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
2023-01-23 05:04:55.201918: step: 2/1541, loss: 14.896011352539062
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2023-01-23 05:27:58.559433: step: 3084/1541, loss: 2.06603741645813
==================================================
Loss: 4.368
--------------------
Dev Chinese: {'event': {'p': 0.6212664277180406, 'r': 0.6924101198402131, 'f1': 0.6549118387909321}, 'argument': {'p': 0.6323709190533013, 'r': 0.49513507557413056, 'f1': 0.5554010786431359}, 'combined': 0.36373874168064324, 'epoch': 0}
Test Chinese: {'event': {'p': 0.6030494216614091, 'r': 0.6872378669862192, 'f1': 0.6423970876505182}, 'argument': {'p': 0.6443200173378743, 'r': 0.5357955277533802, 'f1': 0.5850677675886701}, 'combined': 0.3758458299771519, 'epoch': 0}
Dev Korean: {'event': {'p': 0.5617647058823529, 'r': 0.762982689747004, 'f1': 0.6470920383963863}, 'argument': {'p': 0.6286803309975603, 'r': 0.49483226052711193, 'f1': 0.5537833962577786}, 'combined': 0.3583488267145197, 'epoch': 0}
Test Korean: {'event': {'p': 0.5981887511916111, 'r': 0.7519472738166567, 'f1': 0.6663127156888771}, 'argument': {'p': 0.6443360636425584, 'r': 0.5473124864917484, 'f1': 0.5918744634712435}, 'combined': 0.3943734811024213, 'epoch': 0}
Dev Russian: {'event': {'p': 0.6445497630331753, 'r': 0.7243675099866844, 'f1': 0.6821316614420063}, 'argument': {'p': 0.6206783521749645, 'r': 0.49187592721234025, 'f1': 0.5488212946245071}, 'combined': 0.3743683815369679, 'epoch': 0}
Test Russian: {'event': {'p': 0.6431187859759289, 'r': 0.7363690832834032, 'f1': 0.6865921787709497}, 'argument': {'p': 0.6437771971949983, 'r': 0.5453389035822671, 'f1': 0.5904835543646312}, 'combined': 0.40542139011962663, 'epoch': 0}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6152375889217995, 'r': 0.4875467685795392, 'f1': 0.5439995523098016}, 'combined': 0.3998629187918199, 'epoch': 0}
Korean: {'event': {'p': 0.609375, 'r': 0.6190476190476191, 'f1': 0.6141732283464567}, 'argument': {'p': 0.4080930487137384, 'r': 0.1378692732141008, 'f1': 0.20610760036047393}, 'combined': 0.1265857703001336, 'epoch': 0}
Russian: {'event': {'p': 0.5588235294117647, 'r': 0.5277777777777778, 'f1': 0.5428571428571428}, 'argument': {'p': 0.32857142857142857, 'r': 0.26744186046511625, 'f1': 0.2948717948717949}, 'combined': 0.16007326007326006, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6212664277180406, 'r': 0.6924101198402131, 'f1': 0.6549118387909321}, 'argument': {'p': 0.6323709190533013, 'r': 0.49513507557413056, 'f1': 0.5554010786431359}, 'combined': 0.36373874168064324, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.6030494216614091, 'r': 0.6872378669862192, 'f1': 0.6423970876505182}, 'argument': {'p': 0.6443200173378743, 'r': 0.5357955277533802, 'f1': 0.5850677675886701}, 'combined': 0.3758458299771519, 'epoch': 0}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6152375889217995, 'r': 0.4875467685795392, 'f1': 0.5439995523098016}, 'combined': 0.3998629187918199, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.5617647058823529, 'r': 0.762982689747004, 'f1': 0.6470920383963863}, 'argument': {'p': 0.6286803309975603, 'r': 0.49483226052711193, 'f1': 0.5537833962577786}, 'combined': 0.3583488267145197, 'epoch': 0}
Test for Korean: {'event': {'p': 0.5981887511916111, 'r': 0.7519472738166567, 'f1': 0.6663127156888771}, 'argument': {'p': 0.6443360636425584, 'r': 0.5473124864917484, 'f1': 0.5918744634712435}, 'combined': 0.3943734811024213, 'epoch': 0}
Korean: {'event': {'p': 0.609375, 'r': 0.6190476190476191, 'f1': 0.6141732283464567}, 'argument': {'p': 0.4080930487137384, 'r': 0.1378692732141008, 'f1': 0.20610760036047393}, 'combined': 0.1265857703001336, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.6445497630331753, 'r': 0.7243675099866844, 'f1': 0.6821316614420063}, 'argument': {'p': 0.6206783521749645, 'r': 0.49187592721234025, 'f1': 0.5488212946245071}, 'combined': 0.3743683815369679, 'epoch': 0}
Test for Russian: {'event': {'p': 0.6431187859759289, 'r': 0.7363690832834032, 'f1': 0.6865921787709497}, 'argument': {'p': 0.6437771971949983, 'r': 0.5453389035822671, 'f1': 0.5904835543646312}, 'combined': 0.40542139011962663, 'epoch': 0}
Russian: {'event': {'p': 0.5588235294117647, 'r': 0.5277777777777778, 'f1': 0.5428571428571428}, 'argument': {'p': 0.32857142857142857, 'r': 0.26744186046511625, 'f1': 0.2948717948717949}, 'combined': 0.16007326007326006, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 05:56:38.060717: step: 3084/1541, loss: 0.5273277163505554
==================================================
Loss: 1.719
--------------------
Dev Chinese: {'event': {'p': 0.6367980884109916, 'r': 0.7097203728362184, 'f1': 0.6712846347607053}, 'argument': {'p': 0.6729578904388359, 'r': 0.5990583090121413, 'f1': 0.6338614493379393}, 'combined': 0.4255014515077099, 'epoch': 1}
Test Chinese: {'event': {'p': 0.6151419558359621, 'r': 0.701018573996405, 'f1': 0.6552786334360123}, 'argument': {'p': 0.7112215570847993, 'r': 0.6529247081434223, 'f1': 0.6808274734486968}, 'combined': 0.446131696407155, 'epoch': 1}
Dev Korean: {'event': {'p': 0.5794117647058824, 'r': 0.7869507323568575, 'f1': 0.6674195369847544}, 'argument': {'p': 0.6709033183918488, 'r': 0.5991927054503958, 'f1': 0.633023593329179}, 'combined': 0.42249231356018613, 'epoch': 1}
Test Korean: {'event': {'p': 0.6101048617731173, 'r': 0.7669263031755542, 'f1': 0.6795858773559862}, 'argument': {'p': 0.7136545672623013, 'r': 0.6632701036094307, 'f1': 0.6875405006284541}, 'combined': 0.4672428143373619, 'epoch': 1}
Dev Russian: {'event': {'p': 0.659952606635071, 'r': 0.7416777629826897, 'f1': 0.6984326018808777}, 'argument': {'p': 0.6741587330290469, 'r': 0.5985758502439995, 'f1': 0.6341230011751896}, 'combined': 0.4428921776232986, 'epoch': 1}
Test Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7127746635632035, 'r': 0.6583194973727579, 'f1': 0.684465694079899}, 'combined': 0.476831687440019, 'epoch': 1}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6567493112947658, 'r': 0.5753821399839099, 'f1': 0.6133790737564322}, 'combined': 0.4403747196200026, 'epoch': 1}
Korean: {'event': {'p': 0.609375, 'r': 0.6190476190476191, 'f1': 0.6141732283464567}, 'argument': {'p': 0.45908936301793446, 'r': 0.2171368608868609, 'f1': 0.2948280312959212}, 'combined': 0.1810754837880461, 'epoch': 1}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.4982400932400932, 'r': 0.5793489456280154, 'f1': 0.5357420357420357}, 'combined': 0.2602175602175602, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6367980884109916, 'r': 0.7097203728362184, 'f1': 0.6712846347607053}, 'argument': {'p': 0.6729578904388359, 'r': 0.5990583090121413, 'f1': 0.6338614493379393}, 'combined': 0.4255014515077099, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.6151419558359621, 'r': 0.701018573996405, 'f1': 0.6552786334360123}, 'argument': {'p': 0.7112215570847993, 'r': 0.6529247081434223, 'f1': 0.6808274734486968}, 'combined': 0.446131696407155, 'epoch': 1}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6567493112947658, 'r': 0.5753821399839099, 'f1': 0.6133790737564322}, 'combined': 0.4403747196200026, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.5794117647058824, 'r': 0.7869507323568575, 'f1': 0.6674195369847544}, 'argument': {'p': 0.6709033183918488, 'r': 0.5991927054503958, 'f1': 0.633023593329179}, 'combined': 0.42249231356018613, 'epoch': 1}
Test for Korean: {'event': {'p': 0.6101048617731173, 'r': 0.7669263031755542, 'f1': 0.6795858773559862}, 'argument': {'p': 0.7136545672623013, 'r': 0.6632701036094307, 'f1': 0.6875405006284541}, 'combined': 0.4672428143373619, 'epoch': 1}
Korean: {'event': {'p': 0.609375, 'r': 0.6190476190476191, 'f1': 0.6141732283464567}, 'argument': {'p': 0.45908936301793446, 'r': 0.2171368608868609, 'f1': 0.2948280312959212}, 'combined': 0.1810754837880461, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.659952606635071, 'r': 0.7416777629826897, 'f1': 0.6984326018808777}, 'argument': {'p': 0.6741587330290469, 'r': 0.5985758502439995, 'f1': 0.6341230011751896}, 'combined': 0.4428921776232986, 'epoch': 1}
Test for Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7127746635632035, 'r': 0.6583194973727579, 'f1': 0.684465694079899}, 'combined': 0.476831687440019, 'epoch': 1}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.4982400932400932, 'r': 0.5793489456280154, 'f1': 0.5357420357420357}, 'combined': 0.2602175602175602, 'epoch': 1}
******************************
Epoch: 2
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 06:25:33.012166: step: 3084/1541, loss: 2.1677894592285156
==================================================
Loss: 1.439
--------------------
Dev Chinese: {'event': {'p': 0.6212664277180406, 'r': 0.6924101198402131, 'f1': 0.6549118387909321}, 'argument': {'p': 0.6893128369893616, 'r': 0.624505647186943, 'f1': 0.6553108584831598}, 'combined': 0.42917083930887046, 'epoch': 2}
Test Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.7288561511943926, 'r': 0.6674983629560891, 'f1': 0.6968291831658773}, 'combined': 0.45310483430724363, 'epoch': 2}
Dev Korean: {'event': {'p': 0.5705882352941176, 'r': 0.7749667110519307, 'f1': 0.6572557876905702}, 'argument': {'p': 0.6879318328246672, 'r': 0.6346725941543704, 'f1': 0.6602298798250162}, 'combined': 0.4339399097212416, 'epoch': 2}
Test Korean: {'event': {'p': 0.607721639656816, 'r': 0.7639304973037747, 'f1': 0.6769312450225644}, 'argument': {'p': 0.7251491588582952, 'r': 0.6793663287655232, 'f1': 0.701511554983885}, 'combined': 0.4748750903129564, 'epoch': 2}
Dev Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6840848719584073, 'r': 0.6320573327507272, 'f1': 0.6570427693878401}, 'combined': 0.4547807005668812, 'epoch': 2}
Test Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7226548990349742, 'r': 0.676241111117048, 'f1': 0.6986780265739436}, 'combined': 0.48673268108251827, 'epoch': 2}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6497835497835497, 'r': 0.6037811745776347, 'f1': 0.6259382819015846}, 'combined': 0.4600913866968912, 'epoch': 2}
Korean: {'event': {'p': 0.609375, 'r': 0.6190476190476191, 'f1': 0.6141732283464567}, 'argument': {'p': 0.5048776455026455, 'r': 0.24561615186615185, 'f1': 0.33046536796536796}, 'combined': 0.2029629818999898, 'epoch': 2}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.49764818514818515, 'r': 0.5092213987562825, 'f1': 0.5033682792303481}, 'combined': 0.25887511503275046, 'epoch': 2}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6212664277180406, 'r': 0.6924101198402131, 'f1': 0.6549118387909321}, 'argument': {'p': 0.6893128369893616, 'r': 0.624505647186943, 'f1': 0.6553108584831598}, 'combined': 0.42917083930887046, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.7288561511943926, 'r': 0.6674983629560891, 'f1': 0.6968291831658773}, 'combined': 0.45310483430724363, 'epoch': 2}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6497835497835497, 'r': 0.6037811745776347, 'f1': 0.6259382819015846}, 'combined': 0.4600913866968912, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5705882352941176, 'r': 0.7749667110519307, 'f1': 0.6572557876905702}, 'argument': {'p': 0.6879318328246672, 'r': 0.6346725941543704, 'f1': 0.6602298798250162}, 'combined': 0.4339399097212416, 'epoch': 2}
Test for Korean: {'event': {'p': 0.607721639656816, 'r': 0.7639304973037747, 'f1': 0.6769312450225644}, 'argument': {'p': 0.7251491588582952, 'r': 0.6793663287655232, 'f1': 0.701511554983885}, 'combined': 0.4748750903129564, 'epoch': 2}
Korean: {'event': {'p': 0.609375, 'r': 0.6190476190476191, 'f1': 0.6141732283464567}, 'argument': {'p': 0.5048776455026455, 'r': 0.24561615186615185, 'f1': 0.33046536796536796}, 'combined': 0.2029629818999898, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6840848719584073, 'r': 0.6320573327507272, 'f1': 0.6570427693878401}, 'combined': 0.4547807005668812, 'epoch': 2}
Test for Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7226548990349742, 'r': 0.676241111117048, 'f1': 0.6986780265739436}, 'combined': 0.48673268108251827, 'epoch': 2}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.49764818514818515, 'r': 0.5092213987562825, 'f1': 0.5033682792303481}, 'combined': 0.25887511503275046, 'epoch': 2}
******************************
Epoch: 3
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 06:31:15.003191: step: 2/1541, loss: 0.9028211236000061
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2023-01-23 06:54:27.090284: step: 3084/1541, loss: 1.4200888872146606
==================================================
Loss: 1.008
--------------------
Dev Chinese: {'event': {'p': 0.6272401433691757, 'r': 0.6990679094540613, 'f1': 0.6612090680100756}, 'argument': {'p': 0.6785420951989045, 'r': 0.6342893498598455, 'f1': 0.6556698897427617}, 'combined': 0.43353487671908053, 'epoch': 3}
Test Chinese: {'event': {'p': 0.6146161934805467, 'r': 0.7004194128220491, 'f1': 0.6547185662279473}, 'argument': {'p': 0.7152752499932414, 'r': 0.683166178551939, 'f1': 0.6988520923025249}, 'combined': 0.4575514398777102, 'epoch': 3}
Dev Korean: {'event': {'p': 0.5794117647058824, 'r': 0.7869507323568575, 'f1': 0.6674195369847544}, 'argument': {'p': 0.6730302718272186, 'r': 0.6402230614442431, 'f1': 0.6562168778212484}, 'combined': 0.43797196475703876, 'epoch': 3}
Test Korean: {'event': {'p': 0.6158245948522403, 'r': 0.7741162372678251, 'f1': 0.6859569949561986}, 'argument': {'p': 0.710349703959979, 'r': 0.6981195087758472, 'f1': 0.7041815069913384}, 'combined': 0.48303823043950583, 'epoch': 3}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6726304715079724, 'r': 0.6407450541227186, 'f1': 0.6563007143962181}, 'combined': 0.45509002515499514, 'epoch': 3}
Test Russian: {'event': {'p': 0.6546310832025117, 'r': 0.7495506291192331, 'f1': 0.6988826815642458}, 'argument': {'p': 0.7113660651562723, 'r': 0.6932372099423113, 'f1': 0.7021846453005923}, 'combined': 0.4907446878609168, 'epoch': 3}
Chinese: {'event': {'p': 0.6507936507936508, 'r': 0.7592592592592593, 'f1': 0.7008547008547008}, 'argument': {'p': 0.5846397862926789, 'r': 0.6260302136408331, 'f1': 0.604627471294138}, 'combined': 0.42375600552238724, 'epoch': 3}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5277777777777778, 'r': 0.2368233618233618, 'f1': 0.3269419862340216}, 'combined': 0.1905016297741543, 'epoch': 3}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5364055059457359, 'r': 0.5613545992455375, 'f1': 0.5485965401717753}, 'combined': 0.28213536351691304, 'epoch': 3}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6212664277180406, 'r': 0.6924101198402131, 'f1': 0.6549118387909321}, 'argument': {'p': 0.6893128369893616, 'r': 0.624505647186943, 'f1': 0.6553108584831598}, 'combined': 0.42917083930887046, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.7288561511943926, 'r': 0.6674983629560891, 'f1': 0.6968291831658773}, 'combined': 0.45310483430724363, 'epoch': 2}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6497835497835497, 'r': 0.6037811745776347, 'f1': 0.6259382819015846}, 'combined': 0.4600913866968912, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5705882352941176, 'r': 0.7749667110519307, 'f1': 0.6572557876905702}, 'argument': {'p': 0.6879318328246672, 'r': 0.6346725941543704, 'f1': 0.6602298798250162}, 'combined': 0.4339399097212416, 'epoch': 2}
