Command that produces this log: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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> trainable params:
>>> pad_vector: torch.Size([1, 1, 1024])
>>> xlmr.embeddings.word_embeddings.weight: torch.Size([250002, 1024])
>>> xlmr.embeddings.position_embeddings.weight: torch.Size([514, 1024])
>>> xlmr.embeddings.token_type_embeddings.weight: torch.Size([1, 1024])
>>> xlmr.embeddings.LayerNorm.weight: torch.Size([1024])
>>> xlmr.embeddings.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.0.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.0.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.0.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.1.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.1.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.1.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.2.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.2.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.2.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.3.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.3.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.3.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.4.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.4.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.4.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.5.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.5.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.5.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.6.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.6.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.6.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.7.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.7.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.7.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.8.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.8.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.8.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.9.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.9.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.9.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.10.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.10.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.10.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.11.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.11.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.11.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.12.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.12.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.12.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.13.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.13.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.13.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.14.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.14.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.14.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.15.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.15.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.15.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.16.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.16.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.16.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.17.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.17.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.17.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.18.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.18.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.18.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.19.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.19.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.19.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.20.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.20.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.20.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.21.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.21.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.21.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.22.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.22.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.22.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.23.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.23.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.23.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.pooler.dense.weight: torch.Size([1024, 1024])
>>> xlmr.pooler.dense.bias: torch.Size([1024])
>>> upos_embedding.weight: torch.Size([19, 50])
>>> dist_embedding.weight: torch.Size([104, 50])
>>> deprel_embedding.weight: torch.Size([38, 50])
>>> gcn_layer.W.0.weight: torch.Size([450, 50])
>>> gcn_layer.W.0.bias: torch.Size([450])
>>> gcn_layer.W.1.weight: torch.Size([450, 450])
>>> gcn_layer.W.1.bias: torch.Size([450])
>>> trans_rep.weight: torch.Size([1024, 2924])
>>> trans_rep.bias: torch.Size([1024])
>>> mention_label_ffn.layers.0.weight: torch.Size([350, 1024])
>>> mention_label_ffn.layers.0.bias: torch.Size([350])
>>> mention_label_ffn.layers.1.weight: torch.Size([11, 350])
>>> mention_label_ffn.layers.1.bias: torch.Size([11])
>>> trigger_label_ffn.layers.0.weight: torch.Size([450, 1024])
>>> trigger_label_ffn.layers.0.bias: torch.Size([450])
>>> trigger_label_ffn.layers.1.weight: torch.Size([233, 450])
>>> trigger_label_ffn.layers.1.bias: torch.Size([233])
>>> event_type_ffn.layers.0.weight: torch.Size([450, 1024])
>>> event_type_ffn.layers.0.bias: torch.Size([450])
>>> event_type_ffn.layers.1.weight: torch.Size([117, 450])
>>> event_type_ffn.layers.1.bias: torch.Size([117])
>>> role_type_ffn.layers.0.weight: torch.Size([350, 1024])
>>> role_type_ffn.layers.0.bias: torch.Size([350])
>>> role_type_ffn.layers.1.weight: torch.Size([6, 350])
>>> role_type_ffn.layers.1.bias: torch.Size([6])
>>> trigger_crf.transition: torch.Size([235, 235])
>>> mention_crf.transition: torch.Size([13, 13])
n_trainable_params: 564979817, n_nontrainable_params: 0
----------------------------------------------------------------------------------------------------
******************************
Epoch: 0
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:11:20.843025: step: 3064/1532, loss: 2.6584081649780273
==================================================
Loss: 4.282
--------------------
Dev Chinese: {'event': {'p': 0.5803757828810021, 'r': 0.7403462050599201, 'f1': 0.6506729081334114}, 'argument': {'p': 0.5776911101160835, 'r': 0.5182905759839446, 'f1': 0.5463811339189524}, 'combined': 0.35551540135627563, 'epoch': 0}
Test Chinese: {'event': {'p': 0.5818767249310027, 'r': 0.7329084588644265, 'f1': 0.6487179487179487}, 'argument': {'p': 0.5929461873378155, 'r': 0.5311850703205988, 'f1': 0.56036901397697}, 'combined': 0.36352143727223957, 'epoch': 0}
Dev Korean: {'event': {'p': 0.5882946518668012, 'r': 0.7762982689747004, 'f1': 0.669345579793341}, 'argument': {'p': 0.5769310291740088, 'r': 0.5247070514797115, 'f1': 0.5495811819529938}, 'combined': 0.36785973487783624, 'epoch': 0}
Test Korean: {'event': {'p': 0.5879120879120879, 'r': 0.7439165701042874, 'f1': 0.656777493606138}, 'argument': {'p': 0.5930536442301012, 'r': 0.5324974468781826, 'f1': 0.561146542185617}, 'combined': 0.3685484195224205, 'epoch': 0}
Dev Russian: {'event': {'p': 0.5835051546391753, 'r': 0.7536617842876165, 'f1': 0.6577571179546776}, 'argument': {'p': 0.5705648661477559, 'r': 0.5153223140785368, 'f1': 0.5415384075054632}, 'combined': 0.35620074218255926, 'epoch': 0}
Test Russian: {'event': {'p': 0.5934622467771639, 'r': 0.7468134414831982, 'f1': 0.6613648024628014}, 'argument': {'p': 0.583265013182632, 'r': 0.5306659638976251, 'f1': 0.5557236432002413}, 'combined': 0.3675360575090359, 'epoch': 0}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.5302364646017896, 'r': 0.5214722255174626, 'f1': 0.5258178273967749}, 'combined': 0.3845533364543578, 'epoch': 0}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.48089660977592014, 'r': 0.20345625798212005, 'f1': 0.2859385247316282}, 'combined': 0.17986455587957256, 'epoch': 0}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.4555176771342185, 'r': 0.3826348487927435, 'f1': 0.4159074443399386}, 'combined': 0.22789449004928142, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5803757828810021, 'r': 0.7403462050599201, 'f1': 0.6506729081334114}, 'argument': {'p': 0.5776911101160835, 'r': 0.5182905759839446, 'f1': 0.5463811339189524}, 'combined': 0.35551540135627563, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.5818767249310027, 'r': 0.7329084588644265, 'f1': 0.6487179487179487}, 'argument': {'p': 0.5929461873378155, 'r': 0.5311850703205988, 'f1': 0.56036901397697}, 'combined': 0.36352143727223957, 'epoch': 0}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.5302364646017896, 'r': 0.5214722255174626, 'f1': 0.5258178273967749}, 'combined': 0.3845533364543578, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.5882946518668012, 'r': 0.7762982689747004, 'f1': 0.669345579793341}, 'argument': {'p': 0.5769310291740088, 'r': 0.5247070514797115, 'f1': 0.5495811819529938}, 'combined': 0.36785973487783624, 'epoch': 0}
Test for Korean: {'event': {'p': 0.5879120879120879, 'r': 0.7439165701042874, 'f1': 0.656777493606138}, 'argument': {'p': 0.5930536442301012, 'r': 0.5324974468781826, 'f1': 0.561146542185617}, 'combined': 0.3685484195224205, 'epoch': 0}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.48089660977592014, 'r': 0.20345625798212005, 'f1': 0.2859385247316282}, 'combined': 0.17986455587957256, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.5835051546391753, 'r': 0.7536617842876165, 'f1': 0.6577571179546776}, 'argument': {'p': 0.5705648661477559, 'r': 0.5153223140785368, 'f1': 0.5415384075054632}, 'combined': 0.35620074218255926, 'epoch': 0}
Test for Russian: {'event': {'p': 0.5934622467771639, 'r': 0.7468134414831982, 'f1': 0.6613648024628014}, 'argument': {'p': 0.583265013182632, 'r': 0.5306659638976251, 'f1': 0.5557236432002413}, 'combined': 0.3675360575090359, 'epoch': 0}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.4555176771342185, 'r': 0.3826348487927435, 'f1': 0.4159074443399386}, 'combined': 0.22789449004928142, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:38:13.664848: step: 3064/1532, loss: 0.443219929933548
==================================================
Loss: 1.703
--------------------
Dev Chinese: {'event': {'p': 0.5918580375782881, 'r': 0.7549933422103862, 'f1': 0.6635459332943242}, 'argument': {'p': 0.5847806404185104, 'r': 0.678430293702924, 'f1': 0.6281340525394399}, 'combined': 0.4167957961262287, 'epoch': 1}
Test Chinese: {'event': {'p': 0.5910763569457221, 'r': 0.7444959443800695, 'f1': 0.658974358974359}, 'argument': {'p': 0.5993864813516712, 'r': 0.7020416319656089, 'f1': 0.6466653966367241}, 'combined': 0.4261359152195848, 'epoch': 1}
Dev Korean: {'event': {'p': 0.5953582240161454, 'r': 0.7856191744340879, 'f1': 0.677382319173364}, 'argument': {'p': 0.5862029963139263, 'r': 0.6878059407535012, 'f1': 0.6329530219471012}, 'combined': 0.4287511859343166, 'epoch': 1}
Test Korean: {'event': {'p': 0.5947802197802198, 'r': 0.7526071842410197, 'f1': 0.6644501278772378}, 'argument': {'p': 0.5994389868541805, 'r': 0.7056585331707019, 'f1': 0.6482262508334059}, 'combined': 0.430714015259639, 'epoch': 1}
Dev Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.5846384023678051, 'r': 0.6730533271831967, 'f1': 0.6257381084205703}, 'combined': 0.4152195931529875, 'epoch': 1}
Test Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.5939983041702589, 'r': 0.7002676937013868, 'f1': 0.642770223752866}, 'combined': 0.4297227304001972, 'epoch': 1}
Chinese: {'event': {'p': 0.55, 'r': 0.8148148148148148, 'f1': 0.6567164179104479}, 'argument': {'p': 0.501643889879184, 'r': 0.6343100425745054, 'f1': 0.5602300376023004}, 'combined': 0.3679122635000182, 'epoch': 1}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.5160596649727084, 'r': 0.38872026712229985, 'f1': 0.44342904545803086}, 'combined': 0.27893117375585813, 'epoch': 1}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.44531957757910573, 'r': 0.6725234436908943, 'f1': 0.5358316868431516}, 'combined': 0.2936064037496721, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5918580375782881, 'r': 0.7549933422103862, 'f1': 0.6635459332943242}, 'argument': {'p': 0.5847806404185104, 'r': 0.678430293702924, 'f1': 0.6281340525394399}, 'combined': 0.4167957961262287, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.5910763569457221, 'r': 0.7444959443800695, 'f1': 0.658974358974359}, 'argument': {'p': 0.5993864813516712, 'r': 0.7020416319656089, 'f1': 0.6466653966367241}, 'combined': 0.4261359152195848, 'epoch': 1}
Chinese: {'event': {'p': 0.55, 'r': 0.8148148148148148, 'f1': 0.6567164179104479}, 'argument': {'p': 0.501643889879184, 'r': 0.6343100425745054, 'f1': 0.5602300376023004}, 'combined': 0.3679122635000182, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.5953582240161454, 'r': 0.7856191744340879, 'f1': 0.677382319173364}, 'argument': {'p': 0.5862029963139263, 'r': 0.6878059407535012, 'f1': 0.6329530219471012}, 'combined': 0.4287511859343166, 'epoch': 1}
Test for Korean: {'event': {'p': 0.5947802197802198, 'r': 0.7526071842410197, 'f1': 0.6644501278772378}, 'argument': {'p': 0.5994389868541805, 'r': 0.7056585331707019, 'f1': 0.6482262508334059}, 'combined': 0.430714015259639, 'epoch': 1}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.5160596649727084, 'r': 0.38872026712229985, 'f1': 0.44342904545803086}, 'combined': 0.27893117375585813, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.5846384023678051, 'r': 0.6730533271831967, 'f1': 0.6257381084205703}, 'combined': 0.4152195931529875, 'epoch': 1}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.5939983041702589, 'r': 0.7002676937013868, 'f1': 0.642770223752866}, 'combined': 0.4297227304001972, 'epoch': 1}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.44531957757910573, 'r': 0.6725234436908943, 'f1': 0.5358316868431516}, 'combined': 0.2936064037496721, 'epoch': 1}
******************************
Epoch: 2
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 15:05:27.454443: step: 3064/1532, loss: 1.8837404251098633
==================================================
Loss: 1.433
--------------------
Dev Chinese: {'event': {'p': 0.5897703549060542, 'r': 0.7523302263648469, 'f1': 0.6612053832650673}, 'argument': {'p': 0.6495151172263042, 'r': 0.636816414717173, 'f1': 0.6431030849904321}, 'combined': 0.4252232217900458, 'epoch': 2}
Test Chinese: {'event': {'p': 0.5984360625574977, 'r': 0.753765932792584, 'f1': 0.6671794871794872}, 'argument': {'p': 0.6857322669173267, 'r': 0.6704226828175397, 'f1': 0.6779910601972827}, 'combined': 0.4523417278546999, 'epoch': 2}
Dev Korean: {'event': {'p': 0.5963673057517659, 'r': 0.7869507323568575, 'f1': 0.6785304247990815}, 'argument': {'p': 0.6527274997428266, 'r': 0.6434227599032996, 'f1': 0.6480417316183268}, 'combined': 0.43971603144251564, 'epoch': 2}
