Command that produces this log: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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> trainable params:
>>> pad_vector: torch.Size([1, 1, 1024])
>>> xlmr.embeddings.word_embeddings.weight: torch.Size([250002, 1024])
>>> xlmr.embeddings.position_embeddings.weight: torch.Size([514, 1024])
>>> xlmr.embeddings.token_type_embeddings.weight: torch.Size([1, 1024])
>>> xlmr.embeddings.LayerNorm.weight: torch.Size([1024])
>>> xlmr.embeddings.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.0.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.0.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.0.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.1.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.1.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.1.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.2.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.2.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.2.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.3.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.3.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.3.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.4.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.4.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.4.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.5.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.5.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.5.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.6.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.6.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.6.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.7.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.7.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.7.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.8.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.8.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.8.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.9.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.9.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.9.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.10.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.10.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.10.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.11.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.11.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.11.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.12.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.12.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.12.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.13.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.13.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.13.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.14.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.14.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.14.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.15.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.15.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.15.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.16.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.16.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.16.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.17.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.17.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.17.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.18.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.18.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.18.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.19.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.19.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.19.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.20.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.20.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.20.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.21.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.21.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.21.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.22.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.22.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.22.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.23.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.23.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.23.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.pooler.dense.weight: torch.Size([1024, 1024])
>>> xlmr.pooler.dense.bias: torch.Size([1024])
>>> upos_embedding.weight: torch.Size([19, 50])
>>> dist_embedding.weight: torch.Size([104, 50])
>>> deprel_embedding.weight: torch.Size([38, 50])
>>> gcn_layer.W.0.weight: torch.Size([450, 50])
>>> gcn_layer.W.0.bias: torch.Size([450])
>>> gcn_layer.W.1.weight: torch.Size([450, 450])
>>> gcn_layer.W.1.bias: torch.Size([450])
>>> trans_rep.weight: torch.Size([1024, 2924])
>>> trans_rep.bias: torch.Size([1024])
>>> mention_label_ffn.layers.0.weight: torch.Size([350, 1024])
>>> mention_label_ffn.layers.0.bias: torch.Size([350])
>>> mention_label_ffn.layers.1.weight: torch.Size([11, 350])
>>> mention_label_ffn.layers.1.bias: torch.Size([11])
>>> trigger_label_ffn.layers.0.weight: torch.Size([450, 1024])
>>> trigger_label_ffn.layers.0.bias: torch.Size([450])
>>> trigger_label_ffn.layers.1.weight: torch.Size([233, 450])
>>> trigger_label_ffn.layers.1.bias: torch.Size([233])
>>> event_type_ffn.layers.0.weight: torch.Size([450, 1024])
>>> event_type_ffn.layers.0.bias: torch.Size([450])
>>> event_type_ffn.layers.1.weight: torch.Size([117, 450])
>>> event_type_ffn.layers.1.bias: torch.Size([117])
>>> role_type_ffn.layers.0.weight: torch.Size([350, 1024])
>>> role_type_ffn.layers.0.bias: torch.Size([350])
>>> role_type_ffn.layers.1.weight: torch.Size([6, 350])
>>> role_type_ffn.layers.1.bias: torch.Size([6])
>>> trigger_crf.transition: torch.Size([235, 235])
>>> mention_crf.transition: torch.Size([13, 13])
n_trainable_params: 564979817, n_nontrainable_params: 0
----------------------------------------------------------------------------------------------------
******************************
Epoch: 0
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 05:07:36.148378: step: 3038/1519, loss: 3.695240020751953
==================================================
Loss: 4.177
--------------------
Dev Chinese: {'event': {'p': 0.6035242290748899, 'r': 0.729693741677763, 'f1': 0.6606389391199519}, 'argument': {'p': 0.5594393803071988, 'r': 0.48836647240784437, 'f1': 0.5214924806512959}, 'combined': 0.34451823917650415, 'epoch': 0}
Test Chinese: {'event': {'p': 0.6024978466838932, 'r': 0.7301670146137788, 'f1': 0.6602170835299669}, 'argument': {'p': 0.6023239095669729, 'r': 0.4975638921668276, 'f1': 0.5449549096132565}, 'combined': 0.359788541080201, 'epoch': 0}
Dev Korean: {'event': {'p': 0.5948827292110874, 'r': 0.7430093209054593, 'f1': 0.6607460035523979}, 'argument': {'p': 0.5577900477246787, 'r': 0.4849274363244808, 'f1': 0.5188130092535899}, 'combined': 0.3428036224553027, 'epoch': 0}
Test Korean: {'event': {'p': 0.5963777490297542, 'r': 0.7218162839248434, 'f1': 0.6531286894923258}, 'argument': {'p': 0.598276856807883, 'r': 0.4986858711755384, 'f1': 0.5439605338093328}, 'combined': 0.35527623058243557, 'epoch': 0}
Dev Russian: {'event': {'p': 0.6035242290748899, 'r': 0.729693741677763, 'f1': 0.6606389391199519}, 'argument': {'p': 0.5594393803071988, 'r': 0.48836647240784437, 'f1': 0.5214924806512959}, 'combined': 0.34451823917650415, 'epoch': 0}
Test Russian: {'event': {'p': 0.6024978466838932, 'r': 0.7301670146137788, 'f1': 0.6602170835299669}, 'argument': {'p': 0.6023239095669729, 'r': 0.4975638921668276, 'f1': 0.5449549096132565}, 'combined': 0.359788541080201, 'epoch': 0}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.5480763865860271, 'r': 0.46129762537657276, 'f1': 0.500956697241527}, 'combined': 0.3600626261423475, 'epoch': 0}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.5045474137931035, 'r': 0.200217227695676, 'f1': 0.2866746669278997}, 'combined': 0.16158026681390708, 'epoch': 0}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.4258404746209624, 'r': 0.3294237633860275, 'f1': 0.3714778608395629}, 'combined': 0.20802760207015522, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6035242290748899, 'r': 0.729693741677763, 'f1': 0.6606389391199519}, 'argument': {'p': 0.5594393803071988, 'r': 0.48836647240784437, 'f1': 0.5214924806512959}, 'combined': 0.34451823917650415, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.6024978466838932, 'r': 0.7301670146137788, 'f1': 0.6602170835299669}, 'argument': {'p': 0.6023239095669729, 'r': 0.4975638921668276, 'f1': 0.5449549096132565}, 'combined': 0.359788541080201, 'epoch': 0}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.5480763865860271, 'r': 0.46129762537657276, 'f1': 0.500956697241527}, 'combined': 0.3600626261423475, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.5948827292110874, 'r': 0.7430093209054593, 'f1': 0.6607460035523979}, 'argument': {'p': 0.5577900477246787, 'r': 0.4849274363244808, 'f1': 0.5188130092535899}, 'combined': 0.3428036224553027, 'epoch': 0}
Test for Korean: {'event': {'p': 0.5963777490297542, 'r': 0.7218162839248434, 'f1': 0.6531286894923258}, 'argument': {'p': 0.598276856807883, 'r': 0.4986858711755384, 'f1': 0.5439605338093328}, 'combined': 0.35527623058243557, 'epoch': 0}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.5045474137931035, 'r': 0.200217227695676, 'f1': 0.2866746669278997}, 'combined': 0.16158026681390708, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.6035242290748899, 'r': 0.729693741677763, 'f1': 0.6606389391199519}, 'argument': {'p': 0.5594393803071988, 'r': 0.48836647240784437, 'f1': 0.5214924806512959}, 'combined': 0.34451823917650415, 'epoch': 0}
Test for Russian: {'event': {'p': 0.6024978466838932, 'r': 0.7301670146137788, 'f1': 0.6602170835299669}, 'argument': {'p': 0.6023239095669729, 'r': 0.4975638921668276, 'f1': 0.5449549096132565}, 'combined': 0.359788541080201, 'epoch': 0}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.4258404746209624, 'r': 0.3294237633860275, 'f1': 0.3714778608395629}, 'combined': 0.20802760207015522, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 05:13:35.349155: step: 2/1519, loss: 1.8502222299575806
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2023-01-23 05:34:42.152531: step: 3038/1519, loss: 1.0663524866104126
==================================================
Loss: 1.764
--------------------
Dev Chinese: {'event': {'p': 0.6046255506607929, 'r': 0.7310252996005326, 'f1': 0.6618444846292948}, 'argument': {'p': 0.6144049924497929, 'r': 0.6637034613833572, 'f1': 0.6381034707299992}, 'combined': 0.4223252627254606, 'epoch': 1}
Test Chinese: {'event': {'p': 0.6093884582256676, 'r': 0.738517745302714, 'f1': 0.6677678150070788}, 'argument': {'p': 0.6403312443917345, 'r': 0.6788651479535337, 'f1': 0.6590354059268732}, 'combined': 0.4400826330280913, 'epoch': 1}
Dev Korean: {'event': {'p': 0.6076759061833689, 'r': 0.758988015978695, 'f1': 0.6749555950266429}, 'argument': {'p': 0.6087043830341977, 'r': 0.647195983726066, 'f1': 0.6273603263488595}, 'combined': 0.42344036236690336, 'epoch': 1}
Test Korean: {'event': {'p': 0.6011211729193618, 'r': 0.7275574112734864, 'f1': 0.658323494687131}, 'argument': {'p': 0.6377490459869358, 'r': 0.6873322286256508, 'f1': 0.6616129613789042}, 'combined': 0.4355553568652621, 'epoch': 1}
Dev Russian: {'event': {'p': 0.6046255506607929, 'r': 0.7310252996005326, 'f1': 0.6618444846292948}, 'argument': {'p': 0.6144049924497929, 'r': 0.6637034613833572, 'f1': 0.6381034707299992}, 'combined': 0.4223252627254606, 'epoch': 1}
Test Russian: {'event': {'p': 0.6093884582256676, 'r': 0.738517745302714, 'f1': 0.6677678150070788}, 'argument': {'p': 0.6403312443917345, 'r': 0.6788651479535337, 'f1': 0.6590354059268732}, 'combined': 0.4400826330280913, 'epoch': 1}
Chinese: {'event': {'p': 0.581081081081081, 'r': 0.7962962962962963, 'f1': 0.6718749999999999}, 'argument': {'p': 0.5854779516196839, 'r': 0.6196308321308321, 'f1': 0.6020704441757072}, 'combined': 0.4045160796805532, 'epoch': 1}
Korean: {'event': {'p': 0.7021276595744681, 'r': 0.5238095238095238, 'f1': 0.6}, 'argument': {'p': 0.6111557666544879, 'r': 0.3351499365524611, 'f1': 0.43290200138026225}, 'combined': 0.2597412008281573, 'epoch': 1}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.5214004394739251, 'r': 0.5314273710022698, 'f1': 0.5263661579451054}, 'combined': 0.2666921866921868, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6046255506607929, 'r': 0.7310252996005326, 'f1': 0.6618444846292948}, 'argument': {'p': 0.6144049924497929, 'r': 0.6637034613833572, 'f1': 0.6381034707299992}, 'combined': 0.4223252627254606, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.6093884582256676, 'r': 0.738517745302714, 'f1': 0.6677678150070788}, 'argument': {'p': 0.6403312443917345, 'r': 0.6788651479535337, 'f1': 0.6590354059268732}, 'combined': 0.4400826330280913, 'epoch': 1}
Chinese: {'event': {'p': 0.581081081081081, 'r': 0.7962962962962963, 'f1': 0.6718749999999999}, 'argument': {'p': 0.5854779516196839, 'r': 0.6196308321308321, 'f1': 0.6020704441757072}, 'combined': 0.4045160796805532, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.6076759061833689, 'r': 0.758988015978695, 'f1': 0.6749555950266429}, 'argument': {'p': 0.6087043830341977, 'r': 0.647195983726066, 'f1': 0.6273603263488595}, 'combined': 0.42344036236690336, 'epoch': 1}
Test for Korean: {'event': {'p': 0.6011211729193618, 'r': 0.7275574112734864, 'f1': 0.658323494687131}, 'argument': {'p': 0.6377490459869358, 'r': 0.6873322286256508, 'f1': 0.6616129613789042}, 'combined': 0.4355553568652621, 'epoch': 1}
Korean: {'event': {'p': 0.7021276595744681, 'r': 0.5238095238095238, 'f1': 0.6}, 'argument': {'p': 0.6111557666544879, 'r': 0.3351499365524611, 'f1': 0.43290200138026225}, 'combined': 0.2597412008281573, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.6046255506607929, 'r': 0.7310252996005326, 'f1': 0.6618444846292948}, 'argument': {'p': 0.6144049924497929, 'r': 0.6637034613833572, 'f1': 0.6381034707299992}, 'combined': 0.4223252627254606, 'epoch': 1}
Test for Russian: {'event': {'p': 0.6093884582256676, 'r': 0.738517745302714, 'f1': 0.6677678150070788}, 'argument': {'p': 0.6403312443917345, 'r': 0.6788651479535337, 'f1': 0.6590354059268732}, 'combined': 0.4400826330280913, 'epoch': 1}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.5214004394739251, 'r': 0.5314273710022698, 'f1': 0.5263661579451054}, 'combined': 0.2666921866921868, '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
2023-01-23 05:40:51.054554: step: 2/1519, loss: 1.0281792879104614
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2023-01-23 06:01:58.553760: step: 3038/1519, loss: 0.3759325444698334
==================================================
Loss: 1.389
--------------------
Dev Chinese: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6305168279871929, 'r': 0.6675509018987084, 'f1': 0.6485055710030789}, 'combined': 0.4339006532931993, 'epoch': 2}