Test for Korean: {'event': {'p': 0.607721639656816, 'r': 0.7639304973037747, 'f1': 0.6769312450225644}, 'argument': {'p': 0.7251491588582952, 'r': 0.6793663287655232, 'f1': 0.701511554983885}, 'combined': 0.4748750903129564, 'epoch': 2}
Korean: {'event': {'p': 0.609375, 'r': 0.6190476190476191, 'f1': 0.6141732283464567}, 'argument': {'p': 0.5048776455026455, 'r': 0.24561615186615185, 'f1': 0.33046536796536796}, 'combined': 0.2029629818999898, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6726304715079724, 'r': 0.6407450541227186, 'f1': 0.6563007143962181}, 'combined': 0.45509002515499514, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6546310832025117, 'r': 0.7495506291192331, 'f1': 0.6988826815642458}, 'argument': {'p': 0.7113660651562723, 'r': 0.6932372099423113, 'f1': 0.7021846453005923}, 'combined': 0.4907446878609168, 'epoch': 3}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5364055059457359, 'r': 0.5613545992455375, 'f1': 0.5485965401717753}, 'combined': 0.28213536351691304, 'epoch': 3}
******************************
Epoch: 4
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 06:59:53.995185: step: 2/1541, loss: 0.35639065504074097
2023-01-23 06:59:54.891614: step: 4/1541, loss: 0.41663384437561035
2023-01-23 06:59:55.781023: step: 6/1541, loss: 0.8641396164894104
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2023-01-23 06:59:58.449460: step: 12/1541, loss: 0.8402842879295349
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2023-01-23 07:23:06.887143: step: 3084/1541, loss: 0.5334597229957581
==================================================
Loss: 0.848
--------------------
Dev Chinese: {'event': {'p': 0.6344086021505376, 'r': 0.7070572569906791, 'f1': 0.6687657430730478}, 'argument': {'p': 0.6514595708798564, 'r': 0.6327452819648294, 'f1': 0.6419660678776685}, 'combined': 0.42932491441189163, 'epoch': 4}
Test Chinese: {'event': {'p': 0.6140904311251314, 'r': 0.6998202516476932, 'f1': 0.6541584990198823}, 'argument': {'p': 0.694574043100388, 'r': 0.6872361023900377, 'f1': 0.6908855891084613}, 'combined': 0.45194867996565824, 'epoch': 4}
Dev Korean: {'event': {'p': 0.5803921568627451, 'r': 0.7882822902796272, 'f1': 0.6685488424618858}, 'argument': {'p': 0.6535413679604629, 'r': 0.6483879951665094, 'f1': 0.6509544823729979}, 'combined': 0.4351948656858438, 'epoch': 4}
Test Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.6941652832691442, 'r': 0.7002565897211375, 'f1': 0.6971976320515535}, 'combined': 0.4763967891958319, 'epoch': 4}
Dev Russian: {'event': {'p': 0.6575829383886256, 'r': 0.7390146471371505, 'f1': 0.695924764890282}, 'argument': {'p': 0.6561752698674627, 'r': 0.6438411482534127, 'f1': 0.6499496980470882}, 'combined': 0.45231609080392965, 'epoch': 4}
Test Russian: {'event': {'p': 0.6530612244897959, 'r': 0.7477531455961653, 'f1': 0.6972067039106145}, 'argument': {'p': 0.6933810842434333, 'r': 0.694128262135937, 'f1': 0.6937544720109311}, 'combined': 0.4836902687539899, 'epoch': 4}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6281347962382445, 'r': 0.6448109412711183, 'f1': 0.6363636363636364}, 'combined': 0.4786324786324786, 'epoch': 4}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6318317099567099, 'r': 0.365797305764411, 'f1': 0.4633432539682539}, 'combined': 0.2772762779652543, 'epoch': 4}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5928679962013296, 'r': 0.6204432518386007, 'f1': 0.6063422688422688}, 'combined': 0.31183316683316686, 'epoch': 4}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6344086021505376, 'r': 0.7070572569906791, 'f1': 0.6687657430730478}, 'argument': {'p': 0.6514595708798564, 'r': 0.6327452819648294, 'f1': 0.6419660678776685}, 'combined': 0.42932491441189163, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6140904311251314, 'r': 0.6998202516476932, 'f1': 0.6541584990198823}, 'argument': {'p': 0.694574043100388, 'r': 0.6872361023900377, 'f1': 0.6908855891084613}, 'combined': 0.45194867996565824, 'epoch': 4}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6281347962382445, 'r': 0.6448109412711183, 'f1': 0.6363636363636364}, 'combined': 0.4786324786324786, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.5803921568627451, 'r': 0.7882822902796272, 'f1': 0.6685488424618858}, 'argument': {'p': 0.6535413679604629, 'r': 0.6483879951665094, 'f1': 0.6509544823729979}, 'combined': 0.4351948656858438, 'epoch': 4}
Test for Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.6941652832691442, 'r': 0.7002565897211375, 'f1': 0.6971976320515535}, 'combined': 0.4763967891958319, 'epoch': 4}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6318317099567099, 'r': 0.365797305764411, 'f1': 0.4633432539682539}, 'combined': 0.2772762779652543, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.6575829383886256, 'r': 0.7390146471371505, 'f1': 0.695924764890282}, 'argument': {'p': 0.6561752698674627, 'r': 0.6438411482534127, 'f1': 0.6499496980470882}, 'combined': 0.45231609080392965, 'epoch': 4}
Test for Russian: {'event': {'p': 0.6530612244897959, 'r': 0.7477531455961653, 'f1': 0.6972067039106145}, 'argument': {'p': 0.6933810842434333, 'r': 0.694128262135937, 'f1': 0.6937544720109311}, 'combined': 0.4836902687539899, 'epoch': 4}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5928679962013296, 'r': 0.6204432518386007, 'f1': 0.6063422688422688}, 'combined': 0.31183316683316686, 'epoch': 4}
******************************
Epoch: 5
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.710
--------------------
Dev Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Dev Korean: {'event': {'p': 0.5784313725490197, 'r': 0.7856191744340879, 'f1': 0.6662902315076229}, 'argument': {'p': 0.6322458242188157, 'r': 0.6853109328512413, 'f1': 0.6577097696348575}, 'combined': 0.4382255946748345, 'epoch': 5}
Test Korean: {'event': {'p': 0.6091515729265967, 'r': 0.7657279808268425, 'f1': 0.6785240244226175}, 'argument': {'p': 0.6886317030465187, 'r': 0.7331063374307094, 'f1': 0.7101733952193441}, 'combined': 0.4818697101621034, 'epoch': 5}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6417200356729511, 'r': 0.6867615174220715, 'f1': 0.663477222445676}, 'combined': 0.4600663373196976, 'epoch': 5}
Test Russian: {'event': {'p': 0.6483516483516484, 'r': 0.7423606950269622, 'f1': 0.6921787709497207}, 'argument': {'p': 0.6895321419510843, 'r': 0.7221785704796148, 'f1': 0.7054778747362179}, 'combined': 0.4883168082671363, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
Korean: {'event': {'p': 0.5625, 'r': 0.5714285714285714, 'f1': 0.5669291338582677}, 'argument': {'p': 0.5587606837606838, 'r': 0.37250712250712253, 'f1': 0.44700854700854703}, 'combined': 0.2534221683827983, 'epoch': 5}
Russian: {'event': {'p': 0.47058823529411764, 'r': 0.4444444444444444, 'f1': 0.45714285714285713}, 'argument': {'p': 0.54672127441524, 'r': 0.6102935156263144, 'f1': 0.5767609048776159}, 'combined': 0.26366212794405297, 'epoch': 5}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5803921568627451, 'r': 0.7882822902796272, 'f1': 0.6685488424618858}, 'argument': {'p': 0.6535413679604629, 'r': 0.6483879951665094, 'f1': 0.6509544823729979}, 'combined': 0.4351948656858438, 'epoch': 4}
Test for Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.6941652832691442, 'r': 0.7002565897211375, 'f1': 0.6971976320515535}, 'combined': 0.4763967891958319, 'epoch': 4}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6318317099567099, 'r': 0.365797305764411, 'f1': 0.4633432539682539}, 'combined': 0.2772762779652543, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.6575829383886256, 'r': 0.7390146471371505, 'f1': 0.695924764890282}, 'argument': {'p': 0.6561752698674627, 'r': 0.6438411482534127, 'f1': 0.6499496980470882}, 'combined': 0.45231609080392965, 'epoch': 4}
Test for Russian: {'event': {'p': 0.6530612244897959, 'r': 0.7477531455961653, 'f1': 0.6972067039106145}, 'argument': {'p': 0.6933810842434333, 'r': 0.694128262135937, 'f1': 0.6937544720109311}, 'combined': 0.4836902687539899, 'epoch': 4}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5928679962013296, 'r': 0.6204432518386007, 'f1': 0.6063422688422688}, 'combined': 0.31183316683316686, 'epoch': 4}
******************************
Epoch: 6
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 08:20:53.038694: step: 3084/1541, loss: 0.10745313763618469
==================================================
Loss: 0.581
--------------------
Dev Chinese: {'event': {'p': 0.6272401433691757, 'r': 0.6990679094540613, 'f1': 0.6612090680100756}, 'argument': {'p': 0.674168746877286, 'r': 0.6563723669131339, 'f1': 0.6651515409788485}, 'combined': 0.4398042304960901, 'epoch': 6}
Test Chinese: {'event': {'p': 0.6125131440588854, 'r': 0.6980227681246255, 'f1': 0.6524782973956875}, 'argument': {'p': 0.7174883835585704, 'r': 0.6942085927686642, 'f1': 0.7056565388046863}, 'combined': 0.4604255769854156, 'epoch': 6}
Dev Korean: {'event': {'p': 0.5705882352941176, 'r': 0.7749667110519307, 'f1': 0.6572557876905702}, 'argument': {'p': 0.6733013090338247, 'r': 0.671367924083261, 'f1': 0.6723332266340636}, 'combined': 0.44189490446191415, 'epoch': 6}
Test Korean: {'event': {'p': 0.6129647283126788, 'r': 0.7705212702216896, 'f1': 0.6827714361560924}, 'argument': {'p': 0.719809576245395, 'r': 0.7112103051307276, 'f1': 0.715484103358802}, 'combined': 0.48851210879714324, 'epoch': 6}
Dev Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6749027227273954, 'r': 0.6759176140397674, 'f1': 0.6754097871321493}, 'combined': 0.467493670842566, 'epoch': 6}
Test Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7154362539653685, 'r': 0.7041128024637584, 'f1': 0.7097293657901553}, 'combined': 0.49443157493872836, 'epoch': 6}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6476210705182667, 'r': 0.6132341110217215, 'f1': 0.6299586776859504}, 'combined': 0.4522780250052977, 'epoch': 6}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5402494331065759, 'r': 0.33938746438746437, 'f1': 0.41688538932633423}, 'combined': 0.24290959693030498, 'epoch': 6}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5630713489409142, 'r': 0.6023553965414431, 'f1': 0.5820512820512821}, 'combined': 0.2993406593406594, 'epoch': 6}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5803921568627451, 'r': 0.7882822902796272, 'f1': 0.6685488424618858}, 'argument': {'p': 0.6535413679604629, 'r': 0.6483879951665094, 'f1': 0.6509544823729979}, 'combined': 0.4351948656858438, 'epoch': 4}
Test for Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.6941652832691442, 'r': 0.7002565897211375, 'f1': 0.6971976320515535}, 'combined': 0.4763967891958319, 'epoch': 4}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6318317099567099, 'r': 0.365797305764411, 'f1': 0.4633432539682539}, 'combined': 0.2772762779652543, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6749027227273954, 'r': 0.6759176140397674, 'f1': 0.6754097871321493}, 'combined': 0.467493670842566, 'epoch': 6}
Test for Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7154362539653685, 'r': 0.7041128024637584, 'f1': 0.7097293657901553}, 'combined': 0.49443157493872836, 'epoch': 6}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5630713489409142, 'r': 0.6023553965414431, 'f1': 0.5820512820512821}, 'combined': 0.2993406593406594, 'epoch': 6}
******************************
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 08:49:34.371461: step: 3084/1541, loss: 0.30947452783584595
==================================================
Loss: 0.484
--------------------
Dev Chinese: {'event': {'p': 0.6296296296296297, 'r': 0.7017310252996005, 'f1': 0.663727959697733}, 'argument': {'p': 0.6672884936953917, 'r': 0.6595293251640499, 'f1': 0.6633862218024361}, 'combined': 0.4403079834885187, 'epoch': 7}
Test Chinese: {'event': {'p': 0.6088328075709779, 'r': 0.6938286399041342, 'f1': 0.6485578269392327}, 'argument': {'p': 0.7017525808926439, 'r': 0.6928081743626939, 'f1': 0.6972516938977305}, 'combined': 0.4522080434240111, 'epoch': 7}
Dev Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.658943151930715, 'r': 0.6660438324472097, 'f1': 0.6624744657138378}, 'combined': 0.43616331283022863, 'epoch': 7}
Test Korean: {'event': {'p': 0.6091515729265967, 'r': 0.7657279808268425, 'f1': 0.6785240244226175}, 'argument': {'p': 0.7013697662599169, 'r': 0.7122207753019127, 'f1': 0.7067536235026656}, 'combined': 0.4795493128942961, 'epoch': 7}
Dev Russian: {'event': {'p': 0.6528436018957346, 'r': 0.7336884154460719, 'f1': 0.6909090909090909}, 'argument': {'p': 0.6689314588101222, 'r': 0.6699381276721389, 'f1': 0.66943441479419}, 'combined': 0.4625183229487131, 'epoch': 7}
Test Russian: {'event': {'p': 0.6457352171637886, 'r': 0.7393648891551827, 'f1': 0.6893854748603351}, 'argument': {'p': 0.7036239331831553, 'r': 0.7031191814376867, 'f1': 0.7033714667557484}, 'combined': 0.48489407261262196, 'epoch': 7}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6462391774891775, 'r': 0.6405202467149369, 'f1': 0.6433670033670033}, 'combined': 0.4619045152378485, 'epoch': 7}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.4995136943666355, 'r': 0.3396693121693122, 'f1': 0.40436822877299067}, 'combined': 0.24198413690352194, 'epoch': 7}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5776661508989095, 'r': 0.5373638613013112, 'f1': 0.5567866514688284}, 'combined': 0.28634742075539754, 'epoch': 7}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5803921568627451, 'r': 0.7882822902796272, 'f1': 0.6685488424618858}, 'argument': {'p': 0.6535413679604629, 'r': 0.6483879951665094, 'f1': 0.6509544823729979}, 'combined': 0.4351948656858438, 'epoch': 4}
Test for Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.6941652832691442, 'r': 0.7002565897211375, 'f1': 0.6971976320515535}, 'combined': 0.4763967891958319, 'epoch': 4}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6318317099567099, 'r': 0.365797305764411, 'f1': 0.4633432539682539}, 'combined': 0.2772762779652543, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6749027227273954, 'r': 0.6759176140397674, 'f1': 0.6754097871321493}, 'combined': 0.467493670842566, 'epoch': 6}
Test for Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7154362539653685, 'r': 0.7041128024637584, 'f1': 0.7097293657901553}, 'combined': 0.49443157493872836, 'epoch': 6}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5630713489409142, 'r': 0.6023553965414431, 'f1': 0.5820512820512821}, 'combined': 0.2993406593406594, 'epoch': 6}
******************************
Epoch: 8
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 09:18:02.673918: step: 3084/1541, loss: 0.41520756483078003
==================================================
Loss: 0.386
--------------------