Test Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.6859436856563397, 'r': 0.6753481821169872, 'f1': 0.6806046992705204}, 'combined': 0.45640550421670184, 'epoch': 2}
Dev Russian: {'event': {'p': 0.5927835051546392, 'r': 0.7656458055925432, 'f1': 0.6682161533991866}, 'argument': {'p': 0.6442451310507491, 'r': 0.639124685644999, 'f1': 0.641674693458599}, 'combined': 0.4287773953965072, 'epoch': 2}
Test Russian: {'event': {'p': 0.6072744014732965, 'r': 0.7641946697566628, 'f1': 0.676757311441765}, 'argument': {'p': 0.6844448046712572, 'r': 0.6679411121272751, 'f1': 0.6760922579023877}, 'combined': 0.4575503787446123, 'epoch': 2}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6700747737111373, 'r': 0.6091588851919431, 'f1': 0.6381664511534642}, 'combined': 0.4667187478585037, 'epoch': 2}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5640291884264883, 'r': 0.4410997499232793, 'f1': 0.4950472013527451}, 'combined': 0.3034160266355535, 'epoch': 2}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.48961753114713646, 'r': 0.6395004488452395, 'f1': 0.5546110087330395}, 'combined': 0.2887016209843219, 'epoch': 2}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5897703549060542, 'r': 0.7523302263648469, 'f1': 0.6612053832650673}, 'argument': {'p': 0.6495151172263042, 'r': 0.636816414717173, 'f1': 0.6431030849904321}, 'combined': 0.4252232217900458, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.5984360625574977, 'r': 0.753765932792584, 'f1': 0.6671794871794872}, 'argument': {'p': 0.6857322669173267, 'r': 0.6704226828175397, 'f1': 0.6779910601972827}, 'combined': 0.4523417278546999, 'epoch': 2}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6700747737111373, 'r': 0.6091588851919431, 'f1': 0.6381664511534642}, 'combined': 0.4667187478585037, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5963673057517659, 'r': 0.7869507323568575, 'f1': 0.6785304247990815}, 'argument': {'p': 0.6527274997428266, 'r': 0.6434227599032996, 'f1': 0.6480417316183268}, 'combined': 0.43971603144251564, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.6859436856563397, 'r': 0.6753481821169872, 'f1': 0.6806046992705204}, 'combined': 0.45640550421670184, 'epoch': 2}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5640291884264883, 'r': 0.4410997499232793, 'f1': 0.4950472013527451}, 'combined': 0.3034160266355535, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.5927835051546392, 'r': 0.7656458055925432, 'f1': 0.6682161533991866}, 'argument': {'p': 0.6442451310507491, 'r': 0.639124685644999, 'f1': 0.641674693458599}, 'combined': 0.4287773953965072, 'epoch': 2}
Test for Russian: {'event': {'p': 0.6072744014732965, 'r': 0.7641946697566628, 'f1': 0.676757311441765}, 'argument': {'p': 0.6844448046712572, 'r': 0.6679411121272751, 'f1': 0.6760922579023877}, 'combined': 0.4575503787446123, 'epoch': 2}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.48961753114713646, 'r': 0.6395004488452395, 'f1': 0.5546110087330395}, 'combined': 0.2887016209843219, '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
2023-01-21 15:11:16.440801: step: 2/1532, loss: 1.0145723819732666
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2023-01-21 15:32:36.206835: step: 3064/1532, loss: 1.6727739572525024
==================================================
Loss: 1.003
--------------------
Dev Chinese: {'event': {'p': 0.593945720250522, 'r': 0.7576564580559254, 'f1': 0.665886483323581}, 'argument': {'p': 0.6884557138824825, 'r': 0.6136235710691692, 'f1': 0.6488892935444088}, 'combined': 0.4320866097446093, 'epoch': 3}
Test Chinese: {'event': {'p': 0.5975160993560258, 'r': 0.7526071842410197, 'f1': 0.6661538461538462}, 'argument': {'p': 0.7167600457187657, 'r': 0.6479510813297642, 'f1': 0.6806208837497523}, 'combined': 0.45339821948252734, 'epoch': 3}
Dev Korean: {'event': {'p': 0.5963673057517659, 'r': 0.7869507323568575, 'f1': 0.6785304247990815}, 'argument': {'p': 0.6917945108679805, 'r': 0.6156427205365516, 'f1': 0.651500882020148}, 'combined': 0.44206317023410735, 'epoch': 3}
Test Korean: {'event': {'p': 0.5993589743589743, 'r': 0.7584009269988412, 'f1': 0.6695652173913043}, 'argument': {'p': 0.7171901348249984, 'r': 0.6521880078015847, 'f1': 0.6831462993111534}, 'combined': 0.4574110004083375, 'epoch': 3}
Dev Russian: {'event': {'p': 0.5917525773195876, 'r': 0.7643142476697736, 'f1': 0.6670540383497966}, 'argument': {'p': 0.6904770315207218, 'r': 0.6200303879285208, 'f1': 0.6533602715342858}, 'combined': 0.435826607624265, 'epoch': 3}
Test Russian: {'event': {'p': 0.6049723756906077, 'r': 0.761297798377752, 'f1': 0.6741918932786043}, 'argument': {'p': 0.7127241929699564, 'r': 0.6399972345036343, 'f1': 0.6744056879715717}, 'combined': 0.4546788476114136, 'epoch': 3}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.6419762845849802, 'r': 0.6101427498121713, 'f1': 0.6256548536209554}, 'combined': 0.43889221074902846, 'epoch': 3}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.6180555555555556, 'r': 0.3755274261603376, 'f1': 0.4671916010498688}, 'combined': 0.2938785877571755, 'epoch': 3}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.5474049612938502, 'r': 0.5911973581973582, 'f1': 0.5684589982666907}, 'combined': 0.31148438261188527, 'epoch': 3}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5897703549060542, 'r': 0.7523302263648469, 'f1': 0.6612053832650673}, 'argument': {'p': 0.6495151172263042, 'r': 0.636816414717173, 'f1': 0.6431030849904321}, 'combined': 0.4252232217900458, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.5984360625574977, 'r': 0.753765932792584, 'f1': 0.6671794871794872}, 'argument': {'p': 0.6857322669173267, 'r': 0.6704226828175397, 'f1': 0.6779910601972827}, 'combined': 0.4523417278546999, 'epoch': 2}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6700747737111373, 'r': 0.6091588851919431, 'f1': 0.6381664511534642}, 'combined': 0.4667187478585037, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5963673057517659, 'r': 0.7869507323568575, 'f1': 0.6785304247990815}, 'argument': {'p': 0.6527274997428266, 'r': 0.6434227599032996, 'f1': 0.6480417316183268}, 'combined': 0.43971603144251564, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.6859436856563397, 'r': 0.6753481821169872, 'f1': 0.6806046992705204}, 'combined': 0.45640550421670184, 'epoch': 2}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5640291884264883, 'r': 0.4410997499232793, 'f1': 0.4950472013527451}, 'combined': 0.3034160266355535, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.5917525773195876, 'r': 0.7643142476697736, 'f1': 0.6670540383497966}, 'argument': {'p': 0.6904770315207218, 'r': 0.6200303879285208, 'f1': 0.6533602715342858}, 'combined': 0.435826607624265, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6049723756906077, 'r': 0.761297798377752, 'f1': 0.6741918932786043}, 'argument': {'p': 0.7127241929699564, 'r': 0.6399972345036343, 'f1': 0.6744056879715717}, 'combined': 0.4546788476114136, 'epoch': 3}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.5474049612938502, 'r': 0.5911973581973582, 'f1': 0.5684589982666907}, 'combined': 0.31148438261188527, '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
2023-01-21 15:38:11.731801: step: 2/1532, loss: 0.7639077305793762
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2023-01-21 15:59:30.776024: step: 3064/1532, loss: 0.6385911703109741
==================================================
Loss: 0.832
--------------------
Dev Chinese: {'event': {'p': 0.5918580375782881, 'r': 0.7549933422103862, 'f1': 0.6635459332943242}, 'argument': {'p': 0.6504755729189613, 'r': 0.695726047556802, 'f1': 0.6723402980590943}, 'combined': 0.4461286705670059, 'epoch': 4}
Test Chinese: {'event': {'p': 0.5896964121435143, 'r': 0.7427578215527231, 'f1': 0.6574358974358976}, 'argument': {'p': 0.6519811481853123, 'r': 0.7063851602180627, 'f1': 0.6780936849963085}, 'combined': 0.4458031303411629, 'epoch': 4}
Dev Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6562281600017277, 'r': 0.6927372873056754, 'f1': 0.6739886723126696}, 'combined': 0.4534527692023242, 'epoch': 4}
Test Korean: {'event': {'p': 0.5934065934065934, 'r': 0.7508690614136733, 'f1': 0.6629156010230179}, 'argument': {'p': 0.6508752984602236, 'r': 0.7140459264901651, 'f1': 0.6809987961546802}, 'combined': 0.4514447262488315, 'epoch': 4}
Dev Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.6126017639077341, 'r': 0.6784184823441022, 'f1': 0.6438324420677362}, 'combined': 0.4516436533908, 'epoch': 4}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.58436473727422, 'r': 0.42902727546714886, 'f1': 0.49479058046576285}, 'combined': 0.30325874286611276, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5918580375782881, 'r': 0.7549933422103862, 'f1': 0.6635459332943242}, 'argument': {'p': 0.6504755729189613, 'r': 0.695726047556802, 'f1': 0.6723402980590943}, 'combined': 0.4461286705670059, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.5896964121435143, 'r': 0.7427578215527231, 'f1': 0.6574358974358976}, 'argument': {'p': 0.6519811481853123, 'r': 0.7063851602180627, 'f1': 0.6780936849963085}, 'combined': 0.4458031303411629, 'epoch': 4}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.6126017639077341, 'r': 0.6784184823441022, 'f1': 0.6438324420677362}, 'combined': 0.4516436533908, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6562281600017277, 'r': 0.6927372873056754, 'f1': 0.6739886723126696}, 'combined': 0.4534527692023242, 'epoch': 4}
Test for Korean: {'event': {'p': 0.5934065934065934, 'r': 0.7508690614136733, 'f1': 0.6629156010230179}, 'argument': {'p': 0.6508752984602236, 'r': 0.7140459264901651, 'f1': 0.6809987961546802}, 'combined': 0.4514447262488315, 'epoch': 4}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.58436473727422, 'r': 0.42902727546714886, 'f1': 0.49479058046576285}, 'combined': 0.30325874286611276, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 5
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 16:05:30.029604: step: 2/1532, loss: 0.4160822033882141
2023-01-21 16:05:30.837081: step: 4/1532, loss: 0.484043687582016
2023-01-21 16:05:31.649043: step: 6/1532, loss: 0.7068039178848267
2023-01-21 16:05:32.520205: step: 8/1532, loss: 0.34384074807167053
2023-01-21 16:05:33.327310: step: 10/1532, loss: 0.586400032043457
2023-01-21 16:05:34.218793: step: 12/1532, loss: 0.6412064433097839
2023-01-21 16:05:35.071434: step: 14/1532, loss: 0.553986132144928
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2023-01-21 16:05:36.736129: step: 18/1532, loss: 1.365375280380249
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2023-01-21 16:26:51.027913: step: 3064/1532, loss: 0.9227203726768494
==================================================
Loss: 0.711
--------------------
Dev Chinese: {'event': {'p': 0.593945720250522, 'r': 0.7576564580559254, 'f1': 0.665886483323581}, 'argument': {'p': 0.6691364239303714, 'r': 0.6705910683302201, 'f1': 0.6698629564210667}, 'combined': 0.44605268835996137, 'epoch': 5}
Test Chinese: {'event': {'p': 0.593376264949402, 'r': 0.7473928157589803, 'f1': 0.6615384615384615}, 'argument': {'p': 0.6771463601105253, 'r': 0.6890576634911619, 'f1': 0.6830500872179209}, 'combined': 0.4518639038518554, 'epoch': 5}
Dev Korean: {'event': {'p': 0.5973763874873865, 'r': 0.7882822902796272, 'f1': 0.6796785304247991}, 'argument': {'p': 0.6683444614060126, 'r': 0.6759717876417446, 'f1': 0.6721364867189545}, 'combined': 0.4568367395380265, 'epoch': 5}
Test Korean: {'event': {'p': 0.5947802197802198, 'r': 0.7526071842410197, 'f1': 0.6644501278772378}, 'argument': {'p': 0.681024915002941, 'r': 0.6905911512671964, 'f1': 0.6857746736263426}, 'combined': 0.45566306958599434, 'epoch': 5}
Dev Russian: {'event': {'p': 0.5917525773195876, 'r': 0.7643142476697736, 'f1': 0.6670540383497966}, 'argument': {'p': 0.6665358821389185, 'r': 0.6718411605061602, 'f1': 0.6691780063564061}, 'combined': 0.44637789151490653, 'epoch': 5}
Test Russian: {'event': {'p': 0.6012891344383057, 'r': 0.7566628041714948, 'f1': 0.6700872242175474}, 'argument': {'p': 0.6781663071710042, 'r': 0.6854121575314289, 'f1': 0.6817699806730754}, 'combined': 0.456845353904072, 'epoch': 5}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6658043853165805, 'r': 0.6768094164788381, 'f1': 0.6712617983109788}, 'combined': 0.4809039749093579, 'epoch': 5}
Korean: {'event': {'p': 0.6557377049180327, 'r': 0.6349206349206349, 'f1': 0.6451612903225806}, 'argument': {'p': 0.6486095426312818, 'r': 0.37063402436073245, 'f1': 0.47171603100456866}, 'combined': 0.30433292322875394, 'epoch': 5}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.597526860113067, 'r': 0.597526860113067, 'f1': 0.597526860113067}, 'combined': 0.311041379236939, 'epoch': 5}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.593945720250522, 'r': 0.7576564580559254, 'f1': 0.665886483323581}, 'argument': {'p': 0.6691364239303714, 'r': 0.6705910683302201, 'f1': 0.6698629564210667}, 'combined': 0.44605268835996137, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.593376264949402, 'r': 0.7473928157589803, 'f1': 0.6615384615384615}, 'argument': {'p': 0.6771463601105253, 'r': 0.6890576634911619, 'f1': 0.6830500872179209}, 'combined': 0.4518639038518554, 'epoch': 5}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6658043853165805, 'r': 0.6768094164788381, 'f1': 0.6712617983109788}, 'combined': 0.4809039749093579, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5973763874873865, 'r': 0.7882822902796272, 'f1': 0.6796785304247991}, 'argument': {'p': 0.6683444614060126, 'r': 0.6759717876417446, 'f1': 0.6721364867189545}, 'combined': 0.4568367395380265, 'epoch': 5}
Test for Korean: {'event': {'p': 0.5947802197802198, 'r': 0.7526071842410197, 'f1': 0.6644501278772378}, 'argument': {'p': 0.681024915002941, 'r': 0.6905911512671964, 'f1': 0.6857746736263426}, 'combined': 0.45566306958599434, 'epoch': 5}
Korean: {'event': {'p': 0.6557377049180327, 'r': 0.6349206349206349, 'f1': 0.6451612903225806}, 'argument': {'p': 0.6486095426312818, 'r': 0.37063402436073245, 'f1': 0.47171603100456866}, 'combined': 0.30433292322875394, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 6
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 16:32:34.358129: step: 2/1532, loss: 0.29282107949256897
2023-01-21 16:32:35.206571: step: 4/1532, loss: 0.21350239217281342
2023-01-21 16:32:36.028145: step: 6/1532, loss: 0.33819735050201416
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2023-01-21 16:53:54.740229: step: 3064/1532, loss: 0.29456982016563416
==================================================
Loss: 0.611
--------------------
Dev Chinese: {'event': {'p': 0.5897703549060542, 'r': 0.7523302263648469, 'f1': 0.6612053832650673}, 'argument': {'p': 0.6139937751220738, 'r': 0.6701354410621182, 'f1': 0.6408373614043018}, 'combined': 0.4237251131579058, 'epoch': 6}
Test Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6519445036816124, 'r': 0.7044996568764659, 'f1': 0.677203960916795}, 'combined': 0.4473018982876062, 'epoch': 6}
Dev Korean: {'event': {'p': 0.5933400605449042, 'r': 0.7829560585885486, 'f1': 0.6750861079219288}, 'argument': {'p': 0.6185486344330944, 'r': 0.6738156675311192, 'f1': 0.6450004234526416}, 'combined': 0.4354308254766398, 'epoch': 6}