Test Chinese: {'event': {'p': 0.6085271317829457, 'r': 0.7374739039665971, 'f1': 0.6668239735724399}, 'argument': {'p': 0.6532405890587217, 'r': 0.6842995741213297, 'f1': 0.6684094716510965}, 'combined': 0.44571145985983934, 'epoch': 2}
Dev Korean: {'event': {'p': 0.605543710021322, 'r': 0.7563249001331558, 'f1': 0.6725873297809354}, 'argument': {'p': 0.628444952877954, 'r': 0.650179252204859, 'f1': 0.6391273806483929}, 'combined': 0.42986897834018606, 'epoch': 2}
Test Korean: {'event': {'p': 0.6058645968089694, 'r': 0.7332985386221295, 'f1': 0.6635182998819362}, 'argument': {'p': 0.652264351113527, 'r': 0.6923331575236183, 'f1': 0.6717017320733222}, 'combined': 0.44568639129304255, 'epoch': 2}
Dev Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6305168279871929, 'r': 0.6675509018987084, 'f1': 0.6485055710030789}, 'combined': 0.4339006532931993, 'epoch': 2}
Test Russian: {'event': {'p': 0.6085271317829457, 'r': 0.7374739039665971, 'f1': 0.6668239735724399}, 'argument': {'p': 0.6532405890587217, 'r': 0.6842995741213297, 'f1': 0.6684094716510965}, 'combined': 0.44571145985983934, 'epoch': 2}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.5887626262626262, 'r': 0.5887626262626262, 'f1': 0.5887626262626262}, 'combined': 0.4139737215909091, 'epoch': 2}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.6637445887445889, 'r': 0.45632440476190483, 'f1': 0.5408289241622575}, 'combined': 0.3146641013307679, 'epoch': 2}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.48622736986273873, 'r': 0.6011538391030224, 'f1': 0.5376172544823777}, 'combined': 0.28672920239060146, 'epoch': 2}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6305168279871929, 'r': 0.6675509018987084, 'f1': 0.6485055710030789}, 'combined': 0.4339006532931993, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.6085271317829457, 'r': 0.7374739039665971, 'f1': 0.6668239735724399}, 'argument': {'p': 0.6532405890587217, 'r': 0.6842995741213297, 'f1': 0.6684094716510965}, 'combined': 0.44571145985983934, 'epoch': 2}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.5887626262626262, 'r': 0.5887626262626262, 'f1': 0.5887626262626262}, 'combined': 0.4139737215909091, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.605543710021322, 'r': 0.7563249001331558, 'f1': 0.6725873297809354}, 'argument': {'p': 0.628444952877954, 'r': 0.650179252204859, 'f1': 0.6391273806483929}, 'combined': 0.42986897834018606, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6058645968089694, 'r': 0.7332985386221295, 'f1': 0.6635182998819362}, 'argument': {'p': 0.652264351113527, 'r': 0.6923331575236183, 'f1': 0.6717017320733222}, 'combined': 0.44568639129304255, 'epoch': 2}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.6637445887445889, 'r': 0.45632440476190483, 'f1': 0.5408289241622575}, 'combined': 0.3146641013307679, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6305168279871929, 'r': 0.6675509018987084, 'f1': 0.6485055710030789}, 'combined': 0.4339006532931993, 'epoch': 2}
Test for Russian: {'event': {'p': 0.6085271317829457, 'r': 0.7374739039665971, 'f1': 0.6668239735724399}, 'argument': {'p': 0.6532405890587217, 'r': 0.6842995741213297, 'f1': 0.6684094716510965}, 'combined': 0.44571145985983934, 'epoch': 2}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.48622736986273873, 'r': 0.6011538391030224, 'f1': 0.5376172544823777}, 'combined': 0.28672920239060146, 'epoch': 2}
******************************
Epoch: 3
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 06:29:20.231393: step: 3038/1519, loss: 0.4957989454269409
==================================================
Loss: 1.005
--------------------
Dev Chinese: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6721693909666001, 'r': 0.6646842084391325, 'f1': 0.6684058445446146}, 'combined': 0.44640969002738573, 'epoch': 3}
Test Chinese: {'event': {'p': 0.607665805340224, 'r': 0.7364300626304802, 'f1': 0.665880132137801}, 'argument': {'p': 0.6834534639938017, 'r': 0.6633025019104464, 'f1': 0.6732272276248171}, 'combined': 0.44828863528957863, 'epoch': 3}
Dev Korean: {'event': {'p': 0.6076759061833689, 'r': 0.758988015978695, 'f1': 0.6749555950266429}, 'argument': {'p': 0.6733122395644301, 'r': 0.6520080259505445, 'f1': 0.6624889026293758}, 'combined': 0.44715059147275804, 'epoch': 3}
Test Korean: {'event': {'p': 0.5998275118585598, 'r': 0.725991649269311, 'f1': 0.6569067296340024}, 'argument': {'p': 0.6825183876132653, 'r': 0.6714561629822247, 'f1': 0.6769420850784259}, 'combined': 0.4446878112604914, 'epoch': 3}
Dev Russian: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6721693909666001, 'r': 0.6646842084391325, 'f1': 0.6684058445446146}, 'combined': 0.44640969002738573, 'epoch': 3}
Test Russian: {'event': {'p': 0.607665805340224, 'r': 0.7364300626304802, 'f1': 0.665880132137801}, 'argument': {'p': 0.6834534639938017, 'r': 0.6633025019104464, 'f1': 0.6732272276248171}, 'combined': 0.44828863528957863, 'epoch': 3}
Chinese: {'event': {'p': 0.5945945945945946, 'r': 0.8148148148148148, 'f1': 0.6875}, 'argument': {'p': 0.6791375291375291, 'r': 0.5885858585858585, 'f1': 0.6306277056277055}, 'combined': 0.43355654761904755, 'epoch': 3}
Korean: {'event': {'p': 0.723404255319149, 'r': 0.5396825396825397, 'f1': 0.6181818181818182}, 'argument': {'p': 0.6371408539377289, 'r': 0.3398084554334554, 'f1': 0.443228420130594}, 'combined': 0.27399575062618536, 'epoch': 3}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6004487101928119, 'r': 0.5559710279563073, 'f1': 0.5773545290315498}, 'combined': 0.2925262947093186, 'epoch': 3}
New best chinese model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6721693909666001, 'r': 0.6646842084391325, 'f1': 0.6684058445446146}, 'combined': 0.44640969002738573, 'epoch': 3}
Test for Chinese: {'event': {'p': 0.607665805340224, 'r': 0.7364300626304802, 'f1': 0.665880132137801}, 'argument': {'p': 0.6834534639938017, 'r': 0.6633025019104464, 'f1': 0.6732272276248171}, 'combined': 0.44828863528957863, 'epoch': 3}
Chinese: {'event': {'p': 0.5945945945945946, 'r': 0.8148148148148148, 'f1': 0.6875}, 'argument': {'p': 0.6791375291375291, 'r': 0.5885858585858585, 'f1': 0.6306277056277055}, 'combined': 0.43355654761904755, 'epoch': 3}
--------------------
Dev for Korean: {'event': {'p': 0.605543710021322, 'r': 0.7563249001331558, 'f1': 0.6725873297809354}, 'argument': {'p': 0.628444952877954, 'r': 0.650179252204859, 'f1': 0.6391273806483929}, 'combined': 0.42986897834018606, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6058645968089694, 'r': 0.7332985386221295, 'f1': 0.6635182998819362}, 'argument': {'p': 0.652264351113527, 'r': 0.6923331575236183, 'f1': 0.6717017320733222}, 'combined': 0.44568639129304255, 'epoch': 2}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.6637445887445889, 'r': 0.45632440476190483, 'f1': 0.5408289241622575}, 'combined': 0.3146641013307679, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6721693909666001, 'r': 0.6646842084391325, 'f1': 0.6684058445446146}, 'combined': 0.44640969002738573, 'epoch': 3}
Test for Russian: {'event': {'p': 0.607665805340224, 'r': 0.7364300626304802, 'f1': 0.665880132137801}, 'argument': {'p': 0.6834534639938017, 'r': 0.6633025019104464, 'f1': 0.6732272276248171}, 'combined': 0.44828863528957863, 'epoch': 3}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6004487101928119, 'r': 0.5559710279563073, 'f1': 0.5773545290315498}, 'combined': 0.2925262947093186, 'epoch': 3}
******************************
Epoch: 4
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 06:56:22.865224: step: 3038/1519, loss: 0.7486160397529602
==================================================
Loss: 0.831
--------------------
Dev Chinese: {'event': {'p': 0.6123348017621145, 'r': 0.7403462050599201, 'f1': 0.6702833031946956}, 'argument': {'p': 0.6697097176702609, 'r': 0.6572708298977821, 'f1': 0.6634319737850879}, 'combined': 0.44468737483364545, 'epoch': 4}
Test Chinese: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6603716787341103, 'r': 0.6966982093098342, 'f1': 0.6780487432613131}, 'combined': 0.4566189507474724, 'epoch': 4}
Dev Korean: {'event': {'p': 0.6098081023454158, 'r': 0.7616511318242344, 'f1': 0.6773238602723506}, 'argument': {'p': 0.6647703387717601, 'r': 0.6457070717040405, 'f1': 0.6551000503673965}, 'combined': 0.4437148949794563, 'epoch': 4}
Test Korean: {'event': {'p': 0.6088831392841743, 'r': 0.7369519832985386, 'f1': 0.6668240850059032}, 'argument': {'p': 0.6572952229623019, 'r': 0.7017451141112213, 'f1': 0.6787932611855768}, 'combined': 0.4526356952982453, 'epoch': 4}
Dev Russian: {'event': {'p': 0.6123348017621145, 'r': 0.7403462050599201, 'f1': 0.6702833031946956}, 'argument': {'p': 0.6697097176702609, 'r': 0.6572708298977821, 'f1': 0.6634319737850879}, 'combined': 0.44468737483364545, 'epoch': 4}
Test Russian: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6603716787341103, 'r': 0.6966982093098342, 'f1': 0.6780487432613131}, 'combined': 0.4566189507474724, 'epoch': 4}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6140900432900432, 'r': 0.6396771284271284, 'f1': 0.6266224931531054}, 'combined': 0.4503849169537944, 'epoch': 4}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.7750965250965252, 'r': 0.4481026785714286, 'f1': 0.5678925035360679}, 'combined': 0.32008486562942, 'epoch': 4}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5619902261481209, 'r': 0.5109002055892009, 'f1': 0.5352287868077342}, 'combined': 0.28545535296412494, 'epoch': 4}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6123348017621145, 'r': 0.7403462050599201, 'f1': 0.6702833031946956}, 'argument': {'p': 0.6697097176702609, 'r': 0.6572708298977821, 'f1': 0.6634319737850879}, 'combined': 0.44468737483364545, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6603716787341103, 'r': 0.6966982093098342, 'f1': 0.6780487432613131}, 'combined': 0.4566189507474724, 'epoch': 4}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6140900432900432, 'r': 0.6396771284271284, 'f1': 0.6266224931531054}, 'combined': 0.4503849169537944, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.6098081023454158, 'r': 0.7616511318242344, 'f1': 0.6773238602723506}, 'argument': {'p': 0.6647703387717601, 'r': 0.6457070717040405, 'f1': 0.6551000503673965}, 'combined': 0.4437148949794563, 'epoch': 4}
Test for Korean: {'event': {'p': 0.6088831392841743, 'r': 0.7369519832985386, 'f1': 0.6668240850059032}, 'argument': {'p': 0.6572952229623019, 'r': 0.7017451141112213, 'f1': 0.6787932611855768}, 'combined': 0.4526356952982453, 'epoch': 4}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.7750965250965252, 'r': 0.4481026785714286, 'f1': 0.5678925035360679}, 'combined': 0.32008486562942, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6721693909666001, 'r': 0.6646842084391325, 'f1': 0.6684058445446146}, 'combined': 0.44640969002738573, 'epoch': 3}
Test for Russian: {'event': {'p': 0.607665805340224, 'r': 0.7364300626304802, 'f1': 0.665880132137801}, 'argument': {'p': 0.6834534639938017, 'r': 0.6633025019104464, 'f1': 0.6732272276248171}, 'combined': 0.44828863528957863, 'epoch': 3}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6004487101928119, 'r': 0.5559710279563073, 'f1': 0.5773545290315498}, 'combined': 0.2925262947093186, 'epoch': 3}
******************************
Epoch: 5
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 07:23:32.326536: step: 3038/1519, loss: 0.21621815860271454
==================================================
Loss: 0.721
--------------------
Dev Chinese: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test Chinese: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Dev Korean: {'event': {'p': 0.6066098081023454, 'r': 0.7576564580559254, 'f1': 0.6737714624037893}, 'argument': {'p': 0.6505611522392057, 'r': 0.659177856242374, 'f1': 0.6548411598197268}, 'combined': 0.44121328589393083, 'epoch': 5}
Test Korean: {'event': {'p': 0.605002156101768, 'r': 0.7322546972860126, 'f1': 0.6625737898465172}, 'argument': {'p': 0.6591300179146072, 'r': 0.6959945635375819, 'f1': 0.6770608627605078}, 'combined': 0.4486027817959823, 'epoch': 5}
Dev Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6291984889545865, 'r': 0.6449284511784512, 'f1': 0.6369663715342728}, 'combined': 0.44786697998503566, 'epoch': 5}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7163690476190476, 'r': 0.49404761904761907, 'f1': 0.5847910592808553}, 'combined': 0.37213976499690793, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, 'epoch': 5}
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6123348017621145, 'r': 0.7403462050599201, 'f1': 0.6702833031946956}, 'argument': {'p': 0.6697097176702609, 'r': 0.6572708298977821, 'f1': 0.6634319737850879}, 'combined': 0.44468737483364545, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6603716787341103, 'r': 0.6966982093098342, 'f1': 0.6780487432613131}, 'combined': 0.4566189507474724, 'epoch': 4}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6140900432900432, 'r': 0.6396771284271284, 'f1': 0.6266224931531054}, 'combined': 0.4503849169537944, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.6066098081023454, 'r': 0.7576564580559254, 'f1': 0.6737714624037893}, 'argument': {'p': 0.6505611522392057, 'r': 0.659177856242374, 'f1': 0.6548411598197268}, 'combined': 0.44121328589393083, 'epoch': 5}
Test for Korean: {'event': {'p': 0.605002156101768, 'r': 0.7322546972860126, 'f1': 0.6625737898465172}, 'argument': {'p': 0.6591300179146072, 'r': 0.6959945635375819, 'f1': 0.6770608627605078}, 'combined': 0.4486027817959823, 'epoch': 5}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7163690476190476, 'r': 0.49404761904761907, 'f1': 0.5847910592808553}, 'combined': 0.37213976499690793, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, 'epoch': 5}
******************************
Epoch: 6
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 07:29:39.820650: step: 2/1519, loss: 0.2388956993818283
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2023-01-23 07:50:47.889347: step: 3038/1519, loss: 0.6593735814094543
==================================================
Loss: 0.574
--------------------
Dev Chinese: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6509215935748099, 'r': 0.6727158433596807, 'f1': 0.6616392927006302}, 'combined': 0.4426881343566604, 'epoch': 6}
Test Chinese: {'event': {'p': 0.6128337639965546, 'r': 0.7426931106471816, 'f1': 0.6715431807456346}, 'argument': {'p': 0.6704660228326289, 'r': 0.7023929763008493, 'f1': 0.6860582559217598}, 'combined': 0.4607177433585012, 'epoch': 6}
Dev Korean: {'event': {'p': 0.6098081023454158, 'r': 0.7616511318242344, 'f1': 0.6773238602723506}, 'argument': {'p': 0.6479258841225458, 'r': 0.6646127170469676, 'f1': 0.6561632273642056}, 'combined': 0.4444350101270878, 'epoch': 6}
Test Korean: {'event': {'p': 0.6067270375161707, 'r': 0.7343423799582464, 'f1': 0.6644628099173554}, 'argument': {'p': 0.6704069717908803, 'r': 0.7062959959766876, 'f1': 0.6878836915977333}, 'combined': 0.45707313061535343, 'epoch': 6}
Dev Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6509215935748099, 'r': 0.6727158433596807, 'f1': 0.6616392927006302}, 'combined': 0.4426881343566604, 'epoch': 6}