Dev Chinese: {'event': {'p': 0.6308243727598566, 'r': 0.7030625832223701, 'f1': 0.6649874055415615}, 'argument': {'p': 0.6804945183739395, 'r': 0.6503560665112241, 'f1': 0.6650840346443746}, 'combined': 0.4422725066652767, 'epoch': 8}
Test Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.7145227646598844, 'r': 0.6991594836413227, 'f1': 0.7067576434377564}, 'combined': 0.46074819208151696, 'epoch': 8}
Dev Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6718530148667579, 'r': 0.6646184167167211, 'f1': 0.6682161345986232}, 'combined': 0.43994354203387614, 'epoch': 8}
Test Korean: {'event': {'p': 0.6110581506196378, 'r': 0.7681246255242661, 'f1': 0.6806477302893549}, 'argument': {'p': 0.7112547814187644, 'r': 0.7160064835449799, 'f1': 0.7136227226921052}, 'combined': 0.4857256864832911, 'epoch': 8}
Dev Russian: {'event': {'p': 0.6504739336492891, 'r': 0.7310252996005326, 'f1': 0.6884012539184953}, 'argument': {'p': 0.6774181562069249, 'r': 0.6646463206826616, 'f1': 0.6709714664828391}, 'combined': 0.46189759887031806, 'epoch': 8}
Test Russian: {'event': {'p': 0.6493982208267922, 'r': 0.7435590173756741, 'f1': 0.6932960893854749}, 'argument': {'p': 0.714479757993221, 'r': 0.7047310216184483, 'f1': 0.7095719071610188}, 'combined': 0.49194342837252764, 'epoch': 8}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6370694479420241, 'r': 0.6427072306671748, 'f1': 0.6398759212809757}, 'combined': 0.45939809732993125, 'epoch': 8}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.5609939325971934, 'r': 0.3590361168622038, 'f1': 0.4378489230026876}, 'combined': 0.26201982793861617, 'epoch': 8}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5501329207191277, 'r': 0.6396894426966601, 'f1': 0.591540774966804}, 'combined': 0.3042209699829278, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5803921568627451, 'r': 0.7882822902796272, 'f1': 0.6685488424618858}, 'argument': {'p': 0.6535413679604629, 'r': 0.6483879951665094, 'f1': 0.6509544823729979}, 'combined': 0.4351948656858438, 'epoch': 4}
Test for Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.6941652832691442, 'r': 0.7002565897211375, 'f1': 0.6971976320515535}, 'combined': 0.4763967891958319, 'epoch': 4}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6318317099567099, 'r': 0.365797305764411, 'f1': 0.4633432539682539}, 'combined': 0.2772762779652543, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6749027227273954, 'r': 0.6759176140397674, 'f1': 0.6754097871321493}, 'combined': 0.467493670842566, 'epoch': 6}
Test for Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7154362539653685, 'r': 0.7041128024637584, 'f1': 0.7097293657901553}, 'combined': 0.49443157493872836, 'epoch': 6}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5630713489409142, 'r': 0.6023553965414431, 'f1': 0.5820512820512821}, 'combined': 0.2993406593406594, 'epoch': 6}
******************************
Epoch: 9
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 09:23:20.147714: step: 2/1541, loss: 0.08849950134754181
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2023-01-23 09:46:17.345575: step: 3084/1541, loss: 2.0369186401367188
==================================================
Loss: 0.324
--------------------
Dev Chinese: {'event': {'p': 0.6320191158900836, 'r': 0.7043941411451398, 'f1': 0.6662468513853904}, 'argument': {'p': 0.6662925586063192, 'r': 0.6559544273633973, 'f1': 0.6610830780855506}, 'combined': 0.44044451927866024, 'epoch': 9}
Test Chinese: {'event': {'p': 0.6093585699263933, 'r': 0.6944278010784901, 'f1': 0.6491178941472977}, 'argument': {'p': 0.7064930019884798, 'r': 0.7057205971029239, 'f1': 0.7061065883137368}, 'combined': 0.45834642164974576, 'epoch': 9}
Dev Korean: {'event': {'p': 0.5784313725490197, 'r': 0.7856191744340879, 'f1': 0.6662902315076229}, 'argument': {'p': 0.6601304922457741, 'r': 0.6658171870820622, 'f1': 0.6629616451789225}, 'combined': 0.44172486804693883, 'epoch': 9}
Test Korean: {'event': {'p': 0.6091515729265967, 'r': 0.7657279808268425, 'f1': 0.6785240244226175}, 'argument': {'p': 0.7048764243703549, 'r': 0.7189987288304991, 'f1': 0.71186754255186}, 'combined': 0.48301922982812695, 'epoch': 9}
Dev Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6601370733441357, 'r': 0.6626206590226313, 'f1': 0.6613765346159047}, 'combined': 0.45778037254919046, 'epoch': 9}
Test Russian: {'event': {'p': 0.6499215070643642, 'r': 0.74415817855003, 'f1': 0.693854748603352}, 'argument': {'p': 0.7045800388777901, 'r': 0.7126728462159413, 'f1': 0.70860333674433}, 'combined': 0.4916677900762334, 'epoch': 9}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6380632411067193, 'r': 0.6493563958165728, 'f1': 0.6436602870813396}, 'combined': 0.47311781785465984, 'epoch': 9}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6246043543144992, 'r': 0.3698315255809535, 'f1': 0.46458175114301603}, 'combined': 0.27801742588086, 'epoch': 9}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5360780268488179, 'r': 0.635813473704412, 'f1': 0.5817016887082918}, 'combined': 0.29916086847855006, 'epoch': 9}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5784313725490197, 'r': 0.7856191744340879, 'f1': 0.6662902315076229}, 'argument': {'p': 0.6601304922457741, 'r': 0.6658171870820622, 'f1': 0.6629616451789225}, 'combined': 0.44172486804693883, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6091515729265967, 'r': 0.7657279808268425, 'f1': 0.6785240244226175}, 'argument': {'p': 0.7048764243703549, 'r': 0.7189987288304991, 'f1': 0.71186754255186}, 'combined': 0.48301922982812695, 'epoch': 9}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6246043543144992, 'r': 0.3698315255809535, 'f1': 0.46458175114301603}, 'combined': 0.27801742588086, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6749027227273954, 'r': 0.6759176140397674, 'f1': 0.6754097871321493}, 'combined': 0.467493670842566, 'epoch': 6}
Test for Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7154362539653685, 'r': 0.7041128024637584, 'f1': 0.7097293657901553}, 'combined': 0.49443157493872836, 'epoch': 6}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5630713489409142, 'r': 0.6023553965414431, 'f1': 0.5820512820512821}, 'combined': 0.2993406593406594, 'epoch': 6}
******************************
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 09:51:40.394128: step: 2/1541, loss: 0.2469751387834549
2023-01-23 09:51:41.318525: step: 4/1541, loss: 0.17699915170669556
2023-01-23 09:51:42.200856: step: 6/1541, loss: 0.0412934273481369
2023-01-23 09:51:43.124384: step: 8/1541, loss: 0.030747033655643463
2023-01-23 09:51:43.979370: step: 10/1541, loss: 0.0745549350976944
2023-01-23 09:51:44.843559: step: 12/1541, loss: 0.25741124153137207
2023-01-23 09:51:45.764586: step: 14/1541, loss: 0.0906505137681961
2023-01-23 09:51:46.646321: step: 16/1541, loss: 0.318698525428772
2023-01-23 09:51:47.523209: step: 18/1541, loss: 0.13808131217956543
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2023-01-23 10:14:38.674294: step: 3084/1541, loss: 0.014107637107372284
==================================================
Loss: 0.269
--------------------
Dev Chinese: {'event': {'p': 0.6164874551971327, 'r': 0.6870838881491345, 'f1': 0.649874055415617}, 'argument': {'p': 0.67583044823236, 'r': 0.6438973286799403, 'f1': 0.6594775496436357}, 'combined': 0.42857734964246347, 'epoch': 10}
Test Chinese: {'event': {'p': 0.6093585699263933, 'r': 0.6944278010784901, 'f1': 0.6491178941472977}, 'argument': {'p': 0.7219347095899677, 'r': 0.7053898253760107, 'f1': 0.7135663771696976}, 'combined': 0.4631887040827105, 'epoch': 10}
Dev Korean: {'event': {'p': 0.5696078431372549, 'r': 0.7736351531291611, 'f1': 0.6561264822134386}, 'argument': {'p': 0.6666608337589293, 'r': 0.6542356139182611, 'f1': 0.660389783947883}, 'combined': 0.43329922583141717, 'epoch': 10}
Test Korean: {'event': {'p': 0.6096282173498571, 'r': 0.7663271420011983, 'f1': 0.6790549508893019}, 'argument': {'p': 0.7181686019868226, 'r': 0.7247272193565654, 'f1': 0.7214330047231264}, 'combined': 0.48989265359218415, 'epoch': 10}
Dev Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6727483627478649, 'r': 0.6595770845615362, 'f1': 0.6660976184773536}, 'combined': 0.4593776679154163, 'epoch': 10}
Test Russian: {'event': {'p': 0.6446886446886447, 'r': 0.738166566806471, 'f1': 0.6882681564245811}, 'argument': {'p': 0.7212116816452776, 'r': 0.7139607203935477, 'f1': 0.717567883903295}, 'combined': 0.4938791245636086, 'epoch': 10}
Chinese: {'event': {'p': 0.6507936507936508, 'r': 0.7592592592592593, 'f1': 0.7008547008547008}, 'argument': {'p': 0.6484713203463203, 'r': 0.6427326360954679, 'f1': 0.6455892255892255}, 'combined': 0.4524642435753546, 'epoch': 10}
Korean: {'event': {'p': 0.5625, 'r': 0.5714285714285714, 'f1': 0.5669291338582677}, 'argument': {'p': 0.6136009959539371, 'r': 0.40641104926819216, 'f1': 0.48896329365079366}, 'combined': 0.27720753655793023, 'epoch': 10}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5730551257490913, 'r': 0.6396894426966601, 'f1': 0.6045416711199205}, 'combined': 0.3109071451473877, 'epoch': 10}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5784313725490197, 'r': 0.7856191744340879, 'f1': 0.6662902315076229}, 'argument': {'p': 0.6601304922457741, 'r': 0.6658171870820622, 'f1': 0.6629616451789225}, 'combined': 0.44172486804693883, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6091515729265967, 'r': 0.7657279808268425, 'f1': 0.6785240244226175}, 'argument': {'p': 0.7048764243703549, 'r': 0.7189987288304991, 'f1': 0.71186754255186}, 'combined': 0.48301922982812695, 'epoch': 9}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6246043543144992, 'r': 0.3698315255809535, 'f1': 0.46458175114301603}, 'combined': 0.27801742588086, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6727483627478649, 'r': 0.6595770845615362, 'f1': 0.6660976184773536}, 'combined': 0.4593776679154163, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6446886446886447, 'r': 0.738166566806471, 'f1': 0.6882681564245811}, 'argument': {'p': 0.7212116816452776, 'r': 0.7139607203935477, 'f1': 0.717567883903295}, 'combined': 0.4938791245636086, 'epoch': 10}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5730551257490913, 'r': 0.6396894426966601, 'f1': 0.6045416711199205}, 'combined': 0.3109071451473877, 'epoch': 10}
******************************
Epoch: 11
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 10:43:04.493696: step: 3084/1541, loss: 0.27826330065727234
==================================================
Loss: 0.220
--------------------
Dev Chinese: {'event': {'p': 0.6248506571087217, 'r': 0.6964047936085219, 'f1': 0.6586901763224181}, 'argument': {'p': 0.6489144528347627, 'r': 0.6569943215081917, 'f1': 0.6529293914447226}, 'combined': 0.43007817597681347, 'epoch': 11}
Test Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7156565118366034, 'r': 0.7073076207032561, 'f1': 0.7114575737400191}, 'combined': 0.463413698269192, 'epoch': 11}
Dev Korean: {'event': {'p': 0.5696078431372549, 'r': 0.7736351531291611, 'f1': 0.6561264822134386}, 'argument': {'p': 0.6482489491292114, 'r': 0.6659708628464128, 'f1': 0.6569904182799956}, 'combined': 0.4310688119939891, 'epoch': 11}
Test Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.7129107352592366, 'r': 0.7181711447604467, 'f1': 0.7155312718320432}, 'combined': 0.4889242085733155, 'epoch': 11}
Dev Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6526182691107695, 'r': 0.6683796159874069, 'f1': 0.6604049151255139}, 'combined': 0.4571078534787256, 'epoch': 11}
Test Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7166244846941695, 'r': 0.713019474710081, 'f1': 0.7148174344806034}, 'combined': 0.49637881064770395, 'epoch': 11}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6380266075388026, 'r': 0.6944891391794046, 'f1': 0.6650616332819721}, 'combined': 0.4888487218995692, 'epoch': 11}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.544949494949495, 'r': 0.38924963924963923, 'f1': 0.4541245791245791}, 'combined': 0.2646080224820382, 'epoch': 11}
Russian: {'event': {'p': 0.5588235294117647, 'r': 0.5277777777777778, 'f1': 0.5428571428571428}, 'argument': {'p': 0.5130588428665352, 'r': 0.6204432518386007, 'f1': 0.5616644174538913}, 'combined': 0.30490354090354094, 'epoch': 11}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5784313725490197, 'r': 0.7856191744340879, 'f1': 0.6662902315076229}, 'argument': {'p': 0.6601304922457741, 'r': 0.6658171870820622, 'f1': 0.6629616451789225}, 'combined': 0.44172486804693883, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6091515729265967, 'r': 0.7657279808268425, 'f1': 0.6785240244226175}, 'argument': {'p': 0.7048764243703549, 'r': 0.7189987288304991, 'f1': 0.71186754255186}, 'combined': 0.48301922982812695, 'epoch': 9}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6246043543144992, 'r': 0.3698315255809535, 'f1': 0.46458175114301603}, 'combined': 0.27801742588086, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6727483627478649, 'r': 0.6595770845615362, 'f1': 0.6660976184773536}, 'combined': 0.4593776679154163, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6446886446886447, 'r': 0.738166566806471, 'f1': 0.6882681564245811}, 'argument': {'p': 0.7212116816452776, 'r': 0.7139607203935477, 'f1': 0.717567883903295}, 'combined': 0.4938791245636086, 'epoch': 10}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5730551257490913, 'r': 0.6396894426966601, 'f1': 0.6045416711199205}, 'combined': 0.3109071451473877, 'epoch': 10}
******************************
Epoch: 12
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 10:48:29.044061: step: 2/1541, loss: 0.15065182745456696
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2023-01-23 11:11:41.537332: step: 3084/1541, loss: 0.05450074374675751
==================================================
Loss: 0.208
--------------------
Dev Chinese: {'event': {'p': 0.6296296296296297, 'r': 0.7017310252996005, 'f1': 0.663727959697733}, 'argument': {'p': 0.6804853485228216, 'r': 0.6380544616312972, 'f1': 0.6585871876693117}, 'combined': 0.43712273035482024, 'epoch': 12}
Test Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.7252073335731545, 'r': 0.6926880109229547, 'f1': 0.7085747581434514}, 'combined': 0.46193280228450156, 'epoch': 12}
Dev Korean: {'event': {'p': 0.5774509803921568, 'r': 0.7842876165113183, 'f1': 0.6651609260304913}, 'argument': {'p': 0.6697063337039494, 'r': 0.6480718864557959, 'f1': 0.6587115197613453}, 'combined': 0.4381491644714087, 'epoch': 12}
Test Korean: {'event': {'p': 0.6110581506196378, 'r': 0.7681246255242661, 'f1': 0.6806477302893549}, 'argument': {'p': 0.7174422676434833, 'r': 0.7071102925913643, 'f1': 0.7122388123147404}, 'combined': 0.48478373102601385, 'epoch': 12}
Dev Russian: {'event': {'p': 0.6575829383886256, 'r': 0.7390146471371505, 'f1': 0.695924764890282}, 'argument': {'p': 0.6818843598661549, 'r': 0.6474301727537625, 'f1': 0.6642107613705911}, 'combined': 0.4622407179444238, 'epoch': 12}