Test Korean: {'event': {'p': 0.5970695970695971, 'r': 0.7555040556199305, 'f1': 0.6670076726342711}, 'argument': {'p': 0.651679138203627, 'r': 0.7046007326483511, 'f1': 0.6771074438220207}, 'combined': 0.4516358602270665, 'epoch': 6}
Dev Russian: {'event': {'p': 0.5927835051546392, 'r': 0.7656458055925432, 'f1': 0.6682161533991866}, 'argument': {'p': 0.6227505078136811, 'r': 0.6760388036236987, 'f1': 0.6483014674527682}, 'combined': 0.4332055128243367, 'epoch': 6}
Test Russian: {'event': {'p': 0.6031307550644567, 'r': 0.7589803012746235, 'f1': 0.6721395587480758}, 'argument': {'p': 0.6547002208040562, 'r': 0.7029528333926298, 'f1': 0.6779690493302101}, 'combined': 0.4556898176616599, 'epoch': 6}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6084321475625823, 'r': 0.6939143501126973, 'f1': 0.6483678483678484}, 'combined': 0.48385660325958835, 'epoch': 6}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5503925120772948, 'r': 0.48072257383966255, 'f1': 0.513203828828829}, 'combined': 0.31454428218541136, 'epoch': 6}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.5357222922796693, 'r': 0.6669195883481597, 'f1': 0.5941647241647241}, 'combined': 0.3255697118710817, 'epoch': 6}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.593945720250522, 'r': 0.7576564580559254, 'f1': 0.665886483323581}, 'argument': {'p': 0.6691364239303714, 'r': 0.6705910683302201, 'f1': 0.6698629564210667}, 'combined': 0.44605268835996137, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.593376264949402, 'r': 0.7473928157589803, 'f1': 0.6615384615384615}, 'argument': {'p': 0.6771463601105253, 'r': 0.6890576634911619, 'f1': 0.6830500872179209}, 'combined': 0.4518639038518554, 'epoch': 5}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6658043853165805, 'r': 0.6768094164788381, 'f1': 0.6712617983109788}, 'combined': 0.4809039749093579, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5973763874873865, 'r': 0.7882822902796272, 'f1': 0.6796785304247991}, 'argument': {'p': 0.6683444614060126, 'r': 0.6759717876417446, 'f1': 0.6721364867189545}, 'combined': 0.4568367395380265, 'epoch': 5}
Test for Korean: {'event': {'p': 0.5947802197802198, 'r': 0.7526071842410197, 'f1': 0.6644501278772378}, 'argument': {'p': 0.681024915002941, 'r': 0.6905911512671964, 'f1': 0.6857746736263426}, 'combined': 0.45566306958599434, 'epoch': 5}
Korean: {'event': {'p': 0.6557377049180327, 'r': 0.6349206349206349, 'f1': 0.6451612903225806}, 'argument': {'p': 0.6486095426312818, 'r': 0.37063402436073245, 'f1': 0.47171603100456866}, 'combined': 0.30433292322875394, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 7
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 17:20:52.580991: step: 3064/1532, loss: 0.11751323193311691
==================================================
Loss: 0.494
--------------------
Dev Chinese: {'event': {'p': 0.5887265135699373, 'r': 0.7509986684420772, 'f1': 0.6600351082504389}, 'argument': {'p': 0.7012944815762062, 'r': 0.6326398368968822, 'f1': 0.6652004079914605}, 'combined': 0.43905562329687975, 'epoch': 7}
Test Chinese: {'event': {'p': 0.5965961361545539, 'r': 0.7514484356894554, 'f1': 0.6651282051282051}, 'argument': {'p': 0.7028316507246697, 'r': 0.6656311738720656, 'f1': 0.6837257809238109}, 'combined': 0.45476530146573474, 'epoch': 7}
Dev Korean: {'event': {'p': 0.5943491422805247, 'r': 0.7842876165113183, 'f1': 0.6762342135476465}, 'argument': {'p': 0.7107030828303497, 'r': 0.6351718707463825, 'f1': 0.6708180510632228}, 'combined': 0.4536301171943034, 'epoch': 7}
Test Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.7054504194692276, 'r': 0.6669154267167194, 'f1': 0.6856419063988599}, 'combined': 0.459783396055706, 'epoch': 7}
Dev Russian: {'event': {'p': 0.5917525773195876, 'r': 0.7643142476697736, 'f1': 0.6670540383497966}, 'argument': {'p': 0.6989861748637237, 'r': 0.6331396511446773, 'f1': 0.6644355274369997}, 'combined': 0.44321440179992777, 'epoch': 7}
Test Russian: {'event': {'p': 0.608195211786372, 'r': 0.7653534183082271, 'f1': 0.6777834787070293}, 'argument': {'p': 0.6994616708466207, 'r': 0.6622786532389267, 'f1': 0.6803625113644615}, 'combined': 0.46113846973445544, 'epoch': 7}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6929299429299429, 'r': 0.6356630054977163, 'f1': 0.6630622729760661}, 'combined': 0.4750296881022563, 'epoch': 7}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7095238095238096, 'r': 0.4093406593406594, 'f1': 0.5191637630662022}, 'combined': 0.3098235360233787, 'epoch': 7}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.5833089568383686, 'r': 0.594975135975136, 'f1': 0.5890842930446891}, 'combined': 0.30664661829723544, 'epoch': 7}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.593945720250522, 'r': 0.7576564580559254, 'f1': 0.665886483323581}, 'argument': {'p': 0.6691364239303714, 'r': 0.6705910683302201, 'f1': 0.6698629564210667}, 'combined': 0.44605268835996137, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.593376264949402, 'r': 0.7473928157589803, 'f1': 0.6615384615384615}, 'argument': {'p': 0.6771463601105253, 'r': 0.6890576634911619, 'f1': 0.6830500872179209}, 'combined': 0.4518639038518554, 'epoch': 5}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6658043853165805, 'r': 0.6768094164788381, 'f1': 0.6712617983109788}, 'combined': 0.4809039749093579, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5943491422805247, 'r': 0.7842876165113183, 'f1': 0.6762342135476465}, 'argument': {'p': 0.7107030828303497, 'r': 0.6351718707463825, 'f1': 0.6708180510632228}, 'combined': 0.4536301171943034, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.7054504194692276, 'r': 0.6669154267167194, 'f1': 0.6856419063988599}, 'combined': 0.459783396055706, 'epoch': 7}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7095238095238096, 'r': 0.4093406593406594, 'f1': 0.5191637630662022}, 'combined': 0.3098235360233787, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
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2023-01-21 17:47:33.220008: step: 3042/1532, loss: 0.5229553580284119
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2023-01-21 17:47:39.101966: step: 3056/1532, loss: 0.06316676735877991
2023-01-21 17:47:39.941134: step: 3058/1532, loss: 0.7080357670783997
2023-01-21 17:47:40.777775: step: 3060/1532, loss: 0.38310766220092773
2023-01-21 17:47:41.632404: step: 3062/1532, loss: 0.2865396738052368
2023-01-21 17:47:42.494288: step: 3064/1532, loss: 0.07823115587234497
==================================================
Loss: 0.371
--------------------
Dev Chinese: {'event': {'p': 0.5918580375782881, 'r': 0.7549933422103862, 'f1': 0.6635459332943242}, 'argument': {'p': 0.6530243930079556, 'r': 0.6743186666929976, 'f1': 0.663500720168511}, 'combined': 0.44026320460567087, 'epoch': 8}
Test Chinese: {'event': {'p': 0.5988960441582337, 'r': 0.7543453070683661, 'f1': 0.6676923076923077}, 'argument': {'p': 0.6786463239794128, 'r': 0.6806815845311672, 'f1': 0.6796624306032383}, 'combined': 0.4538053767412391, 'epoch': 8}
Dev Korean: {'event': {'p': 0.5953582240161454, 'r': 0.7856191744340879, 'f1': 0.677382319173364}, 'argument': {'p': 0.6537401344112578, 'r': 0.6793861453903014, 'f1': 0.6663164573887391}, 'combined': 0.45135098720936406, 'epoch': 8}
Test Korean: {'event': {'p': 0.6016483516483516, 'r': 0.761297798377752, 'f1': 0.6721227621483377}, 'argument': {'p': 0.6798154194912863, 'r': 0.6825547697377253, 'f1': 0.6811823405760543}, 'combined': 0.4578381562746473, 'epoch': 8}
Dev Russian: {'event': {'p': 0.5907216494845361, 'r': 0.762982689747004, 'f1': 0.6658919233004067}, 'argument': {'p': 0.6592052755899726, 'r': 0.6749688800062545, 'f1': 0.6669939523155255}, 'combined': 0.44414588573712505, 'epoch': 8}
Test Russian: {'event': {'p': 0.6077348066298343, 'r': 0.764774044032445, 'f1': 0.6772703950743972}, 'argument': {'p': 0.6802969883009371, 'r': 0.6771095191455779, 'f1': 0.678699511307275}, 'combined': 0.4596630861598785, 'epoch': 8}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.649083694083694, 'r': 0.6437193660334155, 'f1': 0.6463904007472471}, 'combined': 0.4823808960800351, 'epoch': 8}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.6085142100534355, 'r': 0.41347760426707797, 'f1': 0.49238554401270357}, 'combined': 0.29384298594306507, 'epoch': 8}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.5764950327450328, 'r': 0.6588514659943232, 'f1': 0.614928034928035}, 'combined': 0.32009952503103195, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.593945720250522, 'r': 0.7576564580559254, 'f1': 0.665886483323581}, 'argument': {'p': 0.6691364239303714, 'r': 0.6705910683302201, 'f1': 0.6698629564210667}, 'combined': 0.44605268835996137, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.593376264949402, 'r': 0.7473928157589803, 'f1': 0.6615384615384615}, 'argument': {'p': 0.6771463601105253, 'r': 0.6890576634911619, 'f1': 0.6830500872179209}, 'combined': 0.4518639038518554, 'epoch': 5}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6658043853165805, 'r': 0.6768094164788381, 'f1': 0.6712617983109788}, 'combined': 0.4809039749093579, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5943491422805247, 'r': 0.7842876165113183, 'f1': 0.6762342135476465}, 'argument': {'p': 0.7107030828303497, 'r': 0.6351718707463825, 'f1': 0.6708180510632228}, 'combined': 0.4536301171943034, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.7054504194692276, 'r': 0.6669154267167194, 'f1': 0.6856419063988599}, 'combined': 0.459783396055706, 'epoch': 7}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7095238095238096, 'r': 0.4093406593406594, 'f1': 0.5191637630662022}, 'combined': 0.3098235360233787, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-21 17:53:14.135417: step: 2/1532, loss: 0.2226312756538391
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==================================================
Loss: 0.338
--------------------
Dev Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Dev Korean: {'event': {'p': 0.5893037336024218, 'r': 0.7776298268974701, 'f1': 0.6704936854190586}, 'argument': {'p': 0.6584006096371918, 'r': 0.6767156051977129, 'f1': 0.6674324857454755}, 'combined': 0.4475092671358872, 'epoch': 9}
Test Korean: {'event': {'p': 0.5975274725274725, 'r': 0.7560834298957126, 'f1': 0.6675191815856777}, 'argument': {'p': 0.6724934529225833, 'r': 0.7060618028339619, 'f1': 0.6888689267721445}, 'combined': 0.45983322221874606, 'epoch': 9}
Dev Russian: {'event': {'p': 0.5835051546391753, 'r': 0.7536617842876165, 'f1': 0.6577571179546776}, 'argument': {'p': 0.6528037941700503, 'r': 0.6745797002844972, 'f1': 0.6635131288818272}, 'combined': 0.43643048337840123, 'epoch': 9}
Test Russian: {'event': {'p': 0.6003683241252302, 'r': 0.7555040556199305, 'f1': 0.6690610569522831}, 'argument': {'p': 0.6675110941658934, 'r': 0.7008084336472185, 'f1': 0.6837546272429337}, 'combined': 0.45747359359917156, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6152210884353742, 'r': 0.4906193490054249, 'f1': 0.5459004024144869}, 'combined': 0.3169744272084118, 'epoch': 9}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.556535514294135, 'r': 0.6207511505588428, 'f1': 0.5868919968919969}, 'combined': 0.32158465583123114, 'epoch': 9}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5893037336024218, 'r': 0.7776298268974701, 'f1': 0.6704936854190586}, 'argument': {'p': 0.6584006096371918, 'r': 0.6767156051977129, 'f1': 0.6674324857454755}, 'combined': 0.4475092671358872, 'epoch': 9}
Test for Korean: {'event': {'p': 0.5975274725274725, 'r': 0.7560834298957126, 'f1': 0.6675191815856777}, 'argument': {'p': 0.6724934529225833, 'r': 0.7060618028339619, 'f1': 0.6888689267721445}, 'combined': 0.45983322221874606, 'epoch': 9}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6152210884353742, 'r': 0.4906193490054249, 'f1': 0.5459004024144869}, 'combined': 0.3169744272084118, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 10
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 18:41:42.593854: step: 3064/1532, loss: 0.4469231069087982
==================================================
Loss: 0.266
--------------------
Dev Chinese: {'event': {'p': 0.5908141962421712, 'r': 0.7536617842876165, 'f1': 0.6623756582796958}, 'argument': {'p': 0.6616431475465404, 'r': 0.6669247691887842, 'f1': 0.6642734600232343}, 'combined': 0.439998570360621, 'epoch': 10}
Test Chinese: {'event': {'p': 0.5979760809567617, 'r': 0.7531865585168018, 'f1': 0.6666666666666667}, 'argument': {'p': 0.6928664073901913, 'r': 0.6917104741112621, 'f1': 0.6922879582272341}, 'combined': 0.4615253054848228, 'epoch': 10}
Dev Korean: {'event': {'p': 0.5953582240161454, 'r': 0.7856191744340879, 'f1': 0.677382319173364}, 'argument': {'p': 0.6671870124620038, 'r': 0.67386365161459, 'f1': 0.670508711670563}, 'combined': 0.45419074613735044, 'epoch': 10}
Test Korean: {'event': {'p': 0.6016483516483516, 'r': 0.761297798377752, 'f1': 0.6721227621483377}, 'argument': {'p': 0.6939331226796003, 'r': 0.6941659080276981, 'f1': 0.694049495834502}, 'combined': 0.4664864642079467, 'epoch': 10}
Dev Russian: {'event': {'p': 0.5907216494845361, 'r': 0.762982689747004, 'f1': 0.6658919233004067}, 'argument': {'p': 0.6622983023263525, 'r': 0.6723913824053461, 'f1': 0.6673066798376429}, 'combined': 0.44435412846829675, 'epoch': 10}
Test Russian: {'event': {'p': 0.6049723756906077, 'r': 0.761297798377752, 'f1': 0.6741918932786043}, 'argument': {'p': 0.6889967738502244, 'r': 0.6855402682957918, 'f1': 0.6872641750894947}, 'combined': 0.4633479353861446, 'epoch': 10}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.6919148380355276, 'r': 0.6633233158026546, 'f1': 0.6773174785833013}, 'combined': 0.4751331566181367, 'epoch': 10}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6471473823447509, 'r': 0.46692912397026326, 'f1': 0.5424617763772176}, 'combined': 0.31497780563838446, 'epoch': 10}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.5628530743227863, 'r': 0.6088002640634219, 'f1': 0.584925743904072}, 'combined': 0.3205072569337381, 'epoch': 10}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5953582240161454, 'r': 0.7856191744340879, 'f1': 0.677382319173364}, 'argument': {'p': 0.6671870124620038, 'r': 0.67386365161459, 'f1': 0.670508711670563}, 'combined': 0.45419074613735044, 'epoch': 10}
Test for Korean: {'event': {'p': 0.6016483516483516, 'r': 0.761297798377752, 'f1': 0.6721227621483377}, 'argument': {'p': 0.6939331226796003, 'r': 0.6941659080276981, 'f1': 0.694049495834502}, 'combined': 0.4664864642079467, 'epoch': 10}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6471473823447509, 'r': 0.46692912397026326, 'f1': 0.5424617763772176}, 'combined': 0.31497780563838446, 'epoch': 10}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
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2023-01-21 19:08:39.465998: step: 3064/1532, loss: 0.25604087114334106
==================================================
Loss: 0.267
--------------------
Dev Chinese: {'event': {'p': 0.5918580375782881, 'r': 0.7549933422103862, 'f1': 0.6635459332943242}, 'argument': {'p': 0.6650036335448892, 'r': 0.6596913314613789, 'f1': 0.662336830785504}, 'combined': 0.43949091053877215, 'epoch': 11}