Test Russian: {'event': {'p': 0.6128337639965546, 'r': 0.7426931106471816, 'f1': 0.6715431807456346}, 'argument': {'p': 0.6704660228326289, 'r': 0.7023929763008493, 'f1': 0.6860582559217598}, 'combined': 0.4607177433585012, 'epoch': 6}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6327845382963492, 'r': 0.6696969696969696, 'f1': 0.6507177033492821}, 'combined': 0.46770334928229645, 'epoch': 6}
Korean: {'event': {'p': 0.7021276595744681, 'r': 0.5238095238095238, 'f1': 0.6}, 'argument': {'p': 0.6885822510822511, 'r': 0.5049603174603174, 'f1': 0.5826465201465202}, 'combined': 0.3495879120879121, 'epoch': 6}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.5664223135662624, 'r': 0.5978902198754992, 'f1': 0.5817310247437288}, 'combined': 0.2947437192034893, 'epoch': 6}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6509215935748099, 'r': 0.6727158433596807, 'f1': 0.6616392927006302}, 'combined': 0.4426881343566604, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.6128337639965546, 'r': 0.7426931106471816, 'f1': 0.6715431807456346}, 'argument': {'p': 0.6704660228326289, 'r': 0.7023929763008493, 'f1': 0.6860582559217598}, 'combined': 0.4607177433585012, 'epoch': 6}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6327845382963492, 'r': 0.6696969696969696, 'f1': 0.6507177033492821}, 'combined': 0.46770334928229645, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.6066098081023454, 'r': 0.7576564580559254, 'f1': 0.6737714624037893}, 'argument': {'p': 0.6505611522392057, 'r': 0.659177856242374, 'f1': 0.6548411598197268}, 'combined': 0.44121328589393083, 'epoch': 5}
Test for Korean: {'event': {'p': 0.605002156101768, 'r': 0.7322546972860126, 'f1': 0.6625737898465172}, 'argument': {'p': 0.6591300179146072, 'r': 0.6959945635375819, 'f1': 0.6770608627605078}, 'combined': 0.4486027817959823, 'epoch': 5}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7163690476190476, 'r': 0.49404761904761907, 'f1': 0.5847910592808553}, 'combined': 0.37213976499690793, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
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2023-01-23 08:17:52.053975: step: 3038/1519, loss: 0.5308061838150024
==================================================
Loss: 0.472
--------------------
Dev Chinese: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6328272800249278, 'r': 0.6902287136033971, 'f1': 0.6602828021312724}, 'combined': 0.44098453572118734, 'epoch': 7}
Test Chinese: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6527702727367708, 'r': 0.7071343612160778, 'f1': 0.6788656767656341}, 'combined': 0.4571690989828032, 'epoch': 7}
Dev Korean: {'event': {'p': 0.603411513859275, 'r': 0.7536617842876165, 'f1': 0.670219064535228}, 'argument': {'p': 0.6395565763754146, 'r': 0.6814407083087567, 'f1': 0.6598346438130391}, 'combined': 0.44223375772431045, 'epoch': 7}
Test Korean: {'event': {'p': 0.6080206985769728, 'r': 0.7359081419624217, 'f1': 0.6658795749704841}, 'argument': {'p': 0.6533735627376005, 'r': 0.7108063996459286, 'f1': 0.6808810018612167}, 'combined': 0.4533847521248244, 'epoch': 7}
Dev Russian: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6328272800249278, 'r': 0.6902287136033971, 'f1': 0.6602828021312724}, 'combined': 0.44098453572118734, 'epoch': 7}
Test Russian: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6527702727367708, 'r': 0.7071343612160778, 'f1': 0.6788656767656341}, 'combined': 0.4571690989828032, 'epoch': 7}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6225520511234797, 'r': 0.6536796536796536, 'f1': 0.637736247492345}, 'combined': 0.4484082990180552, 'epoch': 7}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.6648981100200613, 'r': 0.47001418122107774, 'f1': 0.5507236870873233}, 'combined': 0.3504605281464785, 'epoch': 7}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5571755951247784, 'r': 0.5571755951247784, 'f1': 0.5571755951247784}, 'combined': 0.26744428565989364, 'epoch': 7}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6509215935748099, 'r': 0.6727158433596807, 'f1': 0.6616392927006302}, 'combined': 0.4426881343566604, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.6128337639965546, 'r': 0.7426931106471816, 'f1': 0.6715431807456346}, 'argument': {'p': 0.6704660228326289, 'r': 0.7023929763008493, 'f1': 0.6860582559217598}, 'combined': 0.4607177433585012, 'epoch': 6}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6327845382963492, 'r': 0.6696969696969696, 'f1': 0.6507177033492821}, 'combined': 0.46770334928229645, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.6066098081023454, 'r': 0.7576564580559254, 'f1': 0.6737714624037893}, 'argument': {'p': 0.6505611522392057, 'r': 0.659177856242374, 'f1': 0.6548411598197268}, 'combined': 0.44121328589393083, 'epoch': 5}
Test for Korean: {'event': {'p': 0.605002156101768, 'r': 0.7322546972860126, 'f1': 0.6625737898465172}, 'argument': {'p': 0.6591300179146072, 'r': 0.6959945635375819, 'f1': 0.6770608627605078}, 'combined': 0.4486027817959823, 'epoch': 5}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7163690476190476, 'r': 0.49404761904761907, 'f1': 0.5847910592808553}, 'combined': 0.37213976499690793, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
2023-01-23 08:23:42.337421: step: 2/1519, loss: 0.1280156970024109
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2023-01-23 08:44:49.984233: step: 3038/1519, loss: 0.18702532351016998
==================================================
Loss: 0.393
--------------------
Dev Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Dev Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.67405369312414, 'r': 0.649254072332229, 'f1': 0.6614215024733877}, 'combined': 0.4472133545438773, 'epoch': 8}
Test Korean: {'event': {'p': 0.6084519189305735, 'r': 0.7364300626304802, 'f1': 0.6663518299881935}, 'argument': {'p': 0.6709985174631097, 'r': 0.6886725978618318, 'f1': 0.6797206868255383}, 'combined': 0.45293312354702925, 'epoch': 8}
Dev Russian: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test Russian: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7253193960511033, 'r': 0.5127257799671593, 'f1': 0.6007696007696007}, 'combined': 0.3823079277624732, 'epoch': 8}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.6335644435074044, 'r': 0.5970126486896695, 'f1': 0.6147456976606498}, 'combined': 0.2950779348771119, 'epoch': 8}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.67405369312414, 'r': 0.649254072332229, 'f1': 0.6614215024733877}, 'combined': 0.4472133545438773, 'epoch': 8}
Test for Korean: {'event': {'p': 0.6084519189305735, 'r': 0.7364300626304802, 'f1': 0.6663518299881935}, 'argument': {'p': 0.6709985174631097, 'r': 0.6886725978618318, 'f1': 0.6797206868255383}, 'combined': 0.45293312354702925, 'epoch': 8}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7253193960511033, 'r': 0.5127257799671593, 'f1': 0.6007696007696007}, 'combined': 0.3823079277624732, 'epoch': 8}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
2023-01-23 08:50:52.556516: step: 2/1519, loss: 0.47688063979148865
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2023-01-23 09:11:56.498792: step: 3028/1519, loss: 0.25476229190826416
2023-01-23 09:11:57.316327: step: 3030/1519, loss: 2.014477252960205
2023-01-23 09:11:58.105172: step: 3032/1519, loss: 2.3710570335388184
2023-01-23 09:11:58.915133: step: 3034/1519, loss: 0.07814032584428787
2023-01-23 09:11:59.740784: step: 3036/1519, loss: 0.1693532019853592
2023-01-23 09:12:00.564845: step: 3038/1519, loss: 0.11391976475715637
==================================================
Loss: 0.354
--------------------
Dev Chinese: {'event': {'p': 0.6134361233480177, 'r': 0.7416777629826897, 'f1': 0.6714888487040387}, 'argument': {'p': 0.6679504181364054, 'r': 0.6600045395563441, 'f1': 0.663953706584355}, 'combined': 0.4458375100271077, 'epoch': 9}
Test Chinese: {'event': {'p': 0.6149870801033591, 'r': 0.7453027139874739, 'f1': 0.6739027843322323}, 'argument': {'p': 0.6665521320260593, 'r': 0.6812961423197234, 'f1': 0.6738434953663627}, 'combined': 0.4541050077315555, 'epoch': 9}
Dev Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.6645863704524296, 'r': 0.6479472418844012, 'f1': 0.656161338164815}, 'combined': 0.4436567484808873, 'epoch': 9}
Test Korean: {'event': {'p': 0.6045709357481673, 'r': 0.7317327766179541, 'f1': 0.6621015348288075}, 'argument': {'p': 0.6704988841553422, 'r': 0.6879330857929085, 'f1': 0.6791041091520513}, 'combined': 0.4496358729781232, 'epoch': 9}
Dev Russian: {'event': {'p': 0.6134361233480177, 'r': 0.7416777629826897, 'f1': 0.6714888487040387}, 'argument': {'p': 0.6679504181364054, 'r': 0.6600045395563441, 'f1': 0.663953706584355}, 'combined': 0.4458375100271077, 'epoch': 9}
Test Russian: {'event': {'p': 0.6149870801033591, 'r': 0.7453027139874739, 'f1': 0.6739027843322323}, 'argument': {'p': 0.6665521320260593, 'r': 0.6812961423197234, 'f1': 0.6738434953663627}, 'combined': 0.4541050077315555, 'epoch': 9}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6470508658008658, 'r': 0.6039141414141415, 'f1': 0.6247387669801463}, 'combined': 0.4392694455329154, 'epoch': 9}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7166666666666666, 'r': 0.49425287356321834, 'f1': 0.5850340136054422}, 'combined': 0.3722943722943723, 'epoch': 9}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5657529137529138, 'r': 0.5143208306844671, 'f1': 0.5388122988122989}, 'combined': 0.2586299034299035, 'epoch': 9}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.67405369312414, 'r': 0.649254072332229, 'f1': 0.6614215024733877}, 'combined': 0.4472133545438773, 'epoch': 8}
Test for Korean: {'event': {'p': 0.6084519189305735, 'r': 0.7364300626304802, 'f1': 0.6663518299881935}, 'argument': {'p': 0.6709985174631097, 'r': 0.6886725978618318, 'f1': 0.6797206868255383}, 'combined': 0.45293312354702925, 'epoch': 8}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7253193960511033, 'r': 0.5127257799671593, 'f1': 0.6007696007696007}, 'combined': 0.3823079277624732, 'epoch': 8}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
2023-01-23 09:17:49.187218: step: 2/1519, loss: 0.05954298749566078
2023-01-23 09:17:50.020665: step: 4/1519, loss: 0.21181784570217133
2023-01-23 09:17:50.863391: step: 6/1519, loss: 0.24861277639865875
2023-01-23 09:17:51.698211: step: 8/1519, loss: 0.9654631614685059
2023-01-23 09:17:52.519214: step: 10/1519, loss: 0.2704594135284424
2023-01-23 09:17:53.340630: step: 12/1519, loss: 0.21022774279117584
2023-01-23 09:17:54.155742: step: 14/1519, loss: 0.3357982337474823
2023-01-23 09:17:54.991109: step: 16/1519, loss: 0.37508681416511536
2023-01-23 09:17:55.843028: step: 18/1519, loss: 0.12036477029323578
2023-01-23 09:17:56.676811: step: 20/1519, loss: 0.20917530357837677
2023-01-23 09:17:57.534910: step: 22/1519, loss: 0.3655928671360016
2023-01-23 09:17:58.400297: step: 24/1519, loss: 0.06532254815101624
2023-01-23 09:17:59.212406: step: 26/1519, loss: 0.12132342159748077
2023-01-23 09:18:00.021487: step: 28/1519, loss: 0.065659299492836
2023-01-23 09:18:00.851170: step: 30/1519, loss: 0.28517457842826843
2023-01-23 09:18:01.685929: step: 32/1519, loss: 0.2427993267774582
2023-01-23 09:18:02.549385: step: 34/1519, loss: 0.22850647568702698
2023-01-23 09:18:03.437296: step: 36/1519, loss: 0.1295837163925171
2023-01-23 09:18:04.257597: step: 38/1519, loss: 0.08321716636419296
2023-01-23 09:18:05.118957: step: 40/1519, loss: 0.05108160153031349
2023-01-23 09:18:05.971350: step: 42/1519, loss: 0.0505940243601799
2023-01-23 09:18:06.799104: step: 44/1519, loss: 0.1785464882850647
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2023-01-23 09:39:01.920452: step: 3038/1519, loss: 0.08388727158308029
==================================================
Loss: 0.268
--------------------
Dev Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6595334130471279, 'r': 0.6756912472197747, 'f1': 0.6675145654602394}, 'combined': 0.44340027193320297, 'epoch': 10}
Test Chinese: {'event': {'p': 0.6162790697674418, 'r': 0.7468684759916493, 'f1': 0.6753185464841907}, 'argument': {'p': 0.6664686238077969, 'r': 0.683667814099611, 'f1': 0.6749586699709536}, 'combined': 0.4558121079416869, 'epoch': 10}
Dev Korean: {'event': {'p': 0.6023454157782516, 'r': 0.7523302263648469, 'f1': 0.6690349319123742}, 'argument': {'p': 0.6542682623008388, 'r': 0.6638898543934982, 'f1': 0.6590439430475603}, 'combined': 0.4409234195640871, 'epoch': 10}
Test Korean: {'event': {'p': 0.6062958171625701, 'r': 0.7338204592901879, 'f1': 0.6639905548996458}, 'argument': {'p': 0.6649874031992672, 'r': 0.6887878088812233, 'f1': 0.6766783913546863}, 'combined': 0.4493080605641978, 'epoch': 10}
Dev Russian: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6595334130471279, 'r': 0.6756912472197747, 'f1': 0.6675145654602394}, 'combined': 0.44340027193320297, 'epoch': 10}
Test Russian: {'event': {'p': 0.6162790697674418, 'r': 0.7468684759916493, 'f1': 0.6753185464841907}, 'argument': {'p': 0.6664686238077969, 'r': 0.683667814099611, 'f1': 0.6749586699709536}, 'combined': 0.4558121079416869, 'epoch': 10}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6675857958466654, 'r': 0.6397697210197211, 'f1': 0.6533818427435449}, 'combined': 0.4594091081790551, 'epoch': 10}
Korean: {'event': {'p': 0.723404255319149, 'r': 0.5396825396825397, 'f1': 0.6181818181818182}, 'argument': {'p': 0.6501790027605244, 'r': 0.5201432022084196, 'f1': 0.5779368913426884}, 'combined': 0.357270078284571, 'epoch': 10}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5528931546372563, 'r': 0.5316280333050541, 'f1': 0.5420521123894669}, 'combined': 0.26018501394694415, 'epoch': 10}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.67405369312414, 'r': 0.649254072332229, 'f1': 0.6614215024733877}, 'combined': 0.4472133545438773, 'epoch': 8}
Test for Korean: {'event': {'p': 0.6084519189305735, 'r': 0.7364300626304802, 'f1': 0.6663518299881935}, 'argument': {'p': 0.6709985174631097, 'r': 0.6886725978618318, 'f1': 0.6797206868255383}, 'combined': 0.45293312354702925, 'epoch': 8}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7253193960511033, 'r': 0.5127257799671593, 'f1': 0.6007696007696007}, 'combined': 0.3823079277624732, 'epoch': 8}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, 'epoch': 5}
******************************
Epoch: 11
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 09:44:53.156651: step: 2/1519, loss: 0.09590153396129608
2023-01-23 09:44:53.975227: step: 4/1519, loss: 0.09350977838039398
2023-01-23 09:44:54.811897: step: 6/1519, loss: 0.13041990995407104
2023-01-23 09:44:55.638772: step: 8/1519, loss: 0.6421312689781189
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2023-01-23 10:06:05.832489: step: 3038/1519, loss: 0.05656857788562775
==================================================
Loss: 0.239
--------------------
Dev Chinese: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6684041708199995, 'r': 0.6485259798662448, 'f1': 0.6583150512604523}, 'combined': 0.44046395834786145, 'epoch': 11}
Test Chinese: {'event': {'p': 0.6162790697674418, 'r': 0.7468684759916493, 'f1': 0.6753185464841907}, 'argument': {'p': 0.6851301068704178, 'r': 0.6790241532443445, 'f1': 0.6820634649350508}, 'combined': 0.46061010774990924, 'epoch': 11}