Test Russian: {'event': {'p': 0.652537938252224, 'r': 0.7471539844218095, 'f1': 0.6966480446927374}, 'argument': {'p': 0.7175449258880758, 'r': 0.6949126844980577, 'f1': 0.7060474835213332}, 'combined': 0.49186659885536455, 'epoch': 12}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6331168831168831, 'r': 0.6275140788415124, 'f1': 0.6303030303030303}, 'combined': 0.4525252525252525, 'epoch': 12}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.57687338501292, 'r': 0.3263888888888889, 'f1': 0.4169000933706817}, 'combined': 0.24948352044229768, 'epoch': 12}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5577695316551499, 'r': 0.5318267627409569, 'f1': 0.5444893047109796}, 'combined': 0.2800230709942181, 'epoch': 12}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5784313725490197, 'r': 0.7856191744340879, 'f1': 0.6662902315076229}, 'argument': {'p': 0.6601304922457741, 'r': 0.6658171870820622, 'f1': 0.6629616451789225}, 'combined': 0.44172486804693883, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6091515729265967, 'r': 0.7657279808268425, 'f1': 0.6785240244226175}, 'argument': {'p': 0.7048764243703549, 'r': 0.7189987288304991, 'f1': 0.71186754255186}, 'combined': 0.48301922982812695, 'epoch': 9}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6246043543144992, 'r': 0.3698315255809535, 'f1': 0.46458175114301603}, 'combined': 0.27801742588086, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6727483627478649, 'r': 0.6595770845615362, 'f1': 0.6660976184773536}, 'combined': 0.4593776679154163, 'epoch': 10}
Test for Russian: {'event': {'p': 0.6446886446886447, 'r': 0.738166566806471, 'f1': 0.6882681564245811}, 'argument': {'p': 0.7212116816452776, 'r': 0.7139607203935477, 'f1': 0.717567883903295}, 'combined': 0.4938791245636086, 'epoch': 10}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5730551257490913, 'r': 0.6396894426966601, 'f1': 0.6045416711199205}, 'combined': 0.3109071451473877, 'epoch': 10}
******************************
Epoch: 13
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 11:40:14.695418: step: 3084/1541, loss: 0.005272303242236376
==================================================
Loss: 0.192
--------------------
Dev Chinese: {'event': {'p': 0.6248506571087217, 'r': 0.6964047936085219, 'f1': 0.6586901763224181}, 'argument': {'p': 0.6575667999224194, 'r': 0.6488944681934193, 'f1': 0.6532018504500723}, 'combined': 0.4302576420470879, 'epoch': 13}
Test Chinese: {'event': {'p': 0.6098843322818086, 'r': 0.695026962252846, 'f1': 0.6496779613553626}, 'argument': {'p': 0.7141576827888098, 'r': 0.7024373707080306, 'f1': 0.708249042280353}, 'combined': 0.46013379392058773, 'epoch': 13}
Dev Korean: {'event': {'p': 0.5656862745098039, 'r': 0.7683089214380826, 'f1': 0.6516092603049125}, 'argument': {'p': 0.6501253071310233, 'r': 0.6482611485442678, 'f1': 0.6491918896046328}, 'combined': 0.4230194469812232, 'epoch': 13}
Test Korean: {'event': {'p': 0.611534795042898, 'r': 0.768723786698622, 'f1': 0.6811786567560393}, 'argument': {'p': 0.7080991336922304, 'r': 0.7143170748029806, 'f1': 0.7111945137136163}, 'combined': 0.4844505235437057, 'epoch': 13}
Dev Russian: {'event': {'p': 0.6504739336492891, 'r': 0.7310252996005326, 'f1': 0.6884012539184953}, 'argument': {'p': 0.653371185022781, 'r': 0.6528791886485169, 'f1': 0.6531250941809645}, 'combined': 0.4496121337998113, 'epoch': 13}
Test Russian: {'event': {'p': 0.6478283621140764, 'r': 0.7417615338526063, 'f1': 0.6916201117318437}, 'argument': {'p': 0.7081306065307763, 'r': 0.708893952495416, 'f1': 0.7085120739072518}, 'combined': 0.49002119971909375, 'epoch': 13}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6373027798647631, 'r': 0.6824215607401447, 'f1': 0.659090909090909}, 'combined': 0.48445998445998434, 'epoch': 13}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5426245210727969, 'r': 0.4087301587301587, 'f1': 0.4662551440329218}, 'combined': 0.271676225656978, 'epoch': 13}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6150902184235518, 'r': 0.6436990657920891, 'f1': 0.6290695415695416}, 'combined': 0.3235214785214786, 'epoch': 13}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5784313725490197, 'r': 0.7856191744340879, 'f1': 0.6662902315076229}, 'argument': {'p': 0.6601304922457741, 'r': 0.6658171870820622, 'f1': 0.6629616451789225}, 'combined': 0.44172486804693883, 'epoch': 9}
Test for Korean: {'event': {'p': 0.6091515729265967, 'r': 0.7657279808268425, 'f1': 0.6785240244226175}, 'argument': {'p': 0.7048764243703549, 'r': 0.7189987288304991, 'f1': 0.71186754255186}, 'combined': 0.48301922982812695, 'epoch': 9}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6246043543144992, 'r': 0.3698315255809535, 'f1': 0.46458175114301603}, 'combined': 0.27801742588086, 'epoch': 9}
--------------------
Dev for Russian: {'event': {'p': 0.6504739336492891, 'r': 0.7310252996005326, 'f1': 0.6884012539184953}, 'argument': {'p': 0.653371185022781, 'r': 0.6528791886485169, 'f1': 0.6531250941809645}, 'combined': 0.4496121337998113, 'epoch': 13}
Test for Russian: {'event': {'p': 0.6478283621140764, 'r': 0.7417615338526063, 'f1': 0.6916201117318437}, 'argument': {'p': 0.7081306065307763, 'r': 0.708893952495416, 'f1': 0.7085120739072518}, 'combined': 0.49002119971909375, 'epoch': 13}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6150902184235518, 'r': 0.6436990657920891, 'f1': 0.6290695415695416}, 'combined': 0.3235214785214786, 'epoch': 13}
******************************
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 11:45:46.867738: step: 2/1541, loss: 0.031666576862335205
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2023-01-23 12:08:59.056258: step: 3084/1541, loss: 0.002729891799390316
==================================================
Loss: 0.162
--------------------
Dev Chinese: {'event': {'p': 0.6296296296296297, 'r': 0.7017310252996005, 'f1': 0.663727959697733}, 'argument': {'p': 0.6748177089933514, 'r': 0.6370949799191888, 'f1': 0.6554140049008907}, 'combined': 0.4350166002301882, 'epoch': 14}
Test Chinese: {'event': {'p': 0.6125131440588854, 'r': 0.6980227681246255, 'f1': 0.6524782973956875}, 'argument': {'p': 0.7171542618958446, 'r': 0.6977929283021589, 'f1': 0.7073411303536308}, 'combined': 0.46152473641107805, 'epoch': 14}
Dev Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6654555335459805, 'r': 0.6519157449960623, 'f1': 0.6586160590862389}, 'combined': 0.4566955243569782, 'epoch': 14}
Test Russian: {'event': {'p': 0.652014652014652, 'r': 0.7465548232474536, 'f1': 0.6960893854748604}, 'argument': {'p': 0.7071902650999489, 'r': 0.702871933739784, 'f1': 0.7050244869506607}, 'combined': 0.4907600618662141, 'epoch': 14}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6410109498344793, 'r': 0.6750469294717082, 'f1': 0.6575888192267503}, 'combined': 0.48335588421795317, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.5810623534386653, 'r': 0.6216015873995024, 'f1': 0.60064872490289}, 'combined': 0.2917436663814037, 'epoch': 14}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6504739336492891, 'r': 0.7310252996005326, 'f1': 0.6884012539184953}, 'argument': {'p': 0.653371185022781, 'r': 0.6528791886485169, 'f1': 0.6531250941809645}, 'combined': 0.4496121337998113, 'epoch': 13}
Test for Russian: {'event': {'p': 0.6478283621140764, 'r': 0.7417615338526063, 'f1': 0.6916201117318437}, 'argument': {'p': 0.7081306065307763, 'r': 0.708893952495416, 'f1': 0.7085120739072518}, 'combined': 0.49002119971909375, 'epoch': 13}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6150902184235518, 'r': 0.6436990657920891, 'f1': 0.6290695415695416}, 'combined': 0.3235214785214786, 'epoch': 13}
******************************
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 12:14:27.730038: step: 2/1541, loss: 0.4067099988460541
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==================================================
Loss: 0.152
--------------------
Dev Chinese: {'event': {'p': 0.6308243727598566, 'r': 0.7030625832223701, 'f1': 0.6649874055415615}, 'argument': {'p': 0.6718781990356308, 'r': 0.6520557055858218, 'f1': 0.6618185569696915}, 'combined': 0.4401010051385353, 'epoch': 15}
Test Chinese: {'event': {'p': 0.6125131440588854, 'r': 0.6980227681246255, 'f1': 0.6524782973956875}, 'argument': {'p': 0.7131147922606513, 'r': 0.6951633436411749, 'f1': 0.7040246535822872}, 'combined': 0.45936080729395945, 'epoch': 15}
Dev Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6635301955532534, 'r': 0.6606722047612078, 'f1': 0.6620981159945053}, 'combined': 0.43591552978520226, 'epoch': 15}
Test Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.7133767687394067, 'r': 0.7146291858193565, 'f1': 0.7140024280699973}, 'combined': 0.4878795460186284, 'epoch': 15}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6707048979170669, 'r': 0.6636342137522785, 'f1': 0.6671508220007766}, 'combined': 0.4626136734375291, 'epoch': 15}
Test Russian: {'event': {'p': 0.6541077969649398, 'r': 0.7489514679448772, 'f1': 0.6983240223463687}, 'argument': {'p': 0.709326223967639, 'r': 0.7032135383750991, 'f1': 0.7062566550215115}, 'combined': 0.49319598814351356, 'epoch': 15}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6254208754208753, 'r': 0.5977473853580048, 'f1': 0.611271081859317}, 'combined': 0.4493103678624039, 'epoch': 15}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5306484295845998, 'r': 0.3239022881880025, 'f1': 0.40226574500768053}, 'combined': 0.23439106402022328, 'epoch': 15}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5357032452434751, 'r': 0.5606196752547995, 'f1': 0.5478783189990086}, 'combined': 0.2817659926280616, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6504739336492891, 'r': 0.7310252996005326, 'f1': 0.6884012539184953}, 'argument': {'p': 0.653371185022781, 'r': 0.6528791886485169, 'f1': 0.6531250941809645}, 'combined': 0.4496121337998113, 'epoch': 13}
Test for Russian: {'event': {'p': 0.6478283621140764, 'r': 0.7417615338526063, 'f1': 0.6916201117318437}, 'argument': {'p': 0.7081306065307763, 'r': 0.708893952495416, 'f1': 0.7085120739072518}, 'combined': 0.49002119971909375, 'epoch': 13}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6150902184235518, 'r': 0.6436990657920891, 'f1': 0.6290695415695416}, 'combined': 0.3235214785214786, 'epoch': 13}
******************************
Epoch: 16
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.132
--------------------
Dev Chinese: {'event': {'p': 0.6236559139784946, 'r': 0.6950732356857523, 'f1': 0.6574307304785894}, 'argument': {'p': 0.6669470437176143, 'r': 0.6529660109688754, 'f1': 0.6598824810732926}, 'combined': 0.4338270215620387, 'epoch': 16}
Test Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.721787349796131, 'r': 0.6978330650290092, 'f1': 0.7096081084033167}, 'combined': 0.46260646215707685, 'epoch': 16}
Dev Korean: {'event': {'p': 0.5676470588235294, 'r': 0.7709720372836218, 'f1': 0.6538678712591757}, 'argument': {'p': 0.6663945785118833, 'r': 0.6601755336298629, 'f1': 0.6632704784323108}, 'combined': 0.43369125580159007, 'epoch': 16}
Test Korean: {'event': {'p': 0.611534795042898, 'r': 0.768723786698622, 'f1': 0.6811786567560393}, 'argument': {'p': 0.7250903249811195, 'r': 0.712101351825504, 'f1': 0.7185371428840014}, 'combined': 0.4894521658190463, 'epoch': 16}
Dev Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Chinese: {'event': {'p': 0.6349206349206349, 'r': 0.7407407407407407, 'f1': 0.6837606837606837}, 'argument': {'p': 0.6703869047619048, 'r': 0.6644542772861356, 'f1': 0.6674074074074073}, 'combined': 0.456346945235834, 'epoch': 16}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5656436817151103, 'r': 0.35995507018234285, 'f1': 0.43994508577841906}, 'combined': 0.2563459554929371, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 17
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 13:34:53.667298: step: 3084/1541, loss: 0.0912754088640213
==================================================
Loss: 0.118
--------------------
Dev Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6640785176976081, 'r': 0.644001725302099, 'f1': 0.6538860493031938}, 'combined': 0.4315318511774227, 'epoch': 17}
Test Chinese: {'event': {'p': 0.6093585699263933, 'r': 0.6944278010784901, 'f1': 0.6491178941472977}, 'argument': {'p': 0.7242476573073298, 'r': 0.6973259149438649, 'f1': 0.7105318643171055}, 'combined': 0.461218947490073, 'epoch': 17}
Dev Korean: {'event': {'p': 0.5696078431372549, 'r': 0.7736351531291611, 'f1': 0.6561264822134386}, 'argument': {'p': 0.6588832184640967, 'r': 0.6489645463581856, 'f1': 0.6538862709784535}, 'combined': 0.429032098744756, 'epoch': 17}
Test Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.7177867167777032, 'r': 0.712991376546066, 'f1': 0.7153810107239413}, 'combined': 0.4888215348031391, 'epoch': 17}
Dev Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6642531440510157, 'r': 0.6507683057883259, 'f1': 0.657441585034921}, 'combined': 0.4550567460053622, 'epoch': 17}
Test Russian: {'event': {'p': 0.6493982208267922, 'r': 0.7435590173756741, 'f1': 0.6932960893854749}, 'argument': {'p': 0.7176641171614032, 'r': 0.7019450826382988, 'f1': 0.7097175731158694}, 'combined': 0.4920444180093821, 'epoch': 17}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.63335837716003, 'r': 0.6781979082864038, 'f1': 0.6550116550116549}, 'combined': 0.4702647779570856, 'epoch': 17}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6135819107517221, 'r': 0.41692104192104196, 'f1': 0.4964861262571187}, 'combined': 0.28929112868525025, 'epoch': 17}
Russian: {'event': {'p': 0.47058823529411764, 'r': 0.4444444444444444, 'f1': 0.45714285714285713}, 'argument': {'p': 0.5756560967767864, 'r': 0.5890434478646186, 'f1': 0.5822728335213472}, 'combined': 0.26618186675261585, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6406723427552294, 'r': 0.6675328291750916, 'f1': 0.6538268319220668}, 'combined': 0.43149277068912223, 'epoch': 5}
Test for Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.6917776725673452, 'r': 0.7202241343577782, 'f1': 0.7057143594990817}, 'combined': 0.460068056262633, 'epoch': 5}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6433357969943335, 'r': 0.7002681684097612, 'f1': 0.6705957883923985}, 'combined': 0.4929165624080878, 'epoch': 5}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 18
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 13:40:17.146115: step: 2/1541, loss: 0.03167138993740082
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2023-01-23 14:03:27.030706: step: 3084/1541, loss: 0.01470599602907896
==================================================
Loss: 0.123
--------------------
Dev Chinese: {'event': {'p': 0.6284348864994026, 'r': 0.7003994673768309, 'f1': 0.6624685138539043}, 'argument': {'p': 0.6868474446486486, 'r': 0.6340950032055018, 'f1': 0.6594178774763145}, 'combined': 0.43684358130043, 'epoch': 18}
Test Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7310372578834318, 'r': 0.6833493318043012, 'f1': 0.706389363899417}, 'combined': 0.4601124784178225, 'epoch': 18}