Test Chinese: {'event': {'p': 0.5970561177552898, 'r': 0.7520278099652375, 'f1': 0.6656410256410257}, 'argument': {'p': 0.6846334123753766, 'r': 0.6928408059303262, 'f1': 0.688712658129351}, 'combined': 0.45843540012917827, 'epoch': 11}
Dev Korean: {'event': {'p': 0.5953582240161454, 'r': 0.7856191744340879, 'f1': 0.677382319173364}, 'argument': {'p': 0.6641001992166664, 'r': 0.6617267174467713, 'f1': 0.6629113338403386}, 'combined': 0.4490444167230767, 'epoch': 11}
Test Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.6887014287560248, 'r': 0.6949350655566995, 'f1': 0.6918042050911513}, 'combined': 0.46285416893055026, 'epoch': 11}
Dev Russian: {'event': {'p': 0.5896907216494846, 'r': 0.7616511318242344, 'f1': 0.6647298082510168}, 'argument': {'p': 0.6634553278244654, 'r': 0.6620088409178714, 'f1': 0.6627312950912994}, 'combined': 0.44053724670798744, 'epoch': 11}
Test Russian: {'event': {'p': 0.60451197053407, 'r': 0.7607184241019699, 'f1': 0.6736788096459724}, 'argument': {'p': 0.6826872634343172, 'r': 0.6895438479348293, 'f1': 0.6860984256433494}, 'combined': 0.46220997068738734, 'epoch': 11}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6434149860979128, 'r': 0.6540499445458122, 'f1': 0.6486888794265843}, 'combined': 0.4744142551030244, 'epoch': 11}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6449065757959989, 'r': 0.4299377171973326, 'f1': 0.5159252606367991}, 'combined': 0.31621225651932855, 'epoch': 11}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.5521863483864392, 'r': 0.6184487101928119, 'f1': 0.583442179427181}, 'combined': 0.31969434489160603, 'epoch': 11}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5953582240161454, 'r': 0.7856191744340879, 'f1': 0.677382319173364}, 'argument': {'p': 0.6671870124620038, 'r': 0.67386365161459, 'f1': 0.670508711670563}, 'combined': 0.45419074613735044, 'epoch': 10}
Test for Korean: {'event': {'p': 0.6016483516483516, 'r': 0.761297798377752, 'f1': 0.6721227621483377}, 'argument': {'p': 0.6939331226796003, 'r': 0.6941659080276981, 'f1': 0.694049495834502}, 'combined': 0.4664864642079467, 'epoch': 10}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6471473823447509, 'r': 0.46692912397026326, 'f1': 0.5424617763772176}, 'combined': 0.31497780563838446, 'epoch': 10}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 12
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 19:14:10.875197: step: 2/1532, loss: 0.0558394193649292
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2023-01-21 19:14:13.411554: step: 8/1532, loss: 0.027224939316511154
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2023-01-21 19:35:28.531239: step: 3064/1532, loss: 0.5899629592895508
==================================================
Loss: 0.197
--------------------
Dev Chinese: {'event': {'p': 0.5897703549060542, 'r': 0.7523302263648469, 'f1': 0.6612053832650673}, 'argument': {'p': 0.6527858380681376, 'r': 0.6684072540625166, 'f1': 0.6605041944480188}, 'combined': 0.4367289290381868, 'epoch': 12}
Test Chinese: {'event': {'p': 0.5956761729530818, 'r': 0.7502896871378911, 'f1': 0.6641025641025641}, 'argument': {'p': 0.6905543900104151, 'r': 0.7032272266639262, 'f1': 0.6968331950113363}, 'combined': 0.4627687115588105, 'epoch': 12}
Dev Korean: {'event': {'p': 0.5973763874873865, 'r': 0.7882822902796272, 'f1': 0.6796785304247991}, 'argument': {'p': 0.6514556723596995, 'r': 0.6700421394740989, 'f1': 0.6606181993971073}, 'combined': 0.4490080069381028, 'epoch': 12}
Test Korean: {'event': {'p': 0.5993589743589743, 'r': 0.7584009269988412, 'f1': 0.6695652173913043}, 'argument': {'p': 0.6914045824340443, 'r': 0.7060559408912744, 'f1': 0.6986534572373968}, 'combined': 0.4677940539763439, 'epoch': 12}
Dev Russian: {'event': {'p': 0.5907216494845361, 'r': 0.762982689747004, 'f1': 0.6658919233004067}, 'argument': {'p': 0.6552297243355784, 'r': 0.6723350688432512, 'f1': 0.6636721975195731}, 'combined': 0.4419339560473159, 'epoch': 12}
Test Russian: {'event': {'p': 0.6040515653775322, 'r': 0.7601390498261877, 'f1': 0.6731657260133402}, 'argument': {'p': 0.685681080690598, 'r': 0.6939395346567611, 'f1': 0.6897855899863848}, 'combined': 0.464340017476725, 'epoch': 12}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6480370599418218, 'r': 0.6748154508485087, 'f1': 0.6611552190499559}, 'combined': 0.4835314288574305, 'epoch': 12}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6569431051108969, 'r': 0.5072598659717051, 'f1': 0.5724789915966386}, 'combined': 0.3324071564109515, 'epoch': 12}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.5867033532398558, 'r': 0.6585445801671851, 'f1': 0.6205516236190781}, 'combined': 0.32302687256883517, 'epoch': 12}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5973763874873865, 'r': 0.7882822902796272, 'f1': 0.6796785304247991}, 'argument': {'p': 0.6514556723596995, 'r': 0.6700421394740989, 'f1': 0.6606181993971073}, 'combined': 0.4490080069381028, 'epoch': 12}
Test for Korean: {'event': {'p': 0.5993589743589743, 'r': 0.7584009269988412, 'f1': 0.6695652173913043}, 'argument': {'p': 0.6914045824340443, 'r': 0.7060559408912744, 'f1': 0.6986534572373968}, 'combined': 0.4677940539763439, 'epoch': 12}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6569431051108969, 'r': 0.5072598659717051, 'f1': 0.5724789915966386}, 'combined': 0.3324071564109515, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-21 19:41:02.559451: step: 2/1532, loss: 0.011506142094731331
2023-01-21 19:41:03.392483: step: 4/1532, loss: 0.4566507339477539
2023-01-21 19:41:04.215109: step: 6/1532, loss: 0.11269031465053558
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==================================================
Loss: 0.191
--------------------
Dev Chinese: {'event': {'p': 0.5855949895615866, 'r': 0.7470039946737683, 'f1': 0.6565242832065535}, 'argument': {'p': 0.6469820253660148, 'r': 0.6727675408697328, 'f1': 0.6596228819895071}, 'combined': 0.43305843978480224, 'epoch': 13}
Test Chinese: {'event': {'p': 0.5984360625574977, 'r': 0.753765932792584, 'f1': 0.6671794871794872}, 'argument': {'p': 0.6803049174808791, 'r': 0.6963947406458165, 'f1': 0.6882558062283921}, 'combined': 0.4591901558477631, 'epoch': 13}
Dev Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6481106446116869, 'r': 0.6758274220006399, 'f1': 0.6616789065365651}, 'combined': 0.4451708831577809, 'epoch': 13}
Test Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.6824586546962226, 'r': 0.6984627102502379, 'f1': 0.6903679437407153}, 'combined': 0.4629526210967149, 'epoch': 13}
Dev Russian: {'event': {'p': 0.5855670103092784, 'r': 0.7563249001331558, 'f1': 0.6600813480534573}, 'argument': {'p': 0.6514238506582977, 'r': 0.6740656644393247, 'f1': 0.6625513755094002}, 'combined': 0.4373378051009173, 'epoch': 13}
Test Russian: {'event': {'p': 0.6058931860036832, 'r': 0.7624565469293163, 'f1': 0.6752180605438687}, 'argument': {'p': 0.678510396669093, 'r': 0.6923807499522079, 'f1': 0.6853754048291221}, 'combined': 0.4627778515931886, 'epoch': 13}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.6272958593569281, 'r': 0.6791384923616329, 'f1': 0.6521885521885523}, 'combined': 0.4575054022815218, 'epoch': 13}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6320130065247253, 'r': 0.5185747745843899, 'f1': 0.5697018650363721}, 'combined': 0.3307946313114419, 'epoch': 13}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.5408573378627825, 'r': 0.6165773651635721, 'f1': 0.5762405281902543}, 'combined': 0.299960822893557, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5973763874873865, 'r': 0.7882822902796272, 'f1': 0.6796785304247991}, 'argument': {'p': 0.6514556723596995, 'r': 0.6700421394740989, 'f1': 0.6606181993971073}, 'combined': 0.4490080069381028, 'epoch': 12}
Test for Korean: {'event': {'p': 0.5993589743589743, 'r': 0.7584009269988412, 'f1': 0.6695652173913043}, 'argument': {'p': 0.6914045824340443, 'r': 0.7060559408912744, 'f1': 0.6986534572373968}, 'combined': 0.4677940539763439, 'epoch': 12}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6569431051108969, 'r': 0.5072598659717051, 'f1': 0.5724789915966386}, 'combined': 0.3324071564109515, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
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2023-01-21 20:28:51.766154: step: 3064/1532, loss: 0.03061683475971222
==================================================
Loss: 0.179
--------------------
Dev Chinese: {'event': {'p': 0.5835073068893528, 'r': 0.7443408788282291, 'f1': 0.6541837331772967}, 'argument': {'p': 0.6851549334351383, 'r': 0.6528597407786597, 'f1': 0.668617588216689}, 'combined': 0.43739874992759414, 'epoch': 14}
Test Chinese: {'event': {'p': 0.5956761729530818, 'r': 0.7502896871378911, 'f1': 0.6641025641025641}, 'argument': {'p': 0.6982882863323739, 'r': 0.6834162562740471, 'f1': 0.6907722334690587}, 'combined': 0.4587436114576569, 'epoch': 14}
Dev Korean: {'event': {'p': 0.5882946518668012, 'r': 0.7762982689747004, 'f1': 0.669345579793341}, 'argument': {'p': 0.6823949576101654, 'r': 0.6580758215584845, 'f1': 0.6700147878422017}, 'combined': 0.4484714366383508, 'epoch': 14}
Test Korean: {'event': {'p': 0.5975274725274725, 'r': 0.7560834298957126, 'f1': 0.6675191815856777}, 'argument': {'p': 0.6991320405105493, 'r': 0.6867186407962917, 'f1': 0.6928697457417375}, 'combined': 0.46250384562300123, 'epoch': 14}
Dev Russian: {'event': {'p': 0.5793814432989691, 'r': 0.748335552596538, 'f1': 0.653108657757118}, 'argument': {'p': 0.6823297693867717, 'r': 0.6566560009293824, 'f1': 0.6692467502097392}, 'combined': 0.437090846737796, 'epoch': 14}
Test Russian: {'event': {'p': 0.6040515653775322, 'r': 0.7601390498261877, 'f1': 0.6731657260133402}, 'argument': {'p': 0.6955526012998461, 'r': 0.6813576502529105, 'f1': 0.6883819559256209}, 'combined': 0.4633951391351538, 'epoch': 14}
Chinese: {'event': {'p': 0.6375, 'r': 0.9444444444444444, 'f1': 0.7611940298507462}, 'argument': {'p': 0.6433480100146767, 'r': 0.6220803072042741, 'f1': 0.6325354384177914}, 'combined': 0.48148219939264714, 'epoch': 14}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.7036019536019537, 'r': 0.505150120534736, 'f1': 0.5880852149508866}, 'combined': 0.3414688344876116, 'epoch': 14}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6168794589249135, 'r': 0.5428539238539238, 'f1': 0.577504174312685}, 'combined': 0.30061861128605516, 'epoch': 14}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5882946518668012, 'r': 0.7762982689747004, 'f1': 0.669345579793341}, 'argument': {'p': 0.6823949576101654, 'r': 0.6580758215584845, 'f1': 0.6700147878422017}, 'combined': 0.4484714366383508, 'epoch': 14}
Test for Korean: {'event': {'p': 0.5975274725274725, 'r': 0.7560834298957126, 'f1': 0.6675191815856777}, 'argument': {'p': 0.6991320405105493, 'r': 0.6867186407962917, 'f1': 0.6928697457417375}, 'combined': 0.46250384562300123, 'epoch': 14}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.7036019536019537, 'r': 0.505150120534736, 'f1': 0.5880852149508866}, 'combined': 0.3414688344876116, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-21 20:34:18.049635: step: 2/1532, loss: 0.0917862206697464
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2023-01-21 20:55:30.443674: step: 3064/1532, loss: 0.19064025580883026
==================================================
Loss: 0.158
--------------------
Dev Chinese: {'event': {'p': 0.5908141962421712, 'r': 0.7536617842876165, 'f1': 0.6623756582796958}, 'argument': {'p': 0.6835020517239647, 'r': 0.6607186499998327, 'f1': 0.6719172711862705}, 'combined': 0.44506164481150284, 'epoch': 15}
Test Chinese: {'event': {'p': 0.5984360625574977, 'r': 0.753765932792584, 'f1': 0.6671794871794872}, 'argument': {'p': 0.717054861475092, 'r': 0.672895357689176, 'f1': 0.694273623389416}, 'combined': 0.463205120015195, 'epoch': 15}
Dev Korean: {'event': {'p': 0.5943491422805247, 'r': 0.7842876165113183, 'f1': 0.6762342135476465}, 'argument': {'p': 0.6865720774334578, 'r': 0.6645194411133254, 'f1': 0.6753657867245332}, 'combined': 0.45670545164265225, 'epoch': 15}
Test Korean: {'event': {'p': 0.6039377289377289, 'r': 0.7641946697566628, 'f1': 0.6746803069053707}, 'argument': {'p': 0.7155707195337165, 'r': 0.680044229600476, 'f1': 0.6973552970206464}, 'combined': 0.4704918858159757, 'epoch': 15}
Dev Russian: {'event': {'p': 0.5927835051546392, 'r': 0.7656458055925432, 'f1': 0.6682161533991866}, 'argument': {'p': 0.6792019647626248, 'r': 0.6604993019648134, 'f1': 0.6697200857541826}, 'combined': 0.4475177795568333, 'epoch': 15}
Test Russian: {'event': {'p': 0.6077348066298343, 'r': 0.764774044032445, 'f1': 0.6772703950743972}, 'argument': {'p': 0.7121546282713296, 'r': 0.6704872728893562, 'f1': 0.6906931059797018}, 'combined': 0.46778599276203514, 'epoch': 15}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6788245867193236, 'r': 0.6395537428595279, 'f1': 0.6586042798808757}, 'combined': 0.4816658166292972, 'epoch': 15}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.612408424908425, 'r': 0.4135745207173779, 'f1': 0.4937246216315983}, 'combined': 0.31056871360697313, 'epoch': 15}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.5845448202591059, 'r': 0.5845448202591059, 'f1': 0.5845448202591059}, 'combined': 0.3042836050663839, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5882946518668012, 'r': 0.7762982689747004, 'f1': 0.669345579793341}, 'argument': {'p': 0.6823949576101654, 'r': 0.6580758215584845, 'f1': 0.6700147878422017}, 'combined': 0.4484714366383508, 'epoch': 14}
Test for Korean: {'event': {'p': 0.5975274725274725, 'r': 0.7560834298957126, 'f1': 0.6675191815856777}, 'argument': {'p': 0.6991320405105493, 'r': 0.6867186407962917, 'f1': 0.6928697457417375}, 'combined': 0.46250384562300123, 'epoch': 14}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.7036019536019537, 'r': 0.505150120534736, 'f1': 0.5880852149508866}, 'combined': 0.3414688344876116, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
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2023-01-21 21:22:03.393947: step: 3064/1532, loss: 0.029709137976169586
==================================================
Loss: 0.139
--------------------
Dev Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6765876796359083, 'r': 0.6511115323940059, 'f1': 0.6636051854141123}, 'combined': 0.4372261198222881, 'epoch': 16}
Test Chinese: {'event': {'p': 0.594296228150874, 'r': 0.7485515643105446, 'f1': 0.6625641025641026}, 'argument': {'p': 0.7156728172231, 'r': 0.6788981712930509, 'f1': 0.696800623070299}, 'combined': 0.46167507949068015, 'epoch': 16}
Dev Korean: {'event': {'p': 0.5933400605449042, 'r': 0.7829560585885486, 'f1': 0.6750861079219288}, 'argument': {'p': 0.6769080416011521, 'r': 0.6585741103247132, 'f1': 0.6676152288913098}, 'combined': 0.45069776646164195, 'epoch': 16}
Test Korean: {'event': {'p': 0.5966117216117216, 'r': 0.7549246813441484, 'f1': 0.6664961636828645}, 'argument': {'p': 0.7158205793041196, 'r': 0.6819284699415001, 'f1': 0.6984636228669948}, 'combined': 0.4655233251128871, 'epoch': 16}
Dev Russian: {'event': {'p': 0.5865979381443299, 'r': 0.7576564580559254, 'f1': 0.6612434631028472}, 'argument': {'p': 0.6777896235778603, 'r': 0.652740876619548, 'f1': 0.6650294645514776}, 'combined': 0.4397463862054512, 'epoch': 16}
