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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2023-01-23 05:00:32.997893: step: 3068/1534, loss: 3.3917770385742188
==================================================
Loss: 4.321
--------------------
Dev Chinese: {'event': {'p': 0.6221461187214612, 'r': 0.725699067909454, 'f1': 0.6699446834665028}, 'argument': {'p': 0.5734248946709944, 'r': 0.5414257376023006, 'f1': 0.5569660859835376}, 'combined': 0.3731364681758181, 'epoch': 0}
Test Chinese: {'event': {'p': 0.6410131514856308, 'r': 0.7668997668997669, 'f1': 0.6983284690899443}, 'argument': {'p': 0.585228588326535, 'r': 0.5575174333163713, 'f1': 0.5710370183535497}, 'combined': 0.39877140682052076, 'epoch': 0}
Dev Korean: {'event': {'p': 0.557711950970378, 'r': 0.7270306258322237, 'f1': 0.6312138728323698}, 'argument': {'p': 0.5718987771391054, 'r': 0.541514053171744, 'f1': 0.5562918198564308}, 'combined': 0.35113911403654474, 'epoch': 0}
Test Korean: {'event': {'p': 0.6187353629976581, 'r': 0.7698135198135199, 'f1': 0.6860555699818228}, 'argument': {'p': 0.5786942775243977, 'r': 0.5535418791875529, 'f1': 0.5658387006226531}, 'combined': 0.3881967922734483, 'epoch': 0}
Dev Russian: {'event': {'p': 0.5705521472392638, 'r': 0.7430093209054593, 'f1': 0.6454598033545402}, 'argument': {'p': 0.5631981140137463, 'r': 0.544480303725254, 'f1': 0.5536810598903448}, 'combined': 0.3573788680379553, 'epoch': 0}
Test Russian: {'event': {'p': 0.6159925615992562, 'r': 0.7721445221445221, 'f1': 0.6852857512283423}, 'argument': {'p': 0.5820240848763077, 'r': 0.557891842375723, 'f1': 0.5697025214858608}, 'combined': 0.3904090204131189, 'epoch': 0}
Chinese: {'event': {'p': 0.6323529411764706, 'r': 0.7962962962962963, 'f1': 0.7049180327868853}, 'argument': {'p': 0.5261584269192965, 'r': 0.5305799263051729, 'f1': 0.5283599266135195}, 'combined': 0.3724504400718252, 'epoch': 0}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.4143156924381865, 'r': 0.2344154575637108, 'f1': 0.2994214247872608}, 'combined': 0.17385760148937726, 'epoch': 0}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.41582207207207206, 'r': 0.3991891891891892, 'f1': 0.4073359073359074}, 'combined': 0.2498326898326898, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6221461187214612, 'r': 0.725699067909454, 'f1': 0.6699446834665028}, 'argument': {'p': 0.5734248946709944, 'r': 0.5414257376023006, 'f1': 0.5569660859835376}, 'combined': 0.3731364681758181, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.6410131514856308, 'r': 0.7668997668997669, 'f1': 0.6983284690899443}, 'argument': {'p': 0.585228588326535, 'r': 0.5575174333163713, 'f1': 0.5710370183535497}, 'combined': 0.39877140682052076, 'epoch': 0}
Chinese: {'event': {'p': 0.6323529411764706, 'r': 0.7962962962962963, 'f1': 0.7049180327868853}, 'argument': {'p': 0.5261584269192965, 'r': 0.5305799263051729, 'f1': 0.5283599266135195}, 'combined': 0.3724504400718252, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.557711950970378, 'r': 0.7270306258322237, 'f1': 0.6312138728323698}, 'argument': {'p': 0.5718987771391054, 'r': 0.541514053171744, 'f1': 0.5562918198564308}, 'combined': 0.35113911403654474, 'epoch': 0}
Test for Korean: {'event': {'p': 0.6187353629976581, 'r': 0.7698135198135199, 'f1': 0.6860555699818228}, 'argument': {'p': 0.5786942775243977, 'r': 0.5535418791875529, 'f1': 0.5658387006226531}, 'combined': 0.3881967922734483, 'epoch': 0}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.4143156924381865, 'r': 0.2344154575637108, 'f1': 0.2994214247872608}, 'combined': 0.17385760148937726, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.5705521472392638, 'r': 0.7430093209054593, 'f1': 0.6454598033545402}, 'argument': {'p': 0.5631981140137463, 'r': 0.544480303725254, 'f1': 0.5536810598903448}, 'combined': 0.3573788680379553, 'epoch': 0}
Test for Russian: {'event': {'p': 0.6159925615992562, 'r': 0.7721445221445221, 'f1': 0.6852857512283423}, 'argument': {'p': 0.5820240848763077, 'r': 0.557891842375723, 'f1': 0.5697025214858608}, 'combined': 0.3904090204131189, 'epoch': 0}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.41582207207207206, 'r': 0.3991891891891892, 'f1': 0.4073359073359074}, 'combined': 0.2498326898326898, '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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2023-01-23 05:26:55.915146: step: 3068/1534, loss: 1.1094367504119873
==================================================
Loss: 1.745
--------------------
Dev Chinese: {'event': {'p': 0.6210045662100456, 'r': 0.7243675099866844, 'f1': 0.6687154271665642}, 'argument': {'p': 0.6621818715931054, 'r': 0.6139336757446858, 'f1': 0.6371456742875019}, 'combined': 0.4260691417484954, 'epoch': 1}
Test Chinese: {'event': {'p': 0.6512420847540185, 'r': 0.7791375291375291, 'f1': 0.7094720084903156}, 'argument': {'p': 0.6766742200386552, 'r': 0.6569304099049013, 'f1': 0.6666561629452161}, 'combined': 0.47297388689718955, 'epoch': 1}
Dev Korean: {'event': {'p': 0.5638406537282942, 'r': 0.7350199733688415, 'f1': 0.638150289017341}, 'argument': {'p': 0.6566035828850952, 'r': 0.6197533818048093, 'f1': 0.637646524061641}, 'combined': 0.40691431362083913, 'epoch': 1}
Test Korean: {'event': {'p': 0.6271662763466043, 'r': 0.7803030303030303, 'f1': 0.6954037912230591}, 'argument': {'p': 0.6711770456722412, 'r': 0.6505119155401775, 'f1': 0.6606829268609986}, 'combined': 0.4594414121354855, 'epoch': 1}
Dev Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6540188109473835, 'r': 0.6241152959925621, 'f1': 0.6387172387667277}, 'combined': 0.4152216173359179, 'epoch': 1}
Test Russian: {'event': {'p': 0.6215713621571363, 'r': 0.7791375291375291, 'f1': 0.6914921127489009}, 'argument': {'p': 0.6749238973040559, 'r': 0.6550731944421719, 'f1': 0.6648504062995177}, 'combined': 0.4597388121140187, 'epoch': 1}
Chinese: {'event': {'p': 0.6029411764705882, 'r': 0.7592592592592593, 'f1': 0.6721311475409837}, 'argument': {'p': 0.5512260765550239, 'r': 0.5558582284588476, 'f1': 0.5535324618125762}, 'combined': 0.3720464087592726, 'epoch': 1}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.39142368716019393, 'r': 0.19825355583438395, 'f1': 0.2631986861939235}, 'combined': 0.16131532379627572, 'epoch': 1}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.491975228817334, 'r': 0.4427777059356006, 'f1': 0.4660817957216849}, 'combined': 0.27343465349005514, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6210045662100456, 'r': 0.7243675099866844, 'f1': 0.6687154271665642}, 'argument': {'p': 0.6621818715931054, 'r': 0.6139336757446858, 'f1': 0.6371456742875019}, 'combined': 0.4260691417484954, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.6512420847540185, 'r': 0.7791375291375291, 'f1': 0.7094720084903156}, 'argument': {'p': 0.6766742200386552, 'r': 0.6569304099049013, 'f1': 0.6666561629452161}, 'combined': 0.47297388689718955, 'epoch': 1}
Chinese: {'event': {'p': 0.6029411764705882, 'r': 0.7592592592592593, 'f1': 0.6721311475409837}, 'argument': {'p': 0.5512260765550239, 'r': 0.5558582284588476, 'f1': 0.5535324618125762}, 'combined': 0.3720464087592726, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.5638406537282942, 'r': 0.7350199733688415, 'f1': 0.638150289017341}, 'argument': {'p': 0.6566035828850952, 'r': 0.6197533818048093, 'f1': 0.637646524061641}, 'combined': 0.40691431362083913, 'epoch': 1}
Test for Korean: {'event': {'p': 0.6271662763466043, 'r': 0.7803030303030303, 'f1': 0.6954037912230591}, 'argument': {'p': 0.6711770456722412, 'r': 0.6505119155401775, 'f1': 0.6606829268609986}, 'combined': 0.4594414121354855, 'epoch': 1}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.39142368716019393, 'r': 0.19825355583438395, 'f1': 0.2631986861939235}, 'combined': 0.16131532379627572, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6540188109473835, 'r': 0.6241152959925621, 'f1': 0.6387172387667277}, 'combined': 0.4152216173359179, 'epoch': 1}
Test for Russian: {'event': {'p': 0.6215713621571363, 'r': 0.7791375291375291, 'f1': 0.6914921127489009}, 'argument': {'p': 0.6749238973040559, 'r': 0.6550731944421719, 'f1': 0.6648504062995177}, 'combined': 0.4597388121140187, 'epoch': 1}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.491975228817334, 'r': 0.4427777059356006, 'f1': 0.4660817957216849}, 'combined': 0.27343465349005514, 'epoch': 1}
******************************
Epoch: 2
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 05:53:11.821139: step: 3068/1534, loss: 1.0721909999847412
==================================================
Loss: 1.474
--------------------
Dev Chinese: {'event': {'p': 0.6301369863013698, 'r': 0.7350199733688415, 'f1': 0.6785494775660725}, 'argument': {'p': 0.6512888499923467, 'r': 0.6324038944907099, 'f1': 0.641707459894383}, 'combined': 0.43543026166158505, 'epoch': 2}
Test Chinese: {'event': {'p': 0.6556259132976132, 'r': 0.7843822843822844, 'f1': 0.7142478110904751}, 'argument': {'p': 0.7024126405003643, 'r': 0.6621217297554828, 'f1': 0.6816723457731817}, 'combined': 0.4868829808494045, 'epoch': 2}
Dev Korean: {'event': {'p': 0.567926455566905, 'r': 0.7403462050599201, 'f1': 0.6427745664739885}, 'argument': {'p': 0.6485527054112823, 'r': 0.6240077704175511, 'f1': 0.6360435286006789}, 'combined': 0.40883260335488725, 'epoch': 2}
Test Korean: {'event': {'p': 0.6313817330210773, 'r': 0.7855477855477856, 'f1': 0.700077901843677}, 'argument': {'p': 0.7014469830294013, 'r': 0.6608332715487981, 'f1': 0.6805347182498487}, 'combined': 0.47642731768413193, 'epoch': 2}
Dev Russian: {'event': {'p': 0.5787321063394683, 'r': 0.7536617842876165, 'f1': 0.6547137073452863}, 'argument': {'p': 0.6468574035124114, 'r': 0.6263449637255134, 'f1': 0.6364359466555235}, 'combined': 0.4166833381226447, 'epoch': 2}
Test Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7034754329342563, 'r': 0.6618782250410513, 'f1': 0.6820431734895331}, 'combined': 0.4748022299027213, 'epoch': 2}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6037188101899059, 'r': 0.6189386121274666, 'f1': 0.6112339821009836}, 'combined': 0.4408900854498898, 'epoch': 2}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.4402280552691539, 'r': 0.2490763996917581, 'f1': 0.31814800632896834}, 'combined': 0.18986251990599723, 'epoch': 2}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.522880037720894, 'r': 0.5548931012548263, 'f1': 0.5384111279502274}, 'combined': 0.3015102316521273, 'epoch': 2}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6301369863013698, 'r': 0.7350199733688415, 'f1': 0.6785494775660725}, 'argument': {'p': 0.6512888499923467, 'r': 0.6324038944907099, 'f1': 0.641707459894383}, 'combined': 0.43543026166158505, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.6556259132976132, 'r': 0.7843822843822844, 'f1': 0.7142478110904751}, 'argument': {'p': 0.7024126405003643, 'r': 0.6621217297554828, 'f1': 0.6816723457731817}, 'combined': 0.4868829808494045, 'epoch': 2}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6037188101899059, 'r': 0.6189386121274666, 'f1': 0.6112339821009836}, 'combined': 0.4408900854498898, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.567926455566905, 'r': 0.7403462050599201, 'f1': 0.6427745664739885}, 'argument': {'p': 0.6485527054112823, 'r': 0.6240077704175511, 'f1': 0.6360435286006789}, 'combined': 0.40883260335488725, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6313817330210773, 'r': 0.7855477855477856, 'f1': 0.700077901843677}, 'argument': {'p': 0.7014469830294013, 'r': 0.6608332715487981, 'f1': 0.6805347182498487}, 'combined': 0.47642731768413193, 'epoch': 2}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.4402280552691539, 'r': 0.2490763996917581, 'f1': 0.31814800632896834}, 'combined': 0.18986251990599723, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.5787321063394683, 'r': 0.7536617842876165, 'f1': 0.6547137073452863}, 'argument': {'p': 0.6468574035124114, 'r': 0.6263449637255134, 'f1': 0.6364359466555235}, 'combined': 0.4166833381226447, 'epoch': 2}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7034754329342563, 'r': 0.6618782250410513, 'f1': 0.6820431734895331}, 'combined': 0.4748022299027213, 'epoch': 2}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.522880037720894, 'r': 0.5548931012548263, 'f1': 0.5384111279502274}, 'combined': 0.3015102316521273, 'epoch': 2}
******************************
Epoch: 3
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 06:19:22.509122: step: 3068/1534, loss: 0.4289502799510956
==================================================
Loss: 1.067
--------------------
Dev Chinese: {'event': {'p': 0.6232876712328768, 'r': 0.7270306258322237, 'f1': 0.6711739397664412}, 'argument': {'p': 0.6888870687228388, 'r': 0.6463441917109373, 'f1': 0.6669378838076773}, 'combined': 0.4476313270546918, 'epoch': 3}
Test Chinese: {'event': {'p': 0.6556259132976132, 'r': 0.7843822843822844, 'f1': 0.7142478110904751}, 'argument': {'p': 0.6935784644960613, 'r': 0.6841498447864001, 'f1': 0.6888318917962887}, 'combined': 0.49199667092481014, 'epoch': 3}
Dev Korean: {'event': {'p': 0.5669050051072523, 'r': 0.7390146471371505, 'f1': 0.6416184971098265}, 'argument': {'p': 0.6848979641779556, 'r': 0.65399281586716, 'f1': 0.6690887036496485}, 'combined': 0.4292996884688496, 'epoch': 3}
Test Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6886303896794499, 'r': 0.6814172399348534, 'f1': 0.6850048265880148}, 'combined': 0.4792009874910703, 'epoch': 3}
Dev Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.682044374966459, 'r': 0.6513624377445165, 'f1': 0.6663504078321874}, 'combined': 0.43318557455371814, 'epoch': 3}