Dev Korean: {'event': {'p': 0.6108742004264393, 'r': 0.762982689747004, 'f1': 0.6785079928952042}, 'argument': {'p': 0.660089585733034, 'r': 0.6396593995865436, 'f1': 0.6497139260770507}, 'combined': 0.4408360919386028, 'epoch': 11}
Test Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.6897003889228465, 'r': 0.6880114990815906, 'f1': 0.6888549088260976}, 'combined': 0.45836909871828646, 'epoch': 11}
Dev Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6684041708199995, 'r': 0.6485259798662448, 'f1': 0.6583150512604523}, 'combined': 0.44046395834786145, 'epoch': 11}
Test Russian: {'event': {'p': 0.6162790697674418, 'r': 0.7468684759916493, 'f1': 0.6753185464841907}, 'argument': {'p': 0.6851301068704178, 'r': 0.6790241532443445, 'f1': 0.6820634649350508}, 'combined': 0.46061010774990924, 'epoch': 11}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6678320621177763, 'r': 0.5843530543530543, 'f1': 0.623309924643258}, 'combined': 0.4480040083373416, 'epoch': 11}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7304639804639804, 'r': 0.49117405582922824, 'f1': 0.5873834069710357}, 'combined': 0.37378944079975, 'epoch': 11}
Russian: {'event': {'p': 0.4358974358974359, 'r': 0.4722222222222222, 'f1': 0.45333333333333337}, 'argument': {'p': 0.6087187790561336, 'r': 0.5644483223975056, 'f1': 0.5857482590917512}, 'combined': 0.2655392107882606, 'epoch': 11}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.67405369312414, 'r': 0.649254072332229, 'f1': 0.6614215024733877}, 'combined': 0.4472133545438773, 'epoch': 8}
Test for Korean: {'event': {'p': 0.6084519189305735, 'r': 0.7364300626304802, 'f1': 0.6663518299881935}, 'argument': {'p': 0.6709985174631097, 'r': 0.6886725978618318, 'f1': 0.6797206868255383}, 'combined': 0.45293312354702925, 'epoch': 8}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7253193960511033, 'r': 0.5127257799671593, 'f1': 0.6007696007696007}, 'combined': 0.3823079277624732, 'epoch': 8}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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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==================================================
Loss: 0.215
--------------------
Dev Chinese: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.663587086697338, 'r': 0.6719619794025011, 'f1': 0.6677482746257241}, 'combined': 0.44516551641714935, 'epoch': 12}
Test Chinese: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6777153020919872, 'r': 0.6816651765520325, 'f1': 0.6796845008593442}, 'combined': 0.45772052039937905, 'epoch': 12}
Dev Korean: {'event': {'p': 0.605543710021322, 'r': 0.7563249001331558, 'f1': 0.6725873297809354}, 'argument': {'p': 0.6597903079242924, 'r': 0.6539686287367251, 'f1': 0.6568665694844505}, 'combined': 0.4418001319919099, 'epoch': 12}
Test Korean: {'event': {'p': 0.6088831392841743, 'r': 0.7369519832985386, 'f1': 0.6668240850059032}, 'argument': {'p': 0.676544941604679, 'r': 0.6870901103155099, 'f1': 0.681776752373722}, 'combined': 0.4546251590799034, 'epoch': 12}
Dev Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.663587086697338, 'r': 0.6719619794025011, 'f1': 0.6677482746257241}, 'combined': 0.44516551641714935, 'epoch': 12}
Test Russian: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6777153020919872, 'r': 0.6816651765520325, 'f1': 0.6796845008593442}, 'combined': 0.45772052039937905, 'epoch': 12}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6800356506238859, 'r': 0.578030303030303, 'f1': 0.6248976248976249}, 'combined': 0.44914516789516784, 'epoch': 12}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7001984126984127, 'r': 0.48289545703338804, 'f1': 0.571590540978296}, 'combined': 0.36373943516800655, 'epoch': 12}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5430824730824731, 'r': 0.5430824730824731, 'f1': 0.5430824730824731}, 'combined': 0.2896439856439857, 'epoch': 12}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.67405369312414, 'r': 0.649254072332229, 'f1': 0.6614215024733877}, 'combined': 0.4472133545438773, 'epoch': 8}
Test for Korean: {'event': {'p': 0.6084519189305735, 'r': 0.7364300626304802, 'f1': 0.6663518299881935}, 'argument': {'p': 0.6709985174631097, 'r': 0.6886725978618318, 'f1': 0.6797206868255383}, 'combined': 0.45293312354702925, 'epoch': 8}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7253193960511033, 'r': 0.5127257799671593, 'f1': 0.6007696007696007}, 'combined': 0.3823079277624732, 'epoch': 8}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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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2023-01-23 10:59:52.046922: step: 3020/1519, loss: 0.9808865189552307
2023-01-23 10:59:52.867772: step: 3022/1519, loss: 0.25891295075416565
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2023-01-23 10:59:55.410801: step: 3028/1519, loss: 0.18602776527404785
2023-01-23 10:59:56.211015: step: 3030/1519, loss: 0.05041025951504707
2023-01-23 10:59:57.065100: step: 3032/1519, loss: 0.3363618850708008
2023-01-23 10:59:57.895985: step: 3034/1519, loss: 0.23136886954307556
2023-01-23 10:59:58.721922: step: 3036/1519, loss: 0.18596015870571136
2023-01-23 10:59:59.580059: step: 3038/1519, loss: 0.15204089879989624
==================================================
Loss: 0.196
--------------------
Dev Chinese: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6718390056984488, 'r': 0.6603673671453202, 'f1': 0.6660537952412926}, 'combined': 0.4456417798178631, 'epoch': 13}
Test Chinese: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6838542939666915, 'r': 0.6825925701770851, 'f1': 0.6832228495585967}, 'combined': 0.4601033536196874, 'epoch': 13}
Dev Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Dev Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6718390056984488, 'r': 0.6603673671453202, 'f1': 0.6660537952412926}, 'combined': 0.4456417798178631, 'epoch': 13}
Test Russian: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6838542939666915, 'r': 0.6825925701770851, 'f1': 0.6832228495585967}, 'combined': 0.4601033536196874, 'epoch': 13}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6508896302474285, 'r': 0.5912247474747475, 'f1': 0.6196241894931852}, 'combined': 0.43567325823739594, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.593835774819214, 'r': 0.5481560998331206, 'f1': 0.5700823438264455}, 'combined': 0.27363952503669386, 'epoch': 13}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
2023-01-23 11:05:52.622955: step: 2/1519, loss: 0.029902158305048943
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2023-01-23 11:27:00.521881: step: 3034/1519, loss: 0.009218227118253708
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2023-01-23 11:27:02.181256: step: 3038/1519, loss: 1.2061469554901123
==================================================
Loss: 0.165
--------------------
Dev Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.666092232095444, 'r': 0.6764844585991618, 'f1': 0.6712481247876683}, 'combined': 0.44588030953346014, 'epoch': 14}
Test Chinese: {'event': {'p': 0.6124031007751938, 'r': 0.7421711899791231, 'f1': 0.6710712600283151}, 'argument': {'p': 0.6683928110081542, 'r': 0.6876950918790502, 'f1': 0.6779065790630856}, 'combined': 0.4549236221933495, 'epoch': 14}
Dev Korean: {'event': {'p': 0.605543710021322, 'r': 0.7563249001331558, 'f1': 0.6725873297809354}, 'argument': {'p': 0.6620982844536233, 'r': 0.6577135276029371, 'f1': 0.6598986223790266}, 'combined': 0.4438394523520273, 'epoch': 14}
Test Korean: {'event': {'p': 0.6058645968089694, 'r': 0.7332985386221295, 'f1': 0.6635182998819362}, 'argument': {'p': 0.676364099260375, 'r': 0.6902223239241196, 'f1': 0.6832229451284612}, 'combined': 0.4533309269919659, 'epoch': 14}
Dev Russian: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.666092232095444, 'r': 0.6764844585991618, 'f1': 0.6712481247876683}, 'combined': 0.44588030953346014, 'epoch': 14}
Test Russian: {'event': {'p': 0.6124031007751938, 'r': 0.7421711899791231, 'f1': 0.6710712600283151}, 'argument': {'p': 0.6683928110081542, 'r': 0.6876950918790502, 'f1': 0.6779065790630856}, 'combined': 0.4549236221933495, 'epoch': 14}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6311123658949747, 'r': 0.6048160173160173, 'f1': 0.6176844432163581}, 'combined': 0.44396069356175727, 'epoch': 14}
Korean: {'event': {'p': 0.723404255319149, 'r': 0.5396825396825397, 'f1': 0.6181818181818182}, 'argument': {'p': 0.6591614906832298, 'r': 0.5053571428571428, 'f1': 0.5721024258760108}, 'combined': 0.3536633178142612, 'epoch': 14}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5857482590917512, 'r': 0.5644483223975056, 'f1': 0.5749010691085705}, 'combined': 0.2759525131721139, 'epoch': 14}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
2023-01-23 11:32:50.614428: step: 2/1519, loss: 0.15600450336933136
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2023-01-23 11:53:59.303611: step: 3038/1519, loss: 0.06630504131317139
==================================================
Loss: 0.160
--------------------
Dev Chinese: {'event': {'p': 0.6123348017621145, 'r': 0.7403462050599201, 'f1': 0.6702833031946956}, 'argument': {'p': 0.6789984288718339, 'r': 0.6583003355010196, 'f1': 0.668489204415909}, 'combined': 0.4480771520858896, 'epoch': 15}
Test Chinese: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6778881255266551, 'r': 0.679347294308539, 'f1': 0.6786169255387403}, 'combined': 0.45700158222925086, 'epoch': 15}
Dev Korean: {'event': {'p': 0.6066098081023454, 'r': 0.7576564580559254, 'f1': 0.6737714624037893}, 'argument': {'p': 0.6753230867459995, 'r': 0.6490246858661038, 'f1': 0.6619127744202078}, 'combined': 0.44597793800485286, 'epoch': 15}
Test Korean: {'event': {'p': 0.6058645968089694, 'r': 0.7332985386221295, 'f1': 0.6635182998819362}, 'argument': {'p': 0.6801471162152789, 'r': 0.6863946402466501, 'f1': 0.6832565970879486}, 'combined': 0.45335325568291274, 'epoch': 15}
Dev Russian: {'event': {'p': 0.6123348017621145, 'r': 0.7403462050599201, 'f1': 0.6702833031946956}, 'argument': {'p': 0.6789984288718339, 'r': 0.6583003355010196, 'f1': 0.668489204415909}, 'combined': 0.4480771520858896, 'epoch': 15}
Test Russian: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6778881255266551, 'r': 0.679347294308539, 'f1': 0.6786169255387403}, 'combined': 0.45700158222925086, 'epoch': 15}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6906468531468531, 'r': 0.598560606060606, 'f1': 0.641314935064935}, 'combined': 0.4509245637175325, 'epoch': 15}
Korean: {'event': {'p': 0.7021276595744681, 'r': 0.5238095238095238, 'f1': 0.6}, 'argument': {'p': 0.7148394241417497, 'r': 0.49577572964669736, 'f1': 0.5854875283446712}, 'combined': 0.3512925170068027, 'epoch': 15}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5284487101928118, 'r': 0.48040791835710167, 'f1': 0.503284485897916}, 'combined': 0.2684183924788885, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, 'epoch': 5}
******************************
Epoch: 16
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 11:59:44.573744: step: 2/1519, loss: 0.06407807022333145
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2023-01-23 12:20:52.514280: step: 3038/1519, loss: 0.04299553111195564
==================================================
Loss: 0.144
--------------------
Dev Chinese: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.652738233381713, 'r': 0.6677827577198789, 'f1': 0.6601747953937795}, 'combined': 0.4393206595025512, 'epoch': 16}
Test Chinese: {'event': {'p': 0.615848406546081, 'r': 0.7463465553235908, 'f1': 0.6748466257668712}, 'argument': {'p': 0.6671145986797437, 'r': 0.6962086628727792, 'f1': 0.6813511891535223}, 'combined': 0.4598075509624997, 'epoch': 16}
Dev Korean: {'event': {'p': 0.6002132196162047, 'r': 0.7496671105193076, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6528252247218019, 'r': 0.6585896858672483, 'f1': 0.6556947861491506}, 'combined': 0.437129857432767, 'epoch': 16}
Test Korean: {'event': {'p': 0.6041397153945667, 'r': 0.7312108559498957, 'f1': 0.661629279811098}, 'argument': {'p': 0.6696684508373734, 'r': 0.700173066910369, 'f1': 0.6845811095094356}, 'combined': 0.4529389064570103, 'epoch': 16}
Dev Russian: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.652738233381713, 'r': 0.6677827577198789, 'f1': 0.6601747953937795}, 'combined': 0.4393206595025512, 'epoch': 16}
Test Russian: {'event': {'p': 0.615848406546081, 'r': 0.7463465553235908, 'f1': 0.6748466257668712}, 'argument': {'p': 0.6671145986797437, 'r': 0.6962086628727792, 'f1': 0.6813511891535223}, 'combined': 0.4598075509624997, 'epoch': 16}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6574675324675325, 'r': 0.6410308441558442, 'f1': 0.6491451586388296}, 'combined': 0.4665730827716587, 'epoch': 16}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.6740388007054673, 'r': 0.5229611384783798, 'f1': 0.5889659423640005}, 'combined': 0.3747965087770912, 'epoch': 16}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5586052673053581, 'r': 0.5687617267109101, 'f1': 0.5636377471909919}, 'combined': 0.30060679850186234, 'epoch': 16}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
2023-01-23 12:26:42.010718: step: 2/1519, loss: 0.12195666134357452
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2023-01-23 12:47:50.795479: step: 3038/1519, loss: 0.2803660035133362
==================================================
Loss: 0.133
--------------------
Dev Chinese: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6716558698381535, 'r': 0.6496998087141723, 'f1': 0.6604954248710543}, 'combined': 0.43953402595397456, 'epoch': 17}
Test Chinese: {'event': {'p': 0.6136950904392765, 'r': 0.7437369519832986, 'f1': 0.6724870221802738}, 'argument': {'p': 0.6892364233153593, 'r': 0.6706141312515731, 'f1': 0.6797977670359083}, 'combined': 0.4571551760387775, 'epoch': 17}
Dev Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.6732086532315503, 'r': 0.6395482205699728, 'f1': 0.6559468928922798}, 'combined': 0.44351175351271965, 'epoch': 17}
Test Korean: {'event': {'p': 0.6037084950409659, 'r': 0.7306889352818372, 'f1': 0.6611570247933883}, 'argument': {'p': 0.6944454435089558, 'r': 0.6755388529698622, 'f1': 0.6848616871944423}, 'combined': 0.45280111550045765, 'epoch': 17}
Dev Russian: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6716558698381535, 'r': 0.6496998087141723, 'f1': 0.6604954248710543}, 'combined': 0.43953402595397456, 'epoch': 17}
Test Russian: {'event': {'p': 0.6136950904392765, 'r': 0.7437369519832986, 'f1': 0.6724870221802738}, 'argument': {'p': 0.6892364233153593, 'r': 0.6706141312515731, 'f1': 0.6797977670359083}, 'combined': 0.4571551760387775, 'epoch': 17}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6693618881118881, 'r': 0.5801136363636363, 'f1': 0.6215503246753247}, 'combined': 0.4467392958603895, 'epoch': 17}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.710686600221484, 'r': 0.49289554531490015, 'f1': 0.5820861678004535}, 'combined': 0.3386683158111729, 'epoch': 17}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5808931546372562, 'r': 0.5280846860338694, 'f1': 0.553231575845006}, 'combined': 0.2655511564056029, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