Test Russian: {'event': {'p': 0.6049723756906077, 'r': 0.761297798377752, 'f1': 0.6741918932786043}, 'argument': {'p': 0.7127167314708155, 'r': 0.6738499441741467, 'f1': 0.6927386012507393}, 'combined': 0.467038749124408, 'epoch': 16}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6949724517906335, 'r': 0.6317931379914851, 'f1': 0.661878525514889}, 'combined': 0.47418163021962195, 'epoch': 16}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6354700854700855, 'r': 0.5228551336146273, 'f1': 0.5736882716049383}, 'combined': 0.3331093189964158, 'epoch': 16}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.6043614805811881, 'r': 0.6290292961151142, 'f1': 0.6164487101928119}, 'combined': 0.33778011517414347, 'epoch': 16}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5882946518668012, 'r': 0.7762982689747004, 'f1': 0.669345579793341}, 'argument': {'p': 0.6823949576101654, 'r': 0.6580758215584845, 'f1': 0.6700147878422017}, 'combined': 0.4484714366383508, 'epoch': 14}
Test for Korean: {'event': {'p': 0.5975274725274725, 'r': 0.7560834298957126, 'f1': 0.6675191815856777}, 'argument': {'p': 0.6991320405105493, 'r': 0.6867186407962917, 'f1': 0.6928697457417375}, 'combined': 0.46250384562300123, 'epoch': 14}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.7036019536019537, 'r': 0.505150120534736, 'f1': 0.5880852149508866}, 'combined': 0.3414688344876116, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-21 21:27:24.850690: step: 2/1532, loss: 0.1478312462568283
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2023-01-21 21:48:32.800366: step: 3056/1532, loss: 0.1683693826198578
2023-01-21 21:48:33.614296: step: 3058/1532, loss: 0.000646581465844065
2023-01-21 21:48:34.440043: step: 3060/1532, loss: 0.13499018549919128
2023-01-21 21:48:35.251911: step: 3062/1532, loss: 0.7349913120269775
2023-01-21 21:48:36.053896: step: 3064/1532, loss: 0.06947252154350281
==================================================
Loss: 0.127
--------------------
Dev Chinese: {'event': {'p': 0.5897703549060542, 'r': 0.7523302263648469, 'f1': 0.6612053832650673}, 'argument': {'p': 0.6783478341993107, 'r': 0.6680251497658429, 'f1': 0.6731469198078593}, 'combined': 0.4450883671052551, 'epoch': 17}
Test Chinese: {'event': {'p': 0.5952161913523459, 'r': 0.7497103128621089, 'f1': 0.6635897435897437}, 'argument': {'p': 0.7041991093509724, 'r': 0.6863415410124092, 'f1': 0.6951556601602537}, 'combined': 0.4612981662807017, 'epoch': 17}
Dev Korean: {'event': {'p': 0.5923309788092835, 'r': 0.7816245006657789, 'f1': 0.6739380022962113}, 'argument': {'p': 0.678247539460207, 'r': 0.6685721109372369, 'f1': 0.6733750715043147}, 'combined': 0.4538130504856863, 'epoch': 17}
Test Korean: {'event': {'p': 0.5984432234432234, 'r': 0.7572421784472769, 'f1': 0.6685421994884911}, 'argument': {'p': 0.7093218985929783, 'r': 0.6912318099040299, 'f1': 0.7001600249877665}, 'combined': 0.46808652309923837, 'epoch': 17}
Dev Russian: {'event': {'p': 0.5865979381443299, 'r': 0.7576564580559254, 'f1': 0.6612434631028472}, 'argument': {'p': 0.6779285724151283, 'r': 0.6651560051087564, 'f1': 0.6714815559985983}, 'combined': 0.44401278949820155, 'epoch': 17}
Test Russian: {'event': {'p': 0.6031307550644567, 'r': 0.7589803012746235, 'f1': 0.6721395587480758}, 'argument': {'p': 0.7021043695368517, 'r': 0.6818965463172503, 'f1': 0.6918529305249385}, 'combined': 0.4650217234415953, 'epoch': 17}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6650678721475182, 'r': 0.6210964425840458, 'f1': 0.6423305089971756}, 'combined': 0.46017708107260336, 'epoch': 17}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.6049450549450549, 'r': 0.4854497354497354, 'f1': 0.5386497064579256}, 'combined': 0.32145224417650403, 'epoch': 17}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.594148398244774, 'r': 0.6179143341745649, 'f1': 0.6057983668378086}, 'combined': 0.3153470950662565, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5882946518668012, 'r': 0.7762982689747004, 'f1': 0.669345579793341}, 'argument': {'p': 0.6823949576101654, 'r': 0.6580758215584845, 'f1': 0.6700147878422017}, 'combined': 0.4484714366383508, 'epoch': 14}
Test for Korean: {'event': {'p': 0.5975274725274725, 'r': 0.7560834298957126, 'f1': 0.6675191815856777}, 'argument': {'p': 0.6991320405105493, 'r': 0.6867186407962917, 'f1': 0.6928697457417375}, 'combined': 0.46250384562300123, 'epoch': 14}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.7036019536019537, 'r': 0.505150120534736, 'f1': 0.5880852149508866}, 'combined': 0.3414688344876116, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-21 21:53:59.363239: step: 2/1532, loss: 0.11488906294107437
2023-01-21 21:54:00.186557: step: 4/1532, loss: 0.00613162899389863
2023-01-21 21:54:01.003081: step: 6/1532, loss: 0.0678439810872078
2023-01-21 21:54:01.837298: step: 8/1532, loss: 0.8278744220733643
2023-01-21 21:54:02.655566: step: 10/1532, loss: 0.7664392590522766
2023-01-21 21:54:03.491819: step: 12/1532, loss: 0.12409790605306625
2023-01-21 21:54:04.308912: step: 14/1532, loss: 0.006596212740987539
2023-01-21 21:54:05.133714: step: 16/1532, loss: 0.0834595337510109
2023-01-21 21:54:05.965528: step: 18/1532, loss: 0.08826925605535507
2023-01-21 21:54:06.753350: step: 20/1532, loss: 0.0265467818826437
2023-01-21 21:54:07.554183: step: 22/1532, loss: 0.009696587920188904
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2023-01-21 21:54:12.549887: step: 34/1532, loss: 0.0006383167346939445
2023-01-21 21:54:13.396173: step: 36/1532, loss: 0.05861770361661911
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2023-01-21 21:54:15.088693: step: 40/1532, loss: 0.01664736494421959
2023-01-21 21:54:15.917049: step: 42/1532, loss: 0.05501240864396095
2023-01-21 21:54:16.745004: step: 44/1532, loss: 0.0007405206561088562
2023-01-21 21:54:17.556057: step: 46/1532, loss: 0.0030152644030749798
2023-01-21 21:54:18.359258: step: 48/1532, loss: 0.01757206954061985
2023-01-21 21:54:19.208099: step: 50/1532, loss: 0.08860518038272858
2023-01-21 21:54:20.000723: step: 52/1532, loss: 0.1331489384174347
2023-01-21 21:54:20.801498: step: 54/1532, loss: 0.06232272461056709
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2023-01-21 22:15:09.627490: step: 3064/1532, loss: 0.2712899446487427
==================================================
Loss: 0.130
--------------------
Dev Chinese: {'event': {'p': 0.5793319415448852, 'r': 0.7390146471371505, 'f1': 0.649502633118783}, 'argument': {'p': 0.6667179415491854, 'r': 0.6681694319879717, 'f1': 0.6674428976291591}, 'combined': 0.43350591946656913, 'epoch': 18}
Test Chinese: {'event': {'p': 0.5970561177552898, 'r': 0.7520278099652375, 'f1': 0.6656410256410257}, 'argument': {'p': 0.6897781572435805, 'r': 0.7054131288077683, 'f1': 0.6975080377136799}, 'combined': 0.4642899656165931, 'epoch': 18}
Dev Korean: {'event': {'p': 0.5842583249243188, 'r': 0.7709720372836218, 'f1': 0.6647531572904707}, 'argument': {'p': 0.6728864006282955, 'r': 0.6671229196807298, 'f1': 0.6699922655718297}, 'combined': 0.4453794738990693, 'epoch': 18}
Test Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.6939360489720307, 'r': 0.7067478927764823, 'f1': 0.7002833768771466}, 'combined': 0.4696017939058512, 'epoch': 18}
Dev Russian: {'event': {'p': 0.5845360824742268, 'r': 0.7549933422103862, 'f1': 0.6589192330040674}, 'argument': {'p': 0.6668247791904369, 'r': 0.6658569638360241, 'f1': 0.6663405200915332}, 'combined': 0.4390645844182444, 'epoch': 18}
Test Russian: {'event': {'p': 0.6049723756906077, 'r': 0.761297798377752, 'f1': 0.6741918932786043}, 'argument': {'p': 0.6881474362991747, 'r': 0.6992205428839419, 'f1': 0.6936398002736277}, 'combined': 0.46764633019987, 'epoch': 18}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6406257698940626, 'r': 0.6512146255947909, 'f1': 0.6458768007948336}, 'combined': 0.46271770803211953, 'epoch': 18}
Korean: {'event': {'p': 0.5737704918032787, 'r': 0.5555555555555556, 'f1': 0.5645161290322581}, 'argument': {'p': 0.6585648148148149, 'r': 0.5927083333333334, 'f1': 0.6239035087719299}, 'combined': 0.3522035936615734, 'epoch': 18}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.5968153384737297, 'r': 0.6326242587821536, 'f1': 0.6141983094972364}, 'combined': 0.33654701890259525, 'epoch': 18}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5842583249243188, 'r': 0.7709720372836218, 'f1': 0.6647531572904707}, 'argument': {'p': 0.6728864006282955, 'r': 0.6671229196807298, 'f1': 0.6699922655718297}, 'combined': 0.4453794738990693, 'epoch': 18}
Test for Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.6939360489720307, 'r': 0.7067478927764823, 'f1': 0.7002833768771466}, 'combined': 0.4696017939058512, 'epoch': 18}
Korean: {'event': {'p': 0.5737704918032787, 'r': 0.5555555555555556, 'f1': 0.5645161290322581}, 'argument': {'p': 0.6585648148148149, 'r': 0.5927083333333334, 'f1': 0.6239035087719299}, 'combined': 0.3522035936615734, 'epoch': 18}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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: 0.112
--------------------
Dev Chinese: {'event': {'p': 0.5887265135699373, 'r': 0.7509986684420772, 'f1': 0.6600351082504389}, 'argument': {'p': 0.6623589304334466, 'r': 0.6604390494756684, 'f1': 0.6613975967172877}, 'combined': 0.4365456343458751, 'epoch': 19}
Test Chinese: {'event': {'p': 0.5961361545538179, 'r': 0.7508690614136733, 'f1': 0.6646153846153845}, 'argument': {'p': 0.7033167035545401, 'r': 0.6812720986415268, 'f1': 0.692118910510142}, 'combined': 0.45999287590827886, 'epoch': 19}
Dev Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Dev Russian: {'event': {'p': 0.5845360824742268, 'r': 0.7549933422103862, 'f1': 0.6589192330040674}, 'argument': {'p': 0.6758501225350796, 'r': 0.6562886139233551, 'f1': 0.6659257448179713}, 'combined': 0.43879128101312, 'epoch': 19}
Test Russian: {'event': {'p': 0.60451197053407, 'r': 0.7607184241019699, 'f1': 0.6736788096459724}, 'argument': {'p': 0.702129057164945, 'r': 0.6782008367069349, 'f1': 0.6899575473364569}, 'combined': 0.4648097791958789, 'epoch': 19}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6510785824345147, 'r': 0.6349361382419234, 'f1': 0.6429060479269685}, 'combined': 0.46058940747006694, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
Russian: {'event': {'p': 0.4864864864864865, 'r': 0.5, 'f1': 0.4931506849315069}, 'argument': {'p': 0.590025641025641, 'r': 0.590025641025641, 'f1': 0.590025641025641}, 'combined': 0.2909715489989463, 'epoch': 19}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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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2023-01-21 23:08:32.771417: step: 3064/1532, loss: 0.21608123183250427
==================================================
Loss: 0.108
--------------------
Dev Chinese: {'event': {'p': 0.5866388308977035, 'r': 0.748335552596538, 'f1': 0.657694558221182}, 'argument': {'p': 0.688401266246968, 'r': 0.6514869954482175, 'f1': 0.6694356319572153}, 'combined': 0.4402841722176185, 'epoch': 20}
Test Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.7157274200927725, 'r': 0.6875472947771412, 'f1': 0.701354405189616}, 'combined': 0.4632535763508849, 'epoch': 20}
Dev Korean: {'event': {'p': 0.5933400605449042, 'r': 0.7829560585885486, 'f1': 0.6750861079219288}, 'argument': {'p': 0.6880479738281133, 'r': 0.6546999608414336, 'f1': 0.6709598576044787}, 'combined': 0.4529556788420591, 'epoch': 20}
Test Korean: {'event': {'p': 0.5970695970695971, 'r': 0.7555040556199305, 'f1': 0.6670076726342711}, 'argument': {'p': 0.7180396710457323, 'r': 0.6912938980638275, 'f1': 0.7044129992238695}, 'combined': 0.4698488751856398, 'epoch': 20}
Dev Russian: {'event': {'p': 0.5855670103092784, 'r': 0.7563249001331558, 'f1': 0.6600813480534573}, 'argument': {'p': 0.6871415817864952, 'r': 0.6528093811034625, 'f1': 0.6695356519152482}, 'combined': 0.4419479956860674, 'epoch': 20}
Test Russian: {'event': {'p': 0.6017495395948435, 'r': 0.7572421784472769, 'f1': 0.6706003078501795}, 'argument': {'p': 0.7122279144104333, 'r': 0.6798322634539722, 'f1': 0.6956531374837018}, 'combined': 0.4665052081535137, 'epoch': 20}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6835746849021186, 'r': 0.6383796644127223, 'f1': 0.6602046102046103}, 'combined': 0.48283620746307326, 'epoch': 20}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6030219780219781, 'r': 0.39692585895117544, 'f1': 0.47873500545256276}, 'combined': 0.293418229148345, 'epoch': 20}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.6135811965811966, 'r': 0.6135811965811966, 'f1': 0.6135811965811966}, 'combined': 0.3362088748390118, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-21 23:13:53.148363: step: 2/1532, loss: 0.021190935745835304
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2023-01-21 23:35:04.530963: step: 3064/1532, loss: 0.012471305206418037
==================================================
Loss: 0.105
--------------------
Dev Chinese: {'event': {'p': 0.5835073068893528, 'r': 0.7443408788282291, 'f1': 0.6541837331772967}, 'argument': {'p': 0.6735369162108867, 'r': 0.6613351604824287, 'f1': 0.6673802716385752}, 'combined': 0.43658931754940145, 'epoch': 21}
Test Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6937087918482295, 'r': 0.6987959896551165, 'f1': 0.6962430982947027}, 'combined': 0.459877492617219, 'epoch': 21}
Dev Korean: {'event': {'p': 0.5893037336024218, 'r': 0.7776298268974701, 'f1': 0.6704936854190586}, 'argument': {'p': 0.6795303711346453, 'r': 0.6606410735763766, 'f1': 0.6699526029834885}, 'combined': 0.4491989898304906, 'epoch': 21}
Test Korean: {'event': {'p': 0.597985347985348, 'r': 0.7566628041714948, 'f1': 0.6680306905370844}, 'argument': {'p': 0.6922992339179888, 'r': 0.7078695758691486, 'f1': 0.699997831208031}, 'combined': 0.4676200345563624, 'epoch': 21}
Dev Russian: {'event': {'p': 0.5814432989690722, 'r': 0.7509986684420772, 'f1': 0.6554328878558978}, 'argument': {'p': 0.6754684205904646, 'r': 0.658349380242408, 'f1': 0.6667990422550535}, 'combined': 0.43704202188477653, 'epoch': 21}
Test Russian: {'event': {'p': 0.6003683241252302, 'r': 0.7555040556199305, 'f1': 0.6690610569522831}, 'argument': {'p': 0.6902093935258133, 'r': 0.6982941755784919, 'f1': 0.6942282470898465}, 'combined': 0.4644810847640635, 'epoch': 21}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6440760059612519, 'r': 0.6493989481592788, 'f1': 0.6467265245043022}, 'combined': 0.463326465316515, 'epoch': 21}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.6353391800507184, 'r': 0.5098400827567494, 'f1': 0.565712968538311}, 'combined': 0.33760290057931464, 'epoch': 21}
Russian: {'event': {'p': 0.4864864864864865, 'r': 0.5, 'f1': 0.4931506849315069}, 'argument': {'p': 0.5911107218524264, 'r': 0.6393646583301755, 'f1': 0.6142915344740901}, 'combined': 0.3029382909735239, 'epoch': 21}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 22
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 23:40:28.648606: step: 2/1532, loss: 0.057309482246637344
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2023-01-22 00:01:40.223599: step: 3064/1532, loss: 0.3909856081008911
==================================================
Loss: 0.098
--------------------
