211403983}, 'combined': 0.4933210318745441, 'epoch': 14}
Dev Russian: {'event': {'p': 0.5787401574803149, 'r': 0.7829560585885486, 'f1': 0.66553480475382}, 'argument': {'p': 0.6663333620967928, 'r': 0.6491499802662165, 'f1': 0.6576294429982861}, 'combined': 0.4376752829462277, 'epoch': 14}
Test Russian: {'event': {'p': 0.6307922272047832, 'r': 0.809462915601023, 'f1': 0.7090450854102492}, 'argument': {'p': 0.7232622949848798, 'r': 0.719740793174092, 'f1': 0.7214972471465462}, 'combined': 0.5115740772262826, 'epoch': 14}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6799915038232794, 'r': 0.6013148009015777, 'f1': 0.6382376395534289}, 'combined': 0.45450256150016904, 'epoch': 14}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6992753623188407, 'r': 0.44063926940639275, 'f1': 0.5406162464985995}, 'combined': 0.34144183989385235, 'epoch': 14}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6076806456298288, 'r': 0.5968292055292962, 'f1': 0.6022060452187493}, 'combined': 0.3354058986028477, 'epoch': 14}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6192560175054704, 'r': 0.7536617842876165, 'f1': 0.6798798798798799}, 'argument': {'p': 0.6632184815074122, 'r': 0.6433020105912738, 'f1': 0.6531084436795553}, 'combined': 0.44403529023739136, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6343915343915344, 'r': 0.7666240409207161, 'f1': 0.694267515923567}, 'argument': {'p': 0.7134384328444346, 'r': 0.7077112894615091, 'f1': 0.7105633211403983}, 'combined': 0.4933210318745441, 'epoch': 14}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6992753623188407, 'r': 0.44063926940639275, 'f1': 0.5406162464985995}, 'combined': 0.34144183989385235, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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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2023-01-23 12:27:00.749596: step: 3086/1545, loss: 0.7292872667312622
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2023-01-23 12:27:02.556895: step: 3090/1545, loss: 0.4995597302913666
==================================================
Loss: 0.148
--------------------
Dev Chinese: {'event': {'p': 0.6030534351145038, 'r': 0.7363515312916112, 'f1': 0.6630695443645084}, 'argument': {'p': 0.6532830345792509, 'r': 0.6659966265695656, 'f1': 0.6595785716063304}, 'combined': 0.43734646294760277, 'epoch': 15}
Test Chinese: {'event': {'p': 0.6430096051227321, 'r': 0.770460358056266, 'f1': 0.7009889470622455}, 'argument': {'p': 0.6948353928350569, 'r': 0.7316987972640924, 'f1': 0.7127907971117018}, 'combined': 0.4996584703429905, 'epoch': 15}
Dev Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6585116653200844, 'r': 0.6649385809726077, 'f1': 0.6617095180093489}, 'combined': 0.4427293712086574, 'epoch': 15}
Test Korean: {'event': {'p': 0.6312169312169312, 'r': 0.7627877237851662, 'f1': 0.6907932831499711}, 'argument': {'p': 0.687670847656362, 'r': 0.7257871599308927, 'f1': 0.7062150680227693}, 'combined': 0.48784862544942903, 'epoch': 15}
Dev Russian: {'event': {'p': 0.5728346456692913, 'r': 0.7749667110519307, 'f1': 0.6587436332767402}, 'argument': {'p': 0.6523178704592026, 'r': 0.6681825396978385, 'f1': 0.660154904945285}, 'combined': 0.43487284060911807, 'epoch': 15}
Test Russian: {'event': {'p': 0.630293971101146, 'r': 0.8088235294117647, 'f1': 0.7084850182021843}, 'argument': {'p': 0.6983500056232312, 'r': 0.7388909235526697, 'f1': 0.7180486864005926}, 'combined': 0.5087267366545785, 'epoch': 15}
Chinese: {'event': {'p': 0.5769230769230769, 'r': 0.8333333333333334, 'f1': 0.6818181818181818}, 'argument': {'p': 0.657262277951933, 'r': 0.6301026796894564, 'f1': 0.643395985168137}, 'combined': 0.438679080796457, 'epoch': 15}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6113428997329925, 'r': 0.47734993540795306, 'f1': 0.5361006966889319}, 'combined': 0.3385899136982728, 'epoch': 15}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5554601722730378, 'r': 0.5356223089775722, 'f1': 0.545360896413528}, 'combined': 0.3175519143673707, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6192560175054704, 'r': 0.7536617842876165, 'f1': 0.6798798798798799}, 'argument': {'p': 0.6632184815074122, 'r': 0.6433020105912738, 'f1': 0.6531084436795553}, 'combined': 0.44403529023739136, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6343915343915344, 'r': 0.7666240409207161, 'f1': 0.694267515923567}, 'argument': {'p': 0.7134384328444346, 'r': 0.7077112894615091, 'f1': 0.7105633211403983}, 'combined': 0.4933210318745441, 'epoch': 14}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6992753623188407, 'r': 0.44063926940639275, 'f1': 0.5406162464985995}, 'combined': 0.34144183989385235, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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
2023-01-23 12:32:17.516305: step: 2/1545, loss: 0.03986426070332527
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2023-01-23 12:55:04.320212: step: 3090/1545, loss: 0.05332675948739052
==================================================
Loss: 0.141
--------------------
Dev Chinese: {'event': {'p': 0.5965103598691385, 'r': 0.7283621837549934, 'f1': 0.6558752997601919}, 'argument': {'p': 0.6570539472018659, 'r': 0.6590182340395396, 'f1': 0.6580346247350031}, 'combined': 0.4315886567506555, 'epoch': 16}
Test Chinese: {'event': {'p': 0.6387406616862327, 'r': 0.7653452685421995, 'f1': 0.6963350785340314}, 'argument': {'p': 0.6985107908580674, 'r': 0.7323699017126379, 'f1': 0.7150397400734531}, 'combined': 0.49790725355900134, 'epoch': 16}
Dev Korean: {'event': {'p': 0.6039387308533917, 'r': 0.7350199733688415, 'f1': 0.6630630630630631}, 'argument': {'p': 0.6579040422602876, 'r': 0.6633453538880043, 'f1': 0.660613493576223}, 'combined': 0.43802840655144154, 'epoch': 16}
Test Korean: {'event': {'p': 0.629100529100529, 'r': 0.760230179028133, 'f1': 0.6884771279675738}, 'argument': {'p': 0.6911946012374106, 'r': 0.7268367980162687, 'f1': 0.7085677666008533}, 'combined': 0.4878327009197536, 'epoch': 16}
Dev Russian: {'event': {'p': 0.5679133858267716, 'r': 0.7683089214380826, 'f1': 0.653084323712507}, 'argument': {'p': 0.6521642958266336, 'r': 0.6633522465770547, 'f1': 0.6577106966416014}, 'combined': 0.42954054551466214, 'epoch': 16}
Test Russian: {'event': {'p': 0.6258096661684106, 'r': 0.80306905370844, 'f1': 0.7034444133295996}, 'argument': {'p': 0.700616930504113, 'r': 0.7410119163886631, 'f1': 0.7202484820502618}, 'combined': 0.5066547709073811, 'epoch': 16}
Chinese: {'event': {'p': 0.5769230769230769, 'r': 0.8333333333333334, 'f1': 0.6818181818181818}, 'argument': {'p': 0.6944444444444444, 'r': 0.6198347107438017, 'f1': 0.6550218340611353}, 'combined': 0.44660579595077404, 'epoch': 16}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6699346405228758, 'r': 0.46803652968036524, 'f1': 0.5510752688172041}, 'combined': 0.3383795510281078, 'epoch': 16}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.6180881691408007, 'r': 0.6070508804061435, 'f1': 0.6125198072566492}, 'combined': 0.3256434418326489, 'epoch': 16}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6192560175054704, 'r': 0.7536617842876165, 'f1': 0.6798798798798799}, 'argument': {'p': 0.6632184815074122, 'r': 0.6433020105912738, 'f1': 0.6531084436795553}, 'combined': 0.44403529023739136, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6343915343915344, 'r': 0.7666240409207161, 'f1': 0.694267515923567}, 'argument': {'p': 0.7134384328444346, 'r': 0.7077112894615091, 'f1': 0.7105633211403983}, 'combined': 0.4933210318745441, 'epoch': 14}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6992753623188407, 'r': 0.44063926940639275, 'f1': 0.5406162464985995}, 'combined': 0.34144183989385235, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6600380148157703, 'r': 0.6373432979739673, 'f1': 0.648492160329197}, 'combined': 0.42792408542379373, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7083565804213369, 'r': 0.7086213866196253, 'f1': 0.7084889587768632}, 'combined': 0.5007634085558115, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6156786087148964, 'r': 0.5826958261051699, 'f1': 0.5987333259062295}, 'combined': 0.3486295315403361, 'epoch': 3}
******************************
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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2023-01-23 13:22:59.831697: step: 3082/1545, loss: 0.09680679440498352
2023-01-23 13:23:00.718617: step: 3084/1545, loss: 0.009383108466863632
2023-01-23 13:23:01.654412: step: 3086/1545, loss: 0.11574792861938477
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2023-01-23 13:23:03.413376: step: 3090/1545, loss: 0.06388762593269348
==================================================
Loss: 0.135
--------------------
Dev Chinese: {'event': {'p': 0.6052344601962922, 'r': 0.7390146471371505, 'f1': 0.6654676258992807}, 'argument': {'p': 0.6499157257873251, 'r': 0.6533184259223374, 'f1': 0.6516126336875532}, 'combined': 0.4336271123460336, 'epoch': 17}
