Command that produces this log: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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> trainable params:
>>> pad_vector: torch.Size([1, 1, 1024])
>>> xlmr.embeddings.word_embeddings.weight: torch.Size([250002, 1024])
>>> xlmr.embeddings.position_embeddings.weight: torch.Size([514, 1024])
>>> xlmr.embeddings.token_type_embeddings.weight: torch.Size([1, 1024])
>>> xlmr.embeddings.LayerNorm.weight: torch.Size([1024])
>>> xlmr.embeddings.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.0.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.0.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.0.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.1.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.1.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.1.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.2.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.2.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.2.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.3.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.3.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.3.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.4.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.4.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.4.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.5.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.5.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.5.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.6.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.6.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.6.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.7.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.7.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.7.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.8.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.8.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.8.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.9.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.9.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.9.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.10.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.10.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.10.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.11.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.11.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.11.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.12.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.12.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.12.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.13.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.13.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.13.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.14.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.14.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.14.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.15.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.15.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.15.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.16.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.16.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.16.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.17.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.17.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.17.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.18.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.18.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.18.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.19.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.19.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.19.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.20.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.20.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.20.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.21.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.21.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.21.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.22.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.22.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.22.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.23.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.23.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.23.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.pooler.dense.weight: torch.Size([1024, 1024])
>>> xlmr.pooler.dense.bias: torch.Size([1024])
>>> upos_embedding.weight: torch.Size([19, 50])
>>> dist_embedding.weight: torch.Size([104, 50])
>>> deprel_embedding.weight: torch.Size([38, 50])
>>> gcn_layer.W.0.weight: torch.Size([450, 50])
>>> gcn_layer.W.0.bias: torch.Size([450])
>>> gcn_layer.W.1.weight: torch.Size([450, 450])
>>> gcn_layer.W.1.bias: torch.Size([450])
>>> trans_rep.weight: torch.Size([1024, 2924])
>>> trans_rep.bias: torch.Size([1024])
>>> mention_label_ffn.layers.0.weight: torch.Size([350, 1024])
>>> mention_label_ffn.layers.0.bias: torch.Size([350])
>>> mention_label_ffn.layers.1.weight: torch.Size([11, 350])
>>> mention_label_ffn.layers.1.bias: torch.Size([11])
>>> trigger_label_ffn.layers.0.weight: torch.Size([450, 1024])
>>> trigger_label_ffn.layers.0.bias: torch.Size([450])
>>> trigger_label_ffn.layers.1.weight: torch.Size([233, 450])
>>> trigger_label_ffn.layers.1.bias: torch.Size([233])
>>> event_type_ffn.layers.0.weight: torch.Size([450, 1024])
>>> event_type_ffn.layers.0.bias: torch.Size([450])
>>> event_type_ffn.layers.1.weight: torch.Size([117, 450])
>>> event_type_ffn.layers.1.bias: torch.Size([117])
>>> role_type_ffn.layers.0.weight: torch.Size([350, 1024])
>>> role_type_ffn.layers.0.bias: torch.Size([350])
>>> role_type_ffn.layers.1.weight: torch.Size([6, 350])
>>> role_type_ffn.layers.1.bias: torch.Size([6])
>>> trigger_crf.transition: torch.Size([235, 235])
>>> mention_crf.transition: torch.Size([13, 13])
n_trainable_params: 564979817, n_nontrainable_params: 0
----------------------------------------------------------------------------------------------------
******************************
Epoch: 0
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:05:09.695897: step: 3062/1531, loss: 1.9758981466293335
==================================================
Loss: 4.270
--------------------
Dev Chinese: {'event': {'p': 0.547783251231527, 'r': 0.7403462050599201, 'f1': 0.6296715741789354}, 'argument': {'p': 0.5853872192061128, 'r': 0.5131119740911343, 'f1': 0.5468719385272959}, 'combined': 0.34434971440676837, 'epoch': 0}
Test Chinese: {'event': {'p': 0.5528455284552846, 'r': 0.7291196388261851, 'f1': 0.6288634704307619}, 'argument': {'p': 0.5963028025464641, 'r': 0.5211150581609945, 'f1': 0.5561793498339604}, 'combined': 0.34976087611850915, 'epoch': 0}
Dev Korean: {'event': {'p': 0.5548780487804879, 'r': 0.7270306258322237, 'f1': 0.6293948126801152}, 'argument': {'p': 0.5902163914560148, 'r': 0.5147784213062264, 'f1': 0.5499223321479239}, 'combined': 0.34611826323085465, 'epoch': 0}
Test Korean: {'event': {'p': 0.5965153599266392, 'r': 0.7341986455981941, 'f1': 0.6582342524664812}, 'argument': {'p': 0.586090898521168, 'r': 0.515783823605379, 'f1': 0.5486943271303076}, 'combined': 0.3611694002512169, 'epoch': 0}
Dev Russian: {'event': {'p': 0.5738575982996812, 'r': 0.7190412782956058, 'f1': 0.6382978723404255}, 'argument': {'p': 0.5933132943142577, 'r': 0.5151653328114968, 'f1': 0.5514845902251471}, 'combined': 0.3520114405692428, 'epoch': 0}
Test Russian: {'event': {'p': 0.5842588121680348, 'r': 0.6828442437923251, 'f1': 0.6297163674212855}, 'argument': {'p': 0.5941211535778187, 'r': 0.516743407216454, 'f1': 0.5527373903793638}, 'combined': 0.3480677816076139, 'epoch': 0}
Chinese: {'event': {'p': 0.5465116279069767, 'r': 0.8703703703703703, 'f1': 0.6714285714285714}, 'argument': {'p': 0.6029421413932283, 'r': 0.47836731879132166, 'f1': 0.5334787610483863}, 'combined': 0.35819288241820224, 'epoch': 0}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.4849867552094564, 'r': 0.23279364250053908, 'f1': 0.31458600337910686}, 'combined': 0.19630166610856273, 'epoch': 0}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.4199348585652182, 'r': 0.2931620710738316, 'f1': 0.3452797725980683}, 'combined': 0.20979024157857315, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.547783251231527, 'r': 0.7403462050599201, 'f1': 0.6296715741789354}, 'argument': {'p': 0.5853872192061128, 'r': 0.5131119740911343, 'f1': 0.5468719385272959}, 'combined': 0.34434971440676837, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.5528455284552846, 'r': 0.7291196388261851, 'f1': 0.6288634704307619}, 'argument': {'p': 0.5963028025464641, 'r': 0.5211150581609945, 'f1': 0.5561793498339604}, 'combined': 0.34976087611850915, 'epoch': 0}
Chinese: {'event': {'p': 0.5465116279069767, 'r': 0.8703703703703703, 'f1': 0.6714285714285714}, 'argument': {'p': 0.6029421413932283, 'r': 0.47836731879132166, 'f1': 0.5334787610483863}, 'combined': 0.35819288241820224, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.5548780487804879, 'r': 0.7270306258322237, 'f1': 0.6293948126801152}, 'argument': {'p': 0.5902163914560148, 'r': 0.5147784213062264, 'f1': 0.5499223321479239}, 'combined': 0.34611826323085465, 'epoch': 0}
Test for Korean: {'event': {'p': 0.5965153599266392, 'r': 0.7341986455981941, 'f1': 0.6582342524664812}, 'argument': {'p': 0.586090898521168, 'r': 0.515783823605379, 'f1': 0.5486943271303076}, 'combined': 0.3611694002512169, 'epoch': 0}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.4849867552094564, 'r': 0.23279364250053908, 'f1': 0.31458600337910686}, 'combined': 0.19630166610856273, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.5738575982996812, 'r': 0.7190412782956058, 'f1': 0.6382978723404255}, 'argument': {'p': 0.5933132943142577, 'r': 0.5151653328114968, 'f1': 0.5514845902251471}, 'combined': 0.3520114405692428, 'epoch': 0}
Test for Russian: {'event': {'p': 0.5842588121680348, 'r': 0.6828442437923251, 'f1': 0.6297163674212855}, 'argument': {'p': 0.5941211535778187, 'r': 0.516743407216454, 'f1': 0.5527373903793638}, 'combined': 0.3480677816076139, 'epoch': 0}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.4199348585652182, 'r': 0.2931620710738316, 'f1': 0.3452797725980683}, 'combined': 0.20979024157857315, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:31:43.969685: step: 3062/1531, loss: 1.1186739206314087
==================================================
Loss: 1.723
--------------------
Dev Chinese: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6669941853636175, 'r': 0.6323201194096966, 'f1': 0.6491944887934987}, 'combined': 0.41833710546262826, 'epoch': 1}
Test Chinese: {'event': {'p': 0.5703893881044073, 'r': 0.7522573363431151, 'f1': 0.6488196641518617}, 'argument': {'p': 0.6980048234494222, 'r': 0.631483558395853, 'f1': 0.6630799873219316}, 'combined': 0.43021933468003637, 'epoch': 1}
Dev Korean: {'event': {'p': 0.5691056910569106, 'r': 0.7456724367509987, 'f1': 0.6455331412103748}, 'argument': {'p': 0.6755577563066916, 'r': 0.6314676924433107, 'f1': 0.6527690763713083}, 'combined': 0.42138407235496567, 'epoch': 1}
Test Korean: {'event': {'p': 0.6102705181109582, 'r': 0.7511286681715575, 'f1': 0.6734125980268151}, 'argument': {'p': 0.6909915880652676, 'r': 0.6235549301991894, 'f1': 0.6555434980469568}, 'combined': 0.4414512501393875, 'epoch': 1}
Dev Russian: {'event': {'p': 0.589798087141339, 'r': 0.7390146471371505, 'f1': 0.6560283687943262}, 'argument': {'p': 0.6766850630797329, 'r': 0.6160715699110637, 'f1': 0.6449573237654641}, 'combined': 0.4231103010518116, 'epoch': 1}
Test Russian: {'event': {'p': 0.6001931434089812, 'r': 0.7014672686230248, 'f1': 0.6468904501691388}, 'argument': {'p': 0.6954534075582562, 'r': 0.6216383736654965, 'f1': 0.6564774473544418}, 'combined': 0.424668991445002, 'epoch': 1}
Chinese: {'event': {'p': 0.5581395348837209, 'r': 0.8888888888888888, 'f1': 0.6857142857142857}, 'argument': {'p': 0.6382044746530727, 'r': 0.5643626346105685, 'f1': 0.599016480595428}, 'combined': 0.4107541581225792, 'epoch': 1}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.52720530504692, 'r': 0.24935386049516484, 'f1': 0.3385722142503156}, 'combined': 0.21126906169219697, 'epoch': 1}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5175671847721561, 'r': 0.45897467328851577, 'f1': 0.4865131536858267}, 'combined': 0.29560292882176814, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6669941853636175, 'r': 0.6323201194096966, 'f1': 0.6491944887934987}, 'combined': 0.41833710546262826, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.5703893881044073, 'r': 0.7522573363431151, 'f1': 0.6488196641518617}, 'argument': {'p': 0.6980048234494222, 'r': 0.631483558395853, 'f1': 0.6630799873219316}, 'combined': 0.43021933468003637, 'epoch': 1}
