Command that produces this log: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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> trainable params:
>>> pad_vector: torch.Size([1, 1, 1024])
>>> xlmr.embeddings.word_embeddings.weight: torch.Size([250002, 1024])
>>> xlmr.embeddings.position_embeddings.weight: torch.Size([514, 1024])
>>> xlmr.embeddings.token_type_embeddings.weight: torch.Size([1, 1024])
>>> xlmr.embeddings.LayerNorm.weight: torch.Size([1024])
>>> xlmr.embeddings.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.0.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.0.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.0.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.1.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.1.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.1.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.2.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.2.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.2.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.3.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.3.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.3.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.4.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.4.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.4.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.5.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.5.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.5.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.6.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.6.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.6.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.7.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.7.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.7.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.8.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.8.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.8.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.9.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.9.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.9.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.10.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.10.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.10.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.11.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.11.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.11.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.12.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.12.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.12.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.13.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.13.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.13.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.14.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.14.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.14.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.15.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.15.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.15.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.16.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.16.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.16.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.17.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.17.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.17.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.18.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.18.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.18.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.19.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.19.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.19.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.20.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.20.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.20.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.21.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.21.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.21.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.22.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.22.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.22.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.23.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.23.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.23.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.pooler.dense.weight: torch.Size([1024, 1024])
>>> xlmr.pooler.dense.bias: torch.Size([1024])
>>> upos_embedding.weight: torch.Size([19, 50])
>>> dist_embedding.weight: torch.Size([104, 50])
>>> deprel_embedding.weight: torch.Size([38, 50])
>>> gcn_layer.W.0.weight: torch.Size([450, 50])
>>> gcn_layer.W.0.bias: torch.Size([450])
>>> gcn_layer.W.1.weight: torch.Size([450, 450])
>>> gcn_layer.W.1.bias: torch.Size([450])
>>> trans_rep.weight: torch.Size([1024, 2924])
>>> trans_rep.bias: torch.Size([1024])
>>> mention_label_ffn.layers.0.weight: torch.Size([350, 1024])
>>> mention_label_ffn.layers.0.bias: torch.Size([350])
>>> mention_label_ffn.layers.1.weight: torch.Size([11, 350])
>>> mention_label_ffn.layers.1.bias: torch.Size([11])
>>> trigger_label_ffn.layers.0.weight: torch.Size([450, 1024])
>>> trigger_label_ffn.layers.0.bias: torch.Size([450])
>>> trigger_label_ffn.layers.1.weight: torch.Size([233, 450])
>>> trigger_label_ffn.layers.1.bias: torch.Size([233])
>>> event_type_ffn.layers.0.weight: torch.Size([450, 1024])
>>> event_type_ffn.layers.0.bias: torch.Size([450])
>>> event_type_ffn.layers.1.weight: torch.Size([117, 450])
>>> event_type_ffn.layers.1.bias: torch.Size([117])
>>> role_type_ffn.layers.0.weight: torch.Size([350, 1024])
>>> role_type_ffn.layers.0.bias: torch.Size([350])
>>> role_type_ffn.layers.1.weight: torch.Size([6, 350])
>>> role_type_ffn.layers.1.bias: torch.Size([6])
>>> trigger_crf.transition: torch.Size([235, 235])
>>> mention_crf.transition: torch.Size([13, 13])
n_trainable_params: 564979817, n_nontrainable_params: 0
----------------------------------------------------------------------------------------------------
******************************
Epoch: 0
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:10:02.630484: step: 3078/1538, loss: 1.7917938232421875
==================================================
Loss: 4.226
--------------------
Dev Chinese: {'event': {'p': 0.6245259165613148, 'r': 0.6577896138482024, 'f1': 0.6407263294422828}, 'argument': {'p': 0.5767140210110092, 'r': 0.5176839912405891, 'f1': 0.5456070147407236}, 'combined': 0.3495847798727853, 'epoch': 0}
Test Chinese: {'event': {'p': 0.585609243697479, 'r': 0.6648777579010137, 'f1': 0.6227310807037141}, 'argument': {'p': 0.5785603391994711, 'r': 0.5440319929008127, 'f1': 0.5607651599742811}, 'combined': 0.3492058940917752, 'epoch': 0}
Dev Korean: {'event': {'p': 0.5764828303850156, 'r': 0.7376830892143809, 'f1': 0.647196261682243}, 'argument': {'p': 0.5676894906491934, 'r': 0.5148811659376406, 'f1': 0.539997320373623}, 'combined': 0.3494842470642373, 'epoch': 0}
Test Korean: {'event': {'p': 0.6096726917440156, 'r': 0.7441860465116279, 'f1': 0.6702470461868958}, 'argument': {'p': 0.5792853357616579, 'r': 0.5540380329923846, 'f1': 0.5663804644204328}, 'combined': 0.3796148332957573, 'epoch': 0}
Dev Russian: {'event': {'p': 0.5212669683257919, 'r': 0.7669773635153129, 'f1': 0.6206896551724138}, 'argument': {'p': 0.5679612273958372, 'r': 0.5242101505151684, 'f1': 0.5452093810944236}, 'combined': 0.338405822748263, 'epoch': 0}
Test Russian: {'event': {'p': 0.5568783068783069, 'r': 0.7531305903398927, 'f1': 0.6403041825095057}, 'argument': {'p': 0.5807825544679676, 'r': 0.5558579625527891, 'f1': 0.5680469815715241}, 'combined': 0.363722858162147, 'epoch': 0}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.5168020079619468, 'r': 0.47527327517929024, 'f1': 0.49516843553563267}, 'combined': 0.35434071533742517, 'epoch': 0}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.3752672022309446, 'r': 0.1876336011154723, 'f1': 0.2501781348206297}, 'combined': 0.15636133426289361, 'epoch': 0}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.4473510124770628, 'r': 0.4148163933878219, 'f1': 0.43046984219490947}, 'combined': 0.23914991233050523, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6245259165613148, 'r': 0.6577896138482024, 'f1': 0.6407263294422828}, 'argument': {'p': 0.5767140210110092, 'r': 0.5176839912405891, 'f1': 0.5456070147407236}, 'combined': 0.3495847798727853, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.585609243697479, 'r': 0.6648777579010137, 'f1': 0.6227310807037141}, 'argument': {'p': 0.5785603391994711, 'r': 0.5440319929008127, 'f1': 0.5607651599742811}, 'combined': 0.3492058940917752, 'epoch': 0}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.5168020079619468, 'r': 0.47527327517929024, 'f1': 0.49516843553563267}, 'combined': 0.35434071533742517, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.5764828303850156, 'r': 0.7376830892143809, 'f1': 0.647196261682243}, 'argument': {'p': 0.5676894906491934, 'r': 0.5148811659376406, 'f1': 0.539997320373623}, 'combined': 0.3494842470642373, 'epoch': 0}
Test for Korean: {'event': {'p': 0.6096726917440156, 'r': 0.7441860465116279, 'f1': 0.6702470461868958}, 'argument': {'p': 0.5792853357616579, 'r': 0.5540380329923846, 'f1': 0.5663804644204328}, 'combined': 0.3796148332957573, 'epoch': 0}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.3752672022309446, 'r': 0.1876336011154723, 'f1': 0.2501781348206297}, 'combined': 0.15636133426289361, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.5212669683257919, 'r': 0.7669773635153129, 'f1': 0.6206896551724138}, 'argument': {'p': 0.5679612273958372, 'r': 0.5242101505151684, 'f1': 0.5452093810944236}, 'combined': 0.338405822748263, 'epoch': 0}
Test for Russian: {'event': {'p': 0.5568783068783069, 'r': 0.7531305903398927, 'f1': 0.6403041825095057}, 'argument': {'p': 0.5807825544679676, 'r': 0.5558579625527891, 'f1': 0.5680469815715241}, 'combined': 0.363722858162147, 'epoch': 0}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.4473510124770628, 'r': 0.4148163933878219, 'f1': 0.43046984219490947}, 'combined': 0.23914991233050523, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:36:57.784133: step: 3078/1538, loss: 0.0702895000576973
==================================================
Loss: 1.774
--------------------
Dev Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6873720520028999, 'r': 0.6292527890693214, 'f1': 0.6570296524238245}, 'combined': 0.42608926875734404, 'epoch': 1}
Test Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.689546729056017, 'r': 0.6463401580550571, 'f1': 0.6672447287932048}, 'combined': 0.42557580579829085, 'epoch': 1}
Dev Korean: {'event': {'p': 0.5806451612903226, 'r': 0.7430093209054593, 'f1': 0.6518691588785047}, 'argument': {'p': 0.6871855445836553, 'r': 0.6222152749139279, 'f1': 0.6530885519134739}, 'combined': 0.4257282850090169, 'epoch': 1}
Test Korean: {'event': {'p': 0.6194430874450415, 'r': 0.7561121049493142, 'f1': 0.6809881847475832}, 'argument': {'p': 0.6889079901235811, 'r': 0.6621542817692672, 'f1': 0.6752662477448962}, 'combined': 0.4598483362731086, 'epoch': 1}
Dev Russian: {'event': {'p': 0.5266968325791855, 'r': 0.7749667110519307, 'f1': 0.6271551724137931}, 'argument': {'p': 0.6731255832730372, 'r': 0.627933406304176, 'f1': 0.6497446215140102}, 'combined': 0.4074907001305539, 'epoch': 1}
Test Russian: {'event': {'p': 0.5687830687830688, 'r': 0.7692307692307693, 'f1': 0.6539923954372625}, 'argument': {'p': 0.6948031399997472, 'r': 0.6642961267049999, 'f1': 0.6792072456096362}, 'combined': 0.4441963735545911, 'epoch': 1}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6562688743101115, 'r': 0.5787098255280074, 'f1': 0.6150539208510224}, 'combined': 0.44013032868238294, 'epoch': 1}
Korean: {'event': {'p': 0.7551020408163265, 'r': 0.5873015873015873, 'f1': 0.6607142857142858}, 'argument': {'p': 0.4516948634171273, 'r': 0.24944343203632402, 'f1': 0.32139826820064826}, 'combined': 0.21235242720399977, 'epoch': 1}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5727350427350427, 'r': 0.5206682206682206, 'f1': 0.5454619454619454}, 'combined': 0.3151557907113462, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6873720520028999, 'r': 0.6292527890693214, 'f1': 0.6570296524238245}, 'combined': 0.42608926875734404, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.689546729056017, 'r': 0.6463401580550571, 'f1': 0.6672447287932048}, 'combined': 0.42557580579829085, 'epoch': 1}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6562688743101115, 'r': 0.5787098255280074, 'f1': 0.6150539208510224}, 'combined': 0.44013032868238294, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.5806451612903226, 'r': 0.7430093209054593, 'f1': 0.6518691588785047}, 'argument': {'p': 0.6871855445836553, 'r': 0.6222152749139279, 'f1': 0.6530885519134739}, 'combined': 0.4257282850090169, 'epoch': 1}
Test for Korean: {'event': {'p': 0.6194430874450415, 'r': 0.7561121049493142, 'f1': 0.6809881847475832}, 'argument': {'p': 0.6889079901235811, 'r': 0.6621542817692672, 'f1': 0.6752662477448962}, 'combined': 0.4598483362731086, 'epoch': 1}
Korean: {'event': {'p': 0.7551020408163265, 'r': 0.5873015873015873, 'f1': 0.6607142857142858}, 'argument': {'p': 0.4516948634171273, 'r': 0.24944343203632402, 'f1': 0.32139826820064826}, 'combined': 0.21235242720399977, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.5266968325791855, 'r': 0.7749667110519307, 'f1': 0.6271551724137931}, 'argument': {'p': 0.6731255832730372, 'r': 0.627933406304176, 'f1': 0.6497446215140102}, 'combined': 0.4074907001305539, 'epoch': 1}
Test for Russian: {'event': {'p': 0.5687830687830688, 'r': 0.7692307692307693, 'f1': 0.6539923954372625}, 'argument': {'p': 0.6948031399997472, 'r': 0.6642961267049999, 'f1': 0.6792072456096362}, 'combined': 0.4441963735545911, 'epoch': 1}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5727350427350427, 'r': 0.5206682206682206, 'f1': 0.5454619454619454}, 'combined': 0.3151557907113462, '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
2023-01-21 14:42:31.146272: step: 2/1538, loss: 1.0345646142959595
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2023-01-21 15:03:42.969086: step: 3078/1538, loss: 0.9846875667572021
==================================================
Loss: 1.404
--------------------
Dev Chinese: {'event': {'p': 0.6359039190897599, 'r': 0.6697736351531292, 'f1': 0.6523994811932554}, 'argument': {'p': 0.6799855580233857, 'r': 0.6307833509794172, 'f1': 0.6544610052338112}, 'combined': 0.42697002027575487, 'epoch': 2}
Test Chinese: {'event': {'p': 0.601890756302521, 'r': 0.6833631484794276, 'f1': 0.6400446802569115}, 'argument': {'p': 0.685105521075721, 'r': 0.6586014388735215, 'f1': 0.671592088765705}, 'combined': 0.42984894371711696, 'epoch': 2}