Dev Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6851670471807466, 'r': 0.6438504413206011, 'f1': 0.6638665172165784}, 'combined': 0.4378295268825317, 'epoch': 18}
Test Korean: {'event': {'p': 0.6110581506196378, 'r': 0.7681246255242661, 'f1': 0.6806477302893549}, 'argument': {'p': 0.7273354643967593, 'r': 0.6979868403947849, 'f1': 0.712358995568536}, 'combined': 0.4848655334849286, 'epoch': 18}
Dev Russian: {'event': {'p': 0.6563981042654028, 'r': 0.7376830892143809, 'f1': 0.6946708463949843}, 'argument': {'p': 0.6913057944643356, 'r': 0.6449758127570119, 'f1': 0.6673376543368226}, 'combined': 0.463580013169404, 'epoch': 18}
Test Russian: {'event': {'p': 0.6514913657770801, 'r': 0.7459556620730977, 'f1': 0.6955307262569833}, 'argument': {'p': 0.726321562078031, 'r': 0.6878064810362723, 'f1': 0.7065395246861376}, 'combined': 0.4914199487342131, 'epoch': 18}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6661522633744856, 'r': 0.6366764995083579, 'f1': 0.6510809451985924}, 'combined': 0.489701907499796, 'epoch': 18}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6270833333333333, 'r': 0.33444444444444443, 'f1': 0.43623188405797103}, 'combined': 0.2610521510898094, 'epoch': 18}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.5550541813172126, 'r': 0.5550541813172126, 'f1': 0.5550541813172126}, 'combined': 0.26959774521121754, 'epoch': 18}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6284348864994026, 'r': 0.7003994673768309, 'f1': 0.6624685138539043}, 'argument': {'p': 0.6868474446486486, 'r': 0.6340950032055018, 'f1': 0.6594178774763145}, 'combined': 0.43684358130043, 'epoch': 18}
Test for Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7310372578834318, 'r': 0.6833493318043012, 'f1': 0.706389363899417}, 'combined': 0.4601124784178225, 'epoch': 18}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6661522633744856, 'r': 0.6366764995083579, 'f1': 0.6510809451985924}, 'combined': 0.489701907499796, 'epoch': 18}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
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 14:08:54.868336: step: 2/1541, loss: 0.006840829737484455
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2023-01-23 14:32:05.855985: step: 3084/1541, loss: 0.008871801197528839
==================================================
Loss: 0.114
--------------------
Dev Chinese: {'event': {'p': 0.6284348864994026, 'r': 0.7003994673768309, 'f1': 0.6624685138539043}, 'argument': {'p': 0.6763328078722167, 'r': 0.6590044051860497, 'f1': 0.6675561729293212}, 'combined': 0.44223494579448736, 'epoch': 19}
Test Chinese: {'event': {'p': 0.6125131440588854, 'r': 0.6980227681246255, 'f1': 0.6524782973956875}, 'argument': {'p': 0.7190813155360521, 'r': 0.6983939798014024, 'f1': 0.708586687062498}, 'combined': 0.4623374351317896, 'epoch': 19}
Dev Korean: {'event': {'p': 0.5735294117647058, 'r': 0.7789613848202397, 'f1': 0.6606437041219649}, 'argument': {'p': 0.6713720494406288, 'r': 0.6636662009535053, 'f1': 0.6674968860960941}, 'combined': 0.440977615320401, 'epoch': 19}
Test Korean: {'event': {'p': 0.6124880838894184, 'r': 0.7699221090473337, 'f1': 0.6822405096894081}, 'argument': {'p': 0.7136783443203177, 'r': 0.7134281064716781, 'f1': 0.7135532034568562}, 'combined': 0.4868149012169155, 'epoch': 19}
Dev Russian: {'event': {'p': 0.6575829383886256, 'r': 0.7390146471371505, 'f1': 0.695924764890282}, 'argument': {'p': 0.6711868599987194, 'r': 0.6661289333973726, 'f1': 0.6686483317838983}, 'combined': 0.46532893309098866, 'epoch': 19}
Test Russian: {'event': {'p': 0.652014652014652, 'r': 0.7465548232474536, 'f1': 0.6960893854748604}, 'argument': {'p': 0.7115327423082188, 'r': 0.7015723054991662, 'f1': 0.7065174202496574}, 'combined': 0.4917992768888677, 'epoch': 19}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6325115562403697, 'r': 0.660498793242156, 'f1': 0.6462022825659188}, 'combined': 0.47498629316811125, 'epoch': 19}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5301587301587302, 'r': 0.34425891568748707, 'f1': 0.4174478190226221}, 'combined': 0.2432373118714491, 'epoch': 19}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5613247843719343, 'r': 0.6396491728889484, 'f1': 0.5979329224831473}, 'combined': 0.3075083601341901, 'epoch': 19}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6284348864994026, 'r': 0.7003994673768309, 'f1': 0.6624685138539043}, 'argument': {'p': 0.6868474446486486, 'r': 0.6340950032055018, 'f1': 0.6594178774763145}, 'combined': 0.43684358130043, 'epoch': 18}
Test for Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7310372578834318, 'r': 0.6833493318043012, 'f1': 0.706389363899417}, 'combined': 0.4601124784178225, 'epoch': 18}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6661522633744856, 'r': 0.6366764995083579, 'f1': 0.6510809451985924}, 'combined': 0.489701907499796, 'epoch': 18}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
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 15:00:38.733586: step: 3084/1541, loss: 0.024751314893364906
==================================================
Loss: 0.098
--------------------
Dev Chinese: {'event': {'p': 0.6272401433691757, 'r': 0.6990679094540613, 'f1': 0.6612090680100756}, 'argument': {'p': 0.6770057855108976, 'r': 0.6476391519023692, 'f1': 0.6619969478273624}, 'combined': 0.43771838489844495, 'epoch': 20}
Test Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.7142728955736691, 'r': 0.7007420429070641, 'f1': 0.7074427759396437}, 'combined': 0.4600061959484325, 'epoch': 20}
Dev Korean: {'event': {'p': 0.5735294117647058, 'r': 0.7789613848202397, 'f1': 0.6606437041219649}, 'argument': {'p': 0.6711870580943137, 'r': 0.6601208915450885, 'f1': 0.6656079824397532}, 'combined': 0.4397297230121463, 'epoch': 20}
Test Korean: {'event': {'p': 0.6101048617731173, 'r': 0.7669263031755542, 'f1': 0.6795858773559862}, 'argument': {'p': 0.7133488674413241, 'r': 0.7173550386870458, 'f1': 0.7153463441439948}, 'combined': 0.4861392728984939, 'epoch': 20}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6753575348562465, 'r': 0.6641946003958127, 'f1': 0.6697295553991112}, 'combined': 0.4644018108284746, 'epoch': 20}
Test Russian: {'event': {'p': 0.6493982208267922, 'r': 0.7435590173756741, 'f1': 0.6932960893854749}, 'argument': {'p': 0.7129042236218599, 'r': 0.7052330877866175, 'f1': 0.7090479079513485}, 'combined': 0.4915801417696221, 'epoch': 20}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6663223140495866, 'r': 0.6486323411102171, 'f1': 0.6573583367305339}, 'combined': 0.4719495750885884, 'epoch': 20}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5610974274767379, 'r': 0.4226448155019584, 'f1': 0.4821281599059377}, 'combined': 0.28092506955149127, 'epoch': 20}
Russian: {'event': {'p': 0.47058823529411764, 'r': 0.4444444444444444, 'f1': 0.45714285714285713}, 'argument': {'p': 0.5890434478646186, 'r': 0.5890434478646186, 'f1': 0.5890434478646186}, 'combined': 0.26927700473811134, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6284348864994026, 'r': 0.7003994673768309, 'f1': 0.6624685138539043}, 'argument': {'p': 0.6868474446486486, 'r': 0.6340950032055018, 'f1': 0.6594178774763145}, 'combined': 0.43684358130043, 'epoch': 18}
Test for Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7310372578834318, 'r': 0.6833493318043012, 'f1': 0.706389363899417}, 'combined': 0.4601124784178225, 'epoch': 18}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6661522633744856, 'r': 0.6366764995083579, 'f1': 0.6510809451985924}, 'combined': 0.489701907499796, 'epoch': 18}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 21
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 15:29:12.676776: step: 3084/1541, loss: 0.002820401219651103
==================================================
Loss: 0.100
--------------------
Dev Chinese: {'event': {'p': 0.6320191158900836, 'r': 0.7043941411451398, 'f1': 0.6662468513853904}, 'argument': {'p': 0.6645425841164653, 'r': 0.642373979681238, 'f1': 0.6532702641492037}, 'combined': 0.4352392565931093, 'epoch': 21}
Test Chinese: {'event': {'p': 0.613564668769716, 'r': 0.6992210904733374, 'f1': 0.6535984318118174}, 'argument': {'p': 0.7128404530056945, 'r': 0.6982767662605947, 'f1': 0.7054834560708773}, 'combined': 0.4611028805571066, 'epoch': 21}
Dev Korean: {'event': {'p': 0.5676470588235294, 'r': 0.7709720372836218, 'f1': 0.6538678712591757}, 'argument': {'p': 0.6612324683819081, 'r': 0.6474666811722345, 'f1': 0.6542771758236653}, 'combined': 0.4278108241692854, 'epoch': 21}
Test Korean: {'event': {'p': 0.6120114394661582, 'r': 0.7693229478729778, 'f1': 0.6817095832227236}, 'argument': {'p': 0.7112166621064147, 'r': 0.7122141609312483, 'f1': 0.7117150620097829}, 'combined': 0.4851829782960239, 'epoch': 21}
Dev Russian: {'event': {'p': 0.6587677725118484, 'r': 0.7403462050599201, 'f1': 0.69717868338558}, 'argument': {'p': 0.66278432199299, 'r': 0.6498178867997487, 'f1': 0.6562370604535456}, 'combined': 0.45751448979582615, 'epoch': 21}
Test Russian: {'event': {'p': 0.6509680795395081, 'r': 0.7453565008987417, 'f1': 0.6949720670391061}, 'argument': {'p': 0.7139491554959331, 'r': 0.701900462387025, 'f1': 0.7078735425485658}, 'combined': 0.49195233906727137, 'epoch': 21}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6268618387262455, 'r': 0.6545990882274068, 'f1': 0.6404302767939131}, 'combined': 0.45979609615973244, 'epoch': 21}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5768849206349207, 'r': 0.41955266955266957, 'f1': 0.4857978279030911}, 'combined': 0.2830633012978641, 'epoch': 21}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.561523351063581, 'r': 0.587640716229329, 'f1': 0.5742852454059351}, 'combined': 0.29534669763733806, 'epoch': 21}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6284348864994026, 'r': 0.7003994673768309, 'f1': 0.6624685138539043}, 'argument': {'p': 0.6868474446486486, 'r': 0.6340950032055018, 'f1': 0.6594178774763145}, 'combined': 0.43684358130043, 'epoch': 18}
Test for Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7310372578834318, 'r': 0.6833493318043012, 'f1': 0.706389363899417}, 'combined': 0.4601124784178225, 'epoch': 18}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6661522633744856, 'r': 0.6366764995083579, 'f1': 0.6510809451985924}, 'combined': 0.489701907499796, 'epoch': 18}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 22
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 15:57:35.341608: step: 3062/1541, loss: 0.05452106148004532
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2023-01-23 15:57:41.576519: step: 3076/1541, loss: 0.0009309180313721299
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2023-01-23 15:57:44.274428: step: 3082/1541, loss: 0.02821112424135208
2023-01-23 15:57:44.888450: step: 3084/1541, loss: 0.014883562922477722
==================================================
Loss: 0.089
--------------------
Dev Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6710048156535444, 'r': 0.6356065786757004, 'f1': 0.6528261991338468}, 'combined': 0.4308324034586093, 'epoch': 22}
Test Chinese: {'event': {'p': 0.6140904311251314, 'r': 0.6998202516476932, 'f1': 0.6541584990198823}, 'argument': {'p': 0.7085278692903634, 'r': 0.7080118256856363, 'f1': 0.7082697534909862}, 'combined': 0.46332067884484557, 'epoch': 22}
Dev Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6622286096414778, 'r': 0.6498771434645205, 'f1': 0.6559947414840996}, 'combined': 0.4326379774440589, 'epoch': 22}
Test Korean: {'event': {'p': 0.6096282173498571, 'r': 0.7663271420011983, 'f1': 0.6790549508893019}, 'argument': {'p': 0.7061214899793525, 'r': 0.7237249400455513, 'f1': 0.7148148532164835}, 'combined': 0.48539856504586276, 'epoch': 22}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6676508942084495, 'r': 0.6485608009203222, 'f1': 0.6579674079565712}, 'combined': 0.4562457386833654, 'epoch': 22}
Test Russian: {'event': {'p': 0.6509680795395081, 'r': 0.7453565008987417, 'f1': 0.6949720670391061}, 'argument': {'p': 0.7046691146552304, 'r': 0.7135217417237634, 'f1': 0.7090677982673104}, 'combined': 0.49278231343270057, 'epoch': 22}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6375080857839478, 'r': 0.6544330792118402, 'f1': 0.6458597200955278}, 'combined': 0.463694158017302, 'epoch': 22}
Korean: {'event': {'p': 0.5625, 'r': 0.5714285714285714, 'f1': 0.5669291338582677}, 'argument': {'p': 0.6333911814681046, 'r': 0.41170426795426796, 'f1': 0.4990354763082036}, 'combined': 0.28291775034795796, 'epoch': 22}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.5723667012647522, 'r': 0.612299261818107, 'f1': 0.5916599608579461}, 'combined': 0.28737769527385953, 'epoch': 22}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6284348864994026, 'r': 0.7003994673768309, 'f1': 0.6624685138539043}, 'argument': {'p': 0.6868474446486486, 'r': 0.6340950032055018, 'f1': 0.6594178774763145}, 'combined': 0.43684358130043, 'epoch': 18}
Test for Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7310372578834318, 'r': 0.6833493318043012, 'f1': 0.706389363899417}, 'combined': 0.4601124784178225, 'epoch': 18}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6661522633744856, 'r': 0.6366764995083579, 'f1': 0.6510809451985924}, 'combined': 0.489701907499796, 'epoch': 18}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 23
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 16:03:07.961972: step: 2/1541, loss: 0.008205832913517952
2023-01-23 16:03:08.836861: step: 4/1541, loss: 0.01076498907059431
2023-01-23 16:03:09.781595: step: 6/1541, loss: 0.11512435972690582
2023-01-23 16:03:10.671096: step: 8/1541, loss: 0.04519951343536377
2023-01-23 16:03:11.596813: step: 10/1541, loss: 0.006529401056468487
2023-01-23 16:03:12.497891: step: 12/1541, loss: 0.013989754021167755
2023-01-23 16:03:13.413936: step: 14/1541, loss: 0.2547847628593445
2023-01-23 16:03:14.313083: step: 16/1541, loss: 0.004363110288977623
2023-01-23 16:03:15.166712: step: 18/1541, loss: 0.0018070545047521591
2023-01-23 16:03:16.057001: step: 20/1541, loss: 0.22251728177070618
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2023-01-23 16:03:18.715836: step: 26/1541, loss: 0.004965516738593578
2023-01-23 16:03:19.597418: step: 28/1541, loss: 0.034720078110694885
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2023-01-23 16:03:23.153909: step: 36/1541, loss: 0.0004744830366689712
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2023-01-23 16:03:25.875681: step: 42/1541, loss: 0.14184445142745972
2023-01-23 16:03:26.901144: step: 44/1541, loss: 0.0008655794081278145
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2023-01-23 16:26:15.634087: step: 3084/1541, loss: 0.00397350313141942
==================================================
Loss: 0.082
--------------------
Dev Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.679679224642488, 'r': 0.6459587049703025, 'f1': 0.6623900869727267}, 'combined': 0.43714408762431844, 'epoch': 23}