Chinese: {'event': {'p': 0.5581395348837209, 'r': 0.8888888888888888, 'f1': 0.6857142857142857}, 'argument': {'p': 0.6382044746530727, 'r': 0.5643626346105685, 'f1': 0.599016480595428}, 'combined': 0.4107541581225792, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.5691056910569106, 'r': 0.7456724367509987, 'f1': 0.6455331412103748}, 'argument': {'p': 0.6755577563066916, 'r': 0.6314676924433107, 'f1': 0.6527690763713083}, 'combined': 0.42138407235496567, 'epoch': 1}
Test for Korean: {'event': {'p': 0.6102705181109582, 'r': 0.7511286681715575, 'f1': 0.6734125980268151}, 'argument': {'p': 0.6909915880652676, 'r': 0.6235549301991894, 'f1': 0.6555434980469568}, 'combined': 0.4414512501393875, 'epoch': 1}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.52720530504692, 'r': 0.24935386049516484, 'f1': 0.3385722142503156}, 'combined': 0.21126906169219697, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.589798087141339, 'r': 0.7390146471371505, 'f1': 0.6560283687943262}, 'argument': {'p': 0.6766850630797329, 'r': 0.6160715699110637, 'f1': 0.6449573237654641}, 'combined': 0.4231103010518116, 'epoch': 1}
Test for Russian: {'event': {'p': 0.6001931434089812, 'r': 0.7014672686230248, 'f1': 0.6468904501691388}, 'argument': {'p': 0.6954534075582562, 'r': 0.6216383736654965, 'f1': 0.6564774473544418}, 'combined': 0.424668991445002, 'epoch': 1}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5175671847721561, 'r': 0.45897467328851577, 'f1': 0.4865131536858267}, 'combined': 0.29560292882176814, 'epoch': 1}
******************************
Epoch: 2
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:58:19.507361: step: 3062/1531, loss: 5.643580436706543
==================================================
Loss: 1.414
--------------------
Dev Chinese: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6911234765105495, 'r': 0.6182159421387718, 'f1': 0.6526398656903586}, 'combined': 0.419818169549404, 'epoch': 2}
Test Chinese: {'event': {'p': 0.5759520753102267, 'r': 0.7595936794582393, 'f1': 0.6551472377707471}, 'argument': {'p': 0.7120922266752931, 'r': 0.6388552706989732, 'f1': 0.673488604286221}, 'combined': 0.4412341987681935, 'epoch': 2}
Dev Korean: {'event': {'p': 0.5619918699186992, 'r': 0.7363515312916112, 'f1': 0.637463976945245}, 'argument': {'p': 0.6978078581967944, 'r': 0.6157723915582743, 'f1': 0.6542284931128004}, 'combined': 0.4170470970505806, 'epoch': 2}
Test Korean: {'event': {'p': 0.6162310866574966, 'r': 0.7584650112866818, 'f1': 0.6799898811029599}, 'argument': {'p': 0.7056764905311897, 'r': 0.6293677446059527, 'f1': 0.6653412817013296}, 'combined': 0.452425339036978, 'epoch': 2}
Dev Russian: {'event': {'p': 0.594048884165781, 'r': 0.7443408788282291, 'f1': 0.6607565011820331}, 'argument': {'p': 0.6918589575175508, 'r': 0.6068402335253279, 'f1': 0.6465667403848848}, 'combined': 0.42722317715738845, 'epoch': 2}
Test Russian: {'event': {'p': 0.6030902945436987, 'r': 0.7048532731376975, 'f1': 0.6500130106687484}, 'argument': {'p': 0.7101750499437411, 'r': 0.6268995944159209, 'f1': 0.6659440483029313}, 'combined': 0.4328722957743228, 'epoch': 2}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6764008577092689, 'r': 0.5981396014453866, 'f1': 0.634867471709577}, 'combined': 0.4534767655068407, 'epoch': 2}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.49442340345439334, 'r': 0.3017909086020323, 'f1': 0.37480483810252396}, 'combined': 0.23987509638561533, 'epoch': 2}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5470185085569701, 'r': 0.40252305346644973, 'f1': 0.46377656160264863}, 'combined': 0.2700471118192637, 'epoch': 2}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6911234765105495, 'r': 0.6182159421387718, 'f1': 0.6526398656903586}, 'combined': 0.419818169549404, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.5759520753102267, 'r': 0.7595936794582393, 'f1': 0.6551472377707471}, 'argument': {'p': 0.7120922266752931, 'r': 0.6388552706989732, 'f1': 0.673488604286221}, 'combined': 0.4412341987681935, 'epoch': 2}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6764008577092689, 'r': 0.5981396014453866, 'f1': 0.634867471709577}, 'combined': 0.4534767655068407, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5619918699186992, 'r': 0.7363515312916112, 'f1': 0.637463976945245}, 'argument': {'p': 0.6978078581967944, 'r': 0.6157723915582743, 'f1': 0.6542284931128004}, 'combined': 0.4170470970505806, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6162310866574966, 'r': 0.7584650112866818, 'f1': 0.6799898811029599}, 'argument': {'p': 0.7056764905311897, 'r': 0.6293677446059527, 'f1': 0.6653412817013296}, 'combined': 0.452425339036978, 'epoch': 2}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.49442340345439334, 'r': 0.3017909086020323, 'f1': 0.37480483810252396}, 'combined': 0.23987509638561533, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.589798087141339, 'r': 0.7390146471371505, 'f1': 0.6560283687943262}, 'argument': {'p': 0.6766850630797329, 'r': 0.6160715699110637, 'f1': 0.6449573237654641}, 'combined': 0.4231103010518116, 'epoch': 1}
Test for Russian: {'event': {'p': 0.6001931434089812, 'r': 0.7014672686230248, 'f1': 0.6468904501691388}, 'argument': {'p': 0.6954534075582562, 'r': 0.6216383736654965, 'f1': 0.6564774473544418}, 'combined': 0.424668991445002, 'epoch': 1}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5175671847721561, 'r': 0.45897467328851577, 'f1': 0.4865131536858267}, 'combined': 0.29560292882176814, 'epoch': 1}
******************************
Epoch: 3
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 15:24:44.053562: step: 3062/1531, loss: 0.8040257096290588
==================================================
Loss: 1.003
--------------------
Dev Chinese: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6462957083296235, 'r': 0.6733607993643658, 'f1': 0.6595507122486893}, 'combined': 0.4235167087712422, 'epoch': 3}
Test Chinese: {'event': {'p': 0.5750962772785623, 'r': 0.7584650112866818, 'f1': 0.6541737649063032}, 'argument': {'p': 0.6727227037027117, 'r': 0.6808413819907766, 'f1': 0.6767576949278529}, 'combined': 0.4427171292202649, 'epoch': 3}
Dev Korean: {'event': {'p': 0.568089430894309, 'r': 0.7443408788282291, 'f1': 0.6443804034582133}, 'argument': {'p': 0.6563776324542244, 'r': 0.6682944376912292, 'f1': 0.6622824330340574}, 'combined': 0.4267618214017731, 'epoch': 3}
Test Korean: {'event': {'p': 0.6139385602934434, 'r': 0.7556433408577878, 'f1': 0.6774601568429043}, 'argument': {'p': 0.6678313932411224, 'r': 0.6712259953755232, 'f1': 0.6695243915339426}, 'combined': 0.4535760992987348, 'epoch': 3}
Dev Russian: {'event': {'p': 0.5951115834218916, 'r': 0.7456724367509987, 'f1': 0.6619385342789599}, 'argument': {'p': 0.6573433697098017, 'r': 0.6621803334309923, 'f1': 0.6597529861676822}, 'combined': 0.4367159246500024, 'epoch': 3}
Test Russian: {'event': {'p': 0.6006760019314341, 'r': 0.7020316027088036, 'f1': 0.6474108769190736}, 'argument': {'p': 0.6684878814674943, 'r': 0.6694121647463985, 'f1': 0.6689497038381109}, 'combined': 0.433085314376586, 'epoch': 3}
Chinese: {'event': {'p': 0.6046511627906976, 'r': 0.9629629629629629, 'f1': 0.7428571428571429}, 'argument': {'p': 0.6143328445747801, 'r': 0.6295642374154772, 'f1': 0.6218552875695734}, 'combined': 0.46194964219454027, 'epoch': 3}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.542828965905889, 'r': 0.2900045160319133, 'f1': 0.37804160125588704}, 'combined': 0.2540439560439561, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.4693156798549591, 'r': 0.5047357311647673, 'f1': 0.48638170457695756}, 'combined': 0.2832096001334183, 'epoch': 3}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6462957083296235, 'r': 0.6733607993643658, 'f1': 0.6595507122486893}, 'combined': 0.4235167087712422, 'epoch': 3}
Test for Chinese: {'event': {'p': 0.5750962772785623, 'r': 0.7584650112866818, 'f1': 0.6541737649063032}, 'argument': {'p': 0.6727227037027117, 'r': 0.6808413819907766, 'f1': 0.6767576949278529}, 'combined': 0.4427171292202649, 'epoch': 3}
Chinese: {'event': {'p': 0.6046511627906976, 'r': 0.9629629629629629, 'f1': 0.7428571428571429}, 'argument': {'p': 0.6143328445747801, 'r': 0.6295642374154772, 'f1': 0.6218552875695734}, 'combined': 0.46194964219454027, 'epoch': 3}
--------------------
Dev for Korean: {'event': {'p': 0.568089430894309, 'r': 0.7443408788282291, 'f1': 0.6443804034582133}, 'argument': {'p': 0.6563776324542244, 'r': 0.6682944376912292, 'f1': 0.6622824330340574}, 'combined': 0.4267618214017731, 'epoch': 3}
Test for Korean: {'event': {'p': 0.6139385602934434, 'r': 0.7556433408577878, 'f1': 0.6774601568429043}, 'argument': {'p': 0.6678313932411224, 'r': 0.6712259953755232, 'f1': 0.6695243915339426}, 'combined': 0.4535760992987348, 'epoch': 3}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.542828965905889, 'r': 0.2900045160319133, 'f1': 0.37804160125588704}, 'combined': 0.2540439560439561, 'epoch': 3}
--------------------
Dev for Russian: {'event': {'p': 0.5951115834218916, 'r': 0.7456724367509987, 'f1': 0.6619385342789599}, 'argument': {'p': 0.6573433697098017, 'r': 0.6621803334309923, 'f1': 0.6597529861676822}, 'combined': 0.4367159246500024, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6006760019314341, 'r': 0.7020316027088036, 'f1': 0.6474108769190736}, 'argument': {'p': 0.6684878814674943, 'r': 0.6694121647463985, 'f1': 0.6689497038381109}, 'combined': 0.433085314376586, 'epoch': 3}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.4693156798549591, 'r': 0.5047357311647673, 'f1': 0.48638170457695756}, 'combined': 0.2832096001334183, 'epoch': 3}
******************************
Epoch: 4
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 15:30:27.159048: step: 2/1531, loss: 0.40401843190193176
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==================================================
Loss: 0.830
--------------------
Dev Chinese: {'event': {'p': 0.5566502463054187, 'r': 0.7523302263648469, 'f1': 0.6398640996602492}, 'argument': {'p': 0.6977268276391273, 'r': 0.5904229906087055, 'f1': 0.6396056644450976}, 'combined': 0.4092607026177578, 'epoch': 4}
Test Chinese: {'event': {'p': 0.5755241762943946, 'r': 0.7590293453724605, 'f1': 0.6546605013385252}, 'argument': {'p': 0.7330472843816535, 'r': 0.6030778531682508, 'f1': 0.6617412847218315}, 'combined': 0.4332158812123939, 'epoch': 4}
Dev Korean: {'event': {'p': 0.5630081300813008, 'r': 0.7376830892143809, 'f1': 0.6386167146974063}, 'argument': {'p': 0.7009319094061505, 'r': 0.5926661394978763, 'f1': 0.642268452941117}, 'combined': 0.41016336937104186, 'epoch': 4}
Test Korean: {'event': {'p': 0.6153140761118753, 'r': 0.7573363431151241, 'f1': 0.6789779913989374}, 'argument': {'p': 0.7237686808311149, 'r': 0.59637752328082, 'f1': 0.653926621093686}, 'combined': 0.444001783712485, 'epoch': 4}
Dev Russian: {'event': {'p': 0.5961742826780021, 'r': 0.7470039946737683, 'f1': 0.6631205673758865}, 'argument': {'p': 0.7006753022316279, 'r': 0.5701019150219189, 'f1': 0.628680348037925}, 'combined': 0.41689086908897866, 'epoch': 4}
Test Russian: {'event': {'p': 0.6069531627233221, 'r': 0.7093679458239278, 'f1': 0.654176424668228}, 'argument': {'p': 0.7297934927591966, 'r': 0.5944306149775325, 'f1': 0.6551936219449909}, 'combined': 0.4286122210694008, 'epoch': 4}
Chinese: {'event': {'p': 0.5581395348837209, 'r': 0.8888888888888888, 'f1': 0.6857142857142857}, 'argument': {'p': 0.6486941486941485, 'r': 0.5950830620252107, 'f1': 0.6207331940090559}, 'combined': 0.4256456187490669, 'epoch': 4}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.5883575883575883, 'r': 0.28271728271728275, 'f1': 0.3819163292847504}, 'combined': 0.25053711201079626, 'epoch': 4}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6527717222049205, 'r': 0.48034145596211136, 'f1': 0.5534368949128674}, 'combined': 0.30824333387552105, 'epoch': 4}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6462957083296235, 'r': 0.6733607993643658, 'f1': 0.6595507122486893}, 'combined': 0.4235167087712422, 'epoch': 3}
Test for Chinese: {'event': {'p': 0.5750962772785623, 'r': 0.7584650112866818, 'f1': 0.6541737649063032}, 'argument': {'p': 0.6727227037027117, 'r': 0.6808413819907766, 'f1': 0.6767576949278529}, 'combined': 0.4427171292202649, 'epoch': 3}
Chinese: {'event': {'p': 0.6046511627906976, 'r': 0.9629629629629629, 'f1': 0.7428571428571429}, 'argument': {'p': 0.6143328445747801, 'r': 0.6295642374154772, 'f1': 0.6218552875695734}, 'combined': 0.46194964219454027, 'epoch': 3}
--------------------
Dev for Korean: {'event': {'p': 0.568089430894309, 'r': 0.7443408788282291, 'f1': 0.6443804034582133}, 'argument': {'p': 0.6563776324542244, 'r': 0.6682944376912292, 'f1': 0.6622824330340574}, 'combined': 0.4267618214017731, 'epoch': 3}
Test for Korean: {'event': {'p': 0.6139385602934434, 'r': 0.7556433408577878, 'f1': 0.6774601568429043}, 'argument': {'p': 0.6678313932411224, 'r': 0.6712259953755232, 'f1': 0.6695243915339426}, 'combined': 0.4535760992987348, 'epoch': 3}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.542828965905889, 'r': 0.2900045160319133, 'f1': 0.37804160125588704}, 'combined': 0.2540439560439561, 'epoch': 3}