Dev Korean: {'event': {'p': 0.5816857440166493, 'r': 0.7443408788282291, 'f1': 0.6530373831775701}, 'argument': {'p': 0.6862015119746094, 'r': 0.6267706677788463, 'f1': 0.6551410403409166}, 'combined': 0.42783159059646303, 'epoch': 2}
Test Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6810659714474888, 'r': 0.6709008076945412, 'f1': 0.6759451746696881}, 'combined': 0.46611901411164314, 'epoch': 2}
Dev Russian: {'event': {'p': 0.530316742081448, 'r': 0.7802929427430093, 'f1': 0.6314655172413793}, 'argument': {'p': 0.6729163359276138, 'r': 0.6301160036071295, 'f1': 0.6508132446015242}, 'combined': 0.41096612212984185, 'epoch': 2}
Test Russian: {'event': {'p': 0.5749559082892416, 'r': 0.7775790101371497, 'f1': 0.6610899873257288}, 'argument': {'p': 0.681667081667301, 'r': 0.6753983448808509, 'f1': 0.6785182345834657}, 'combined': 0.4485616111010592, 'epoch': 2}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.7097496706192357, 'r': 0.5936088154269971, 'f1': 0.6465046504650463}, 'combined': 0.462636355378657, 'epoch': 2}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6381515444015444, 'r': 0.35241204690831557, 'f1': 0.4540693681318681}, 'combined': 0.2919017366562009, 'epoch': 2}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.60316015003515, 'r': 0.5263943127579491, 'f1': 0.5621686835279068}, 'combined': 0.3123159352932815, 'epoch': 2}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6359039190897599, 'r': 0.6697736351531292, 'f1': 0.6523994811932554}, 'argument': {'p': 0.6799855580233857, 'r': 0.6307833509794172, 'f1': 0.6544610052338112}, 'combined': 0.42697002027575487, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.601890756302521, 'r': 0.6833631484794276, 'f1': 0.6400446802569115}, 'argument': {'p': 0.685105521075721, 'r': 0.6586014388735215, 'f1': 0.671592088765705}, 'combined': 0.42984894371711696, 'epoch': 2}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.7097496706192357, 'r': 0.5936088154269971, 'f1': 0.6465046504650463}, 'combined': 0.462636355378657, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5816857440166493, 'r': 0.7443408788282291, 'f1': 0.6530373831775701}, 'argument': {'p': 0.6862015119746094, 'r': 0.6267706677788463, 'f1': 0.6551410403409166}, 'combined': 0.42783159059646303, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6810659714474888, 'r': 0.6709008076945412, 'f1': 0.6759451746696881}, 'combined': 0.46611901411164314, 'epoch': 2}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6381515444015444, 'r': 0.35241204690831557, 'f1': 0.4540693681318681}, 'combined': 0.2919017366562009, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.530316742081448, 'r': 0.7802929427430093, 'f1': 0.6314655172413793}, 'argument': {'p': 0.6729163359276138, 'r': 0.6301160036071295, 'f1': 0.6508132446015242}, 'combined': 0.41096612212984185, 'epoch': 2}
Test for Russian: {'event': {'p': 0.5749559082892416, 'r': 0.7775790101371497, 'f1': 0.6610899873257288}, 'argument': {'p': 0.681667081667301, 'r': 0.6753983448808509, 'f1': 0.6785182345834657}, 'combined': 0.4485616111010592, 'epoch': 2}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.60316015003515, 'r': 0.5263943127579491, 'f1': 0.5621686835279068}, 'combined': 0.3123159352932815, 'epoch': 2}
******************************
Epoch: 3
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 15:09:12.837305: step: 2/1538, loss: 0.557913064956665
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2023-01-21 15:30:23.149538: step: 3072/1538, loss: 0.9517433047294617
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2023-01-21 15:30:24.775266: step: 3076/1538, loss: 0.4228972792625427
2023-01-21 15:30:25.351208: step: 3078/1538, loss: 1.1764159202575684
==================================================
Loss: 1.015
--------------------
Dev Chinese: {'event': {'p': 0.6308470290771175, 'r': 0.6644474034620506, 'f1': 0.6472114137483787}, 'argument': {'p': 0.6640003184339204, 'r': 0.6618479673206823, 'f1': 0.6629223958390601}, 'combined': 0.42905094101646035, 'epoch': 3}
Test Chinese: {'event': {'p': 0.6003151260504201, 'r': 0.6815742397137746, 'f1': 0.638369170622731}, 'argument': {'p': 0.6520923122048193, 'r': 0.7022532592974977, 'f1': 0.6762438793235164}, 'combined': 0.4316932443824513, 'epoch': 3}
Dev Korean: {'event': {'p': 0.5816857440166493, 'r': 0.7443408788282291, 'f1': 0.6530373831775701}, 'argument': {'p': 0.6602525423069414, 'r': 0.6640884321678386, 'f1': 0.6621649320022548}, 'combined': 0.4324184544267061, 'epoch': 3}
Test Korean: {'event': {'p': 0.6243282852955545, 'r': 0.7620751341681574, 'f1': 0.686358754027927}, 'argument': {'p': 0.653674204691147, 'r': 0.7110379139477756, 'f1': 0.6811504588507848}, 'combined': 0.46751358024237544, 'epoch': 3}
Dev Russian: {'event': {'p': 0.5221719457013575, 'r': 0.7683089214380826, 'f1': 0.6217672413793104}, 'argument': {'p': 0.6544206925735921, 'r': 0.668285537755236, 'f1': 0.6612804482609463}, 'combined': 0.4111625200932824, 'epoch': 3}
Test Russian: {'event': {'p': 0.5701058201058201, 'r': 0.7710196779964222, 'f1': 0.6555133079847908}, 'argument': {'p': 0.657788418340658, 'r': 0.7186450339150319, 'f1': 0.68687139132321}, 'combined': 0.4502533378863931, 'epoch': 3}
Chinese: {'event': {'p': 0.6909090909090909, 'r': 0.7037037037037037, 'f1': 0.6972477064220184}, 'argument': {'p': 0.5914997431946584, 'r': 0.6345179063360881, 'f1': 0.6122541201488568}, 'combined': 0.4268927810212213, 'epoch': 3}
Korean: {'event': {'p': 0.7551020408163265, 'r': 0.5873015873015873, 'f1': 0.6607142857142858}, 'argument': {'p': 0.6005099302200751, 'r': 0.4033275650731848, 'f1': 0.48255262249827463}, 'combined': 0.31882941129350295, 'epoch': 3}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.4987333547678375, 'r': 0.5259369923006286, 'f1': 0.5119740633014969}, 'combined': 0.29580723657419816, 'epoch': 3}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6359039190897599, 'r': 0.6697736351531292, 'f1': 0.6523994811932554}, 'argument': {'p': 0.6799855580233857, 'r': 0.6307833509794172, 'f1': 0.6544610052338112}, 'combined': 0.42697002027575487, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.601890756302521, 'r': 0.6833631484794276, 'f1': 0.6400446802569115}, 'argument': {'p': 0.685105521075721, 'r': 0.6586014388735215, 'f1': 0.671592088765705}, 'combined': 0.42984894371711696, 'epoch': 2}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.7097496706192357, 'r': 0.5936088154269971, 'f1': 0.6465046504650463}, 'combined': 0.462636355378657, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5816857440166493, 'r': 0.7443408788282291, 'f1': 0.6530373831775701}, 'argument': {'p': 0.6602525423069414, 'r': 0.6640884321678386, 'f1': 0.6621649320022548}, 'combined': 0.4324184544267061, 'epoch': 3}
Test for Korean: {'event': {'p': 0.6243282852955545, 'r': 0.7620751341681574, 'f1': 0.686358754027927}, 'argument': {'p': 0.653674204691147, 'r': 0.7110379139477756, 'f1': 0.6811504588507848}, 'combined': 0.46751358024237544, 'epoch': 3}
Korean: {'event': {'p': 0.7551020408163265, 'r': 0.5873015873015873, 'f1': 0.6607142857142858}, 'argument': {'p': 0.6005099302200751, 'r': 0.4033275650731848, 'f1': 0.48255262249827463}, 'combined': 0.31882941129350295, 'epoch': 3}
--------------------
Dev for Russian: {'event': {'p': 0.530316742081448, 'r': 0.7802929427430093, 'f1': 0.6314655172413793}, 'argument': {'p': 0.6729163359276138, 'r': 0.6301160036071295, 'f1': 0.6508132446015242}, 'combined': 0.41096612212984185, 'epoch': 2}
Test for Russian: {'event': {'p': 0.5749559082892416, 'r': 0.7775790101371497, 'f1': 0.6610899873257288}, 'argument': {'p': 0.681667081667301, 'r': 0.6753983448808509, 'f1': 0.6785182345834657}, 'combined': 0.4485616111010592, 'epoch': 2}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.60316015003515, 'r': 0.5263943127579491, 'f1': 0.5621686835279068}, 'combined': 0.3123159352932815, 'epoch': 2}
******************************
Epoch: 4
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 15:35:54.829628: step: 2/1538, loss: 0.6802728772163391
2023-01-21 15:35:55.653932: step: 4/1538, loss: 1.422450304031372
2023-01-21 15:35:56.479222: step: 6/1538, loss: 0.5196142792701721
2023-01-21 15:35:57.307068: step: 8/1538, loss: 0.5517090559005737
2023-01-21 15:35:58.140972: step: 10/1538, loss: 0.3469089865684509
2023-01-21 15:35:58.951761: step: 12/1538, loss: 0.27840688824653625
2023-01-21 15:35:59.783491: step: 14/1538, loss: 1.0566987991333008
2023-01-21 15:36:00.586584: step: 16/1538, loss: 0.5264607667922974
2023-01-21 15:36:01.434902: step: 18/1538, loss: 0.3756818175315857
2023-01-21 15:36:02.283719: step: 20/1538, loss: 0.6060910224914551
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2023-01-21 15:36:12.169072: step: 44/1538, loss: 0.6303220987319946
2023-01-21 15:36:12.988571: step: 46/1538, loss: 2.1706464290618896
2023-01-21 15:36:13.783366: step: 48/1538, loss: 0.3105222284793854
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==================================================
Loss: 0.832
--------------------
Dev Chinese: {'event': {'p': 0.6308470290771175, 'r': 0.6644474034620506, 'f1': 0.6472114137483787}, 'argument': {'p': 0.6624190684265232, 'r': 0.6785232597172816, 'f1': 0.6703744615621501}, 'combined': 0.43387400300844725, 'epoch': 4}
Test Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6740418220905992, 'r': 0.6850796248540915, 'f1': 0.679515902941362}, 'combined': 0.43643858608353325, 'epoch': 4}
Dev Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6557579829804582, 'r': 0.6805395346628592, 'f1': 0.6679189726790972}, 'combined': 0.44163801230884225, 'epoch': 4}
Test Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6740767145213707, 'r': 0.6959562763877412, 'f1': 0.6848417860750591}, 'combined': 0.4715183511000138, 'epoch': 4}
Dev Russian: {'event': {'p': 0.5285067873303168, 'r': 0.7776298268974701, 'f1': 0.6293103448275863}, 'argument': {'p': 0.65316747255161, 'r': 0.6840729956172582, 'f1': 0.6682630988575631}, 'combined': 0.4205448811776044, 'epoch': 4}
Test Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.6797831453833191, 'r': 0.7075766641042726, 'f1': 0.6934015055579432}, 'combined': 0.45558851772019987, 'epoch': 4}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6535763035763035, 'r': 0.659517906336088, 'f1': 0.6565336624160152}, 'combined': 0.48185956874569924, 'epoch': 4}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6105590062111801, 'r': 0.4070393374741201, 'f1': 0.48844720496894406}, 'combined': 0.3140017746228926, 'epoch': 4}
Russian: {'event': {'p': 0.4444444444444444, 'r': 0.6666666666666666, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5625841653619431, 'r': 0.5523553623553624, 'f1': 0.5574228427439436}, 'combined': 0.29729218279676994, 'epoch': 4}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6308470290771175, 'r': 0.6644474034620506, 'f1': 0.6472114137483787}, 'argument': {'p': 0.6624190684265232, 'r': 0.6785232597172816, 'f1': 0.6703744615621501}, 'combined': 0.43387400300844725, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6740418220905992, 'r': 0.6850796248540915, 'f1': 0.679515902941362}, 'combined': 0.43643858608353325, 'epoch': 4}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6535763035763035, 'r': 0.659517906336088, 'f1': 0.6565336624160152}, 'combined': 0.48185956874569924, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6557579829804582, 'r': 0.6805395346628592, 'f1': 0.6679189726790972}, 'combined': 0.44163801230884225, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6740767145213707, 'r': 0.6959562763877412, 'f1': 0.6848417860750591}, 'combined': 0.4715183511000138, 'epoch': 4}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6105590062111801, 'r': 0.4070393374741201, 'f1': 0.48844720496894406}, 'combined': 0.3140017746228926, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.530316742081448, 'r': 0.7802929427430093, 'f1': 0.6314655172413793}, 'argument': {'p': 0.6729163359276138, 'r': 0.6301160036071295, 'f1': 0.6508132446015242}, 'combined': 0.41096612212984185, 'epoch': 2}
Test for Russian: {'event': {'p': 0.5749559082892416, 'r': 0.7775790101371497, 'f1': 0.6610899873257288}, 'argument': {'p': 0.681667081667301, 'r': 0.6753983448808509, 'f1': 0.6785182345834657}, 'combined': 0.4485616111010592, 'epoch': 2}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.60316015003515, 'r': 0.5263943127579491, 'f1': 0.5621686835279068}, 'combined': 0.3123159352932815, 'epoch': 2}
******************************
Epoch: 5
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 16:23:50.484055: step: 3078/1538, loss: 0.0054840086959302425
==================================================
Loss: 0.712
--------------------
Dev Chinese: {'event': {'p': 0.638432364096081, 'r': 0.6724367509986684, 'f1': 0.6549935149156939}, 'argument': {'p': 0.6860099367391231, 'r': 0.6538445557117432, 'f1': 0.6695411551452204}, 'combined': 0.43854511458928186, 'epoch': 5}
Test Chinese: {'event': {'p': 0.604516806722689, 'r': 0.6863446630888491, 'f1': 0.6428371963138788}, 'argument': {'p': 0.7058189643912194, 'r': 0.6772360001272918, 'f1': 0.6912321267357686}, 'combined': 0.44434972235290127, 'epoch': 5}
Dev Korean: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.680575642093921, 'r': 0.6613419826434407, 'f1': 0.6708209746769188}, 'combined': 0.442773190061284, 'epoch': 5}
Test Korean: {'event': {'p': 0.6257938446507083, 'r': 0.7638640429338104, 'f1': 0.6879699248120301}, 'argument': {'p': 0.7041457005839209, 'r': 0.6885197210980086, 'f1': 0.6962450475619414}, 'combined': 0.47899565302193714, 'epoch': 5}
Dev Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6669758812615955, 'r': 0.5942148760330578, 'f1': 0.6284965034965034}, 'combined': 0.44974979149291067, 'epoch': 5}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6068783068783069, 'r': 0.39579020013802624, 'f1': 0.4791144527986633}, 'combined': 0.3080021482277121, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6308470290771175, 'r': 0.6644474034620506, 'f1': 0.6472114137483787}, 'argument': {'p': 0.6624190684265232, 'r': 0.6785232597172816, 'f1': 0.6703744615621501}, 'combined': 0.43387400300844725, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6740418220905992, 'r': 0.6850796248540915, 'f1': 0.679515902941362}, 'combined': 0.43643858608353325, 'epoch': 4}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6535763035763035, 'r': 0.659517906336088, 'f1': 0.6565336624160152}, 'combined': 0.48185956874569924, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6557579829804582, 'r': 0.6805395346628592, 'f1': 0.6679189726790972}, 'combined': 0.44163801230884225, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6740767145213707, 'r': 0.6959562763877412, 'f1': 0.6848417860750591}, 'combined': 0.4715183511000138, 'epoch': 4}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6105590062111801, 'r': 0.4070393374741201, 'f1': 0.48844720496894406}, 'combined': 0.3140017746228926, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, '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
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2023-01-21 16:50:20.831195: step: 3078/1538, loss: 0.0048278807662427425