Test Chinese: {'event': {'p': 0.6414087513340448, 'r': 0.768542199488491, 'f1': 0.6992437463641652}, 'argument': {'p': 0.7023112844001405, 'r': 0.7119687352169491, 'f1': 0.7071070367215375}, 'combined': 0.49444017343763125, 'epoch': 17}
Dev Korean: {'event': {'p': 0.6105032822757112, 'r': 0.7430093209054593, 'f1': 0.6702702702702702}, 'argument': {'p': 0.6585233819851708, 'r': 0.6550600734397942, 'f1': 0.6567871621456619}, 'combined': 0.44022490868141656, 'epoch': 17}
Test Korean: {'event': {'p': 0.6322751322751323, 'r': 0.7640664961636828, 'f1': 0.6919513607411696}, 'argument': {'p': 0.6974316896181407, 'r': 0.7096907105285093, 'f1': 0.7035077990352685}, 'combined': 0.4867931788344793, 'epoch': 17}
Dev Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Chinese: {'event': {'p': 0.6153846153846154, 'r': 0.8888888888888888, 'f1': 0.7272727272727274}, 'argument': {'p': 0.6713113275613275, 'r': 0.6213790800567659, 'f1': 0.6453808470975851}, 'combined': 0.4693678887982438, 'epoch': 17}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6641681363903587, 'r': 0.49130245705588177, 'f1': 0.5648043994500689}, 'combined': 0.3468097189605686, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6105032822757112, 'r': 0.7430093209054593, 'f1': 0.6702702702702702}, 'argument': {'p': 0.6585233819851708, 'r': 0.6550600734397942, 'f1': 0.6567871621456619}, 'combined': 0.44022490868141656, 'epoch': 17}
Test for Korean: {'event': {'p': 0.6322751322751323, 'r': 0.7640664961636828, 'f1': 0.6919513607411696}, 'argument': {'p': 0.6974316896181407, 'r': 0.7096907105285093, 'f1': 0.7035077990352685}, 'combined': 0.4867931788344793, 'epoch': 17}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6641681363903587, 'r': 0.49130245705588177, 'f1': 0.5648043994500689}, 'combined': 0.3468097189605686, 'epoch': 17}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 13:28:28.201934: step: 2/1545, loss: 0.14539097249507904
2023-01-23 13:28:29.041417: step: 4/1545, loss: 0.033329740166664124
2023-01-23 13:28:29.942900: step: 6/1545, loss: 0.07417663186788559
2023-01-23 13:28:30.877081: step: 8/1545, loss: 0.08235273510217667
2023-01-23 13:28:31.758554: step: 10/1545, loss: 0.024416405707597733
2023-01-23 13:28:32.626380: step: 12/1545, loss: 0.0021931547671556473
2023-01-23 13:28:33.528238: step: 14/1545, loss: 0.06490126997232437
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2023-01-23 13:51:12.238541: step: 3090/1545, loss: 0.10804931819438934
==================================================
Loss: 0.116
--------------------
Dev Chinese: {'event': {'p': 0.6030534351145038, 'r': 0.7363515312916112, 'f1': 0.6630695443645084}, 'argument': {'p': 0.6464189693864957, 'r': 0.6570715666348125, 'f1': 0.6517017395672173}, 'combined': 0.4321235755163923, 'epoch': 18}
Test Chinese: {'event': {'p': 0.6366061899679829, 'r': 0.7627877237851662, 'f1': 0.6940081442699244}, 'argument': {'p': 0.7044881340243597, 'r': 0.708524544265141, 'f1': 0.7065005739425402}, 'combined': 0.49031715224749883, 'epoch': 18}
Dev Korean: {'event': {'p': 0.6061269146608315, 'r': 0.7376830892143809, 'f1': 0.6654654654654655}, 'argument': {'p': 0.6571334539663469, 'r': 0.6596057393462053, 'f1': 0.658367275697414}, 'combined': 0.4381206855692101, 'epoch': 18}
Test Korean: {'event': {'p': 0.6275132275132275, 'r': 0.7583120204603581, 'f1': 0.6867400115807759}, 'argument': {'p': 0.6972332451712081, 'r': 0.7022972480986748, 'f1': 0.6997560849461563}, 'combined': 0.48055050187964177, 'epoch': 18}
Dev Russian: {'event': {'p': 0.5688976377952756, 'r': 0.7696404793608522, 'f1': 0.6542161856253538}, 'argument': {'p': 0.6484735469415961, 'r': 0.6619158148910645, 'f1': 0.6551257340166957}, 'combined': 0.4285938588134127, 'epoch': 18}
Test Russian: {'event': {'p': 0.6233183856502242, 'r': 0.7998721227621484, 'f1': 0.7006440772892747}, 'argument': {'p': 0.7072013830251471, 'r': 0.7175274234362219, 'f1': 0.7123269830879572}, 'combined': 0.49908768179391455, 'epoch': 18}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6680632411067193, 'r': 0.6349361382419234, 'f1': 0.6510785824345146}, 'combined': 0.463646869309427, 'epoch': 18}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6462780514504654, 'r': 0.5134811915633833, 'f1': 0.572276747849267}, 'combined': 0.35139800306533936, 'epoch': 18}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5516353328059318, 'r': 0.5713365946918579, 'f1': 0.5613131456621762}, 'combined': 0.3268405658286088, 'epoch': 18}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6061269146608315, 'r': 0.7376830892143809, 'f1': 0.6654654654654655}, 'argument': {'p': 0.6571334539663469, 'r': 0.6596057393462053, 'f1': 0.658367275697414}, 'combined': 0.4381206855692101, 'epoch': 18}
Test for Korean: {'event': {'p': 0.6275132275132275, 'r': 0.7583120204603581, 'f1': 0.6867400115807759}, 'argument': {'p': 0.6972332451712081, 'r': 0.7022972480986748, 'f1': 0.6997560849461563}, 'combined': 0.48055050187964177, 'epoch': 18}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6462780514504654, 'r': 0.5134811915633833, 'f1': 0.572276747849267}, 'combined': 0.35139800306533936, 'epoch': 18}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
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==================================================
Loss: 0.109
--------------------
Dev Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6669509987520128, 'r': 0.6669509987520128, 'f1': 0.6669509987520128}, 'combined': 0.44063549198124585, 'epoch': 19}
Test Chinese: {'event': {'p': 0.6392742796157951, 'r': 0.7659846547314578, 'f1': 0.6969168121000582}, 'argument': {'p': 0.7165364993103626, 'r': 0.7044984672486382, 'f1': 0.7104664943104287}, 'combined': 0.4951360443187281, 'epoch': 19}
Dev Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.665609309737802, 'r': 0.6721299736613826, 'f1': 0.6688537495677951}, 'combined': 0.44590249971186335, 'epoch': 19}
Test Korean: {'event': {'p': 0.6312169312169312, 'r': 0.7627877237851662, 'f1': 0.6907932831499711}, 'argument': {'p': 0.7096480242524722, 'r': 0.7004141808446711, 'f1': 0.7050008684696171}, 'combined': 0.48700986455370776, 'epoch': 19}
Dev Russian: {'event': {'p': 0.5679133858267716, 'r': 0.7683089214380826, 'f1': 0.653084323712507}, 'argument': {'p': 0.6637001701988704, 'r': 0.6722640433627267, 'f1': 0.667954658469376}, 'combined': 0.436230716397091, 'epoch': 19}
Test Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7186624970441664, 'r': 0.7122050279580339, 'f1': 0.7154191913034632}, 'combined': 0.5056617302856178, 'epoch': 19}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6338609307359306, 'r': 0.5867142499373904, 'f1': 0.6093770321238132}, 'combined': 0.43395031075483664, 'epoch': 19}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6713836477987422, 'r': 0.48744292237442927, 'f1': 0.5648148148148148}, 'combined': 0.346816114359974, 'epoch': 19}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5577002310554942, 'r': 0.5577002310554942, 'f1': 0.5577002310554942}, 'combined': 0.3106178502081233, 'epoch': 19}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.665609309737802, 'r': 0.6721299736613826, 'f1': 0.6688537495677951}, 'combined': 0.44590249971186335, 'epoch': 19}
Test for Korean: {'event': {'p': 0.6312169312169312, 'r': 0.7627877237851662, 'f1': 0.6907932831499711}, 'argument': {'p': 0.7096480242524722, 'r': 0.7004141808446711, 'f1': 0.7050008684696171}, 'combined': 0.48700986455370776, 'epoch': 19}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6713836477987422, 'r': 0.48744292237442927, 'f1': 0.5648148148148148}, 'combined': 0.346816114359974, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
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2023-01-23 14:47:30.134544: step: 3090/1545, loss: 0.05127254128456116
==================================================
Loss: 0.095
--------------------
Dev Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6717678270879244, 'r': 0.6642255236400809, 'f1': 0.6679753854596523}, 'combined': 0.4413122750458854, 'epoch': 20}
Test Chinese: {'event': {'p': 0.6414087513340448, 'r': 0.768542199488491, 'f1': 0.6992437463641652}, 'argument': {'p': 0.7142472009963787, 'r': 0.7035990202475795, 'f1': 0.7088831261188785}, 'combined': 0.4956820928417056, 'epoch': 20}
Dev Korean: {'event': {'p': 0.6050328227571116, 'r': 0.7363515312916112, 'f1': 0.6642642642642642}, 'argument': {'p': 0.6764248869158849, 'r': 0.6647361022434091, 'f1': 0.6705295582311311}, 'combined': 0.44540882366584444, 'epoch': 20}
Test Korean: {'event': {'p': 0.6333333333333333, 'r': 0.7653452685421995, 'f1': 0.6931094383323683}, 'argument': {'p': 0.7105687610691339, 'r': 0.7005109782996015, 'f1': 0.7055040251493577}, 'combined': 0.48899149861249636, 'epoch': 20}
Dev Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6774217798792312, 'r': 0.6686933675400573, 'f1': 0.6730292755428386}, 'combined': 0.44259197406948414, 'epoch': 20}