Test Russian: {'event': {'p': 0.6234309623430963, 'r': 0.7814685314685315, 'f1': 0.6935608999224205}, 'argument': {'p': 0.6918986779227967, 'r': 0.6842058868667551, 'f1': 0.6880307800192493}, 'combined': 0.4771912469644755, 'epoch': 3}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6066972243442832, 'r': 0.6066972243442832, 'f1': 0.6066972243442832}, 'combined': 0.4475635261556188, 'epoch': 3}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.5435112506541078, 'r': 0.3458707958707959, 'f1': 0.42273097273097276}, 'combined': 0.2522749353394515, 'epoch': 3}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.5369525597430265, 'r': 0.5691697133276081, 'f1': 0.5525919546869982}, 'combined': 0.32418728008303893, 'epoch': 3}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6232876712328768, 'r': 0.7270306258322237, 'f1': 0.6711739397664412}, 'argument': {'p': 0.6888870687228388, 'r': 0.6463441917109373, 'f1': 0.6669378838076773}, 'combined': 0.4476313270546918, 'epoch': 3}
Test for Chinese: {'event': {'p': 0.6556259132976132, 'r': 0.7843822843822844, 'f1': 0.7142478110904751}, 'argument': {'p': 0.6935784644960613, 'r': 0.6841498447864001, 'f1': 0.6888318917962887}, 'combined': 0.49199667092481014, 'epoch': 3}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6066972243442832, 'r': 0.6066972243442832, 'f1': 0.6066972243442832}, 'combined': 0.4475635261556188, 'epoch': 3}
--------------------
Dev for Korean: {'event': {'p': 0.5669050051072523, 'r': 0.7390146471371505, 'f1': 0.6416184971098265}, 'argument': {'p': 0.6848979641779556, 'r': 0.65399281586716, 'f1': 0.6690887036496485}, 'combined': 0.4292996884688496, 'epoch': 3}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6886303896794499, 'r': 0.6814172399348534, 'f1': 0.6850048265880148}, 'combined': 0.4792009874910703, 'epoch': 3}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.5435112506541078, 'r': 0.3458707958707959, 'f1': 0.42273097273097276}, 'combined': 0.2522749353394515, 'epoch': 3}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.682044374966459, 'r': 0.6513624377445165, 'f1': 0.6663504078321874}, 'combined': 0.43318557455371814, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6234309623430963, 'r': 0.7814685314685315, 'f1': 0.6935608999224205}, 'argument': {'p': 0.6918986779227967, 'r': 0.6842058868667551, 'f1': 0.6880307800192493}, 'combined': 0.4771912469644755, 'epoch': 3}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.5369525597430265, 'r': 0.5691697133276081, 'f1': 0.5525919546869982}, 'combined': 0.32418728008303893, 'epoch': 3}
******************************
Epoch: 4
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 06:24:54.626482: step: 2/1534, loss: 0.5345631837844849
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2023-01-23 06:45:45.697943: step: 3068/1534, loss: 1.323855996131897
==================================================
Loss: 0.859
--------------------
Dev Chinese: {'event': {'p': 0.6244292237442922, 'r': 0.7283621837549934, 'f1': 0.6724031960663798}, 'argument': {'p': 0.6442363407417968, 'r': 0.6826122955142047, 'f1': 0.6628693513200121}, 'combined': 0.4457154704020241, 'epoch': 4}
Test Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.6622711145496808, 'r': 0.7165989010969313, 'f1': 0.6883647443620546}, 'combined': 0.4909324576400114, 'epoch': 4}
Dev Korean: {'event': {'p': 0.5648621041879469, 'r': 0.7363515312916112, 'f1': 0.6393063583815031}, 'argument': {'p': 0.6451842498567286, 'r': 0.694090344598055, 'f1': 0.6687443488683507}, 'combined': 0.4275325143632347, 'epoch': 4}
Test Korean: {'event': {'p': 0.6313817330210773, 'r': 0.7855477855477856, 'f1': 0.700077901843677}, 'argument': {'p': 0.654369063440815, 'r': 0.709631655377879, 'f1': 0.680880875370421}, 'combined': 0.4766696546348105, 'epoch': 4}
Dev Russian: {'event': {'p': 0.5787321063394683, 'r': 0.7536617842876165, 'f1': 0.6547137073452863}, 'argument': {'p': 0.6314868542396288, 'r': 0.6860943649166699, 'f1': 0.6576589980391458}, 'combined': 0.43057836077519557, 'epoch': 4}
Test Russian: {'event': {'p': 0.6252905625290562, 'r': 0.7837995337995338, 'f1': 0.6956296870959401}, 'argument': {'p': 0.6586138255222455, 'r': 0.7143957028310295, 'f1': 0.685371626433659}, 'combined': 0.47676485004048175, 'epoch': 4}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.5816814764183185, 'r': 0.6501145912910619, 'f1': 0.613997113997114}, 'combined': 0.44288316419463963, 'epoch': 4}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.5108919313837347, 'r': 0.4047325690182833, 'f1': 0.45165808426677995}, 'combined': 0.2695378889979171, 'epoch': 4}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5746051295569413, 'r': 0.6801448472306652, 'f1': 0.6229364021364971}, 'combined': 0.34884438519643834, 'epoch': 4}
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6232876712328768, 'r': 0.7270306258322237, 'f1': 0.6711739397664412}, 'argument': {'p': 0.6888870687228388, 'r': 0.6463441917109373, 'f1': 0.6669378838076773}, 'combined': 0.4476313270546918, 'epoch': 3}
Test for Chinese: {'event': {'p': 0.6556259132976132, 'r': 0.7843822843822844, 'f1': 0.7142478110904751}, 'argument': {'p': 0.6935784644960613, 'r': 0.6841498447864001, 'f1': 0.6888318917962887}, 'combined': 0.49199667092481014, 'epoch': 3}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6066972243442832, 'r': 0.6066972243442832, 'f1': 0.6066972243442832}, 'combined': 0.4475635261556188, 'epoch': 3}
--------------------
Dev for Korean: {'event': {'p': 0.5648621041879469, 'r': 0.7363515312916112, 'f1': 0.6393063583815031}, 'argument': {'p': 0.6451842498567286, 'r': 0.694090344598055, 'f1': 0.6687443488683507}, 'combined': 0.4275325143632347, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6313817330210773, 'r': 0.7855477855477856, 'f1': 0.700077901843677}, 'argument': {'p': 0.654369063440815, 'r': 0.709631655377879, 'f1': 0.680880875370421}, 'combined': 0.4766696546348105, 'epoch': 4}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.5108919313837347, 'r': 0.4047325690182833, 'f1': 0.45165808426677995}, 'combined': 0.2695378889979171, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.5787321063394683, 'r': 0.7536617842876165, 'f1': 0.6547137073452863}, 'argument': {'p': 0.6314868542396288, 'r': 0.6860943649166699, 'f1': 0.6576589980391458}, 'combined': 0.43057836077519557, 'epoch': 4}
Test for Russian: {'event': {'p': 0.6252905625290562, 'r': 0.7837995337995338, 'f1': 0.6956296870959401}, 'argument': {'p': 0.6586138255222455, 'r': 0.7143957028310295, 'f1': 0.685371626433659}, 'combined': 0.47676485004048175, 'epoch': 4}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5746051295569413, 'r': 0.6801448472306652, 'f1': 0.6229364021364971}, 'combined': 0.34884438519643834, '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
2023-01-23 06:51:08.867283: step: 2/1534, loss: 0.4297473728656769
2023-01-23 06:51:09.711821: step: 4/1534, loss: 2.6981589794158936
2023-01-23 06:51:10.572235: step: 6/1534, loss: 0.290738970041275
2023-01-23 06:51:11.410973: step: 8/1534, loss: 0.38757967948913574
2023-01-23 06:51:12.206200: step: 10/1534, loss: 0.31367728114128113
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2023-01-23 07:11:59.460600: step: 3068/1534, loss: 0.6818187832832336
==================================================
Loss: 0.712
--------------------
Dev Chinese: {'event': {'p': 0.6255707762557078, 'r': 0.729693741677763, 'f1': 0.6736324523663184}, 'argument': {'p': 0.6757534385325703, 'r': 0.6460886670493697, 'f1': 0.66058818449171}, 'combined': 0.44499363872336456, 'epoch': 5}
Test Chinese: {'event': {'p': 0.6561130053580126, 'r': 0.784965034965035, 'f1': 0.714778455823826}, 'argument': {'p': 0.7184211003723899, 'r': 0.6655224322852686, 'f1': 0.6909607897176601}, 'combined': 0.49388388630920044, 'epoch': 5}
Dev Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6714220450102752, 'r': 0.6416353215589103, 'f1': 0.6561908271799939}, 'combined': 0.42405858080186887, 'epoch': 5}
Test Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7195879275039677, 'r': 0.6658662354702727, 'f1': 0.6916855384783036}, 'combined': 0.4856706559452955, 'epoch': 5}
Dev Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6676398584393834, 'r': 0.6380982717827736, 'f1': 0.6525348842665467}, 'combined': 0.42948796893888386, 'epoch': 5}
Test Russian: {'event': {'p': 0.6248256624825662, 'r': 0.7832167832167832, 'f1': 0.6951124903025601}, 'argument': {'p': 0.7235630473527254, 'r': 0.6658483093487813, 'f1': 0.6935069725222751}, 'combined': 0.48206535871214773, 'epoch': 5}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6465063861758076, 'r': 0.6573720397249808, 'f1': 0.6518939393939392}, 'combined': 0.48090536512667653, 'epoch': 5}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6285714285714286, 'r': 0.4489795918367347, 'f1': 0.5238095238095237}, 'combined': 0.3210445468509985, 'epoch': 5}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.5327052515497767, 'r': 0.6332156763704893, 'f1': 0.5786281180626885}, 'combined': 0.3548919124117822, 'epoch': 5}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6255707762557078, 'r': 0.729693741677763, 'f1': 0.6736324523663184}, 'argument': {'p': 0.6757534385325703, 'r': 0.6460886670493697, 'f1': 0.66058818449171}, 'combined': 0.44499363872336456, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6561130053580126, 'r': 0.784965034965035, 'f1': 0.714778455823826}, 'argument': {'p': 0.7184211003723899, 'r': 0.6655224322852686, 'f1': 0.6909607897176601}, 'combined': 0.49388388630920044, 'epoch': 5}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6465063861758076, 'r': 0.6573720397249808, 'f1': 0.6518939393939392}, 'combined': 0.48090536512667653, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6714220450102752, 'r': 0.6416353215589103, 'f1': 0.6561908271799939}, 'combined': 0.42405858080186887, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7195879275039677, 'r': 0.6658662354702727, 'f1': 0.6916855384783036}, 'combined': 0.4856706559452955, 'epoch': 5}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6285714285714286, 'r': 0.4489795918367347, 'f1': 0.5238095238095237}, 'combined': 0.3210445468509985, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6676398584393834, 'r': 0.6380982717827736, 'f1': 0.6525348842665467}, 'combined': 0.42948796893888386, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6248256624825662, 'r': 0.7832167832167832, 'f1': 0.6951124903025601}, 'argument': {'p': 0.7235630473527254, 'r': 0.6658483093487813, 'f1': 0.6935069725222751}, 'combined': 0.48206535871214773, 'epoch': 5}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.5327052515497767, 'r': 0.6332156763704893, 'f1': 0.5786281180626885}, 'combined': 0.3548919124117822, 'epoch': 5}
******************************
Epoch: 6
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 07:17:19.632208: step: 2/1534, loss: 0.26572275161743164
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2023-01-23 07:38:09.955161: step: 3068/1534, loss: 0.32408180832862854
==================================================
Loss: 0.590
--------------------
Dev Chinese: {'event': {'p': 0.6255707762557078, 'r': 0.729693741677763, 'f1': 0.6736324523663184}, 'argument': {'p': 0.6672581399544486, 'r': 0.6667620372481629, 'f1': 0.6670099963546143}, 'combined': 0.449319579597208, 'epoch': 6}
Test Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.6913039028948416, 'r': 0.7000053100859658, 'f1': 0.6956273966917481}, 'combined': 0.4961120833927883, 'epoch': 6}
Dev Korean: {'event': {'p': 0.5699693564862104, 'r': 0.7430093209054593, 'f1': 0.6450867052023121}, 'argument': {'p': 0.665666169284083, 'r': 0.6690574816457923, 'f1': 0.6673575170826269}, 'combined': 0.43050346188682753, 'epoch': 6}
Test Korean: {'event': {'p': 0.62903981264637, 'r': 0.7826340326340326, 'f1': 0.6974811737211114}, 'argument': {'p': 0.6909398206946751, 'r': 0.699310735352731, 'f1': 0.6951000767346592}, 'combined': 0.4848192173745247, 'epoch': 6}
Dev Russian: {'event': {'p': 0.5807770961145194, 'r': 0.7563249001331558, 'f1': 0.6570271833429728}, 'argument': {'p': 0.6565403882312763, 'r': 0.6628346544901307, 'f1': 0.6596725075145815}, 'combined': 0.4334227695411016, 'epoch': 6}
Test Russian: {'event': {'p': 0.6243607624360762, 'r': 0.7826340326340326, 'f1': 0.6945952935091801}, 'argument': {'p': 0.6915916546741157, 'r': 0.7010840891500348, 'f1': 0.6963055218432673}, 'combined': 0.4836505383167871, 'epoch': 6}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6080745992386407, 'r': 0.7000522697117124, 'f1': 0.6508298444976076}, 'combined': 0.46945103537532357, 'epoch': 6}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.5928803339517624, 'r': 0.4311856974194636, 'f1': 0.4992676496435895}, 'combined': 0.31405545703387083, 'epoch': 6}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.6187382041579942, 'r': 0.5692391478253547, 'f1': 0.5929574456514112}, 'combined': 0.34786836811549454, 'epoch': 6}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6255707762557078, 'r': 0.729693741677763, 'f1': 0.6736324523663184}, 'argument': {'p': 0.6757534385325703, 'r': 0.6460886670493697, 'f1': 0.66058818449171}, 'combined': 0.44499363872336456, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.6561130053580126, 'r': 0.784965034965035, 'f1': 0.714778455823826}, 'argument': {'p': 0.7184211003723899, 'r': 0.6655224322852686, 'f1': 0.6909607897176601}, 'combined': 0.49388388630920044, 'epoch': 5}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6465063861758076, 'r': 0.6573720397249808, 'f1': 0.6518939393939392}, 'combined': 0.48090536512667653, 'epoch': 5}
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Dev for Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6714220450102752, 'r': 0.6416353215589103, 'f1': 0.6561908271799939}, 'combined': 0.42405858080186887, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7195879275039677, 'r': 0.6658662354702727, 'f1': 0.6916855384783036}, 'combined': 0.4856706559452955, 'epoch': 5}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6285714285714286, 'r': 0.4489795918367347, 'f1': 0.5238095238095237}, 'combined': 0.3210445468509985, 'epoch': 5}