==================================================
Loss: 0.585
--------------------
Dev Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6872044301823219, 'r': 0.6521486576305112, 'f1': 0.6692177749677453}, 'combined': 0.43572934245630107, 'epoch': 6}
Test Chinese: {'event': {'p': 0.6029411764705882, 'r': 0.6845557543231962, 'f1': 0.6411616866796984}, 'argument': {'p': 0.6885980867841335, 'r': 0.6767820553570235, 'f1': 0.6826389429655265}, 'combined': 0.4376819360650234, 'epoch': 6}
Dev Korean: {'event': {'p': 0.5848074921956296, 'r': 0.748335552596538, 'f1': 0.6565420560747663}, 'argument': {'p': 0.6850826623658798, 'r': 0.656205631539411, 'f1': 0.6703332954701037}, 'combined': 0.44010200006331573, 'epoch': 6}
Test Korean: {'event': {'p': 0.6233512457254519, 'r': 0.7608825283243887, 'f1': 0.6852846401718582}, 'argument': {'p': 0.6896528607744112, 'r': 0.6913267754850286, 'f1': 0.690488803635956}, 'combined': 0.47318137134236293, 'epoch': 6}
Dev Russian: {'event': {'p': 0.5266968325791855, 'r': 0.7749667110519307, 'f1': 0.6271551724137931}, 'argument': {'p': 0.672919070022266, 'r': 0.6600879860599771, 'f1': 0.6664417741610892}, 'combined': 0.41796240577775207, 'epoch': 6}
Test Russian: {'event': {'p': 0.5687830687830688, 'r': 0.7692307692307693, 'f1': 0.6539923954372625}, 'argument': {'p': 0.6950479206194136, 'r': 0.698125448878287, 'f1': 0.6965832856097369}, 'combined': 0.45556017157747064, 'epoch': 6}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6989378319275225, 'r': 0.6163360881542699, 'f1': 0.6550431854779679}, 'combined': 0.48076564071777467, 'epoch': 6}
Korean: {'event': {'p': 0.6938775510204082, 'r': 0.5396825396825397, 'f1': 0.6071428571428572}, 'argument': {'p': 0.6911764705882353, 'r': 0.3357142857142857, 'f1': 0.4519230769230769}, 'combined': 0.27438186813186816, 'epoch': 6}
Russian: {'event': {'p': 0.42592592592592593, 'r': 0.6388888888888888, 'f1': 0.5111111111111111}, 'argument': {'p': 0.5454871073097673, 'r': 0.48597942287597456, 'f1': 0.5140166972726654}, 'combined': 0.26271964527269565, 'epoch': 6}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6872044301823219, 'r': 0.6521486576305112, 'f1': 0.6692177749677453}, 'combined': 0.43572934245630107, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.6029411764705882, 'r': 0.6845557543231962, 'f1': 0.6411616866796984}, 'argument': {'p': 0.6885980867841335, 'r': 0.6767820553570235, 'f1': 0.6826389429655265}, 'combined': 0.4376819360650234, 'epoch': 6}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6989378319275225, 'r': 0.6163360881542699, 'f1': 0.6550431854779679}, 'combined': 0.48076564071777467, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6557579829804582, 'r': 0.6805395346628592, 'f1': 0.6679189726790972}, 'combined': 0.44163801230884225, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6740767145213707, 'r': 0.6959562763877412, 'f1': 0.6848417860750591}, 'combined': 0.4715183511000138, 'epoch': 4}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6105590062111801, 'r': 0.4070393374741201, 'f1': 0.48844720496894406}, 'combined': 0.3140017746228926, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 7
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 16:55:43.224939: step: 2/1538, loss: 0.2325260490179062
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==================================================
Loss: 0.481
--------------------
Dev Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6945446947440382, 'r': 0.6551458871005352, 'f1': 0.6742702457731947}, 'combined': 0.4372699388931224, 'epoch': 7}
Test Chinese: {'event': {'p': 0.6055672268907563, 'r': 0.6875372689326178, 'f1': 0.6439542027366657}, 'argument': {'p': 0.6854141684281151, 'r': 0.6676274703439059, 'f1': 0.6764039099651644}, 'combined': 0.43557314056958085, 'epoch': 7}
Dev Korean: {'event': {'p': 0.5806451612903226, 'r': 0.7430093209054593, 'f1': 0.6518691588785047}, 'argument': {'p': 0.6885724674420276, 'r': 0.6530429287876788, 'f1': 0.6703372398447192}, 'combined': 0.43697217270251554, 'epoch': 7}
Test Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6927779527569125, 'r': 0.682664406001337, 'f1': 0.6876839972219352}, 'combined': 0.4742138842282303, 'epoch': 7}
Dev Russian: {'event': {'p': 0.530316742081448, 'r': 0.7802929427430093, 'f1': 0.6314655172413793}, 'argument': {'p': 0.6817774774723526, 'r': 0.6528886013082699, 'f1': 0.6670203892153753}, 'combined': 0.42120037508643315, 'epoch': 7}
Test Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.6962895361554382, 'r': 0.6929648248641094, 'f1': 0.6946232022227585}, 'combined': 0.45639121423609375, 'epoch': 7}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.7136569779426921, 'r': 0.635803489439853, 'f1': 0.6724844599844598}, 'combined': 0.4935665761353834, 'epoch': 7}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.5899470899470899, 'r': 0.3169864960909737, 'f1': 0.4123901987979657}, 'combined': 0.26510798494154936, 'epoch': 7}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.6102302459445316, 'r': 0.5436596736596736, 'f1': 0.5750246548323471}, 'combined': 0.28112316458470304, 'epoch': 7}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6945446947440382, 'r': 0.6551458871005352, 'f1': 0.6742702457731947}, 'combined': 0.4372699388931224, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6055672268907563, 'r': 0.6875372689326178, 'f1': 0.6439542027366657}, 'argument': {'p': 0.6854141684281151, 'r': 0.6676274703439059, 'f1': 0.6764039099651644}, 'combined': 0.43557314056958085, 'epoch': 7}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.7136569779426921, 'r': 0.635803489439853, 'f1': 0.6724844599844598}, 'combined': 0.4935665761353834, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6557579829804582, 'r': 0.6805395346628592, 'f1': 0.6679189726790972}, 'combined': 0.44163801230884225, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6740767145213707, 'r': 0.6959562763877412, 'f1': 0.6848417860750591}, 'combined': 0.4715183511000138, 'epoch': 4}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6105590062111801, 'r': 0.4070393374741201, 'f1': 0.48844720496894406}, 'combined': 0.3140017746228926, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, '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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2023-01-21 17:43:25.844227: step: 3078/1538, loss: 0.03214036673307419
==================================================
Loss: 0.376
--------------------
Dev Chinese: {'event': {'p': 0.6333754740834386, 'r': 0.6671105193075899, 'f1': 0.649805447470817}, 'argument': {'p': 0.6644602496432934, 'r': 0.6590930424571603, 'f1': 0.6617657636350077}, 'combined': 0.4300189981597131, 'epoch': 8}
Test Chinese: {'event': {'p': 0.6008403361344538, 'r': 0.6821705426356589, 'f1': 0.6389276738341245}, 'argument': {'p': 0.6815677534156046, 'r': 0.6636970767055087, 'f1': 0.672513716838866}, 'combined': 0.4296876247213977, 'epoch': 8}
Dev Korean: {'event': {'p': 0.5837669094693028, 'r': 0.7470039946737683, 'f1': 0.655373831775701}, 'argument': {'p': 0.6495550838685014, 'r': 0.6575684629873435, 'f1': 0.653537210239134}, 'combined': 0.42831118568242316, 'epoch': 8}
Test Korean: {'event': {'p': 0.621885686370298, 'r': 0.7590936195587359, 'f1': 0.6836734693877551}, 'argument': {'p': 0.6784059909015469, 'r': 0.6840456666139255, 'f1': 0.681214156438221}, 'combined': 0.4657280457281715, 'epoch': 8}
Dev Russian: {'event': {'p': 0.5248868778280543, 'r': 0.7723035952063915, 'f1': 0.625}, 'argument': {'p': 0.6496284416097321, 'r': 0.6615566474584983, 'f1': 0.6555382874746212}, 'combined': 0.4097114296716382, 'epoch': 8}
Test Russian: {'event': {'p': 0.568342151675485, 'r': 0.7686344663088849, 'f1': 0.6534854245880861}, 'argument': {'p': 0.6816352772837962, 'r': 0.6886049631455938, 'f1': 0.6851023946657078}, 'combined': 0.4477044292644346, 'epoch': 8}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6414283017086756, 'r': 0.6239348025711662, 'f1': 0.6325606293348228}, 'combined': 0.45265806502858874, 'epoch': 8}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.5992266237164197, 'r': 0.41945863660149374, 'f1': 0.49348074894293376}, 'combined': 0.3172376243204574, 'epoch': 8}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.5886480803147469, 'r': 0.5779453879453879, 'f1': 0.5832476392109418}, 'combined': 0.28514329028090485, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6945446947440382, 'r': 0.6551458871005352, 'f1': 0.6742702457731947}, 'combined': 0.4372699388931224, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6055672268907563, 'r': 0.6875372689326178, 'f1': 0.6439542027366657}, 'argument': {'p': 0.6854141684281151, 'r': 0.6676274703439059, 'f1': 0.6764039099651644}, 'combined': 0.43557314056958085, 'epoch': 7}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.7136569779426921, 'r': 0.635803489439853, 'f1': 0.6724844599844598}, 'combined': 0.4935665761353834, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6557579829804582, 'r': 0.6805395346628592, 'f1': 0.6679189726790972}, 'combined': 0.44163801230884225, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6740767145213707, 'r': 0.6959562763877412, 'f1': 0.6848417860750591}, 'combined': 0.4715183511000138, 'epoch': 4}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6105590062111801, 'r': 0.4070393374741201, 'f1': 0.48844720496894406}, 'combined': 0.3140017746228926, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, '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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2023-01-21 18:09:54.398401: step: 3078/1538, loss: 0.18547794222831726
==================================================
Loss: 0.332
--------------------
Dev Chinese: {'event': {'p': 0.6333754740834386, 'r': 0.6671105193075899, 'f1': 0.649805447470817}, 'argument': {'p': 0.6841529097538968, 'r': 0.67084685640374, 'f1': 0.6774345505746441}, 'combined': 0.44020066126834845, 'epoch': 9}
Test Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6825195808936952, 'r': 0.6862560019569819, 'f1': 0.6843826916695652}, 'combined': 0.43956442078748953, 'epoch': 9}
Dev Korean: {'event': {'p': 0.5848074921956296, 'r': 0.748335552596538, 'f1': 0.6565420560747663}, 'argument': {'p': 0.6828920187987633, 'r': 0.6640741096512991, 'f1': 0.673351615594957}, 'combined': 0.4420836541639788, 'epoch': 9}
Test Korean: {'event': {'p': 0.6243282852955545, 'r': 0.7620751341681574, 'f1': 0.686358754027927}, 'argument': {'p': 0.6829949259743664, 'r': 0.7017103609906601, 'f1': 0.6922261662056709}, 'combined': 0.475115488942453, 'epoch': 9}
Dev Russian: {'event': {'p': 0.5257918552036199, 'r': 0.7736351531291611, 'f1': 0.6260775862068966}, 'argument': {'p': 0.6735223091719424, 'r': 0.6635336308579517, 'f1': 0.6684906590500601}, 'combined': 0.4185270182199191, 'epoch': 9}
Test Russian: {'event': {'p': 0.5674603174603174, 'r': 0.7674418604651163, 'f1': 0.6524714828897339}, 'argument': {'p': 0.6859882924937469, 'r': 0.7075058250233838, 'f1': 0.6965809280945232}, 'combined': 0.45449919110654063, 'epoch': 9}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6528016009148084, 'r': 0.6290633608815426, 'f1': 0.6407126823793489}, 'combined': 0.4584916442714606, 'epoch': 9}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.637531328320802, 'r': 0.36158493248045487, 'f1': 0.4614512471655329}, 'combined': 0.2884070294784581, 'epoch': 9}
Russian: {'event': {'p': 0.4444444444444444, 'r': 0.6666666666666666, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5875716344107148, 'r': 0.5768885137850654, 'f1': 0.5821810689574055}, 'combined': 0.31049657011061627, 'epoch': 9}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6945446947440382, 'r': 0.6551458871005352, 'f1': 0.6742702457731947}, 'combined': 0.4372699388931224, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6055672268907563, 'r': 0.6875372689326178, 'f1': 0.6439542027366657}, 'argument': {'p': 0.6854141684281151, 'r': 0.6676274703439059, 'f1': 0.6764039099651644}, 'combined': 0.43557314056958085, 'epoch': 7}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.7136569779426921, 'r': 0.635803489439853, 'f1': 0.6724844599844598}, 'combined': 0.4935665761353834, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6557579829804582, 'r': 0.6805395346628592, 'f1': 0.6679189726790972}, 'combined': 0.44163801230884225, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6740767145213707, 'r': 0.6959562763877412, 'f1': 0.6848417860750591}, 'combined': 0.4715183511000138, 'epoch': 4}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6105590062111801, 'r': 0.4070393374741201, 'f1': 0.48844720496894406}, 'combined': 0.3140017746228926, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, '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
2023-01-21 18:15:09.905446: step: 2/1538, loss: 0.06616567075252533
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2023-01-21 18:36:22.457299: step: 3078/1538, loss: 0.041747864335775375
==================================================
Loss: 0.284
--------------------
Dev Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6708260611396356, 'r': 0.663764734180271, 'f1': 0.6672767169008015}, 'combined': 0.4344655147655024, 'epoch': 10}
Test Chinese: {'event': {'p': 0.6024159663865546, 'r': 0.6839594514013119, 'f1': 0.6406031834683048}, 'argument': {'p': 0.6832271168656409, 'r': 0.6832271168656409, 'f1': 0.6832271168656409}, 'combined': 0.43767746609600106, 'epoch': 10}
Dev Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6631564171166042, 'r': 0.6583439612594445, 'f1': 0.6607414265225889}, 'combined': 0.436892111462366, 'epoch': 10}
Test Korean: {'event': {'p': 0.6238397655105031, 'r': 0.7614788312462731, 'f1': 0.6858216970998926}, 'argument': {'p': 0.6822516386598142, 'r': 0.7004670950317995, 'f1': 0.6912393847978476}, 'combined': 0.47406696798434556, 'epoch': 10}
Dev Russian: {'event': {'p': 0.5266968325791855, 'r': 0.7749667110519307, 'f1': 0.6271551724137931}, 'argument': {'p': 0.667098986872297, 'r': 0.6638011811462334, 'f1': 0.6654459982322595}, 'combined': 0.4173378997534214, 'epoch': 10}
Test Russian: {'event': {'p': 0.568342151675485, 'r': 0.7686344663088849, 'f1': 0.6534854245880861}, 'argument': {'p': 0.6865649916774251, 'r': 0.707625267495751, 'f1': 0.6969360640592592}, 'combined': 0.4554375597325146, 'epoch': 10}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6788600288600288, 'r': 0.6480027548209366, 'f1': 0.6630725863284003}, 'combined': 0.47449230948270843, 'epoch': 10}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.521540442418102, 'r': 0.3552521854152289, 'f1': 0.42262759989053095}, 'combined': 0.2716891713581984, 'epoch': 10}