Test Russian: {'event': {'p': 0.6283009466865969, 'r': 0.8062659846547314, 'f1': 0.7062447493699243}, 'argument': {'p': 0.7173603528297395, 'r': 0.7103748213040566, 'f1': 0.7138504978855218}, 'combined': 0.5041531659667561, 'epoch': 20}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.657937869258624, 'r': 0.5763753234827614, 'f1': 0.6144617986027677}, 'combined': 0.4282612535716261, 'epoch': 20}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.7246031746031747, 'r': 0.4592555331991952, 'f1': 0.5621921182266011}, 'combined': 0.3452056866303691, 'epoch': 20}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6398969860548809, 'r': 0.5713365946918579, 'f1': 0.6036764019385669}, 'combined': 0.3362248314594549, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.665609309737802, 'r': 0.6721299736613826, 'f1': 0.6688537495677951}, 'combined': 0.44590249971186335, 'epoch': 19}
Test for Korean: {'event': {'p': 0.6312169312169312, 'r': 0.7627877237851662, 'f1': 0.6907932831499711}, 'argument': {'p': 0.7096480242524722, 'r': 0.7004141808446711, 'f1': 0.7050008684696171}, 'combined': 0.48700986455370776, 'epoch': 19}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6713836477987422, 'r': 0.48744292237442927, 'f1': 0.5648148148148148}, 'combined': 0.346816114359974, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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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2023-01-23 15:15:34.700874: step: 3090/1545, loss: 0.0005659238086082041
==================================================
Loss: 0.108
--------------------
Dev Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6752094979248321, 'r': 0.6468648713252889, 'f1': 0.6607333371729981}, 'combined': 0.436527660410458, 'epoch': 21}
Test Chinese: {'event': {'p': 0.6419423692636073, 'r': 0.7691815856777494, 'f1': 0.6998254799301921}, 'argument': {'p': 0.7117491540587041, 'r': 0.703334118293708, 'f1': 0.7075166154483417}, 'combined': 0.49513815496472086, 'epoch': 21}
Dev Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Dev Russian: {'event': {'p': 0.5738188976377953, 'r': 0.7762982689747004, 'f1': 0.6598754951895868}, 'argument': {'p': 0.6755193864987635, 'r': 0.6532760852969431, 'f1': 0.6642115656185077}, 'combined': 0.4382969357731635, 'epoch': 21}
Test Russian: {'event': {'p': 0.6307922272047832, 'r': 0.809462915601023, 'f1': 0.7090450854102492}, 'argument': {'p': 0.7168265240567604, 'r': 0.7149493128078276, 'f1': 0.7158866878199376}, 'combined': 0.5075959377093481, 'epoch': 21}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6905940594059405, 'r': 0.5764462809917356, 'f1': 0.6283783783783784}, 'combined': 0.4379606879606881, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.6180519634839396, 'r': 0.5849420368687286, 'f1': 0.6010413589843816}, 'combined': 0.3195409756625826, 'epoch': 21}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 15:20:56.820070: step: 2/1545, loss: 0.09232566505670547
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==================================================
Loss: 0.096
--------------------
Dev Chinese: {'event': {'p': 0.5954198473282443, 'r': 0.7270306258322237, 'f1': 0.6546762589928057}, 'argument': {'p': 0.6449224411644022, 'r': 0.6642170542134495, 'f1': 0.6544275618890065}, 'combined': 0.42843818799927763, 'epoch': 22}
Test Chinese: {'event': {'p': 0.6419423692636073, 'r': 0.7691815856777494, 'f1': 0.6998254799301921}, 'argument': {'p': 0.6899783438979525, 'r': 0.720527003398778, 'f1': 0.7049218629218812}, 'combined': 0.49332228103259057, 'epoch': 22}
Dev Korean: {'event': {'p': 0.6050328227571116, 'r': 0.7363515312916112, 'f1': 0.6642642642642642}, 'argument': {'p': 0.6495165582802774, 'r': 0.6661207409731568, 'f1': 0.6577138719334065}, 'combined': 0.43689582123624476, 'epoch': 22}
Test Korean: {'event': {'p': 0.6328042328042328, 'r': 0.7647058823529411, 'f1': 0.6925303995367689}, 'argument': {'p': 0.6861019053788218, 'r': 0.7136298763626874, 'f1': 0.699595200015416}, 'combined': 0.48449094338068177, 'epoch': 22}
Dev Russian: {'event': {'p': 0.5708661417322834, 'r': 0.7723035952063915, 'f1': 0.656479909451047}, 'argument': {'p': 0.6477106149487214, 'r': 0.6722661675898389, 'f1': 0.6597599875679852}, 'combined': 0.43311917689805474, 'epoch': 22}
Test Russian: {'event': {'p': 0.6243148978574987, 'r': 0.801150895140665, 'f1': 0.7017642117054047}, 'argument': {'p': 0.6955400831529507, 'r': 0.7317498477815125, 'f1': 0.7131856519717606}, 'combined': 0.5004881668555677, 'epoch': 22}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6623009758602978, 'r': 0.6458802905083896, 'f1': 0.6539875744896664}, 'combined': 0.4657184242577927, 'epoch': 22}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.654040404040404, 'r': 0.4927701674277017, 'f1': 0.5620659722222222}, 'combined': 0.34512822855750486, 'epoch': 22}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5379664923375895, 'r': 0.5667861258556747, 'f1': 0.5520004008333528}, 'combined': 0.3074432612236395, 'epoch': 22}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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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2023-01-23 16:11:58.870629: step: 3088/1545, loss: 0.05891648307442665
2023-01-23 16:11:59.774676: step: 3090/1545, loss: 0.043314337730407715
==================================================
Loss: 0.083
--------------------
Dev Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6723718371838484, 'r': 0.6638097988077244, 'f1': 0.6680633859090178}, 'combined': 0.4405693791965945, 'epoch': 23}
Test Chinese: {'event': {'p': 0.6376734258271078, 'r': 0.7640664961636828, 'f1': 0.6951716114019779}, 'argument': {'p': 0.7092168493286913, 'r': 0.7021788195261929, 'f1': 0.7056802865970946}, 'combined': 0.4905689019683118, 'epoch': 23}
Dev Korean: {'event': {'p': 0.6050328227571116, 'r': 0.7363515312916112, 'f1': 0.6642642642642642}, 'argument': {'p': 0.6705292368440877, 'r': 0.6604460904253796, 'f1': 0.6654494698982992}, 'combined': 0.44203430252703835, 'epoch': 23}
Test Korean: {'event': {'p': 0.6301587301587301, 'r': 0.7615089514066496, 'f1': 0.6896352055587724}, 'argument': {'p': 0.7054837411912293, 'r': 0.6984720526319892, 'f1': 0.7019603878929249}, 'combined': 0.48409659639865293, 'epoch': 23}
Dev Russian: {'event': {'p': 0.5669291338582677, 'r': 0.7669773635153129, 'f1': 0.6519524617996605}, 'argument': {'p': 0.6732697025728679, 'r': 0.6650884409976435, 'f1': 0.669154066131394}, 'combined': 0.43625664073761516, 'epoch': 23}
Test Russian: {'event': {'p': 0.6273044344793224, 'r': 0.8049872122762148, 'f1': 0.7051246149537944}, 'argument': {'p': 0.7137762458468431, 'r': 0.7113720593900237, 'f1': 0.7125721247198288}, 'combined': 0.5024521450698765, 'epoch': 23}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6691588785046728, 'r': 0.5917355371900826, 'f1': 0.6280701754385964}, 'combined': 0.43774587985114305, 'epoch': 23}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6718020541549953, 'r': 0.469341161121983, 'f1': 0.552611367127496}, 'combined': 0.3393227692888134, 'epoch': 23}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5526021400184079, 'r': 0.5427342446609363, 'f1': 0.5476237423605844}, 'combined': 0.305005628656528, 'epoch': 23}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 16:17:19.140070: step: 2/1545, loss: 0.06070634350180626
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2023-01-23 16:40:00.828587: step: 3084/1545, loss: 0.0008712914423085749
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2023-01-23 16:40:03.555918: step: 3090/1545, loss: 0.0007412445265799761
==================================================
Loss: 0.086
--------------------
Dev Chinese: {'event': {'p': 0.6030534351145038, 'r': 0.7363515312916112, 'f1': 0.6630695443645084}, 'argument': {'p': 0.6570935553400805, 'r': 0.6634874297558149, 'f1': 0.6602750138947997}, 'combined': 0.43780825261849426, 'epoch': 24}
Test Chinese: {'event': {'p': 0.6371398078975453, 'r': 0.7634271099744245, 'f1': 0.694589877835951}, 'argument': {'p': 0.7025436650262697, 'r': 0.7167337619189498, 'f1': 0.7095677763725049}, 'combined': 0.4928585951069056, 'epoch': 24}
Dev Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6614245020593517, 'r': 0.6644038917082676, 'f1': 0.6629108492549906}, 'combined': 0.44194056616999366, 'epoch': 24}
Test Korean: {'event': {'p': 0.6301587301587301, 'r': 0.7615089514066496, 'f1': 0.6896352055587724}, 'argument': {'p': 0.6986998567150585, 'r': 0.7120338234462619, 'f1': 0.7053038251150118}, 'combined': 0.48640234841457963, 'epoch': 24}
Dev Russian: {'event': {'p': 0.5728346456692913, 'r': 0.7749667110519307, 'f1': 0.6587436332767402}, 'argument': {'p': 0.6546270102563835, 'r': 0.669611319504026, 'f1': 0.6620343880244897}, 'combined': 0.43611093812139556, 'epoch': 24}
Test Russian: {'event': {'p': 0.6263079222720478, 'r': 0.8037084398976982, 'f1': 0.7040044805376645}, 'argument': {'p': 0.7047270861568067, 'r': 0.727409170516644, 'f1': 0.7158885096208002}, 'combined': 0.5039887183384743, 'epoch': 24}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6790024135156878, 'r': 0.6341096919609316, 'f1': 0.6557886557886557}, 'combined': 0.4570648207011844, 'epoch': 24}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5396825396825397, 'f1': 0.5964912280701754}, 'argument': {'p': 0.613232074438971, 'r': 0.4939925044091711, 'f1': 0.5471916971916972}, 'combined': 0.32639504744767905, 'epoch': 24}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.575557373912637, 'r': 0.575557373912637, 'f1': 0.575557373912637}, 'combined': 0.30599252790292086, 'epoch': 24}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 16:45:21.097340: step: 2/1545, loss: 0.0007386198849417269