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Dev for Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6676398584393834, 'r': 0.6380982717827736, 'f1': 0.6525348842665467}, 'combined': 0.42948796893888386, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6248256624825662, 'r': 0.7832167832167832, 'f1': 0.6951124903025601}, 'argument': {'p': 0.7235630473527254, 'r': 0.6658483093487813, 'f1': 0.6935069725222751}, 'combined': 0.48206535871214773, 'epoch': 5}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.5327052515497767, 'r': 0.6332156763704893, 'f1': 0.5786281180626885}, 'combined': 0.3548919124117822, 'epoch': 5}
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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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==================================================
Loss: 0.490
--------------------
Dev Chinese: {'event': {'p': 0.6244292237442922, 'r': 0.7283621837549934, 'f1': 0.6724031960663798}, 'argument': {'p': 0.6579682745374089, 'r': 0.6662907899147422, 'f1': 0.6621033801444832}, 'combined': 0.4452004289355037, 'epoch': 7}
Test Chinese: {'event': {'p': 0.6502679006332197, 'r': 0.777972027972028, 'f1': 0.7084107190236136}, 'argument': {'p': 0.6966131410351314, 'r': 0.6889960987375305, 'f1': 0.6927836834882117}, 'combined': 0.49077538734771164, 'epoch': 7}
Dev Korean: {'event': {'p': 0.5648621041879469, 'r': 0.7363515312916112, 'f1': 0.6393063583815031}, 'argument': {'p': 0.6517760028894124, 'r': 0.6655325560799459, 'f1': 0.658582450164815}, 'combined': 0.4210359479088356, 'epoch': 7}
Test Korean: {'event': {'p': 0.6281030444964871, 'r': 0.7814685314685315, 'f1': 0.6964424824720853}, 'argument': {'p': 0.6901161948874946, 'r': 0.6871804911788764, 'f1': 0.6886452143114105}, 'combined': 0.4796017825975599, 'epoch': 7}
Dev Russian: {'event': {'p': 0.5766871165644172, 'r': 0.7509986684420772, 'f1': 0.6524002313475998}, 'argument': {'p': 0.6466470863741226, 'r': 0.6614303162248585, 'f1': 0.6539551650024849}, 'combined': 0.4266405009385789, 'epoch': 7}
Test Russian: {'event': {'p': 0.6225011622501162, 'r': 0.7803030303030303, 'f1': 0.6925265063356607}, 'argument': {'p': 0.6929604801677359, 'r': 0.6870725806630428, 'f1': 0.6900039700784085}, 'combined': 0.477846038756136, 'epoch': 7}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.674622636135794, 'r': 0.6349389516572178, 'f1': 0.6541795259498608}, 'combined': 0.4825914535695695, 'epoch': 7}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5149180884475002, 'r': 0.34104964299769497, 'f1': 0.41032535173160173}, 'combined': 0.2514897317064656, 'epoch': 7}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.5897534743998765, 'r': 0.6133436133758715, 'f1': 0.6013172680155602}, 'combined': 0.3527727972357953, 'epoch': 7}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6244292237442922, 'r': 0.7283621837549934, 'f1': 0.6724031960663798}, 'argument': {'p': 0.6579682745374089, 'r': 0.6662907899147422, 'f1': 0.6621033801444832}, 'combined': 0.4452004289355037, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6502679006332197, 'r': 0.777972027972028, 'f1': 0.7084107190236136}, 'argument': {'p': 0.6966131410351314, 'r': 0.6889960987375305, 'f1': 0.6927836834882117}, 'combined': 0.49077538734771164, 'epoch': 7}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.674622636135794, 'r': 0.6349389516572178, 'f1': 0.6541795259498608}, 'combined': 0.4825914535695695, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6714220450102752, 'r': 0.6416353215589103, 'f1': 0.6561908271799939}, 'combined': 0.42405858080186887, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7195879275039677, 'r': 0.6658662354702727, 'f1': 0.6916855384783036}, 'combined': 0.4856706559452955, 'epoch': 5}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6285714285714286, 'r': 0.4489795918367347, 'f1': 0.5238095238095237}, 'combined': 0.3210445468509985, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6676398584393834, 'r': 0.6380982717827736, 'f1': 0.6525348842665467}, 'combined': 0.42948796893888386, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6248256624825662, 'r': 0.7832167832167832, 'f1': 0.6951124903025601}, 'argument': {'p': 0.7235630473527254, 'r': 0.6658483093487813, 'f1': 0.6935069725222751}, 'combined': 0.48206535871214773, 'epoch': 5}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.5327052515497767, 'r': 0.6332156763704893, 'f1': 0.5786281180626885}, 'combined': 0.3548919124117822, 'epoch': 5}
******************************
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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2023-01-23 08:30:20.573944: step: 3068/1534, loss: 0.8862339854240417
==================================================
Loss: 0.402
--------------------
Dev Chinese: {'event': {'p': 0.6267123287671232, 'r': 0.7310252996005326, 'f1': 0.6748617086662568}, 'argument': {'p': 0.6383818395598335, 'r': 0.6810988399764767, 'f1': 0.659048877531195}, 'combined': 0.4447668515852809, 'epoch': 8}
Test Chinese: {'event': {'p': 0.648319532391622, 'r': 0.7756410256410257, 'f1': 0.7062881400902096}, 'argument': {'p': 0.6703695741878966, 'r': 0.7068341215069065, 'f1': 0.6881191076342664}, 'combined': 0.48601036469154074, 'epoch': 8}
Dev Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6389689127763922, 'r': 0.6877274417309861, 'f1': 0.6624521944850101}, 'combined': 0.4265732628071106, 'epoch': 8}
Test Korean: {'event': {'p': 0.6295081967213115, 'r': 0.7832167832167832, 'f1': 0.6980005193456246}, 'argument': {'p': 0.6673631851786658, 'r': 0.702337999993139, 'f1': 0.6844040580846005}, 'combined': 0.47771438798530413, 'epoch': 8}
Dev Russian: {'event': {'p': 0.5787321063394683, 'r': 0.7536617842876165, 'f1': 0.6547137073452863}, 'argument': {'p': 0.6368645214110006, 'r': 0.6838655931018494, 'f1': 0.6595287392548084}, 'combined': 0.43180250597827824, 'epoch': 8}
Test Russian: {'event': {'p': 0.6192468619246861, 'r': 0.7762237762237763, 'f1': 0.6889061287820014}, 'argument': {'p': 0.6687514229941122, 'r': 0.7054791899320039, 'f1': 0.6866245122500796}, 'combined': 0.47301983466103226, 'epoch': 8}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6645652958152958, 'r': 0.6701498781330714, 'f1': 0.6673459037475774}, 'combined': 0.48136425844087555, 'epoch': 8}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5865336122443146, 'r': 0.4121587545500589, 'f1': 0.4841229815349898}, 'combined': 0.2967205370698325, 'epoch': 8}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.6170586723218302, 'r': 0.6170586723218302, 'f1': 0.6170586723218302}, 'combined': 0.3455528565002249, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6244292237442922, 'r': 0.7283621837549934, 'f1': 0.6724031960663798}, 'argument': {'p': 0.6579682745374089, 'r': 0.6662907899147422, 'f1': 0.6621033801444832}, 'combined': 0.4452004289355037, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6502679006332197, 'r': 0.777972027972028, 'f1': 0.7084107190236136}, 'argument': {'p': 0.6966131410351314, 'r': 0.6889960987375305, 'f1': 0.6927836834882117}, 'combined': 0.49077538734771164, 'epoch': 7}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.674622636135794, 'r': 0.6349389516572178, 'f1': 0.6541795259498608}, 'combined': 0.4825914535695695, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6714220450102752, 'r': 0.6416353215589103, 'f1': 0.6561908271799939}, 'combined': 0.42405858080186887, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7195879275039677, 'r': 0.6658662354702727, 'f1': 0.6916855384783036}, 'combined': 0.4856706559452955, 'epoch': 5}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6285714285714286, 'r': 0.4489795918367347, 'f1': 0.5238095238095237}, 'combined': 0.3210445468509985, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6676398584393834, 'r': 0.6380982717827736, 'f1': 0.6525348842665467}, 'combined': 0.42948796893888386, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6248256624825662, 'r': 0.7832167832167832, 'f1': 0.6951124903025601}, 'argument': {'p': 0.7235630473527254, 'r': 0.6658483093487813, 'f1': 0.6935069725222751}, 'combined': 0.48206535871214773, 'epoch': 5}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.5327052515497767, 'r': 0.6332156763704893, 'f1': 0.5786281180626885}, 'combined': 0.3548919124117822, 'epoch': 5}
******************************
Epoch: 9
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.327
--------------------
Dev Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Dev Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6794493018744256, 'r': 0.6566356756801018, 'f1': 0.6678477176553373}, 'combined': 0.43159176204547234, 'epoch': 9}
Test Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.7070904600374679, 'r': 0.6871658926838564, 'f1': 0.6969858107248686}, 'combined': 0.4875823874559325, 'epoch': 9}
Dev Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6784927708019279, 'r': 0.6569612658982505, 'f1': 0.6675534422888111}, 'combined': 0.4393729423508774, 'epoch': 9}
Test Russian: {'event': {'p': 0.6276150627615062, 'r': 0.7867132867132867, 'f1': 0.6982156710628393}, 'argument': {'p': 0.7136756690771788, 'r': 0.6926646091436014, 'f1': 0.7030131839814041}, 'combined': 0.4908548220195994, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.680873015873016, 'r': 0.453915343915344, 'f1': 0.5446984126984128}, 'combined': 0.32506195596518184, 'epoch': 9}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.5799795615013007, 'r': 0.5335811965811966, 'f1': 0.5558137464387465}, 'combined': 0.32607739791073126, 'epoch': 9}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6794493018744256, 'r': 0.6566356756801018, 'f1': 0.6678477176553373}, 'combined': 0.43159176204547234, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.7070904600374679, 'r': 0.6871658926838564, 'f1': 0.6969858107248686}, 'combined': 0.4875823874559325, 'epoch': 9}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.680873015873016, 'r': 0.453915343915344, 'f1': 0.5446984126984128}, 'combined': 0.32506195596518184, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6676398584393834, 'r': 0.6380982717827736, 'f1': 0.6525348842665467}, 'combined': 0.42948796893888386, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6248256624825662, 'r': 0.7832167832167832, 'f1': 0.6951124903025601}, 'argument': {'p': 0.7235630473527254, 'r': 0.6658483093487813, 'f1': 0.6935069725222751}, 'combined': 0.48206535871214773, 'epoch': 5}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.5327052515497767, 'r': 0.6332156763704893, 'f1': 0.5786281180626885}, 'combined': 0.3548919124117822, 'epoch': 5}
******************************
Epoch: 10
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 09:22:52.536633: step: 3068/1534, loss: 0.23488199710845947
==================================================
Loss: 0.296
--------------------
Dev Chinese: {'event': {'p': 0.6289954337899544, 'r': 0.7336884154460719, 'f1': 0.677320221266134}, 'argument': {'p': 0.6678263047819832, 'r': 0.6569027518413114, 'f1': 0.6623194911743358}, 'combined': 0.44860238431107435, 'epoch': 10}
Test Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.712616686441532, 'r': 0.6889966438017728, 'f1': 0.7006076421806365}, 'combined': 0.4996639273498411, 'epoch': 10}
Dev Korean: {'event': {'p': 0.5669050051072523, 'r': 0.7390146471371505, 'f1': 0.6416184971098265}, 'argument': {'p': 0.6748275619818728, 'r': 0.6684380803316438, 'f1': 0.6716176248044983}, 'combined': 0.43092229105953356, 'epoch': 10}
Test Korean: {'event': {'p': 0.6318501170960187, 'r': 0.7861305361305362, 'f1': 0.7005972474681901}, 'argument': {'p': 0.7082232296943743, 'r': 0.6810731255416084, 'f1': 0.6943828893111704}, 'combined': 0.4864827409404149, 'epoch': 10}
Dev Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.5999644886363635, 'r': 0.6453399541634834, 'f1': 0.6218255428781743}, 'combined': 0.4485298997809782, 'epoch': 10}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6171681316204642, 'r': 0.4648798913504795, 'f1': 0.5303074316146211}, 'combined': 0.3250271355057356, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6794493018744256, 'r': 0.6566356756801018, 'f1': 0.6678477176553373}, 'combined': 0.43159176204547234, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.7070904600374679, 'r': 0.6871658926838564, 'f1': 0.6969858107248686}, 'combined': 0.4875823874559325, 'epoch': 9}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.680873015873016, 'r': 0.453915343915344, 'f1': 0.5446984126984128}, 'combined': 0.32506195596518184, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
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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2023-01-23 09:49:03.722789: step: 3068/1534, loss: 0.637133002281189
==================================================
Loss: 0.253
--------------------
Dev Chinese: {'event': {'p': 0.6278538812785388, 'r': 0.7323568575233023, 'f1': 0.6760909649661956}, 'argument': {'p': 0.6561010277575889, 'r': 0.6775645558031903, 'f1': 0.6666600786798033}, 'combined': 0.4507228558990681, 'epoch': 11}
Test Chinese: {'event': {'p': 0.6536775450560156, 'r': 0.782051282051282, 'f1': 0.7121252321570708}, 'argument': {'p': 0.6872099059165221, 'r': 0.706103056691968, 'f1': 0.6965283869149}, 'combined': 0.49601543923576324, 'epoch': 11}
Dev Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6515186062554778, 'r': 0.688056794661648, 'f1': 0.6692893936028642}, 'combined': 0.43097594478242246, 'epoch': 11}
Test Korean: {'event': {'p': 0.6327868852459017, 'r': 0.7872960372960373, 'f1': 0.7016359387172163}, 'argument': {'p': 0.6821329595751042, 'r': 0.702471786351426, 'f1': 0.6921529917496537}, 'combined': 0.48563941410219796, 'epoch': 11}
Dev Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6462448345818439, 'r': 0.6815639762059623, 'f1': 0.6634346684276584}, 'combined': 0.4374294517105439, 'epoch': 11}