Dev Chinese: {'event': {'p': 0.5824634655532359, 'r': 0.7430093209054593, 'f1': 0.6530134581626682}, 'argument': {'p': 0.6632878827573742, 'r': 0.6637681781105657, 'f1': 0.6635279435184156}, 'combined': 0.43329267698452417, 'epoch': 22}
Test Chinese: {'event': {'p': 0.5961361545538179, 'r': 0.7508690614136733, 'f1': 0.6646153846153845}, 'argument': {'p': 0.6892574271097738, 'r': 0.6949916819110198, 'f1': 0.6921126774292011}, 'combined': 0.4599887333067921, 'epoch': 22}
Dev Korean: {'event': {'p': 0.58627648839556, 'r': 0.7736351531291611, 'f1': 0.6670493685419058}, 'argument': {'p': 0.6698476971040409, 'r': 0.6645996026110605, 'f1': 0.6672133300435673}, 'combined': 0.44506423048830374, 'epoch': 22}
Test Korean: {'event': {'p': 0.5975274725274725, 'r': 0.7560834298957126, 'f1': 0.6675191815856777}, 'argument': {'p': 0.69324984302888, 'r': 0.7011514503580728, 'f1': 0.6971782588060438}, 'combined': 0.4653798607375381, 'epoch': 22}
Dev Russian: {'event': {'p': 0.5824742268041238, 'r': 0.7523302263648469, 'f1': 0.6565950029052877}, 'argument': {'p': 0.6654364849657739, 'r': 0.6654364849657739, 'f1': 0.6654364849657739}, 'combined': 0.4369222707793868, 'epoch': 22}
Test Russian: {'event': {'p': 0.6058931860036832, 'r': 0.7624565469293163, 'f1': 0.6752180605438687}, 'argument': {'p': 0.6870723517439911, 'r': 0.691211341814738, 'f1': 0.6891356320795586}, 'combined': 0.46531682494443266, 'epoch': 22}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6432806324110671, 'r': 0.6113824192336589, 'f1': 0.6269260400616332}, 'combined': 0.46785525377733816, 'epoch': 22}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6717948717948719, 'r': 0.5598290598290598, 'f1': 0.6107226107226108}, 'combined': 0.3546131288066773, 'epoch': 22}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.5681307375751821, 'r': 0.6261032618175476, 'f1': 0.5957098995933948}, 'combined': 0.3264163833388465, 'epoch': 22}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-22 00:07:04.153606: step: 2/1532, loss: 0.003764599794521928
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2023-01-22 00:28:18.528526: step: 3064/1532, loss: 0.0019233853090554476
==================================================
Loss: 0.091
--------------------
Dev Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6823043311776537, 'r': 0.6442061197920994, 'f1': 0.6627081232326036}, 'combined': 0.43663507709766625, 'epoch': 23}
Test Chinese: {'event': {'p': 0.5956761729530818, 'r': 0.7502896871378911, 'f1': 0.6641025641025641}, 'argument': {'p': 0.7209342725985198, 'r': 0.6870503617863893, 'f1': 0.7035845998836553}, 'combined': 0.4672523368458121, 'epoch': 23}
Dev Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6869507093605023, 'r': 0.6491953884320522, 'f1': 0.6675396250960037}, 'combined': 0.44911391539180046, 'epoch': 23}
Test Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.7271833817281934, 'r': 0.6961510263923465, 'f1': 0.711328911431287}, 'combined': 0.47700879943039237, 'epoch': 23}
Dev Russian: {'event': {'p': 0.5804123711340207, 'r': 0.7496671105193076, 'f1': 0.6542707728065078}, 'argument': {'p': 0.6862787280155462, 'r': 0.642547715658292, 'f1': 0.6636936389858648}, 'combined': 0.43423535008604514, 'epoch': 23}
Test Russian: {'event': {'p': 0.6022099447513812, 'r': 0.7578215527230591, 'f1': 0.6711133914828117}, 'argument': {'p': 0.7194900970174269, 'r': 0.6846101023442317, 'f1': 0.7016168634954932}, 'combined': 0.4708644727819934, 'epoch': 23}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.661408271577763, 'r': 0.6450097193898846, 'f1': 0.653106075700218}, 'combined': 0.45814903817776487, 'epoch': 23}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.6707650273224043, 'r': 0.5051440329218106, 'f1': 0.5762910798122065}, 'combined': 0.34391564440405875, 'epoch': 23}
Russian: {'event': {'p': 0.4864864864864865, 'r': 0.5, 'f1': 0.4931506849315069}, 'argument': {'p': 0.6403793672214724, 'r': 0.5881035005095155, 'f1': 0.6131291813822608}, 'combined': 0.30236507575015603, 'epoch': 23}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
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==================================================
Loss: 0.089
--------------------
Dev Chinese: {'event': {'p': 0.5855949895615866, 'r': 0.7470039946737683, 'f1': 0.6565242832065535}, 'argument': {'p': 0.641122427044199, 'r': 0.6732017943147208, 'f1': 0.6567706221153167}, 'combined': 0.4311858619153805, 'epoch': 24}
Test Chinese: {'event': {'p': 0.5919963201471942, 'r': 0.7456546929316339, 'f1': 0.6600000000000001}, 'argument': {'p': 0.6772463861260335, 'r': 0.6934462996812076, 'f1': 0.6852506113067744}, 'combined': 0.45226540346247124, 'epoch': 24}
Dev Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6489459032595757, 'r': 0.6813469112682563, 'f1': 0.664751822963184}, 'combined': 0.44723831028292294, 'epoch': 24}
Test Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.6775068861705444, 'r': 0.6988278260098585, 'f1': 0.6880022136754935}, 'combined': 0.4603104324744478, 'epoch': 24}
Dev Russian: {'event': {'p': 0.5876288659793815, 'r': 0.758988015978695, 'f1': 0.6624055781522371}, 'argument': {'p': 0.6399813544465304, 'r': 0.6742991951922139, 'f1': 0.6566922296155646}, 'combined': 0.4349965960265797, 'epoch': 24}
Test Russian: {'event': {'p': 0.6022099447513812, 'r': 0.7578215527230591, 'f1': 0.6711133914828117}, 'argument': {'p': 0.6718447090253826, 'r': 0.69406001503524, 'f1': 0.6827717052786294}, 'combined': 0.4582172347380437, 'epoch': 24}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6281107326189292, 'r': 0.6333017304091684, 'f1': 0.6306955504486368}, 'combined': 0.4612549548057195, 'epoch': 24}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6467633928571428, 'r': 0.5110229276895943, 'f1': 0.5709359605911329}, 'combined': 0.33151120292388364, 'epoch': 24}
Russian: {'event': {'p': 0.4864864864864865, 'r': 0.5, 'f1': 0.4931506849315069}, 'argument': {'p': 0.5517679872169037, 'r': 0.5742891295522875, 'f1': 0.5628033469612419}, 'combined': 0.27754685603568097, 'epoch': 24}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 25
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 01:21:24.699052: step: 3064/1532, loss: 0.03415262699127197
==================================================
Loss: 0.081
--------------------
Dev Chinese: {'event': {'p': 0.592901878914405, 'r': 0.7563249001331558, 'f1': 0.6647162083089526}, 'argument': {'p': 0.6759223054136241, 'r': 0.658779348392264, 'f1': 0.6672407345184033}, 'combined': 0.44352573107835347, 'epoch': 25}
Test Chinese: {'event': {'p': 0.5970561177552898, 'r': 0.7520278099652375, 'f1': 0.6656410256410257}, 'argument': {'p': 0.6995048862991301, 'r': 0.6789586813611049, 'f1': 0.6890786617065957}, 'combined': 0.4586790271257237, 'epoch': 25}
Dev Korean: {'event': {'p': 0.5983854692230071, 'r': 0.7896138482023968, 'f1': 0.6808266360505166}, 'argument': {'p': 0.6843284012642538, 'r': 0.6633397270457353, 'f1': 0.6736706246418841}, 'combined': 0.4586529051809842, 'epoch': 25}
Test Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.7051235344001635, 'r': 0.6835840105228843, 'f1': 0.6941867282589645}, 'combined': 0.4655134530677762, 'epoch': 25}
Dev Russian: {'event': {'p': 0.5907216494845361, 'r': 0.762982689747004, 'f1': 0.6658919233004067}, 'argument': {'p': 0.6800336464541096, 'r': 0.6603082324887982, 'f1': 0.6700257929374929}, 'combined': 0.4461647639200272, 'epoch': 25}
Test Russian: {'event': {'p': 0.608195211786372, 'r': 0.7653534183082271, 'f1': 0.6777834787070293}, 'argument': {'p': 0.6945982414091072, 'r': 0.6734370629798375, 'f1': 0.6838539884823304}, 'combined': 0.4635049352412306, 'epoch': 25}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.7311894454751597, 'r': 0.6345032378090228, 'f1': 0.6794238210167413}, 'combined': 0.48675139416124746, 'epoch': 25}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6194259483454398, 'r': 0.46260925256178415, 'f1': 0.5296540717736369}, 'combined': 0.32462668915158394, 'epoch': 25}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.6017148324881945, 'r': 0.6017148324881945, 'f1': 0.6017148324881945}, 'combined': 0.3297067575277778, 'epoch': 25}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 26
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 01:26:47.742303: step: 2/1532, loss: 0.0005181316519156098
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2023-01-22 01:47:59.274421: step: 3064/1532, loss: 0.4127650260925293
==================================================
Loss: 0.079
--------------------
Dev Chinese: {'event': {'p': 0.5887265135699373, 'r': 0.7509986684420772, 'f1': 0.6600351082504389}, 'argument': {'p': 0.6737394012761585, 'r': 0.6610457603825497, 'f1': 0.6673322233561949}, 'combined': 0.44046269628191215, 'epoch': 26}
Test Chinese: {'event': {'p': 0.5952161913523459, 'r': 0.7497103128621089, 'f1': 0.6635897435897437}, 'argument': {'p': 0.7052835189474606, 'r': 0.6827369928672221, 'f1': 0.6938271369137963}, 'combined': 0.46041657188023205, 'epoch': 26}
Dev Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6756036573324449, 'r': 0.6635651032103415, 'f1': 0.6695302695462848}, 'combined': 0.45045320086581275, 'epoch': 26}
Test Korean: {'event': {'p': 0.6011904761904762, 'r': 0.7607184241019699, 'f1': 0.6716112531969309}, 'argument': {'p': 0.7062521158822801, 'r': 0.6875356055989188, 'f1': 0.6967681931974764}, 'combined': 0.46795735942111844, 'epoch': 26}
Dev Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.676355991845347, 'r': 0.6601587873962204, 'f1': 0.6681592427704676}, 'combined': 0.4433688874165451, 'epoch': 26}
Test Russian: {'event': {'p': 0.6068139963167587, 'r': 0.7636152954808807, 'f1': 0.6762442278091328}, 'argument': {'p': 0.7024313637759876, 'r': 0.6789465304835774, 'f1': 0.6904893140688133}, 'combined': 0.46693941300292247, 'epoch': 26}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.71267217630854, 'r': 0.6478837966441272, 'f1': 0.6787354060081333}, 'combined': 0.4862582013192597, 'epoch': 26}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.6592261904761905, 'r': 0.4672995780590717, 'f1': 0.5469135802469136}, 'combined': 0.326383910792513, 'epoch': 26}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.5685405238036817, 'r': 0.5685405238036817, 'f1': 0.5685405238036817}, 'combined': 0.31152905413900367, 'epoch': 26}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-22 01:53:23.338658: step: 2/1532, loss: 0.004524226766079664
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2023-01-22 02:14:31.477214: step: 3058/1532, loss: 0.0005755729507654905
2023-01-22 02:14:32.361780: step: 3060/1532, loss: 0.00018664819072000682
2023-01-22 02:14:33.196606: step: 3062/1532, loss: 0.08024115115404129
2023-01-22 02:14:34.054942: step: 3064/1532, loss: 0.0007101779337972403
==================================================
Loss: 0.079
--------------------
Dev Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6868383314041928, 'r': 0.6559580003330834, 'f1': 0.6710430878778353}, 'combined': 0.4421266921886732, 'epoch': 27}
Test Chinese: {'event': {'p': 0.5975160993560258, 'r': 0.7526071842410197, 'f1': 0.6661538461538462}, 'argument': {'p': 0.7125066312611809, 'r': 0.6746554604056755, 'f1': 0.6930646277652949}, 'combined': 0.46168766741903494, 'epoch': 27}
Dev Korean: {'event': {'p': 0.5893037336024218, 'r': 0.7776298268974701, 'f1': 0.6704936854190586}, 'argument': {'p': 0.694186758946316, 'r': 0.6600220753747783, 'f1': 0.6766734549728989}, 'combined': 0.4537052786500264, 'epoch': 27}
Test Korean: {'event': {'p': 0.5998168498168498, 'r': 0.7589803012746235, 'f1': 0.6700767263427111}, 'argument': {'p': 0.7149668546955795, 'r': 0.6808177482822689, 'f1': 0.697474557423565}, 'combined': 0.4673614681457137, 'epoch': 27}
Dev Russian: {'event': {'p': 0.5876288659793815, 'r': 0.758988015978695, 'f1': 0.6624055781522371}, 'argument': {'p': 0.6920793417601117, 'r': 0.655970854363932, 'f1': 0.6735415022486801}, 'combined': 0.4461576482065633, 'epoch': 27}
Test Russian: {'event': {'p': 0.6072744014732965, 'r': 0.7641946697566628, 'f1': 0.676757311441765}, 'argument': {'p': 0.7117981843462267, 'r': 0.6732196276837681, 'f1': 0.6919716186887148}, 'combined': 0.46829685225778084, 'epoch': 27}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.7127348069825945, 'r': 0.6656118445374642, 'f1': 0.6883678050344716}, 'combined': 0.48288487815850994, 'epoch': 27}
Korean: {'event': {'p': 0.5737704918032787, 'r': 0.5555555555555556, 'f1': 0.5645161290322581}, 'argument': {'p': 0.6636904761904762, 'r': 0.5040687160940325, 'f1': 0.5729701952723535}, 'combined': 0.323450916686006, 'epoch': 27}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6054996950293703, 'r': 0.5807854217628654, 'f1': 0.5928851180495918}, 'combined': 0.30862512994362307, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-22 02:19:54.331922: step: 2/1532, loss: 0.009841369464993477
2023-01-22 02:19:55.131912: step: 4/1532, loss: 0.016045304015278816
2023-01-22 02:19:55.943570: step: 6/1532, loss: 0.002809332450851798
2023-01-22 02:19:56.723700: step: 8/1532, loss: 0.0001409054093528539
2023-01-22 02:19:57.552820: step: 10/1532, loss: 0.16592280566692352
2023-01-22 02:19:58.376942: step: 12/1532, loss: 0.02265843003988266
2023-01-22 02:19:59.240719: step: 14/1532, loss: 0.007155044004321098
2023-01-22 02:20:00.064157: step: 16/1532, loss: 0.12274059653282166
2023-01-22 02:20:00.935004: step: 18/1532, loss: 0.13828249275684357
2023-01-22 02:20:01.765912: step: 20/1532, loss: 0.08464264869689941
2023-01-22 02:20:02.576672: step: 22/1532, loss: 1.3064273595809937
2023-01-22 02:20:03.424833: step: 24/1532, loss: 0.000310709816403687
2023-01-22 02:20:04.237874: step: 26/1532, loss: 0.09484507888555527
2023-01-22 02:20:05.043678: step: 28/1532, loss: 0.03292800113558769
2023-01-22 02:20:05.849234: step: 30/1532, loss: 0.009965559467673302
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2023-01-22 02:20:08.282604: step: 36/1532, loss: 0.0008374081808142364
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==================================================
Loss: 0.074
--------------------
Dev Chinese: {'event': {'p': 0.5897703549060542, 'r': 0.7523302263648469, 'f1': 0.6612053832650673}, 'argument': {'p': 0.7028470753806204, 'r': 0.6384875321408252, 'f1': 0.6691232629289989}, 'combined': 0.4424279035165411, 'epoch': 28}
Test Chinese: {'event': {'p': 0.5952161913523459, 'r': 0.7497103128621089, 'f1': 0.6635897435897437}, 'argument': {'p': 0.7194635480617807, 'r': 0.6726672198856476, 'f1': 0.6952788571684784}, 'combined': 0.46137991855180055, 'epoch': 28}
Dev Korean: {'event': {'p': 0.5943491422805247, 'r': 0.7842876165113183, 'f1': 0.6762342135476465}, 'argument': {'p': 0.7036111696355059, 'r': 0.6387784261476628, 'f1': 0.6696292000050378}, 'combined': 0.4528261754339464, 'epoch': 28}
Test Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7253827548529543, 'r': 0.6788901017734529, 'f1': 0.7013667891436607}, 'combined': 0.46925205125314995, 'epoch': 28}
Dev Russian: {'event': {'p': 0.5876288659793815, 'r': 0.758988015978695, 'f1': 0.6624055781522371}, 'argument': {'p': 0.6963741974262817, 'r': 0.6392280214573857, 'f1': 0.6665785577787353}, 'combined': 0.4415453549493076, 'epoch': 28}