2023-01-23 16:45:21.983872: step: 4/1545, loss: 0.006372077856212854
2023-01-23 16:45:22.856924: step: 6/1545, loss: 0.10809977352619171
2023-01-23 16:45:23.722420: step: 8/1545, loss: 0.06674730777740479
2023-01-23 16:45:24.638499: step: 10/1545, loss: 0.0004942889208905399
2023-01-23 16:45:25.498265: step: 12/1545, loss: 0.0008639309089630842
2023-01-23 16:45:26.396325: step: 14/1545, loss: 0.0014895333442837
2023-01-23 16:45:27.269077: step: 16/1545, loss: 0.0013995087938383222
2023-01-23 16:45:28.139725: step: 18/1545, loss: 0.02467469871044159
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2023-01-23 16:45:41.400140: step: 48/1545, loss: 0.006285474169999361
2023-01-23 16:45:42.258607: step: 50/1545, loss: 6.276088242884725e-05
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2023-01-23 17:08:09.977918: step: 3090/1545, loss: 0.43655842542648315
==================================================
Loss: 0.083
--------------------
Dev Chinese: {'event': {'p': 0.5965103598691385, 'r': 0.7283621837549934, 'f1': 0.6558752997601919}, 'argument': {'p': 0.6594986522834787, 'r': 0.6659111298299898, 'f1': 0.6626893789227364}, 'combined': 0.4346415950488451, 'epoch': 25}
Test Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.6950880457507395, 'r': 0.7263696587936789, 'f1': 0.710384649517727}, 'combined': 0.4959055144975407, 'epoch': 25}
Dev Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6600102330990629, 'r': 0.6704157998371112, 'f1': 0.6651723243076589}, 'combined': 0.4450462277950343, 'epoch': 25}
Test Korean: {'event': {'p': 0.6317460317460317, 'r': 0.7634271099744245, 'f1': 0.6913723219455704}, 'argument': {'p': 0.6909323864014446, 'r': 0.7210303199321151, 'f1': 0.705660563673278}, 'combined': 0.48787418241221425, 'epoch': 25}
Dev Russian: {'event': {'p': 0.5688976377952756, 'r': 0.7696404793608522, 'f1': 0.6542161856253538}, 'argument': {'p': 0.6565281496607026, 'r': 0.6748433126340699, 'f1': 0.6655597538359292}, 'combined': 0.43541996346029105, 'epoch': 25}
Test Russian: {'event': {'p': 0.6283009466865969, 'r': 0.8062659846547314, 'f1': 0.7062447493699243}, 'argument': {'p': 0.6987320765508586, 'r': 0.7368827434081604, 'f1': 0.7173004935833348}, 'combined': 0.5065897073136852, 'epoch': 25}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7073496873496874, 'r': 0.6138158443943568, 'f1': 0.6572718333780282}, 'combined': 0.4580985505362016, 'epoch': 25}
Korean: {'event': {'p': 0.6666666666666666, 'r': 0.5396825396825397, 'f1': 0.5964912280701754}, 'argument': {'p': 0.6894444444444444, 'r': 0.4722222222222222, 'f1': 0.5605239385727191}, 'combined': 0.33434761248197276, 'epoch': 25}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.563729326629349, 'r': 0.5335295412742054, 'f1': 0.5482138405753303}, 'combined': 0.3053342909533485, 'epoch': 25}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6584459487309184, 'r': 0.6574580328288466, 'f1': 0.6579516199405799}, 'combined': 0.4346898592173376, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6424759871931697, 'r': 0.7698209718670077, 'f1': 0.7004072134962187}, 'argument': {'p': 0.7021344597431646, 'r': 0.7123297633236919, 'f1': 0.7071953682162753}, 'combined': 0.4953247372497937, 'epoch': 7}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7120517097581317, 'r': 0.641435011269722, 'f1': 0.674901185770751}, 'combined': 0.4703856749311296, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
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2023-01-23 17:36:06.995709: step: 3090/1545, loss: 0.07705379277467728
==================================================
Loss: 0.074
--------------------
Dev Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6559451265403835, 'r': 0.6613377994790564, 'f1': 0.6586304247882229}, 'combined': 0.43355887674908195, 'epoch': 26}
Test Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6980547354393568, 'r': 0.7185779178100443, 'f1': 0.7081676638737557}, 'combined': 0.49271002093834315, 'epoch': 26}
Dev Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6562787457968984, 'r': 0.6646672711191144, 'f1': 0.6604463732449922}, 'combined': 0.44188424011706984, 'epoch': 26}
Test Korean: {'event': {'p': 0.6301587301587301, 'r': 0.7615089514066496, 'f1': 0.6896352055587724}, 'argument': {'p': 0.6927764210970498, 'r': 0.7120863404327065, 'f1': 0.7022986727897791}, 'combined': 0.48432988957303236, 'epoch': 26}
Dev Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6530729561342159, 'r': 0.668477822147389, 'f1': 0.6606856045102064}, 'combined': 0.4344746306965817, 'epoch': 26}
Test Russian: {'event': {'p': 0.6292974588938715, 'r': 0.8075447570332481, 'f1': 0.7073648837860543}, 'argument': {'p': 0.7008390992866921, 'r': 0.7289251605689677, 'f1': 0.7146062708717255}, 'combined': 0.5054873817479637, 'epoch': 26}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6657901327322815, 'r': 0.6657901327322815, 'f1': 0.6657901327322815}, 'combined': 0.47412327633965495, 'epoch': 26}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6436097427476738, 'r': 0.5113611654707546, 'f1': 0.5699139706773294}, 'combined': 0.34994717497730754, 'epoch': 26}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5765709872358072, 'r': 0.5868668977221609, 'f1': 0.5816733853529382}, 'combined': 0.32396998677885164, 'epoch': 26}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6559451265403835, 'r': 0.6613377994790564, 'f1': 0.6586304247882229}, 'combined': 0.43355887674908195, 'epoch': 26}
Test for Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6980547354393568, 'r': 0.7185779178100443, 'f1': 0.7081676638737557}, 'combined': 0.49271002093834315, 'epoch': 26}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6657901327322815, 'r': 0.6657901327322815, 'f1': 0.6657901327322815}, 'combined': 0.47412327633965495, 'epoch': 26}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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: 0.080
--------------------
Dev Chinese: {'event': {'p': 0.5965103598691385, 'r': 0.7283621837549934, 'f1': 0.6558752997601919}, 'argument': {'p': 0.6752334003169094, 'r': 0.6438742461448882, 'f1': 0.6591810725486392}, 'combined': 0.43234058355408356, 'epoch': 27}
Test Chinese: {'event': {'p': 0.6387406616862327, 'r': 0.7653452685421995, 'f1': 0.6963350785340314}, 'argument': {'p': 0.7105504586733332, 'r': 0.7078353250903247, 'f1': 0.709190293170513}, 'combined': 0.4938340784904619, 'epoch': 27}
Dev Korean: {'event': {'p': 0.6050328227571116, 'r': 0.7363515312916112, 'f1': 0.6642642642642642}, 'argument': {'p': 0.6772961615056954, 'r': 0.6503061640923106, 'f1': 0.6635268110799947}, 'combined': 0.4407571489816661, 'epoch': 27}
Test Korean: {'event': {'p': 0.6312169312169312, 'r': 0.7627877237851662, 'f1': 0.6907932831499711}, 'argument': {'p': 0.7036217845798122, 'r': 0.7020079731472897, 'f1': 0.7028139524505588}, 'combined': 0.4854991576569292, 'epoch': 27}
Dev Russian: {'event': {'p': 0.5639763779527559, 'r': 0.762982689747004, 'f1': 0.6485568760611204}, 'argument': {'p': 0.6680186568351105, 'r': 0.65417123650043, 'f1': 0.6610224339935727}, 'combined': 0.42871064479718973, 'epoch': 27}
Test Russian: {'event': {'p': 0.6292974588938715, 'r': 0.8075447570332481, 'f1': 0.7073648837860543}, 'argument': {'p': 0.713305570760347, 'r': 0.7149085046272241, 'f1': 0.7141061381798312}, 'combined': 0.5051336054444843, 'epoch': 27}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7091836734693877, 'r': 0.5743801652892562, 'f1': 0.6347031963470319}, 'combined': 0.451985609519856, 'epoch': 27}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.7329122125040493, 'r': 0.4919547727766906, 'f1': 0.5887327608639084}, 'combined': 0.3615025724602946, 'epoch': 27}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5467835375265716, 'r': 0.497963578818842, 'f1': 0.521232904931872}, 'combined': 0.29030693439243505, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6559451265403835, 'r': 0.6613377994790564, 'f1': 0.6586304247882229}, 'combined': 0.43355887674908195, 'epoch': 26}
Test for Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6980547354393568, 'r': 0.7185779178100443, 'f1': 0.7081676638737557}, 'combined': 0.49271002093834315, 'epoch': 26}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6657901327322815, 'r': 0.6657901327322815, 'f1': 0.6657901327322815}, 'combined': 0.47412327633965495, 'epoch': 26}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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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2023-01-23 18:32:10.079534: step: 3068/1545, loss: 0.0008653621189296246