Test Russian: {'event': {'p': 0.6229660622966062, 'r': 0.7808857808857809, 'f1': 0.6930437031290406}, 'argument': {'p': 0.6871039310641708, 'r': 0.7050615640223981, 'f1': 0.6959669292075331}, 'combined': 0.48233549787333563, 'epoch': 11}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6174949494949494, 'r': 0.6486291486291486, 'f1': 0.6326792515317106}, 'combined': 0.46673059539224554, 'epoch': 11}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5868675595238095, 'r': 0.4942042606516291, 'f1': 0.5365646258503401}, 'combined': 0.3288621900373053, 'epoch': 11}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5827834811387442, 'r': 0.6660382641585648, 'f1': 0.6216357132146604}, 'combined': 0.34811599940020976, 'epoch': 11}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6515186062554778, 'r': 0.688056794661648, 'f1': 0.6692893936028642}, 'combined': 0.43097594478242246, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6327868852459017, 'r': 0.7872960372960373, 'f1': 0.7016359387172163}, 'argument': {'p': 0.6821329595751042, 'r': 0.702471786351426, 'f1': 0.6921529917496537}, 'combined': 0.48563941410219796, 'epoch': 11}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5868675595238095, 'r': 0.4942042606516291, 'f1': 0.5365646258503401}, 'combined': 0.3288621900373053, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
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
2023-01-23 09:54:21.013170: step: 2/1534, loss: 0.2610008716583252
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2023-01-23 10:15:15.832672: step: 3068/1534, loss: 0.041259780526161194
==================================================
Loss: 0.223
--------------------
Dev Chinese: {'event': {'p': 0.6289954337899544, 'r': 0.7336884154460719, 'f1': 0.677320221266134}, 'argument': {'p': 0.6852668257149318, 'r': 0.64657419662553, 'f1': 0.6653584622767305}, 'combined': 0.45066074089056973, 'epoch': 12}
Test Chinese: {'event': {'p': 0.6570871894788115, 'r': 0.7861305361305362, 'f1': 0.7158397452905281}, 'argument': {'p': 0.7062210409688812, 'r': 0.6961555770107566, 'f1': 0.7011521868941588}, 'combined': 0.5019126028762114, 'epoch': 12}
Dev Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6859791457786035, 'r': 0.6495333833027389, 'f1': 0.6672589672209072}, 'combined': 0.42966849103126625, 'epoch': 12}
Test Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7018211427627443, 'r': 0.6930914526072667, 'f1': 0.6974289814608694}, 'combined': 0.4897034447858195, 'epoch': 12}
Dev Russian: {'event': {'p': 0.5807770961145194, 'r': 0.7563249001331558, 'f1': 0.6570271833429728}, 'argument': {'p': 0.6791003238686427, 'r': 0.6455211934633297, 'f1': 0.661885143505722}, 'combined': 0.4348765315341239, 'epoch': 12}
Test Russian: {'event': {'p': 0.6252905625290562, 'r': 0.7837995337995338, 'f1': 0.6956296870959401}, 'argument': {'p': 0.7028426033978984, 'r': 0.6943441797620414, 'f1': 0.6985675456425602}, 'combined': 0.485944323190713, 'epoch': 12}
Chinese: {'event': {'p': 0.6323529411764706, 'r': 0.7962962962962963, 'f1': 0.7049180327868853}, 'argument': {'p': 0.6713181115835983, 'r': 0.6374701395709799, 'f1': 0.6539564362840224}, 'combined': 0.4609856845936552, 'epoch': 12}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.6601527403414197, 'r': 0.4728120978120979, 'f1': 0.5509936257967755}, 'combined': 0.3465927646141007, 'epoch': 12}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.5892749138251387, 'r': 0.5421329207191277, 'f1': 0.5647217924157579}, 'combined': 0.331303451550578, 'epoch': 12}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6859791457786035, 'r': 0.6495333833027389, 'f1': 0.6672589672209072}, 'combined': 0.42966849103126625, 'epoch': 12}
Test for Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7018211427627443, 'r': 0.6930914526072667, 'f1': 0.6974289814608694}, 'combined': 0.4897034447858195, 'epoch': 12}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.6601527403414197, 'r': 0.4728120978120979, 'f1': 0.5509936257967755}, 'combined': 0.3465927646141007, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
Epoch: 13
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.180
--------------------
Dev Chinese: {'event': {'p': 0.6278538812785388, 'r': 0.7323568575233023, 'f1': 0.6760909649661956}, 'argument': {'p': 0.6702035160888974, 'r': 0.6537598610473111, 'f1': 0.6618795732846319}, 'combined': 0.4474907993934205, 'epoch': 13}
Test Chinese: {'event': {'p': 0.6561130053580126, 'r': 0.784965034965035, 'f1': 0.714778455823826}, 'argument': {'p': 0.6902194160123041, 'r': 0.7037261972875034, 'f1': 0.6969073689675884}, 'combined': 0.49813437304289826, 'epoch': 13}
Dev Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6753943289026956, 'r': 0.6675351948936461, 'f1': 0.6714417651636894}, 'combined': 0.4323619227701445, 'epoch': 13}
Test Korean: {'event': {'p': 0.6337236533957845, 'r': 0.7884615384615384, 'f1': 0.7026746299662425}, 'argument': {'p': 0.6867582183540631, 'r': 0.6977697212007088, 'f1': 0.6922201811359734}, 'combined': 0.48640555963488546, 'epoch': 13}
Dev Russian: {'event': {'p': 0.5777096114519428, 'r': 0.7523302263648469, 'f1': 0.6535569693464431}, 'argument': {'p': 0.6666638631578143, 'r': 0.6622292920059886, 'f1': 0.664439178408678}, 'combined': 0.4342488557558163, 'epoch': 13}
Test Russian: {'event': {'p': 0.6266852626685263, 'r': 0.7855477855477856, 'f1': 0.6971812774760797}, 'argument': {'p': 0.6900355743924588, 'r': 0.701532410163127, 'f1': 0.6957365000840212}, 'combined': 0.48505446191531454, 'epoch': 13}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6353837520504186, 'r': 0.6247050335285629, 'f1': 0.6299991439821947}, 'combined': 0.4647534668721109, 'epoch': 13}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6227556596409056, 'r': 0.49984335839599003, 'f1': 0.5545707334028502}, 'combined': 0.3398981914404567, 'epoch': 13}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.59185258563758, 'r': 0.5563414304993252, 'f1': 0.5735478664941497}, 'combined': 0.32118680523672377, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6859791457786035, 'r': 0.6495333833027389, 'f1': 0.6672589672209072}, 'combined': 0.42966849103126625, 'epoch': 12}
Test for Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7018211427627443, 'r': 0.6930914526072667, 'f1': 0.6974289814608694}, 'combined': 0.4897034447858195, 'epoch': 12}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.6601527403414197, 'r': 0.4728120978120979, 'f1': 0.5509936257967755}, 'combined': 0.3465927646141007, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
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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2023-01-23 11:07:22.722691: step: 3068/1534, loss: 0.2116895318031311
==================================================
Loss: 0.178
--------------------
Dev Chinese: {'event': {'p': 0.6289954337899544, 'r': 0.7336884154460719, 'f1': 0.677320221266134}, 'argument': {'p': 0.6892573018912624, 'r': 0.6452185738358029, 'f1': 0.6665112819516429}, 'combined': 0.45144156896786136, 'epoch': 14}
Test Chinese: {'event': {'p': 0.6575742815392109, 'r': 0.7867132867132867, 'f1': 0.7163703900238789}, 'argument': {'p': 0.7313715207321474, 'r': 0.6884352822016034, 'f1': 0.7092541861738713}, 'combined': 0.508088697975445, 'epoch': 14}
Dev Korean: {'event': {'p': 0.5699693564862104, 'r': 0.7430093209054593, 'f1': 0.6450867052023121}, 'argument': {'p': 0.6977259368083214, 'r': 0.6474531073002983, 'f1': 0.671650109045383}, 'combined': 0.4332725558928598, 'epoch': 14}
Test Korean: {'event': {'p': 0.6346604215456675, 'r': 0.7896270396270396, 'f1': 0.7037133212152688}, 'argument': {'p': 0.7277081320811548, 'r': 0.6843553071912136, 'f1': 0.7053662157453298}, 'combined': 0.4963756023551918, 'epoch': 14}
Dev Russian: {'event': {'p': 0.5787321063394683, 'r': 0.7536617842876165, 'f1': 0.6547137073452863}, 'argument': {'p': 0.6908157157803145, 'r': 0.6474804125763833, 'f1': 0.6684464449836186}, 'combined': 0.4376410501570019, 'epoch': 14}
Test Russian: {'event': {'p': 0.6285448628544863, 'r': 0.7878787878787878, 'f1': 0.6992500646495992}, 'argument': {'p': 0.7324805555642512, 'r': 0.6862967113225327, 'f1': 0.7086369483414648}, 'combined': 0.495514431940864, 'epoch': 14}
Chinese: {'event': {'p': 0.6176470588235294, 'r': 0.7777777777777778, 'f1': 0.6885245901639345}, 'argument': {'p': 0.6702710333145117, 'r': 0.6477409145476373, 'f1': 0.6588134088134089}, 'combined': 0.453609232297757, 'epoch': 14}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6296031746031746, 'r': 0.41973544973544974, 'f1': 0.5036825396825397}, 'combined': 0.30870865335381475, 'epoch': 14}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.568025641025641, 'r': 0.568025641025641, 'f1': 0.568025641025641}, 'combined': 0.3332417094017094, 'epoch': 14}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6859791457786035, 'r': 0.6495333833027389, 'f1': 0.6672589672209072}, 'combined': 0.42966849103126625, 'epoch': 12}
Test for Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7018211427627443, 'r': 0.6930914526072667, 'f1': 0.6974289814608694}, 'combined': 0.4897034447858195, 'epoch': 12}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.6601527403414197, 'r': 0.4728120978120979, 'f1': 0.5509936257967755}, 'combined': 0.3465927646141007, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
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
2023-01-23 11:12:27.463627: step: 2/1534, loss: 0.07727231830358505
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2023-01-23 11:33:17.656215: step: 3068/1534, loss: 0.13106366991996765
==================================================
Loss: 0.158
--------------------
Dev Chinese: {'event': {'p': 0.6289954337899544, 'r': 0.7336884154460719, 'f1': 0.677320221266134}, 'argument': {'p': 0.6641418202802752, 'r': 0.6616728915803486, 'f1': 0.6629050571140177}, 'combined': 0.44899899996290565, 'epoch': 15}
Test Chinese: {'event': {'p': 0.6531904529956162, 'r': 0.7814685314685315, 'f1': 0.7115945874237197}, 'argument': {'p': 0.6933207858457102, 'r': 0.7059726250034787, 'f1': 0.6995895090450385}, 'combined': 0.49782410805486677, 'epoch': 15}
Dev Korean: {'event': {'p': 0.567926455566905, 'r': 0.7403462050599201, 'f1': 0.6427745664739885}, 'argument': {'p': 0.6698647276326484, 'r': 0.6688882484378486, 'f1': 0.6693761319159215}, 'combined': 0.43025795300029174, 'epoch': 15}
Test Korean: {'event': {'p': 0.6323185011709602, 'r': 0.7867132867132867, 'f1': 0.7011165930927032}, 'argument': {'p': 0.68590446893883, 'r': 0.7002877867671671, 'f1': 0.6930215062300239}, 'combined': 0.485888877387968, 'epoch': 15}
Dev Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6580286032439187, 'r': 0.6653238205747825, 'f1': 0.6616561038130693}, 'combined': 0.43625677174488076, 'epoch': 15}
Test Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.6925520463668609, 'r': 0.7016020567734843, 'f1': 0.6970476779563893}, 'combined': 0.4856080797554986, 'epoch': 15}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6229632389468455, 'r': 0.6386681945505475, 'f1': 0.6307179680623664}, 'combined': 0.4652837469312539, 'epoch': 15}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.6294162826420892, 'r': 0.5134711779448622, 'f1': 0.5655624568668047}, 'combined': 0.3375130790979319, 'epoch': 15}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.555719023135291, 'r': 0.6112909254488201, 'f1': 0.5821818337607811}, 'combined': 0.3260218269060374, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6859791457786035, 'r': 0.6495333833027389, 'f1': 0.6672589672209072}, 'combined': 0.42966849103126625, 'epoch': 12}
Test for Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7018211427627443, 'r': 0.6930914526072667, 'f1': 0.6974289814608694}, 'combined': 0.4897034447858195, 'epoch': 12}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.6601527403414197, 'r': 0.4728120978120979, 'f1': 0.5509936257967755}, 'combined': 0.3465927646141007, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
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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2023-01-23 11:59:18.540184: step: 3068/1534, loss: 0.013036670163273811
==================================================
Loss: 0.149
--------------------
Dev Chinese: {'event': {'p': 0.6255707762557078, 'r': 0.729693741677763, 'f1': 0.6736324523663184}, 'argument': {'p': 0.6643084124258206, 'r': 0.6672718700277201, 'f1': 0.6657868436107445}, 'combined': 0.4484956242147363, 'epoch': 16}
Test Chinese: {'event': {'p': 0.6536775450560156, 'r': 0.782051282051282, 'f1': 0.7121252321570708}, 'argument': {'p': 0.6921231537354209, 'r': 0.702220441124134, 'f1': 0.6971352371396932}, 'combined': 0.4964475925929786, 'epoch': 16}
Dev Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Dev Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6579711233371459, 'r': 0.6662261116004163, 'f1': 0.6620728868489654}, 'combined': 0.4365315737465705, 'epoch': 16}
Test Russian: {'event': {'p': 0.6266852626685263, 'r': 0.7855477855477856, 'f1': 0.6971812774760797}, 'argument': {'p': 0.6893149554613033, 'r': 0.6990014074498118, 'f1': 0.69412438967919}, 'combined': 0.48393052872384185, 'epoch': 16}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6320789865871833, 'r': 0.6480137509549274, 'f1': 0.6399471897397209}, 'combined': 0.47209218915225315, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5802694100771023, 'r': 0.6034801864801864, 'f1': 0.5916472416472416}, 'combined': 0.33132245532245524, 'epoch': 16}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
Epoch: 17
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 12:25:27.702015: step: 3068/1534, loss: 0.21224334836006165
==================================================
Loss: 0.118
--------------------
Dev Chinese: {'event': {'p': 0.6244292237442922, 'r': 0.7283621837549934, 'f1': 0.6724031960663798}, 'argument': {'p': 0.673930224727911, 'r': 0.6488770565224125, 'f1': 0.6611663947141247}, 'combined': 0.4445703969374631, 'epoch': 17}