Test Chinese: {'event': {'p': 0.6151419558359621, 'r': 0.701018573996405, 'f1': 0.6552786334360123}, 'argument': {'p': 0.720803339496083, 'r': 0.694252961199546, 'f1': 0.7072790710047019}, 'combined': 0.4634648631058534, 'epoch': 23}
Dev Korean: {'event': {'p': 0.5705882352941176, 'r': 0.7749667110519307, 'f1': 0.6572557876905702}, 'argument': {'p': 0.6710994412739938, 'r': 0.6556940021629696, 'f1': 0.6633072852069517}, 'combined': 0.4359625522195888, 'epoch': 23}
Test Korean: {'event': {'p': 0.611534795042898, 'r': 0.768723786698622, 'f1': 0.6811786567560393}, 'argument': {'p': 0.7159306392023576, 'r': 0.7063815467875841, 'f1': 0.7111240377377481}, 'combined': 0.48440251681313024, 'epoch': 23}
Dev Russian: {'event': {'p': 0.6528436018957346, 'r': 0.7336884154460719, 'f1': 0.6909090909090909}, 'argument': {'p': 0.676523549274366, 'r': 0.6541254784180288, 'f1': 0.6651360067464118}, 'combined': 0.45954851375206635, 'epoch': 23}
Test Russian: {'event': {'p': 0.6509680795395081, 'r': 0.7453565008987417, 'f1': 0.6949720670391061}, 'argument': {'p': 0.7174344421937103, 'r': 0.6986291587897101, 'f1': 0.7079069336835883}, 'combined': 0.49197554497339874, 'epoch': 23}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.666347687400319, 'r': 0.6722445695897024, 'f1': 0.6692831397677212}, 'combined': 0.4919517095728549, 'epoch': 23}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5628867366155502, 'r': 0.43130282415996707, 'f1': 0.4883870214752568}, 'combined': 0.28457196526904727, 'epoch': 23}
Russian: {'event': {'p': 0.47058823529411764, 'r': 0.4444444444444444, 'f1': 0.45714285714285713}, 'argument': {'p': 0.5939748501817468, 'r': 0.6216015873995024, 'f1': 0.6074742785949683}, 'combined': 0.2777025273576998, 'epoch': 23}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.679679224642488, 'r': 0.6459587049703025, 'f1': 0.6623900869727267}, 'combined': 0.43714408762431844, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.6151419558359621, 'r': 0.701018573996405, 'f1': 0.6552786334360123}, 'argument': {'p': 0.720803339496083, 'r': 0.694252961199546, 'f1': 0.7072790710047019}, 'combined': 0.4634648631058534, 'epoch': 23}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.666347687400319, 'r': 0.6722445695897024, 'f1': 0.6692831397677212}, 'combined': 0.4919517095728549, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
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 16:54:54.099343: step: 3084/1541, loss: 0.7902599573135376
==================================================
Loss: 0.093
--------------------
Dev Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6547812559679388, 'r': 0.6532585088610365, 'f1': 0.6540189960657643}, 'combined': 0.4316195893431493, 'epoch': 24}
Test Chinese: {'event': {'p': 0.613564668769716, 'r': 0.6992210904733374, 'f1': 0.6535984318118174}, 'argument': {'p': 0.701673136441013, 'r': 0.711383107010845, 'f1': 0.7064947601633925}, 'combined': 0.4617638673260594, 'epoch': 24}
Dev Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6431283877150464, 'r': 0.6629382162764698, 'f1': 0.6528830687280083}, 'combined': 0.42984848003210485, 'epoch': 24}
Test Korean: {'event': {'p': 0.6124880838894184, 'r': 0.7699221090473337, 'f1': 0.6822405096894081}, 'argument': {'p': 0.6987936688487971, 'r': 0.7267454156027491, 'f1': 0.7124955054928911}, 'combined': 0.48609329681888247, 'epoch': 24}
Dev Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6509545947330577, 'r': 0.6622117042660355, 'f1': 0.6565348987505234}, 'combined': 0.4544291712981679, 'epoch': 24}
Test Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.6959848476747131, 'r': 0.7159628842064595, 'f1': 0.7058325285756641}, 'combined': 0.490139571519302, 'epoch': 24}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6790953008344311, 'r': 0.6911146866899078, 'f1': 0.6850522771575401}, 'combined': 0.49183240411310575, 'epoch': 24}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5879442195231669, 'r': 0.4468376068376068, 'f1': 0.5077700077700078}, 'combined': 0.2958659887793746, 'epoch': 24}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.526577365163572, 'r': 0.612299261818107, 'f1': 0.5662122206059914}, 'combined': 0.2750173642943387, 'epoch': 24}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.679679224642488, 'r': 0.6459587049703025, 'f1': 0.6623900869727267}, 'combined': 0.43714408762431844, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.6151419558359621, 'r': 0.701018573996405, 'f1': 0.6552786334360123}, 'argument': {'p': 0.720803339496083, 'r': 0.694252961199546, 'f1': 0.7072790710047019}, 'combined': 0.4634648631058534, 'epoch': 23}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.666347687400319, 'r': 0.6722445695897024, 'f1': 0.6692831397677212}, 'combined': 0.4919517095728549, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
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 17:23:28.589345: step: 3084/1541, loss: 0.006555307190865278
==================================================
Loss: 0.082
--------------------
Dev Chinese: {'event': {'p': 0.6320191158900836, 'r': 0.7043941411451398, 'f1': 0.6662468513853904}, 'argument': {'p': 0.6748395028808764, 'r': 0.6385747632400816, 'f1': 0.6562064793919685}, 'combined': 0.4371955007535911, 'epoch': 25}
Test Chinese: {'event': {'p': 0.6109358569926393, 'r': 0.6962252846015579, 'f1': 0.6507980957714926}, 'argument': {'p': 0.7154126636351134, 'r': 0.6940648330608487, 'f1': 0.7045770821024057}, 'combined': 0.45853742335648023, 'epoch': 25}
Dev Korean: {'event': {'p': 0.5774509803921568, 'r': 0.7842876165113183, 'f1': 0.6651609260304913}, 'argument': {'p': 0.6698169538960423, 'r': 0.6525065372842279, 'f1': 0.6610484410450397}, 'combined': 0.4397035931965312, 'epoch': 25}
Test Korean: {'event': {'p': 0.611534795042898, 'r': 0.768723786698622, 'f1': 0.6811786567560393}, 'argument': {'p': 0.7157954795955861, 'r': 0.7080069011935282, 'f1': 0.711879887594975}, 'combined': 0.4849173856035853, 'epoch': 25}
Dev Russian: {'event': {'p': 0.6563981042654028, 'r': 0.7376830892143809, 'f1': 0.6946708463949843}, 'argument': {'p': 0.6773132344375045, 'r': 0.6472444001020548, 'f1': 0.6619375203814012}, 'combined': 0.4598286975439452, 'epoch': 25}
Test Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7165382631843054, 'r': 0.6985347892349509, 'f1': 0.7074220002939199}, 'combined': 0.4912433219918114, 'epoch': 25}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6613345847859121, 'r': 0.7050076234038498, 'f1': 0.6824731331580647}, 'combined': 0.5133131257940999, 'epoch': 25}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5668613357292602, 'r': 0.3901772830344259, 'f1': 0.4622100122100122}, 'combined': 0.26931921971292044, 'epoch': 25}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5875966625966627, 'r': 0.573931623931624, 'f1': 0.5806837606837608}, 'combined': 0.29863736263736274, 'epoch': 25}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6320191158900836, 'r': 0.7043941411451398, 'f1': 0.6662468513853904}, 'argument': {'p': 0.6748395028808764, 'r': 0.6385747632400816, 'f1': 0.6562064793919685}, 'combined': 0.4371955007535911, 'epoch': 25}
Test for Chinese: {'event': {'p': 0.6109358569926393, 'r': 0.6962252846015579, 'f1': 0.6507980957714926}, 'argument': {'p': 0.7154126636351134, 'r': 0.6940648330608487, 'f1': 0.7045770821024057}, 'combined': 0.45853742335648023, 'epoch': 25}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6613345847859121, 'r': 0.7050076234038498, 'f1': 0.6824731331580647}, 'combined': 0.5133131257940999, 'epoch': 25}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 26
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 17:52:02.015896: step: 3076/1541, loss: 6.282328104134649e-05
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2023-01-23 17:52:04.750219: step: 3082/1541, loss: 0.002873768797144294
2023-01-23 17:52:05.376180: step: 3084/1541, loss: 0.012348378077149391
==================================================
Loss: 0.075
--------------------
Dev Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6810820418882222, 'r': 0.6435670496662952, 'f1': 0.6617933203189905}, 'combined': 0.43675025169666387, 'epoch': 26}
Test Chinese: {'event': {'p': 0.6077812828601472, 'r': 0.6926303175554224, 'f1': 0.6474376925231027}, 'argument': {'p': 0.7213274123810446, 'r': 0.7003127754580717, 'f1': 0.7106647750952937}, 'combined': 0.4601111621451467, 'epoch': 26}
Dev Korean: {'event': {'p': 0.5686274509803921, 'r': 0.7723035952063915, 'f1': 0.6549971767363071}, 'argument': {'p': 0.6699095436188013, 'r': 0.6554925520057711, 'f1': 0.6626226377781327}, 'combined': 0.43401595698624157, 'epoch': 26}
Test Korean: {'event': {'p': 0.6086749285033365, 'r': 0.7651288196524865, 'f1': 0.6779930979559331}, 'argument': {'p': 0.719367683224033, 'r': 0.715075211965764, 'f1': 0.7172150251330616}, 'combined': 0.4862668367905068, 'epoch': 26}
Dev Russian: {'event': {'p': 0.6504739336492891, 'r': 0.7310252996005326, 'f1': 0.6884012539184953}, 'argument': {'p': 0.6782118548747985, 'r': 0.649634079199111, 'f1': 0.6636154429328351}, 'combined': 0.4568337030346413, 'epoch': 26}
Test Russian: {'event': {'p': 0.6493982208267922, 'r': 0.7435590173756741, 'f1': 0.6932960893854749}, 'argument': {'p': 0.7201609983745072, 'r': 0.7043929363139024, 'f1': 0.7121897008058545}, 'combined': 0.4937583344693104, 'epoch': 26}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6636363636363637, 'r': 0.7012006861063464, 'f1': 0.6819015846538783}, 'combined': 0.5128832431584726, 'epoch': 26}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5598387503149408, 'r': 0.45804988662131524, 'f1': 0.5038548752834466}, 'combined': 0.2935847304801185, 'epoch': 26}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.5638570649208947, 'r': 0.6163088849135361, 'f1': 0.5889173789173789}, 'combined': 0.28604558404558406, 'epoch': 26}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6320191158900836, 'r': 0.7043941411451398, 'f1': 0.6662468513853904}, 'argument': {'p': 0.6748395028808764, 'r': 0.6385747632400816, 'f1': 0.6562064793919685}, 'combined': 0.4371955007535911, 'epoch': 25}
Test for Chinese: {'event': {'p': 0.6109358569926393, 'r': 0.6962252846015579, 'f1': 0.6507980957714926}, 'argument': {'p': 0.7154126636351134, 'r': 0.6940648330608487, 'f1': 0.7045770821024057}, 'combined': 0.45853742335648023, 'epoch': 25}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6613345847859121, 'r': 0.7050076234038498, 'f1': 0.6824731331580647}, 'combined': 0.5133131257940999, 'epoch': 25}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 27
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 17:57:27.944399: step: 2/1541, loss: 0.07283709198236465
2023-01-23 17:57:28.858117: step: 4/1541, loss: 0.015200561843812466
2023-01-23 17:57:29.802403: step: 6/1541, loss: 0.1604229062795639
2023-01-23 17:57:30.710900: step: 8/1541, loss: 0.0003460958250798285
2023-01-23 17:57:31.609446: step: 10/1541, loss: 0.0002640494203660637
2023-01-23 17:57:32.479405: step: 12/1541, loss: 0.0008807028061710298
2023-01-23 17:57:33.375015: step: 14/1541, loss: 0.0030832148622721434
2023-01-23 17:57:34.266732: step: 16/1541, loss: 0.00011614768300205469
2023-01-23 17:57:35.121177: step: 18/1541, loss: 0.26672494411468506
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2023-01-23 18:20:36.190489: step: 3084/1541, loss: 0.2414318472146988
==================================================
Loss: 0.063
--------------------
Dev Chinese: {'event': {'p': 0.6272401433691757, 'r': 0.6990679094540613, 'f1': 0.6612090680100756}, 'argument': {'p': 0.6800046033188152, 'r': 0.644549110383126, 'f1': 0.6618023226866295}, 'combined': 0.43758969699052963, 'epoch': 27}
Test Chinese: {'event': {'p': 0.6098843322818086, 'r': 0.695026962252846, 'f1': 0.6496779613553626}, 'argument': {'p': 0.7224781577231381, 'r': 0.6800104748485761, 'f1': 0.7006013506151025}, 'combined': 0.45516525719043344, 'epoch': 27}
Dev Korean: {'event': {'p': 0.5705882352941176, 'r': 0.7749667110519307, 'f1': 0.6572557876905702}, 'argument': {'p': 0.673183849651783, 'r': 0.6519355789310668, 'f1': 0.6623893564357923}, 'combined': 0.4353592382220566, 'epoch': 27}
Test Korean: {'event': {'p': 0.607721639656816, 'r': 0.7639304973037747, 'f1': 0.6769312450225644}, 'argument': {'p': 0.7239969439454942, 'r': 0.6947625167847737, 'f1': 0.7090785335255458}, 'combined': 0.4799974145182219, 'epoch': 27}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6790052558154206, 'r': 0.6514158774752906, 'f1': 0.6649245016625662}, 'combined': 0.4610699052280867, 'epoch': 27}
Test Russian: {'event': {'p': 0.6488749345892203, 'r': 0.7429598562013181, 'f1': 0.6927374301675977}, 'argument': {'p': 0.72095499117386, 'r': 0.6841292326318859, 'f1': 0.7020595299803036}, 'combined': 0.48634291462322704, 'epoch': 27}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6734006734006733, 'r': 0.6436041834271922, 'f1': 0.658165364047717}, 'combined': 0.48377966930003125, 'epoch': 27}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5715783168613356, 'r': 0.3934240362811791, 'f1': 0.466056166056166}, 'combined': 0.27156028573351404, 'epoch': 27}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.554375696767001, 'r': 0.5930530709600477, 'f1': 0.5730625180063381}, 'combined': 0.27834465160307853, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6320191158900836, 'r': 0.7043941411451398, 'f1': 0.6662468513853904}, 'argument': {'p': 0.6748395028808764, 'r': 0.6385747632400816, 'f1': 0.6562064793919685}, 'combined': 0.4371955007535911, 'epoch': 25}
Test for Chinese: {'event': {'p': 0.6109358569926393, 'r': 0.6962252846015579, 'f1': 0.6507980957714926}, 'argument': {'p': 0.7154126636351134, 'r': 0.6940648330608487, 'f1': 0.7045770821024057}, 'combined': 0.45853742335648023, 'epoch': 25}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6613345847859121, 'r': 0.7050076234038498, 'f1': 0.6824731331580647}, 'combined': 0.5133131257940999, 'epoch': 25}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 28
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.075
--------------------
Dev Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6809153876089666, 'r': 0.6491957266955055, 'f1': 0.6646773417995319}, 'combined': 0.4386535605830664, 'epoch': 28}
Test Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.716394349621888, 'r': 0.6884285723838208, 'f1': 0.7021331039642987}, 'combined': 0.4565536453108658, 'epoch': 28}
Dev Korean: {'event': {'p': 0.5676470588235294, 'r': 0.7709720372836218, 'f1': 0.6538678712591757}, 'argument': {'p': 0.6799844555377508, 'r': 0.6614349872603391, 'f1': 0.670581468161319}, 'combined': 0.43847167709249435, 'epoch': 28}
Test Korean: {'event': {'p': 0.6101048617731173, 'r': 0.7669263031755542, 'f1': 0.6795858773559862}, 'argument': {'p': 0.7167363642351611, 'r': 0.7044135284991881, 'f1': 0.7105215205176734}, 'combined': 0.4828603909013124, 'epoch': 28}
Dev Russian: {'event': {'p': 0.6528436018957346, 'r': 0.7336884154460719, 'f1': 0.6909090909090909}, 'argument': {'p': 0.6710021009827256, 'r': 0.6598777255331251, 'f1': 0.665393420807338}, 'combined': 0.45972636346688805, 'epoch': 28}