Russian: {'event': {'p': 0.4444444444444444, 'r': 0.6666666666666666, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5420541180788415, 'r': 0.5223430592396109, 'f1': 0.5320160788551592}, 'combined': 0.28374190872275157, 'epoch': 10}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6945446947440382, 'r': 0.6551458871005352, 'f1': 0.6742702457731947}, 'combined': 0.4372699388931224, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6055672268907563, 'r': 0.6875372689326178, 'f1': 0.6439542027366657}, 'argument': {'p': 0.6854141684281151, 'r': 0.6676274703439059, 'f1': 0.6764039099651644}, 'combined': 0.43557314056958085, 'epoch': 7}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.7136569779426921, 'r': 0.635803489439853, 'f1': 0.6724844599844598}, 'combined': 0.4935665761353834, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6557579829804582, 'r': 0.6805395346628592, 'f1': 0.6679189726790972}, 'combined': 0.44163801230884225, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6740767145213707, 'r': 0.6959562763877412, 'f1': 0.6848417860750591}, 'combined': 0.4715183511000138, 'epoch': 4}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6105590062111801, 'r': 0.4070393374741201, 'f1': 0.48844720496894406}, 'combined': 0.3140017746228926, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
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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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==================================================
Loss: 0.241
--------------------
Dev Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6812506658803518, 'r': 0.6464986076208682, 'f1': 0.6634198424692748}, 'combined': 0.4319542943185161, 'epoch': 11}
Test Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6829404237028909, 'r': 0.673990348983814, 'f1': 0.6784358697905962}, 'combined': 0.43574490380295205, 'epoch': 11}
Dev Korean: {'event': {'p': 0.5910509885535901, 'r': 0.7563249001331558, 'f1': 0.6635514018691588}, 'argument': {'p': 0.6737506808524313, 'r': 0.6434587720686381, 'f1': 0.658256414453006}, 'combined': 0.43678696659965816, 'epoch': 11}
Test Korean: {'event': {'p': 0.626770884220811, 'r': 0.7650566487775791, 'f1': 0.6890440386680988}, 'argument': {'p': 0.6857860195859213, 'r': 0.6881639184055673, 'f1': 0.6869729112778319}, 'combined': 0.4733545892424588, 'epoch': 11}
Dev Russian: {'event': {'p': 0.5312217194570136, 'r': 0.7816245006657789, 'f1': 0.6325431034482759}, 'argument': {'p': 0.6711015271592774, 'r': 0.6507220316311355, 'f1': 0.6607546767943262}, 'combined': 0.41795581387744557, 'epoch': 11}
Test Russian: {'event': {'p': 0.5705467372134039, 'r': 0.7716159809183065, 'f1': 0.6560202788339671}, 'argument': {'p': 0.689827444045646, 'r': 0.6942946292661187, 'f1': 0.6920538278467105}, 'combined': 0.4540013451121133, 'epoch': 11}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6889673433362753, 'r': 0.6823426573426572, 'f1': 0.6856389986824768}, 'combined': 0.5032212834366803, 'epoch': 11}
Korean: {'event': {'p': 0.7551020408163265, 'r': 0.5873015873015873, 'f1': 0.6607142857142858}, 'argument': {'p': 0.5398627748200729, 'r': 0.3488344083452779, 'f1': 0.42381750546622554}, 'combined': 0.2800222803973276, 'epoch': 11}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.5577320052689511, 'r': 0.4968885137850655, 'f1': 0.5255551588111269}, 'combined': 0.25693807764099535, 'epoch': 11}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6812506658803518, 'r': 0.6464986076208682, 'f1': 0.6634198424692748}, 'combined': 0.4319542943185161, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6829404237028909, 'r': 0.673990348983814, 'f1': 0.6784358697905962}, 'combined': 0.43574490380295205, 'epoch': 11}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6889673433362753, 'r': 0.6823426573426572, 'f1': 0.6856389986824768}, 'combined': 0.5032212834366803, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6557579829804582, 'r': 0.6805395346628592, 'f1': 0.6679189726790972}, 'combined': 0.44163801230884225, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6740767145213707, 'r': 0.6959562763877412, 'f1': 0.6848417860750591}, 'combined': 0.4715183511000138, 'epoch': 4}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6105590062111801, 'r': 0.4070393374741201, 'f1': 0.48844720496894406}, 'combined': 0.3140017746228926, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 12
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 19:29:24.065507: step: 3078/1538, loss: 0.0926971435546875
==================================================
Loss: 0.214
--------------------
Dev Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6640693144522788, 'r': 0.6791251369661766, 'f1': 0.6715128455990618}, 'combined': 0.4372236686001673, 'epoch': 12}
Test Chinese: {'event': {'p': 0.6013655462184874, 'r': 0.6827668455575432, 'f1': 0.6394861770455179}, 'argument': {'p': 0.6644581971737245, 'r': 0.6908332676369204, 'f1': 0.6773890922803012}, 'combined': 0.43318096099466336, 'epoch': 12}
Dev Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6517852083024894, 'r': 0.6806587723594625, 'f1': 0.665909150649364}, 'combined': 0.4410870191801278, 'epoch': 12}
Test Korean: {'event': {'p': 0.6209086468001954, 'r': 0.7579010137149672, 'f1': 0.6825993555316865}, 'argument': {'p': 0.6619178322174862, 'r': 0.7103620440100251, 'f1': 0.6852848495502435}, 'combined': 0.46777499665862493, 'epoch': 12}
Dev Russian: {'event': {'p': 0.5312217194570136, 'r': 0.7816245006657789, 'f1': 0.6325431034482759}, 'argument': {'p': 0.6423349602286983, 'r': 0.6831613348195054, 'f1': 0.6621194045889252}, 'combined': 0.41881906303200334, 'epoch': 12}
Test Russian: {'event': {'p': 0.5692239858906526, 'r': 0.7698270721526536, 'f1': 0.6544993662864386}, 'argument': {'p': 0.6658011319263619, 'r': 0.7179408187983617, 'f1': 0.6908886581948848}, 'combined': 0.4521861889630399, 'epoch': 12}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6433360150174309, 'r': 0.6608815426997244, 'f1': 0.6519907596140779}, 'combined': 0.47852532815712145, 'epoch': 12}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6397174254317112, 'r': 0.4478021978021978, 'f1': 0.5268261150614092}, 'combined': 0.3386739311109059, 'epoch': 12}
Russian: {'event': {'p': 0.3888888888888889, 'r': 0.5833333333333334, 'f1': 0.4666666666666666}, 'argument': {'p': 0.5865773651635721, 'r': 0.5332521501487019, 'f1': 0.5586451096795925}, 'combined': 0.2607010511838098, 'epoch': 12}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6812506658803518, 'r': 0.6464986076208682, 'f1': 0.6634198424692748}, 'combined': 0.4319542943185161, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6829404237028909, 'r': 0.673990348983814, 'f1': 0.6784358697905962}, 'combined': 0.43574490380295205, 'epoch': 11}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6889673433362753, 'r': 0.6823426573426572, 'f1': 0.6856389986824768}, 'combined': 0.5032212834366803, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6517852083024894, 'r': 0.6806587723594625, 'f1': 0.665909150649364}, 'combined': 0.4410870191801278, 'epoch': 12}
Test for Korean: {'event': {'p': 0.6209086468001954, 'r': 0.7579010137149672, 'f1': 0.6825993555316865}, 'argument': {'p': 0.6619178322174862, 'r': 0.7103620440100251, 'f1': 0.6852848495502435}, 'combined': 0.46777499665862493, 'epoch': 12}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6397174254317112, 'r': 0.4478021978021978, 'f1': 0.5268261150614092}, 'combined': 0.3386739311109059, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 13
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 19:56:10.435981: step: 3078/1538, loss: 1.0337607860565186
==================================================
Loss: 0.177
--------------------
Dev Chinese: {'event': {'p': 0.6333754740834386, 'r': 0.6671105193075899, 'f1': 0.649805447470817}, 'argument': {'p': 0.6734928120030234, 'r': 0.6647885430272169, 'f1': 0.6691123709493453}, 'combined': 0.4347928636129986, 'epoch': 13}
Test Chinese: {'event': {'p': 0.6024159663865546, 'r': 0.6839594514013119, 'f1': 0.6406031834683048}, 'argument': {'p': 0.677393673654791, 'r': 0.6884944439805251, 'f1': 0.682898950034503}, 'combined': 0.4374672413792654, 'epoch': 13}
Dev Korean: {'event': {'p': 0.5848074921956296, 'r': 0.748335552596538, 'f1': 0.6565420560747663}, 'argument': {'p': 0.6593067758124687, 'r': 0.6702952220760098, 'f1': 0.6647555921414974}, 'combined': 0.43644000325177745, 'epoch': 13}
Test Korean: {'event': {'p': 0.6189545676599902, 'r': 0.75551580202743, 'f1': 0.6804511278195488}, 'argument': {'p': 0.6782275308883711, 'r': 0.7047740189349196, 'f1': 0.6912459971683755}, 'combined': 0.4703591183739697, 'epoch': 13}
Dev Russian: {'event': {'p': 0.5294117647058824, 'r': 0.7789613848202397, 'f1': 0.6303879310344827}, 'argument': {'p': 0.6598090854717152, 'r': 0.6677361204208454, 'f1': 0.6637489359996462}, 'combined': 0.4184193184911562, 'epoch': 13}
Test Russian: {'event': {'p': 0.5692239858906526, 'r': 0.7698270721526536, 'f1': 0.6544993662864386}, 'argument': {'p': 0.6818281702121234, 'r': 0.7136328890813984, 'f1': 0.6973680902452395}, 'combined': 0.45642697313389313, 'epoch': 13}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.7107474747474747, 'r': 0.6461340679522497, 'f1': 0.6769023569023569}, 'combined': 0.49680906928613355, 'epoch': 13}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6089159067882473, 'r': 0.4147688060731539, 'f1': 0.493431855500821}, 'combined': 0.31720619282195633, 'epoch': 13}
Russian: {'event': {'p': 0.42592592592592593, 'r': 0.6388888888888888, 'f1': 0.5111111111111111}, 'argument': {'p': 0.5784565108094519, 'r': 0.5363869463869464, 'f1': 0.5566279632317368}, 'combined': 0.28449873676288767, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6812506658803518, 'r': 0.6464986076208682, 'f1': 0.6634198424692748}, 'combined': 0.4319542943185161, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6829404237028909, 'r': 0.673990348983814, 'f1': 0.6784358697905962}, 'combined': 0.43574490380295205, 'epoch': 11}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6889673433362753, 'r': 0.6823426573426572, 'f1': 0.6856389986824768}, 'combined': 0.5032212834366803, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6517852083024894, 'r': 0.6806587723594625, 'f1': 0.665909150649364}, 'combined': 0.4410870191801278, 'epoch': 12}
Test for Korean: {'event': {'p': 0.6209086468001954, 'r': 0.7579010137149672, 'f1': 0.6825993555316865}, 'argument': {'p': 0.6619178322174862, 'r': 0.7103620440100251, 'f1': 0.6852848495502435}, 'combined': 0.46777499665862493, 'epoch': 12}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6397174254317112, 'r': 0.4478021978021978, 'f1': 0.5268261150614092}, 'combined': 0.3386739311109059, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 14
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 20:22:44.265586: step: 3076/1538, loss: 0.04932969808578491
2023-01-21 20:22:44.844099: step: 3078/1538, loss: 0.00045865660649724305
==================================================
Loss: 0.161
--------------------
Dev Chinese: {'event': {'p': 0.6333754740834386, 'r': 0.6671105193075899, 'f1': 0.649805447470817}, 'argument': {'p': 0.6760448453423469, 'r': 0.6573877635715354, 'f1': 0.6665857816626598}, 'combined': 0.433151072130989, 'epoch': 14}
Test Chinese: {'event': {'p': 0.6024159663865546, 'r': 0.6839594514013119, 'f1': 0.6406031834683048}, 'argument': {'p': 0.6796251312534255, 'r': 0.6709437883359547, 'f1': 0.6752565583252583}, 'combined': 0.43257150092101154, 'epoch': 14}
Dev Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6599867788996122, 'r': 0.6456079819736947, 'f1': 0.6527182020394844}, 'combined': 0.432349556724752, 'epoch': 14}
Test Korean: {'event': {'p': 0.6194430874450415, 'r': 0.7561121049493142, 'f1': 0.6809881847475832}, 'argument': {'p': 0.6833598931759218, 'r': 0.6881021547524375, 'f1': 0.6857228250333766}, 'combined': 0.4669691418594637, 'epoch': 14}
Dev Russian: {'event': {'p': 0.5285067873303168, 'r': 0.7776298268974701, 'f1': 0.6293103448275863}, 'argument': {'p': 0.6604553109981173, 'r': 0.6492611531845899, 'f1': 0.6548103938100991}, 'combined': 0.412078954725321, 'epoch': 14}
Test Russian: {'event': {'p': 0.5661375661375662, 'r': 0.7656529516994633, 'f1': 0.6509505703422054}, 'argument': {'p': 0.6882661347360869, 'r': 0.6953012229957402, 'f1': 0.6917657930483042}, 'combined': 0.45030533752802165, 'epoch': 14}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6501206757843925, 'r': 0.7063811188811188, 'f1': 0.677084206116464}, 'combined': 0.4969425365992397, 'epoch': 14}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.5830687830687831, 'r': 0.3802622498274672, 'f1': 0.4603174603174603}, 'combined': 0.2959183673469387, 'epoch': 14}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.5115530338934594, 'r': 0.4371453198725926, 'f1': 0.4714312273135802}, 'combined': 0.23047748890886144, 'epoch': 14}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6812506658803518, 'r': 0.6464986076208682, 'f1': 0.6634198424692748}, 'combined': 0.4319542943185161, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6829404237028909, 'r': 0.673990348983814, 'f1': 0.6784358697905962}, 'combined': 0.43574490380295205, 'epoch': 11}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6889673433362753, 'r': 0.6823426573426572, 'f1': 0.6856389986824768}, 'combined': 0.5032212834366803, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6517852083024894, 'r': 0.6806587723594625, 'f1': 0.665909150649364}, 'combined': 0.4410870191801278, 'epoch': 12}
Test for Korean: {'event': {'p': 0.6209086468001954, 'r': 0.7579010137149672, 'f1': 0.6825993555316865}, 'argument': {'p': 0.6619178322174862, 'r': 0.7103620440100251, 'f1': 0.6852848495502435}, 'combined': 0.46777499665862493, 'epoch': 12}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6397174254317112, 'r': 0.4478021978021978, 'f1': 0.5268261150614092}, 'combined': 0.3386739311109059, 'epoch': 12}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 15
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 20:28:03.704712: step: 2/1538, loss: 0.09399234503507614
2023-01-21 20:28:04.517942: step: 4/1538, loss: 0.1051344946026802
2023-01-21 20:28:05.328604: step: 6/1538, loss: 0.010149789042770863
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2023-01-21 20:28:08.648272: step: 14/1538, loss: 0.035101309418678284
2023-01-21 20:28:09.465623: step: 16/1538, loss: 0.0018968872027471662
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2023-01-21 20:49:19.032841: step: 3078/1538, loss: 0.003708434058353305
==================================================
Loss: 0.173
--------------------
Dev Chinese: {'event': {'p': 0.6308470290771175, 'r': 0.6644474034620506, 'f1': 0.6472114137483787}, 'argument': {'p': 0.6825166375665978, 'r': 0.6593055454388269, 'f1': 0.6707103365872744}, 'combined': 0.43409138515830076, 'epoch': 15}
Test Chinese: {'event': {'p': 0.6024159663865546, 'r': 0.6839594514013119, 'f1': 0.6406031834683048}, 'argument': {'p': 0.6924239150241178, 'r': 0.6676854740207042, 'f1': 0.679829716124273}, 'combined': 0.43550108036556323, 'epoch': 15}
Dev Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Dev Russian: {'event': {'p': 0.5294117647058824, 'r': 0.7789613848202397, 'f1': 0.6303879310344827}, 'argument': {'p': 0.6639611523414282, 'r': 0.662553460852365, 'f1': 0.6632565596788798}, 'combined': 0.4181089304010179, 'epoch': 15}