Test Chinese: {'event': {'p': 0.6536775450560156, 'r': 0.782051282051282, 'f1': 0.7121252321570708}, 'argument': {'p': 0.7065424665102374, 'r': 0.6948214567804724, 'f1': 0.7006329443748223}, 'combined': 0.4989383981698124, 'epoch': 17}
Dev Korean: {'event': {'p': 0.5638406537282942, 'r': 0.7350199733688415, 'f1': 0.638150289017341}, 'argument': {'p': 0.6761760529390354, 'r': 0.6545069004777698, 'f1': 0.6651650439348468}, 'combined': 0.4244752650312548, 'epoch': 17}
Test Korean: {'event': {'p': 0.6327868852459017, 'r': 0.7872960372960373, 'f1': 0.7016359387172163}, 'argument': {'p': 0.7058727470391223, 'r': 0.6908640036444402, 'f1': 0.6982877365320065}, 'combined': 0.4899437715163546, 'epoch': 17}
Dev Russian: {'event': {'p': 0.5736196319018405, 'r': 0.7470039946737683, 'f1': 0.64893001735107}, 'argument': {'p': 0.6697383592864704, 'r': 0.6489942508129957, 'f1': 0.6592031491403911}, 'combined': 0.427776711009554, 'epoch': 17}
Test Russian: {'event': {'p': 0.6229660622966062, 'r': 0.7808857808857809, 'f1': 0.6930437031290406}, 'argument': {'p': 0.7108557704613582, 'r': 0.6950641362269994, 'f1': 0.7028712656064898}, 'combined': 0.4871205047389172, 'epoch': 17}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6964333601501742, 'r': 0.6613190730837789, 'f1': 0.6784221525600834}, 'combined': 0.4893536838138307, 'epoch': 17}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5970017636684304, 'r': 0.4356499356499357, 'f1': 0.5037202380952381}, 'combined': 0.3087317588325654, 'epoch': 17}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.6101705434265116, 'r': 0.647527923636298, 'f1': 0.6282944209540317}, 'combined': 0.3350903578421502, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
Epoch: 18
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 12:30:35.663436: step: 2/1534, loss: 0.3365136682987213
2023-01-23 12:30:36.498963: step: 4/1534, loss: 0.02048736810684204
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2023-01-23 12:51:26.556309: step: 3068/1534, loss: 0.017219962552189827
==================================================
Loss: 0.121
--------------------
Dev Chinese: {'event': {'p': 0.6278538812785388, 'r': 0.7323568575233023, 'f1': 0.6760909649661956}, 'argument': {'p': 0.6759055540009904, 'r': 0.6628397217303392, 'f1': 0.6693088781736534}, 'combined': 0.45251368530486713, 'epoch': 18}
Test Chinese: {'event': {'p': 0.654164637116415, 'r': 0.7826340326340326, 'f1': 0.712655876890422}, 'argument': {'p': 0.6988599541394687, 'r': 0.7102103478261885, 'f1': 0.7044894359334575}, 'combined': 0.5020585367251968, 'epoch': 18}
Dev Korean: {'event': {'p': 0.567926455566905, 'r': 0.7403462050599201, 'f1': 0.6427745664739885}, 'argument': {'p': 0.6751529682635421, 'r': 0.6687604055633046, 'f1': 0.6719414832333825}, 'combined': 0.43190689558122625, 'epoch': 18}
Test Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6924333384847046, 'r': 0.7037661263486112, 'f1': 0.698053738959418}, 'combined': 0.48832946578983955, 'epoch': 18}
Dev Russian: {'event': {'p': 0.5777096114519428, 'r': 0.7523302263648469, 'f1': 0.6535569693464431}, 'argument': {'p': 0.6707590220791544, 'r': 0.6588871809804083, 'f1': 0.6647701022391619}, 'combined': 0.4344651333315518, 'epoch': 18}
Test Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7001716893423292, 'r': 0.7102362284900138, 'f1': 0.7051680491105845}, 'combined': 0.4909005400066443, 'epoch': 18}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6503381270330423, 'r': 0.6448731007554537, 'f1': 0.6475940843029451}, 'combined': 0.4671170444152391, 'epoch': 18}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6543598921049901, 'r': 0.4775058672117496, 'f1': 0.5521161589635855}, 'combined': 0.33839377484864924, 'epoch': 18}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.6171626816160932, 'r': 0.5801329207191277, 'f1': 0.5980751759991006}, 'combined': 0.35087076991947236, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
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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2023-01-23 13:17:24.761813: step: 3068/1534, loss: 0.037656158208847046
==================================================
Loss: 0.127
--------------------
Dev Chinese: {'event': {'p': 0.6267123287671232, 'r': 0.7310252996005326, 'f1': 0.6748617086662568}, 'argument': {'p': 0.6663350843032897, 'r': 0.665840035949424, 'f1': 0.6660874681441283}, 'combined': 0.4495169268729274, 'epoch': 19}
Test Chinese: {'event': {'p': 0.6531904529956162, 'r': 0.7814685314685315, 'f1': 0.7115945874237197}, 'argument': {'p': 0.6901210518365383, 'r': 0.7068249101866931, 'f1': 0.6983731135538301}, 'combined': 0.4969585276071563, 'epoch': 19}
Dev Korean: {'event': {'p': 0.5628192032686414, 'r': 0.7336884154460719, 'f1': 0.6369942196531792}, 'argument': {'p': 0.6631846928079577, 'r': 0.6675286973460011, 'f1': 0.665349604754012}, 'combined': 0.423823852276833, 'epoch': 19}
Test Korean: {'event': {'p': 0.6323185011709602, 'r': 0.7867132867132867, 'f1': 0.7011165930927032}, 'argument': {'p': 0.684994415912107, 'r': 0.701351822638173, 'f1': 0.6930766193001114}, 'combined': 0.4859275180759026, 'epoch': 19}
Dev Russian: {'event': {'p': 0.5777096114519428, 'r': 0.7523302263648469, 'f1': 0.6535569693464431}, 'argument': {'p': 0.6569739351174604, 'r': 0.663272357799265, 'f1': 0.6601081226978373}, 'combined': 0.4314182641113685, 'epoch': 19}
Test Russian: {'event': {'p': 0.6266852626685263, 'r': 0.7855477855477856, 'f1': 0.6971812774760797}, 'argument': {'p': 0.6909810594671998, 'r': 0.7056587617107841, 'f1': 0.6982427844252221}, 'combined': 0.4868017964340313, 'epoch': 19}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6063264221158958, 'r': 0.6776589423648247, 'f1': 0.6400112233445566}, 'combined': 0.4616474397895163, 'epoch': 19}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6439153439153439, 'r': 0.5017522160379303, 'f1': 0.5640134399258486}, 'combined': 0.345685656728746, 'epoch': 19}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.5430080649933443, 'r': 0.5984170512171549, 'f1': 0.5693676797988464}, 'combined': 0.3340290388153232, 'epoch': 19}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
Epoch: 20
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 13:43:25.227911: step: 3068/1534, loss: 0.05770539492368698
==================================================
Loss: 0.110
--------------------
Dev Chinese: {'event': {'p': 0.6278538812785388, 'r': 0.7323568575233023, 'f1': 0.6760909649661956}, 'argument': {'p': 0.6675367174233017, 'r': 0.6655514781149796, 'f1': 0.6665426195566997}, 'combined': 0.4506434428471849, 'epoch': 20}
Test Chinese: {'event': {'p': 0.6507549926936191, 'r': 0.7785547785547785, 'f1': 0.7089413637569647}, 'argument': {'p': 0.697863855786223, 'r': 0.7017974385277237, 'f1': 0.6998251197221408}, 'combined': 0.4961349747671956, 'epoch': 20}
Dev Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6668675326730429, 'r': 0.6722102441511226, 'f1': 0.6695282301193263}, 'combined': 0.43267778108289406, 'epoch': 20}
Test Korean: {'event': {'p': 0.6262295081967213, 'r': 0.7791375291375291, 'f1': 0.6943650999740326}, 'argument': {'p': 0.692450656994549, 'r': 0.6983419256731676, 'f1': 0.6953838138993809}, 'combined': 0.4828502514585677, 'epoch': 20}
Dev Russian: {'event': {'p': 0.5807770961145194, 'r': 0.7563249001331558, 'f1': 0.6570271833429728}, 'argument': {'p': 0.6639440000503527, 'r': 0.6614940221903883, 'f1': 0.6627167468155093}, 'combined': 0.43542291751441214, 'epoch': 20}
Test Russian: {'event': {'p': 0.6225011622501162, 'r': 0.7803030303030303, 'f1': 0.6925265063356607}, 'argument': {'p': 0.6979235042978634, 'r': 0.7036217693558147, 'f1': 0.7007610531037582}, 'combined': 0.4852956038820441, 'epoch': 20}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6471040251528055, 'r': 0.6688554209562612, 'f1': 0.6577999594528519}, 'combined': 0.4744786592774669, 'epoch': 20}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6217159277504105, 'r': 0.46830550401978976, 'f1': 0.534215167548501}, 'combined': 0.32742219946521034, 'epoch': 20}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5714821447108898, 'r': 0.5943414304993253, 'f1': 0.5826876769601229}, 'combined': 0.3263050990976688, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
Epoch: 21
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 14:09:23.461852: step: 3068/1534, loss: 0.23380731046199799
==================================================
Loss: 0.097
--------------------
Dev Chinese: {'event': {'p': 0.6267123287671232, 'r': 0.7310252996005326, 'f1': 0.6748617086662568}, 'argument': {'p': 0.6885791319483747, 'r': 0.6548402880667171, 'f1': 0.6712860487259269}, 'combined': 0.45302524984699916, 'epoch': 21}
Test Chinese: {'event': {'p': 0.6551388212372138, 'r': 0.7837995337995338, 'f1': 0.7137171663571239}, 'argument': {'p': 0.7063965340307572, 'r': 0.7063965340307572, 'f1': 0.7063965340307572}, 'combined': 0.5041673325929257, 'epoch': 21}
Dev Korean: {'event': {'p': 0.5638406537282942, 'r': 0.7350199733688415, 'f1': 0.638150289017341}, 'argument': {'p': 0.6813908011709565, 'r': 0.6615540966244948, 'f1': 0.671325944432833}, 'combined': 0.4284068454646518, 'epoch': 21}
Test Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.7001606478770667, 'r': 0.699473092412703, 'f1': 0.6998167012676625}, 'combined': 0.48956276115686387, 'epoch': 21}
Dev Russian: {'event': {'p': 0.5756646216768916, 'r': 0.7496671105193076, 'f1': 0.6512434933487565}, 'argument': {'p': 0.6740900522517327, 'r': 0.6547025065011298, 'f1': 0.6642548438574875}, 'combined': 0.43259164498758296, 'epoch': 21}
Test Russian: {'event': {'p': 0.6252905625290562, 'r': 0.7837995337995338, 'f1': 0.6956296870959401}, 'argument': {'p': 0.7041020593643452, 'r': 0.7020311709544501, 'f1': 0.7030650902047513}, 'combined': 0.48907294870721, 'epoch': 21}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6600907029478458, 'r': 0.6212618380685607, 'f1': 0.6400879543736686}, 'combined': 0.46170278676133475, 'epoch': 21}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6895238095238095, 'r': 0.45968253968253964, 'f1': 0.5516190476190476}, 'combined': 0.33808909370199697, 'epoch': 21}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.5490510518288296, 'r': 0.592975135975136, 'f1': 0.5701683999760923}, 'combined': 0.3344987946526408, 'epoch': 21}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
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
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2023-01-23 14:35:23.156892: step: 3068/1534, loss: 0.04629424586892128
==================================================
Loss: 0.093
--------------------
Dev Chinese: {'event': {'p': 0.6335616438356164, 'r': 0.7390146471371505, 'f1': 0.6822372464658882}, 'argument': {'p': 0.684030144463843, 'r': 0.6448700544090357, 'f1': 0.6638731138003467}, 'combined': 0.45291896516188385, 'epoch': 22}
Test Chinese: {'event': {'p': 0.6522162688748173, 'r': 0.7803030303030303, 'f1': 0.7105332979570179}, 'argument': {'p': 0.7162474243971284, 'r': 0.6818352718001821, 'f1': 0.6986178408735189}, 'combined': 0.4963912384874725, 'epoch': 22}
Dev Korean: {'event': {'p': 0.575076608784474, 'r': 0.7496671105193076, 'f1': 0.6508670520231213}, 'argument': {'p': 0.6861351958853341, 'r': 0.6506281995968074, 'f1': 0.6679101308244068}, 'combined': 0.43472069786605894, 'epoch': 22}
Test Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7092512875799923, 'r': 0.6772235789130947, 'f1': 0.6928675116030859}, 'combined': 0.4865005846208113, 'epoch': 22}
Dev Russian: {'event': {'p': 0.5838445807770961, 'r': 0.7603195739014648, 'f1': 0.6604973973395026}, 'argument': {'p': 0.6853226032398666, 'r': 0.6468838446817634, 'f1': 0.6655486784690884}, 'combined': 0.4395931699315784, 'epoch': 22}
Test Russian: {'event': {'p': 0.6252905625290562, 'r': 0.7837995337995338, 'f1': 0.6956296870959401}, 'argument': {'p': 0.7148353095041132, 'r': 0.678171100555356, 'f1': 0.6960207003520351}, 'combined': 0.48417266199818326, 'epoch': 22}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.627104377104377, 'r': 0.6007554536966302, 'f1': 0.6136472016300343}, 'combined': 0.4426307683888772, 'epoch': 22}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6445279866332498, 'r': 0.48984126984126986, 'f1': 0.5566378066378066}, 'combined': 0.34116510729413957, 'epoch': 22}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.6063422688422689, 'r': 0.5335811965811966, 'f1': 0.5676395708310603}, 'combined': 0.33301521488755537, 'epoch': 22}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5828220858895705, 'r': 0.758988015978695, 'f1': 0.6593406593406592}, 'argument': {'p': 0.6675442642210367, 'r': 0.6636030434728682, 'f1': 0.665567819323269}, 'combined': 0.43883592482852896, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7113255968780491, 'r': 0.6859792451095929, 'f1': 0.6984225364587269}, 'combined': 0.48620467239381765, 'epoch': 10}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.608621132849808, 'r': 0.5964487101928119, 'f1': 0.6024734446392038}, 'combined': 0.35345108752166626, 'epoch': 10}
******************************
Epoch: 23
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 15:01:24.180555: step: 3066/1534, loss: 0.2832956910133362
2023-01-23 15:01:24.995953: step: 3068/1534, loss: 0.010320807807147503
==================================================
Loss: 0.094
--------------------
Dev Chinese: {'event': {'p': 0.6221461187214612, 'r': 0.725699067909454, 'f1': 0.6699446834665028}, 'argument': {'p': 0.6767872736473232, 'r': 0.6531374581369707, 'f1': 0.664752085655865}, 'combined': 0.44534712560841605, 'epoch': 23}
Test Chinese: {'event': {'p': 0.6531904529956162, 'r': 0.7814685314685315, 'f1': 0.7115945874237197}, 'argument': {'p': 0.7060253769280681, 'r': 0.6842474797292919, 'f1': 0.6949658586436971}, 'combined': 0.49453394345513274, 'epoch': 23}
Dev Korean: {'event': {'p': 0.5607763023493361, 'r': 0.7310252996005326, 'f1': 0.6346820809248556}, 'argument': {'p': 0.6833547404103886, 'r': 0.6555033099424251, 'f1': 0.669139337192342}, 'combined': 0.4246907469579142, 'epoch': 23}