Test Russian: {'event': {'p': 0.6514913657770801, 'r': 0.7459556620730977, 'f1': 0.6955307262569833}, 'argument': {'p': 0.7113260130880612, 'r': 0.6962728442314631, 'f1': 0.7037189377260537}, 'combined': 0.4894581438373949, 'epoch': 28}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6800847457627118, 'r': 0.7101769911504425, 'f1': 0.6948051948051949}, 'combined': 0.5225885225885226, 'epoch': 28}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6119194591416814, 'r': 0.4236365486365487, 'f1': 0.5006613756613757}, 'combined': 0.29172395117277006, 'epoch': 28}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.561250654107797, 'r': 0.6395646988670244, 'f1': 0.5978539576365662}, 'combined': 0.2903862079949036, 'epoch': 28}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6809153876089666, 'r': 0.6491957266955055, 'f1': 0.6646773417995319}, 'combined': 0.4386535605830664, 'epoch': 28}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.716394349621888, 'r': 0.6884285723838208, 'f1': 0.7021331039642987}, 'combined': 0.4565536453108658, 'epoch': 28}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6800847457627118, 'r': 0.7101769911504425, 'f1': 0.6948051948051949}, 'combined': 0.5225885225885226, 'epoch': 28}
--------------------
Dev for Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6636893013672245, 'r': 0.6465372447633335, 'f1': 0.6550010050584172}, 'combined': 0.4319825939628635, 'epoch': 14}
Test for Korean: {'event': {'p': 0.6143946615824595, 'r': 0.7723187537447573, 'f1': 0.6843642155561453}, 'argument': {'p': 0.7065465973991794, 'r': 0.7144881116656963, 'f1': 0.7104951637835268}, 'combined': 0.48623746541914836, 'epoch': 14}
Korean: {'event': {'p': 0.59375, 'r': 0.6031746031746031, 'f1': 0.5984251968503936}, 'argument': {'p': 0.6660879629629629, 'r': 0.42068713450292394, 'f1': 0.5156810035842294}, 'combined': 0.30859650608190103, 'epoch': 14}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 29
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 19:17:40.595547: step: 3084/1541, loss: 5.870097447768785e-05
==================================================
Loss: 0.067
--------------------
Dev Chinese: {'event': {'p': 0.6272401433691757, 'r': 0.6990679094540613, 'f1': 0.6612090680100756}, 'argument': {'p': 0.6785674678881605, 'r': 0.6511506004987399, 'f1': 0.6645763860760334}, 'combined': 0.43942393285883824, 'epoch': 29}
Test Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7160994091858569, 'r': 0.6967876904250139, 'f1': 0.7063115709369184}, 'combined': 0.4600618073366767, 'epoch': 29}
Dev Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Dev Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6750913025256584, 'r': 0.6593323941082673, 'f1': 0.6671187957148792}, 'combined': 0.46175495327224236, 'epoch': 29}
Test Russian: {'event': {'p': 0.6493982208267922, 'r': 0.7435590173756741, 'f1': 0.6932960893854749}, 'argument': {'p': 0.7161345911435275, 'r': 0.702263978546056, 'f1': 0.7091314640298458}, 'combined': 0.4916380708720887, 'epoch': 29}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6429075235109718, 'r': 0.6599758648431215, 'f1': 0.6513298928146091}, 'combined': 0.48988915015115897, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.5761148272017838, 'r': 0.6163088849135361, 'f1': 0.5955344281186978}, 'combined': 0.28925957937193897, 'epoch': 29}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6809153876089666, 'r': 0.6491957266955055, 'f1': 0.6646773417995319}, 'combined': 0.4386535605830664, 'epoch': 28}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.716394349621888, 'r': 0.6884285723838208, 'f1': 0.7021331039642987}, 'combined': 0.4565536453108658, 'epoch': 28}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6800847457627118, 'r': 0.7101769911504425, 'f1': 0.6948051948051949}, 'combined': 0.5225885225885226, 'epoch': 28}
--------------------
Dev for Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 30
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 19:46:18.536957: step: 3084/1541, loss: 0.006110209506005049
==================================================
Loss: 0.064
--------------------
Dev Chinese: {'event': {'p': 0.6272401433691757, 'r': 0.6990679094540613, 'f1': 0.6612090680100756}, 'argument': {'p': 0.6792395718258888, 'r': 0.6544728845676911, 'f1': 0.6666262727836854}, 'combined': 0.440779336538331, 'epoch': 30}
Test Chinese: {'event': {'p': 0.6072555205047319, 'r': 0.6920311563810665, 'f1': 0.6468776253150378}, 'argument': {'p': 0.7308811240268683, 'r': 0.695442848600119, 'f1': 0.7127217387297314}, 'combined': 0.46104374585989344, 'epoch': 30}
Dev Korean: {'event': {'p': 0.5735294117647058, 'r': 0.7789613848202397, 'f1': 0.6606437041219649}, 'argument': {'p': 0.6632830910458017, 'r': 0.6632830910458017, 'f1': 0.6632830910458017}, 'combined': 0.43819379814996495, 'epoch': 30}
Test Korean: {'event': {'p': 0.6105815061963775, 'r': 0.7675254643499101, 'f1': 0.6801168038226706}, 'argument': {'p': 0.7241965327770653, 'r': 0.7089395833505823, 'f1': 0.7164868464806451}, 'combined': 0.4872947440094008, 'epoch': 30}
Dev Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.6721237710474757, 'r': 0.6620014250979654, 'f1': 0.6670241976707877}, 'combined': 0.46168947600536026, 'epoch': 30}
Test Russian: {'event': {'p': 0.6509680795395081, 'r': 0.7453565008987417, 'f1': 0.6949720670391061}, 'argument': {'p': 0.722776026511748, 'r': 0.7025331072773077, 'f1': 0.7125108171038972}, 'combined': 0.49517511535041786, 'epoch': 30}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.662472179421332, 'r': 0.6917851077143113, 'f1': 0.6768114040841314}, 'combined': 0.5090547312769536, 'epoch': 30}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6009700176366843, 'r': 0.3512162440733869, 'f1': 0.4433385376008327}, 'combined': 0.25832324238158755, 'epoch': 30}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.5812767094017094, 'r': 0.6488670244484197, 'f1': 0.613214990138067}, 'combined': 0.297847280924204, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6809153876089666, 'r': 0.6491957266955055, 'f1': 0.6646773417995319}, 'combined': 0.4386535605830664, 'epoch': 28}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.716394349621888, 'r': 0.6884285723838208, 'f1': 0.7021331039642987}, 'combined': 0.4565536453108658, 'epoch': 28}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6800847457627118, 'r': 0.7101769911504425, 'f1': 0.6948051948051949}, 'combined': 0.5225885225885226, 'epoch': 28}
--------------------
Dev for Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 31
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 20:14:44.841455: step: 3082/1541, loss: 0.0029203947633504868
2023-01-23 20:14:45.557495: step: 3084/1541, loss: 3.368854595464654e-05
==================================================
Loss: 0.065
--------------------
Dev Chinese: {'event': {'p': 0.6284348864994026, 'r': 0.7003994673768309, 'f1': 0.6624685138539043}, 'argument': {'p': 0.6619326401514553, 'r': 0.6567814133798486, 'f1': 0.6593469657758637}, 'combined': 0.4367966045316175, 'epoch': 31}
Test Chinese: {'event': {'p': 0.6056782334384858, 'r': 0.6902336728579987, 'f1': 0.6451974236908429}, 'argument': {'p': 0.7146322356778463, 'r': 0.7062952938106603, 'f1': 0.7104393072690287}, 'combined': 0.45837361073868443, 'epoch': 31}
Dev Korean: {'event': {'p': 0.5686274509803921, 'r': 0.7723035952063915, 'f1': 0.6549971767363071}, 'argument': {'p': 0.6473164976644228, 'r': 0.6626403458874687, 'f1': 0.6548887927452189}, 'combined': 0.4289503103243669, 'epoch': 31}
Test Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.7099116610679151, 'r': 0.7181201701131994, 'f1': 0.7139923238122525}, 'combined': 0.48294463527306064, 'epoch': 31}
Dev Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6602125580861498, 'r': 0.6656811862028272, 'f1': 0.6629355945087024}, 'combined': 0.45719696173013963, 'epoch': 31}
Test Russian: {'event': {'p': 0.6467817896389325, 'r': 0.7405632115038946, 'f1': 0.6905027932960894}, 'argument': {'p': 0.7085942728862843, 'r': 0.7139277781660736, 'f1': 0.7112510269892324}, 'combined': 0.49112082087077724, 'epoch': 31}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6657224418093983, 'r': 0.6775051398945203, 'f1': 0.671562112351586}, 'combined': 0.49362685181398624, 'epoch': 31}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6135819107517221, 'r': 0.41692104192104196, 'f1': 0.4964861262571187}, 'combined': 0.28929112868525025, 'epoch': 31}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.5755840455840456, 'r': 0.6023553965414431, 'f1': 0.5886655011655012}, 'combined': 0.28592324342324343, 'epoch': 31}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6809153876089666, 'r': 0.6491957266955055, 'f1': 0.6646773417995319}, 'combined': 0.4386535605830664, 'epoch': 28}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.716394349621888, 'r': 0.6884285723838208, 'f1': 0.7021331039642987}, 'combined': 0.4565536453108658, 'epoch': 28}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6800847457627118, 'r': 0.7101769911504425, 'f1': 0.6948051948051949}, 'combined': 0.5225885225885226, 'epoch': 28}
--------------------
Dev for Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 32
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 20:20:09.207162: step: 2/1541, loss: 0.27946367859840393
2023-01-23 20:20:10.102644: step: 4/1541, loss: 0.0004815998545382172
2023-01-23 20:20:11.059076: step: 6/1541, loss: 0.0016038863686844707
2023-01-23 20:20:11.911994: step: 8/1541, loss: 3.907667996827513e-05
2023-01-23 20:20:12.785185: step: 10/1541, loss: 0.005859008524566889
2023-01-23 20:20:13.665880: step: 12/1541, loss: 8.379944483749568e-05
2023-01-23 20:20:14.551855: step: 14/1541, loss: 0.00010252413630951196
2023-01-23 20:20:15.456751: step: 16/1541, loss: 0.0010895368177443743
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2023-01-23 20:43:15.713581: step: 3084/1541, loss: 0.3598845899105072
==================================================
Loss: 0.058
--------------------
Dev Chinese: {'event': {'p': 0.6320191158900836, 'r': 0.7043941411451398, 'f1': 0.6662468513853904}, 'argument': {'p': 0.7064294519407052, 'r': 0.6290350830800685, 'f1': 0.6654896439983955}, 'combined': 0.44338037994351537, 'epoch': 32}
Test Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7537890914616138, 'r': 0.677915353590204, 'f1': 0.7138417432965972}, 'combined': 0.4649666465717965, 'epoch': 32}
Dev Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.7001227469542347, 'r': 0.6333726929435443, 'f1': 0.6650770844232173}, 'combined': 0.4378768381916835, 'epoch': 32}
Test Korean: {'event': {'p': 0.6105815061963775, 'r': 0.7675254643499101, 'f1': 0.6801168038226706}, 'argument': {'p': 0.7525362332215595, 'r': 0.6901771914237055, 'f1': 0.7200090260713234}, 'combined': 0.4896902375351024, 'epoch': 32}
Dev Russian: {'event': {'p': 0.6540284360189573, 'r': 0.7350199733688415, 'f1': 0.6921630094043887}, 'argument': {'p': 0.7095563060360507, 'r': 0.6348661685585717, 'f1': 0.67013651125627}, 'combined': 0.46384370434289784, 'epoch': 32}
Test Russian: {'event': {'p': 0.6509680795395081, 'r': 0.7453565008987417, 'f1': 0.6949720670391061}, 'argument': {'p': 0.7505422699780423, 'r': 0.6888280222850542, 'f1': 0.7183621202261284}, 'combined': 0.49924160757614733, 'epoch': 32}
Chinese: {'event': {'p': 0.6666666666666666, 'r': 0.7777777777777778, 'f1': 0.717948717948718}, 'argument': {'p': 0.6883971291866028, 'r': 0.6944891391794046, 'f1': 0.6914297156587905}, 'combined': 0.49641107790887523, 'epoch': 32}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5905943937858831, 'r': 0.3701058201058201, 'f1': 0.455048139474369}, 'combined': 0.26514616000868746, 'epoch': 32}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5533618233618234, 'r': 0.5790995825879547, 'f1': 0.5659382284382285}, 'combined': 0.29105394605394613, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6809153876089666, 'r': 0.6491957266955055, 'f1': 0.6646773417995319}, 'combined': 0.4386535605830664, 'epoch': 28}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.716394349621888, 'r': 0.6884285723838208, 'f1': 0.7021331039642987}, 'combined': 0.4565536453108658, 'epoch': 28}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6800847457627118, 'r': 0.7101769911504425, 'f1': 0.6948051948051949}, 'combined': 0.5225885225885226, 'epoch': 28}
--------------------
Dev for Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
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 20:48:33.439034: step: 2/1541, loss: 0.0019863639026880264
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2023-01-23 21:11:40.133598: step: 3084/1541, loss: 7.023812213446945e-05
==================================================
Loss: 0.062
--------------------
Dev Chinese: {'event': {'p': 0.6200716845878136, 'r': 0.6910785619174434, 'f1': 0.6536523929471032}, 'argument': {'p': 0.667344626189574, 'r': 0.6549192801627705, 'f1': 0.6610735725370487}, 'combined': 0.43211232260293225, 'epoch': 33}
Test Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7172937437299021, 'r': 0.7034393091753522, 'f1': 0.7102989749318}, 'combined': 0.46265903547784015, 'epoch': 33}
Dev Korean: {'event': {'p': 0.5627450980392157, 'r': 0.7643142476697736, 'f1': 0.6482213438735178}, 'argument': {'p': 0.6636872750304308, 'r': 0.6608306583516772, 'f1': 0.6622558862273458}, 'combined': 0.4292884005584376, 'epoch': 33}
Test Korean: {'event': {'p': 0.613441372735939, 'r': 0.7711204313960456, 'f1': 0.6833023626227768}, 'argument': {'p': 0.7113677591330053, 'r': 0.7181046688126234, 'f1': 0.7147203389020036}, 'combined': 0.4883700961862908, 'epoch': 33}
Dev Russian: {'event': {'p': 0.6481042654028436, 'r': 0.7283621837549934, 'f1': 0.6858934169278997}, 'argument': {'p': 0.6738499304583855, 'r': 0.663701588132205, 'f1': 0.6687372602728135}, 'combined': 0.4586824844755223, 'epoch': 33}
Test Russian: {'event': {'p': 0.6509680795395081, 'r': 0.7453565008987417, 'f1': 0.6949720670391061}, 'argument': {'p': 0.7144920197618627, 'r': 0.7057755760331569, 'f1': 0.710107050687512}, 'combined': 0.4935045648353435, 'epoch': 33}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6442550505050505, 'r': 0.6841646554035935, 'f1': 0.6636103524515542}, 'combined': 0.49912573517723735, 'epoch': 33}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5351696234049176, 'r': 0.35912698412698413, 'f1': 0.4298212723409574}, 'combined': 0.2504470405766208, 'epoch': 33}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.5601886863442256, 'r': 0.612299261818107, 'f1': 0.585085961292858}, 'combined': 0.2841846097708167, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6809153876089666, 'r': 0.6491957266955055, 'f1': 0.6646773417995319}, 'combined': 0.4386535605830664, 'epoch': 28}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.716394349621888, 'r': 0.6884285723838208, 'f1': 0.7021331039642987}, 'combined': 0.4565536453108658, 'epoch': 28}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6800847457627118, 'r': 0.7101769911504425, 'f1': 0.6948051948051949}, 'combined': 0.5225885225885226, 'epoch': 28}