Test Russian: {'event': {'p': 0.5701058201058201, 'r': 0.7710196779964222, 'f1': 0.6555133079847908}, 'argument': {'p': 0.694726133931258, 'r': 0.6909375796903241, 'f1': 0.692826677652567}, 'combined': 0.4541571073281465, 'epoch': 15}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6457336523125995, 'r': 0.6692148760330577, 'f1': 0.6572646103896103}, 'combined': 0.47033614321458345, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.5504344170251783, 'r': 0.5304186200424446, 'f1': 0.5402411870802676}, 'combined': 0.26411791368368637, 'epoch': 15}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6812506658803518, 'r': 0.6464986076208682, 'f1': 0.6634198424692748}, 'combined': 0.4319542943185161, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6829404237028909, 'r': 0.673990348983814, 'f1': 0.6784358697905962}, 'combined': 0.43574490380295205, 'epoch': 11}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6889673433362753, 'r': 0.6823426573426572, 'f1': 0.6856389986824768}, 'combined': 0.5032212834366803, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
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Epoch: 16
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 21:15:58.311139: step: 3076/1538, loss: 0.06580979377031326
2023-01-21 21:15:58.880837: step: 3078/1538, loss: 0.020069243386387825
==================================================
Loss: 0.151
--------------------
Dev Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Dev Korean: {'event': {'p': 0.5910509885535901, 'r': 0.7563249001331558, 'f1': 0.6635514018691588}, 'argument': {'p': 0.6732103289453999, 'r': 0.6458321311088405, 'f1': 0.6592370975069483}, 'combined': 0.43743770021489087, 'epoch': 16}
Test Korean: {'event': {'p': 0.6199316072300928, 'r': 0.7567084078711985, 'f1': 0.6815252416756176}, 'argument': {'p': 0.6837276665440077, 'r': 0.6967759044551529, 'f1': 0.6901901208591685}, 'combined': 0.4703819889206685, 'epoch': 16}
Dev Russian: {'event': {'p': 0.5294117647058824, 'r': 0.7789613848202397, 'f1': 0.6303879310344827}, 'argument': {'p': 0.6753831161242486, 'r': 0.6534271985682659, 'f1': 0.6642237686595519}, 'combined': 0.41871864726922176, 'epoch': 16}
Test Russian: {'event': {'p': 0.5656966490299824, 'r': 0.7650566487775791, 'f1': 0.6504435994930293}, 'argument': {'p': 0.691860593461015, 'r': 0.6996422565776521, 'f1': 0.6957296664290228}, 'combined': 0.45253290850617817, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
Korean: {'event': {'p': 0.7551020408163265, 'r': 0.5873015873015873, 'f1': 0.6607142857142858}, 'argument': {'p': 0.6053273897550738, 'r': 0.45632372458459414, 'f1': 0.5203691596140109}, 'combined': 0.34381533760211436, 'epoch': 16}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.5721053122992779, 'r': 0.4992919089157334, 'f1': 0.533224368744958}, 'combined': 0.2606874691642017, 'epoch': 16}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 17
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 21:21:22.026404: step: 2/1538, loss: 0.11634708195924759
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2023-01-21 21:42:36.526074: step: 3078/1538, loss: 0.00017269210366066545
==================================================
Loss: 0.121
--------------------
Dev Chinese: {'event': {'p': 0.629582806573957, 'r': 0.6631158455392809, 'f1': 0.6459143968871596}, 'argument': {'p': 0.680605138695111, 'r': 0.655785664431513, 'f1': 0.667964927699948}, 'combined': 0.431448163417087, 'epoch': 17}
Test Chinese: {'event': {'p': 0.6055672268907563, 'r': 0.6875372689326178, 'f1': 0.6439542027366657}, 'argument': {'p': 0.6792313450572453, 'r': 0.6755088495600372, 'f1': 0.677364983053974}, 'combined': 0.4361920276242569, 'epoch': 17}
Dev Korean: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6750447623381771, 'r': 0.64759196154592, 'f1': 0.6610334551880889}, 'combined': 0.4347684991482407, 'epoch': 17}
Test Korean: {'event': {'p': 0.6248168050806058, 'r': 0.7626714370900417, 'f1': 0.6868958109559614}, 'argument': {'p': 0.6857744137001428, 'r': 0.6962515227983394, 'f1': 0.6909732548791239}, 'combined': 0.47462663425907603, 'epoch': 17}
Dev Russian: {'event': {'p': 0.5285067873303168, 'r': 0.7776298268974701, 'f1': 0.6293103448275863}, 'argument': {'p': 0.6715929119487853, 'r': 0.6535699383230411, 'f1': 0.6624588637547789}, 'combined': 0.4168922159836109, 'epoch': 17}
Test Russian: {'event': {'p': 0.5687830687830688, 'r': 0.7692307692307693, 'f1': 0.6539923954372625}, 'argument': {'p': 0.6874606085785537, 'r': 0.7003519599156899, 'f1': 0.6938464105498121}, 'combined': 0.4537702761010179, 'epoch': 17}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6417565485362094, 'r': 0.6884297520661157, 'f1': 0.6642743221690589}, 'combined': 0.4875407869130708, 'epoch': 17}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.6606280193236715, 'r': 0.45356550580431176, 'f1': 0.5378564405113078}, 'combined': 0.33616027531956744, 'epoch': 17}
Russian: {'event': {'p': 0.42592592592592593, 'r': 0.6388888888888888, 'f1': 0.5111111111111111}, 'argument': {'p': 0.5834141330084535, 'r': 0.5409840142442023, 'f1': 0.5613985053477571}, 'combined': 0.28693701384440917, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 18
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 22:09:08.794202: step: 3078/1538, loss: 0.001112401019781828
==================================================
Loss: 0.128
--------------------
Dev Chinese: {'event': {'p': 0.638432364096081, 'r': 0.6724367509986684, 'f1': 0.6549935149156939}, 'argument': {'p': 0.6832620182988141, 'r': 0.6638826255593826, 'f1': 0.6734329304893367}, 'combined': 0.44109420220118684, 'epoch': 18}
Test Chinese: {'event': {'p': 0.6013655462184874, 'r': 0.6827668455575432, 'f1': 0.6394861770455179}, 'argument': {'p': 0.6789188547920246, 'r': 0.6868420507633393, 'f1': 0.6828574703383163}, 'combined': 0.43667791317362303, 'epoch': 18}
Dev Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6732044968851298, 'r': 0.6678227748896819, 'f1': 0.6705028371019353}, 'combined': 0.4433465021024478, 'epoch': 18}
Test Korean: {'event': {'p': 0.621885686370298, 'r': 0.7590936195587359, 'f1': 0.6836734693877551}, 'argument': {'p': 0.682673214596629, 'r': 0.7172689415425185, 'f1': 0.6995436088639119}, 'combined': 0.4782594060600214, 'epoch': 18}
Dev Russian: {'event': {'p': 0.5330316742081448, 'r': 0.7842876165113183, 'f1': 0.634698275862069}, 'argument': {'p': 0.66600444935031, 'r': 0.6688265021017944, 'f1': 0.6674124925836088}, 'combined': 0.4236055583316224, 'epoch': 18}
Test Russian: {'event': {'p': 0.5674603174603174, 'r': 0.7674418604651163, 'f1': 0.6524714828897339}, 'argument': {'p': 0.6875865429351846, 'r': 0.7220479209820554, 'f1': 0.7043959927624709}, 'combined': 0.45959829793931567, 'epoch': 18}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6456304112554113, 'r': 0.6573691460055097, 'f1': 0.6514469014469014}, 'combined': 0.4661730120445716, 'epoch': 18}
Korean: {'event': {'p': 0.7551020408163265, 'r': 0.5873015873015873, 'f1': 0.6607142857142858}, 'argument': {'p': 0.5588609940783853, 'r': 0.4587664876762864, 'f1': 0.5038910602346096}, 'combined': 0.33292802194072424, 'epoch': 18}
Russian: {'event': {'p': 0.3888888888888889, 'r': 0.5833333333333334, 'f1': 0.4666666666666666}, 'argument': {'p': 0.5520951253553134, 'r': 0.5520951253553134, 'f1': 0.5520951253553134}, 'combined': 0.25764439183247956, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 19
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 22:14:28.238410: step: 2/1538, loss: 0.020990731194615364
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2023-01-21 22:35:33.071277: step: 3056/1538, loss: 0.283299058675766
2023-01-21 22:35:33.930747: step: 3058/1538, loss: 0.002114134142175317
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2023-01-21 22:35:39.673930: step: 3072/1538, loss: 0.06497102975845337
2023-01-21 22:35:40.485313: step: 3074/1538, loss: 0.5481203198432922
2023-01-21 22:35:41.308145: step: 3076/1538, loss: 0.0009065017220564187
2023-01-21 22:35:41.894708: step: 3078/1538, loss: 0.07790535688400269
==================================================
Loss: 0.114
--------------------
Dev Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6808426278654566, 'r': 0.6340965669861643, 'f1': 0.6566386866824521}, 'combined': 0.4275390670746964, 'epoch': 19}
Test Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.7086107991981928, 'r': 0.6634352473367415, 'f1': 0.6852793053393624}, 'combined': 0.4401402966435447, 'epoch': 19}
Dev Korean: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.6778130045321942, 'r': 0.6354496917489321, 'f1': 0.6559480689021234}, 'combined': 0.4329563772543221, 'epoch': 19}
Test Korean: {'event': {'p': 0.625305324865657, 'r': 0.7632677400119261, 'f1': 0.6874328678839957}, 'argument': {'p': 0.7084754306693172, 'r': 0.6826724753379685, 'f1': 0.6953346569155142}, 'combined': 0.47799589734256615, 'epoch': 19}
Dev Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6798832082247294, 'r': 0.6386200071609843, 'f1': 0.6586059316936789}, 'combined': 0.4173063446507362, 'epoch': 19}
Test Russian: {'event': {'p': 0.5709876543209876, 'r': 0.7722122838401908, 'f1': 0.6565272496831432}, 'argument': {'p': 0.7199748517637071, 'r': 0.6912544093305828, 'f1': 0.7053223804371541}, 'combined': 0.46306336256837244, 'epoch': 19}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6445236680530798, 'r': 0.5976492194674012, 'f1': 0.6202020202020202}, 'combined': 0.4438142896858493, 'epoch': 19}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.6247920394261858, 'r': 0.3823354271115465, 'f1': 0.47437914104580764}, 'combined': 0.2964869631536298, 'epoch': 19}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.5460024649679822, 'r': 0.48643855969874783, 'f1': 0.514502322758291}, 'combined': 0.25153446890405334, 'epoch': 19}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 20
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 22:40:55.967841: step: 2/1538, loss: 0.16802267730236053
2023-01-21 22:40:56.773384: step: 4/1538, loss: 0.0259974654763937
2023-01-21 22:40:57.574358: step: 6/1538, loss: 0.08625760674476624
2023-01-21 22:40:58.452996: step: 8/1538, loss: 0.3291322588920593
2023-01-21 22:40:59.256085: step: 10/1538, loss: 0.0012548570521175861
2023-01-21 22:41:00.067285: step: 12/1538, loss: 0.05399011820554733
2023-01-21 22:41:00.886024: step: 14/1538, loss: 0.016804860904812813
2023-01-21 22:41:01.784015: step: 16/1538, loss: 0.0013063600054010749
2023-01-21 22:41:02.579004: step: 18/1538, loss: 0.26340654492378235
2023-01-21 22:41:03.421273: step: 20/1538, loss: 0.10229279845952988
2023-01-21 22:41:04.236531: step: 22/1538, loss: 0.008904960937798023
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==================================================
Loss: 0.104
--------------------
Dev Chinese: {'event': {'p': 0.6257901390644753, 'r': 0.6591211717709721, 'f1': 0.642023346303502}, 'argument': {'p': 0.674297127473204, 'r': 0.6568537094625796, 'f1': 0.6654611290787967}, 'combined': 0.4272415809260758, 'epoch': 20}
Test Chinese: {'event': {'p': 0.6024159663865546, 'r': 0.6839594514013119, 'f1': 0.6406031834683048}, 'argument': {'p': 0.6876431609998439, 'r': 0.6751223459779968, 'f1': 0.6813252341255345}, 'combined': 0.4364591139581055, 'epoch': 20}
Dev Korean: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6641530001908172, 'r': 0.6564302908862728, 'f1': 0.6602690645171867}, 'combined': 0.43426575154576647, 'epoch': 20}
Test Korean: {'event': {'p': 0.621885686370298, 'r': 0.7590936195587359, 'f1': 0.6836734693877551}, 'argument': {'p': 0.6937508100715571, 'r': 0.6968823241447968, 'f1': 0.6953130412392222}, 'combined': 0.4753670792145703, 'epoch': 20}
Dev Russian: {'event': {'p': 0.5285067873303168, 'r': 0.7776298268974701, 'f1': 0.6293103448275863}, 'argument': {'p': 0.6618362433608989, 'r': 0.6599666494530998, 'f1': 0.6609001242048015}, 'combined': 0.41591128505991826, 'epoch': 20}
Test Russian: {'event': {'p': 0.5692239858906526, 'r': 0.7698270721526536, 'f1': 0.6544993662864386}, 'argument': {'p': 0.6972962577116717, 'r': 0.7008623824527815, 'f1': 0.6990747722271751}, 'combined': 0.45754399540952245, 'epoch': 20}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6678055060408, 'r': 0.6192378328741964, 'f1': 0.6426052982656755}, 'combined': 0.4598459932543365, 'epoch': 20}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6708037825059102, 'r': 0.47056384742951907, 'f1': 0.5531189083820662}, 'combined': 0.3555764411027568, 'epoch': 20}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.594577365163572, 'r': 0.5405248774214291, 'f1': 0.56626415729864}, 'combined': 0.2768402546793351, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 21
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 23:28:42.301695: step: 3076/1538, loss: 2.1263177394866943
2023-01-21 23:28:42.846309: step: 3078/1538, loss: 0.0057215215638279915
==================================================
Loss: 0.105
--------------------
Dev Chinese: {'event': {'p': 0.6359039190897599, 'r': 0.6697736351531292, 'f1': 0.6523994811932554}, 'argument': {'p': 0.6874108693234154, 'r': 0.6579345132763758, 'f1': 0.6723497795862756}, 'combined': 0.43864064738248587, 'epoch': 21}
Test Chinese: {'event': {'p': 0.6034663865546218, 'r': 0.6851520572450805, 'f1': 0.6417201898910918}, 'argument': {'p': 0.7036215321867307, 'r': 0.6730069207314398, 'f1': 0.6879738098301207}, 'combined': 0.4414866838842829, 'epoch': 21}
Dev Korean: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6806625680747969, 'r': 0.6584186933011107, 'f1': 0.6693558808974747}, 'combined': 0.4410242018997379, 'epoch': 21}
Test Korean: {'event': {'p': 0.6248168050806058, 'r': 0.7626714370900417, 'f1': 0.6868958109559614}, 'argument': {'p': 0.6992874610601437, 'r': 0.6859144393778391, 'f1': 0.6925363972795034}, 'combined': 0.4757003502258244, 'epoch': 21}
Dev Russian: {'event': {'p': 0.5312217194570136, 'r': 0.7816245006657789, 'f1': 0.6325431034482759}, 'argument': {'p': 0.6738870001914916, 'r': 0.6648447311211255, 'f1': 0.6693353283096436}, 'combined': 0.42338344581655263, 'epoch': 21}
Test Russian: {'event': {'p': 0.5687830687830688, 'r': 0.7692307692307693, 'f1': 0.6539923954372625}, 'argument': {'p': 0.7049750986698158, 'r': 0.6876632954282651, 'f1': 0.6962115960620419}, 'combined': 0.4553170894398145, 'epoch': 21}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6806060606060607, 'r': 0.6544289044289044, 'f1': 0.667260843731432}, 'combined': 0.48973272934417034, 'epoch': 21}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.6208401527550464, 'r': 0.42911010558069385, 'f1': 0.507469342251951}, 'combined': 0.3171683389074694, 'epoch': 21}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.5268420092094389, 'r': 0.4885262267214797, 'f1': 0.5069611786732336}, 'combined': 0.24784768735135868, 'epoch': 21}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 22
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 23:33:59.067064: step: 2/1538, loss: 0.1038723886013031
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2023-01-21 23:55:12.894678: step: 3078/1538, loss: 0.0005655884742736816
==================================================