2023-01-23 18:32:10.976516: step: 3070/1545, loss: 0.00350892823189497
2023-01-23 18:32:11.913654: step: 3072/1545, loss: 0.03901705518364906
2023-01-23 18:32:12.865498: step: 3074/1545, loss: 0.001282014767639339
2023-01-23 18:32:13.742280: step: 3076/1545, loss: 0.046293705701828
2023-01-23 18:32:14.644697: step: 3078/1545, loss: 0.07354967296123505
2023-01-23 18:32:15.518707: step: 3080/1545, loss: 0.2509703040122986
2023-01-23 18:32:16.345634: step: 3082/1545, loss: 4.72398241981864e-05
2023-01-23 18:32:17.282473: step: 3084/1545, loss: 0.002963283099234104
2023-01-23 18:32:18.160790: step: 3086/1545, loss: 0.0007737764972262084
2023-01-23 18:32:19.034227: step: 3088/1545, loss: 0.2595474123954773
2023-01-23 18:32:19.881613: step: 3090/1545, loss: 0.008310087025165558
==================================================
Loss: 0.064
--------------------
Dev Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6637511851641956, 'r': 0.6582861679210773, 'f1': 0.66100738094142}, 'combined': 0.43591613851052874, 'epoch': 28}
Test Chinese: {'event': {'p': 0.6414087513340448, 'r': 0.768542199488491, 'f1': 0.6992437463641652}, 'argument': {'p': 0.701797632651199, 'r': 0.7175954427719241, 'f1': 0.7096086230919498}, 'combined': 0.49618939206313184, 'epoch': 28}
Dev Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6654420309320569, 'r': 0.6559428584394129, 'f1': 0.660658300857252}, 'combined': 0.4420260343273145, 'epoch': 28}
Test Korean: {'event': {'p': 0.6322751322751323, 'r': 0.7640664961636828, 'f1': 0.6919513607411696}, 'argument': {'p': 0.6964149686755481, 'r': 0.7134396279556227, 'f1': 0.7048245079199321}, 'combined': 0.4877042773389223, 'epoch': 28}
Dev Russian: {'event': {'p': 0.5728346456692913, 'r': 0.7749667110519307, 'f1': 0.6587436332767402}, 'argument': {'p': 0.663895185623841, 'r': 0.6676970191850369, 'f1': 0.6657906750902901}, 'combined': 0.4385853683107514, 'epoch': 28}
Test Russian: {'event': {'p': 0.6292974588938715, 'r': 0.8075447570332481, 'f1': 0.7073648837860543}, 'argument': {'p': 0.7004601572194137, 'r': 0.729034317824547, 'f1': 0.714461653118353}, 'combined': 0.505385084227656, 'epoch': 28}
Chinese: {'event': {'p': 0.5641025641025641, 'r': 0.8148148148148148, 'f1': 0.6666666666666667}, 'argument': {'p': 0.7147408666100254, 'r': 0.6320435762584522, 'f1': 0.67085326953748}, 'combined': 0.44723551302498676, 'epoch': 28}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6932773109243697, 'r': 0.48434442270058703, 'f1': 0.5702764976958524}, 'combined': 0.35016977928692694, 'epoch': 28}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5618667600955051, 'r': 0.5217334200886833, 'f1': 0.5410568800919678}, 'combined': 0.3013481357474251, 'epoch': 28}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6559451265403835, 'r': 0.6613377994790564, 'f1': 0.6586304247882229}, 'combined': 0.43355887674908195, 'epoch': 26}
Test for Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6980547354393568, 'r': 0.7185779178100443, 'f1': 0.7081676638737557}, 'combined': 0.49271002093834315, 'epoch': 26}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6657901327322815, 'r': 0.6657901327322815, 'f1': 0.6657901327322815}, 'combined': 0.47412327633965495, 'epoch': 26}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 18:37:37.376595: step: 2/1545, loss: 0.0005658617010340095
2023-01-23 18:37:38.238068: step: 4/1545, loss: 0.11908487230539322
2023-01-23 18:37:39.135783: step: 6/1545, loss: 0.0008357217884622514
2023-01-23 18:37:39.982278: step: 8/1545, loss: 7.941525836940855e-05
2023-01-23 18:37:40.921679: step: 10/1545, loss: 0.0031601418741047382
2023-01-23 18:37:41.766814: step: 12/1545, loss: 0.039306171238422394
2023-01-23 18:37:42.652655: step: 14/1545, loss: 3.412686783121899e-05
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==================================================
Loss: 0.066
--------------------
Dev Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6422869359839323, 'r': 0.6644016059354748, 'f1': 0.6531571343118429}, 'combined': 0.43073911735193476, 'epoch': 29}
Test Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6963799774551439, 'r': 0.7216015168597335, 'f1': 0.7087664402632768}, 'combined': 0.4931266216142403, 'epoch': 29}
Dev Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6419906505971872, 'r': 0.6651254488169057, 'f1': 0.6533533169794383}, 'combined': 0.4355688779862922, 'epoch': 29}
Test Korean: {'event': {'p': 0.6275132275132275, 'r': 0.7583120204603581, 'f1': 0.6867400115807759}, 'argument': {'p': 0.6896292603073065, 'r': 0.7133188150506873, 'f1': 0.7012740320573361}, 'combined': 0.4815929368963524, 'epoch': 29}
Dev Russian: {'event': {'p': 0.5679133858267716, 'r': 0.7683089214380826, 'f1': 0.653084323712507}, 'argument': {'p': 0.6431259565585519, 'r': 0.6725891356707547, 'f1': 0.6575276574751885}, 'combined': 0.42942100550445245, 'epoch': 29}
Test Russian: {'event': {'p': 0.6253114100647733, 'r': 0.8024296675191815, 'f1': 0.7028843461215345}, 'argument': {'p': 0.695042191367221, 'r': 0.7306787109096271, 'f1': 0.7124150759169946}, 'combined': 0.5007454048030401, 'epoch': 29}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.7034534534534534, 'r': 0.6453168044077134, 'f1': 0.673132183908046}, 'combined': 0.4691527342389412, 'epoch': 29}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6985714285714286, 'r': 0.47847358121330724, 'f1': 0.5679442508710801}, 'combined': 0.34873769790329484, 'epoch': 29}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5763393342633109, 'r': 0.5557557866110497, 'f1': 0.5658604372767052}, 'combined': 0.31516277519208896, 'epoch': 29}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6559451265403835, 'r': 0.6613377994790564, 'f1': 0.6586304247882229}, 'combined': 0.43355887674908195, 'epoch': 26}
Test for Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6980547354393568, 'r': 0.7185779178100443, 'f1': 0.7081676638737557}, 'combined': 0.49271002093834315, 'epoch': 26}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6657901327322815, 'r': 0.6657901327322815, 'f1': 0.6657901327322815}, 'combined': 0.47412327633965495, 'epoch': 26}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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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2023-01-23 19:28:27.870936: step: 3090/1545, loss: 0.0011728546814993024
==================================================
Loss: 0.060
--------------------
Dev Chinese: {'event': {'p': 0.5965103598691385, 'r': 0.7283621837549934, 'f1': 0.6558752997601919}, 'argument': {'p': 0.6568691654364283, 'r': 0.6529299200664946, 'f1': 0.6548936190742135}, 'combined': 0.4295285487213367, 'epoch': 30}
Test Chinese: {'event': {'p': 0.6398078975453575, 'r': 0.7666240409207161, 'f1': 0.6974985456660848}, 'argument': {'p': 0.6989078550712907, 'r': 0.7154532109378123, 'f1': 0.7070837582815586}, 'combined': 0.49318989306549654, 'epoch': 30}
Dev Korean: {'event': {'p': 0.6039387308533917, 'r': 0.7350199733688415, 'f1': 0.6630630630630631}, 'argument': {'p': 0.6603470462480774, 'r': 0.6504170154774296, 'f1': 0.6553444171098344}, 'combined': 0.4345346765701244, 'epoch': 30}
Test Korean: {'event': {'p': 0.6322751322751323, 'r': 0.7640664961636828, 'f1': 0.6919513607411696}, 'argument': {'p': 0.6924051969707655, 'r': 0.7143653159423259, 'f1': 0.7032138543618833}, 'combined': 0.48658978341774783, 'epoch': 30}
Dev Russian: {'event': {'p': 0.562992125984252, 'r': 0.7616511318242344, 'f1': 0.6474250141482739}, 'argument': {'p': 0.6530003386611138, 'r': 0.6600067800630571, 'f1': 0.656484865548313}, 'combined': 0.4250247233657442, 'epoch': 30}
Test Russian: {'event': {'p': 0.6263079222720478, 'r': 0.8037084398976982, 'f1': 0.7040044805376645}, 'argument': {'p': 0.7005633317324845, 'r': 0.7254804101729885, 'f1': 0.7128041845032378}, 'combined': 0.5018173396362755, 'epoch': 30}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6680506993006993, 'r': 0.5741923365890308, 'f1': 0.6175757575757576}, 'combined': 0.4397887970615243, 'epoch': 30}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6238229755178908, 'r': 0.504185692541857, 'f1': 0.5576599326599326}, 'combined': 0.35220627325890486, 'epoch': 30}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5686085018372468, 'r': 0.527993608848872, 'f1': 0.5475489276951265}, 'combined': 0.3049639597289312, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6559451265403835, 'r': 0.6613377994790564, 'f1': 0.6586304247882229}, 'combined': 0.43355887674908195, 'epoch': 26}
Test for Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6980547354393568, 'r': 0.7185779178100443, 'f1': 0.7081676638737557}, 'combined': 0.49271002093834315, 'epoch': 26}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6657901327322815, 'r': 0.6657901327322815, 'f1': 0.6657901327322815}, 'combined': 0.47412327633965495, 'epoch': 26}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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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2023-01-23 19:56:26.376319: step: 3084/1545, loss: 0.0003583995276130736