--------------------
Dev for Russian: {'event': {'p': 0.5961742826780021, 'r': 0.7470039946737683, 'f1': 0.6631205673758865}, 'argument': {'p': 0.7006753022316279, 'r': 0.5701019150219189, 'f1': 0.628680348037925}, 'combined': 0.41689086908897866, 'epoch': 4}
Test for Russian: {'event': {'p': 0.6069531627233221, 'r': 0.7093679458239278, 'f1': 0.654176424668228}, 'argument': {'p': 0.7297934927591966, 'r': 0.5944306149775325, 'f1': 0.6551936219449909}, 'combined': 0.4286122210694008, 'epoch': 4}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6527717222049205, 'r': 0.48034145596211136, 'f1': 0.5534368949128674}, 'combined': 0.30824333387552105, '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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2023-01-21 16:17:37.201881: step: 3056/1531, loss: 0.8585737347602844
2023-01-21 16:17:37.998934: step: 3058/1531, loss: 1.3401678800582886
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2023-01-21 16:17:39.626698: step: 3062/1531, loss: 0.7155558466911316
==================================================
Loss: 0.732
--------------------
Dev Chinese: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6610479621654668, 'r': 0.6739441290574232, 'f1': 0.6674337564332549}, 'combined': 0.42782277026185994, 'epoch': 5}
Test Chinese: {'event': {'p': 0.5703893881044073, 'r': 0.7522573363431151, 'f1': 0.6488196641518617}, 'argument': {'p': 0.6726722869932877, 'r': 0.6859635344589957, 'f1': 0.6792528979918955}, 'combined': 0.4407126371492804, 'epoch': 5}
Dev Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6630535272715522, 'r': 0.672676945664463, 'f1': 0.6678305699752378}, 'combined': 0.42956709861231435, 'epoch': 5}
Test Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6654323762971, 'r': 0.6814370167839204, 'f1': 0.6733396062079107}, 'combined': 0.45513873710790215, 'epoch': 5}
Dev Russian: {'event': {'p': 0.5876726886291179, 'r': 0.7363515312916112, 'f1': 0.6536643026004727}, 'argument': {'p': 0.6617323757359359, 'r': 0.6554116403321393, 'f1': 0.6585568420022455}, 'combined': 0.4304750988501675, 'epoch': 5}
Test Russian: {'event': {'p': 0.5982617093191694, 'r': 0.6992099322799097, 'f1': 0.644808743169399}, 'argument': {'p': 0.6734726605940627, 'r': 0.6737056961167597, 'f1': 0.6735891582001161}, 'combined': 0.4343361785115503, 'epoch': 5}
Chinese: {'event': {'p': 0.5581395348837209, 'r': 0.8888888888888888, 'f1': 0.6857142857142857}, 'argument': {'p': 0.6402097006935716, 'r': 0.6560826684793627, 'f1': 0.648049003151044}, 'combined': 0.444376459303573, 'epoch': 5}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.6671367521367522, 'r': 0.44475783475783476, 'f1': 0.5337094017094017}, 'combined': 0.35865271794871795, 'epoch': 5}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6185075680376433, 'r': 0.5134779810123831, 'f1': 0.5611202679104392}, 'combined': 0.3125226808615104, 'epoch': 5}
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6462957083296235, 'r': 0.6733607993643658, 'f1': 0.6595507122486893}, 'combined': 0.4235167087712422, 'epoch': 3}
Test for Chinese: {'event': {'p': 0.5750962772785623, 'r': 0.7584650112866818, 'f1': 0.6541737649063032}, 'argument': {'p': 0.6727227037027117, 'r': 0.6808413819907766, 'f1': 0.6767576949278529}, 'combined': 0.4427171292202649, 'epoch': 3}
Chinese: {'event': {'p': 0.6046511627906976, 'r': 0.9629629629629629, 'f1': 0.7428571428571429}, 'argument': {'p': 0.6143328445747801, 'r': 0.6295642374154772, 'f1': 0.6218552875695734}, 'combined': 0.46194964219454027, 'epoch': 3}
--------------------
Dev for Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6630535272715522, 'r': 0.672676945664463, 'f1': 0.6678305699752378}, 'combined': 0.42956709861231435, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6654323762971, 'r': 0.6814370167839204, 'f1': 0.6733396062079107}, 'combined': 0.45513873710790215, 'epoch': 5}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.6671367521367522, 'r': 0.44475783475783476, 'f1': 0.5337094017094017}, 'combined': 0.35865271794871795, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5876726886291179, 'r': 0.7363515312916112, 'f1': 0.6536643026004727}, 'argument': {'p': 0.6617323757359359, 'r': 0.6554116403321393, 'f1': 0.6585568420022455}, 'combined': 0.4304750988501675, 'epoch': 5}
Test for Russian: {'event': {'p': 0.5982617093191694, 'r': 0.6992099322799097, 'f1': 0.644808743169399}, 'argument': {'p': 0.6734726605940627, 'r': 0.6737056961167597, 'f1': 0.6735891582001161}, 'combined': 0.4343361785115503, 'epoch': 5}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6185075680376433, 'r': 0.5134779810123831, 'f1': 0.5611202679104392}, 'combined': 0.3125226808615104, 'epoch': 5}
******************************
Epoch: 6
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 16:23:22.100946: step: 2/1531, loss: 0.3764267563819885
2023-01-21 16:23:22.926357: step: 4/1531, loss: 0.21308544278144836
2023-01-21 16:23:23.721087: step: 6/1531, loss: 0.18736515939235687
2023-01-21 16:23:24.551707: step: 8/1531, loss: 0.2258596122264862
2023-01-21 16:23:25.339305: step: 10/1531, loss: 0.21530310809612274
2023-01-21 16:23:26.153463: step: 12/1531, loss: 0.1352149397134781
2023-01-21 16:23:26.948891: step: 14/1531, loss: 0.21595615148544312
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==================================================
Loss: 0.598
--------------------
Dev Chinese: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6519192341743013, 'r': 0.6636867293760035, 'f1': 0.6577503543368621}, 'combined': 0.4216157424175131, 'epoch': 6}
Test Chinese: {'event': {'p': 0.5712451861360719, 'r': 0.7533860045146726, 'f1': 0.6497931370163056}, 'argument': {'p': 0.6870356700297854, 'r': 0.6812835294568635, 'f1': 0.6841475093183771}, 'combined': 0.4445543562618804, 'epoch': 6}
Dev Korean: {'event': {'p': 0.5630081300813008, 'r': 0.7376830892143809, 'f1': 0.6386167146974063}, 'argument': {'p': 0.6507287395557891, 'r': 0.6620539678004149, 'f1': 0.6563425029451992}, 'combined': 0.4191512929471359, 'epoch': 6}
Test Korean: {'event': {'p': 0.6143970655662541, 'r': 0.7562076749435666, 'f1': 0.6779661016949152}, 'argument': {'p': 0.6811989403309595, 'r': 0.6735839115264468, 'f1': 0.6773700244680875}, 'combined': 0.4592339148936186, 'epoch': 6}
Dev Russian: {'event': {'p': 0.5844845908607864, 'r': 0.7323568575233023, 'f1': 0.6501182033096926}, 'argument': {'p': 0.660429417623526, 'r': 0.6497616884108112, 'f1': 0.655052124067746}, 'combined': 0.42586130997312094, 'epoch': 6}
Test Russian: {'event': {'p': 0.6026074360212458, 'r': 0.7042889390519187, 'f1': 0.6494925839188134}, 'argument': {'p': 0.6844655262767186, 'r': 0.6617368197659733, 'f1': 0.6729093021257223}, 'combined': 0.43704960138064086, 'epoch': 6}
Chinese: {'event': {'p': 0.6046511627906976, 'r': 0.9629629629629629, 'f1': 0.7428571428571429}, 'argument': {'p': 0.6476611351611351, 'r': 0.6423085637961671, 'f1': 0.6449737445588067}, 'combined': 0.4791233531008279, 'epoch': 6}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6027596535409036, 'r': 0.37574627753199186, 'f1': 0.462919413919414}, 'combined': 0.30367513553113556, 'epoch': 6}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.648202099110195, 'r': 0.46474867483372473, 'f1': 0.5413555992568662}, 'combined': 0.328924921067463, 'epoch': 6}
New best chinese model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6519192341743013, 'r': 0.6636867293760035, 'f1': 0.6577503543368621}, 'combined': 0.4216157424175131, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.5712451861360719, 'r': 0.7533860045146726, 'f1': 0.6497931370163056}, 'argument': {'p': 0.6870356700297854, 'r': 0.6812835294568635, 'f1': 0.6841475093183771}, 'combined': 0.4445543562618804, 'epoch': 6}
Chinese: {'event': {'p': 0.6046511627906976, 'r': 0.9629629629629629, 'f1': 0.7428571428571429}, 'argument': {'p': 0.6476611351611351, 'r': 0.6423085637961671, 'f1': 0.6449737445588067}, 'combined': 0.4791233531008279, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6630535272715522, 'r': 0.672676945664463, 'f1': 0.6678305699752378}, 'combined': 0.42956709861231435, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6654323762971, 'r': 0.6814370167839204, 'f1': 0.6733396062079107}, 'combined': 0.45513873710790215, 'epoch': 5}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.6671367521367522, 'r': 0.44475783475783476, 'f1': 0.5337094017094017}, 'combined': 0.35865271794871795, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5844845908607864, 'r': 0.7323568575233023, 'f1': 0.6501182033096926}, 'argument': {'p': 0.660429417623526, 'r': 0.6497616884108112, 'f1': 0.655052124067746}, 'combined': 0.42586130997312094, 'epoch': 6}
Test for Russian: {'event': {'p': 0.6026074360212458, 'r': 0.7042889390519187, 'f1': 0.6494925839188134}, 'argument': {'p': 0.6844655262767186, 'r': 0.6617368197659733, 'f1': 0.6729093021257223}, 'combined': 0.43704960138064086, 'epoch': 6}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.648202099110195, 'r': 0.46474867483372473, 'f1': 0.5413555992568662}, 'combined': 0.328924921067463, '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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2023-01-21 17:10:51.780029: step: 3062/1531, loss: 0.7716228365898132
==================================================
Loss: 0.497
--------------------
Dev Chinese: {'event': {'p': 0.554679802955665, 'r': 0.7496671105193076, 'f1': 0.637599093997735}, 'argument': {'p': 0.6759887486805959, 'r': 0.6647629427458278, 'f1': 0.6703288501659787}, 'combined': 0.4274010675463714, 'epoch': 7}
Test Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.708407213487476, 'r': 0.6614331249842877, 'f1': 0.684114759682398}, 'combined': 0.4451990429278978, 'epoch': 7}
Dev Korean: {'event': {'p': 0.5640243902439024, 'r': 0.7390146471371505, 'f1': 0.6397694524495676}, 'argument': {'p': 0.6839595265353466, 'r': 0.6626322896673119, 'f1': 0.6731270183802749}, 'combined': 0.4306461039781585, 'epoch': 7}
Test Korean: {'event': {'p': 0.6116460339293902, 'r': 0.7528216704288939, 'f1': 0.6749304325828486}, 'argument': {'p': 0.7023302859626394, 'r': 0.6530981934484956, 'f1': 0.6768201316909659}, 'combined': 0.45680650426296415, 'epoch': 7}
Dev Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Chinese: {'event': {'p': 0.5348837209302325, 'r': 0.8518518518518519, 'f1': 0.6571428571428571}, 'argument': {'p': 0.649668958492488, 'r': 0.6389306286000501, 'f1': 0.6442550505050505}, 'combined': 0.4233676046176046, 'epoch': 7}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.5423925667828107, 'r': 0.2965079365079365, 'f1': 0.38341543513957305}, 'combined': 0.25152052545155995, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6519192341743013, 'r': 0.6636867293760035, 'f1': 0.6577503543368621}, 'combined': 0.4216157424175131, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.5712451861360719, 'r': 0.7533860045146726, 'f1': 0.6497931370163056}, 'argument': {'p': 0.6870356700297854, 'r': 0.6812835294568635, 'f1': 0.6841475093183771}, 'combined': 0.4445543562618804, 'epoch': 6}
Chinese: {'event': {'p': 0.6046511627906976, 'r': 0.9629629629629629, 'f1': 0.7428571428571429}, 'argument': {'p': 0.6476611351611351, 'r': 0.6423085637961671, 'f1': 0.6449737445588067}, 'combined': 0.4791233531008279, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6630535272715522, 'r': 0.672676945664463, 'f1': 0.6678305699752378}, 'combined': 0.42956709861231435, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6654323762971, 'r': 0.6814370167839204, 'f1': 0.6733396062079107}, 'combined': 0.45513873710790215, 'epoch': 5}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.6671367521367522, 'r': 0.44475783475783476, 'f1': 0.5337094017094017}, 'combined': 0.35865271794871795, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 8
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 17:37:13.693737: step: 3062/1531, loss: 0.23135899007320404
==================================================
Loss: 0.393
--------------------
Dev Chinese: {'event': {'p': 0.5527093596059113, 'r': 0.7470039946737683, 'f1': 0.6353340883352209}, 'argument': {'p': 0.6790674816702553, 'r': 0.675637847924446, 'f1': 0.6773483234888117}, 'combined': 0.43034247958915445, 'epoch': 8}
Test Chinese: {'event': {'p': 0.5729567821994009, 'r': 0.7556433408577878, 'f1': 0.6517400827451936}, 'argument': {'p': 0.6983153712691405, 'r': 0.6826412909089955, 'f1': 0.6903893794217691}, 'combined': 0.44995443127074664, 'epoch': 8}
Dev Korean: {'event': {'p': 0.5558943089430894, 'r': 0.7283621837549934, 'f1': 0.6305475504322767}, 'argument': {'p': 0.6859405722848795, 'r': 0.675487196364718, 'f1': 0.680673752534246}, 'combined': 0.4291971673040145, 'epoch': 8}
Test Korean: {'event': {'p': 0.6139385602934434, 'r': 0.7556433408577878, 'f1': 0.6774601568429043}, 'argument': {'p': 0.6930105115534614, 'r': 0.675855622661158, 'f1': 0.6843255729537355}, 'combined': 0.463603309984848, 'epoch': 8}