Loss: 0.094
--------------------
Dev Chinese: {'event': {'p': 0.6270543615676359, 'r': 0.6604527296937417, 'f1': 0.6433203631647211}, 'argument': {'p': 0.6883367138645985, 'r': 0.6599803548927936, 'f1': 0.6738603541090957}, 'combined': 0.433508087727771, 'epoch': 22}
Test Chinese: {'event': {'p': 0.6013655462184874, 'r': 0.6827668455575432, 'f1': 0.6394861770455179}, 'argument': {'p': 0.6754421595835804, 'r': 0.6877408150749056, 'f1': 0.6815360080099931}, 'combined': 0.43583285628117396, 'epoch': 22}
Dev Korean: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6791363145324393, 'r': 0.662355330016376, 'f1': 0.6706408639459408}, 'combined': 0.44187084960924133, 'epoch': 22}
Test Korean: {'event': {'p': 0.6233512457254519, 'r': 0.7608825283243887, 'f1': 0.6852846401718582}, 'argument': {'p': 0.6762203135427505, 'r': 0.7050605002853851, 'f1': 0.6903393253515705}, 'combined': 0.4730789361700343, 'epoch': 22}
Dev Russian: {'event': {'p': 0.5276018099547511, 'r': 0.7762982689747004, 'f1': 0.6282327586206897}, 'argument': {'p': 0.670812516939306, 'r': 0.6646539274476315, 'f1': 0.6677190218274894}, 'combined': 0.4194829630661922, 'epoch': 22}
Test Russian: {'event': {'p': 0.568342151675485, 'r': 0.7686344663088849, 'f1': 0.6534854245880861}, 'argument': {'p': 0.6814708338098486, 'r': 0.7149058593868906, 'f1': 0.6977880602928247}, 'combined': 0.4559943268529536, 'epoch': 22}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6546015712682378, 'r': 0.6797785547785546, 'f1': 0.6669525443110347}, 'combined': 0.48950645454020897, 'epoch': 22}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6507936507936508, 'r': 0.45938375350140054, 'f1': 0.5385878489326765}, 'combined': 0.34623504574243485, 'epoch': 22}
Russian: {'event': {'p': 0.4444444444444444, 'r': 0.6666666666666666, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5591658324260205, 'r': 0.5591658324260205, 'f1': 0.5591658324260205}, 'combined': 0.29822177729387755, 'epoch': 22}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 23
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 00:21:38.100813: step: 3056/1538, loss: 0.15240369737148285
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2023-01-22 00:21:43.057605: step: 3068/1538, loss: 0.07869401574134827
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2023-01-22 00:21:44.707193: step: 3072/1538, loss: 0.006392639596015215
2023-01-22 00:21:45.570180: step: 3074/1538, loss: 0.01677718758583069
2023-01-22 00:21:46.386868: step: 3076/1538, loss: 0.073042131960392
2023-01-22 00:21:46.939939: step: 3078/1538, loss: 0.00014050006575416774
==================================================
Loss: 0.077
--------------------
Dev Chinese: {'event': {'p': 0.6359039190897599, 'r': 0.6697736351531292, 'f1': 0.6523994811932554}, 'argument': {'p': 0.6726448870101099, 'r': 0.650314166064835, 'f1': 0.6612910622396635}, 'combined': 0.43142594592289324, 'epoch': 23}
Test Chinese: {'event': {'p': 0.6071428571428571, 'r': 0.6893261776982708, 'f1': 0.6456297123708461}, 'argument': {'p': 0.6797580630123231, 'r': 0.6902005789766675, 'f1': 0.6849395219318364}, 'combined': 0.44221730653627633, 'epoch': 23}
Dev Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6670058085800157, 'r': 0.6553804350099937, 'f1': 0.6611420212518397}, 'combined': 0.43792935286190793, 'epoch': 23}
Test Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6815886711148459, 'r': 0.7047977363578114, 'f1': 0.6929989358516506}, 'combined': 0.4778789654315357, 'epoch': 23}
Dev Russian: {'event': {'p': 0.534841628959276, 'r': 0.7869507323568575, 'f1': 0.6368534482758621}, 'argument': {'p': 0.6616688982861753, 'r': 0.654654740353813, 'f1': 0.6581431315102277}, 'combined': 0.4191407227613627, 'epoch': 23}
Test Russian: {'event': {'p': 0.5709876543209876, 'r': 0.7722122838401908, 'f1': 0.6565272496831432}, 'argument': {'p': 0.6858481990129828, 'r': 0.7118219793236075, 'f1': 0.6985937456546103}, 'combined': 0.4586458304804666, 'epoch': 23}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6415436415436415, 'r': 0.6823691460055097, 'f1': 0.6613269256441063}, 'combined': 0.48537756010576616, 'epoch': 23}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.6612540849673203, 'r': 0.4600028417163967, 'f1': 0.5425674543321602}, 'combined': 0.3391046589576002, 'epoch': 23}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.5571044259723505, 'r': 0.5368460832097196, 'f1': 0.5467876773432329}, 'combined': 0.2673184200344694, 'epoch': 23}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 24
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 00:27:06.910927: step: 2/1538, loss: 0.007679430767893791
2023-01-22 00:27:07.738196: step: 4/1538, loss: 0.01151560340076685
2023-01-22 00:27:08.584157: step: 6/1538, loss: 0.0010207996238023043
2023-01-22 00:27:09.384547: step: 8/1538, loss: 0.24328987300395966
2023-01-22 00:27:10.210175: step: 10/1538, loss: 0.0007018783362582326
2023-01-22 00:27:11.029215: step: 12/1538, loss: 9.366545418743044e-05
2023-01-22 00:27:11.831563: step: 14/1538, loss: 0.0076081957668066025
2023-01-22 00:27:12.648605: step: 16/1538, loss: 0.3176814317703247
2023-01-22 00:27:13.475051: step: 18/1538, loss: 0.3073682487010956
2023-01-22 00:27:14.290030: step: 20/1538, loss: 0.014983238652348518
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2023-01-22 00:27:20.938706: step: 36/1538, loss: 0.11640999466180801
2023-01-22 00:27:21.751891: step: 38/1538, loss: 0.0001246286992682144
2023-01-22 00:27:22.553758: step: 40/1538, loss: 0.0009941176977008581
2023-01-22 00:27:23.362165: step: 42/1538, loss: 0.0008405431290157139
2023-01-22 00:27:24.153446: step: 44/1538, loss: 0.07414880394935608
2023-01-22 00:27:24.959156: step: 46/1538, loss: 0.05189834535121918
2023-01-22 00:27:25.798966: step: 48/1538, loss: 0.0037213326431810856
2023-01-22 00:27:26.616700: step: 50/1538, loss: 0.0013126528356224298
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2023-01-22 00:48:19.643748: step: 3078/1538, loss: 0.0002354358439333737
==================================================
Loss: 0.089
--------------------
Dev Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6796415540335632, 'r': 0.6675541537193042, 'f1': 0.6735436281817123}, 'combined': 0.4367987212592168, 'epoch': 24}
Test Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6769150570384497, 'r': 0.6821068818002494, 'f1': 0.6795010523445817}, 'combined': 0.4364290478616414, 'epoch': 24}
Dev Korean: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.6637155777293907, 'r': 0.6632339263667424, 'f1': 0.663474664634026}, 'combined': 0.43792428214745877, 'epoch': 24}
Test Korean: {'event': {'p': 0.6213971665852467, 'r': 0.7584973166368515, 'f1': 0.6831364124597208}, 'argument': {'p': 0.674657044964829, 'r': 0.696435283486776, 'f1': 0.6853732029804959}, 'combined': 0.468203391080124, 'epoch': 24}
Dev Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6605055071077445, 'r': 0.6623713418735856, 'f1': 0.6614371086692505}, 'combined': 0.4191002369585337, 'epoch': 24}
Test Russian: {'event': {'p': 0.5687830687830688, 'r': 0.7692307692307693, 'f1': 0.6539923954372625}, 'argument': {'p': 0.6831416252996528, 'r': 0.7057190486911575, 'f1': 0.694246826850527}, 'combined': 0.45403214531669456, 'epoch': 24}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6567911255411255, 'r': 0.668732782369146, 'f1': 0.6627081627081628}, 'combined': 0.4863913120793856, 'epoch': 24}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.6755050505050505, 'r': 0.43075684380032203, 'f1': 0.5260570304818092}, 'combined': 0.3287856440511308, 'epoch': 24}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.5426945965097103, 'r': 0.4637572006537524, 'f1': 0.5001303144305174}, 'combined': 0.2778501746836207, 'epoch': 24}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
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Epoch: 25
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 01:14:51.705937: step: 3076/1538, loss: 0.04947960004210472
2023-01-22 01:14:52.271422: step: 3078/1538, loss: 4.9114227294921875e-05
==================================================
Loss: 0.072
--------------------
Dev Chinese: {'event': {'p': 0.6333754740834386, 'r': 0.6671105193075899, 'f1': 0.649805447470817}, 'argument': {'p': 0.6863551884895794, 'r': 0.654651585779769, 'f1': 0.6701286245145044}, 'combined': 0.4354532307156507, 'epoch': 25}
Test Chinese: {'event': {'p': 0.6034663865546218, 'r': 0.6851520572450805, 'f1': 0.6417201898910918}, 'argument': {'p': 0.683874027790879, 'r': 0.6698663666510513, 'f1': 0.6767977259512099}, 'combined': 0.4343147652152695, 'epoch': 25}
Dev Korean: {'event': {'p': 0.5910509885535901, 'r': 0.7563249001331558, 'f1': 0.6635514018691588}, 'argument': {'p': 0.6718050269153004, 'r': 0.6571793732088715, 'f1': 0.6644117214099963}, 'combined': 0.44087132915990407, 'epoch': 25}
Test Korean: {'event': {'p': 0.6233512457254519, 'r': 0.7608825283243887, 'f1': 0.6852846401718582}, 'argument': {'p': 0.6841540507550651, 'r': 0.6905702228184158, 'f1': 0.6873471638700844}, 'combined': 0.47102845386585807, 'epoch': 25}
Dev Russian: {'event': {'p': 0.5312217194570136, 'r': 0.7816245006657789, 'f1': 0.6325431034482759}, 'argument': {'p': 0.6715590650417194, 'r': 0.6516258631111523, 'f1': 0.6614423215941755}, 'combined': 0.41839077885321235, 'epoch': 25}
Test Russian: {'event': {'p': 0.5705467372134039, 'r': 0.7716159809183065, 'f1': 0.6560202788339671}, 'argument': {'p': 0.6873430422007736, 'r': 0.6943758836966888, 'f1': 0.6908415646415647}, 'combined': 0.45320607586625333, 'epoch': 25}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6472914990614106, 'r': 0.6649449035812672, 'f1': 0.655999456447887}, 'combined': 0.4814674909709263, 'epoch': 25}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.5635244963369963, 'r': 0.41861819727891153, 'f1': 0.480381537861046}, 'combined': 0.30881670291067237, 'epoch': 25}
Russian: {'event': {'p': 0.42592592592592593, 'r': 0.6388888888888888, 'f1': 0.5111111111111111}, 'argument': {'p': 0.5554680050623254, 'r': 0.5150703319668836, 'f1': 0.5345069482675207}, 'combined': 0.27319244022562167, 'epoch': 25}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 26
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 01:41:23.681270: step: 3078/1538, loss: 3.737381484825164e-05
==================================================
Loss: 0.075
--------------------
Dev Chinese: {'event': {'p': 0.6333754740834386, 'r': 0.6671105193075899, 'f1': 0.649805447470817}, 'argument': {'p': 0.6920722845115101, 'r': 0.6495864253654078, 'f1': 0.6701566621686457}, 'combined': 0.43547144973604596, 'epoch': 26}
Test Chinese: {'event': {'p': 0.6008403361344538, 'r': 0.6821705426356589, 'f1': 0.6389276738341245}, 'argument': {'p': 0.6894807768260597, 'r': 0.6698029500450744, 'f1': 0.6794994292773529}, 'combined': 0.43415098971979427, 'epoch': 26}
Dev Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6803822034209128, 'r': 0.6413762570709476, 'f1': 0.6603036849038221}, 'combined': 0.4366026701583683, 'epoch': 26}
Test Korean: {'event': {'p': 0.6233512457254519, 'r': 0.7608825283243887, 'f1': 0.6852846401718582}, 'argument': {'p': 0.6862485714227674, 'r': 0.6852961098385582, 'f1': 0.6857720099148349}, 'combined': 0.4699490250544196, 'epoch': 26}
Dev Russian: {'event': {'p': 0.5330316742081448, 'r': 0.7842876165113183, 'f1': 0.634698275862069}, 'argument': {'p': 0.6811440371909427, 'r': 0.6426613232253529, 'f1': 0.6613433384353922}, 'combined': 0.41975347665780816, 'epoch': 26}
Test Russian: {'event': {'p': 0.5687830687830688, 'r': 0.7692307692307693, 'f1': 0.6539923954372625}, 'argument': {'p': 0.6917011827149671, 'r': 0.6931166694463073, 'f1': 0.6924082026632277}, 'combined': 0.45282969908013376, 'epoch': 26}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6669472502805837, 'r': 0.6548209366391184, 'f1': 0.6608284681679178}, 'combined': 0.48501171975627, 'epoch': 26}
Korean: {'event': {'p': 0.6938775510204082, 'r': 0.5396825396825397, 'f1': 0.6071428571428572}, 'argument': {'p': 0.6519939180316539, 'r': 0.4799399674399674, 'f1': 0.5528908424908424}, 'combined': 0.3356837257980115, 'epoch': 26}
Russian: {'event': {'p': 0.42592592592592593, 'r': 0.6388888888888888, 'f1': 0.5111111111111111}, 'argument': {'p': 0.554577365163572, 'r': 0.5041612410577927, 'f1': 0.5281689192034019}, 'combined': 0.2699530031484054, 'epoch': 26}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 27
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 02:07:53.116363: step: 3078/1538, loss: 2.9039329092483968e-05
==================================================
Loss: 0.065
--------------------
Dev Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6798491204335512, 'r': 0.6501229240745132, 'f1': 0.6646538172450528}, 'combined': 0.43103360392026774, 'epoch': 27}
Test Chinese: {'event': {'p': 0.6050420168067226, 'r': 0.6869409660107334, 'f1': 0.6433956995252722}, 'argument': {'p': 0.6795826926082935, 'r': 0.6763572179810909, 'f1': 0.6779661189570751}, 'combined': 0.4362004853608213, 'epoch': 27}
Dev Korean: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6681025231921004, 'r': 0.6511209775771959, 'f1': 0.6595024539343867}, 'combined': 0.4337615438844155, 'epoch': 27}
Test Korean: {'event': {'p': 0.625305324865657, 'r': 0.7632677400119261, 'f1': 0.6874328678839957}, 'argument': {'p': 0.6768555650972021, 'r': 0.6986744793273719, 'f1': 0.6875919743678894}, 'combined': 0.4726733228737371, 'epoch': 27}
Dev Russian: {'event': {'p': 0.5285067873303168, 'r': 0.7776298268974701, 'f1': 0.6293103448275863}, 'argument': {'p': 0.6582719771778688, 'r': 0.6512938290099055, 'f1': 0.6547643112248783}, 'combined': 0.4120499544777252, 'epoch': 27}
Test Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.6828800291650469, 'r': 0.7043000641746562, 'f1': 0.6934246687560479}, 'combined': 0.45560373673401167, 'epoch': 27}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6616404428904429, 'r': 0.6255509641873278, 'f1': 0.6430897762673464}, 'combined': 0.46019268393443136, 'epoch': 27}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.6535489667565139, 'r': 0.48786049631120043, 'f1': 0.5586789554531488}, 'combined': 0.3491743471582181, 'epoch': 27}
Russian: {'event': {'p': 0.4444444444444444, 'r': 0.6666666666666666, 'f1': 0.5333333333333333}, 'argument': {'p': 0.52366486170834, 'r': 0.43797424797424805, 'f1': 0.47700165620957713}, 'combined': 0.25440088331177446, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 28
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 02:13:11.577209: step: 2/1538, loss: 8.105832239380106e-05
2023-01-22 02:13:12.394609: step: 4/1538, loss: 0.0002282033092342317
2023-01-22 02:13:13.266266: step: 6/1538, loss: 2.9540271759033203
2023-01-22 02:13:14.131999: step: 8/1538, loss: 0.0037796401884406805
2023-01-22 02:13:14.970800: step: 10/1538, loss: 0.018371403217315674
2023-01-22 02:13:15.798690: step: 12/1538, loss: 0.1475106179714203
2023-01-22 02:13:16.605917: step: 14/1538, loss: 0.024945378303527832
2023-01-22 02:13:17.435825: step: 16/1538, loss: 0.00022059210459701717