Test Korean: {'event': {'p': 0.6318501170960187, 'r': 0.7861305361305362, 'f1': 0.7005972474681901}, 'argument': {'p': 0.6987199927536646, 'r': 0.681091736055751, 'f1': 0.6897932564931648}, 'combined': 0.4832672568212305, 'epoch': 23}
Dev Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6469583048530417, 'r': 0.6469583048530417, 'f1': 0.6469583048530417}, 'combined': 0.47726432325224394, 'epoch': 23}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6247519841269841, 'r': 0.4052445302445302, 'f1': 0.49160811865729886}, 'combined': 0.30130820175769935, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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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2023-01-23 15:27:33.436171: step: 3068/1534, loss: 0.005370371509343386
==================================================
Loss: 0.093
--------------------
Dev Chinese: {'event': {'p': 0.6267123287671232, 'r': 0.7310252996005326, 'f1': 0.6748617086662568}, 'argument': {'p': 0.6805051285685851, 'r': 0.644582553008459, 'f1': 0.662056917752102}, 'combined': 0.446796862748499, 'epoch': 24}
Test Chinese: {'event': {'p': 0.6561130053580126, 'r': 0.784965034965035, 'f1': 0.714778455823826}, 'argument': {'p': 0.7116426096617581, 'r': 0.6882752403892824, 'f1': 0.6997639014579958}, 'combined': 0.5001761609254022, 'epoch': 24}
Dev Korean: {'event': {'p': 0.5658835546475995, 'r': 0.7376830892143809, 'f1': 0.6404624277456648}, 'argument': {'p': 0.678582284114601, 'r': 0.6518742350313732, 'f1': 0.6649601862178767}, 'combined': 0.42588201521931063, 'epoch': 24}
Test Korean: {'event': {'p': 0.6323185011709602, 'r': 0.7867132867132867, 'f1': 0.7011165930927032}, 'argument': {'p': 0.7073945603340311, 'r': 0.6869978626572963, 'f1': 0.6970470335206344}, 'combined': 0.4887112413673625, 'epoch': 24}
Dev Russian: {'event': {'p': 0.5787321063394683, 'r': 0.7536617842876165, 'f1': 0.6547137073452863}, 'argument': {'p': 0.6750753337664459, 'r': 0.642667729117392, 'f1': 0.6584730270336351}, 'combined': 0.4311113167160642, 'epoch': 24}
Test Russian: {'event': {'p': 0.6238958623895863, 'r': 0.782051282051282, 'f1': 0.6940780967158003}, 'argument': {'p': 0.712012432140467, 'r': 0.6913035738200417, 'f1': 0.7015052017540466}, 'combined': 0.4868993952696822, 'epoch': 24}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6198347107438017, 'r': 0.6302521008403361, 'f1': 0.625}, 'combined': 0.46106557377049184, 'epoch': 24}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6163522012578617, 'r': 0.42982456140350883, 'f1': 0.5064599483204135}, 'combined': 0.31041093606735026, 'epoch': 24}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.562687462446049, 'r': 0.608621132849808, 'f1': 0.5847536374439333}, 'combined': 0.34305546730044084, 'epoch': 24}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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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2023-01-23 15:53:36.051763: step: 3068/1534, loss: 0.0043128919787704945
==================================================
Loss: 0.088
--------------------
Dev Chinese: {'event': {'p': 0.6289954337899544, 'r': 0.7336884154460719, 'f1': 0.677320221266134}, 'argument': {'p': 0.6942351103400983, 'r': 0.6411101353289319, 'f1': 0.6666158687931574}, 'combined': 0.4515124077504975, 'epoch': 25}
Test Chinese: {'event': {'p': 0.6551388212372138, 'r': 0.7837995337995338, 'f1': 0.7137171663571239}, 'argument': {'p': 0.7229859374866586, 'r': 0.6788780420962258, 'f1': 0.7002380899323588}, 'combined': 0.49977194532184804, 'epoch': 25}
Dev Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6918200998394499, 'r': 0.6510062410725195, 'f1': 0.6707929223208775}, 'combined': 0.4334950792801971, 'epoch': 25}
Test Korean: {'event': {'p': 0.62903981264637, 'r': 0.7826340326340326, 'f1': 0.6974811737211114}, 'argument': {'p': 0.7196307290536543, 'r': 0.6762596009351242, 'f1': 0.6972713818490152}, 'combined': 0.48633366181419235, 'epoch': 25}
Dev Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6857325543774475, 'r': 0.6391735912758053, 'f1': 0.6616350009004707}, 'combined': 0.43547751938966783, 'epoch': 25}
Test Russian: {'event': {'p': 0.6271501627150162, 'r': 0.7861305361305362, 'f1': 0.6976984742694595}, 'argument': {'p': 0.7243800708713659, 'r': 0.6784703244652748, 'f1': 0.7006739754346174}, 'combined': 0.4888591636210493, 'epoch': 25}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.65319715007215, 'r': 0.6147737883032, 'f1': 0.6334032970396606}, 'combined': 0.4672647273243398, 'epoch': 25}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.6092103508069894, 'r': 0.41460148874364555, 'f1': 0.4934100361907848}, 'combined': 0.310370829216784, 'epoch': 25}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5970126486896695, 'r': 0.6335644435074044, 'f1': 0.6147456976606498}, 'combined': 0.34425759068996387, 'epoch': 25}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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
2023-01-23 15:58:45.196252: step: 2/1534, loss: 0.0005525123560801148
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2023-01-23 16:19:30.445172: step: 3048/1534, loss: 0.05651552975177765
2023-01-23 16:19:31.244431: step: 3050/1534, loss: 4.753816392621957e-05
2023-01-23 16:19:32.045902: step: 3052/1534, loss: 0.00010416103032184765
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2023-01-23 16:19:33.629048: step: 3056/1534, loss: 0.00017256841238122433
2023-01-23 16:19:34.427505: step: 3058/1534, loss: 0.0010575943160802126
2023-01-23 16:19:35.249674: step: 3060/1534, loss: 0.7377400398254395
2023-01-23 16:19:36.056509: step: 3062/1534, loss: 0.008746607229113579
2023-01-23 16:19:36.875663: step: 3064/1534, loss: 0.0005551915965043008
2023-01-23 16:19:37.691362: step: 3066/1534, loss: 3.70829802704975e-05
2023-01-23 16:19:38.478105: step: 3068/1534, loss: 0.3946511745452881
==================================================
Loss: 0.082
--------------------
Dev Chinese: {'event': {'p': 0.6278538812785388, 'r': 0.7323568575233023, 'f1': 0.6760909649661956}, 'argument': {'p': 0.6713208908888796, 'r': 0.6628231580928179, 'f1': 0.6670449616475491}, 'combined': 0.4509830717961304, 'epoch': 26}
Test Chinese: {'event': {'p': 0.6517291768144179, 'r': 0.7797202797202797, 'f1': 0.7100026532236667}, 'argument': {'p': 0.7090655766019173, 'r': 0.6968362532243717, 'f1': 0.7028977261530547}, 'combined': 0.49905925051355116, 'epoch': 26}
Dev Korean: {'event': {'p': 0.5628192032686414, 'r': 0.7336884154460719, 'f1': 0.6369942196531792}, 'argument': {'p': 0.6703691896822545, 'r': 0.6723207739316933, 'f1': 0.671343563504467}, 'combined': 0.42764196935371246, 'epoch': 26}
Test Korean: {'event': {'p': 0.629976580796253, 'r': 0.7837995337995338, 'f1': 0.6985198649701376}, 'argument': {'p': 0.7027031291695787, 'r': 0.69304239875219, 'f1': 0.697839330319031}, 'combined': 0.4874546347853008, 'epoch': 26}
Dev Russian: {'event': {'p': 0.5807770961145194, 'r': 0.7563249001331558, 'f1': 0.6570271833429728}, 'argument': {'p': 0.6654311276685302, 'r': 0.6673940513489683, 'f1': 0.6664111440568491}, 'combined': 0.43785023692803965, 'epoch': 26}
Test Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.7099918149404594, 'r': 0.6925957397622383, 'f1': 0.7011858969446707}, 'combined': 0.4884910282826333, 'epoch': 26}
Chinese: {'event': {'p': 0.6323529411764706, 'r': 0.7962962962962963, 'f1': 0.7049180327868853}, 'argument': {'p': 0.6275252525252525, 'r': 0.6433452168746286, 'f1': 0.635336770191542}, 'combined': 0.44786034620059517, 'epoch': 26}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.5917487684729064, 'r': 0.4515977443609022, 'f1': 0.5122601279317697}, 'combined': 0.30570362473347545, 'epoch': 26}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.5462009130137785, 'r': 0.5898969860548807, 'f1': 0.5672086404373854}, 'combined': 0.3478879661349296, 'epoch': 26}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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
2023-01-23 16:24:48.306604: step: 2/1534, loss: -3.2040725272963755e-06
2023-01-23 16:24:49.098503: step: 4/1534, loss: 0.0011513778008520603
2023-01-23 16:24:49.916922: step: 6/1534, loss: 0.0025082360953092575
2023-01-23 16:24:50.758972: step: 8/1534, loss: 0.29410505294799805
2023-01-23 16:24:51.573461: step: 10/1534, loss: 0.03599289059638977
2023-01-23 16:24:52.372893: step: 12/1534, loss: 0.029688376933336258
2023-01-23 16:24:53.182257: step: 14/1534, loss: 0.0006344335852190852
2023-01-23 16:24:53.985684: step: 16/1534, loss: 0.0014294798020273447
2023-01-23 16:24:54.801009: step: 18/1534, loss: 0.00038940407102927566
2023-01-23 16:24:55.569020: step: 20/1534, loss: 0.0067858947440981865
2023-01-23 16:24:56.404962: step: 22/1534, loss: 0.00448579341173172
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2023-01-23 16:45:37.351999: step: 3068/1534, loss: 0.09965724498033524
==================================================
Loss: 0.077
--------------------
Dev Chinese: {'event': {'p': 0.6255707762557078, 'r': 0.729693741677763, 'f1': 0.6736324523663184}, 'argument': {'p': 0.684254719636094, 'r': 0.6511620434632174, 'f1': 0.6672983502970372}, 'combined': 0.44951382417059177, 'epoch': 27}
Test Chinese: {'event': {'p': 0.6570871894788115, 'r': 0.7861305361305362, 'f1': 0.7158397452905281}, 'argument': {'p': 0.7039698179583936, 'r': 0.6820218369009844, 'f1': 0.6928220479300767}, 'combined': 0.49594955832192816, 'epoch': 27}
Dev Korean: {'event': {'p': 0.5628192032686414, 'r': 0.7336884154460719, 'f1': 0.6369942196531792}, 'argument': {'p': 0.6865303782519057, 'r': 0.6560067972946416, 'f1': 0.6709215996185228}, 'combined': 0.42737318079746367, 'epoch': 27}
Test Korean: {'event': {'p': 0.6351288056206089, 'r': 0.7902097902097902, 'f1': 0.7042326668397818}, 'argument': {'p': 0.7005397287705564, 'r': 0.6776012962633345, 'f1': 0.6888796134448149}, 'combined': 0.48513152730779996, 'epoch': 27}
Dev Russian: {'event': {'p': 0.5756646216768916, 'r': 0.7496671105193076, 'f1': 0.6512434933487565}, 'argument': {'p': 0.6805466211018414, 'r': 0.6503671700551965, 'f1': 0.6651147249317316}, 'combined': 0.4331516369422382, 'epoch': 27}
Test Russian: {'event': {'p': 0.6276150627615062, 'r': 0.7867132867132867, 'f1': 0.6982156710628393}, 'argument': {'p': 0.7081555651755733, 'r': 0.6815418756346612, 'f1': 0.6945938849099295}, 'combined': 0.484976335468531, 'epoch': 27}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6615971747550694, 'r': 0.6337989741351086, 'f1': 0.6473998104899392}, 'combined': 0.4669769124845463, 'epoch': 27}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6347080498866214, 'r': 0.47391534391534396, 'f1': 0.542651157154974}, 'combined': 0.33259264470788735, 'epoch': 27}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5763414304993252, 'r': 0.5881035005095155, 'f1': 0.5821630611104295}, 'combined': 0.3104869659255624, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
Epoch: 28
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 17:11:37.287107: step: 3068/1534, loss: 0.8673397898674011
==================================================
Loss: 0.074
--------------------
Dev Chinese: {'event': {'p': 0.6244292237442922, 'r': 0.7283621837549934, 'f1': 0.6724031960663798}, 'argument': {'p': 0.6766869943284507, 'r': 0.6525555116183722, 'f1': 0.6644022077445756}, 'combined': 0.44674616796101146, 'epoch': 28}
Test Chinese: {'event': {'p': 0.6522162688748173, 'r': 0.7803030303030303, 'f1': 0.7105332979570179}, 'argument': {'p': 0.6948827396768363, 'r': 0.7001784000521072, 'f1': 0.6975205186853471}, 'combined': 0.4956115545341894, 'epoch': 28}
Dev Korean: {'event': {'p': 0.5648621041879469, 'r': 0.7363515312916112, 'f1': 0.6393063583815031}, 'argument': {'p': 0.6751821398842387, 'r': 0.6584624073597929, 'f1': 0.6667174670390824}, 'combined': 0.42623671592209555, 'epoch': 28}
Test Korean: {'event': {'p': 0.6318501170960187, 'r': 0.7861305361305362, 'f1': 0.7005972474681901}, 'argument': {'p': 0.6932703408386757, 'r': 0.6959934976603005, 'f1': 0.6946292503600844}, 'combined': 0.4866553408131674, 'epoch': 28}
Dev Russian: {'event': {'p': 0.5807770961145194, 'r': 0.7563249001331558, 'f1': 0.6570271833429728}, 'argument': {'p': 0.6658253976574973, 'r': 0.6530494121673903, 'f1': 0.6593755242077599}, 'combined': 0.43322764343552067, 'epoch': 28}
Test Russian: {'event': {'p': 0.6229660622966062, 'r': 0.7808857808857809, 'f1': 0.6930437031290406}, 'argument': {'p': 0.7003413207912137, 'r': 0.699654711653183, 'f1': 0.699997847853111}, 'combined': 0.4851291006584788, 'epoch': 28}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6168803418803419, 'r': 0.6739028944911297, 'f1': 0.6441320838911201}, 'combined': 0.47517940614918697, 'epoch': 28}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6316249663707292, 'r': 0.5035928785928786, 'f1': 0.5603890679078649}, 'combined': 0.34346426742740116, 'epoch': 28}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.564158264304463, 'r': 0.6217254341314491, 'f1': 0.5915445878143883}, 'combined': 0.33126496917605747, 'epoch': 28}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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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2023-01-23 17:37:31.717289: step: 3068/1534, loss: 0.11044441908597946
==================================================
Loss: 0.069
--------------------
Dev Chinese: {'event': {'p': 0.6278538812785388, 'r': 0.7323568575233023, 'f1': 0.6760909649661956}, 'argument': {'p': 0.6837240310382416, 'r': 0.6573293394168692, 'f1': 0.6702669342880566}, 'combined': 0.4531614183877458, 'epoch': 29}
Test Chinese: {'event': {'p': 0.6561130053580126, 'r': 0.784965034965035, 'f1': 0.714778455823826}, 'argument': {'p': 0.7092328753722549, 'r': 0.6967612952811038, 'f1': 0.702941772042593}, 'combined': 0.5024476343546685, 'epoch': 29}