Test Russian: {'event': {'p': 0.6054327808471455, 'r': 0.7618771726535342, 'f1': 0.6747049769112365}, 'argument': {'p': 0.7154888828326997, 'r': 0.6676140830446302, 'f1': 0.6907229125027344}, 'combined': 0.46603418673221947, 'epoch': 28}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6851220538720538, 'r': 0.5435679105100593, 'f1': 0.6061909416748127}, 'combined': 0.44333367376217653, 'epoch': 28}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7147120121258052, 'r': 0.5181662087912088, 'f1': 0.6007724159898072}, 'combined': 0.3585254740584334, 'epoch': 28}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.591219430456758, 'r': 0.5188252144824611, 'f1': 0.5526616415139259}, 'combined': 0.2876868818839614, 'epoch': 28}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 29
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 03:07:32.647646: step: 3064/1532, loss: 0.0814451053738594
==================================================
Loss: 0.070
--------------------
Dev Chinese: {'event': {'p': 0.592901878914405, 'r': 0.7563249001331558, 'f1': 0.6647162083089526}, 'argument': {'p': 0.6732069553408458, 'r': 0.6492858960140138, 'f1': 0.6610300853476007}, 'combined': 0.43939741191040044, 'epoch': 29}
Test Chinese: {'event': {'p': 0.5975160993560258, 'r': 0.7526071842410197, 'f1': 0.6661538461538462}, 'argument': {'p': 0.7040328195264431, 'r': 0.6798529661048947, 'f1': 0.6917316523659303}, 'combined': 0.4607997007299197, 'epoch': 29}
Dev Korean: {'event': {'p': 0.5973763874873865, 'r': 0.7882822902796272, 'f1': 0.6796785304247991}, 'argument': {'p': 0.6764672096032914, 'r': 0.6499674257513106, 'f1': 0.6629526086123066}, 'combined': 0.45059465476289956, 'epoch': 29}
Test Korean: {'event': {'p': 0.6007326007326007, 'r': 0.7601390498261877, 'f1': 0.6710997442455243}, 'argument': {'p': 0.7049547798535464, 'r': 0.6827179512272936, 'f1': 0.6936581979738611}, 'combined': 0.4655138392540695, 'epoch': 29}
Dev Russian: {'event': {'p': 0.5907216494845361, 'r': 0.762982689747004, 'f1': 0.6658919233004067}, 'argument': {'p': 0.676107099809867, 'r': 0.6497081241123017, 'f1': 0.6626447903003785}, 'combined': 0.4412498138781137, 'epoch': 29}
Test Russian: {'event': {'p': 0.60451197053407, 'r': 0.7607184241019699, 'f1': 0.6736788096459724}, 'argument': {'p': 0.7017791977922074, 'r': 0.674318272661208, 'f1': 0.6877747348650902}, 'combined': 0.46333926468848824, 'epoch': 29}
Chinese: {'event': {'p': 0.6125, 'r': 0.9074074074074074, 'f1': 0.7313432835820897}, 'argument': {'p': 0.6998155408532768, 'r': 0.6130615481855152, 'f1': 0.6535722231757475}, 'combined': 0.4779856557553975, 'epoch': 29}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6946693657219973, 'r': 0.4949519230769231, 'f1': 0.5780460415496912}, 'combined': 0.35428628353045594, 'epoch': 29}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.5508799530818864, 'r': 0.5398623540202487, 'f1': 0.5453155091113624}, 'combined': 0.2838628677565996, 'epoch': 29}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-22 03:12:54.635775: step: 2/1532, loss: 0.5633144378662109
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2023-01-22 03:34:03.670483: step: 3064/1532, loss: 0.0032802901696413755
==================================================
Loss: 0.066
--------------------
Dev Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6641428375926544, 'r': 0.6708854044717676, 'f1': 0.6674970943481728}, 'combined': 0.4397903617530969, 'epoch': 30}
Test Chinese: {'event': {'p': 0.5988960441582337, 'r': 0.7543453070683661, 'f1': 0.6676923076923077}, 'argument': {'p': 0.6908424187966343, 'r': 0.6977508429846007, 'f1': 0.6942794457558215}, 'combined': 0.4635650453200408, 'epoch': 30}
Dev Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6673422510794297, 'r': 0.6740061537292955, 'f1': 0.6706576490620813}, 'combined': 0.45121169041375386, 'epoch': 30}
Test Korean: {'event': {'p': 0.6025641025641025, 'r': 0.7624565469293163, 'f1': 0.6731457800511509}, 'argument': {'p': 0.6932439092832524, 'r': 0.7016214791839563, 'f1': 0.6974075363660346}, 'combined': 0.46945694008066574, 'epoch': 30}
Dev Russian: {'event': {'p': 0.5917525773195876, 'r': 0.7643142476697736, 'f1': 0.6670540383497966}, 'argument': {'p': 0.6646771729318687, 'r': 0.6719019248115629, 'f1': 0.6682700225152841}, 'combined': 0.4457722172269298, 'epoch': 30}
Test Russian: {'event': {'p': 0.6058931860036832, 'r': 0.7624565469293163, 'f1': 0.6752180605438687}, 'argument': {'p': 0.68724045297866, 'r': 0.69575901135729, 'f1': 0.6914734972204131}, 'combined': 0.4668953937106535, 'epoch': 30}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.677957258535771, 'r': 0.677957258535771, 'f1': 0.677957258535771}, 'combined': 0.48570072253308966, 'epoch': 30}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.5991784773610938, 'r': 0.4956167652246084, 'f1': 0.5424994322053146}, 'combined': 0.3149996703127633, 'epoch': 30}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.5924972093100748, 'r': 0.6398969860548808, 'f1': 0.6152855635143084}, 'combined': 0.3202856358019687, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-22 03:39:29.520495: step: 2/1532, loss: 0.11291315406560898
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2023-01-22 04:00:32.998414: step: 3056/1532, loss: 0.14264659583568573
2023-01-22 04:00:33.825246: step: 3058/1532, loss: 0.00026273675030097365
2023-01-22 04:00:34.652134: step: 3060/1532, loss: 0.00030628536478616297
2023-01-22 04:00:35.472642: step: 3062/1532, loss: 9.86628292594105e-05
2023-01-22 04:00:36.314145: step: 3064/1532, loss: 0.0014333765720948577
==================================================
Loss: 0.066
--------------------
Dev Chinese: {'event': {'p': 0.593945720250522, 'r': 0.7576564580559254, 'f1': 0.665886483323581}, 'argument': {'p': 0.6739702432264246, 'r': 0.6671279057317401, 'f1': 0.6705316195364939}, 'combined': 0.4464979420904213, 'epoch': 31}
Test Chinese: {'event': {'p': 0.5965961361545539, 'r': 0.7514484356894554, 'f1': 0.6651282051282051}, 'argument': {'p': 0.7090988112795155, 'r': 0.6880341269658049, 'f1': 0.6984076721631548}, 'combined': 0.4645306414336471, 'epoch': 31}
Dev Korean: {'event': {'p': 0.6004036326942482, 'r': 0.7922769640479361, 'f1': 0.6831228473019517}, 'argument': {'p': 0.682260278854097, 'r': 0.6700944393595518, 'f1': 0.6761226369068669}, 'combined': 0.46187482084912257, 'epoch': 31}
Test Korean: {'event': {'p': 0.6016483516483516, 'r': 0.761297798377752, 'f1': 0.6721227621483377}, 'argument': {'p': 0.7067612387961539, 'r': 0.6920518743096278, 'f1': 0.6993292176283518}, 'combined': 0.4700350854034037, 'epoch': 31}
Dev Russian: {'event': {'p': 0.5948453608247423, 'r': 0.7683089214380826, 'f1': 0.6705403834979663}, 'argument': {'p': 0.6781029359780419, 'r': 0.6727016816002299, 'f1': 0.6753915102071375}, 'combined': 0.4528772822655646, 'epoch': 31}
Test Russian: {'event': {'p': 0.606353591160221, 'r': 0.7630359212050984, 'f1': 0.6757311441765007}, 'argument': {'p': 0.7053600938227733, 'r': 0.6841214158555436, 'f1': 0.6945784347799661}, 'combined': 0.4693482804541894, 'epoch': 31}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6512095747389866, 'r': 0.6404457801152017, 'f1': 0.6457828282828284}, 'combined': 0.46265038444142925, 'epoch': 31}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6383928571428572, 'r': 0.45485491071428574, 'f1': 0.5312174139728885}, 'combined': 0.3084488210165159, 'epoch': 31}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.582786706470917, 'r': 0.6065739189799341, 'f1': 0.5944424406003355}, 'combined': 0.3094357909974349, 'epoch': 31}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 32
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 04:05:59.380246: step: 2/1532, loss: 3.244876643293537e-05
2023-01-22 04:06:00.200142: step: 4/1532, loss: 0.024845831096172333
2023-01-22 04:06:01.001158: step: 6/1532, loss: 0.0001718508719932288
2023-01-22 04:06:01.811628: step: 8/1532, loss: 3.8222318835323676e-05
2023-01-22 04:06:02.650062: step: 10/1532, loss: 0.0012004277668893337
2023-01-22 04:06:03.486037: step: 12/1532, loss: 0.009724329225718975
2023-01-22 04:06:04.278193: step: 14/1532, loss: 0.12172255665063858
2023-01-22 04:06:05.100476: step: 16/1532, loss: 0.05756881460547447
2023-01-22 04:06:05.950875: step: 18/1532, loss: 6.794450746383518e-05
2023-01-22 04:06:06.840583: step: 20/1532, loss: 0.14590968191623688
2023-01-22 04:06:07.660863: step: 22/1532, loss: 0.00046991551062092185
2023-01-22 04:06:08.497411: step: 24/1532, loss: 0.004724592436105013
2023-01-22 04:06:09.367230: step: 26/1532, loss: 0.03033088706433773
2023-01-22 04:06:10.197093: step: 28/1532, loss: 0.02533652074635029
2023-01-22 04:06:11.009460: step: 30/1532, loss: 0.1869962513446808
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==================================================
Loss: 0.060
--------------------
Dev Chinese: {'event': {'p': 0.5949895615866388, 'r': 0.758988015978695, 'f1': 0.6670567583382095}, 'argument': {'p': 0.6794741399218326, 'r': 0.6632022628409759, 'f1': 0.6712396020527837}, 'combined': 0.44775491301355963, 'epoch': 32}
Test Chinese: {'event': {'p': 0.594296228150874, 'r': 0.7485515643105446, 'f1': 0.6625641025641026}, 'argument': {'p': 0.7024269209721998, 'r': 0.6893324111271788, 'f1': 0.6958180655329452}, 'combined': 0.4610240721377258, 'epoch': 32}
Dev Korean: {'event': {'p': 0.5983854692230071, 'r': 0.7896138482023968, 'f1': 0.6808266360505166}, 'argument': {'p': 0.6850742934760038, 'r': 0.6713728076064838, 'f1': 0.6781543511176604}, 'combined': 0.46170554559445764, 'epoch': 32}
Test Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7077759600689543, 'r': 0.6938004642041389, 'f1': 0.7007185354206412}, 'combined': 0.4688183346957538, 'epoch': 32}
Dev Russian: {'event': {'p': 0.5938144329896907, 'r': 0.7669773635153129, 'f1': 0.6693782684485764}, 'argument': {'p': 0.6818961742882654, 'r': 0.6670400484867781, 'f1': 0.6743863045273815}, 'combined': 0.45141953678997293, 'epoch': 32}
Test Russian: {'event': {'p': 0.602670349907919, 'r': 0.7584009269988412, 'f1': 0.6716264751154438}, 'argument': {'p': 0.7003585249063193, 'r': 0.6863326532459793, 'f1': 0.6932746558074692}, 'combined': 0.46562161336684305, 'epoch': 32}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.672663610898905, 'r': 0.6615452041071875, 'f1': 0.6670580808080807}, 'combined': 0.477892356399819, 'epoch': 32}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6402116402116401, 'r': 0.510548523206751, 'f1': 0.568075117370892}, 'combined': 0.34817507193699837, 'epoch': 32}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.5609640724778976, 'r': 0.5838605652320975, 'f1': 0.5721833539274556}, 'combined': 0.2978488691677166, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-22 04:32:28.440635: step: 2/1532, loss: 0.000676521158311516
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2023-01-22 04:53:37.797271: step: 3064/1532, loss: 2.1895739337196574e-05
==================================================
Loss: 0.062
--------------------
Dev Chinese: {'event': {'p': 0.5918580375782881, 'r': 0.7549933422103862, 'f1': 0.6635459332943242}, 'argument': {'p': 0.6887571881670813, 'r': 0.6537948943514934, 'f1': 0.6708208030585636}, 'combined': 0.4451204158387427, 'epoch': 33}
Test Chinese: {'event': {'p': 0.5947562097516099, 'r': 0.7491309385863267, 'f1': 0.663076923076923}, 'argument': {'p': 0.719101483285694, 'r': 0.6843564932568951, 'f1': 0.7012989025999461}, 'combined': 0.465015118493195, 'epoch': 33}
Dev Korean: {'event': {'p': 0.5953582240161454, 'r': 0.7856191744340879, 'f1': 0.677382319173364}, 'argument': {'p': 0.6954547006468279, 'r': 0.6543416378593018, 'f1': 0.6742720437096951}, 'combined': 0.4567399607218371, 'epoch': 33}
Test Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.7247805109981217, 'r': 0.6905102051460978, 'f1': 0.7072304419528297}, 'combined': 0.47426041401542696, 'epoch': 33}
Dev Russian: {'event': {'p': 0.5896907216494846, 'r': 0.7616511318242344, 'f1': 0.6647298082510168}, 'argument': {'p': 0.6892576596751154, 'r': 0.6562931629080447, 'f1': 0.6723716145605804}, 'combined': 0.44694545442028116, 'epoch': 33}
Test Russian: {'event': {'p': 0.6040515653775322, 'r': 0.7601390498261877, 'f1': 0.6731657260133402}, 'argument': {'p': 0.7191081671720841, 'r': 0.67820873979863, 'f1': 0.6980598909290995}, 'combined': 0.46990999327808036, 'epoch': 33}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6570036649682667, 'r': 0.6135654061273896, 'f1': 0.6345420012086679}, 'combined': 0.4545972545972546, 'epoch': 33}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.7144444444444444, 'r': 0.457977207977208, 'f1': 0.5581597222222222}, 'combined': 0.3240927419354839, 'epoch': 33}
Russian: {'event': {'p': 0.4864864864864865, 'r': 0.5, 'f1': 0.4931506849315069}, 'argument': {'p': 0.6155071719216457, 'r': 0.6029458010661018, 'f1': 0.6091617371595668}, 'combined': 0.30040852791430694, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 34
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 04:58:58.342194: step: 2/1532, loss: 0.00015007558977231383
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2023-01-22 05:20:07.538070: step: 3064/1532, loss: 7.427731179632246e-05
==================================================
Loss: 0.063
--------------------
Dev Chinese: {'event': {'p': 0.5887265135699373, 'r': 0.7509986684420772, 'f1': 0.6600351082504389}, 'argument': {'p': 0.6918044344635019, 'r': 0.6506375522965883, 'f1': 0.6705897883804777}, 'combined': 0.4426128035653475, 'epoch': 34}
Test Chinese: {'event': {'p': 0.5915363385464582, 'r': 0.7450753186558516, 'f1': 0.6594871794871794}, 'argument': {'p': 0.7182164054887457, 'r': 0.6727596709641415, 'f1': 0.6947452811263684}, 'combined': 0.4581756059120562, 'epoch': 34}
Dev Korean: {'event': {'p': 0.5893037336024218, 'r': 0.7776298268974701, 'f1': 0.6704936854190586}, 'argument': {'p': 0.6991544778140869, 'r': 0.6492505179415611, 'f1': 0.6732790345197092}, 'combined': 0.45142934117050537, 'epoch': 34}
Test Korean: {'event': {'p': 0.5993589743589743, 'r': 0.7584009269988412, 'f1': 0.6695652173913043}, 'argument': {'p': 0.720082870308785, 'r': 0.6792322475890185, 'f1': 0.6990612767551169}, 'combined': 0.4680671157403825, 'epoch': 34}
Dev Russian: {'event': {'p': 0.5876288659793815, 'r': 0.758988015978695, 'f1': 0.6624055781522371}, 'argument': {'p': 0.6904118817577795, 'r': 0.6518609645022689, 'f1': 0.6705828198796188}, 'combined': 0.44419780050131635, 'epoch': 34}
Test Russian: {'event': {'p': 0.6040515653775322, 'r': 0.7601390498261877, 'f1': 0.6731657260133402}, 'argument': {'p': 0.7144286526117735, 'r': 0.668745400971717, 'f1': 0.6908326172237657}, 'combined': 0.46504484032713217, 'epoch': 34}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.6534439219784047, 'r': 0.6264421070206193, 'f1': 0.6396581852278055}, 'combined': 0.44871544336875907, 'epoch': 34}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6971861471861471, 'r': 0.47339800117577896, 'f1': 0.5639005602240896}, 'combined': 0.3274261317430198, 'epoch': 34}
Russian: {'event': {'p': 0.5405405405405406, 'r': 0.5555555555555556, 'f1': 0.547945205479452}, 'argument': {'p': 0.5648874092960281, 'r': 0.5309941647382663, 'f1': 0.5474166646786252}, 'combined': 0.29995433681020556, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-22 05:25:28.021547: step: 2/1532, loss: 0.0001089374563889578