Dev Russian: {'event': {'p': 0.589798087141339, 'r': 0.7390146471371505, 'f1': 0.6560283687943262}, 'argument': {'p': 0.6788910003681168, 'r': 0.6599359246781914, 'f1': 0.6692792797965861}, 'combined': 0.43906619419279586, 'epoch': 8}
Test Russian: {'event': {'p': 0.6035731530661517, 'r': 0.7054176072234764, 'f1': 0.6505334374186834}, 'argument': {'p': 0.6970366230052902, 'r': 0.6678628314509353, 'f1': 0.6821379423157231}, 'combined': 0.44375354040835496, 'epoch': 8}
Chinese: {'event': {'p': 0.5697674418604651, 'r': 0.9074074074074074, 'f1': 0.7}, 'argument': {'p': 0.6848318839469282, 'r': 0.6395537428595279, 'f1': 0.6614188280854948}, 'combined': 0.4629931796598463, 'epoch': 8}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6120368702335915, 'r': 0.4848603777175206, 'f1': 0.5410760736847694}, 'combined': 0.35494590433720874, 'epoch': 8}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.587453583533068, 'r': 0.5431174640211383, 'f1': 0.5644161881003986}, 'combined': 0.34293641808631814, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6519192341743013, 'r': 0.6636867293760035, 'f1': 0.6577503543368621}, 'combined': 0.4216157424175131, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.5712451861360719, 'r': 0.7533860045146726, 'f1': 0.6497931370163056}, 'argument': {'p': 0.6870356700297854, 'r': 0.6812835294568635, 'f1': 0.6841475093183771}, 'combined': 0.4445543562618804, 'epoch': 6}
Chinese: {'event': {'p': 0.6046511627906976, 'r': 0.9629629629629629, 'f1': 0.7428571428571429}, 'argument': {'p': 0.6476611351611351, 'r': 0.6423085637961671, 'f1': 0.6449737445588067}, 'combined': 0.4791233531008279, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6630535272715522, 'r': 0.672676945664463, 'f1': 0.6678305699752378}, 'combined': 0.42956709861231435, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6654323762971, 'r': 0.6814370167839204, 'f1': 0.6733396062079107}, 'combined': 0.45513873710790215, 'epoch': 5}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.6671367521367522, 'r': 0.44475783475783476, 'f1': 0.5337094017094017}, 'combined': 0.35865271794871795, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 9
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 17:42:39.894267: step: 2/1531, loss: 0.9774797558784485
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==================================================
Loss: 0.318
--------------------
Dev Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Dev Korean: {'event': {'p': 0.5691056910569106, 'r': 0.7456724367509987, 'f1': 0.6455331412103748}, 'argument': {'p': 0.6860668958732771, 'r': 0.6691392656412803, 'f1': 0.6774973608035888}, 'combined': 0.4373469994812793, 'epoch': 9}
Test Korean: {'event': {'p': 0.6111875286565795, 'r': 0.7522573363431151, 'f1': 0.6744244877308374}, 'argument': {'p': 0.6987196051642067, 'r': 0.6643641142354817, 'f1': 0.6811089076587643}, 'combined': 0.4593565261366724, 'epoch': 9}
Dev Russian: {'event': {'p': 0.5951115834218916, 'r': 0.7456724367509987, 'f1': 0.6619385342789599}, 'argument': {'p': 0.6902749561399558, 'r': 0.6527434743219127, 'f1': 0.6709847949789449}, 'combined': 0.4441506917118312, 'epoch': 9}
Test Russian: {'event': {'p': 0.5982617093191694, 'r': 0.6992099322799097, 'f1': 0.644808743169399}, 'argument': {'p': 0.7047872085451831, 'r': 0.6583553189392244, 'f1': 0.6807804732236368}, 'combined': 0.4389732013136019, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.6190419662641885, 'r': 0.43413332699046986, 'f1': 0.5103552088284914}, 'combined': 0.31846165030897866, 'epoch': 9}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.6500732246032999, 'r': 0.5396834317461356, 'f1': 0.5897571522174266}, 'combined': 0.3135417771282521, 'epoch': 9}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6630535272715522, 'r': 0.672676945664463, 'f1': 0.6678305699752378}, 'combined': 0.42956709861231435, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6654323762971, 'r': 0.6814370167839204, 'f1': 0.6733396062079107}, 'combined': 0.45513873710790215, 'epoch': 5}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.6671367521367522, 'r': 0.44475783475783476, 'f1': 0.5337094017094017}, 'combined': 0.35865271794871795, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 10
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 18:29:57.102417: step: 3062/1531, loss: 0.30994755029678345
==================================================
Loss: 0.279
--------------------
Dev Chinese: {'event': {'p': 0.5566502463054187, 'r': 0.7523302263648469, 'f1': 0.6398640996602492}, 'argument': {'p': 0.6984569005312914, 'r': 0.6414708862641174, 'f1': 0.6687521095038034}, 'combined': 0.42791046644354347, 'epoch': 10}
Test Chinese: {'event': {'p': 0.5738125802310655, 'r': 0.7567720090293454, 'f1': 0.6527135556096374}, 'argument': {'p': 0.7197998049692932, 'r': 0.661587807319092, 'f1': 0.6894672725339724}, 'combined': 0.450024634932128, 'epoch': 10}
Dev Korean: {'event': {'p': 0.5711382113821138, 'r': 0.748335552596538, 'f1': 0.6478386167146974}, 'argument': {'p': 0.6959294611374637, 'r': 0.6454265974845272, 'f1': 0.669727297691023}, 'combined': 0.43387520611222474, 'epoch': 10}
Test Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.7122662225793635, 'r': 0.6522655353512106, 'f1': 0.6809467149198454}, 'combined': 0.462003311261074, 'epoch': 10}
Dev Russian: {'event': {'p': 0.5929861849096706, 'r': 0.7430093209054593, 'f1': 0.6595744680851064}, 'argument': {'p': 0.6912886297338143, 'r': 0.6308453182434955, 'f1': 0.659685346238492}, 'combined': 0.4351116113487926, 'epoch': 10}
Test Russian: {'event': {'p': 0.6026074360212458, 'r': 0.7042889390519187, 'f1': 0.6494925839188134}, 'argument': {'p': 0.7175808314176925, 'r': 0.6464445687537548, 'f1': 0.6801577610702343}, 'combined': 0.44175742170994137, 'epoch': 10}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6329793088267665, 'r': 0.6172856069550284, 'f1': 0.6250339618540456}, 'combined': 0.4464528298957468, 'epoch': 10}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6387171855921856, 'r': 0.3981613624470767, 'f1': 0.49053479853479853}, 'combined': 0.31394227106227107, 'epoch': 10}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.6168358765783177, 'r': 0.5470053999845459, 'f1': 0.5798257239836186}, 'combined': 0.33762004181324623, 'epoch': 10}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6630535272715522, 'r': 0.672676945664463, 'f1': 0.6678305699752378}, 'combined': 0.42956709861231435, 'epoch': 5}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6654323762971, 'r': 0.6814370167839204, 'f1': 0.6733396062079107}, 'combined': 0.45513873710790215, 'epoch': 5}
Korean: {'event': {'p': 0.6774193548387096, 'r': 0.6666666666666666, 'f1': 0.6719999999999999}, 'argument': {'p': 0.6671367521367522, 'r': 0.44475783475783476, 'f1': 0.5337094017094017}, 'combined': 0.35865271794871795, 'epoch': 5}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 11
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.242
--------------------
Dev Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.667032680537494, 'r': 0.6564448602115021, 'f1': 0.661696419093194}, 'combined': 0.427142648791756, 'epoch': 11}
Test Chinese: {'event': {'p': 0.5708172871202396, 'r': 0.7528216704288939, 'f1': 0.6493064005840837}, 'argument': {'p': 0.693373977268071, 'r': 0.6924429615214874, 'f1': 0.6929081566585527}, 'combined': 0.44990970113531725, 'epoch': 11}
Dev Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Dev Russian: {'event': {'p': 0.5929861849096706, 'r': 0.7430093209054593, 'f1': 0.6595744680851064}, 'argument': {'p': 0.6637849617373468, 'r': 0.6384422172363615, 'f1': 0.650866990925093}, 'combined': 0.42929524933357205, 'epoch': 11}
Test Russian: {'event': {'p': 0.6026074360212458, 'r': 0.7042889390519187, 'f1': 0.6494925839188134}, 'argument': {'p': 0.6882755554919464, 'r': 0.673732843720208, 'f1': 0.6809265604131772}, 'combined': 0.44225675118170443, 'epoch': 11}
Chinese: {'event': {'p': 0.5697674418604651, 'r': 0.9074074074074074, 'f1': 0.7}, 'argument': {'p': 0.6592500257678829, 'r': 0.6102148998843214, 'f1': 0.6337854324978789}, 'combined': 0.4436498027485152, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5553250112460639, 'r': 0.5448471808451948, 'f1': 0.5500362016151491}, 'combined': 0.3202742439784412, 'epoch': 11}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 12
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 19:22:28.760216: step: 3040/1531, loss: 0.27198687195777893
2023-01-21 19:22:29.598804: step: 3042/1531, loss: 0.024354567751288414
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2023-01-21 19:22:33.694937: step: 3052/1531, loss: 6.8381829261779785
2023-01-21 19:22:34.487885: step: 3054/1531, loss: 0.582915723323822
2023-01-21 19:22:35.333200: step: 3056/1531, loss: 0.057927537709474564
2023-01-21 19:22:36.158153: step: 3058/1531, loss: 0.05046948045492172
2023-01-21 19:22:36.960903: step: 3060/1531, loss: 0.2856330871582031
2023-01-21 19:22:37.844572: step: 3062/1531, loss: 0.014783894643187523
==================================================
Loss: 0.219
--------------------
Dev Chinese: {'event': {'p': 0.554679802955665, 'r': 0.7496671105193076, 'f1': 0.637599093997735}, 'argument': {'p': 0.6784337951112089, 'r': 0.6671755142399551, 'f1': 0.6727575574656804}, 'combined': 0.428949609120247, 'epoch': 12}
Test Chinese: {'event': {'p': 0.5678219940094138, 'r': 0.7488713318284425, 'f1': 0.6458992455585301}, 'argument': {'p': 0.6998776018076266, 'r': 0.67778609926568, 'f1': 0.6886547265824616}, 'combined': 0.4448015683499278, 'epoch': 12}
Dev Korean: {'event': {'p': 0.5640243902439024, 'r': 0.7390146471371505, 'f1': 0.6397694524495676}, 'argument': {'p': 0.6852064591338849, 'r': 0.6688092052169754, 'f1': 0.6769085460507956}, 'combined': 0.43306540986535047, 'epoch': 12}
Test Korean: {'event': {'p': 0.6107290233837689, 'r': 0.7516930022573364, 'f1': 0.6739185428788261}, 'argument': {'p': 0.6905017118046753, 'r': 0.6684544424739554, 'f1': 0.6792992332217206}, 'combined': 0.45779234943148583, 'epoch': 12}
Dev Russian: {'event': {'p': 0.589798087141339, 'r': 0.7390146471371505, 'f1': 0.6560283687943262}, 'argument': {'p': 0.6770313861917412, 'r': 0.6507051474036544, 'f1': 0.6636072696679243}, 'combined': 0.4353451946403049, 'epoch': 12}
Test Russian: {'event': {'p': 0.6006760019314341, 'r': 0.7020316027088036, 'f1': 0.6474108769190736}, 'argument': {'p': 0.7033346118670741, 'r': 0.6593914566653826, 'f1': 0.6806545276434852}, 'combined': 0.44066314462060663, 'epoch': 12}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6841646554035935, 'r': 0.6389306286000501, 'f1': 0.6607744107744108}, 'combined': 0.47198172198172195, 'epoch': 12}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6440493468795356, 'r': 0.4551282051282052, 'f1': 0.5333533653846154}, 'combined': 0.34134615384615385, 'epoch': 12}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5963542163388369, 'r': 0.4950865192246948, 'f1': 0.5410223818331715}, 'combined': 0.31502569068766945, 'epoch': 12}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 13
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 19:28:01.316954: step: 2/1531, loss: 0.13663657009601593
2023-01-21 19:28:02.151031: step: 4/1531, loss: 0.12332615256309509
2023-01-21 19:28:02.958195: step: 6/1531, loss: 0.03656141459941864
2023-01-21 19:28:03.791087: step: 8/1531, loss: 0.05172951519489288
2023-01-21 19:28:04.639488: step: 10/1531, loss: 0.11511288583278656
2023-01-21 19:28:05.446902: step: 12/1531, loss: 0.10821326822042465
2023-01-21 19:28:06.248564: step: 14/1531, loss: 0.002364424290135503
2023-01-21 19:28:07.055699: step: 16/1531, loss: 0.01711910218000412
2023-01-21 19:28:07.844606: step: 18/1531, loss: 0.012612473219633102
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2023-01-21 19:48:52.856075: step: 3062/1531, loss: 0.038051798939704895
==================================================
Loss: 0.188
--------------------
Dev Chinese: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6717863137821081, 'r': 0.6548220129290245, 'f1': 0.6631956959588075}, 'combined': 0.4266083299032873, 'epoch': 13}
Test Chinese: {'event': {'p': 0.5712451861360719, 'r': 0.7533860045146726, 'f1': 0.6497931370163056}, 'argument': {'p': 0.7066079049657724, 'r': 0.674607882432938, 'f1': 0.6902372052626011}, 'combined': 0.4485113988929532, 'epoch': 13}
Dev Korean: {'event': {'p': 0.5660569105691057, 'r': 0.7416777629826897, 'f1': 0.6420749279538904}, 'argument': {'p': 0.6768998371509453, 'r': 0.6538292843256411, 'f1': 0.6651645762339057}, 'combined': 0.42708549736286505, 'epoch': 13}
Test Korean: {'event': {'p': 0.6134800550206327, 'r': 0.755079006772009, 'f1': 0.676954211990893}, 'argument': {'p': 0.7005470784759281, 'r': 0.6632257716144688, 'f1': 0.6813757535115355}, 'combined': 0.46126018628810245, 'epoch': 13}
Dev Russian: {'event': {'p': 0.5961742826780021, 'r': 0.7470039946737683, 'f1': 0.6631205673758865}, 'argument': {'p': 0.6707733015497815, 'r': 0.6387378389482122, 'f1': 0.6543637175826247}, 'combined': 0.43392203967358445, 'epoch': 13}