Dev Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6809753200357401, 'r': 0.6641121292992397, 'f1': 0.6724380188258525}, 'combined': 0.43300344102427735, 'epoch': 29}
Test Korean: {'event': {'p': 0.6318501170960187, 'r': 0.7861305361305362, 'f1': 0.7005972474681901}, 'argument': {'p': 0.7075417320545476, 'r': 0.6913190261116002, 'f1': 0.6993363110399986}, 'combined': 0.48995309456918107, 'epoch': 29}
Dev Russian: {'event': {'p': 0.5807770961145194, 'r': 0.7563249001331558, 'f1': 0.6570271833429728}, 'argument': {'p': 0.6775331799811177, 'r': 0.653549704583556, 'f1': 0.6653253749364129}, 'combined': 0.4371368571010787, 'epoch': 29}
Test Russian: {'event': {'p': 0.6248256624825662, 'r': 0.7832167832167832, 'f1': 0.6951124903025601}, 'argument': {'p': 0.7115564864091583, 'r': 0.69527905044555, 'f1': 0.7033236014424407}, 'combined': 0.4888890200872201, 'epoch': 29}
Chinese: {'event': {'p': 0.6764705882352942, 'r': 0.8518518518518519, 'f1': 0.7540983606557378}, 'argument': {'p': 0.6664724164724164, 'r': 0.6552711993888464, 'f1': 0.660824345146379}, 'combined': 0.49832655535628584, 'epoch': 29}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.6334154351395731, 'r': 0.4964607464607465, 'f1': 0.5566378066378067}, 'combined': 0.3321870781548201, 'epoch': 29}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5821534745447788, 'r': 0.5465114250828537, 'f1': 0.5637696806117858}, 'combined': 0.3157110211426, 'epoch': 29}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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
2023-01-23 17:42:41.691730: step: 2/1534, loss: 0.10560312122106552
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2023-01-23 18:03:30.678199: step: 3064/1534, loss: 0.00010501701035536826
2023-01-23 18:03:31.468685: step: 3066/1534, loss: 0.14411181211471558
2023-01-23 18:03:32.252696: step: 3068/1534, loss: 0.000999230658635497
==================================================
Loss: 0.069
--------------------
Dev Chinese: {'event': {'p': 0.6255707762557078, 'r': 0.729693741677763, 'f1': 0.6736324523663184}, 'argument': {'p': 0.6763145205803295, 'r': 0.6536868972151884, 'f1': 0.6648082243889817}, 'combined': 0.4478363945484474, 'epoch': 30}
Test Chinese: {'event': {'p': 0.6531904529956162, 'r': 0.7814685314685315, 'f1': 0.7115945874237197}, 'argument': {'p': 0.6979496111015875, 'r': 0.6863788284241739, 'f1': 0.692115863098398}, 'combined': 0.4925059020509162, 'epoch': 30}
Dev Korean: {'event': {'p': 0.567926455566905, 'r': 0.7403462050599201, 'f1': 0.6427745664739885}, 'argument': {'p': 0.6725596770582285, 'r': 0.6578856477405944, 'f1': 0.6651417394436157}, 'combined': 0.42753619321462466, 'epoch': 30}
Test Korean: {'event': {'p': 0.6318501170960187, 'r': 0.7861305361305362, 'f1': 0.7005972474681901}, 'argument': {'p': 0.6944750560507128, 'r': 0.6810629354919592, 'f1': 0.6877036086359066}, 'combined': 0.4818032552842576, 'epoch': 30}
Dev Russian: {'event': {'p': 0.5797546012269938, 'r': 0.7549933422103862, 'f1': 0.6558704453441295}, 'argument': {'p': 0.6615431218793242, 'r': 0.6566572790294031, 'f1': 0.6590911458901496}, 'combined': 0.4322784033773451, 'epoch': 30}
Test Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7028403095533621, 'r': 0.6853955468080793, 'f1': 0.6940083215364878}, 'combined': 0.4831317304308832, 'epoch': 30}
Chinese: {'event': {'p': 0.6764705882352942, 'r': 0.8518518518518519, 'f1': 0.7540983606557378}, 'argument': {'p': 0.6317340067340067, 'r': 0.6370426958662252, 'f1': 0.6343772452559062}, 'combined': 0.47838284068478176, 'epoch': 30}
Korean: {'event': {'p': 0.5901639344262295, 'r': 0.5714285714285714, 'f1': 0.5806451612903226}, 'argument': {'p': 0.639172335600907, 'r': 0.4648526077097505, 'f1': 0.538250387874448}, 'combined': 0.31253248328193756, 'epoch': 30}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.6121329207191276, 'r': 0.6246254293052322, 'f1': 0.6183160815344724}, 'combined': 0.3462570056593045, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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
2023-01-23 18:08:41.026406: step: 2/1534, loss: 0.0017100762343034148
2023-01-23 18:08:41.822215: step: 4/1534, loss: 0.1347518414258957
2023-01-23 18:08:42.619072: step: 6/1534, loss: 0.002695840084925294
2023-01-23 18:08:43.458508: step: 8/1534, loss: 0.0002272312412969768
2023-01-23 18:08:44.274503: step: 10/1534, loss: 0.02439136430621147
2023-01-23 18:08:45.087567: step: 12/1534, loss: 0.018641600385308266
2023-01-23 18:08:45.949875: step: 14/1534, loss: 0.0004062188963871449
2023-01-23 18:08:46.740992: step: 16/1534, loss: 0.011939850635826588
2023-01-23 18:08:47.546939: step: 18/1534, loss: 0.14471076428890228
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2023-01-23 18:29:32.456266: step: 3068/1534, loss: 0.0066605908796191216
==================================================
Loss: 0.063
--------------------
Dev Chinese: {'event': {'p': 0.6267123287671232, 'r': 0.7310252996005326, 'f1': 0.6748617086662568}, 'argument': {'p': 0.6749059946570456, 'r': 0.647849629615115, 'f1': 0.661101099311788}, 'combined': 0.44615181748269395, 'epoch': 31}
Test Chinese: {'event': {'p': 0.654164637116415, 'r': 0.7826340326340326, 'f1': 0.712655876890422}, 'argument': {'p': 0.6943762999340722, 'r': 0.7028976674622848, 'f1': 0.6986109996369833}, 'combined': 0.49786923455158855, 'epoch': 31}
Dev Korean: {'event': {'p': 0.5730337078651685, 'r': 0.7470039946737683, 'f1': 0.6485549132947976}, 'argument': {'p': 0.6654696887753047, 'r': 0.6528587471512828, 'f1': 0.6591039007296248}, 'combined': 0.4274650731899647, 'epoch': 31}
Test Korean: {'event': {'p': 0.6346604215456675, 'r': 0.7896270396270396, 'f1': 0.7037133212152688}, 'argument': {'p': 0.6919206272786171, 'r': 0.6966768738163752, 'f1': 0.6942906049613525}, 'combined': 0.48858154750591154, 'epoch': 31}
Dev Russian: {'event': {'p': 0.5797546012269938, 'r': 0.7549933422103862, 'f1': 0.6558704453441295}, 'argument': {'p': 0.6602407512222797, 'r': 0.6500082377347018, 'f1': 0.6550845385872228}, 'combined': 0.42965058806125545, 'epoch': 31}
Test Russian: {'event': {'p': 0.6271501627150162, 'r': 0.7861305361305362, 'f1': 0.6976984742694595}, 'argument': {'p': 0.6951837611859524, 'r': 0.7017699516708895, 'f1': 0.6984613305363657}, 'combined': 0.487315404651439, 'epoch': 31}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.716947466947467, 'r': 0.6687493179089818, 'f1': 0.6920101637492942}, 'combined': 0.4991548722126057, 'epoch': 31}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.5773809523809524, 'r': 0.43693693693693697, 'f1': 0.49743589743589745}, 'combined': 0.31290322580645163, 'epoch': 31}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.62931077064204, 'r': 0.6549969245457967, 'f1': 0.6418969860548808}, 'combined': 0.3594623121907332, 'epoch': 31}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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
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2023-01-23 18:55:35.645543: step: 3068/1534, loss: 0.000150049920193851
==================================================
Loss: 0.067
--------------------
Dev Chinese: {'event': {'p': 0.6232876712328768, 'r': 0.7270306258322237, 'f1': 0.6711739397664412}, 'argument': {'p': 0.684808293743922, 'r': 0.6496768645481372, 'f1': 0.6667801471325787}, 'combined': 0.44752545830902024, 'epoch': 32}
Test Chinese: {'event': {'p': 0.6561130053580126, 'r': 0.784965034965035, 'f1': 0.714778455823826}, 'argument': {'p': 0.7105567185176811, 'r': 0.686761549893581, 'f1': 0.6984565282343754}, 'combined': 0.49924167871143743, 'epoch': 32}
Dev Korean: {'event': {'p': 0.5658835546475995, 'r': 0.7376830892143809, 'f1': 0.6404624277456648}, 'argument': {'p': 0.6784786712911612, 'r': 0.6577089160475543, 'f1': 0.6679323707011432}, 'combined': 0.42778558770917147, 'epoch': 32}
Test Korean: {'event': {'p': 0.6341920374707259, 'r': 0.789044289044289, 'f1': 0.7031939755907557}, 'argument': {'p': 0.7066627061614337, 'r': 0.68121822246986, 'f1': 0.6937072232151408}, 'combined': 0.4878107401886786, 'epoch': 32}
Dev Russian: {'event': {'p': 0.5787321063394683, 'r': 0.7536617842876165, 'f1': 0.6547137073452863}, 'argument': {'p': 0.6734034885919628, 'r': 0.6490516281188955, 'f1': 0.6610033491928624}, 'combined': 0.43276795331770984, 'epoch': 32}
Test Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.7137405436613897, 'r': 0.6866925518075115, 'f1': 0.6999553450159149}, 'combined': 0.48763374695445433, 'epoch': 32}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.663544536271809, 'r': 0.6133604957134369, 'f1': 0.6374663667240087}, 'combined': 0.47026207381279334, 'epoch': 32}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.5570175438596491, 'r': 0.41776315789473684, 'f1': 0.47744360902255634}, 'combined': 0.2926267281105991, 'epoch': 32}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.6193230348101568, 'r': 0.5821636527215475, 'f1': 0.6001687141459252}, 'combined': 0.3360944799217181, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
Epoch: 33
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 19:21:33.132980: step: 3068/1534, loss: 0.0017825725954025984
==================================================
Loss: 0.060
--------------------
Dev Chinese: {'event': {'p': 0.6244292237442922, 'r': 0.7283621837549934, 'f1': 0.6724031960663798}, 'argument': {'p': 0.6839331788726242, 'r': 0.6483381435409634, 'f1': 0.6656601550096152}, 'combined': 0.447592015722507, 'epoch': 33}
Test Chinese: {'event': {'p': 0.6556259132976132, 'r': 0.7843822843822844, 'f1': 0.7142478110904751}, 'argument': {'p': 0.7162939615136623, 'r': 0.6927816597265759, 'f1': 0.7043416435986357}, 'combined': 0.503074477200193, 'epoch': 33}
Dev Korean: {'event': {'p': 0.567926455566905, 'r': 0.7403462050599201, 'f1': 0.6427745664739885}, 'argument': {'p': 0.6760901761622039, 'r': 0.6509951259553098, 'f1': 0.6633053786151989}, 'combined': 0.42635582717924925, 'epoch': 33}
Test Korean: {'event': {'p': 0.6337236533957845, 'r': 0.7884615384615384, 'f1': 0.7026746299662425}, 'argument': {'p': 0.7135647676718775, 'r': 0.6909082104004627, 'f1': 0.702053744478102}, 'combined': 0.4933153551175653, 'epoch': 33}
Dev Russian: {'event': {'p': 0.5756646216768916, 'r': 0.7496671105193076, 'f1': 0.6512434933487565}, 'argument': {'p': 0.6732940001033355, 'r': 0.6405230531956511, 'f1': 0.6564998186948623}, 'combined': 0.4275412353096674, 'epoch': 33}
Test Russian: {'event': {'p': 0.6248256624825662, 'r': 0.7832167832167832, 'f1': 0.6951124903025601}, 'argument': {'p': 0.7180294794242841, 'r': 0.6943375560587653, 'f1': 0.7059848062102244}, 'combined': 0.49073885676055934, 'epoch': 33}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6376424341941582, 'r': 0.6215674148447257, 'f1': 0.6295023180129562}, 'combined': 0.4643869559111972, 'epoch': 33}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.5849642564162663, 'r': 0.4189608863521907, 'f1': 0.4882378833080647}, 'combined': 0.3071173782099117, 'epoch': 33}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5764861553775328, 'r': 0.5529561082192661, 'f1': 0.5644760271405008}, 'combined': 0.3161065751986804, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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
2023-01-23 19:26:42.251664: step: 2/1534, loss: 0.0006829613703303039
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2023-01-23 19:47:30.941836: step: 3068/1534, loss: 0.0012640851782634854
==================================================
Loss: 0.051
--------------------
Dev Chinese: {'event': {'p': 0.6210045662100456, 'r': 0.7243675099866844, 'f1': 0.6687154271665642}, 'argument': {'p': 0.6852453967538563, 'r': 0.6414955050531647, 'f1': 0.6626491144989362}, 'combined': 0.4431236856637016, 'epoch': 34}
Test Chinese: {'event': {'p': 0.6551388212372138, 'r': 0.7837995337995338, 'f1': 0.7137171663571239}, 'argument': {'p': 0.7134553498305819, 'r': 0.6855323875486553, 'f1': 0.6992152058386996}, 'combined': 0.49904189538500976, 'epoch': 34}
Dev Korean: {'event': {'p': 0.5658835546475995, 'r': 0.7376830892143809, 'f1': 0.6404624277456648}, 'argument': {'p': 0.6828716365274206, 'r': 0.6554970446841202, 'f1': 0.6689043847576147}, 'combined': 0.42840812619158214, 'epoch': 34}
Test Korean: {'event': {'p': 0.6327868852459017, 'r': 0.7872960372960373, 'f1': 0.7016359387172163}, 'argument': {'p': 0.7144156928514674, 'r': 0.6830796199080643, 'f1': 0.6983963315994433}, 'combined': 0.49001996571843565, 'epoch': 34}
Dev Russian: {'event': {'p': 0.5756646216768916, 'r': 0.7496671105193076, 'f1': 0.6512434933487565}, 'argument': {'p': 0.6689593510350567, 'r': 0.6418060194432278, 'f1': 0.6551014360418634}, 'combined': 0.42663054770569014, 'epoch': 34}
Test Russian: {'event': {'p': 0.6243607624360762, 'r': 0.7826340326340326, 'f1': 0.6945952935091801}, 'argument': {'p': 0.7169992570386449, 'r': 0.6860698773232523, 'f1': 0.7011936621941056}, 'combined': 0.4870458175984917, 'epoch': 34}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6535858585858586, 'r': 0.6041549953314659, 'f1': 0.6278990781174187}, 'combined': 0.4632042379554729, 'epoch': 34}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6552380952380952, 'r': 0.4427284427284427, 'f1': 0.5284178187403993}, 'combined': 0.3238689856795996, 'epoch': 34}
Russian: {'event': {'p': 0.5641025641025641, 'r': 0.6111111111111112, 'f1': 0.5866666666666667}, 'argument': {'p': 0.587353176388264, 'r': 0.6343414304993251, 'f1': 0.609943683172428}, 'combined': 0.35783362746115777, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
Epoch: 35
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 20:13:25.242499: step: 3066/1534, loss: 0.12460190802812576
2023-01-23 20:13:26.065794: step: 3068/1534, loss: 0.004772025626152754
==================================================
Loss: 0.065
--------------------
Dev Chinese: {'event': {'p': 0.6210045662100456, 'r': 0.7243675099866844, 'f1': 0.6687154271665642}, 'argument': {'p': 0.7051452183613653, 'r': 0.6428495383428038, 'f1': 0.6725579247749567}, 'combined': 0.4497498599601431, 'epoch': 35}