2023-01-23 12:53:36.355740: step: 2/1519, loss: 0.05214913189411163
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2023-01-23 13:14:39.062878: step: 3028/1519, loss: 0.022221682593226433
2023-01-23 13:14:39.903345: step: 3030/1519, loss: 0.3594456613063812
2023-01-23 13:14:40.749315: step: 3032/1519, loss: 0.1905059665441513
2023-01-23 13:14:41.563559: step: 3034/1519, loss: 0.042866144329309464
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2023-01-23 13:14:43.229014: step: 3038/1519, loss: 0.0034179696813225746
==================================================
Loss: 0.123
--------------------
Dev Chinese: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6626290795029863, 'r': 0.6572138344253244, 'f1': 0.6599103477332986}, 'combined': 0.43994023182219905, 'epoch': 18}
Test Chinese: {'event': {'p': 0.6119724375538329, 'r': 0.7416492693110647, 'f1': 0.6705993393109957}, 'argument': {'p': 0.6760048717017377, 'r': 0.6882694127319968, 'f1': 0.6820820143850231}, 'combined': 0.45740374820250956, 'epoch': 18}
Dev Korean: {'event': {'p': 0.6076759061833689, 'r': 0.758988015978695, 'f1': 0.6749555950266429}, 'argument': {'p': 0.6594537080210029, 'r': 0.6429552340896459, 'f1': 0.6510999725244626}, 'combined': 0.43946356937707953, 'epoch': 18}
Test Korean: {'event': {'p': 0.6080206985769728, 'r': 0.7359081419624217, 'f1': 0.6658795749704841}, 'argument': {'p': 0.6815120355647268, 'r': 0.6933952371637447, 'f1': 0.6874022836355668}, 'combined': 0.4577271404609914, 'epoch': 18}
Dev Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6626290795029863, 'r': 0.6572138344253244, 'f1': 0.6599103477332986}, 'combined': 0.43994023182219905, 'epoch': 18}
Test Russian: {'event': {'p': 0.6119724375538329, 'r': 0.7416492693110647, 'f1': 0.6705993393109957}, 'argument': {'p': 0.6760048717017377, 'r': 0.6882694127319968, 'f1': 0.6820820143850231}, 'combined': 0.45740374820250956, 'epoch': 18}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6792613636363637, 'r': 0.6339772727272728, 'f1': 0.6558385579937304}, 'combined': 0.4713839635579936, 'epoch': 18}
Korean: {'event': {'p': 0.7021276595744681, 'r': 0.5238095238095238, 'f1': 0.6}, 'argument': {'p': 0.7047619047619048, 'r': 0.5683563748079877, 'f1': 0.6292517006802721}, 'combined': 0.3775510204081633, 'epoch': 18}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5740082171630301, 'r': 0.5531351910843744, 'f1': 0.5633784353637146}, 'combined': 0.30046849886064775, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
2023-01-23 13:20:31.773573: step: 2/1519, loss: 0.01607096754014492
2023-01-23 13:20:32.649890: step: 4/1519, loss: 0.0018442654982209206
2023-01-23 13:20:33.492781: step: 6/1519, loss: 0.03666844591498375
2023-01-23 13:20:34.281640: step: 8/1519, loss: 0.006444805767387152
2023-01-23 13:20:35.105235: step: 10/1519, loss: 0.002225582953542471
2023-01-23 13:20:35.938016: step: 12/1519, loss: 0.0647049993276596
2023-01-23 13:20:36.781581: step: 14/1519, loss: 0.002645250177010894
2023-01-23 13:20:37.586180: step: 16/1519, loss: 0.0014879604568704963
2023-01-23 13:20:38.424583: step: 18/1519, loss: 0.14135697484016418
2023-01-23 13:20:39.233614: step: 20/1519, loss: 0.1975678652524948
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2023-01-23 13:41:39.776198: step: 3038/1519, loss: 0.2777736783027649
==================================================
Loss: 0.122
--------------------
Dev Chinese: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6618469276052416, 'r': 0.6603706891050068, 'f1': 0.6611079842547333}, 'combined': 0.44073865616982216, 'epoch': 19}
Test Chinese: {'event': {'p': 0.6111111111111112, 'r': 0.7406054279749478, 'f1': 0.6696554978763567}, 'argument': {'p': 0.6694654731294164, 'r': 0.6787264464742746, 'f1': 0.6740641521541186}, 'combined': 0.4513907654113706, 'epoch': 19}
Dev Korean: {'event': {'p': 0.6066098081023454, 'r': 0.7576564580559254, 'f1': 0.6737714624037893}, 'argument': {'p': 0.663159310237424, 'r': 0.6475440799742912, 'f1': 0.6552586780975889}, 'combined': 0.4414945977945863, 'epoch': 19}
Test Korean: {'event': {'p': 0.6067270375161707, 'r': 0.7343423799582464, 'f1': 0.6644628099173554}, 'argument': {'p': 0.6728320035533559, 'r': 0.6849829335226391, 'f1': 0.6788530998994272}, 'combined': 0.4510726382802806, 'epoch': 19}
Dev Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6618469276052416, 'r': 0.6603706891050068, 'f1': 0.6611079842547333}, 'combined': 0.44073865616982216, 'epoch': 19}
Test Russian: {'event': {'p': 0.6111111111111112, 'r': 0.7406054279749478, 'f1': 0.6696554978763567}, 'argument': {'p': 0.6694654731294164, 'r': 0.6787264464742746, 'f1': 0.6740641521541186}, 'combined': 0.4513907654113706, 'epoch': 19}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6533057851239669, 'r': 0.5988636363636364, 'f1': 0.6249011857707509}, 'combined': 0.44914772727272717, 'epoch': 19}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7070267131242741, 'r': 0.4997947454844006, 'f1': 0.5856180856180856}, 'combined': 0.3726660544842363, 'epoch': 19}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.546585298262319, 'r': 0.5167715547207381, 'f1': 0.531260476815712}, 'combined': 0.2833389209683797, 'epoch': 19}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
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2023-01-23 14:08:34.182585: step: 3038/1519, loss: 0.0008166576735675335
==================================================
Loss: 0.102
--------------------
Dev Chinese: {'event': {'p': 0.6057268722466961, 'r': 0.7323568575233023, 'f1': 0.6630500301386378}, 'argument': {'p': 0.6400500395750606, 'r': 0.6619077246681955, 'f1': 0.650795405202963}, 'combined': 0.4315099130339116, 'epoch': 20}
Test Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6604610859837139, 'r': 0.6844040685170714, 'f1': 0.6722194456919334}, 'combined': 0.45332779041707066, 'epoch': 20}
Dev Korean: {'event': {'p': 0.603411513859275, 'r': 0.7536617842876165, 'f1': 0.670219064535228}, 'argument': {'p': 0.639067235199477, 'r': 0.6461157708818241, 'f1': 0.6425721743321979}, 'combined': 0.4306641215772931, 'epoch': 20}
Test Korean: {'event': {'p': 0.6054333764553687, 'r': 0.732776617954071, 'f1': 0.6630460448642267}, 'argument': {'p': 0.6649951952771266, 'r': 0.6914404003508575, 'f1': 0.6779600086223615}, 'combined': 0.4495187022931738, 'epoch': 20}
Dev Russian: {'event': {'p': 0.6057268722466961, 'r': 0.7323568575233023, 'f1': 0.6630500301386378}, 'argument': {'p': 0.6400500395750606, 'r': 0.6619077246681955, 'f1': 0.650795405202963}, 'combined': 0.4315099130339116, 'epoch': 20}
Test Russian: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6604610859837139, 'r': 0.6844040685170714, 'f1': 0.6722194456919334}, 'combined': 0.45332779041707066, 'epoch': 20}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.634658551325218, 'r': 0.6187920875420876, 'f1': 0.6266248987767975}, 'combined': 0.4503866459958231, 'epoch': 20}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.7254901960784313, 'r': 0.578125, 'f1': 0.6434782608695652}, 'combined': 0.362687747035573, 'epoch': 20}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5485701987063148, 'r': 0.578492209544841, 'f1': 0.5631340092914382}, 'combined': 0.27030432445989033, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
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Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
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Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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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2023-01-23 14:35:31.872831: step: 3036/1519, loss: 0.1250089555978775
2023-01-23 14:35:32.710222: step: 3038/1519, loss: 0.004380092956125736
==================================================
Loss: 0.106
--------------------
Dev Chinese: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.675404027799106, 'r': 0.6367377749065177, 'f1': 0.6555011919244289}, 'combined': 0.4362105580979924, 'epoch': 21}
Test Chinese: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.693931668533036, 'r': 0.6606536665990816, 'f1': 0.6768838984084449}, 'combined': 0.45583450827222793, 'epoch': 21}
Dev Korean: {'event': {'p': 0.6076759061833689, 'r': 0.758988015978695, 'f1': 0.6749555950266429}, 'argument': {'p': 0.6741882744507347, 'r': 0.6250390556211156, 'f1': 0.6486840179850782}, 'combined': 0.43783290734339203, 'epoch': 21}
Test Korean: {'event': {'p': 0.605002156101768, 'r': 0.7322546972860126, 'f1': 0.6625737898465172}, 'argument': {'p': 0.6994398853291475, 'r': 0.6673260246620608, 'f1': 0.6830056774971657}, 'combined': 0.45254166022598513, 'epoch': 21}
Dev Russian: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.675404027799106, 'r': 0.6367377749065177, 'f1': 0.6555011919244289}, 'combined': 0.4362105580979924, 'epoch': 21}
Test Russian: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.693931668533036, 'r': 0.6606536665990816, 'f1': 0.6768838984084449}, 'combined': 0.45583450827222793, 'epoch': 21}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.664929107589658, 'r': 0.6039772727272726, 'f1': 0.6329892814608971}, 'combined': 0.4549610460500197, 'epoch': 21}
Korean: {'event': {'p': 0.7021276595744681, 'r': 0.5238095238095238, 'f1': 0.6}, 'argument': {'p': 0.6799242424242424, 'r': 0.4825268817204301, 'f1': 0.5644654088050314}, 'combined': 0.3386792452830188, 'epoch': 21}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.5985483317995633, 'r': 0.5332521501487019, 'f1': 0.5640166972726655}, 'combined': 0.28576845995148387, 'epoch': 21}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6506920398144073, 'r': 0.6666569746202626, 'f1': 0.6585777678033442}, 'combined': 0.43905184520222945, 'epoch': 5}
Test for Russian: {'event': {'p': 0.6106804478897502, 'r': 0.7400835073068893, 'f1': 0.6691835771590373}, 'argument': {'p': 0.6599026870951604, 'r': 0.6911526913425942, 'f1': 0.6751662818402272}, 'combined': 0.45181018765901, 'epoch': 5}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.6117440662196338, 'r': 0.6004154724007518, 'f1': 0.6060268319558989}, 'combined': 0.3070535948576555, '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
2023-01-23 14:41:16.437303: step: 2/1519, loss: 0.03749644756317139
2023-01-23 14:41:17.242266: step: 4/1519, loss: 0.37046539783477783
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2023-01-23 14:41:19.737348: step: 10/1519, loss: 0.0823647752404213
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2023-01-23 14:41:21.408603: step: 14/1519, loss: 0.005289451219141483
2023-01-23 14:41:22.206056: step: 16/1519, loss: 0.003677637316286564
2023-01-23 14:41:23.030283: step: 18/1519, loss: 0.02512933313846588
2023-01-23 14:41:23.860809: step: 20/1519, loss: 0.2832756042480469
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2023-01-23 15:02:22.222417: step: 3038/1519, loss: 0.021893002092838287
==================================================
Loss: 0.093
--------------------
Dev Chinese: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test Chinese: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Dev Korean: {'event': {'p': 0.6076759061833689, 'r': 0.758988015978695, 'f1': 0.6749555950266429}, 'argument': {'p': 0.6603038647507287, 'r': 0.6335807502832598, 'f1': 0.6466663459518964}, 'combined': 0.4364710683156672, 'epoch': 22}
Test Korean: {'event': {'p': 0.6067270375161707, 'r': 0.7343423799582464, 'f1': 0.6644628099173554}, 'argument': {'p': 0.6866294532773872, 'r': 0.6792689537782423, 'f1': 0.682929371543021}, 'combined': 0.4537811691905694, 'epoch': 22}
Dev Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6815693652142251, 'r': 0.607732683982684, 'f1': 0.6425367583957893}, 'combined': 0.4517836582470394, 'epoch': 22}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.6746841593780369, 'r': 0.5165550595238095, 'f1': 0.5851243152128108}, 'combined': 0.34043596521472624, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 23
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 15:29:21.013353: step: 3038/1519, loss: 0.01921706832945347
==================================================
Loss: 0.088
--------------------
Dev Chinese: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6732662371031413, 'r': 0.653258325153568, 'f1': 0.6631113918979656}, 'combined': 0.443673083186704, 'epoch': 23}
Test Chinese: {'event': {'p': 0.6149870801033591, 'r': 0.7453027139874739, 'f1': 0.6739027843322323}, 'argument': {'p': 0.6764238248835093, 'r': 0.6743444402389243, 'f1': 0.6753825320496368}, 'combined': 0.45514216883760333, 'epoch': 23}
Dev Korean: {'event': {'p': 0.6066098081023454, 'r': 0.7576564580559254, 'f1': 0.6737714624037893}, 'argument': {'p': 0.6747103591185675, 'r': 0.6404787306044637, 'f1': 0.6571490559200834}, 'combined': 0.4427682804245441, 'epoch': 23}
Test Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.6800795058691629, 'r': 0.6796634278600658, 'f1': 0.6798714032051882}, 'combined': 0.45239140831929653, 'epoch': 23}
Dev Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6732662371031413, 'r': 0.653258325153568, 'f1': 0.6631113918979656}, 'combined': 0.443673083186704, 'epoch': 23}
Test Russian: {'event': {'p': 0.6149870801033591, 'r': 0.7453027139874739, 'f1': 0.6739027843322323}, 'argument': {'p': 0.6764238248835093, 'r': 0.6743444402389243, 'f1': 0.6753825320496368}, 'combined': 0.45514216883760333, 'epoch': 23}
Chinese: {'event': {'p': 0.6351351351351351, 'r': 0.8703703703703703, 'f1': 0.734375}, 'argument': {'p': 0.6420454545454545, 'r': 0.6206439393939394, 'f1': 0.6311633281972264}, 'combined': 0.4635105691448382, 'epoch': 23}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.6915760869565217, 'r': 0.5131048387096774, 'f1': 0.5891203703703705}, 'combined': 0.3427609427609427, 'epoch': 23}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.5830430575713594, 'r': 0.5618414918414918, 'f1': 0.5722459639126306}, 'combined': 0.2899379550490662, 'epoch': 23}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 24
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 15:56:12.406106: step: 3038/1519, loss: 0.006036375183612108
==================================================
Loss: 0.101
--------------------
Dev Chinese: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6709793127519623, 'r': 0.6689853177660724, 'f1': 0.6699808316317957}, 'combined': 0.44746158013744997, 'epoch': 24}
Test Chinese: {'event': {'p': 0.6171403962101637, 'r': 0.7479123173277662, 'f1': 0.6762623879188296}, 'argument': {'p': 0.6686799119473132, 'r': 0.6760777228969455, 'f1': 0.6723584689201285}, 'combined': 0.4546907437293743, 'epoch': 24}
Dev Korean: {'event': {'p': 0.6044776119402985, 'r': 0.7549933422103862, 'f1': 0.6714031971580817}, 'argument': {'p': 0.662069954345274, 'r': 0.647465470058246, 'f1': 0.6546862745570368}, 'combined': 0.43955845787310815, 'epoch': 24}
Test Korean: {'event': {'p': 0.6080206985769728, 'r': 0.7359081419624217, 'f1': 0.6658795749704841}, 'argument': {'p': 0.6764977401508999, 'r': 0.6781527804937767, 'f1': 0.67732424930197}, 'combined': 0.451016383242398, 'epoch': 24}