--------------------
Dev for Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 34
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-23 21:39:59.111162: step: 3062/1541, loss: 0.00017041787214111537
2023-01-23 21:40:00.028931: step: 3064/1541, loss: 0.00029931587050668895
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2023-01-23 21:40:05.323831: step: 3076/1541, loss: 0.06125633418560028
2023-01-23 21:40:06.203867: step: 3078/1541, loss: 6.515049790323246e-06
2023-01-23 21:40:07.083961: step: 3080/1541, loss: 0.0006469248910434544
2023-01-23 21:40:07.965220: step: 3082/1541, loss: 0.2375534325838089
2023-01-23 21:40:08.563614: step: 3084/1541, loss: 5.828607754665427e-05
==================================================
Loss: 0.056
--------------------
Dev Chinese: {'event': {'p': 0.6236559139784946, 'r': 0.6950732356857523, 'f1': 0.6574307304785894}, 'argument': {'p': 0.6857984262495748, 'r': 0.6537517708173516, 'f1': 0.6693917653345131}, 'combined': 0.4400787172602214, 'epoch': 34}
Test Chinese: {'event': {'p': 0.61198738170347, 'r': 0.6974236069502696, 'f1': 0.6519182301876226}, 'argument': {'p': 0.7289310758677934, 'r': 0.6912094239825067, 'f1': 0.7095692702611291}, 'combined': 0.4625811428641582, 'epoch': 34}
Dev Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6797819022259629, 'r': 0.6563747779025437, 'f1': 0.6678733141577707}, 'combined': 0.4397178341659857, 'epoch': 34}
Test Korean: {'event': {'p': 0.6129647283126788, 'r': 0.7705212702216896, 'f1': 0.6827714361560924}, 'argument': {'p': 0.7272593327512444, 'r': 0.7030003311320843, 'f1': 0.714924100362141}, 'combined': 0.4881297547468614, 'epoch': 34}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6884964799060531, 'r': 0.6610188342471519, 'f1': 0.6744779192318231}, 'combined': 0.4676944066899037, 'epoch': 34}
Test Russian: {'event': {'p': 0.652014652014652, 'r': 0.7465548232474536, 'f1': 0.6960893854748604}, 'argument': {'p': 0.7251475848977346, 'r': 0.6962665723668237, 'f1': 0.7104136691145296}, 'combined': 0.4945114143668738, 'epoch': 34}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6703869047619045, 'r': 0.6644542772861355, 'f1': 0.6674074074074071}, 'combined': 0.5019816397594173, 'epoch': 34}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5878585352269563, 'r': 0.44089390142021717, 'f1': 0.5038787444802483}, 'combined': 0.29359863851605017, 'epoch': 34}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5669764957264958, 'r': 0.5274199960246473, 'f1': 0.5464833693749357}, 'combined': 0.2810485899642527, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6809153876089666, 'r': 0.6491957266955055, 'f1': 0.6646773417995319}, 'combined': 0.4386535605830664, 'epoch': 28}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.716394349621888, 'r': 0.6884285723838208, 'f1': 0.7021331039642987}, 'combined': 0.4565536453108658, 'epoch': 28}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6800847457627118, 'r': 0.7101769911504425, 'f1': 0.6948051948051949}, 'combined': 0.5225885225885226, 'epoch': 28}
--------------------
Dev for Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
Epoch: 35
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-23 21:45:30.466167: step: 2/1541, loss: 0.0003253375762142241
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2023-01-23 22:08:35.336271: step: 3084/1541, loss: 0.0002987682819366455
==================================================
Loss: 0.052
--------------------
Dev Chinese: {'event': {'p': 0.6272401433691757, 'r': 0.6990679094540613, 'f1': 0.6612090680100756}, 'argument': {'p': 0.6873583196168412, 'r': 0.6505926420559405, 'f1': 0.6684703371184095}, 'combined': 0.4419986485984446, 'epoch': 35}
Test Chinese: {'event': {'p': 0.6114616193480547, 'r': 0.6968244457759137, 'f1': 0.6513581629795576}, 'argument': {'p': 0.7268315472720632, 'r': 0.6966241843887183, 'f1': 0.7114073483910106}, 'combined': 0.4633809835781268, 'epoch': 35}
Dev Korean: {'event': {'p': 0.5725490196078431, 'r': 0.7776298268974701, 'f1': 0.6595143986448334}, 'argument': {'p': 0.6815636922678625, 'r': 0.6571173618421859, 'f1': 0.6691173136654546}, 'combined': 0.44129250274491866, 'epoch': 35}
Test Korean: {'event': {'p': 0.6124880838894184, 'r': 0.7699221090473337, 'f1': 0.6822405096894081}, 'argument': {'p': 0.7243608933407139, 'r': 0.7081002489444298, 'f1': 0.7161382794397195}, 'combined': 0.48857854477305, 'epoch': 35}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.687408505687428, 'r': 0.6599949234440617, 'f1': 0.673422843191676}, 'combined': 0.46696279910344435, 'epoch': 35}
Test Russian: {'event': {'p': 0.6493982208267922, 'r': 0.7435590173756741, 'f1': 0.6932960893854749}, 'argument': {'p': 0.725344132470029, 'r': 0.7032379111505768, 'f1': 0.7141199833238517}, 'combined': 0.49509659179044696, 'epoch': 35}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6881789594053744, 'r': 0.6455484043979618, 'f1': 0.6661823716618236}, 'combined': 0.4896725125035626, 'epoch': 35}
Korean: {'event': {'p': 0.5625, 'r': 0.5714285714285714, 'f1': 0.5669291338582677}, 'argument': {'p': 0.6058730158730159, 'r': 0.3883801383801384, 'f1': 0.47333829365079366}, 'combined': 0.2683492688413948, 'epoch': 35}
Russian: {'event': {'p': 0.5, 'r': 0.4722222222222222, 'f1': 0.4857142857142857}, 'argument': {'p': 0.5553464266230224, 'r': 0.6070065593321408, 'f1': 0.58002849002849}, 'combined': 0.2817281237281237, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6260454002389486, 'r': 0.6977363515312917, 'f1': 0.659949622166247}, 'argument': {'p': 0.6809153876089666, 'r': 0.6491957266955055, 'f1': 0.6646773417995319}, 'combined': 0.4386535605830664, 'epoch': 28}
Test for Chinese: {'event': {'p': 0.610410094637224, 'r': 0.6956261234272019, 'f1': 0.6502380285634276}, 'argument': {'p': 0.716394349621888, 'r': 0.6884285723838208, 'f1': 0.7021331039642987}, 'combined': 0.4565536453108658, 'epoch': 28}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.6800847457627118, 'r': 0.7101769911504425, 'f1': 0.6948051948051949}, 'combined': 0.5225885225885226, 'epoch': 28}
--------------------
Dev for Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
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 22:13:56.897337: step: 2/1541, loss: 0.0030942445155233145
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2023-01-23 22:37:01.682074: step: 3084/1541, loss: 9.774533964446164e-07
==================================================
Loss: 0.049
--------------------
Dev Chinese: {'event': {'p': 0.6308243727598566, 'r': 0.7030625832223701, 'f1': 0.6649874055415615}, 'argument': {'p': 0.6788322431410229, 'r': 0.6497771665863487, 'f1': 0.6639870052194498}, 'combined': 0.44154299591419316, 'epoch': 36}
Test Chinese: {'event': {'p': 0.6125131440588854, 'r': 0.6980227681246255, 'f1': 0.6524782973956875}, 'argument': {'p': 0.7303126454040291, 'r': 0.7023771890224542, 'f1': 0.7160725659671817}, 'combined': 0.46722180865402785, 'epoch': 36}
Dev Korean: {'event': {'p': 0.5764705882352941, 'r': 0.7829560585885486, 'f1': 0.6640316205533597}, 'argument': {'p': 0.675707796918868, 'r': 0.6543798607176985, 'f1': 0.6648728322452419}, 'combined': 0.44149658425771005, 'epoch': 36}
Test Korean: {'event': {'p': 0.6153479504289799, 'r': 0.7735170760934691, 'f1': 0.6854260684895142}, 'argument': {'p': 0.724640180117913, 'r': 0.7159831997510792, 'f1': 0.720285679212291}, 'combined': 0.49370258129178, 'epoch': 36}
Dev Russian: {'event': {'p': 0.6552132701421801, 'r': 0.7363515312916112, 'f1': 0.6934169278996866}, 'argument': {'p': 0.6855655882974819, 'r': 0.6551303966424394, 'f1': 0.6700025372357077}, 'combined': 0.4645911010549798, 'epoch': 36}
Test Russian: {'event': {'p': 0.6562009419152276, 'r': 0.7513481126423007, 'f1': 0.7005586592178771}, 'argument': {'p': 0.7269449017502853, 'r': 0.7050190950188584, 'f1': 0.7158141376695298}, 'combined': 0.5014697925349667, 'epoch': 36}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.7283362470862471, 'r': 0.6703271654599089, 'f1': 0.6981287529674627}, 'combined': 0.5250882928302283, 'epoch': 36}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5849631519274376, 'r': 0.4310254803675856, 'f1': 0.49633237133237135}, 'combined': 0.2892015392015392, 'epoch': 36}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.5348601934464002, 'r': 0.6219304574958143, 'f1': 0.5751184875767745}, 'combined': 0.2957752221823412, 'epoch': 36}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6308243727598566, 'r': 0.7030625832223701, 'f1': 0.6649874055415615}, 'argument': {'p': 0.6788322431410229, 'r': 0.6497771665863487, 'f1': 0.6639870052194498}, 'combined': 0.44154299591419316, 'epoch': 36}
Test for Chinese: {'event': {'p': 0.6125131440588854, 'r': 0.6980227681246255, 'f1': 0.6524782973956875}, 'argument': {'p': 0.7303126454040291, 'r': 0.7023771890224542, 'f1': 0.7160725659671817}, 'combined': 0.46722180865402785, 'epoch': 36}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.7283362470862471, 'r': 0.6703271654599089, 'f1': 0.6981287529674627}, 'combined': 0.5250882928302283, 'epoch': 36}
--------------------
Dev for Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
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 23:05:40.948384: step: 3084/1541, loss: 0.0014171141665428877
==================================================
Loss: 0.049
--------------------
Dev Chinese: {'event': {'p': 0.6272401433691757, 'r': 0.6990679094540613, 'f1': 0.6612090680100756}, 'argument': {'p': 0.6727793514338454, 'r': 0.6586760575761482, 'f1': 0.6656530107164214}, 'combined': 0.44013580683390585, 'epoch': 37}
Test Chinese: {'event': {'p': 0.6098843322818086, 'r': 0.695026962252846, 'f1': 0.6496779613553626}, 'argument': {'p': 0.7182761838136468, 'r': 0.7101585893192163, 'f1': 0.7141943209908744}, 'combined': 0.4639963104729288, 'epoch': 37}
Dev Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6586894230381533, 'r': 0.6657771858398551, 'f1': 0.6622143396794563}, 'combined': 0.435992049726847, 'epoch': 37}
Test Korean: {'event': {'p': 0.605815061963775, 'r': 0.7615338526063511, 'f1': 0.6748075391558269}, 'argument': {'p': 0.7170915247015461, 'r': 0.7216141762238405, 'f1': 0.7193457418591828}, 'combined': 0.48541992986621785, 'epoch': 37}
Dev Russian: {'event': {'p': 0.6528436018957346, 'r': 0.7336884154460719, 'f1': 0.6909090909090909}, 'argument': {'p': 0.6694168528800274, 'r': 0.6663923791471358, 'f1': 0.6679011920810538}, 'combined': 0.461459005437819, 'epoch': 37}
Test Russian: {'event': {'p': 0.6488749345892203, 'r': 0.7429598562013181, 'f1': 0.6927374301675977}, 'argument': {'p': 0.7169875215279904, 'r': 0.7121030601048883, 'f1': 0.7145369435627033}, 'combined': 0.49498648604343687, 'epoch': 37}
Chinese: {'event': {'p': 0.6825396825396826, 'r': 0.7962962962962963, 'f1': 0.735042735042735}, 'argument': {'p': 0.6756258234519104, 'r': 0.6875838026280503, 'f1': 0.681552365762892}, 'combined': 0.5009701150052026, 'epoch': 37}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.5475570436507936, 'r': 0.44927757427757425, 'f1': 0.4935725463894478}, 'combined': 0.28759345222692234, 'epoch': 37}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.533530809639184, 'r': 0.6079769691237213, 'f1': 0.5683262972243481}, 'combined': 0.29228209571537905, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.6308243727598566, 'r': 0.7030625832223701, 'f1': 0.6649874055415615}, 'argument': {'p': 0.6788322431410229, 'r': 0.6497771665863487, 'f1': 0.6639870052194498}, 'combined': 0.44154299591419316, 'epoch': 36}
Test for Chinese: {'event': {'p': 0.6125131440588854, 'r': 0.6980227681246255, 'f1': 0.6524782973956875}, 'argument': {'p': 0.7303126454040291, 'r': 0.7023771890224542, 'f1': 0.7160725659671817}, 'combined': 0.46722180865402785, 'epoch': 36}
Chinese: {'event': {'p': 0.6984126984126984, 'r': 0.8148148148148148, 'f1': 0.7521367521367521}, 'argument': {'p': 0.7283362470862471, 'r': 0.6703271654599089, 'f1': 0.6981287529674627}, 'combined': 0.5250882928302283, 'epoch': 36}
--------------------
Dev for Korean: {'event': {'p': 0.5715686274509804, 'r': 0.7762982689747004, 'f1': 0.6583850931677019}, 'argument': {'p': 0.6716276506286475, 'r': 0.6576454525897165, 'f1': 0.6645630144776751}, 'combined': 0.4375383822026929, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6072449952335558, 'r': 0.7633313361294188, 'f1': 0.67640031855588}, 'argument': {'p': 0.716767199678779, 'r': 0.7094763372478128, 'f1': 0.7131031332605374}, 'combined': 0.4823431865006237, 'epoch': 29}
Korean: {'event': {'p': 0.578125, 'r': 0.5873015873015873, 'f1': 0.5826771653543307}, 'argument': {'p': 0.6237824675324675, 'r': 0.4536599763872491, 'f1': 0.5252904989747095}, 'combined': 0.3060747789301457, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6516587677725119, 'r': 0.7323568575233023, 'f1': 0.6896551724137931}, 'argument': {'p': 0.6690746367862752, 'r': 0.6640364241598725, 'f1': 0.6665460100410511}, 'combined': 0.45968690347658697, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6504447933019362, 'r': 0.7447573397243858, 'f1': 0.6944134078212291}, 'argument': {'p': 0.7205532683201716, 'r': 0.7065719975730321, 'f1': 0.7134941470412731}, 'combined': 0.49545990210743157, 'epoch': 16}
Russian: {'event': {'p': 0.5294117647058824, 'r': 0.5, 'f1': 0.5142857142857143}, 'argument': {'p': 0.6021764497133955, 'r': 0.6862010706036367, 'f1': 0.6414488268686168}, 'combined': 0.3298879681038601, 'epoch': 16}
******************************
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 23:11:05.002816: step: 2/1541, loss: 0.0005544863524846733
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2023-01-23 23:26:09.037648: step: 2008/1541, loss: 0.00012412334035616368
2023-01-23 23:26:10.034129: step: 2010/1541, loss: 0.08043722808361053
2023-01-23 23:26:10.917165: step: 2012/1541, loss: 0.00013395525456871837
2023-01-23 23:26:11.779769: step: 2014/1541, loss: 0.0024820331018418074
2023-01-23 23:26:12.665195: step: 2016/1541, loss: 0.0008486578008159995
2023-01-23 23:26:13.523432: step: 2018/1541, loss: 0.014951150864362717
2023-01-23 23:26:14.434639: step: 2020/1541, loss: 0.0006466421182267368
2023-01-23 23:26:15.333983: step: 2022/1541, loss: 0.0004136962816119194
2023-01-23 23:26:16.223270: step: 2024/1541, loss: 8.894711936591193e-05
2023-01-23 23:26:17.154938: step: 2026/1541, loss: 0.11921858787536621
2023-01-23 23:26:18.043537: step: 2028/1541, loss: 0.00313932984136045
2023-01-23 23:26:18.986720: step: 2030/1541, loss: 0.0018991122487932444
2023-01-23 23:26:19.873541: step: 2032/1541, loss: 0.0026681581512093544
2023-01-23 23:26:20.757813: step: 2034/1541, loss: 1.1035230159759521
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2023-01-23 23:26:22.498691: step: 2038/1541, loss: 0.07907050102949142
2023-01-23 23:26:23.357314: step: 2040/1541, loss: 0.0010988981230184436