2023-01-22 02:13:18.278209: step: 18/1538, loss: 0.0004075080796610564
2023-01-22 02:13:19.096715: step: 20/1538, loss: 0.10239075124263763
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2023-01-22 02:13:21.578085: step: 26/1538, loss: 0.0004837042943108827
2023-01-22 02:13:22.380283: step: 28/1538, loss: 0.002158017596229911
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2023-01-22 02:13:28.188296: step: 42/1538, loss: 0.0008998556877486408
2023-01-22 02:13:29.023640: step: 44/1538, loss: 0.0008172426023520529
2023-01-22 02:13:29.826438: step: 46/1538, loss: 0.0006957942969165742
2023-01-22 02:13:30.681952: step: 48/1538, loss: 0.7550947070121765
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==================================================
Loss: 0.070
--------------------
Dev Chinese: {'event': {'p': 0.6333754740834386, 'r': 0.6671105193075899, 'f1': 0.649805447470817}, 'argument': {'p': 0.6716845672143988, 'r': 0.6727705568137754, 'f1': 0.6722271234076252}, 'combined': 0.43681684672791204, 'epoch': 28}
Test Chinese: {'event': {'p': 0.604516806722689, 'r': 0.6863446630888491, 'f1': 0.6428371963138788}, 'argument': {'p': 0.6719560063148993, 'r': 0.6861902825626217, 'f1': 0.6789985520984669}, 'combined': 0.43648552553216163, 'epoch': 28}
Dev Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6582688741490995, 'r': 0.6702026825999534, 'f1': 0.6641821770070684}, 'combined': 0.43916718713317837, 'epoch': 28}
Test Korean: {'event': {'p': 0.6262823644357597, 'r': 0.7644603458556947, 'f1': 0.6885069817400644}, 'argument': {'p': 0.6737495770628111, 'r': 0.7022804512043116, 'f1': 0.6877192317877708}, 'combined': 0.4734994925627939, 'epoch': 28}
Dev Russian: {'event': {'p': 0.5285067873303168, 'r': 0.7776298268974701, 'f1': 0.6293103448275863}, 'argument': {'p': 0.6570534991714951, 'r': 0.6705006067762752, 'f1': 0.6637089485169277}, 'combined': 0.41767890725634255, 'epoch': 28}
Test Russian: {'event': {'p': 0.5670194003527337, 'r': 0.7668455575432319, 'f1': 0.6519645120405577}, 'argument': {'p': 0.6783821952170629, 'r': 0.7130643115369536, 'f1': 0.6952910236123037}, 'combined': 0.4533050729355754, 'epoch': 28}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.636034255599473, 'r': 0.6649449035812672, 'f1': 0.6501683501683502}, 'combined': 0.47718777994007355, 'epoch': 28}
Korean: {'event': {'p': 0.6938775510204082, 'r': 0.5396825396825397, 'f1': 0.6071428571428572}, 'argument': {'p': 0.6528440204910793, 'r': 0.48253688471079775, 'f1': 0.5549174174174174}, 'combined': 0.3369141462891463, 'epoch': 28}
Russian: {'event': {'p': 0.4074074074074074, 'r': 0.6111111111111112, 'f1': 0.4888888888888889}, 'argument': {'p': 0.5411343436698406, 'r': 0.5017791186756704, 'f1': 0.5207141797577711}, 'combined': 0.2545713767704659, 'epoch': 28}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6659933361258289, 'r': 0.6597103801246419, 'f1': 0.6628369696039056}, 'combined': 0.4390520581371665, 'epoch': 15}
Test for Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6899086709927581, 'r': 0.6875148171725195, 'f1': 0.6887096639176925}, 'combined': 0.4712224016278948, 'epoch': 15}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.632716049382716, 'r': 0.5099502487562189, 'f1': 0.5647382920110192}, 'combined': 0.3630460448642266, 'epoch': 15}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 29
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 03:00:55.223026: step: 3078/1538, loss: 3.0517578125e-05
==================================================
Loss: 0.066
--------------------
Dev Chinese: {'event': {'p': 0.6333754740834386, 'r': 0.6671105193075899, 'f1': 0.649805447470817}, 'argument': {'p': 0.678765982372494, 'r': 0.6677738207146399, 'f1': 0.6732250355776165}, 'combined': 0.4374652954920698, 'epoch': 29}
Test Chinese: {'event': {'p': 0.6071428571428571, 'r': 0.6893261776982708, 'f1': 0.6456297123708461}, 'argument': {'p': 0.6821948973802955, 'r': 0.6849326368444266, 'f1': 0.6835610258840609}, 'combined': 0.4413273085294467, 'epoch': 29}
Dev Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Dev Russian: {'event': {'p': 0.530316742081448, 'r': 0.7802929427430093, 'f1': 0.6314655172413793}, 'argument': {'p': 0.6629238246683722, 'r': 0.6690142909020745, 'f1': 0.665955133047088}, 'combined': 0.420527702549131, 'epoch': 29}
Test Russian: {'event': {'p': 0.5731922398589065, 'r': 0.7751937984496124, 'f1': 0.6590621039290241}, 'argument': {'p': 0.6834521741256784, 'r': 0.7069873495728974, 'f1': 0.6950205786148176}, 'combined': 0.4580617248158494, 'epoch': 29}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.659018759018759, 'r': 0.6290633608815426, 'f1': 0.6436927413671599}, 'combined': 0.47243503953553023, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
Russian: {'event': {'p': 0.42592592592592593, 'r': 0.6388888888888888, 'f1': 0.5111111111111111}, 'argument': {'p': 0.6034510976917432, 'r': 0.5156763925729443, 'f1': 0.5561215998335672}, 'combined': 0.2842399288038232, 'epoch': 29}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 30
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 03:27:31.766370: step: 3076/1538, loss: 0.00014796432515140623
2023-01-22 03:27:32.328826: step: 3078/1538, loss: -3.051757857974735e-06
==================================================
Loss: 0.072
--------------------
Dev Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6715366663412474, 'r': 0.6715366663412474, 'f1': 0.6715366663412474}, 'combined': 0.4354971895857636, 'epoch': 30}
Test Chinese: {'event': {'p': 0.6055672268907563, 'r': 0.6875372689326178, 'f1': 0.6439542027366657}, 'argument': {'p': 0.6658981296485696, 'r': 0.6904619393982064, 'f1': 0.6779576080589167}, 'combined': 0.4365736509868366, 'epoch': 30}
Dev Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6567395240126546, 'r': 0.669607424700856, 'f1': 0.6631110537102262}, 'combined': 0.4384589443925093, 'epoch': 30}
Test Korean: {'event': {'p': 0.626770884220811, 'r': 0.7650566487775791, 'f1': 0.6890440386680988}, 'argument': {'p': 0.6616688712641807, 'r': 0.7100948621891693, 'f1': 0.6850270997796135}, 'combined': 0.4720138394292396, 'epoch': 30}
Dev Russian: {'event': {'p': 0.530316742081448, 'r': 0.7802929427430093, 'f1': 0.6314655172413793}, 'argument': {'p': 0.6507052708793634, 'r': 0.6718589404627208, 'f1': 0.6611129351562933}, 'combined': 0.41747002155343527, 'epoch': 30}
Test Russian: {'event': {'p': 0.5692239858906526, 'r': 0.7698270721526536, 'f1': 0.6544993662864386}, 'argument': {'p': 0.6693549823219591, 'r': 0.714313363337106, 'f1': 0.6911037752491282}, 'combined': 0.4523269829387197, 'epoch': 30}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6812121212121213, 'r': 0.6812121212121213, 'f1': 0.6812121212121213}, 'combined': 0.4999721990547679, 'epoch': 30}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.5975355054302423, 'r': 0.49361628709454797, 'f1': 0.5406273620559335}, 'combined': 0.34754616132167154, 'epoch': 30}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.487775419025419, 'r': 0.49664406300769937, 'f1': 0.4921697921697921}, 'combined': 0.27342766231655113, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6842044228520048, 'r': 0.6403665076814383, 'f1': 0.66156003684224}, 'combined': 0.43074337029157517, 'epoch': 16}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6828325134004063, 'r': 0.6778483344704763, 'f1': 0.6803312954026025}, 'combined': 0.43392255758155374, 'epoch': 16}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6651776384535005, 'r': 0.7419289044289044, 'f1': 0.7014600550964186}, 'combined': 0.5148330679606743, 'epoch': 16}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 31
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 03:32:53.517971: step: 2/1538, loss: 0.0006561473128385842
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==================================================
Loss: 0.066
--------------------
Dev Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Dev Korean: {'event': {'p': 0.5910509885535901, 'r': 0.7563249001331558, 'f1': 0.6635514018691588}, 'argument': {'p': 0.6743481053974448, 'r': 0.6572202507610801, 'f1': 0.6656740209840267}, 'combined': 0.4417089298118308, 'epoch': 31}
Test Korean: {'event': {'p': 0.626770884220811, 'r': 0.7650566487775791, 'f1': 0.6890440386680988}, 'argument': {'p': 0.6720841879468042, 'r': 0.703832520548668, 'f1': 0.6875920687681083}, 'combined': 0.47378121602013046, 'epoch': 31}
Dev Russian: {'event': {'p': 0.5312217194570136, 'r': 0.7816245006657789, 'f1': 0.6325431034482759}, 'argument': {'p': 0.6649847671352664, 'r': 0.6574655047506981, 'f1': 0.6612037592197852}, 'combined': 0.41823987786854955, 'epoch': 31}
Test Russian: {'event': {'p': 0.5696649029982364, 'r': 0.7704233750745378, 'f1': 0.6550063371356146}, 'argument': {'p': 0.6760213868850368, 'r': 0.7096956013119271, 'f1': 0.6924493366994868}, 'combined': 0.4535587036835168, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.6820216049382716, 'r': 0.4580742122719735, 'f1': 0.5480530753968254}, 'combined': 0.3425331721230159, 'epoch': 31}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.5245973044049967, 'r': 0.495982905982906, 'f1': 0.5098889687674735}, 'combined': 0.283271649315263, 'epoch': 31}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
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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2023-01-22 04:20:40.262326: step: 3074/1538, loss: 0.0002882076078094542
2023-01-22 04:20:41.054857: step: 3076/1538, loss: 0.0004722372395917773
2023-01-22 04:20:41.620545: step: 3078/1538, loss: 0.00037494959542527795
==================================================
Loss: 0.056
--------------------
Dev Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6784302485268275, 'r': 0.6663350577103039, 'f1': 0.6723282593740924}, 'combined': 0.43949862869590467, 'epoch': 32}
Test Chinese: {'event': {'p': 0.6055672268907563, 'r': 0.6875372689326178, 'f1': 0.6439542027366657}, 'argument': {'p': 0.6830555734921288, 'r': 0.6873090331072812, 'f1': 0.6851757021664698}, 'combined': 0.4412217730231442, 'epoch': 32}
Dev Korean: {'event': {'p': 0.5910509885535901, 'r': 0.7563249001331558, 'f1': 0.6635514018691588}, 'argument': {'p': 0.6693559893842578, 'r': 0.6581838647428659, 'f1': 0.6637229166598385}, 'combined': 0.44041427180232273, 'epoch': 32}
Test Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.682449407026679, 'r': 0.701663311082696, 'f1': 0.691922997766895}, 'combined': 0.4771370188682563, 'epoch': 32}
Dev Russian: {'event': {'p': 0.5330316742081448, 'r': 0.7842876165113183, 'f1': 0.634698275862069}, 'argument': {'p': 0.6617496052954254, 'r': 0.6664229782141783, 'f1': 0.6640780697757047}, 'combined': 0.4214892059244505, 'epoch': 32}
Test Russian: {'event': {'p': 0.5692239858906526, 'r': 0.7698270721526536, 'f1': 0.6544993662864386}, 'argument': {'p': 0.690538412727102, 'r': 0.7110284679478585, 'f1': 0.7006336642658784}, 'combined': 0.45856428926096277, 'epoch': 32}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6858730158730157, 'r': 0.6924679487179486, 'f1': 0.6891547049441785}, 'combined': 0.505801618307654, 'epoch': 32}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.5756359835600906, 'r': 0.467182827237175, 'f1': 0.5157698412698413}, 'combined': 0.3315663265306122, 'epoch': 32}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.5423209372135852, 'r': 0.5226001758603639, 'f1': 0.532277956894815}, 'combined': 0.29570997605267496, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 33
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 04:26:01.781180: step: 2/1538, loss: 0.2673555612564087
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2023-01-22 04:47:13.528725: step: 3078/1538, loss: 0.14669901132583618
==================================================
Loss: 0.059
--------------------
Dev Chinese: {'event': {'p': 0.6359039190897599, 'r': 0.6697736351531292, 'f1': 0.6523994811932554}, 'argument': {'p': 0.6725296096604796, 'r': 0.6572572422067136, 'f1': 0.6648057257103183}, 'combined': 0.4337189105477173, 'epoch': 33}
Test Chinese: {'event': {'p': 0.6029411764705882, 'r': 0.6845557543231962, 'f1': 0.6411616866796984}, 'argument': {'p': 0.6743691860516342, 'r': 0.6795471578640966, 'f1': 0.676948270536625}, 'combined': 0.43403329493216725, 'epoch': 33}
Dev Korean: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6643372761568964, 'r': 0.6590302971897752, 'f1': 0.6616731456291072}, 'combined': 0.4351892301275553, 'epoch': 33}
Test Korean: {'event': {'p': 0.6233512457254519, 'r': 0.7608825283243887, 'f1': 0.6852846401718582}, 'argument': {'p': 0.6804684308803018, 'r': 0.6977464119078712, 'f1': 0.6889991187481126}, 'combined': 0.4721605131700277, 'epoch': 33}
Dev Russian: {'event': {'p': 0.5266968325791855, 'r': 0.7749667110519307, 'f1': 0.6271551724137931}, 'argument': {'p': 0.6560610150811821, 'r': 0.658379251883589, 'f1': 0.6572180891818544}, 'combined': 0.4121777240343096, 'epoch': 33}
Test Russian: {'event': {'p': 0.5687830687830688, 'r': 0.7692307692307693, 'f1': 0.6539923954372625}, 'argument': {'p': 0.6843515081871056, 'r': 0.7058176993746996, 'f1': 0.6949188695085579}, 'combined': 0.45447165610445617, 'epoch': 33}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6434469696969696, 'r': 0.7019421487603305, 'f1': 0.6714229249011857}, 'combined': 0.4804677811219494, 'epoch': 33}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.5843445980542755, 'r': 0.5489297739297739, 'f1': 0.5660838293650794}, 'combined': 0.3639110331632653, 'epoch': 33}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.5213176638176638, 'r': 0.5687101787101786, 'f1': 0.5439836492010405}, 'combined': 0.30221313844502246, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 34
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 04:52:33.114917: step: 2/1538, loss: 0.27409520745277405
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2023-01-22 05:13:44.456397: step: 3078/1538, loss: 0.0006123881321400404
==================================================
Loss: 0.058
--------------------
Dev Chinese: {'event': {'p': 0.6359039190897599, 'r': 0.6697736351531292, 'f1': 0.6523994811932554}, 'argument': {'p': 0.687047364686338, 'r': 0.6642385048060628, 'f1': 0.6754504351053829}, 'combined': 0.4406635134345105, 'epoch': 34}
Test Chinese: {'event': {'p': 0.6034663865546218, 'r': 0.6851520572450805, 'f1': 0.6417201898910918}, 'argument': {'p': 0.6873638035425053, 'r': 0.6841002062283882, 'f1': 0.6857281217841211}, 'combined': 0.44004558052496795, 'epoch': 34}
Dev Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6717384136586199, 'r': 0.6590641039669477, 'f1': 0.6653409049571091}, 'combined': 0.4399333553805183, 'epoch': 34}
Test Korean: {'event': {'p': 0.6233512457254519, 'r': 0.7608825283243887, 'f1': 0.6852846401718582}, 'argument': {'p': 0.6916494944378743, 'r': 0.7065443428766197, 'f1': 0.6990175818356661}, 'combined': 0.47902601204205686, 'epoch': 34}
Dev Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6623985840719213, 'r': 0.6623985840719213, 'f1': 0.6623985840719213}, 'combined': 0.41970944766626045, 'epoch': 34}