Test Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.7097865360485611, 'r': 0.6581388671053187, 'f1': 0.6829876916454332}, 'combined': 0.4450172208899726, 'epoch': 13}
Chinese: {'event': {'p': 0.5697674418604651, 'r': 0.9074074074074074, 'f1': 0.7}, 'argument': {'p': 0.6640304140304141, 'r': 0.6420789953847805, 'f1': 0.6528702390046929}, 'combined': 0.457009167303285, 'epoch': 13}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.6186371100164203, 'r': 0.4659863945578231, 'f1': 0.5315696649029982}, 'combined': 0.33169947089947094, 'epoch': 13}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6222938196121154, 'r': 0.5283626770291545, 'f1': 0.5714943241335753}, 'combined': 0.31830063622629506, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 14
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 20:15:03.189926: step: 3048/1531, loss: 0.0023639528080821037
2023-01-21 20:15:04.074903: step: 3050/1531, loss: 0.07703115791082382
2023-01-21 20:15:04.868594: step: 3052/1531, loss: 0.0033971481025218964
2023-01-21 20:15:05.677453: step: 3054/1531, loss: 0.007362942676991224
2023-01-21 20:15:06.455597: step: 3056/1531, loss: 0.9327265024185181
2023-01-21 20:15:07.282027: step: 3058/1531, loss: 0.000630134716629982
2023-01-21 20:15:08.093065: step: 3060/1531, loss: 0.11714952439069748
2023-01-21 20:15:08.881026: step: 3062/1531, loss: 0.012166710570454597
==================================================
Loss: 0.162
--------------------
Dev Chinese: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6888719692162228, 'r': 0.6595265207080949, 'f1': 0.6738799197201856}, 'combined': 0.431954739027888, 'epoch': 14}
Test Chinese: {'event': {'p': 0.5691056910569106, 'r': 0.7505643340857788, 'f1': 0.6473594548551959}, 'argument': {'p': 0.7080605126953863, 'r': 0.6630064278286336, 'f1': 0.6847932180893462}, 'combined': 0.44330736435085444, 'epoch': 14}
Dev Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6867522174147687, 'r': 0.6583451953591506, 'f1': 0.6722487433900773}, 'combined': 0.43240898998462607, 'epoch': 14}
Test Korean: {'event': {'p': 0.6102705181109582, 'r': 0.7511286681715575, 'f1': 0.6734125980268151}, 'argument': {'p': 0.6997155672629832, 'r': 0.6556427276046984, 'f1': 0.6769625785372988}, 'combined': 0.45587512877973424, 'epoch': 14}
Dev Russian: {'event': {'p': 0.59192348565356, 'r': 0.7416777629826897, 'f1': 0.6583924349881797}, 'argument': {'p': 0.6841082893506865, 'r': 0.6449590255433838, 'f1': 0.6639570633048582}, 'combined': 0.4371443076368865, 'epoch': 14}
Test Russian: {'event': {'p': 0.6016417189763399, 'r': 0.7031602708803611, 'f1': 0.6484517304189436}, 'argument': {'p': 0.7085945841670652, 'r': 0.6465404374746916, 'f1': 0.6761467235708177}, 'combined': 0.43844851291659587, 'epoch': 14}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.698790150179039, 'r': 0.623713522473853, 'f1': 0.6591208403435477}, 'combined': 0.4708006002453911, 'epoch': 14}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.5967567155067155, 'r': 0.4262547967905111, 'f1': 0.49729726292226295}, 'combined': 0.32622700447700453, 'epoch': 14}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5750671225530248, 'r': 0.5208155072178338, 'f1': 0.5465984531197068}, 'combined': 0.31827251700641146, 'epoch': 14}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 15
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 20:20:32.785222: step: 2/1531, loss: 0.12320782989263535
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2023-01-21 20:20:36.100517: step: 10/1531, loss: 0.007685885298997164
2023-01-21 20:20:36.911964: step: 12/1531, loss: 0.06666342169046402
2023-01-21 20:20:37.700751: step: 14/1531, loss: 0.01162828877568245
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2023-01-21 20:20:40.164850: step: 20/1531, loss: 0.03483264893293381
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2023-01-21 20:41:24.039976: step: 3062/1531, loss: 0.27734771370887756
==================================================
Loss: 0.153
--------------------
Dev Chinese: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6705890496903765, 'r': 0.6458780370211502, 'f1': 0.6580016218161736}, 'combined': 0.4240123701171039, 'epoch': 15}
Test Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.7087709607153142, 'r': 0.6642495735289643, 'f1': 0.6857884447358165}, 'combined': 0.446288221276119, 'epoch': 15}
Dev Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.665816204438278, 'r': 0.6431069434741278, 'f1': 0.6542645759375032}, 'combined': 0.4208410759344401, 'epoch': 15}
Test Korean: {'event': {'p': 0.6143970655662541, 'r': 0.7562076749435666, 'f1': 0.6779661016949152}, 'argument': {'p': 0.6998370096348489, 'r': 0.6546248260814245, 'f1': 0.6764763223842127}, 'combined': 0.4586280151757374, 'epoch': 15}
Dev Russian: {'event': {'p': 0.5908607863974495, 'r': 0.7403462050599201, 'f1': 0.657210401891253}, 'argument': {'p': 0.672117170435586, 'r': 0.6291845758482909, 'f1': 0.6499426562801458}, 'combined': 0.42714907434014315, 'epoch': 15}
Test Russian: {'event': {'p': 0.6001931434089812, 'r': 0.7014672686230248, 'f1': 0.6468904501691388}, 'argument': {'p': 0.712177250032363, 'r': 0.64763618674818, 'f1': 0.6783750564956387}, 'combined': 0.4388343456799787, 'epoch': 15}
Chinese: {'event': {'p': 0.5465116279069767, 'r': 0.8703703703703703, 'f1': 0.6714285714285714}, 'argument': {'p': 0.7134343434343434, 'r': 0.6190959178562484, 'f1': 0.6629257173504961}, 'combined': 0.4451072673639045, 'epoch': 15}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.5691208791208792, 'r': 0.37941391941391944, 'f1': 0.45529670329670335}, 'combined': 0.2986746373626374, 'epoch': 15}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5961829184253596, 'r': 0.528690512565885, 'f1': 0.5604119433198381}, 'combined': 0.3121281709629478, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 16
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 21:07:38.727894: step: 3062/1531, loss: 0.5183573961257935
==================================================
Loss: 0.136
--------------------
Dev Chinese: {'event': {'p': 0.5566502463054187, 'r': 0.7523302263648469, 'f1': 0.6398640996602492}, 'argument': {'p': 0.6976407137878978, 'r': 0.6563661549635056, 'f1': 0.6763743425869285}, 'combined': 0.4327876597526779, 'epoch': 16}
Test Chinese: {'event': {'p': 0.5716730851519042, 'r': 0.7539503386004515, 'f1': 0.6502798734485276}, 'argument': {'p': 0.7166511514568857, 'r': 0.6762086276259295, 'f1': 0.695842753004629}, 'combined': 0.4524925373639252, 'epoch': 16}
Dev Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6979472025816884, 'r': 0.6564449033708846, 'f1': 0.6765601806789611}, 'combined': 0.4351822257277928, 'epoch': 16}
Test Korean: {'event': {'p': 0.6121045392022009, 'r': 0.7533860045146726, 'f1': 0.6754363774348595}, 'argument': {'p': 0.7053209086286604, 'r': 0.6667617689718813, 'f1': 0.6854995320726796}, 'combined': 0.46301132067646195, 'epoch': 16}
Dev Russian: {'event': {'p': 0.5887353878852285, 'r': 0.7376830892143809, 'f1': 0.6548463356973995}, 'argument': {'p': 0.6945609265354523, 'r': 0.642035393911993, 'f1': 0.667266086614372}, 'combined': 0.4369567517545651, 'epoch': 16}
Test Russian: {'event': {'p': 0.6055045871559633, 'r': 0.7076749435665914, 'f1': 0.6526151444184232}, 'argument': {'p': 0.7125111886990556, 'r': 0.6583494565487282, 'f1': 0.6843603768056598}, 'combined': 0.4466239461432721, 'epoch': 16}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.681783703022641, 'r': 0.6367070945583342, 'f1': 0.6584748584748584}, 'combined': 0.4797459683173969, 'epoch': 16}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6294642857142857, 'r': 0.4577922077922078, 'f1': 0.5300751879699249}, 'combined': 0.33924812030075197, 'epoch': 16}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5801704869351761, 'r': 0.5254374221299709, 'f1': 0.5514491757007615}, 'combined': 0.3210969883827218, 'epoch': 16}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
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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2023-01-21 21:33:55.046713: step: 3062/1531, loss: 0.0632132887840271
==================================================
Loss: 0.124
--------------------
Dev Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.7019824008196097, 'r': 0.6447087464567102, 'f1': 0.6721276731972478}, 'combined': 0.4277868089885088, 'epoch': 17}
Test Chinese: {'event': {'p': 0.5729567821994009, 'r': 0.7556433408577878, 'f1': 0.6517400827451936}, 'argument': {'p': 0.7166758933171224, 'r': 0.6649520540881256, 'f1': 0.6898457841297324}, 'combined': 0.44960014843013474, 'epoch': 17}
Dev Korean: {'event': {'p': 0.5660569105691057, 'r': 0.7416777629826897, 'f1': 0.6420749279538904}, 'argument': {'p': 0.7011374881984427, 'r': 0.6441509869805722, 'f1': 0.6714372617694617}, 'combined': 0.4311130314761846, 'epoch': 17}
Test Korean: {'event': {'p': 0.6143970655662541, 'r': 0.7562076749435666, 'f1': 0.6779661016949152}, 'argument': {'p': 0.7125870318012912, 'r': 0.6595786894220511, 'f1': 0.6850589739490276}, 'combined': 0.4644467619993407, 'epoch': 17}
Dev Russian: {'event': {'p': 0.589798087141339, 'r': 0.7390146471371505, 'f1': 0.6560283687943262}, 'argument': {'p': 0.696027016250384, 'r': 0.6320068712678247, 'f1': 0.6624738434654638}, 'combined': 0.43460163489755604, 'epoch': 17}
Test Russian: {'event': {'p': 0.6040560115886046, 'r': 0.7059819413092551, 'f1': 0.6510538641686182}, 'argument': {'p': 0.717037337875779, 'r': 0.6506311744304675, 'f1': 0.6822221043400756}, 'combined': 0.4441633372518524, 'epoch': 17}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6858869305297877, 'r': 0.6348705472672415, 'f1': 0.6593934439428002}, 'combined': 0.4709953171020001, 'epoch': 17}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.63003663003663, 'r': 0.42547928262213974, 'f1': 0.5079365079365079}, 'combined': 0.316952380952381, 'epoch': 17}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5866254820979306, 'r': 0.5202150501623158, 'f1': 0.5514279531720547}, 'combined': 0.3210846309609432, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 18
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 21:39:18.430109: step: 2/1531, loss: 0.06692253798246384
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2023-01-21 22:00:09.422643: step: 3062/1531, loss: 0.043271712958812714
==================================================
Loss: 0.124
--------------------
Dev Chinese: {'event': {'p': 0.5556650246305419, 'r': 0.7509986684420772, 'f1': 0.638731596828992}, 'argument': {'p': 0.6746565935459866, 'r': 0.6639322835329724, 'f1': 0.669251478812552}, 'combined': 0.42747206574210567, 'epoch': 18}
Test Chinese: {'event': {'p': 0.5708172871202396, 'r': 0.7528216704288939, 'f1': 0.6493064005840837}, 'argument': {'p': 0.6928452171642879, 'r': 0.6856570639741632, 'f1': 0.6892323993792429}, 'combined': 0.4475230084068678, 'epoch': 18}
Dev Korean: {'event': {'p': 0.5660569105691057, 'r': 0.7416777629826897, 'f1': 0.6420749279538904}, 'argument': {'p': 0.6746249140371126, 'r': 0.6638622250531993, 'f1': 0.6692002985002644}, 'combined': 0.42967673344627927, 'epoch': 18}
Test Korean: {'event': {'p': 0.6121045392022009, 'r': 0.7533860045146726, 'f1': 0.6754363774348595}, 'argument': {'p': 0.6866670438380955, 'r': 0.6759705710929034, 'f1': 0.681276824722643}, 'combined': 0.4601591505209857, 'epoch': 18}
Dev Russian: {'event': {'p': 0.5929861849096706, 'r': 0.7430093209054593, 'f1': 0.6595744680851064}, 'argument': {'p': 0.6711795715772739, 'r': 0.6534391423725883, 'f1': 0.6621905594579355}, 'combined': 0.43676398602544686, 'epoch': 18}
Test Russian: {'event': {'p': 0.6026074360212458, 'r': 0.7042889390519187, 'f1': 0.6494925839188134}, 'argument': {'p': 0.6948325477950936, 'r': 0.6664818421796644, 'f1': 0.6803619793800175}, 'combined': 0.441890059987646, 'epoch': 18}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6406926406926406, 'r': 0.667167543200601, 'f1': 0.6536621273463378}, 'combined': 0.46690151953309833, 'epoch': 18}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.601851851851852, 'r': 0.4924242424242425, 'f1': 0.5416666666666667}, 'combined': 0.3553333333333334, 'epoch': 18}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5435032488218615, 'r': 0.5537580271015192, 'f1': 0.5485827184370191}, 'combined': 0.3333160820883154, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 19
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 22:26:29.366797: step: 3062/1531, loss: 0.10068699717521667
==================================================
Loss: 0.108
--------------------
Dev Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6910575307013501, 'r': 0.6386290330686609, 'f1': 0.6638096746278834}, 'combined': 0.42850681148119313, 'epoch': 19}
Test Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7177313372910802, 'r': 0.661760355693927, 'f1': 0.6886103736234462}, 'combined': 0.4454432643200585, 'epoch': 19}