2023-01-23 19:56:27.266463: step: 3086/1545, loss: 0.0011432861210778356
2023-01-23 19:56:28.151443: step: 3088/1545, loss: 0.49811333417892456
2023-01-23 19:56:29.032271: step: 3090/1545, loss: 0.004055042751133442
==================================================
Loss: 0.063
--------------------
Dev Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6520749314359015, 'r': 0.6652827507523076, 'f1': 0.6586126303400119}, 'combined': 0.43512656992487597, 'epoch': 31}
Test Chinese: {'event': {'p': 0.6414087513340448, 'r': 0.768542199488491, 'f1': 0.6992437463641652}, 'argument': {'p': 0.7110094532177335, 'r': 0.7229591919272752, 'f1': 0.7169345319945478}, 'combined': 0.5013119880497071, 'epoch': 31}
Dev Korean: {'event': {'p': 0.6105032822757112, 'r': 0.7430093209054593, 'f1': 0.6702702702702702}, 'argument': {'p': 0.6514317684862074, 'r': 0.6592626537572662, 'f1': 0.6553238178871705}, 'combined': 0.4392440725297791, 'epoch': 31}
Test Korean: {'event': {'p': 0.6322751322751323, 'r': 0.7640664961636828, 'f1': 0.6919513607411696}, 'argument': {'p': 0.7013586510852526, 'r': 0.7182750350892232, 'f1': 0.7097160550985628}, 'combined': 0.49108899006530543, 'epoch': 31}
Dev Russian: {'event': {'p': 0.5688976377952756, 'r': 0.7696404793608522, 'f1': 0.6542161856253538}, 'argument': {'p': 0.6502762935859703, 'r': 0.6614398351067595, 'f1': 0.6558105599143614}, 'combined': 0.42904188300000107, 'epoch': 31}
Test Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7093639649934588, 'r': 0.734053653894803, 'f1': 0.7214976506186919}, 'combined': 0.5099580146069947, 'epoch': 31}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6526806526806527, 'r': 0.6311044327573253, 'f1': 0.6417112299465241}, 'combined': 0.45697617890131254, 'epoch': 31}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6652186020607074, 'r': 0.5194172646227441, 'f1': 0.5833455433455434}, 'combined': 0.3581946318788425, 'epoch': 31}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5291288024840657, 'r': 0.5291288024840657, 'f1': 0.5291288024840657}, 'combined': 0.30810031537046856, 'epoch': 31}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5986913849509269, 'r': 0.7310252996005326, 'f1': 0.658273381294964}, 'argument': {'p': 0.6559451265403835, 'r': 0.6613377994790564, 'f1': 0.6586304247882229}, 'combined': 0.43355887674908195, 'epoch': 26}
Test for Chinese: {'event': {'p': 0.6382070437566703, 'r': 0.7647058823529411, 'f1': 0.6957533449680047}, 'argument': {'p': 0.6980547354393568, 'r': 0.7185779178100443, 'f1': 0.7081676638737557}, 'combined': 0.49271002093834315, 'epoch': 26}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.6657901327322815, 'r': 0.6657901327322815, 'f1': 0.6657901327322815}, 'combined': 0.47412327633965495, 'epoch': 26}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 20:01:44.246618: step: 2/1545, loss: 0.0009108028607442975
2023-01-23 20:01:45.101666: step: 4/1545, loss: 9.49410823523067e-05
2023-01-23 20:01:45.950476: step: 6/1545, loss: 0.00018773427291307598
2023-01-23 20:01:46.782537: step: 8/1545, loss: 0.011837727390229702
2023-01-23 20:01:47.680057: step: 10/1545, loss: 0.3349662721157074
2023-01-23 20:01:48.609009: step: 12/1545, loss: 0.393801212310791
2023-01-23 20:01:49.535793: step: 14/1545, loss: 0.00013034367293585092
2023-01-23 20:01:50.432196: step: 16/1545, loss: 0.027653034776449203
2023-01-23 20:01:51.349642: step: 18/1545, loss: 0.3327631950378418
2023-01-23 20:01:52.229658: step: 20/1545, loss: 0.0001711147924652323
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2023-01-23 20:01:59.178555: step: 36/1545, loss: -3.11529693135526e-05
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2023-01-23 20:24:26.504460: step: 3090/1545, loss: 0.0044409772381186485
==================================================
Loss: 0.049
--------------------
Dev Chinese: {'event': {'p': 0.6019629225736096, 'r': 0.7350199733688415, 'f1': 0.6618705035971223}, 'argument': {'p': 0.6768856506816677, 'r': 0.6515909701629208, 'f1': 0.6639975004402042}, 'combined': 0.4394803600035884, 'epoch': 32}
Test Chinese: {'event': {'p': 0.6430096051227321, 'r': 0.770460358056266, 'f1': 0.7009889470622455}, 'argument': {'p': 0.7188174928872716, 'r': 0.7075602288657368, 'f1': 0.7131444385641958}, 'combined': 0.4999063690924118, 'epoch': 32}
Dev Korean: {'event': {'p': 0.6094091903719913, 'r': 0.7416777629826897, 'f1': 0.669069069069069}, 'argument': {'p': 0.6792670818181646, 'r': 0.6527491476931312, 'f1': 0.6657441536962104}, 'combined': 0.44542882115169874, 'epoch': 32}
Test Korean: {'event': {'p': 0.6338624338624339, 'r': 0.7659846547314578, 'f1': 0.6936884771279677}, 'argument': {'p': 0.7140723622898609, 'r': 0.7045259403341141, 'f1': 0.7092670301075603}, 'combined': 0.4920103659923899, 'epoch': 32}
Dev Russian: {'event': {'p': 0.5688976377952756, 'r': 0.7696404793608522, 'f1': 0.6542161856253538}, 'argument': {'p': 0.6814615854379754, 'r': 0.6614759452355741, 'f1': 0.6713200518615844}, 'combined': 0.43918844366270043, 'epoch': 32}
Test Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7217740480829588, 'r': 0.7190707744946331, 'f1': 0.720419875384867}, 'combined': 0.5083892707840562, 'epoch': 32}
Chinese: {'event': {'p': 0.6153846153846154, 'r': 0.8888888888888888, 'f1': 0.7272727272727274}, 'argument': {'p': 0.699276418026418, 'r': 0.6010309708656816, 'f1': 0.6464421997755332}, 'combined': 0.4701397816549333, 'epoch': 32}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6967960023515579, 'r': 0.5154381387258099, 'f1': 0.5925509311336083}, 'combined': 0.36384706297677705, 'epoch': 32}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5467097046336813, 'r': 0.5271843580396213, 'f1': 0.5367695281857962}, 'combined': 0.3125493455259066, 'epoch': 32}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6019629225736096, 'r': 0.7350199733688415, 'f1': 0.6618705035971223}, 'argument': {'p': 0.6768856506816677, 'r': 0.6515909701629208, 'f1': 0.6639975004402042}, 'combined': 0.4394803600035884, 'epoch': 32}
Test for Chinese: {'event': {'p': 0.6430096051227321, 'r': 0.770460358056266, 'f1': 0.7009889470622455}, 'argument': {'p': 0.7188174928872716, 'r': 0.7075602288657368, 'f1': 0.7131444385641958}, 'combined': 0.4999063690924118, 'epoch': 32}
Chinese: {'event': {'p': 0.6153846153846154, 'r': 0.8888888888888888, 'f1': 0.7272727272727274}, 'argument': {'p': 0.699276418026418, 'r': 0.6010309708656816, 'f1': 0.6464421997755332}, 'combined': 0.4701397816549333, 'epoch': 32}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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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2023-01-23 20:52:33.472706: step: 3090/1545, loss: 2.2707690732204355e-05
==================================================
Loss: 0.052
--------------------
Dev Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6758987924276707, 'r': 0.6414709887684336, 'f1': 0.6582350269068861}, 'combined': 0.43408784748056034, 'epoch': 33}
Test Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.7201409239897084, 'r': 0.7110669833666995, 'f1': 0.7155751890249322}, 'combined': 0.49952892776609586, 'epoch': 33}
Dev Korean: {'event': {'p': 0.6083150984682714, 'r': 0.7403462050599201, 'f1': 0.667867867867868}, 'argument': {'p': 0.6759653888065972, 'r': 0.6404149927010662, 'f1': 0.6577101506829622}, 'combined': 0.4392634760116842, 'epoch': 33}
Test Korean: {'event': {'p': 0.6322751322751323, 'r': 0.7640664961636828, 'f1': 0.6919513607411696}, 'argument': {'p': 0.7140529651685205, 'r': 0.7064131168594955, 'f1': 0.7102124958975411}, 'combined': 0.4914325029516859, 'epoch': 33}
Dev Russian: {'event': {'p': 0.5728346456692913, 'r': 0.7749667110519307, 'f1': 0.6587436332767402}, 'argument': {'p': 0.6686780590067114, 'r': 0.6533611671754911, 'f1': 0.6609308838118473}, 'combined': 0.43538401174702335, 'epoch': 33}
Test Russian: {'event': {'p': 0.6287992027902342, 'r': 0.8069053708439897, 'f1': 0.7068048165779893}, 'argument': {'p': 0.7210169141479743, 'r': 0.7193972580323292, 'f1': 0.7202061754888874}, 'combined': 0.5090451937647583, 'epoch': 33}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7175290642271774, 'r': 0.6285791802320727, 'f1': 0.6701152494104036}, 'combined': 0.4772032836710449, 'epoch': 33}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6791554357592093, 'r': 0.4999338624338624, 'f1': 0.5759238095238095}, 'combined': 0.3536374269005848, 'epoch': 33}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5817314502918499, 'r': 0.5505672654547865, 'f1': 0.5657204929443678}, 'combined': 0.3294068693093786, 'epoch': 33}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6758987924276707, 'r': 0.6414709887684336, 'f1': 0.6582350269068861}, 'combined': 0.43408784748056034, 'epoch': 33}
Test for Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.7201409239897084, 'r': 0.7110669833666995, 'f1': 0.7155751890249322}, 'combined': 0.49952892776609586, 'epoch': 33}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7175290642271774, 'r': 0.6285791802320727, 'f1': 0.6701152494104036}, 'combined': 0.4772032836710449, 'epoch': 33}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