Dev Russian: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6709793127519623, 'r': 0.6689853177660724, 'f1': 0.6699808316317957}, 'combined': 0.44746158013744997, 'epoch': 24}
Test Russian: {'event': {'p': 0.6171403962101637, 'r': 0.7479123173277662, 'f1': 0.6762623879188296}, 'argument': {'p': 0.6686799119473132, 'r': 0.6760777228969455, 'f1': 0.6723584689201285}, 'combined': 0.4546907437293743, 'epoch': 24}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6444805194805195, 'r': 0.6337391774891775, 'f1': 0.6390647167958093}, 'combined': 0.44934237899705354, 'epoch': 24}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.7211765336765336, 'r': 0.5583302196205422, 'f1': 0.6293904293904292}, 'combined': 0.3661907952817042, 'epoch': 24}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.6173440258089868, 'r': 0.5050996574800802, 'f1': 0.5556096232280882}, 'combined': 0.2963251323883137, 'epoch': 24}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 25
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 16:22:58.630183: step: 3022/1519, loss: 0.07687688618898392
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2023-01-23 16:23:00.288384: step: 3026/1519, loss: 0.00018761272076517344
2023-01-23 16:23:01.123462: step: 3028/1519, loss: 0.0047099655494093895
2023-01-23 16:23:01.936548: step: 3030/1519, loss: 0.13867059350013733
2023-01-23 16:23:02.757602: step: 3032/1519, loss: 0.04951087757945061
2023-01-23 16:23:03.598813: step: 3034/1519, loss: 0.009993710555136204
2023-01-23 16:23:04.449222: step: 3036/1519, loss: 0.0005780637729912996
2023-01-23 16:23:05.291528: step: 3038/1519, loss: 0.001184430904686451
==================================================
Loss: 0.091
--------------------
Dev Chinese: {'event': {'p': 0.6057268722466961, 'r': 0.7323568575233023, 'f1': 0.6630500301386378}, 'argument': {'p': 0.6683183803576053, 'r': 0.6688152713467187, 'f1': 0.6685667335275635}, 'combined': 0.4432931928151416, 'epoch': 25}
Test Chinese: {'event': {'p': 0.6124031007751938, 'r': 0.7421711899791231, 'f1': 0.6710712600283151}, 'argument': {'p': 0.6865907545348099, 'r': 0.6783567325708315, 'f1': 0.6824489077557135}, 'combined': 0.45797184843257405, 'epoch': 25}
Dev Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.6624665505796764, 'r': 0.6497924296708673, 'f1': 0.6560682852323243}, 'combined': 0.44359383169645605, 'epoch': 25}
Test Korean: {'event': {'p': 0.6032772746873653, 'r': 0.7301670146137788, 'f1': 0.6606847697756788}, 'argument': {'p': 0.6891537365855507, 'r': 0.6828254194451875, 'f1': 0.6859749831880159}, 'combined': 0.4532132238394494, 'epoch': 25}
Dev Russian: {'event': {'p': 0.6057268722466961, 'r': 0.7323568575233023, 'f1': 0.6630500301386378}, 'argument': {'p': 0.6683183803576053, 'r': 0.6688152713467187, 'f1': 0.6685667335275635}, 'combined': 0.4432931928151416, 'epoch': 25}
Test Russian: {'event': {'p': 0.6124031007751938, 'r': 0.7421711899791231, 'f1': 0.6710712600283151}, 'argument': {'p': 0.6865907545348099, 'r': 0.6783567325708315, 'f1': 0.6824489077557135}, 'combined': 0.45797184843257405, 'epoch': 25}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6458644600237522, 'r': 0.6081890331890333, 'f1': 0.6264608067183175}, 'combined': 0.4502687048287906, 'epoch': 25}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.6914285714285714, 'r': 0.5401785714285714, 'f1': 0.6065162907268169}, 'combined': 0.3418546365914785, 'epoch': 25}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.6005098979463771, 'r': 0.48040791835710167, 'f1': 0.5337865759523353}, 'combined': 0.28468617384124545, 'epoch': 25}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 26
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 16:28:50.682523: step: 2/1519, loss: 0.00019173964392393827
2023-01-23 16:28:51.511672: step: 4/1519, loss: 0.002175187459215522
2023-01-23 16:28:52.357998: step: 6/1519, loss: 0.09853827208280563
2023-01-23 16:28:53.189879: step: 8/1519, loss: 0.0010739730205386877
2023-01-23 16:28:54.017201: step: 10/1519, loss: 0.282663494348526
2023-01-23 16:28:54.814829: step: 12/1519, loss: 0.002312310505658388
2023-01-23 16:28:55.647226: step: 14/1519, loss: 0.039582084864377975
2023-01-23 16:28:56.451428: step: 16/1519, loss: 0.00805243942886591
2023-01-23 16:28:57.242424: step: 18/1519, loss: 6.510332605103031e-05
2023-01-23 16:28:58.070166: step: 20/1519, loss: 0.01422545313835144
2023-01-23 16:28:58.858270: step: 22/1519, loss: 9.731922546052374e-06
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2023-01-23 16:49:54.627541: step: 3038/1519, loss: 0.0003839224227704108
==================================================
Loss: 0.075
--------------------
Dev Chinese: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6686525334703103, 'r': 0.6457841196861956, 'f1': 0.657019395596016}, 'combined': 0.4388049971792559, 'epoch': 26}
Test Chinese: {'event': {'p': 0.6132644272179156, 'r': 0.7432150313152401, 'f1': 0.6720151014629543}, 'argument': {'p': 0.6862324564673293, 'r': 0.6609180098715471, 'f1': 0.6733373899505615}, 'combined': 0.4524928944264274, 'epoch': 26}
Dev Korean: {'event': {'p': 0.6044776119402985, 'r': 0.7549933422103862, 'f1': 0.6714031971580817}, 'argument': {'p': 0.667688920893215, 'r': 0.6288469291770412, 'f1': 0.6476861052881472}, 'combined': 0.43485852184532797, 'epoch': 26}
Test Korean: {'event': {'p': 0.6015523932729625, 'r': 0.7280793319415448, 'f1': 0.6587957497048406}, 'argument': {'p': 0.6916219080070378, 'r': 0.6685537354327517, 'f1': 0.6798922070621543}, 'combined': 0.4479100962699906, 'epoch': 26}
Dev Russian: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6686525334703103, 'r': 0.6457841196861956, 'f1': 0.657019395596016}, 'combined': 0.4388049971792559, 'epoch': 26}
Test Russian: {'event': {'p': 0.6132644272179156, 'r': 0.7432150313152401, 'f1': 0.6720151014629543}, 'argument': {'p': 0.6862324564673293, 'r': 0.6609180098715471, 'f1': 0.6733373899505615}, 'combined': 0.4524928944264274, 'epoch': 26}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6686016004197822, 'r': 0.6128848003848003, 'f1': 0.639531965618922}, 'combined': 0.4596636002886001, 'epoch': 26}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.7388716356107661, 'r': 0.5310639880952381, 'f1': 0.617965367965368}, 'combined': 0.35954348681621406, 'epoch': 26}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.6020669806384092, 'r': 0.5673323471400394, 'f1': 0.5841838029956842}, 'combined': 0.2804082254379284, 'epoch': 26}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 27
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.066
--------------------
Dev Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6966836873540085, 'r': 0.6386698784442324, 'f1': 0.6664165915496452}, 'combined': 0.44267093664117474, 'epoch': 27}
Test Chinese: {'event': {'p': 0.6188630490956072, 'r': 0.75, 'f1': 0.6781500707881075}, 'argument': {'p': 0.7078721221449918, 'r': 0.6551628748960416, 'f1': 0.6804983520013216}, 'combined': 0.46148000558088675, 'epoch': 27}
Dev Korean: {'event': {'p': 0.6108742004264393, 'r': 0.762982689747004, 'f1': 0.6785079928952042}, 'argument': {'p': 0.6913389983154136, 'r': 0.6241396258723836, 'f1': 0.6560229194540999}, 'combined': 0.44511679437205354, 'epoch': 27}
Test Korean: {'event': {'p': 0.6062958171625701, 'r': 0.7338204592901879, 'f1': 0.6639905548996458}, 'argument': {'p': 0.7126553541908487, 'r': 0.6574327494097507, 'f1': 0.6839311539980361}, 'combined': 0.45412382645631105, 'epoch': 27}
Dev Russian: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6966836873540085, 'r': 0.6386698784442324, 'f1': 0.6664165915496452}, 'combined': 0.44267093664117474, 'epoch': 27}
Test Russian: {'event': {'p': 0.6188630490956072, 'r': 0.75, 'f1': 0.6781500707881075}, 'argument': {'p': 0.7078721221449918, 'r': 0.6551628748960416, 'f1': 0.6804983520013216}, 'combined': 0.46148000558088675, 'epoch': 27}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6361798578779712, 'r': 0.5619588744588745, 'f1': 0.5967704861510172}, 'combined': 0.4289287869210436, 'epoch': 27}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.726449275362319, 'r': 0.5221354166666667, 'f1': 0.6075757575757575}, 'combined': 0.3424517906336087, 'epoch': 27}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5842637027471419, 'r': 0.5099028678520512, 'f1': 0.5445564608128701}, 'combined': 0.26138710119017766, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
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
2023-01-23 17:22:25.917552: step: 2/1519, loss: 3.252364695072174e-05
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2023-01-23 17:43:19.830948: step: 3014/1519, loss: 0.014091513119637966
2023-01-23 17:43:20.677834: step: 3016/1519, loss: 0.049599528312683105
2023-01-23 17:43:21.493426: step: 3018/1519, loss: 1.2879276275634766
2023-01-23 17:43:22.349780: step: 3020/1519, loss: 0.0006885414477437735
2023-01-23 17:43:23.199104: step: 3022/1519, loss: 0.055947206914424896
2023-01-23 17:43:24.022319: step: 3024/1519, loss: 0.03691067174077034
2023-01-23 17:43:24.854345: step: 3026/1519, loss: 0.006143668200820684
2023-01-23 17:43:25.722112: step: 3028/1519, loss: 0.003982544410973787
2023-01-23 17:43:26.574545: step: 3030/1519, loss: 0.004197655711323023
2023-01-23 17:43:27.429712: step: 3032/1519, loss: 0.12113949656486511
2023-01-23 17:43:28.258314: step: 3034/1519, loss: 3.126109004369937e-05
2023-01-23 17:43:29.083330: step: 3036/1519, loss: 0.0006034723483026028
2023-01-23 17:43:29.942288: step: 3038/1519, loss: 0.04569686949253082
==================================================
Loss: 0.082
--------------------
Dev Chinese: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6794594849876187, 'r': 0.6426365136408508, 'f1': 0.6605352032615232}, 'combined': 0.43956049692629384, 'epoch': 28}
Test Chinese: {'event': {'p': 0.6136950904392765, 'r': 0.7437369519832986, 'f1': 0.6724870221802738}, 'argument': {'p': 0.6872450938541829, 'r': 0.6762660631689548, 'f1': 0.6817113767776241}, 'combined': 0.45844205375559904, 'epoch': 28}
Dev Korean: {'event': {'p': 0.605543710021322, 'r': 0.7563249001331558, 'f1': 0.6725873297809354}, 'argument': {'p': 0.6741191620452548, 'r': 0.6314910385629814, 'f1': 0.6521091969974598}, 'combined': 0.43860038353411146, 'epoch': 28}
Test Korean: {'event': {'p': 0.6062958171625701, 'r': 0.7338204592901879, 'f1': 0.6639905548996458}, 'argument': {'p': 0.690501294322911, 'r': 0.6801416606971232, 'f1': 0.6852823272672541}, 'combined': 0.4550209927451047, 'epoch': 28}
Dev Russian: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6794594849876187, 'r': 0.6426365136408508, 'f1': 0.6605352032615232}, 'combined': 0.43956049692629384, 'epoch': 28}
Test Russian: {'event': {'p': 0.6136950904392765, 'r': 0.7437369519832986, 'f1': 0.6724870221802738}, 'argument': {'p': 0.6872450938541829, 'r': 0.6762660631689548, 'f1': 0.6817113767776241}, 'combined': 0.45844205375559904, 'epoch': 28}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6911439189741075, 'r': 0.6105104617604618, 'f1': 0.6483296939049151}, 'combined': 0.45585681602689354, 'epoch': 28}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.7282647907647907, 'r': 0.5006820436507936, 'f1': 0.5934009406231628}, 'combined': 0.3452514563625674, 'epoch': 28}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5302748496957996, 'r': 0.5109921278886796, 'f1': 0.5204549450718032}, 'combined': 0.2775759707049617, 'epoch': 28}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 29
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 17:49:14.968110: step: 2/1519, loss: 0.08062605559825897
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2023-01-23 17:49:18.291248: step: 10/1519, loss: 0.06073344126343727
2023-01-23 17:49:19.108985: step: 12/1519, loss: 0.22980573773384094
2023-01-23 17:49:19.898214: step: 14/1519, loss: 0.11511576920747757
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2023-01-23 18:10:19.118454: step: 3038/1519, loss: 0.029576679691672325
==================================================
Loss: 0.057
--------------------
Dev Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6698454838851566, 'r': 0.6399416676402836, 'f1': 0.6545522079973677}, 'combined': 0.4347899537149482, 'epoch': 29}
Test Chinese: {'event': {'p': 0.6128337639965546, 'r': 0.7426931106471816, 'f1': 0.6715431807456346}, 'argument': {'p': 0.6876512010348843, 'r': 0.6677622124511061, 'f1': 0.6775607837858555}, 'combined': 0.45501132389205856, 'epoch': 29}
Dev Korean: {'event': {'p': 0.6108742004264393, 'r': 0.762982689747004, 'f1': 0.6785079928952042}, 'argument': {'p': 0.6572745245807425, 'r': 0.6258144037429308, 'f1': 0.6411587780331197}, 'combined': 0.43503135561039386, 'epoch': 29}
Test Korean: {'event': {'p': 0.6045709357481673, 'r': 0.7317327766179541, 'f1': 0.6621015348288075}, 'argument': {'p': 0.6968157772668582, 'r': 0.6771630944009627, 'f1': 0.6868488849303298}, 'combined': 0.45476370090782636, 'epoch': 29}
Dev Russian: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6698454838851566, 'r': 0.6399416676402836, 'f1': 0.6545522079973677}, 'combined': 0.4347899537149482, 'epoch': 29}
Test Russian: {'event': {'p': 0.6128337639965546, 'r': 0.7426931106471816, 'f1': 0.6715431807456346}, 'argument': {'p': 0.6876512010348843, 'r': 0.6677622124511061, 'f1': 0.6775607837858555}, 'combined': 0.45501132389205856, 'epoch': 29}
Chinese: {'event': {'p': 0.6351351351351351, 'r': 0.8703703703703703, 'f1': 0.734375}, 'argument': {'p': 0.6751028307103074, 'r': 0.6019666907166907, 'f1': 0.6364405540616995}, 'combined': 0.4673860318890606, 'epoch': 29}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.66243961352657, 'r': 0.4914874551971326, 'f1': 0.5643004115226337}, 'combined': 0.3283202394313504, 'epoch': 29}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5450198991162748, 'r': 0.5152915409826598, 'f1': 0.5297389673653512}, 'combined': 0.2542747043353686, 'epoch': 29}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 30
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 18:16:03.913003: step: 2/1519, loss: 0.0012867520563304424
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2023-01-23 18:37:06.436897: step: 3032/1519, loss: -1.4829635802016128e-05
2023-01-23 18:37:07.266039: step: 3034/1519, loss: 0.047443099319934845
2023-01-23 18:37:08.088029: step: 3036/1519, loss: 0.0001537349889986217
2023-01-23 18:37:08.931523: step: 3038/1519, loss: 0.003838649019598961
==================================================
Loss: 0.064
--------------------
Dev Chinese: {'event': {'p': 0.6123348017621145, 'r': 0.7403462050599201, 'f1': 0.6702833031946956}, 'argument': {'p': 0.6747219415880444, 'r': 0.6566892839064041, 'f1': 0.6655834954265811}, 'combined': 0.4461295038664004, 'epoch': 30}
Test Chinese: {'event': {'p': 0.6124031007751938, 'r': 0.7421711899791231, 'f1': 0.6710712600283151}, 'argument': {'p': 0.6941322320612985, 'r': 0.6753488260276594, 'f1': 0.684611715162702}, 'combined': 0.45942324632438036, 'epoch': 30}
Dev Korean: {'event': {'p': 0.6108742004264393, 'r': 0.762982689747004, 'f1': 0.6785079928952042}, 'argument': {'p': 0.6682985424116985, 'r': 0.6456610365568104, 'f1': 0.6567847847521712}, 'combined': 0.4456337260663044, 'epoch': 30}