Dev Korean: {'event': {'p': 0.5640243902439024, 'r': 0.7390146471371505, 'f1': 0.6397694524495676}, 'argument': {'p': 0.6931490234571287, 'r': 0.635805780587671, 'f1': 0.6632402465176462}, 'combined': 0.4243208493571108, 'epoch': 19}
Test Korean: {'event': {'p': 0.609353507565337, 'r': 0.75, 'f1': 0.672400708322793}, 'argument': {'p': 0.7112005738300302, 'r': 0.6485471899450037, 'f1': 0.678430435309842}, 'combined': 0.45617710525007854, 'epoch': 19}
Dev Russian: {'event': {'p': 0.5929861849096706, 'r': 0.7430093209054593, 'f1': 0.6595744680851064}, 'argument': {'p': 0.6869284365158602, 'r': 0.6202559706187326, 'f1': 0.6518919011139693}, 'combined': 0.42997125392623514, 'epoch': 19}
Test Russian: {'event': {'p': 0.6021245774987929, 'r': 0.70372460496614, 'f1': 0.6489721571688786}, 'argument': {'p': 0.7160466149614196, 'r': 0.642075886389753, 'f1': 0.6770468268368017}, 'combined': 0.4393845397166234, 'epoch': 19}
Chinese: {'event': {'p': 0.5697674418604651, 'r': 0.9074074074074074, 'f1': 0.7}, 'argument': {'p': 0.7487362805544624, 'r': 0.612602411362742, 'f1': 0.6738626524990161}, 'combined': 0.47170385674931126, 'epoch': 19}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.5900550696469063, 'r': 0.38550264550264546, 'f1': 0.4663338453661034}, 'combined': 0.29099231950844856, 'epoch': 19}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6299546944283786, 'r': 0.49920938049041325, 'f1': 0.5570125719156189}, 'combined': 0.310234850180851, 'epoch': 19}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 20
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 22:52:31.889476: step: 3040/1531, loss: 0.004133615642786026
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2023-01-21 22:52:37.559793: step: 3054/1531, loss: 0.0018492513336241245
2023-01-21 22:52:38.378681: step: 3056/1531, loss: 0.0007073386223055422
2023-01-21 22:52:39.225508: step: 3058/1531, loss: 0.20788472890853882
2023-01-21 22:52:40.041482: step: 3060/1531, loss: 0.28018108010292053
2023-01-21 22:52:40.844606: step: 3062/1531, loss: 0.00028830842347815633
==================================================
Loss: 0.107
--------------------
Dev Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6759733810191013, 'r': 0.6403701654243001, 'f1': 0.6576902921660467}, 'combined': 0.4245565872419554, 'epoch': 20}
Test Chinese: {'event': {'p': 0.5750962772785623, 'r': 0.7584650112866818, 'f1': 0.6541737649063032}, 'argument': {'p': 0.7053850266095052, 'r': 0.6705891880485665, 'f1': 0.6875471461843208}, 'combined': 0.44977530516998154, 'epoch': 20}
Dev Korean: {'event': {'p': 0.5650406504065041, 'r': 0.7403462050599201, 'f1': 0.640922190201729}, 'argument': {'p': 0.676037186828425, 'r': 0.6392159892887503, 'f1': 0.65711117375932}, 'combined': 0.42115713269185234, 'epoch': 20}
Test Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.7007737762253238, 'r': 0.6601250938187666, 'f1': 0.6798423673633971}, 'combined': 0.4595342285846185, 'epoch': 20}
Dev Russian: {'event': {'p': 0.5972369819341127, 'r': 0.748335552596538, 'f1': 0.6643026004728132}, 'argument': {'p': 0.6710172017689359, 'r': 0.6231930514591734, 'f1': 0.6462215185035695}, 'combined': 0.4292866352234114, 'epoch': 20}
Test Russian: {'event': {'p': 0.607436021245775, 'r': 0.7099322799097065, 'f1': 0.6546968514181629}, 'argument': {'p': 0.7060765531212131, 'r': 0.6536839101364906, 'f1': 0.6788708667027868}, 'combined': 0.4444546189498339, 'epoch': 20}
Chinese: {'event': {'p': 0.5697674418604651, 'r': 0.9074074074074074, 'f1': 0.7}, 'argument': {'p': 0.7097341910426023, 'r': 0.6276161854674251, 'f1': 0.6661540214171793}, 'combined': 0.46630781499202545, 'epoch': 20}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.6744791666666667, 'r': 0.5464135021097047, 'f1': 0.6037296037296038}, 'combined': 0.37672727272727285, 'epoch': 20}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5661528566469584, 'r': 0.5341064685348663, 'f1': 0.5496629676184062}, 'combined': 0.3200569178537554, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 21
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 22:58:05.027040: step: 2/1531, loss: 0.03455916419625282
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2023-01-21 23:18:55.799267: step: 3062/1531, loss: 0.047395091503858566
==================================================
Loss: 0.115
--------------------
Dev Chinese: {'event': {'p': 0.554679802955665, 'r': 0.7496671105193076, 'f1': 0.637599093997735}, 'argument': {'p': 0.6777688495783836, 'r': 0.6508343938509189, 'f1': 0.6640286038257809}, 'combined': 0.42338403618789877, 'epoch': 21}
Test Chinese: {'event': {'p': 0.5716730851519042, 'r': 0.7539503386004515, 'f1': 0.6502798734485276}, 'argument': {'p': 0.698171681915097, 'r': 0.6747037262204719, 'f1': 0.6862371232498817}, 'combined': 0.4462461896626147, 'epoch': 21}
Dev Korean: {'event': {'p': 0.5619918699186992, 'r': 0.7363515312916112, 'f1': 0.637463976945245}, 'argument': {'p': 0.6794197736787361, 'r': 0.6518291229709702, 'f1': 0.6653385348460162}, 'combined': 0.424129348437864, 'epoch': 21}
Test Korean: {'event': {'p': 0.6121045392022009, 'r': 0.7533860045146726, 'f1': 0.6754363774348595}, 'argument': {'p': 0.6960847001211041, 'r': 0.6660769754383545, 'f1': 0.6807503103699541}, 'combined': 0.4598035235739381, 'epoch': 21}
Dev Russian: {'event': {'p': 0.5929861849096706, 'r': 0.7430093209054593, 'f1': 0.6595744680851064}, 'argument': {'p': 0.6767920226492873, 'r': 0.6375360976938587, 'f1': 0.6565778185701593}, 'combined': 0.43306196543989234, 'epoch': 21}
Test Russian: {'event': {'p': 0.6050217286335103, 'r': 0.7071106094808126, 'f1': 0.6520947176684881}, 'argument': {'p': 0.703177634705828, 'r': 0.6556326836768278, 'f1': 0.6785733571591499}, 'combined': 0.442494101754054, 'epoch': 21}
Chinese: {'event': {'p': 0.5348837209302325, 'r': 0.8518518518518519, 'f1': 0.6571428571428571}, 'argument': {'p': 0.6627081627081627, 'r': 0.60793889306286, 'f1': 0.6341431556948798}, 'combined': 0.416722645170921, 'epoch': 21}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6413690476190476, 'r': 0.4664502164502164, 'f1': 0.5401002506265664}, 'combined': 0.3456641604010025, 'epoch': 21}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5925145398486905, 'r': 0.5254374221299709, 'f1': 0.5569636674577692}, 'combined': 0.32430795826654907, 'epoch': 21}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 22
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 23:24:19.465166: step: 2/1531, loss: 0.003470722585916519
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2023-01-21 23:45:11.667850: step: 3062/1531, loss: 0.011720298789441586
==================================================
Loss: 0.091
--------------------
Dev Chinese: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6767205262489451, 'r': 0.6512946105228286, 'f1': 0.6637641686035307}, 'combined': 0.42622229173069304, 'epoch': 22}
Test Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7104972728673117, 'r': 0.6776171173825065, 'f1': 0.693667780307118}, 'combined': 0.4487147627296957, 'epoch': 22}
Dev Korean: {'event': {'p': 0.5650406504065041, 'r': 0.7403462050599201, 'f1': 0.640922190201729}, 'argument': {'p': 0.673742131423267, 'r': 0.6571065232399765, 'f1': 0.6653203547804761}, 'combined': 0.42641857897169416, 'epoch': 22}
Test Korean: {'event': {'p': 0.6088950022925264, 'r': 0.7494356659142212, 'f1': 0.6718947634707817}, 'argument': {'p': 0.7046738057539453, 'r': 0.6714705315167255, 'f1': 0.687671608392411}, 'combined': 0.462042952666391, 'epoch': 22}
Dev Russian: {'event': {'p': 0.5929861849096706, 'r': 0.7430093209054593, 'f1': 0.6595744680851064}, 'argument': {'p': 0.6751883183762651, 'r': 0.6423977157178151, 'f1': 0.6583849891859055}, 'combined': 0.4342539290375122, 'epoch': 22}
Test Russian: {'event': {'p': 0.6006760019314341, 'r': 0.7020316027088036, 'f1': 0.6474108769190736}, 'argument': {'p': 0.7146134615407131, 'r': 0.6647740077691368, 'f1': 0.6887933454577213}, 'combined': 0.4459323037988058, 'epoch': 22}
Chinese: {'event': {'p': 0.5581395348837209, 'r': 0.8888888888888888, 'f1': 0.6857142857142857}, 'argument': {'p': 0.6856395816572807, 'r': 0.6403080390683695, 'f1': 0.6621989121989121}, 'combined': 0.45407925407925404, 'epoch': 22}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.5833333333333334, 'r': 0.5, 'f1': 0.5384615384615384}, 'combined': 0.3446153846153846, 'epoch': 22}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5794667967855196, 'r': 0.4701334389014593, 'f1': 0.5191056721203613}, 'combined': 0.31540597799718156, 'epoch': 22}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
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
2023-01-21 23:50:35.547802: step: 2/1531, loss: 0.13818088173866272
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2023-01-21 23:50:38.021801: step: 8/1531, loss: 0.0012531309621408582
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2023-01-22 00:11:28.019922: step: 3062/1531, loss: 0.012233803048729897
==================================================
Loss: 0.090
--------------------
Dev Chinese: {'event': {'p': 0.5635467980295567, 'r': 0.7616511318242344, 'f1': 0.6477916194790486}, 'argument': {'p': 0.6814600451562614, 'r': 0.6568230225548057, 'f1': 0.6689147571379207}, 'combined': 0.43331737381980817, 'epoch': 23}
Test Chinese: {'event': {'p': 0.5708172871202396, 'r': 0.7528216704288939, 'f1': 0.6493064005840837}, 'argument': {'p': 0.7144638592087927, 'r': 0.6713083240887986, 'f1': 0.6922141196486573}, 'combined': 0.44945905846254997, 'epoch': 23}
Dev Korean: {'event': {'p': 0.5721544715447154, 'r': 0.7496671105193076, 'f1': 0.6489913544668587}, 'argument': {'p': 0.6839805921398462, 'r': 0.655647093773588, 'f1': 0.6695142122689849}, 'combined': 0.4345089354552605, 'epoch': 23}
Test Korean: {'event': {'p': 0.6134800550206327, 'r': 0.755079006772009, 'f1': 0.676954211990893}, 'argument': {'p': 0.7027587104214643, 'r': 0.664313534621989, 'f1': 0.6829955397004415}, 'combined': 0.46235670737120704, 'epoch': 23}
Dev Russian: {'event': {'p': 0.594048884165781, 'r': 0.7443408788282291, 'f1': 0.6607565011820331}, 'argument': {'p': 0.680228496276159, 'r': 0.6407152233307056, 'f1': 0.6598808812796363}, 'combined': 0.436020582311249, 'epoch': 23}
Test Russian: {'event': {'p': 0.6040560115886046, 'r': 0.7059819413092551, 'f1': 0.6510538641686182}, 'argument': {'p': 0.7059809876218635, 'r': 0.6536135937393405, 'f1': 0.6787887753556505}, 'combined': 0.4419280551495804, 'epoch': 23}
Chinese: {'event': {'p': 0.5581395348837209, 'r': 0.8888888888888888, 'f1': 0.6857142857142857}, 'argument': {'p': 0.6813308523834839, 'r': 0.6419150179480757, 'f1': 0.6610358908231247}, 'combined': 0.4532817537072856, 'epoch': 23}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6514778325123154, 'r': 0.4971804511278196, 'f1': 0.5639658848614073}, 'combined': 0.36093816631130066, 'epoch': 23}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5883655147478499, 'r': 0.4995556257293065, 'f1': 0.5403356768092499}, 'combined': 0.3146258371294366, 'epoch': 23}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6887893958819851, 'r': 0.6674045987535191, 'f1': 0.6779283969736882}, 'combined': 0.4376208225084964, 'epoch': 9}
Test for Chinese: {'event': {'p': 0.5686777920410783, 'r': 0.75, 'f1': 0.646872718422974}, 'argument': {'p': 0.7069501139492907, 'r': 0.6730487503360283, 'f1': 0.6895830178670458}, 'combined': 0.4460724413459742, 'epoch': 9}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6710508445802563, 'r': 0.6599590950830619, 'f1': 0.6654587542087542}, 'combined': 0.4753276815776815, 'epoch': 9}
--------------------
Dev for Korean: {'event': {'p': 0.5701219512195121, 'r': 0.7470039946737683, 'f1': 0.646685878962536}, 'argument': {'p': 0.6701303993771388, 'r': 0.6618571845700136, 'f1': 0.6659680987598895}, 'combined': 0.43067216530754815, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6148555708390646, 'r': 0.7567720090293454, 'f1': 0.6784720465469265}, 'argument': {'p': 0.6805918035587584, 'r': 0.6856846673949124, 'f1': 0.6831287435245823}, 'combined': 0.4634837566741538, 'epoch': 11}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6956233421750663, 'r': 0.5107108081791626, 'f1': 0.5889949466591802}, 'combined': 0.3769567658618753, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 24
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 00:16:53.799240: step: 2/1531, loss: 0.07479982078075409
2023-01-22 00:16:54.601653: step: 4/1531, loss: 0.11063440144062042
2023-01-22 00:16:55.408371: step: 6/1531, loss: 0.0965094342827797
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2023-01-22 00:37:43.797715: step: 3062/1531, loss: 0.045929502695798874
==================================================
Loss: 0.087
--------------------
Dev Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Dev Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Dev Russian: {'event': {'p': 0.589798087141339, 'r': 0.7390146471371505, 'f1': 0.6560283687943262}, 'argument': {'p': 0.6809646274727356, 'r': 0.6394362923660222, 'f1': 0.6595473997045518}, 'combined': 0.4326818047707166, 'epoch': 24}