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2023-01-23 21:20:22.578983: step: 3068/1545, loss: 0.000458867521956563
2023-01-23 21:20:23.445730: step: 3070/1545, loss: 0.1331682950258255
2023-01-23 21:20:24.300252: step: 3072/1545, loss: 0.0001653057843213901
2023-01-23 21:20:25.191236: step: 3074/1545, loss: 0.05594516545534134
2023-01-23 21:20:26.065544: step: 3076/1545, loss: 0.027075180783867836
2023-01-23 21:20:26.951082: step: 3078/1545, loss: 0.03286608308553696
2023-01-23 21:20:27.768697: step: 3080/1545, loss: 0.11180433630943298
2023-01-23 21:20:28.644771: step: 3082/1545, loss: 0.013995649293065071
2023-01-23 21:20:29.526700: step: 3084/1545, loss: 0.0820547565817833
2023-01-23 21:20:30.407914: step: 3086/1545, loss: 0.0001297267444897443
2023-01-23 21:20:31.287441: step: 3088/1545, loss: 4.4712418457493186e-05
2023-01-23 21:20:32.143409: step: 3090/1545, loss: 0.024229073897004128
==================================================
Loss: 0.059
--------------------
Dev Chinese: {'event': {'p': 0.604143947655398, 'r': 0.7376830892143809, 'f1': 0.6642685851318945}, 'argument': {'p': 0.692643354981853, 'r': 0.6418356837331842, 'f1': 0.666272318156592}, 'combined': 0.44258377009442684, 'epoch': 34}
Test Chinese: {'event': {'p': 0.6398078975453575, 'r': 0.7666240409207161, 'f1': 0.6974985456660848}, 'argument': {'p': 0.7283177912220395, 'r': 0.6999417733822199, 'f1': 0.7138479013302109}, 'combined': 0.4979078730046089, 'epoch': 34}
Dev Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6949801740994155, 'r': 0.6400306874424587, 'f1': 0.666374561288581}, 'combined': 0.4442497075257206, 'epoch': 34}
Test Korean: {'event': {'p': 0.6328042328042328, 'r': 0.7647058823529411, 'f1': 0.6925303995367689}, 'argument': {'p': 0.7233338635735137, 'r': 0.6959913463937589, 'f1': 0.70939923572884}, 'combined': 0.49128053615037204, 'epoch': 34}
Dev Russian: {'event': {'p': 0.5708661417322834, 'r': 0.7723035952063915, 'f1': 0.656479909451047}, 'argument': {'p': 0.6868872536107843, 'r': 0.6485630720788521, 'f1': 0.6671752573702983}, 'combined': 0.43798715254643233, 'epoch': 34}
Test Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7334263866419518, 'r': 0.7108932417544883, 'f1': 0.7219840419492881}, 'combined': 0.509493079168918, 'epoch': 34}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7149041434755721, 'r': 0.5790132732281493, 'f1': 0.639822886398229}, 'combined': 0.45563144940479944, 'epoch': 34}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.7491556906450524, 'r': 0.49591996422982343, 'f1': 0.596785041700296}, 'combined': 0.3664469554300063, 'epoch': 34}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5787600329172, 'r': 0.5064150288025501, 'f1': 0.5401760307227199}, 'combined': 0.30085753609873006, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6758987924276707, 'r': 0.6414709887684336, 'f1': 0.6582350269068861}, 'combined': 0.43408784748056034, 'epoch': 33}
Test for Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.7201409239897084, 'r': 0.7110669833666995, 'f1': 0.7155751890249322}, 'combined': 0.49952892776609586, 'epoch': 33}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7175290642271774, 'r': 0.6285791802320727, 'f1': 0.6701152494104036}, 'combined': 0.4772032836710449, 'epoch': 33}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 21:25:42.842224: step: 2/1545, loss: 5.040541145717725e-05
2023-01-23 21:25:43.835928: step: 4/1545, loss: 0.13711418211460114
2023-01-23 21:25:44.707302: step: 6/1545, loss: 0.13833855092525482
2023-01-23 21:25:45.570281: step: 8/1545, loss: 3.8700527511537075e-05
2023-01-23 21:25:46.427285: step: 10/1545, loss: 0.0016633578343316913
2023-01-23 21:25:47.304082: step: 12/1545, loss: 1.1907724142074585
2023-01-23 21:25:48.170587: step: 14/1545, loss: 0.004788549151271582
2023-01-23 21:25:49.083587: step: 16/1545, loss: 0.0010091199073940516
2023-01-23 21:25:50.038061: step: 18/1545, loss: 0.00012225423415657133
2023-01-23 21:25:50.894672: step: 20/1545, loss: -6.983679668337572e-06
2023-01-23 21:25:51.798722: step: 22/1545, loss: 0.0007519216160289943
2023-01-23 21:25:52.662596: step: 24/1545, loss: 0.0021949775982648134
2023-01-23 21:25:53.516288: step: 26/1545, loss: 1.4517705494654365e-05
2023-01-23 21:25:54.358991: step: 28/1545, loss: 0.0005313888541422784
2023-01-23 21:25:55.269302: step: 30/1545, loss: 0.0008807789999991655
2023-01-23 21:25:56.174095: step: 32/1545, loss: 0.000600870349444449
2023-01-23 21:25:57.085201: step: 34/1545, loss: 0.024078859016299248
2023-01-23 21:25:57.961378: step: 36/1545, loss: 0.0001013085784506984
2023-01-23 21:25:58.835314: step: 38/1545, loss: 0.0023916333448141813
2023-01-23 21:25:59.689591: step: 40/1545, loss: 0.03748514875769615
2023-01-23 21:26:00.574237: step: 42/1545, loss: 0.01957590878009796
2023-01-23 21:26:01.493734: step: 44/1545, loss: 0.00041911762673407793
2023-01-23 21:26:02.377450: step: 46/1545, loss: 3.7712601624662057e-05
2023-01-23 21:26:03.252395: step: 48/1545, loss: 0.00015831067867111415
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2023-01-23 21:48:20.365731: step: 3090/1545, loss: 0.03884175419807434
==================================================
Loss: 0.051
--------------------
Dev Chinese: {'event': {'p': 0.6019629225736096, 'r': 0.7350199733688415, 'f1': 0.6618705035971223}, 'argument': {'p': 0.66422522252087, 'r': 0.6488243385282396, 'f1': 0.6564344613032586}, 'combined': 0.4344746074812934, 'epoch': 35}
Test Chinese: {'event': {'p': 0.6366061899679829, 'r': 0.7627877237851662, 'f1': 0.6940081442699244}, 'argument': {'p': 0.7234055447323137, 'r': 0.7131856114924909, 'f1': 0.7182592257253736}, 'combined': 0.4984777523504193, 'epoch': 35}
Dev Korean: {'event': {'p': 0.6050328227571116, 'r': 0.7363515312916112, 'f1': 0.6642642642642642}, 'argument': {'p': 0.6693489929114083, 'r': 0.647708386373671, 'f1': 0.6583509009377015}, 'combined': 0.4373189768390978, 'epoch': 35}
Test Korean: {'event': {'p': 0.6285714285714286, 'r': 0.7595907928388747, 'f1': 0.6878980891719745}, 'argument': {'p': 0.714131533396885, 'r': 0.7056689655317078, 'f1': 0.7098750293524121}, 'combined': 0.48832167624242356, 'epoch': 35}
Dev Russian: {'event': {'p': 0.5698818897637795, 'r': 0.7709720372836218, 'f1': 0.6553480475382004}, 'argument': {'p': 0.6639555099566563, 'r': 0.6539819293349899, 'f1': 0.6589309817732005}, 'combined': 0.4318291323674965, 'epoch': 35}
Test Russian: {'event': {'p': 0.6238166417538615, 'r': 0.8005115089514067, 'f1': 0.7012041444973396}, 'argument': {'p': 0.7261557140920908, 'r': 0.7234350107796439, 'f1': 0.7247928092232994}, 'combined': 0.5082277217292471, 'epoch': 35}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6915450804339692, 'r': 0.6172468486518072, 'f1': 0.652287062767412}, 'combined': 0.45462431647425694, 'epoch': 35}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6574900793650793, 'r': 0.5404028049575994, 'f1': 0.5932241317579663}, 'combined': 0.36426043178120737, 'epoch': 35}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.5699463722992983, 'r': 0.5394135309261217, 'f1': 0.5542597748965654}, 'combined': 0.2946697537424778, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6758987924276707, 'r': 0.6414709887684336, 'f1': 0.6582350269068861}, 'combined': 0.43408784748056034, 'epoch': 33}
Test for Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.7201409239897084, 'r': 0.7110669833666995, 'f1': 0.7155751890249322}, 'combined': 0.49952892776609586, 'epoch': 33}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7175290642271774, 'r': 0.6285791802320727, 'f1': 0.6701152494104036}, 'combined': 0.4772032836710449, 'epoch': 33}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 21:53:32.401018: step: 2/1545, loss: 0.23937709629535675
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2023-01-23 22:16:19.265919: step: 3090/1545, loss: 0.0011297168675810099
==================================================
Loss: 0.057
--------------------
Dev Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6538672241198886, 'r': 0.6558219841322053, 'f1': 0.6548431453499183}, 'combined': 0.4326361787623561, 'epoch': 36}
Test Chinese: {'event': {'p': 0.6414087513340448, 'r': 0.768542199488491, 'f1': 0.6992437463641652}, 'argument': {'p': 0.7148759095527157, 'r': 0.7102409398874235, 'f1': 0.7125508874631048}, 'combined': 0.49824675202481206, 'epoch': 36}
Dev Korean: {'event': {'p': 0.6105032822757112, 'r': 0.7430093209054593, 'f1': 0.6702702702702702}, 'argument': {'p': 0.6524414022670175, 'r': 0.6548905066298817, 'f1': 0.653663660420384}, 'combined': 0.4381313183358249, 'epoch': 36}
Test Korean: {'event': {'p': 0.6285714285714286, 'r': 0.7595907928388747, 'f1': 0.6878980891719745}, 'argument': {'p': 0.7084413106854012, 'r': 0.7087123287843626, 'f1': 0.7085767938199747}, 'combined': 0.4874286225003648, 'epoch': 36}
Dev Russian: {'event': {'p': 0.5708661417322834, 'r': 0.7723035952063915, 'f1': 0.656479909451047}, 'argument': {'p': 0.6517103789889689, 'r': 0.6619661932505978, 'f1': 0.656798252778095}, 'combined': 0.43117485751136964, 'epoch': 36}