Test Korean: {'event': {'p': 0.6045709357481673, 'r': 0.7317327766179541, 'f1': 0.6621015348288075}, 'argument': {'p': 0.6975494036452717, 'r': 0.6776682836517044, 'f1': 0.6874651358792201}, 'combined': 0.4551717216069263, 'epoch': 30}
Dev Russian: {'event': {'p': 0.6123348017621145, 'r': 0.7403462050599201, 'f1': 0.6702833031946956}, 'argument': {'p': 0.6747219415880444, 'r': 0.6566892839064041, 'f1': 0.6655834954265811}, 'combined': 0.4461295038664004, 'epoch': 30}
Test Russian: {'event': {'p': 0.6124031007751938, 'r': 0.7421711899791231, 'f1': 0.6710712600283151}, 'argument': {'p': 0.6941322320612985, 'r': 0.6753488260276594, 'f1': 0.684611715162702}, 'combined': 0.45942324632438036, 'epoch': 30}
Chinese: {'event': {'p': 0.5945945945945946, 'r': 0.8148148148148148, 'f1': 0.6875}, 'argument': {'p': 0.6740932678432678, 'r': 0.5842141654641655, 'f1': 0.625943748711606}, 'combined': 0.4303363272392291, 'epoch': 30}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.7552910052910053, 'r': 0.5310639880952381, 'f1': 0.6236347750109219}, 'combined': 0.36284205091544536, 'epoch': 30}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5762902595675444, 'r': 0.4505542029346256, 'f1': 0.505724105334784}, 'combined': 0.2697195228452181, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 31
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 18:42:51.571150: step: 2/1519, loss: 0.12406864017248154
2023-01-23 18:42:52.465056: step: 4/1519, loss: 5.106374010210857e-05
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2023-01-23 18:42:54.966156: step: 10/1519, loss: 0.015082811005413532
2023-01-23 18:42:55.794893: step: 12/1519, loss: 2.0626121113309637e-05
2023-01-23 18:42:56.645818: step: 14/1519, loss: 0.006822343450039625
2023-01-23 18:42:57.479773: step: 16/1519, loss: 0.00039099325658753514
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2023-01-23 19:03:56.638140: step: 3038/1519, loss: 0.0006128399982117116
==================================================
Loss: 0.067
--------------------
Dev Chinese: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6884653046032059, 'r': 0.6460431663091702, 'f1': 0.6665799655445823}, 'combined': 0.4435830512123079, 'epoch': 31}
Test Chinese: {'event': {'p': 0.6124031007751938, 'r': 0.7421711899791231, 'f1': 0.6710712600283151}, 'argument': {'p': 0.688698481117189, 'r': 0.6706808231008883, 'f1': 0.6795702461421443}, 'combined': 0.45604006135636105, 'epoch': 31}
Dev Korean: {'event': {'p': 0.6066098081023454, 'r': 0.7576564580559254, 'f1': 0.6737714624037893}, 'argument': {'p': 0.6780353240361557, 'r': 0.6336638947426132, 'f1': 0.6550991234131159}, 'combined': 0.44138709440149554, 'epoch': 31}
Test Korean: {'event': {'p': 0.6032772746873653, 'r': 0.7301670146137788, 'f1': 0.6606847697756788}, 'argument': {'p': 0.6934733162518716, 'r': 0.6800759476766284, 'f1': 0.6867092941240084}, 'combined': 0.4536983718911394, 'epoch': 31}
Dev Russian: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6884653046032059, 'r': 0.6460431663091702, 'f1': 0.6665799655445823}, 'combined': 0.4435830512123079, 'epoch': 31}
Test Russian: {'event': {'p': 0.6124031007751938, 'r': 0.7421711899791231, 'f1': 0.6710712600283151}, 'argument': {'p': 0.688698481117189, 'r': 0.6706808231008883, 'f1': 0.6795702461421443}, 'combined': 0.45604006135636105, 'epoch': 31}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6434132730592909, 'r': 0.6058808321308322, 'f1': 0.6240832605639474}, 'combined': 0.4388085425840256, 'epoch': 31}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.7304964539007093, 'r': 0.5364583333333334, 'f1': 0.6186186186186188}, 'combined': 0.3486759486759487, 'epoch': 31}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5699254127017724, 'r': 0.548005204520935, 'f1': 0.5587504046095808}, 'combined': 0.31290022658136524, 'epoch': 31}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 32
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 19:09:39.372117: step: 2/1519, loss: 0.4413014352321625
2023-01-23 19:09:40.236714: step: 4/1519, loss: -1.7068696251953952e-05
2023-01-23 19:09:41.066767: step: 6/1519, loss: 0.22309979796409607
2023-01-23 19:09:41.976960: step: 8/1519, loss: 0.10062035918235779
2023-01-23 19:09:42.846161: step: 10/1519, loss: 0.00012178275210317224
2023-01-23 19:09:43.665871: step: 12/1519, loss: 0.0003505778731778264
2023-01-23 19:09:44.508329: step: 14/1519, loss: 0.000302989938063547
2023-01-23 19:09:45.366459: step: 16/1519, loss: 0.0004599973908625543
2023-01-23 19:09:46.190078: step: 18/1519, loss: 8.675995923113078e-05
2023-01-23 19:09:47.027398: step: 20/1519, loss: 0.002396884374320507
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2023-01-23 19:30:42.225146: step: 3038/1519, loss: 0.00028894178103655577
==================================================
Loss: 0.068
--------------------
Dev Chinese: {'event': {'p': 0.6134361233480177, 'r': 0.7416777629826897, 'f1': 0.6714888487040387}, 'argument': {'p': 0.6651732799592904, 'r': 0.6523149117221592, 'f1': 0.6586813485482763}, 'combined': 0.44229718039950566, 'epoch': 32}
Test Chinese: {'event': {'p': 0.611541774332472, 'r': 0.7411273486430062, 'f1': 0.6701274185936762}, 'argument': {'p': 0.6925742724560511, 'r': 0.6861812791718414, 'f1': 0.6893629543457448}, 'combined': 0.46196101706982423, 'epoch': 32}
Dev Korean: {'event': {'p': 0.6130063965884861, 'r': 0.7656458055925432, 'f1': 0.6808762581409117}, 'argument': {'p': 0.6610169618489026, 'r': 0.6396625225527559, 'f1': 0.6501644445648161}, 'combined': 0.4426815341915562, 'epoch': 32}
Test Korean: {'event': {'p': 0.6037084950409659, 'r': 0.7306889352818372, 'f1': 0.6611570247933883}, 'argument': {'p': 0.6954730225243106, 'r': 0.6946202045935636, 'f1': 0.695046351958345}, 'combined': 0.4595347781542776, 'epoch': 32}
Dev Russian: {'event': {'p': 0.6134361233480177, 'r': 0.7416777629826897, 'f1': 0.6714888487040387}, 'argument': {'p': 0.6651732799592904, 'r': 0.6523149117221592, 'f1': 0.6586813485482763}, 'combined': 0.44229718039950566, 'epoch': 32}
Test Russian: {'event': {'p': 0.611541774332472, 'r': 0.7411273486430062, 'f1': 0.6701274185936762}, 'argument': {'p': 0.6925742724560511, 'r': 0.6861812791718414, 'f1': 0.6893629543457448}, 'combined': 0.46196101706982423, 'epoch': 32}
Chinese: {'event': {'p': 0.5945945945945946, 'r': 0.8148148148148148, 'f1': 0.6875}, 'argument': {'p': 0.6579306047487865, 'r': 0.6031030543530543, 'f1': 0.629324926281448}, 'combined': 0.43266088681849546, 'epoch': 32}
Korean: {'event': {'p': 0.7021276595744681, 'r': 0.5238095238095238, 'f1': 0.6}, 'argument': {'p': 0.6956018518518517, 'r': 0.5385304659498208, 'f1': 0.607070707070707}, 'combined': 0.3642424242424242, 'epoch': 32}
Russian: {'event': {'p': 0.48717948717948717, 'r': 0.5277777777777778, 'f1': 0.5066666666666667}, 'argument': {'p': 0.619069587980609, 'r': 0.5388198265757153, 'f1': 0.5761637749522499}, 'combined': 0.29192297930913996, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
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
2023-01-23 19:36:29.136925: step: 2/1519, loss: 0.00018511226517148316
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2023-01-23 19:57:22.497859: step: 3014/1519, loss: 0.04184044152498245
2023-01-23 19:57:23.319258: step: 3016/1519, loss: 0.16951890289783478
2023-01-23 19:57:24.182198: step: 3018/1519, loss: 0.0005926179001107812
2023-01-23 19:57:25.015153: step: 3020/1519, loss: 0.0044084955006837845
2023-01-23 19:57:25.829019: step: 3022/1519, loss: 0.06414801627397537
2023-01-23 19:57:26.658833: step: 3024/1519, loss: 0.0007593114278279245
2023-01-23 19:57:27.531427: step: 3026/1519, loss: 6.356442463584244e-05
2023-01-23 19:57:28.360371: step: 3028/1519, loss: 0.11163166910409927
2023-01-23 19:57:29.196780: step: 3030/1519, loss: 0.0012995826546102762
2023-01-23 19:57:30.126477: step: 3032/1519, loss: 1.5401783457491547e-05
2023-01-23 19:57:30.959807: step: 3034/1519, loss: 0.00011072576307924464
2023-01-23 19:57:31.789506: step: 3036/1519, loss: 0.00020976501400582492
2023-01-23 19:57:32.668599: step: 3038/1519, loss: 0.003744803834706545
==================================================
Loss: 0.061
--------------------
Dev Chinese: {'event': {'p': 0.6134361233480177, 'r': 0.7416777629826897, 'f1': 0.6714888487040387}, 'argument': {'p': 0.6582802136466767, 'r': 0.6592576155971542, 'f1': 0.6587685520841}, 'combined': 0.4423557366013789, 'epoch': 33}
Test Chinese: {'event': {'p': 0.6136950904392765, 'r': 0.7437369519832986, 'f1': 0.6724870221802738}, 'argument': {'p': 0.6830850092689246, 'r': 0.679726263064775, 'f1': 0.6814014972312933}, 'combined': 0.4582336637822525, 'epoch': 33}
Dev Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6486319373812643, 'r': 0.6481542997248717, 'f1': 0.648393030590332}, 'combined': 0.440707637133038, 'epoch': 33}
Test Korean: {'event': {'p': 0.6045709357481673, 'r': 0.7317327766179541, 'f1': 0.6621015348288075}, 'argument': {'p': 0.6884658887009102, 'r': 0.6878334950493172, 'f1': 0.6881495465863287}, 'combined': 0.4556248709865562, 'epoch': 33}
Dev Russian: {'event': {'p': 0.6134361233480177, 'r': 0.7416777629826897, 'f1': 0.6714888487040387}, 'argument': {'p': 0.6582802136466767, 'r': 0.6592576155971542, 'f1': 0.6587685520841}, 'combined': 0.4423557366013789, 'epoch': 33}
Test Russian: {'event': {'p': 0.6136950904392765, 'r': 0.7437369519832986, 'f1': 0.6724870221802738}, 'argument': {'p': 0.6830850092689246, 'r': 0.679726263064775, 'f1': 0.6814014972312933}, 'combined': 0.4582336637822525, 'epoch': 33}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6531688567086796, 'r': 0.61506734006734, 'f1': 0.6335457580092773}, 'combined': 0.44546186110027314, 'epoch': 33}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.7167565139263253, 'r': 0.5935639880952381, 'f1': 0.6493691493691495}, 'combined': 0.36600806600806596, 'epoch': 33}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5197163784164692, 'r': 0.5291657671149504, 'f1': 0.5243985079517527}, 'combined': 0.2517112838168413, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 34
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 20:03:20.013742: step: 2/1519, loss: 0.14904670417308807
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2023-01-23 20:03:24.886744: step: 14/1519, loss: 0.013502547517418861
2023-01-23 20:03:25.747804: step: 16/1519, loss: 0.6679490804672241
2023-01-23 20:03:26.544553: step: 18/1519, loss: 0.0015469281934201717
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2023-01-23 20:24:14.222373: step: 3020/1519, loss: 7.768846262479201e-05
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2023-01-23 20:24:18.432385: step: 3030/1519, loss: 0.038057733327150345
2023-01-23 20:24:19.273620: step: 3032/1519, loss: 0.0006038586143404245
2023-01-23 20:24:20.089443: step: 3034/1519, loss: 5.366106779547408e-05
2023-01-23 20:24:20.922164: step: 3036/1519, loss: 5.9295711253071204e-05
2023-01-23 20:24:21.794078: step: 3038/1519, loss: 0.11762133240699768
==================================================
Loss: 0.054
--------------------
Dev Chinese: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.668132201479761, 'r': 0.6745803964457869, 'f1': 0.6713408156723126}, 'combined': 0.44756054378154175, 'epoch': 34}
Test Chinese: {'event': {'p': 0.615848406546081, 'r': 0.7463465553235908, 'f1': 0.6748466257668712}, 'argument': {'p': 0.670534875063578, 'r': 0.6874321890633363, 'f1': 0.6788784046167211}, 'combined': 0.4581388006615909, 'epoch': 34}
Dev Korean: {'event': {'p': 0.605543710021322, 'r': 0.7563249001331558, 'f1': 0.6725873297809354}, 'argument': {'p': 0.6663200930865723, 'r': 0.6594609156577399, 'f1': 0.6628727607498347}, 'combined': 0.44583982013724816, 'epoch': 34}
Test Korean: {'event': {'p': 0.6058645968089694, 'r': 0.7332985386221295, 'f1': 0.6635182998819362}, 'argument': {'p': 0.6744700850494565, 'r': 0.6990601402335512, 'f1': 0.6865449970580145}, 'combined': 0.45553516924038273, 'epoch': 34}
Dev Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.668132201479761, 'r': 0.6745803964457869, 'f1': 0.6713408156723126}, 'combined': 0.44756054378154175, 'epoch': 34}
Test Russian: {'event': {'p': 0.615848406546081, 'r': 0.7463465553235908, 'f1': 0.6748466257668712}, 'argument': {'p': 0.670534875063578, 'r': 0.6874321890633363, 'f1': 0.6788784046167211}, 'combined': 0.4581388006615909, 'epoch': 34}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6323387356445209, 'r': 0.6376082251082252, 'f1': 0.6349625478256185}, 'combined': 0.44645804143988804, 'epoch': 34}
Korean: {'event': {'p': 0.6595744680851063, 'r': 0.49206349206349204, 'f1': 0.5636363636363635}, 'argument': {'p': 0.7190170940170939, 'r': 0.5842013888888888, 'f1': 0.6446360153256705}, 'combined': 0.363340299547196, 'epoch': 34}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5737128053404825, 'r': 0.5319882376793565, 'f1': 0.5520632655163134}, 'combined': 0.29443374160870045, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 35
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 20:30:09.244138: step: 2/1519, loss: 2.7908086849492975e-05
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2023-01-23 20:51:08.214657: step: 3026/1519, loss: 0.0016002083430066705
2023-01-23 20:51:09.032521: step: 3028/1519, loss: 0.5992271304130554
2023-01-23 20:51:09.842010: step: 3030/1519, loss: -3.513322326398338e-06
2023-01-23 20:51:10.669906: step: 3032/1519, loss: 0.25995197892189026
2023-01-23 20:51:11.509594: step: 3034/1519, loss: -3.8486261473735794e-05
2023-01-23 20:51:12.329175: step: 3036/1519, loss: 0.15032663941383362
2023-01-23 20:51:13.166005: step: 3038/1519, loss: 8.154078386723995e-06
==================================================
Loss: 0.052
--------------------
Dev Chinese: {'event': {'p': 0.6057268722466961, 'r': 0.7323568575233023, 'f1': 0.6630500301386378}, 'argument': {'p': 0.682833703863998, 'r': 0.6595149582235051, 'f1': 0.6709717890687775}, 'combined': 0.44488786496422866, 'epoch': 35}
Test Chinese: {'event': {'p': 0.6171403962101637, 'r': 0.7479123173277662, 'f1': 0.6762623879188296}, 'argument': {'p': 0.6878263541181306, 'r': 0.6888838977130232, 'f1': 0.6883547197304506}, 'combined': 0.4655084065001112, 'epoch': 35}
Dev Korean: {'event': {'p': 0.6044776119402985, 'r': 0.7549933422103862, 'f1': 0.6714031971580817}, 'argument': {'p': 0.6752565141248157, 'r': 0.6369969470993478, 'f1': 0.6555689898583974}, 'combined': 0.4401511157486221, 'epoch': 35}
Test Korean: {'event': {'p': 0.6037084950409659, 'r': 0.7306889352818372, 'f1': 0.6611570247933883}, 'argument': {'p': 0.6959693743927283, 'r': 0.6934114471361548, 'f1': 0.6946880561213701}, 'combined': 0.4592978883447074, 'epoch': 35}
Dev Russian: {'event': {'p': 0.6057268722466961, 'r': 0.7323568575233023, 'f1': 0.6630500301386378}, 'argument': {'p': 0.682833703863998, 'r': 0.6595149582235051, 'f1': 0.6709717890687775}, 'combined': 0.44488786496422866, 'epoch': 35}