Test Russian: {'event': {'p': 0.6026074360212458, 'r': 0.7042889390519187, 'f1': 0.6494925839188134}, 'argument': {'p': 0.7033846542606331, 'r': 0.6533347222497239, 'f1': 0.677436507036694}, 'combined': 0.43998998739619777, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.566370224166608, 'r': 0.4915666096540371, 'f1': 0.5263238446800801}, 'combined': 0.30646704880105924, 'epoch': 24}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 25
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 01:04:12.234060: step: 3062/1531, loss: 0.027878940105438232
==================================================
Loss: 0.084
--------------------
Dev Chinese: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6748213545264731, 'r': 0.6567806102219358, 'f1': 0.6656787730114678}, 'combined': 0.42820559804135194, 'epoch': 25}
Test Chinese: {'event': {'p': 0.5708172871202396, 'r': 0.7528216704288939, 'f1': 0.6493064005840837}, 'argument': {'p': 0.6910973605146797, 'r': 0.6753061573846401, 'f1': 0.6831105113448975}, 'combined': 0.4435480273225083, 'epoch': 25}
Dev Korean: {'event': {'p': 0.5660569105691057, 'r': 0.7416777629826897, 'f1': 0.6420749279538904}, 'argument': {'p': 0.6723377262418639, 'r': 0.6537972083919431, 'f1': 0.6629378610479012}, 'combined': 0.4256557793702374, 'epoch': 25}
Test Korean: {'event': {'p': 0.6180651077487391, 'r': 0.7607223476297968, 'f1': 0.6820136605110043}, 'argument': {'p': 0.6829541888041607, 'r': 0.6676225641575366, 'f1': 0.6752013548755273}, 'combined': 0.46049654762064807, 'epoch': 25}
Dev Russian: {'event': {'p': 0.5887353878852285, 'r': 0.7376830892143809, 'f1': 0.6548463356973995}, 'argument': {'p': 0.6747573434353993, 'r': 0.6444924920018997, 'f1': 0.6592777654175132}, 'combined': 0.4317256288904282, 'epoch': 25}
Test Russian: {'event': {'p': 0.6030902945436987, 'r': 0.7048532731376975, 'f1': 0.6500130106687484}, 'argument': {'p': 0.6930419158082843, 'r': 0.6651567745548119, 'f1': 0.6788130906341674}, 'combined': 0.4412373407244731, 'epoch': 25}
Chinese: {'event': {'p': 0.5581395348837209, 'r': 0.8888888888888888, 'f1': 0.6857142857142857}, 'argument': {'p': 0.6879097997519049, 'r': 0.6481133650555138, 'f1': 0.667418869546529}, 'combined': 0.45765865340333417, 'epoch': 25}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6512820512820512, 'r': 0.5582417582417581, 'f1': 0.6011834319526627}, 'combined': 0.38475739644970414, 'epoch': 25}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5849871917917904, 'r': 0.5077247324985351, 'f1': 0.5436244610590375}, 'combined': 0.31654082542678125, 'epoch': 25}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
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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==================================================
Loss: 0.073
--------------------
Dev Chinese: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6639687062775212, 'r': 0.6471897137001109, 'f1': 0.6554718489759808}, 'combined': 0.42238219939675314, 'epoch': 26}
Test Chinese: {'event': {'p': 0.5725288831835686, 'r': 0.755079006772009, 'f1': 0.6512533463129715}, 'argument': {'p': 0.7064362015622015, 'r': 0.6765667514290347, 'f1': 0.6911789232974025}, 'combined': 0.45013258669843004, 'epoch': 26}
Dev Korean: {'event': {'p': 0.5609756097560976, 'r': 0.7350199733688415, 'f1': 0.6363112391930836}, 'argument': {'p': 0.6654531505741521, 'r': 0.6456681040378648, 'f1': 0.6554113474186349}, 'combined': 0.41704560665716023, 'epoch': 26}
Test Korean: {'event': {'p': 0.6134800550206327, 'r': 0.755079006772009, 'f1': 0.676954211990893}, 'argument': {'p': 0.6974317955007764, 'r': 0.6687767030540552, 'f1': 0.6828037408542877}, 'combined': 0.46222686833444826, 'epoch': 26}
Dev Russian: {'event': {'p': 0.59192348565356, 'r': 0.7416777629826897, 'f1': 0.6583924349881797}, 'argument': {'p': 0.6658245336478954, 'r': 0.6349811912803826, 'f1': 0.6500371999558301}, 'combined': 0.4279795749118172, 'epoch': 26}
Test Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.7039762089912205, 'r': 0.658558389056303, 'f1': 0.6805103353581797}, 'combined': 0.44340303922375285, 'epoch': 26}
Chinese: {'event': {'p': 0.5697674418604651, 'r': 0.9074074074074074, 'f1': 0.7}, 'argument': {'p': 0.6518939393939394, 'r': 0.6465063861758076, 'f1': 0.6491889852885703}, 'combined': 0.45443228970199917, 'epoch': 26}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6409205548549811, 'r': 0.5077422577422578, 'f1': 0.5666109253065775}, 'combined': 0.36263099219620964, 'epoch': 26}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.559813410572698, 'r': 0.5175633418502302, 'f1': 0.5378599434914157}, 'combined': 0.3131842708937357, 'epoch': 26}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 27
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 01:56:45.172707: step: 3062/1531, loss: 0.009076397866010666
==================================================
Loss: 0.076
--------------------
Dev Chinese: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6785379162725215, 'r': 0.655004809349775, 'f1': 0.6665637177500652}, 'combined': 0.42726507842189915, 'epoch': 27}
Test Chinese: {'event': {'p': 0.5729567821994009, 'r': 0.7556433408577878, 'f1': 0.6517400827451936}, 'argument': {'p': 0.6989595249598055, 'r': 0.6707851403460411, 'f1': 0.6845825720981021}, 'combined': 0.4461699021851345, 'epoch': 27}
Dev Korean: {'event': {'p': 0.5650406504065041, 'r': 0.7403462050599201, 'f1': 0.640922190201729}, 'argument': {'p': 0.6736484744390598, 'r': 0.6550583204603494, 'f1': 0.6642233485080273}, 'combined': 0.42571548330889125, 'epoch': 27}
Test Korean: {'event': {'p': 0.6134800550206327, 'r': 0.755079006772009, 'f1': 0.676954211990893}, 'argument': {'p': 0.6926619780513139, 'r': 0.6688745384786575, 'f1': 0.680560462780049}, 'combined': 0.46070827179342555, 'epoch': 27}
Dev Russian: {'event': {'p': 0.5929861849096706, 'r': 0.7430093209054593, 'f1': 0.6595744680851064}, 'argument': {'p': 0.6724403312886663, 'r': 0.6403016389844286, 'f1': 0.6559775736488308}, 'combined': 0.4326660592151863, 'epoch': 27}
Test Russian: {'event': {'p': 0.6016417189763399, 'r': 0.7031602708803611, 'f1': 0.6484517304189436}, 'argument': {'p': 0.6973945083305976, 'r': 0.6598741512354075, 'f1': 0.6781157230994147}, 'combined': 0.4397253140681086, 'epoch': 27}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6674156822461906, 'r': 0.6508681859921528, 'f1': 0.6590380795401714}, 'combined': 0.4707414853858366, 'epoch': 27}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6508097165991903, 'r': 0.4817682317682318, 'f1': 0.5536739380022963}, 'combined': 0.3543513203214696, 'epoch': 27}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5643174597899084, 'r': 0.5004324643419943, 'f1': 0.530458412202514}, 'combined': 0.30887451849766634, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 28
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 02:02:11.537339: step: 2/1531, loss: 0.05005870386958122
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2023-01-22 02:23:01.048750: step: 3062/1531, loss: 4.354524571681395e-05
==================================================
Loss: 0.071
--------------------
Dev Chinese: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6513590223907614, 'r': 0.6565285384414817, 'f1': 0.6539335639812387}, 'combined': 0.4199097743797988, 'epoch': 28}
Test Chinese: {'event': {'p': 0.5673940949935815, 'r': 0.7483069977426636, 'f1': 0.6454125091263081}, 'argument': {'p': 0.689922134885906, 'r': 0.6859876577664628, 'f1': 0.6879492709173509}, 'combined': 0.44401106509438176, 'epoch': 28}
Dev Korean: {'event': {'p': 0.5660569105691057, 'r': 0.7416777629826897, 'f1': 0.6420749279538904}, 'argument': {'p': 0.6528104033989123, 'r': 0.6636984666898296, 'f1': 0.6582094106905969}, 'combined': 0.4226197599477377, 'epoch': 28}
Test Korean: {'event': {'p': 0.6098120128381477, 'r': 0.7505643340857788, 'f1': 0.6729066531748039}, 'argument': {'p': 0.6828632153698665, 'r': 0.6761503227441966, 'f1': 0.6794901897787021}, 'combined': 0.4572334694690988, 'epoch': 28}
Dev Russian: {'event': {'p': 0.594048884165781, 'r': 0.7443408788282291, 'f1': 0.6607565011820331}, 'argument': {'p': 0.6532419917060025, 'r': 0.6417141918523671, 'f1': 0.6474267810973436}, 'combined': 0.4277914546494268, 'epoch': 28}
Test Russian: {'event': {'p': 0.5987445678416224, 'r': 0.6997742663656885, 'f1': 0.645329169919334}, 'argument': {'p': 0.6859905732035476, 'r': 0.6734014110221805, 'f1': 0.679637698770595}, 'combined': 0.43859003199351443, 'epoch': 28}
Chinese: {'event': {'p': 0.5581395348837209, 'r': 0.8888888888888888, 'f1': 0.6857142857142857}, 'argument': {'p': 0.6684848484848485, 'r': 0.6905835211620336, 'f1': 0.6793545208179353}, 'combined': 0.4658430999894414, 'epoch': 28}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6128364389233955, 'r': 0.549165120593692, 'f1': 0.579256360078278}, 'combined': 0.37999217221135034, 'epoch': 28}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5039864163924315, 'r': 0.4659497057213045, 'f1': 0.4842222432005714}, 'combined': 0.28195219224337065, 'epoch': 28}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 29
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 02:49:11.884037: step: 3040/1531, loss: 0.0063722999766469
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2023-01-22 02:49:16.756731: step: 3052/1531, loss: 8.224546036217362e-05
2023-01-22 02:49:17.590471: step: 3054/1531, loss: 0.010928632691502571
2023-01-22 02:49:18.383031: step: 3056/1531, loss: 0.02276497706770897
2023-01-22 02:49:19.181919: step: 3058/1531, loss: 0.0001476318866480142
2023-01-22 02:49:19.968646: step: 3060/1531, loss: 0.04449429735541344
2023-01-22 02:49:20.808441: step: 3062/1531, loss: 0.000117544608656317
==================================================
Loss: 0.072
--------------------
Dev Chinese: {'event': {'p': 0.5566502463054187, 'r': 0.7523302263648469, 'f1': 0.6398640996602492}, 'argument': {'p': 0.6831651076281116, 'r': 0.6560553811349326, 'f1': 0.6693358544372591}, 'combined': 0.42828398386982036, 'epoch': 29}
Test Chinese: {'event': {'p': 0.5712451861360719, 'r': 0.7533860045146726, 'f1': 0.6497931370163056}, 'argument': {'p': 0.7008939561728155, 'r': 0.6794692500889303, 'f1': 0.6900153360104879}, 'combined': 0.4483672297756151, 'epoch': 29}
Dev Korean: {'event': {'p': 0.5619918699186992, 'r': 0.7363515312916112, 'f1': 0.637463976945245}, 'argument': {'p': 0.6837248811423685, 'r': 0.6599086298398767, 'f1': 0.671605680885783}, 'combined': 0.42812442827647035, 'epoch': 29}
Test Korean: {'event': {'p': 0.6116460339293902, 'r': 0.7528216704288939, 'f1': 0.6749304325828486}, 'argument': {'p': 0.6933966619249904, 'r': 0.6660474566263508, 'f1': 0.6794469545397496}, 'combined': 0.45857942694461223, 'epoch': 29}
Dev Russian: {'event': {'p': 0.589798087141339, 'r': 0.7390146471371505, 'f1': 0.6560283687943262}, 'argument': {'p': 0.679043331931422, 'r': 0.640126814745051, 'f1': 0.6590110399909337}, 'combined': 0.4323299375827047, 'epoch': 29}
Test Russian: {'event': {'p': 0.6001931434089812, 'r': 0.7014672686230248, 'f1': 0.6468904501691388}, 'argument': {'p': 0.7026975836984934, 'r': 0.6611900822291592, 'f1': 0.6813122293789905}, 'combined': 0.44073437476871474, 'epoch': 29}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.7140713060524382, 'r': 0.6255500697649458, 'f1': 0.6668859774586646}, 'combined': 0.47634712675618895, 'epoch': 29}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6435472739820566, 'r': 0.5766852195423624, 'f1': 0.6082844096542728}, 'combined': 0.3893020221787346, 'epoch': 29}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.6054914290636575, 'r': 0.536945229547017, 'f1': 0.569161943319838}, 'combined': 0.31700158868446676, 'epoch': 29}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 30
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 03:15:35.240233: step: 3062/1531, loss: 0.009812982752919197
==================================================
Loss: 0.069
--------------------
Dev Chinese: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6741232165719327, 'r': 0.6682866519262883, 'f1': 0.6711922460650983}, 'combined': 0.43023194934637116, 'epoch': 30}
Test Chinese: {'event': {'p': 0.5708172871202396, 'r': 0.7528216704288939, 'f1': 0.6493064005840837}, 'argument': {'p': 0.701382973313459, 'r': 0.6865600523745969, 'f1': 0.6938923601046527}, 'combined': 0.4505487507323469, 'epoch': 30}
Dev Korean: {'event': {'p': 0.5650406504065041, 'r': 0.7403462050599201, 'f1': 0.640922190201729}, 'argument': {'p': 0.6729658588633305, 'r': 0.6685737684138962, 'f1': 0.6707626239670883}, 'combined': 0.42990665005844503, 'epoch': 30}
Test Korean: {'event': {'p': 0.6130215497478221, 'r': 0.7545146726862303, 'f1': 0.6764482671388818}, 'argument': {'p': 0.6917590816034438, 'r': 0.6748640158383664, 'f1': 0.6832071149352693}, 'combined': 0.4621542689949178, 'epoch': 30}
Dev Russian: {'event': {'p': 0.594048884165781, 'r': 0.7443408788282291, 'f1': 0.6607565011820331}, 'argument': {'p': 0.6750477298152376, 'r': 0.6531918798432947, 'f1': 0.6639399885617333}, 'combined': 0.43870266383689, 'epoch': 30}