Test Russian: {'event': {'p': 0.6253114100647733, 'r': 0.8024296675191815, 'f1': 0.7028843461215345}, 'argument': {'p': 0.7177601603131445, 'r': 0.7201777656435405, 'f1': 0.7189669306181732}, 'combined': 0.5053506009105613, 'epoch': 36}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6891643709825528, 'r': 0.6265130645295934, 'f1': 0.6563470199833835}, 'combined': 0.45745398362478257, 'epoch': 36}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6670524691358024, 'r': 0.49343607305936066, 'f1': 0.567257217847769}, 'combined': 0.3483158355205599, 'epoch': 36}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5519637400308907, 'r': 0.5716767307462796, 'f1': 0.5616473144173976}, 'combined': 0.3128162257514619, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6758987924276707, 'r': 0.6414709887684336, 'f1': 0.6582350269068861}, 'combined': 0.43408784748056034, 'epoch': 33}
Test for Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.7201409239897084, 'r': 0.7110669833666995, 'f1': 0.7155751890249322}, 'combined': 0.49952892776609586, 'epoch': 33}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7175290642271774, 'r': 0.6285791802320727, 'f1': 0.6701152494104036}, 'combined': 0.4772032836710449, 'epoch': 33}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
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2023-01-23 22:44:16.003961: step: 3088/1545, loss: 0.0003960269968956709
2023-01-23 22:44:16.910137: step: 3090/1545, loss: 0.117294080555439
==================================================
Loss: 0.054
--------------------
Dev Chinese: {'event': {'p': 0.6008724100327154, 'r': 0.7336884154460719, 'f1': 0.6606714628297362}, 'argument': {'p': 0.6853368328247768, 'r': 0.6360541841722085, 'f1': 0.6597764847473957}, 'combined': 0.43589549531872307, 'epoch': 37}
Test Chinese: {'event': {'p': 0.6419423692636073, 'r': 0.7691815856777494, 'f1': 0.6998254799301921}, 'argument': {'p': 0.7281808458432854, 'r': 0.7117640824100587, 'f1': 0.7198788806442992}, 'combined': 0.5037895831385061, 'epoch': 37}
Dev Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6921142718794929, 'r': 0.6343947495815037, 'f1': 0.6619987547573354}, 'combined': 0.4413325031715569, 'epoch': 37}
Test Korean: {'event': {'p': 0.6322751322751323, 'r': 0.7640664961636828, 'f1': 0.6919513607411696}, 'argument': {'p': 0.7237467474202179, 'r': 0.7065674601537351, 'f1': 0.7150539348785571}, 'combined': 0.4947825432425452, 'epoch': 37}
Dev Russian: {'event': {'p': 0.5688976377952756, 'r': 0.7696404793608522, 'f1': 0.6542161856253538}, 'argument': {'p': 0.6844853539606232, 'r': 0.642438396503042, 'f1': 0.6627956927813256}, 'combined': 0.43361166998031264, 'epoch': 37}
Test Russian: {'event': {'p': 0.6263079222720478, 'r': 0.8037084398976982, 'f1': 0.7040044805376645}, 'argument': {'p': 0.7338946921670021, 'r': 0.72096141487722, 'f1': 0.7273705668536204}, 'combined': 0.5120721380761697, 'epoch': 37}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7135076252723311, 'r': 0.6014692378328741, 'f1': 0.6527154957648231}, 'combined': 0.4648125500143437, 'epoch': 37}
Korean: {'event': {'p': 0.7058823529411765, 'r': 0.5714285714285714, 'f1': 0.6315789473684211}, 'argument': {'p': 0.6593073593073593, 'r': 0.4967384213959556, 'f1': 0.5665922619047619}, 'combined': 0.3578477443609023, 'epoch': 37}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5823017333543649, 'r': 0.5719034881158941, 'f1': 0.5770557717926139}, 'combined': 0.3213981513781647, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6758987924276707, 'r': 0.6414709887684336, 'f1': 0.6582350269068861}, 'combined': 0.43408784748056034, 'epoch': 33}
Test for Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.7201409239897084, 'r': 0.7110669833666995, 'f1': 0.7155751890249322}, 'combined': 0.49952892776609586, 'epoch': 33}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7175290642271774, 'r': 0.6285791802320727, 'f1': 0.6701152494104036}, 'combined': 0.4772032836710449, 'epoch': 33}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 22:49:27.649002: step: 2/1545, loss: 0.09286477416753769
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2023-01-23 23:12:06.982758: step: 3082/1545, loss: 0.008942573331296444
2023-01-23 23:12:07.838679: step: 3084/1545, loss: 0.0005663189804181457
2023-01-23 23:12:08.705753: step: 3086/1545, loss: 0.5272532105445862
2023-01-23 23:12:09.581279: step: 3088/1545, loss: 0.0008671785471960902
2023-01-23 23:12:10.479562: step: 3090/1545, loss: 0.01255625206977129
==================================================
Loss: 0.051
--------------------
Dev Chinese: {'event': {'p': 0.604143947655398, 'r': 0.7376830892143809, 'f1': 0.6642685851318945}, 'argument': {'p': 0.6547518161109622, 'r': 0.6547518161109622, 'f1': 0.6547518161109622}, 'combined': 0.43493106250056723, 'epoch': 38}
Test Chinese: {'event': {'p': 0.6430096051227321, 'r': 0.770460358056266, 'f1': 0.7009889470622455}, 'argument': {'p': 0.6996405349315086, 'r': 0.7063164942342901, 'f1': 0.7029626647270049}, 'combined': 0.49276905817105343, 'epoch': 38}
Dev Korean: {'event': {'p': 0.6083150984682714, 'r': 0.7403462050599201, 'f1': 0.667867867867868}, 'argument': {'p': 0.6626196217973507, 'r': 0.6546602269409261, 'f1': 0.6586158778589982}, 'combined': 0.4398683820896133, 'epoch': 38}
Test Korean: {'event': {'p': 0.6301587301587301, 'r': 0.7615089514066496, 'f1': 0.6896352055587724}, 'argument': {'p': 0.6924399079547182, 'r': 0.7051452273667315, 'f1': 0.698734816208852}, 'combined': 0.48187212860726275, 'epoch': 38}
Dev Russian: {'event': {'p': 0.5757874015748031, 'r': 0.7789613848202397, 'f1': 0.6621392190152802}, 'argument': {'p': 0.6564582780597298, 'r': 0.6606874294573944, 'f1': 0.6585660641826472}, 'combined': 0.4360624194078649, 'epoch': 38}
Test Russian: {'event': {'p': 0.6292974588938715, 'r': 0.8075447570332481, 'f1': 0.7073648837860543}, 'argument': {'p': 0.7022199678478288, 'r': 0.7195977398305647, 'f1': 0.7108026563437467}, 'combined': 0.5027968383994131, 'epoch': 38}
Chinese: {'event': {'p': 0.5897435897435898, 'r': 0.8518518518518519, 'f1': 0.6969696969696971}, 'argument': {'p': 0.6666457431457431, 'r': 0.5509468951617712, 'f1': 0.603299315064021}, 'combined': 0.42048134080219657, 'epoch': 38}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6619500139236981, 'r': 0.5168650793650793, 'f1': 0.580479242979243}, 'combined': 0.35643462288199135, 'epoch': 38}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5566564116082233, 'r': 0.5765369977370884, 'f1': 0.5664223135662623}, 'combined': 0.31547571894829796, 'epoch': 38}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5997818974918212, 'r': 0.7323568575233023, 'f1': 0.6594724220623501}, 'argument': {'p': 0.6758987924276707, 'r': 0.6414709887684336, 'f1': 0.6582350269068861}, 'combined': 0.43408784748056034, 'epoch': 33}
Test for Chinese: {'event': {'p': 0.6403415154749199, 'r': 0.7672634271099744, 'f1': 0.6980802792321118}, 'argument': {'p': 0.7201409239897084, 'r': 0.7110669833666995, 'f1': 0.7155751890249322}, 'combined': 0.49952892776609586, 'epoch': 33}
Chinese: {'event': {'p': 0.6025641025641025, 'r': 0.8703703703703703, 'f1': 0.712121212121212}, 'argument': {'p': 0.7175290642271774, 'r': 0.6285791802320727, 'f1': 0.6701152494104036}, 'combined': 0.4772032836710449, 'epoch': 33}
--------------------
Dev for Korean: {'event': {'p': 0.6072210065645515, 'r': 0.7390146471371505, 'f1': 0.6666666666666666}, 'argument': {'p': 0.679479351255927, 'r': 0.6457861602845586, 'f1': 0.6622044524951831}, 'combined': 0.4414696349967887, 'epoch': 21}
Test for Korean: {'event': {'p': 0.6349206349206349, 'r': 0.7672634271099744, 'f1': 0.6948465547191662}, 'argument': {'p': 0.7120765802085607, 'r': 0.7036480296218995, 'f1': 0.7078372151256981}, 'combined': 0.4918382502321006, 'epoch': 21}
Korean: {'event': {'p': 0.6862745098039216, 'r': 0.5555555555555556, 'f1': 0.6140350877192983}, 'argument': {'p': 0.6946010860484545, 'r': 0.5423597521200261, 'f1': 0.6091117216117216}, 'combined': 0.37401596941070625, 'epoch': 21}
--------------------
Dev for Russian: {'event': {'p': 0.5718503937007874, 'r': 0.7736351531291611, 'f1': 0.6576117713638936}, 'argument': {'p': 0.6589821494992243, 'r': 0.6537970252900029, 'f1': 0.656379347472477}, 'combined': 0.4316427853780522, 'epoch': 17}
Test for Russian: {'event': {'p': 0.6278026905829597, 'r': 0.8056265984654731, 'f1': 0.7056846821618594}, 'argument': {'p': 0.7071220972103918, 'r': 0.7219364770995731, 'f1': 0.7144525002914325}, 'combined': 0.5041781855879053, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5898371784374884, 'r': 0.610902791953113, 'f1': 0.6001851991118303}, 'combined': 0.3494749260651163, 'epoch': 17}
******************************
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
2023-01-23 23:17:26.384149: step: 2/1545, loss: 0.0006336256046779454
2023-01-23 23:17:27.313713: step: 4/1545, loss: 0.00010930249118246138
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