Test Russian: {'event': {'p': 0.5652557319223986, 'r': 0.7644603458556947, 'f1': 0.649936628643853}, 'argument': {'p': 0.6947551908100725, 'r': 0.7158514760308492, 'f1': 0.7051455809935874}, 'combined': 0.4582999416140832, 'epoch': 34}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6960784313725489, 'r': 0.6454545454545453, 'f1': 0.6698113207547167}, 'combined': 0.4793145231088798, 'epoch': 34}
Korean: {'event': {'p': 0.6938775510204082, 'r': 0.5396825396825397, 'f1': 0.6071428571428572}, 'argument': {'p': 0.6718235596707819, 'r': 0.5182638888888889, 'f1': 0.5851366487455196}, 'combined': 0.35526153673835126, 'epoch': 34}
Russian: {'event': {'p': 0.4444444444444444, 'r': 0.6666666666666666, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5585501644325174, 'r': 0.5179283342919706, 'f1': 0.5374727997369507}, 'combined': 0.28665215985970705, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 35
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 05:19:03.145580: step: 2/1538, loss: 0.0003742890839930624
2023-01-22 05:19:03.979102: step: 4/1538, loss: 0.38349518179893494
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2023-01-22 05:40:08.399358: step: 3062/1538, loss: 0.051402170211076736
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2023-01-22 05:40:10.014420: step: 3066/1538, loss: 0.00020783219952136278
2023-01-22 05:40:10.814654: step: 3068/1538, loss: 0.004308525938540697
2023-01-22 05:40:11.635037: step: 3070/1538, loss: 0.009269160218536854
2023-01-22 05:40:12.456614: step: 3072/1538, loss: 0.00026435969630256295
2023-01-22 05:40:13.268751: step: 3074/1538, loss: 0.0020877730567008257
2023-01-22 05:40:14.091315: step: 3076/1538, loss: 0.7954326272010803
2023-01-22 05:40:14.654692: step: 3078/1538, loss: 6.111462880653562e-06
==================================================
Loss: 0.053
--------------------
Dev Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6946707019911553, 'r': 0.6580794575588821, 'f1': 0.6758801919497798}, 'combined': 0.44006725857171136, 'epoch': 35}
Test Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6959132809462611, 'r': 0.6753331510606653, 'f1': 0.6854687792311144}, 'combined': 0.44026199168711616, 'epoch': 35}
Dev Korean: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.6802010948616913, 'r': 0.6456480639180641, 'f1': 0.6624743351296294}, 'combined': 0.4372640179301876, 'epoch': 35}
Test Korean: {'event': {'p': 0.6233512457254519, 'r': 0.7608825283243887, 'f1': 0.6852846401718582}, 'argument': {'p': 0.6990807746809595, 'r': 0.6947054429896322, 'f1': 0.6968862413806007}, 'combined': 0.4775654371652237, 'epoch': 35}
Dev Russian: {'event': {'p': 0.5312217194570136, 'r': 0.7816245006657789, 'f1': 0.6325431034482759}, 'argument': {'p': 0.6747918111503913, 'r': 0.6543002519134797, 'f1': 0.6643880650480367}, 'combined': 0.42025408855948015, 'epoch': 35}
Test Russian: {'event': {'p': 0.5709876543209876, 'r': 0.7722122838401908, 'f1': 0.6565272496831432}, 'argument': {'p': 0.7035303629306487, 'r': 0.7028105160381549, 'f1': 0.7031702552546903}, 'combined': 0.46165043374135567, 'epoch': 35}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.6822582258225821, 'r': 0.6625777000777, 'f1': 0.6722739591032273}, 'combined': 0.4934120800757632, 'epoch': 35}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.6640931372549019, 'r': 0.49806985294117645, 'f1': 0.5692226890756302}, 'combined': 0.3659288715486194, 'epoch': 35}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.5447923202779602, 'r': 0.5249816904496708, 'f1': 0.5347035736061461}, 'combined': 0.29705754089230335, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
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
2023-01-22 05:45:32.691524: step: 2/1538, loss: 7.177233055699617e-05
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2023-01-22 05:45:35.939114: step: 10/1538, loss: 6.220934301381931e-05
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2023-01-22 06:06:43.268415: step: 3078/1538, loss: 2.7020718107451103e-07
==================================================
Loss: 0.048
--------------------
Dev Chinese: {'event': {'p': 0.6321112515802781, 'r': 0.6657789613848203, 'f1': 0.648508430609598}, 'argument': {'p': 0.6708116394734648, 'r': 0.6773296473064053, 'f1': 0.6740548867231351}, 'combined': 0.43713027673355065, 'epoch': 36}
Test Chinese: {'event': {'p': 0.5982142857142857, 'r': 0.6791890280262374, 'f1': 0.6361351577771573}, 'argument': {'p': 0.6653862939378787, 'r': 0.6969672425366097, 'f1': 0.6808107265719593}, 'combined': 0.43308763896423447, 'epoch': 36}
Dev Korean: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6582520452415649, 'r': 0.6697082084654935, 'f1': 0.6639307113399824}, 'combined': 0.4374496742941005, 'epoch': 36}
Test Korean: {'event': {'p': 0.6223742061553493, 'r': 0.7596899224806202, 'f1': 0.6842105263157894}, 'argument': {'p': 0.6692722584170099, 'r': 0.71510007729367, 'f1': 0.6914276331285123}, 'combined': 0.47308206477213993, 'epoch': 36}
Dev Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6491990464094393, 'r': 0.6744329316055659, 'f1': 0.6615754580394286}, 'combined': 0.41918789798187933, 'epoch': 36}
Test Russian: {'event': {'p': 0.5679012345679012, 'r': 0.7680381633870006, 'f1': 0.6529784537389101}, 'argument': {'p': 0.6703173375779496, 'r': 0.7263104326023606, 'f1': 0.6971914576412122}, 'combined': 0.45525099997053553, 'epoch': 36}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6686914186914187, 'r': 0.6747704315886134, 'f1': 0.6717171717171716}, 'combined': 0.480678343063664, 'epoch': 36}
Korean: {'event': {'p': 0.6938775510204082, 'r': 0.5396825396825397, 'f1': 0.6071428571428572}, 'argument': {'p': 0.5898401027077498, 'r': 0.43596877156659763, 'f1': 0.5013640873015873}, 'combined': 0.30439962443310664, 'epoch': 36}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.49799988848780774, 'r': 0.5704362359042161, 'f1': 0.5317625927920658}, 'combined': 0.2954236626622588, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 37
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 06:33:16.285222: step: 3078/1538, loss: 3.7515161238843575e-05
==================================================
Loss: 0.050
--------------------
Dev Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6664331015833134, 'r': 0.6766776233714065, 'f1': 0.671516292643162}, 'combined': 0.4372259129790757, 'epoch': 37}
Test Chinese: {'event': {'p': 0.6050420168067226, 'r': 0.6869409660107334, 'f1': 0.6433956995252722}, 'argument': {'p': 0.6658490730149295, 'r': 0.6991536816323246, 'f1': 0.6820950788925867}, 'combined': 0.43885704042684154, 'epoch': 37}
Dev Korean: {'event': {'p': 0.5931321540062435, 'r': 0.758988015978695, 'f1': 0.6658878504672897}, 'argument': {'p': 0.6615686025074905, 'r': 0.6754913089463275, 'f1': 0.6684574676682508}, 'combined': 0.4451177062744193, 'epoch': 37}
Test Korean: {'event': {'p': 0.625305324865657, 'r': 0.7632677400119261, 'f1': 0.6874328678839957}, 'argument': {'p': 0.6642455532542465, 'r': 0.7186956923886689, 'f1': 0.6903986981604864}, 'combined': 0.4746027570598403, 'epoch': 37}
Dev Russian: {'event': {'p': 0.5330316742081448, 'r': 0.7842876165113183, 'f1': 0.634698275862069}, 'argument': {'p': 0.6534688359061585, 'r': 0.6793305000833633, 'f1': 0.6661487578780035}, 'combined': 0.42280346809282765, 'epoch': 37}
Test Russian: {'event': {'p': 0.5701058201058201, 'r': 0.7710196779964222, 'f1': 0.6555133079847908}, 'argument': {'p': 0.6708251301552737, 'r': 0.7264221651579106, 'f1': 0.6975175334019956}, 'combined': 0.45723202569773397, 'epoch': 37}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6420786815523657, 'r': 0.665426997245179, 'f1': 0.6535443722943722}, 'combined': 0.467673954485881, 'epoch': 37}
Korean: {'event': {'p': 0.7551020408163265, 'r': 0.5873015873015873, 'f1': 0.6607142857142858}, 'argument': {'p': 0.635630641145347, 'r': 0.4987255799755799, 'f1': 0.5589165982484948}, 'combined': 0.3692841809856127, 'epoch': 37}
Russian: {'event': {'p': 0.4444444444444444, 'r': 0.6666666666666666, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5433309745809746, 'r': 0.553209719573356, 'f1': 0.5482258482258483}, 'combined': 0.2923871190537858, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
Epoch: 38
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 06:38:38.877964: step: 2/1538, loss: 0.12221691757440567
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2023-01-22 06:59:49.393015: step: 3078/1538, loss: 0.00012169002729933709
==================================================
Loss: 0.054
--------------------
Dev Chinese: {'event': {'p': 0.6333754740834386, 'r': 0.6671105193075899, 'f1': 0.649805447470817}, 'argument': {'p': 0.6888346525862704, 'r': 0.6509376005022361, 'f1': 0.6693501449423992}, 'combined': 0.43494737044895193, 'epoch': 38}
Test Chinese: {'event': {'p': 0.6039915966386554, 'r': 0.6857483601669648, 'f1': 0.6422786931024853}, 'argument': {'p': 0.6800538147199343, 'r': 0.6830343281518697, 'f1': 0.681540812857077}, 'combined': 0.43773914257784896, 'epoch': 38}
Dev Korean: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.6807426557116677, 'r': 0.6422100525581771, 'f1': 0.6609151997200656}, 'combined': 0.4362349157030807, 'epoch': 38}
Test Korean: {'event': {'p': 0.6228627259404006, 'r': 0.7602862254025045, 'f1': 0.6847475832438238}, 'argument': {'p': 0.6837501699890576, 'r': 0.7017873143553084, 'f1': 0.6926513369844616}, 'combined': 0.47429132903071347, 'epoch': 38}
Dev Russian: {'event': {'p': 0.530316742081448, 'r': 0.7802929427430093, 'f1': 0.6314655172413793}, 'argument': {'p': 0.6731240820696077, 'r': 0.647929466003443, 'f1': 0.660286523109662}, 'combined': 0.41694817084295466, 'epoch': 38}
Test Russian: {'event': {'p': 0.5701058201058201, 'r': 0.7710196779964222, 'f1': 0.6555133079847908}, 'argument': {'p': 0.6839839155761804, 'r': 0.7061533021267561, 'f1': 0.6948918343238282}, 'combined': 0.4555108450092318, 'epoch': 38}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.68016801680168, 'r': 0.6605477855477855, 'f1': 0.6702143385070214}, 'combined': 0.49190043193175886, 'epoch': 38}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.6218779178338002, 'r': 0.45964889579020013, 'f1': 0.5285962301587301}, 'combined': 0.3303726438492064, 'epoch': 38}
Russian: {'event': {'p': 0.46296296296296297, 'r': 0.6944444444444444, 'f1': 0.5555555555555555}, 'argument': {'p': 0.5464193710362293, 'r': 0.5364844733810251, 'f1': 0.5414063492836033}, 'combined': 0.30078130515755735, 'epoch': 38}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}
******************************
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
2023-01-22 07:05:07.816903: step: 2/1538, loss: 0.11957941949367523
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2023-01-22 07:26:18.423834: step: 3078/1538, loss: 3.058910442632623e-05
==================================================
Loss: 0.047
--------------------
Dev Chinese: {'event': {'p': 0.6346396965865992, 'r': 0.6684420772303595, 'f1': 0.6511024643320363}, 'argument': {'p': 0.6909837030768384, 'r': 0.6612579431230459, 'f1': 0.6757940986541622}, 'combined': 0.44001120301477226, 'epoch': 39}
Test Chinese: {'event': {'p': 0.6008403361344538, 'r': 0.6821705426356589, 'f1': 0.6389276738341245}, 'argument': {'p': 0.6864809069465384, 'r': 0.6819629338843674, 'f1': 0.6842144622794367}, 'combined': 0.43716355478786684, 'epoch': 39}
Dev Korean: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6900836869126673, 'r': 0.6574615489858867, 'f1': 0.6733777503579845}, 'combined': 0.44524743773670467, 'epoch': 39}
Test Korean: {'event': {'p': 0.6248168050806058, 'r': 0.7626714370900417, 'f1': 0.6868958109559614}, 'argument': {'p': 0.6891391852506161, 'r': 0.698005023169348, 'f1': 0.6935437715097753}, 'combined': 0.47639231136466315, 'epoch': 39}
Dev Russian: {'event': {'p': 0.5339366515837104, 'r': 0.7856191744340879, 'f1': 0.6357758620689654}, 'argument': {'p': 0.6819062734894792, 'r': 0.6539553449648221, 'f1': 0.6676383933082419}, 'combined': 0.4244683750558865, 'epoch': 39}
Test Russian: {'event': {'p': 0.5692239858906526, 'r': 0.7698270721526536, 'f1': 0.6544993662864386}, 'argument': {'p': 0.6940638411836948, 'r': 0.7011702968954733, 'f1': 0.6975989711048343}, 'combined': 0.4565780845101856, 'epoch': 39}
Chinese: {'event': {'p': 0.7090909090909091, 'r': 0.7222222222222222, 'f1': 0.7155963302752293}, 'argument': {'p': 0.6753150553150552, 'r': 0.6446189164370981, 'f1': 0.6596100540286586}, 'combined': 0.47201453407555377, 'epoch': 39}
Korean: {'event': {'p': 0.7346938775510204, 'r': 0.5714285714285714, 'f1': 0.6428571428571428}, 'argument': {'p': 0.601428952991453, 'r': 0.466780679933665, 'f1': 0.5256185807656396}, 'combined': 0.33789765906362546, 'epoch': 39}
Russian: {'event': {'p': 0.42592592592592593, 'r': 0.6388888888888888, 'f1': 0.5111111111111111}, 'argument': {'p': 0.5665773651635719, 'r': 0.5150703319668836, 'f1': 0.5395974906319733}, 'combined': 0.2757942729896752, 'epoch': 39}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6371681415929203, 'r': 0.6711051930758988, 'f1': 0.6536964980544746}, 'argument': {'p': 0.6813504222578696, 'r': 0.6620408961210069, 'f1': 0.6715568843609392}, 'combined': 0.4389943835511197, 'epoch': 31}
Test for Chinese: {'event': {'p': 0.5997899159663865, 'r': 0.6809779367918902, 'f1': 0.6378106674113375}, 'argument': {'p': 0.6731620076950452, 'r': 0.6872016104900023, 'f1': 0.680109361386629}, 'combined': 0.4337810056987044, 'epoch': 31}
Chinese: {'event': {'p': 0.7272727272727273, 'r': 0.7407407407407407, 'f1': 0.7339449541284404}, 'argument': {'p': 0.699354657687991, 'r': 0.7262529137529138, 'f1': 0.7125500285877645}, 'combined': 0.5229724980460657, 'epoch': 31}
--------------------
Dev for Korean: {'event': {'p': 0.5920915712799167, 'r': 0.7576564580559254, 'f1': 0.6647196261682243}, 'argument': {'p': 0.6623423823997137, 'r': 0.6642677963020384, 'f1': 0.6633036920984069}, 'combined': 0.440910982247656, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6272594040058622, 'r': 0.7656529516994633, 'f1': 0.6895810955961332}, 'argument': {'p': 0.6769761165667345, 'r': 0.6993069259673733, 'f1': 0.6879603575699523}, 'combined': 0.4744044570997953, 'epoch': 29}
Korean: {'event': {'p': 0.7142857142857143, 'r': 0.5555555555555556, 'f1': 0.6250000000000001}, 'argument': {'p': 0.7329931972789115, 'r': 0.5205314009661836, 'f1': 0.6087570621468926}, 'combined': 0.38047316384180796, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.5321266968325792, 'r': 0.7829560585885486, 'f1': 0.6336206896551724}, 'argument': {'p': 0.6761366762011285, 'r': 0.6685021054042208, 'f1': 0.6722977170743655}, 'combined': 0.4259817431462574, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5714285714285714, 'r': 0.7728085867620751, 'f1': 0.6570342205323193}, 'argument': {'p': 0.7103498031185339, 'r': 0.6982566842367066, 'f1': 0.7042513329681345}, 'combined': 0.4627172256155651, 'epoch': 5}
Russian: {'event': {'p': 0.48148148148148145, 'r': 0.7222222222222222, 'f1': 0.5777777777777777}, 'argument': {'p': 0.5511344211344211, 'r': 0.5511344211344211, 'f1': 0.5511344211344211}, 'combined': 0.31843322109988775, 'epoch': 5}