Test Chinese: {'event': {'p': 0.6566000974184121, 'r': 0.7855477855477856, 'f1': 0.7153091005571769}, 'argument': {'p': 0.7168422380469243, 'r': 0.6959125376659921, 'f1': 0.7062223530388216}, 'combined': 0.5051672761455726, 'epoch': 35}
Dev Korean: {'event': {'p': 0.5669050051072523, 'r': 0.7390146471371505, 'f1': 0.6416184971098265}, 'argument': {'p': 0.697541951644528, 'r': 0.655374448376869, 'f1': 0.675801064680016}, 'combined': 0.43360646346521253, 'epoch': 35}
Test Korean: {'event': {'p': 0.6327868852459017, 'r': 0.7872960372960373, 'f1': 0.7016359387172163}, 'argument': {'p': 0.7119836893585318, 'r': 0.6930938502369985, 'f1': 0.7024117924559524}, 'combined': 0.4928373573658747, 'epoch': 35}
Dev Russian: {'event': {'p': 0.5777096114519428, 'r': 0.7523302263648469, 'f1': 0.6535569693464431}, 'argument': {'p': 0.6885477206367977, 'r': 0.6478954566877617, 'f1': 0.6676033032790244}, 'combined': 0.43631679161671355, 'epoch': 35}
Test Russian: {'event': {'p': 0.6276150627615062, 'r': 0.7867132867132867, 'f1': 0.6982156710628393}, 'argument': {'p': 0.715266422614734, 'r': 0.6975016356478322, 'f1': 0.7062723378829804}, 'combined': 0.4931304143480855, 'epoch': 35}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.7020923520923521, 'r': 0.619493251846193, 'f1': 0.65821158008658}, 'combined': 0.48556591973600166, 'epoch': 35}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.6773049645390071, 'r': 0.4301801801801802, 'f1': 0.5261707988980717}, 'combined': 0.3140051541811073, 'epoch': 35}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.5799457689808567, 'r': 0.6391239086727808, 'f1': 0.6080984762129372}, 'combined': 0.3081032279478882, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
Epoch: 36
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 20:18:33.911782: step: 2/1534, loss: 0.0031920652836561203
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2023-01-23 20:18:37.949999: step: 12/1534, loss: 0.002045238623395562
2023-01-23 20:18:38.787352: step: 14/1534, loss: 0.25498276948928833
2023-01-23 20:18:39.589235: step: 16/1534, loss: 0.0015194523148238659
2023-01-23 20:18:40.386883: step: 18/1534, loss: 0.004550717771053314
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2023-01-23 20:39:24.835294: step: 3068/1534, loss: 0.00011037618241971359
==================================================
Loss: 0.052
--------------------
Dev Chinese: {'event': {'p': 0.6267123287671232, 'r': 0.7310252996005326, 'f1': 0.6748617086662568}, 'argument': {'p': 0.6795091835364591, 'r': 0.6446236674173997, 'f1': 0.6616068797319475}, 'combined': 0.4464931493212528, 'epoch': 36}
Test Chinese: {'event': {'p': 0.6566000974184121, 'r': 0.7855477855477856, 'f1': 0.7153091005571769}, 'argument': {'p': 0.7148641464498268, 'r': 0.6838037142160199, 'f1': 0.6989890484357689}, 'combined': 0.4999932275359068, 'epoch': 36}
Dev Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6727441976222619, 'r': 0.6438353063915893, 'f1': 0.6579723674548955}, 'combined': 0.4252098883321232, 'epoch': 36}
Test Korean: {'event': {'p': 0.6337236533957845, 'r': 0.7884615384615384, 'f1': 0.7026746299662425}, 'argument': {'p': 0.7118069621321326, 'r': 0.6807878513970167, 'f1': 0.6959519418736207}, 'combined': 0.48902777323033436, 'epoch': 36}
Dev Russian: {'event': {'p': 0.5807770961145194, 'r': 0.7563249001331558, 'f1': 0.6570271833429728}, 'argument': {'p': 0.6678014270099089, 'r': 0.6411682829489525, 'f1': 0.6542139073947864}, 'combined': 0.42983632087939694, 'epoch': 36}
Test Russian: {'event': {'p': 0.6257554625755463, 'r': 0.7843822843822844, 'f1': 0.6961468838893199}, 'argument': {'p': 0.7162388427494071, 'r': 0.6855562762352893, 'f1': 0.7005617686641653}, 'combined': 0.4876938922275493, 'epoch': 36}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.6643234143234144, 'r': 0.6196630167218402, 'f1': 0.6412165129556433}, 'combined': 0.4625168290171853, 'epoch': 36}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.6315748521630875, 'r': 0.4352745602745603, 'f1': 0.5153650793650795}, 'combined': 0.3241812596006145, 'epoch': 36}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5668803345634493, 'r': 0.6131562802420982, 'f1': 0.5891109359188786}, 'combined': 0.329902124114572, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
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
2023-01-23 20:44:35.277033: step: 2/1534, loss: 0.03340624272823334
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2023-01-23 20:44:40.165668: step: 14/1534, loss: 0.00015509300283156335
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2023-01-23 21:05:20.754686: step: 3068/1534, loss: 0.34635603427886963
==================================================
Loss: 0.048
--------------------
Dev Chinese: {'event': {'p': 0.6267123287671232, 'r': 0.7310252996005326, 'f1': 0.6748617086662568}, 'argument': {'p': 0.6806626375750005, 'r': 0.649309678043314, 'f1': 0.6646165982101147}, 'combined': 0.4485242930760331, 'epoch': 37}
Test Chinese: {'event': {'p': 0.6595226497808085, 'r': 0.789044289044289, 'f1': 0.718492968957283}, 'argument': {'p': 0.7116035157426299, 'r': 0.6825825500807123, 'f1': 0.6967909869837294}, 'combined': 0.5006394249806153, 'epoch': 37}
Dev Korean: {'event': {'p': 0.5709908069458631, 'r': 0.7443408788282291, 'f1': 0.6462427745664739}, 'argument': {'p': 0.6759020186611205, 'r': 0.6611335841674326, 'f1': 0.6684362378953498}, 'combined': 0.4319720889982665, 'epoch': 37}
Test Korean: {'event': {'p': 0.6370023419203747, 'r': 0.7925407925407926, 'f1': 0.7063100493378344}, 'argument': {'p': 0.7082438469516469, 'r': 0.6804058968585589, 'f1': 0.6940458413328366}, 'combined': 0.4902115524345146, 'epoch': 37}
Dev Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6761756993937634, 'r': 0.6527043124871852, 'f1': 0.6642327238689582}, 'combined': 0.43718729887962665, 'epoch': 37}
Test Russian: {'event': {'p': 0.6276150627615062, 'r': 0.7867132867132867, 'f1': 0.6982156710628393}, 'argument': {'p': 0.7136580363218973, 'r': 0.6833491357267171, 'f1': 0.6981748012214556}, 'combined': 0.48747658735400307, 'epoch': 37}
Chinese: {'event': {'p': 0.6764705882352942, 'r': 0.8518518518518519, 'f1': 0.7540983606557378}, 'argument': {'p': 0.6650263316929983, 'r': 0.6538494185553009, 'f1': 0.6593905153227186}, 'combined': 0.4972453066368043, 'epoch': 37}
Korean: {'event': {'p': 0.639344262295082, 'r': 0.6190476190476191, 'f1': 0.6290322580645161}, 'argument': {'p': 0.6180952380952381, 'r': 0.41763191763191765, 'f1': 0.49846390168970817}, 'combined': 0.3135498736435261, 'epoch': 37}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.6193514220453876, 'r': 0.6067115971056858, 'f1': 0.612966355838734}, 'combined': 0.34326115926969103, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
Epoch: 38
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 21:31:11.096334: step: 3068/1534, loss: 0.27502521872520447
==================================================
Loss: 0.045
--------------------
Dev Chinese: {'event': {'p': 0.6289954337899544, 'r': 0.7336884154460719, 'f1': 0.677320221266134}, 'argument': {'p': 0.6743806567356319, 'r': 0.6512819894163177, 'f1': 0.6626300846864503}, 'combined': 0.44881275557742367, 'epoch': 38}
Test Chinese: {'event': {'p': 0.6566000974184121, 'r': 0.7855477855477856, 'f1': 0.7153091005571769}, 'argument': {'p': 0.7057080249865422, 'r': 0.6893288599503824, 'f1': 0.697422288363084}, 'combined': 0.4988725097975256, 'epoch': 38}
Dev Korean: {'event': {'p': 0.5699693564862104, 'r': 0.7430093209054593, 'f1': 0.6450867052023121}, 'argument': {'p': 0.673152047128406, 'r': 0.6579423434396448, 'f1': 0.66546029870932}, 'combined': 0.4292795915373417, 'epoch': 38}
Test Korean: {'event': {'p': 0.6346604215456675, 'r': 0.7896270396270396, 'f1': 0.7037133212152688}, 'argument': {'p': 0.7032094999703146, 'r': 0.6873268631461078, 'f1': 0.6951774762163084}, 'combined': 0.48920565062222693, 'epoch': 38}
Dev Russian: {'event': {'p': 0.581799591002045, 'r': 0.7576564580559254, 'f1': 0.6581839213418161}, 'argument': {'p': 0.6634123999956077, 'r': 0.6511632788730892, 'f1': 0.657230771221888}, 'combined': 0.4325787262293283, 'epoch': 38}
Test Russian: {'event': {'p': 0.6248256624825662, 'r': 0.7832167832167832, 'f1': 0.6951124903025601}, 'argument': {'p': 0.7060346383011118, 'r': 0.6891968308538784, 'f1': 0.6975141343176464}, 'combined': 0.4848507869267736, 'epoch': 38}
Chinese: {'event': {'p': 0.6617647058823529, 'r': 0.8333333333333334, 'f1': 0.7377049180327869}, 'argument': {'p': 0.6635485287659201, 'r': 0.6412443765384941, 'f1': 0.6522058188724855}, 'combined': 0.4811354401518336, 'epoch': 38}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6315192743764173, 'r': 0.4181681681681682, 'f1': 0.5031616982836495}, 'combined': 0.3083894279803014, 'epoch': 38}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5924915985287501, 'r': 0.616674929080944, 'f1': 0.6043414304993251}, 'combined': 0.32231542959964005, 'epoch': 38}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}
******************************
Epoch: 39
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 21:36:17.975097: step: 2/1534, loss: 0.00013491304707713425
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2023-01-23 21:36:20.480783: step: 8/1534, loss: 0.0031102471984922886
2023-01-23 21:36:21.309399: step: 10/1534, loss: 0.0002223812771262601
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2023-01-23 21:36:22.957704: step: 14/1534, loss: 0.00012716490891762078
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2023-01-23 21:57:01.245401: step: 3068/1534, loss: 0.02004532516002655
==================================================
Loss: 0.041
--------------------
Dev Chinese: {'event': {'p': 0.6278538812785388, 'r': 0.7323568575233023, 'f1': 0.6760909649661956}, 'argument': {'p': 0.6982057742263954, 'r': 0.6271402533727429, 'f1': 0.6607677346690506}, 'combined': 0.4467390953509255, 'epoch': 39}
Test Chinese: {'event': {'p': 0.6551388212372138, 'r': 0.7837995337995338, 'f1': 0.7137171663571239}, 'argument': {'p': 0.7176701389066802, 'r': 0.6831782031620768, 'f1': 0.6999995377619241}, 'combined': 0.49960168654273707, 'epoch': 39}
Dev Korean: {'event': {'p': 0.567926455566905, 'r': 0.7403462050599201, 'f1': 0.6427745664739885}, 'argument': {'p': 0.7015426485926333, 'r': 0.6356293188996619, 'f1': 0.6669614481079371}, 'combined': 0.42870585566244285, 'epoch': 39}
Test Korean: {'event': {'p': 0.6332552693208431, 'r': 0.7878787878787878, 'f1': 0.7021552843417295}, 'argument': {'p': 0.7127317925769758, 'r': 0.6784142119976062, 'f1': 0.6951497194927645}, 'combined': 0.48810304895051554, 'epoch': 39}
Dev Russian: {'event': {'p': 0.5756646216768916, 'r': 0.7496671105193076, 'f1': 0.6512434933487565}, 'argument': {'p': 0.6987087813594531, 'r': 0.6265707065878282, 'f1': 0.6606764215653927}, 'combined': 0.4302612207534021, 'epoch': 39}
Test Russian: {'event': {'p': 0.6252905625290562, 'r': 0.7837995337995338, 'f1': 0.6956296870959401}, 'argument': {'p': 0.7174359775507726, 'r': 0.6792196166550942, 'f1': 0.6978049444232293}, 'combined': 0.48541383514313086, 'epoch': 39}
Chinese: {'event': {'p': 0.6764705882352942, 'r': 0.8518518518518519, 'f1': 0.7540983606557378}, 'argument': {'p': 0.6732552800734618, 'r': 0.6223368135132841, 'f1': 0.646795465572758}, 'combined': 0.4877474002679815, 'epoch': 39}
Korean: {'event': {'p': 0.6065573770491803, 'r': 0.5873015873015873, 'f1': 0.5967741935483871}, 'argument': {'p': 0.64, 'r': 0.43243243243243246, 'f1': 0.5161290322580645}, 'combined': 0.30801248699271594, 'epoch': 39}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.6131291813822608, 'r': 0.5763414304993252, 'f1': 0.5941664231951806}, 'combined': 0.33273319698930115, 'epoch': 39}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6312785388127854, 'r': 0.7363515312916112, 'f1': 0.6797787338660111}, 'argument': {'p': 0.6780919129175775, 'r': 0.654431690269377, 'f1': 0.6660517467267478}, 'combined': 0.4527678130791537, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.6546517291768145, 'r': 0.7832167832167832, 'f1': 0.7131865216237729}, 'argument': {'p': 0.7125440529651725, 'r': 0.6900998603154075, 'f1': 0.7011423879775204}, 'combined': 0.5000453008446736, 'epoch': 9}
Chinese: {'event': {'p': 0.6470588235294118, 'r': 0.8148148148148148, 'f1': 0.7213114754098361}, 'argument': {'p': 0.7140786749482402, 'r': 0.6900760304121649, 'f1': 0.7018722018722019}, 'combined': 0.5062684734815883, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5689479060265578, 'r': 0.7416777629826897, 'f1': 0.6439306358381504}, 'argument': {'p': 0.6625770586133328, 'r': 0.680419381152411, 'f1': 0.671379698171212}, 'combined': 0.432321955932214, 'epoch': 16}
Test for Korean: {'event': {'p': 0.6309133489461358, 'r': 0.784965034965035, 'f1': 0.6995585562191639}, 'argument': {'p': 0.6861410529773848, 'r': 0.695580268249265, 'f1': 0.6908284186613552}, 'combined': 0.48327493115390574, 'epoch': 16}
Korean: {'event': {'p': 0.6229508196721312, 'r': 0.6031746031746031, 'f1': 0.6129032258064517}, 'argument': {'p': 0.6297713529856387, 'r': 0.522047305764411, 'f1': 0.570871873929428}, 'combined': 0.34988921305352044, 'epoch': 16}
--------------------
Dev for Russian: {'event': {'p': 0.5746421267893661, 'r': 0.748335552596538, 'f1': 0.6500867553499133}, 'argument': {'p': 0.6749622827872511, 'r': 0.651550306926734, 'f1': 0.6630496927418134}, 'combined': 0.4310398233902824, 'epoch': 23}
Test for Russian: {'event': {'p': 0.6262203626220363, 'r': 0.784965034965035, 'f1': 0.6966640806826998}, 'argument': {'p': 0.705025247603358, 'r': 0.6836050097637265, 'f1': 0.6941499203472439}, 'combined': 0.483589316114682, 'epoch': 23}
Russian: {'event': {'p': 0.5897435897435898, 'r': 0.6388888888888888, 'f1': 0.6133333333333332}, 'argument': {'p': 0.6040593604738342, 'r': 0.5798969860548808, 'f1': 0.5917316184233478}, 'combined': 0.36292872596631987, 'epoch': 23}