Test Russian: {'event': {'p': 0.6171403962101637, 'r': 0.7479123173277662, 'f1': 0.6762623879188296}, 'argument': {'p': 0.6878263541181306, 'r': 0.6888838977130232, 'f1': 0.6883547197304506}, 'combined': 0.4655084065001112, 'epoch': 35}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6407935285053928, 'r': 0.6301136363636364, 'f1': 0.6354087089381206}, 'combined': 0.45670000954927414, 'epoch': 35}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.7367149758454106, 'r': 0.5295138888888888, 'f1': 0.6161616161616161}, 'combined': 0.35849403122130385, 'epoch': 35}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5907153768594785, 'r': 0.5370139789631623, 'f1': 0.5625860731995034}, 'combined': 0.27004131513576163, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 36
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 20:56:58.278447: step: 2/1519, loss: 0.0009271954768337309
2023-01-23 20:56:59.078475: step: 4/1519, loss: 1.5574256394756958e-05
2023-01-23 20:56:59.856308: step: 6/1519, loss: 0.0001011126660159789
2023-01-23 20:57:00.667250: step: 8/1519, loss: 0.021467827260494232
2023-01-23 20:57:01.484121: step: 10/1519, loss: 0.10431009531021118
2023-01-23 20:57:02.300499: step: 12/1519, loss: 0.03247220441699028
2023-01-23 20:57:03.117927: step: 14/1519, loss: 3.893680332112126e-05
2023-01-23 20:57:03.919571: step: 16/1519, loss: 8.126757165882736e-06
2023-01-23 20:57:04.771483: step: 18/1519, loss: 1.840528602770064e-05
2023-01-23 20:57:05.585125: step: 20/1519, loss: 0.0001130610762629658
2023-01-23 20:57:06.426818: step: 22/1519, loss: 0.0009359741234220564
2023-01-23 20:57:07.230470: step: 24/1519, loss: 0.005285915452986956
2023-01-23 20:57:08.014825: step: 26/1519, loss: 0.0016426272923126817
2023-01-23 20:57:08.847760: step: 28/1519, loss: 0.023287830874323845
2023-01-23 20:57:09.665967: step: 30/1519, loss: 0.03846726939082146
2023-01-23 20:57:10.481300: step: 32/1519, loss: 3.574837319320068e-05
2023-01-23 20:57:11.302173: step: 34/1519, loss: 4.75491069664713e-05
2023-01-23 20:57:12.131769: step: 36/1519, loss: 0.027812320739030838
2023-01-23 20:57:12.925844: step: 38/1519, loss: 3.755632496904582e-05
2023-01-23 20:57:13.729541: step: 40/1519, loss: 4.830849138670601e-05
2023-01-23 20:57:14.562911: step: 42/1519, loss: 0.005614185705780983
2023-01-23 20:57:15.415607: step: 44/1519, loss: 6.075501732993871e-05
2023-01-23 20:57:16.217945: step: 46/1519, loss: 0.034781359136104584
2023-01-23 20:57:17.047832: step: 48/1519, loss: 4.6891553211025894e-05
2023-01-23 20:57:17.852150: step: 50/1519, loss: 5.833196337334812e-05
2023-01-23 20:57:18.711231: step: 52/1519, loss: 0.11622076481580734
2023-01-23 20:57:19.542604: step: 54/1519, loss: 0.06149333715438843
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2023-01-23 21:18:00.564827: step: 3038/1519, loss: 0.008612573146820068
==================================================
Loss: 0.052
--------------------
Dev Chinese: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6653635346719596, 'r': 0.6698058728114609, 'f1': 0.6675773135303876}, 'combined': 0.44424674752112586, 'epoch': 36}
Test Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6805519736231002, 'r': 0.6820177778739807, 'f1': 0.6812840873184071}, 'combined': 0.45944075544030377, 'epoch': 36}
Dev Korean: {'event': {'p': 0.603411513859275, 'r': 0.7536617842876165, 'f1': 0.670219064535228}, 'argument': {'p': 0.6590522347755837, 'r': 0.6493674113402335, 'f1': 0.6541739798919748}, 'combined': 0.43843987284648633, 'epoch': 36}
Test Korean: {'event': {'p': 0.6054333764553687, 'r': 0.732776617954071, 'f1': 0.6630460448642267}, 'argument': {'p': 0.6838227382177928, 'r': 0.6859158595468954, 'f1': 0.6848676996148249}, 'combined': 0.4540988194848709, 'epoch': 36}
Dev Russian: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6653635346719596, 'r': 0.6698058728114609, 'f1': 0.6675773135303876}, 'combined': 0.44424674752112586, 'epoch': 36}
Test Russian: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6805519736231002, 'r': 0.6820177778739807, 'f1': 0.6812840873184071}, 'combined': 0.45944075544030377, 'epoch': 36}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.676357733175915, 'r': 0.6199945887445888, 'f1': 0.6469508752117448}, 'combined': 0.4649959415584415, 'epoch': 36}
Korean: {'event': {'p': 0.7021276595744681, 'r': 0.5238095238095238, 'f1': 0.6}, 'argument': {'p': 0.6692063492063492, 'r': 0.5396825396825398, 'f1': 0.5975056689342404}, 'combined': 0.3585034013605442, 'epoch': 36}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5829403166404074, 'r': 0.5935392314884148, 'f1': 0.5881920312047354}, 'combined': 0.282332174978273, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 37
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 21:44:37.851008: step: 3012/1519, loss: 0.29529792070388794
2023-01-23 21:44:38.688217: step: 3014/1519, loss: 0.00043212322634644806
2023-01-23 21:44:39.495492: step: 3016/1519, loss: 0.3165275752544403
2023-01-23 21:44:40.341417: step: 3018/1519, loss: 0.04044939950108528
2023-01-23 21:44:41.189119: step: 3020/1519, loss: 0.0029691392555832863
2023-01-23 21:44:42.033267: step: 3022/1519, loss: 0.000344898464391008
2023-01-23 21:44:42.854759: step: 3024/1519, loss: 3.013453897438012e-05
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2023-01-23 21:44:44.507202: step: 3028/1519, loss: 0.0005181872984394431
2023-01-23 21:44:45.286835: step: 3030/1519, loss: 0.00019725182210095227
2023-01-23 21:44:46.120372: step: 3032/1519, loss: 6.78838841849938e-05
2023-01-23 21:44:46.940708: step: 3034/1519, loss: 0.0004014568985439837
2023-01-23 21:44:47.754274: step: 3036/1519, loss: 0.00017147057224065065
2023-01-23 21:44:48.562645: step: 3038/1519, loss: 0.05595241114497185
==================================================
Loss: 0.055
--------------------
Dev Chinese: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6587751172102947, 'r': 0.6646395841290806, 'f1': 0.66169435702068}, 'combined': 0.4403318686864561, 'epoch': 37}
Test Chinese: {'event': {'p': 0.6132644272179156, 'r': 0.7432150313152401, 'f1': 0.6720151014629543}, 'argument': {'p': 0.6778904652133771, 'r': 0.6845548261617114, 'f1': 0.6812063464250597}, 'combined': 0.45778095201004493, 'epoch': 37}
Dev Korean: {'event': {'p': 0.6012793176972282, 'r': 0.7509986684420772, 'f1': 0.6678507992895205}, 'argument': {'p': 0.6579567885792476, 'r': 0.6531188710161648, 'f1': 0.655528903750542}, 'combined': 0.4377955023271826, 'epoch': 37}
Test Korean: {'event': {'p': 0.6067270375161707, 'r': 0.7343423799582464, 'f1': 0.6644628099173554}, 'argument': {'p': 0.6795061747013781, 'r': 0.6907445616642799, 'f1': 0.6850792812375073}, 'combined': 0.4552097042272363, 'epoch': 37}
Dev Russian: {'event': {'p': 0.6079295154185022, 'r': 0.7350199733688415, 'f1': 0.6654611211573236}, 'argument': {'p': 0.6587751172102947, 'r': 0.6646395841290806, 'f1': 0.66169435702068}, 'combined': 0.4403318686864561, 'epoch': 37}
Test Russian: {'event': {'p': 0.6132644272179156, 'r': 0.7432150313152401, 'f1': 0.6720151014629543}, 'argument': {'p': 0.6778904652133771, 'r': 0.6845548261617114, 'f1': 0.6812063464250597}, 'combined': 0.45778095201004493, 'epoch': 37}
Chinese: {'event': {'p': 0.6081081081081081, 'r': 0.8333333333333334, 'f1': 0.7031250000000001}, 'argument': {'p': 0.6588901436727523, 'r': 0.6314363876863877, 'f1': 0.6448712044456725}, 'combined': 0.45342506562586354, 'epoch': 37}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.7006802721088434, 'r': 0.5364583333333333, 'f1': 0.607669616519174}, 'combined': 0.3535532314293375, 'epoch': 37}
Russian: {'event': {'p': 0.5128205128205128, 'r': 0.5555555555555556, 'f1': 0.5333333333333333}, 'argument': {'p': 0.5954018663096564, 'r': 0.5629254008745842, 'f1': 0.5787083560392922}, 'combined': 0.3086444565542891, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 38
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 21:50:36.993512: step: 2/1519, loss: 0.04166743904352188
2023-01-23 21:50:37.827942: step: 4/1519, loss: 0.00742704002186656
2023-01-23 21:50:38.620545: step: 6/1519, loss: 3.419577842578292e-05
2023-01-23 21:50:39.476788: step: 8/1519, loss: 0.0016869500977918506
2023-01-23 21:50:40.303278: step: 10/1519, loss: 2.316792779311072e-05
2023-01-23 21:50:41.121141: step: 12/1519, loss: 0.0033904453739523888
2023-01-23 21:50:41.977898: step: 14/1519, loss: 8.386004628846422e-05
2023-01-23 21:50:42.779317: step: 16/1519, loss: 9.28034060052596e-05
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2023-01-23 21:50:44.416636: step: 20/1519, loss: 0.19956815242767334
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2023-01-23 22:11:31.040116: step: 3020/1519, loss: 0.015062779188156128
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2023-01-23 22:11:35.184840: step: 3030/1519, loss: 0.00015631139103788882
2023-01-23 22:11:36.016613: step: 3032/1519, loss: 0.0010269477497786283
2023-01-23 22:11:36.871613: step: 3034/1519, loss: 0.0001502042869105935
2023-01-23 22:11:37.682410: step: 3036/1519, loss: 0.00021582384943030775
2023-01-23 22:11:38.551598: step: 3038/1519, loss: 0.0025854287669062614
==================================================
Loss: 0.043
--------------------
Dev Chinese: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6766787440158761, 'r': 0.6535873328699338, 'f1': 0.6649326224216382}, 'combined': 0.4440900214847348, 'epoch': 38}
Test Chinese: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6836693246375141, 'r': 0.6830388272585063, 'f1': 0.6833539305157377}, 'combined': 0.46019162758185833, 'epoch': 38}
Dev Korean: {'event': {'p': 0.6130063965884861, 'r': 0.7656458055925432, 'f1': 0.6808762581409117}, 'argument': {'p': 0.6722365496257419, 'r': 0.6455644161358153, 'f1': 0.6586305624893888}, 'combined': 0.44844591288501895, 'epoch': 38}
Test Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.688590091590806, 'r': 0.6871133358796737, 'f1': 0.6878509211198452}, 'combined': 0.4577010379494036, 'epoch': 38}
Dev Russian: {'event': {'p': 0.6101321585903083, 'r': 0.7376830892143809, 'f1': 0.6678722121760096}, 'argument': {'p': 0.6766787440158761, 'r': 0.6535873328699338, 'f1': 0.6649326224216382}, 'combined': 0.4440900214847348, 'epoch': 38}
Test Russian: {'event': {'p': 0.6145564168819982, 'r': 0.7447807933194155, 'f1': 0.6734308636149128}, 'argument': {'p': 0.6836693246375141, 'r': 0.6830388272585063, 'f1': 0.6833539305157377}, 'combined': 0.46019162758185833, 'epoch': 38}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.6962620712620713, 'r': 0.6034271284271284, 'f1': 0.6465290661719233}, 'combined': 0.4646927663110698, 'epoch': 38}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.6680158730158731, 'r': 0.5387224782386073, 'f1': 0.5964427437641725}, 'combined': 0.34702123273551844, 'epoch': 38}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.5845848516706696, 'r': 0.5208119587611421, 'f1': 0.5508588025358233}, 'combined': 0.2644122252171952, 'epoch': 38}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}
******************************
Epoch: 39
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 22:17:24.591705: step: 2/1519, loss: 0.3297885060310364
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2023-01-23 22:38:27.570175: step: 3038/1519, loss: 0.00018164084758609533
==================================================
Loss: 0.047
--------------------
Dev Chinese: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6976964200952559, 'r': 0.6531515855531683, 'f1': 0.6746895596166547}, 'combined': 0.4497930397444364, 'epoch': 39}
Test Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6969843805626047, 'r': 0.6693449753696825, 'f1': 0.6828851199239697}, 'combined': 0.4605204513314548, 'epoch': 39}
Dev Korean: {'event': {'p': 0.6087420042643923, 'r': 0.7603195739014648, 'f1': 0.6761397276494967}, 'argument': {'p': 0.6939132112423494, 'r': 0.6397890019769418, 'f1': 0.6657528741858069}, 'combined': 0.4501419670338611, 'epoch': 39}
Test Korean: {'event': {'p': 0.6058645968089694, 'r': 0.7332985386221295, 'f1': 0.6635182998819362}, 'argument': {'p': 0.6985181520121272, 'r': 0.6733116865878364, 'f1': 0.6856833432395593}, 'combined': 0.4549634461636745, 'epoch': 39}
Dev Russian: {'event': {'p': 0.6090308370044053, 'r': 0.7363515312916112, 'f1': 0.6666666666666666}, 'argument': {'p': 0.6976964200952559, 'r': 0.6531515855531683, 'f1': 0.6746895596166547}, 'combined': 0.4497930397444364, 'epoch': 39}
Test Russian: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6969843805626047, 'r': 0.6693449753696825, 'f1': 0.6828851199239697}, 'combined': 0.4605204513314548, 'epoch': 39}
Chinese: {'event': {'p': 0.6216216216216216, 'r': 0.8518518518518519, 'f1': 0.7187499999999999}, 'argument': {'p': 0.662875319125319, 'r': 0.5744919432419432, 'f1': 0.6155270820449391}, 'combined': 0.44241009021979993, 'epoch': 39}
Korean: {'event': {'p': 0.6808510638297872, 'r': 0.5079365079365079, 'f1': 0.5818181818181817}, 'argument': {'p': 0.7115384615384616, 'r': 0.5393920595533499, 'f1': 0.613620324629499}, 'combined': 0.3570154616026175, 'epoch': 39}
Russian: {'event': {'p': 0.46153846153846156, 'r': 0.5, 'f1': 0.48000000000000004}, 'argument': {'p': 0.581716677373924, 'r': 0.5182566762058595, 'f1': 0.5481560998331206}, 'combined': 0.26311492791989793, 'epoch': 39}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6068281938325991, 'r': 0.7336884154460719, 'f1': 0.6642555756479807}, 'argument': {'p': 0.6785482645350069, 'r': 0.6659452135592453, 'f1': 0.6721876696293545}, 'combined': 0.44650440743312153, 'epoch': 8}
Test for Chinese: {'event': {'p': 0.6154177433247201, 'r': 0.7458246346555324, 'f1': 0.6743747050495517}, 'argument': {'p': 0.6733537305173153, 'r': 0.6830765032774732, 'f1': 0.6781802709068705}, 'combined': 0.4573476201632458, 'epoch': 8}
Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8888888888888888, 'f1': 0.75}, 'argument': {'p': 0.6591008608249989, 'r': 0.6371308321308322, 'f1': 0.6479296597940666}, 'combined': 0.48594724484554996, 'epoch': 8}
--------------------
Dev for Korean: {'event': {'p': 0.6119402985074627, 'r': 0.7643142476697736, 'f1': 0.679692125518058}, 'argument': {'p': 0.6705309176892111, 'r': 0.6498233452311619, 'f1': 0.6600147494506201}, 'combined': 0.4486068279273605, 'epoch': 13}
Test for Korean: {'event': {'p': 0.6075894782233722, 'r': 0.7353862212943633, 'f1': 0.6654073199527746}, 'argument': {'p': 0.681843878787907, 'r': 0.6874806775408995, 'f1': 0.6846506762764378}, 'combined': 0.45557157160495915, 'epoch': 13}
Korean: {'event': {'p': 0.7446808510638298, 'r': 0.5555555555555556, 'f1': 0.6363636363636364}, 'argument': {'p': 0.7148394241417497, 'r': 0.529967159277504, 'f1': 0.6086751532296085}, 'combined': 0.38733873387338724, 'epoch': 13}
--------------------
Dev for Russian: {'event': {'p': 0.6112334801762115, 'r': 0.7390146471371505, 'f1': 0.6690777576853527}, 'argument': {'p': 0.6692894332428253, 'r': 0.6543610443972604, 'f1': 0.661741056176177}, 'combined': 0.4427562220346935, 'epoch': 22}
Test for Russian: {'event': {'p': 0.6141257536606374, 'r': 0.744258872651357, 'f1': 0.6729589428975933}, 'argument': {'p': 0.6839793926454413, 'r': 0.6726218012546498, 'f1': 0.6782530535442236}, 'combined': 0.45643645793018545, 'epoch': 22}
Russian: {'event': {'p': 0.5384615384615384, 'r': 0.5833333333333334, 'f1': 0.5599999999999999}, 'argument': {'p': 0.5901329207191277, 'r': 0.5364844733810251, 'f1': 0.5620313530658358}, 'combined': 0.31473755771686807, 'epoch': 22}