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2023-01-22 05:46:36.654661: step: 3064/1532, loss: 0.0003472333773970604
==================================================
Loss: 0.048
--------------------
Dev Chinese: {'event': {'p': 0.5908141962421712, 'r': 0.7536617842876165, 'f1': 0.6623756582796958}, 'argument': {'p': 0.6866499398035841, 'r': 0.66073862132043, 'f1': 0.6734451332688998}, 'combined': 0.44607366346424493, 'epoch': 35}
Test Chinese: {'event': {'p': 0.5952161913523459, 'r': 0.7497103128621089, 'f1': 0.6635897435897437}, 'argument': {'p': 0.7081610470346974, 'r': 0.6817230346120687, 'f1': 0.6946905923356679}, 'combined': 0.46098955204223296, 'epoch': 35}
Dev Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6908389947101713, 'r': 0.6627521026285167, 'f1': 0.6765041484420681}, 'combined': 0.45514515612749934, 'epoch': 35}
Test Korean: {'event': {'p': 0.6007326007326007, 'r': 0.7601390498261877, 'f1': 0.6710997442455243}, 'argument': {'p': 0.7103092415978058, 'r': 0.6869500786190861, 'f1': 0.698434402690166}, 'combined': 0.46871914901764594, 'epoch': 35}
Dev Russian: {'event': {'p': 0.5907216494845361, 'r': 0.762982689747004, 'f1': 0.6658919233004067}, 'argument': {'p': 0.68186457833225, 'r': 0.6610669659212445, 'f1': 0.6713047286678125}, 'combined': 0.4470163968932674, 'epoch': 35}
Test Russian: {'event': {'p': 0.606353591160221, 'r': 0.7630359212050984, 'f1': 0.6757311441765007}, 'argument': {'p': 0.7049884868348099, 'r': 0.6790697924658831, 'f1': 0.691786455246143}, 'combined': 0.4674616529292818, 'epoch': 35}
Chinese: {'event': {'p': 0.575, 'r': 0.8518518518518519, 'f1': 0.6865671641791045}, 'argument': {'p': 0.6710508445802564, 'r': 0.659959095083062, 'f1': 0.6654587542087542}, 'combined': 0.4568821297552641, 'epoch': 35}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.6716517916208319, 'r': 0.4726438533628076, 'f1': 0.5548427843824264}, 'combined': 0.3221667780285057, 'epoch': 35}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.579051288689093, 'r': 0.5211461598201836, 'f1': 0.5485749050738776}, 'combined': 0.2855595396274979, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 36
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 06:13:07.346499: step: 3064/1532, loss: 0.007713891565799713
==================================================
Loss: 0.046
--------------------
Dev Chinese: {'event': {'p': 0.5908141962421712, 'r': 0.7536617842876165, 'f1': 0.6623756582796958}, 'argument': {'p': 0.677840579124545, 'r': 0.6513355890646424, 'f1': 0.664323817206995}, 'combined': 0.4400319257333636, 'epoch': 36}
Test Chinese: {'event': {'p': 0.5952161913523459, 'r': 0.7497103128621089, 'f1': 0.6635897435897437}, 'argument': {'p': 0.7054994642916446, 'r': 0.6838929560643372, 'f1': 0.6945282082546548}, 'combined': 0.46088179563155046, 'epoch': 36}
Dev Korean: {'event': {'p': 0.5893037336024218, 'r': 0.7776298268974701, 'f1': 0.6704936854190586}, 'argument': {'p': 0.6771278461152065, 'r': 0.6563600163128143, 'f1': 0.666582211423807}, 'combined': 0.4469391635723345, 'epoch': 36}
Test Korean: {'event': {'p': 0.6025641025641025, 'r': 0.7624565469293163, 'f1': 0.6731457800511509}, 'argument': {'p': 0.7062864877285071, 'r': 0.6888006395880222, 'f1': 0.697433980937872}, 'combined': 0.46947474113260335, 'epoch': 36}
Dev Russian: {'event': {'p': 0.5896907216494846, 'r': 0.7616511318242344, 'f1': 0.6647298082510168}, 'argument': {'p': 0.6734247536611162, 'r': 0.6543793835430716, 'f1': 0.6637654798866464}, 'combined': 0.4412247001686946, 'epoch': 36}
Test Russian: {'event': {'p': 0.6072744014732965, 'r': 0.7641946697566628, 'f1': 0.676757311441765}, 'argument': {'p': 0.702917120929777, 'r': 0.6843388497509706, 'f1': 0.6935035843043401}, 'combined': 0.4693336211890326, 'epoch': 36}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.6837121212121212, 'r': 0.6554595542198848, 'f1': 0.6692878148574352}, 'combined': 0.46950040743730526, 'epoch': 36}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6186155913978494, 'r': 0.4854957805907173, 'f1': 0.5440307328605201}, 'combined': 0.3334381911080607, 'epoch': 36}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6105223215635115, 'r': 0.5616805358384306, 'f1': 0.5850838914983651}, 'combined': 0.30456421749229967, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 37
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 06:18:29.114638: step: 2/1532, loss: 0.00010514257155591622
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2023-01-22 06:39:35.122345: step: 3064/1532, loss: 0.00010944903624476865
==================================================
Loss: 0.049
--------------------
Dev Chinese: {'event': {'p': 0.5887265135699373, 'r': 0.7509986684420772, 'f1': 0.6600351082504389}, 'argument': {'p': 0.6819924933140606, 'r': 0.6661896259816565, 'f1': 0.6739984421103793}, 'combined': 0.4448626346989514, 'epoch': 37}
Test Chinese: {'event': {'p': 0.5979760809567617, 'r': 0.7531865585168018, 'f1': 0.6666666666666667}, 'argument': {'p': 0.7120041696065589, 'r': 0.6885158614455962, 'f1': 0.7000630527522392}, 'combined': 0.4667087018348262, 'epoch': 37}
Dev Korean: {'event': {'p': 0.5933400605449042, 'r': 0.7829560585885486, 'f1': 0.6750861079219288}, 'argument': {'p': 0.6858584874762417, 'r': 0.6721608857292126, 'f1': 0.6789406064786427}, 'combined': 0.4583433715378208, 'epoch': 37}
Test Korean: {'event': {'p': 0.6002747252747253, 'r': 0.7595596755504056, 'f1': 0.6705882352941176}, 'argument': {'p': 0.7137169095437667, 'r': 0.6959996833424119, 'f1': 0.7047469619713757}, 'combined': 0.4725950215572754, 'epoch': 37}
Dev Russian: {'event': {'p': 0.5907216494845361, 'r': 0.762982689747004, 'f1': 0.6658919233004067}, 'argument': {'p': 0.6802352777672048, 'r': 0.6659611655709321, 'f1': 0.6730225453886861}, 'combined': 0.44816027717340745, 'epoch': 37}
Test Russian: {'event': {'p': 0.6072744014732965, 'r': 0.7641946697566628, 'f1': 0.676757311441765}, 'argument': {'p': 0.7077618170843887, 'r': 0.6866805581417814, 'f1': 0.6970618337787133}, 'combined': 0.4717416925367485, 'epoch': 37}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.678661616161616, 'r': 0.6506177477251857, 'f1': 0.664343860546392}, 'combined': 0.475947840391445, 'epoch': 37}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.6434027777777779, 'r': 0.4825520833333334, 'f1': 0.5514880952380953}, 'combined': 0.3291138632872504, 'epoch': 37}
Russian: {'event': {'p': 0.4864864864864865, 'r': 0.5, 'f1': 0.4931506849315069}, 'argument': {'p': 0.5808931546372562, 'r': 0.5808931546372562, 'f1': 0.5808931546372562}, 'combined': 0.28646785708138667, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
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
2023-01-22 06:44:58.484842: step: 2/1532, loss: 0.01824115216732025
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2023-01-22 07:06:04.964878: step: 3064/1532, loss: 0.0885438472032547
==================================================
Loss: 0.047
--------------------
Dev Chinese: {'event': {'p': 0.5887265135699373, 'r': 0.7509986684420772, 'f1': 0.6600351082504389}, 'argument': {'p': 0.6801548691221692, 'r': 0.6451613939716808, 'f1': 0.6621961500044028}, 'combined': 0.4370727075511799, 'epoch': 38}
Test Chinese: {'event': {'p': 0.593836246550138, 'r': 0.7479721900347625, 'f1': 0.662051282051282}, 'argument': {'p': 0.7139442123177994, 'r': 0.675468176923427, 'f1': 0.6941734495151526}, 'combined': 0.4595784222174677, 'epoch': 38}
Dev Korean: {'event': {'p': 0.5893037336024218, 'r': 0.7776298268974701, 'f1': 0.6704936854190586}, 'argument': {'p': 0.6861756497439623, 'r': 0.647538531903782, 'f1': 0.6662974406021314}, 'combined': 0.4467482265346094, 'epoch': 38}
Test Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7160117268318819, 'r': 0.6798034981809729, 'f1': 0.6974379816417745}, 'combined': 0.46662346802426646, 'epoch': 38}
Dev Russian: {'event': {'p': 0.5855670103092784, 'r': 0.7563249001331558, 'f1': 0.6600813480534573}, 'argument': {'p': 0.6771575113338785, 'r': 0.6443523390566768, 'f1': 0.6603477472511183}, 'combined': 0.4358832311895819, 'epoch': 38}
Test Russian: {'event': {'p': 0.6035911602209945, 'r': 0.7595596755504056, 'f1': 0.672652642380708}, 'argument': {'p': 0.7118071122158838, 'r': 0.6735046573138926, 'f1': 0.6921263728947851}, 'combined': 0.4655606335890524, 'epoch': 38}
Chinese: {'event': {'p': 0.5875, 'r': 0.8703703703703703, 'f1': 0.7014925373134329}, 'argument': {'p': 0.6847177128427129, 'r': 0.6337882961849904, 'f1': 0.6582693891706768}, 'combined': 0.46177106404510165, 'epoch': 38}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6448087431693988, 'r': 0.48559670781893, 'f1': 0.5539906103286385}, 'combined': 0.33954263213690755, 'epoch': 38}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6039474236842658, 'r': 0.5556316297895245, 'f1': 0.5787829476974214}, 'combined': 0.3012842741438632, 'epoch': 38}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}
******************************
Epoch: 39
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 07:32:09.462769: step: 3012/1532, loss: 0.011944486759603024
2023-01-22 07:32:10.290637: step: 3014/1532, loss: 0.0021447231993079185
2023-01-22 07:32:11.125225: step: 3016/1532, loss: 0.0003007798222824931
2023-01-22 07:32:11.961778: step: 3018/1532, loss: 0.019475501030683517
2023-01-22 07:32:12.767209: step: 3020/1532, loss: 0.0007747708004899323
2023-01-22 07:32:13.602521: step: 3022/1532, loss: 0.0005687007796950638
2023-01-22 07:32:14.472220: step: 3024/1532, loss: 0.020696554332971573
2023-01-22 07:32:15.299462: step: 3026/1532, loss: 0.005081039387732744
2023-01-22 07:32:16.115356: step: 3028/1532, loss: 0.0026301683392375708
2023-01-22 07:32:16.923273: step: 3030/1532, loss: 7.366299541899934e-05
2023-01-22 07:32:17.739455: step: 3032/1532, loss: 0.4610954523086548
2023-01-22 07:32:18.593290: step: 3034/1532, loss: 0.06138867884874344
2023-01-22 07:32:19.409261: step: 3036/1532, loss: 0.005590824410319328
2023-01-22 07:32:20.213542: step: 3038/1532, loss: 0.006398666184395552
2023-01-22 07:32:21.008602: step: 3040/1532, loss: 4.59204147773562e-06
2023-01-22 07:32:21.820039: step: 3042/1532, loss: 0.0001667261094553396
2023-01-22 07:32:22.626037: step: 3044/1532, loss: 0.00010661692067515105
2023-01-22 07:32:23.474644: step: 3046/1532, loss: 0.10508590191602707
2023-01-22 07:32:24.335127: step: 3048/1532, loss: 0.0029613550286740065
2023-01-22 07:32:25.163703: step: 3050/1532, loss: 0.6540667414665222
2023-01-22 07:32:25.997574: step: 3052/1532, loss: 0.0016140337102115154
2023-01-22 07:32:26.829634: step: 3054/1532, loss: 0.00017340319755021483
2023-01-22 07:32:27.634867: step: 3056/1532, loss: 0.21569699048995972
2023-01-22 07:32:28.468899: step: 3058/1532, loss: 0.00021067188936285675
2023-01-22 07:32:29.289952: step: 3060/1532, loss: 0.0017170681385323405
2023-01-22 07:32:30.114169: step: 3062/1532, loss: 0.0981346070766449
2023-01-22 07:32:30.967206: step: 3064/1532, loss: 7.320269651245326e-05
==================================================
Loss: 0.050
--------------------
Dev Chinese: {'event': {'p': 0.5887265135699373, 'r': 0.7509986684420772, 'f1': 0.6600351082504389}, 'argument': {'p': 0.6673090606902597, 'r': 0.6532960536229045, 'f1': 0.6602282107963638}, 'combined': 0.4357737985829716, 'epoch': 39}
Test Chinese: {'event': {'p': 0.5956761729530818, 'r': 0.7502896871378911, 'f1': 0.6641025641025641}, 'argument': {'p': 0.6929389721534824, 'r': 0.6855599215348763, 'f1': 0.6892296969596197}, 'combined': 0.45771920900651664, 'epoch': 39}
Dev Korean: {'event': {'p': 0.5903128153380424, 'r': 0.7789613848202397, 'f1': 0.6716417910447761}, 'argument': {'p': 0.6735331169221427, 'r': 0.6548105284973647, 'f1': 0.6640398781942917}, 'combined': 0.445996933115569, 'epoch': 39}
Test Korean: {'event': {'p': 0.5993589743589743, 'r': 0.7584009269988412, 'f1': 0.6695652173913043}, 'argument': {'p': 0.696316433517064, 'r': 0.6895606032822045, 'f1': 0.6929220518441787}, 'combined': 0.46395650427827606, 'epoch': 39}
Dev Russian: {'event': {'p': 0.5876288659793815, 'r': 0.758988015978695, 'f1': 0.6624055781522371}, 'argument': {'p': 0.6633229783250599, 'r': 0.6580394498952441, 'f1': 0.6606706508944618}, 'combined': 0.43763192447396077, 'epoch': 39}
Test Russian: {'event': {'p': 0.606353591160221, 'r': 0.7630359212050984, 'f1': 0.6757311441765007}, 'argument': {'p': 0.6909974482244126, 'r': 0.6824609188139608, 'f1': 0.6867026547447715}, 'combined': 0.464026370599725, 'epoch': 39}
Chinese: {'event': {'p': 0.6, 'r': 0.8888888888888888, 'f1': 0.7164179104477612}, 'argument': {'p': 0.6682365319865319, 'r': 0.6627139160197011, 'f1': 0.6654637662936417}, 'combined': 0.476750160926788, 'epoch': 39}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.646760343481655, 'r': 0.487066431510876, 'f1': 0.5556673373574782}, 'combined': 0.3316079271326886, 'epoch': 39}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.5323975759427597, 'r': 0.5643414304993253, 'f1': 0.5479043014556557}, 'combined': 0.28521045829198516, 'epoch': 39}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5876826722338204, 'r': 0.7496671105193076, 'f1': 0.6588648332358104}, 'argument': {'p': 0.6535613144530148, 'r': 0.678188262533853, 'f1': 0.6656470855595428}, 'combined': 0.4385714560210913, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5924563017479301, 'r': 0.746234067207416, 'f1': 0.6605128205128206}, 'argument': {'p': 0.6735983753392938, 'r': 0.7027584781678347, 'f1': 0.6878695273591942}, 'combined': 0.4543466416608421, 'epoch': 9}
Chinese: {'event': {'p': 0.625, 'r': 0.9259259259259259, 'f1': 0.7462686567164178}, 'argument': {'p': 0.6647397318129026, 'r': 0.6757271653965868, 'f1': 0.6701884181392378}, 'combined': 0.5001406105516699, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.591321897073663, 'r': 0.7802929427430093, 'f1': 0.6727898966704937}, 'argument': {'p': 0.6734564213967323, 'r': 0.6657707561026185, 'f1': 0.6695915351907252}, 'combined': 0.4504944197724053, 'epoch': 19}
Test for Korean: {'event': {'p': 0.5989010989010989, 'r': 0.7578215527230591, 'f1': 0.6690537084398976}, 'argument': {'p': 0.7045769833116922, 'r': 0.6870572599363342, 'f1': 0.695706840956288}, 'combined': 0.46546524192881056, 'epoch': 19}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.7125, 'r': 0.5277777777777778, 'f1': 0.6063829787234042}, 'combined': 0.3618737131091283, 'epoch': 19}
--------------------
Dev for Russian: {'event': {'p': 0.588659793814433, 'r': 0.7603195739014648, 'f1': 0.6635676932016271}, 'argument': {'p': 0.6513653320490216, 'r': 0.691456608822495, 'f1': 0.6708124880813949}, 'combined': 0.44512949528701523, 'epoch': 4}
Test for Russian: {'event': {'p': 0.5999079189686924, 'r': 0.7549246813441484, 'f1': 0.6685479733196511}, 'argument': {'p': 0.6474332199870393, 'r': 0.7034777400327088, 'f1': 0.674292935504682}, 'combined': 0.4507971754554133, 'epoch': 4}
Russian: {'event': {'p': 0.5135135135135135, 'r': 0.5277777777777778, 'f1': 0.5205479452054794}, 'argument': {'p': 0.6115974412212656, 'r': 0.6727571853433922, 'f1': 0.6407211288984687}, 'combined': 0.333526067097833, 'epoch': 4}