Test Russian: {'event': {'p': 0.6040560115886046, 'r': 0.7059819413092551, 'f1': 0.6510538641686182}, 'argument': {'p': 0.7005603559876046, 'r': 0.6694783020651043, 'f1': 0.6846667498964288}, 'combined': 0.4457549331878389, 'epoch': 30}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.6483901515151514, 'r': 0.685900325569747, 'f1': 0.6666179870999147}, 'combined': 0.48567881917279504, 'epoch': 30}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.680687128962991, 'r': 0.5127253698682269, 'f1': 0.5848867182200514}, 'combined': 0.3836856871523538, 'epoch': 30}
Russian: {'event': {'p': 0.4883720930232558, 'r': 0.5833333333333334, 'f1': 0.5316455696202531}, 'argument': {'p': 0.5607421272627391, 'r': 0.5819022075368048, 'f1': 0.5711262407305676}, 'combined': 0.3036367355782764, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
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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2023-01-22 03:41:51.329274: step: 3062/1531, loss: 0.0003064890333916992
==================================================
Loss: 0.067
--------------------
Dev Chinese: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6999725410091282, 'r': 0.6565712154671058, 'f1': 0.6775775862000565}, 'combined': 0.4350922892133998, 'epoch': 31}
Test Chinese: {'event': {'p': 0.5703893881044073, 'r': 0.7522573363431151, 'f1': 0.6488196641518617}, 'argument': {'p': 0.7201652036689802, 'r': 0.6678599972155506, 'f1': 0.6930270871315588}, 'combined': 0.44964960192084097, 'epoch': 31}
Dev Korean: {'event': {'p': 0.5640243902439024, 'r': 0.7390146471371505, 'f1': 0.6397694524495676}, 'argument': {'p': 0.6993984659482725, 'r': 0.6557495050835762, 'f1': 0.6768710247229722}, 'combined': 0.4330414048659937, 'epoch': 31}
Test Korean: {'event': {'p': 0.6116460339293902, 'r': 0.7528216704288939, 'f1': 0.6749304325828486}, 'argument': {'p': 0.7117154937203681, 'r': 0.6601699649334118, 'f1': 0.6849743753286873}, 'combined': 0.4623100514487574, 'epoch': 31}
Dev Russian: {'event': {'p': 0.5908607863974495, 'r': 0.7403462050599201, 'f1': 0.657210401891253}, 'argument': {'p': 0.6997684623906424, 'r': 0.6370872932190871, 'f1': 0.6669584115022265}, 'combined': 0.43833200566812996, 'epoch': 31}
Test Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.721198654248517, 'r': 0.6535627957396252, 'f1': 0.6857169420314799}, 'combined': 0.4467955302750002, 'epoch': 31}
Chinese: {'event': {'p': 0.5581395348837209, 'r': 0.8888888888888888, 'f1': 0.6857142857142857}, 'argument': {'p': 0.6942359373552035, 'r': 0.6253860923282412, 'f1': 0.6580149319279756}, 'combined': 0.4512102390363261, 'epoch': 31}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.6680075876504448, 'r': 0.49877899877899873, 'f1': 0.5711209909683191}, 'combined': 0.3563794983642312, 'epoch': 31}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5974995874995875, 'r': 0.5073109705185177, 'f1': 0.548724110969009}, 'combined': 0.3195102418300558, 'epoch': 31}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 32
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 03:47:14.292784: step: 2/1531, loss: 0.0009393127402290702
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==================================================
Loss: 0.054
--------------------
Dev Chinese: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6901451003955698, 'r': 0.6682357321290437, 'f1': 0.6790137278085444}, 'combined': 0.43524549257036943, 'epoch': 32}
Test Chinese: {'event': {'p': 0.5708172871202396, 'r': 0.7528216704288939, 'f1': 0.6493064005840837}, 'argument': {'p': 0.7043244072568996, 'r': 0.6726746854016264, 'f1': 0.6881358188225214}, 'combined': 0.44681099163263255, 'epoch': 32}
Dev Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6902655745143638, 'r': 0.669742812690514, 'f1': 0.679849347589414}, 'combined': 0.43181209282048083, 'epoch': 32}
Test Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6979551167347872, 'r': 0.6622050412168249, 'f1': 0.6796102563993732}, 'combined': 0.45937733496056793, 'epoch': 32}
Dev Russian: {'event': {'p': 0.59192348565356, 'r': 0.7416777629826897, 'f1': 0.6583924349881797}, 'argument': {'p': 0.6911420487295907, 'r': 0.6571431373750514, 'f1': 0.6737139278169514}, 'combined': 0.4435681534208533, 'epoch': 32}
Test Russian: {'event': {'p': 0.5992274263640753, 'r': 0.7003386004514672, 'f1': 0.6458495966692687}, 'argument': {'p': 0.70928822480003, 'r': 0.6569032068102912, 'f1': 0.6820913945781746}, 'combined': 0.44052845207989305, 'epoch': 32}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.7152861952861952, 'r': 0.6207028967359546, 'f1': 0.6646464646464646}, 'combined': 0.47474747474747464, 'epoch': 32}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.614406779661017, 'r': 0.4707792207792208, 'f1': 0.5330882352941176}, 'combined': 0.33264705882352946, 'epoch': 32}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5919548383221153, 'r': 0.5137721238267415, 'f1': 0.5500994457134809}, 'combined': 0.33423763790186184, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 33
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 04:34:12.170026: step: 3054/1531, loss: 7.829068636056036e-06
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2023-01-22 04:34:14.590721: step: 3060/1531, loss: 7.68326863180846e-05
2023-01-22 04:34:15.392760: step: 3062/1531, loss: 7.89839104982093e-05
==================================================
Loss: 0.065
--------------------
Dev Chinese: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6767212364375513, 'r': 0.6723301108946976, 'f1': 0.6745185271688576}, 'combined': 0.43389187251632066, 'epoch': 33}
Test Chinese: {'event': {'p': 0.5712451861360719, 'r': 0.7533860045146726, 'f1': 0.6497931370163056}, 'argument': {'p': 0.7025078638754623, 'r': 0.6942707532437568, 'f1': 0.6983650205333154}, 'combined': 0.45379279747479967, 'epoch': 33}
Dev Korean: {'event': {'p': 0.568089430894309, 'r': 0.7443408788282291, 'f1': 0.6443804034582133}, 'argument': {'p': 0.6735495912388487, 'r': 0.6750159546450291, 'f1': 0.6742819757164552}, 'combined': 0.4344940915567706, 'epoch': 33}
Test Korean: {'event': {'p': 0.6116460339293902, 'r': 0.7528216704288939, 'f1': 0.6749304325828486}, 'argument': {'p': 0.6942030739692889, 'r': 0.6850223619025536, 'f1': 0.6895821625706198}, 'combined': 0.4654199872852046, 'epoch': 33}
Dev Russian: {'event': {'p': 0.5908607863974495, 'r': 0.7403462050599201, 'f1': 0.657210401891253}, 'argument': {'p': 0.6713773081042349, 'r': 0.6605327407192912, 'f1': 0.6659108755438007}, 'combined': 0.4376435541398974, 'epoch': 33}
Test Russian: {'event': {'p': 0.6016417189763399, 'r': 0.7031602708803611, 'f1': 0.6484517304189436}, 'argument': {'p': 0.7040611423135137, 'r': 0.6762692551169276, 'f1': 0.6898854146159262}, 'combined': 0.4473573908984877, 'epoch': 33}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.6605216817081224, 'r': 0.6441451110872598, 'f1': 0.6522306145737108}, 'combined': 0.47519659061798936, 'epoch': 33}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.5598686598686599, 'r': 0.5244339345605168, 'f1': 0.5415722984350435}, 'combined': 0.33794111422346723, 'epoch': 33}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5439963029746312, 'r': 0.52346814059823, 'f1': 0.5335348356097344}, 'combined': 0.3106658536461744, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 34
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 04:39:42.060652: step: 2/1531, loss: 0.0002973449882119894
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==================================================
Loss: 0.051
--------------------
Dev Chinese: {'event': {'p': 0.5556650246305419, 'r': 0.7509986684420772, 'f1': 0.638731596828992}, 'argument': {'p': 0.6781395497360153, 'r': 0.6722639724097826, 'f1': 0.6751889788162067}, 'combined': 0.43126453460061215, 'epoch': 34}
Test Chinese: {'event': {'p': 0.5755241762943946, 'r': 0.7590293453724605, 'f1': 0.6546605013385252}, 'argument': {'p': 0.6979795583832972, 'r': 0.6942320171302325, 'f1': 0.6961007439596543}, 'combined': 0.4557096620227476, 'epoch': 34}
Dev Korean: {'event': {'p': 0.5630081300813008, 'r': 0.7376830892143809, 'f1': 0.6386167146974063}, 'argument': {'p': 0.6802217932777549, 'r': 0.6787409040325928, 'f1': 0.6794805417776338}, 'combined': 0.43392763129084627, 'epoch': 34}
Test Korean: {'event': {'p': 0.6166895919303071, 'r': 0.7590293453724605, 'f1': 0.6804958259549709}, 'argument': {'p': 0.6856370405170169, 'r': 0.6893620634875678, 'f1': 0.6874945062549567}, 'combined': 0.4678371418734717, 'epoch': 34}
Dev Russian: {'event': {'p': 0.594048884165781, 'r': 0.7443408788282291, 'f1': 0.6607565011820331}, 'argument': {'p': 0.67567923468082, 'r': 0.6617477040688443, 'f1': 0.6686409093195614}, 'combined': 0.44180882778916647, 'epoch': 34}
Test Russian: {'event': {'p': 0.6059874456784162, 'r': 0.7082392776523702, 'f1': 0.653135571168358}, 'argument': {'p': 0.6965880973329246, 'r': 0.682569322299854, 'f1': 0.6895074612226356}, 'combined': 0.45034184951049056, 'epoch': 34}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.6344842149720198, 'r': 0.6449715573682515, 'f1': 0.6396849052586757}, 'combined': 0.46605614525989236, 'epoch': 34}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6132154882154883, 'r': 0.5123066104078763, 'f1': 0.5582375478927203}, 'combined': 0.357272030651341, 'epoch': 34}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5678662062197432, 'r': 0.5250083793352343, 'f1': 0.5455969432307336}, 'combined': 0.3038767785082567, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 35
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 05:26:46.148317: step: 3062/1531, loss: 0.11198868602514267
==================================================
Loss: 0.052
--------------------
Dev Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.6685446311449738, 'r': 0.6767505941265366, 'f1': 0.6726225854791913}, 'combined': 0.4281018041215238, 'epoch': 35}
Test Chinese: {'event': {'p': 0.5725288831835686, 'r': 0.755079006772009, 'f1': 0.6512533463129715}, 'argument': {'p': 0.6933428518339402, 'r': 0.6914802786926805, 'f1': 0.6924103126922672}, 'combined': 0.45093453316244997, 'epoch': 35}
Dev Korean: {'event': {'p': 0.5640243902439024, 'r': 0.7390146471371505, 'f1': 0.6397694524495676}, 'argument': {'p': 0.6706660917975737, 'r': 0.6794202539820235, 'f1': 0.6750147912400651}, 'combined': 0.43185384338701566, 'epoch': 35}
Test Korean: {'event': {'p': 0.6102705181109582, 'r': 0.7511286681715575, 'f1': 0.6734125980268151}, 'argument': {'p': 0.6873976867514198, 'r': 0.6852941236161723, 'f1': 0.6863442933952538}, 'combined': 0.4621928937561765, 'epoch': 35}
Dev Russian: {'event': {'p': 0.5929861849096706, 'r': 0.7430093209054593, 'f1': 0.6595744680851064}, 'argument': {'p': 0.671804696097323, 'r': 0.6639069186266712, 'f1': 0.6678324584263854}, 'combined': 0.4404852385365521, 'epoch': 35}
Test Russian: {'event': {'p': 0.6006760019314341, 'r': 0.7020316027088036, 'f1': 0.6474108769190736}, 'argument': {'p': 0.6963320463489241, 'r': 0.6808526260757976, 'f1': 0.6885053426316197}, 'combined': 0.44574584763660413, 'epoch': 35}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6401515151515151, 'r': 0.6031179564237414, 'f1': 0.6210831721470019}, 'combined': 0.44363083724785846, 'epoch': 35}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.6115051139879907, 'r': 0.5650616876091559, 'f1': 0.5873667542253067}, 'combined': 0.36651685463659145, 'epoch': 35}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5644647726354008, 'r': 0.4686122640746724, 'f1': 0.5120917524939719}, 'combined': 0.31114435594570444, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 36
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 05:32:13.339443: step: 2/1531, loss: 0.0007859353208914399
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2023-01-22 05:52:57.500748: step: 3052/1531, loss: 0.09387808293104172
2023-01-22 05:52:58.287109: step: 3054/1531, loss: 0.05388376861810684
2023-01-22 05:52:59.100403: step: 3056/1531, loss: 0.11183395236730576
2023-01-22 05:52:59.920113: step: 3058/1531, loss: 0.00461967010051012
2023-01-22 05:53:00.808818: step: 3060/1531, loss: 0.029503915458917618
2023-01-22 05:53:01.631447: step: 3062/1531, loss: 0.01718146726489067
==================================================
Loss: 0.044
--------------------
Dev Chinese: {'event': {'p': 0.5556650246305419, 'r': 0.7509986684420772, 'f1': 0.638731596828992}, 'argument': {'p': 0.6716941247403609, 'r': 0.6697528122411114, 'f1': 0.6707220637783633}, 'combined': 0.42841137482559105, 'epoch': 36}
Test Chinese: {'event': {'p': 0.57466837826273, 'r': 0.7579006772009029, 'f1': 0.6536870284740812}, 'argument': {'p': 0.7076198675666673, 'r': 0.6758220599697748, 'f1': 0.6913555344184796}, 'combined': 0.45193014491312633, 'epoch': 36}
Dev Korean: {'event': {'p': 0.5650406504065041, 'r': 0.7403462050599201, 'f1': 0.640922190201729}, 'argument': {'p': 0.6736631869349113, 'r': 0.6707299655113922, 'f1': 0.6721933763452351}, 'combined': 0.4308236510062832, 'epoch': 36}
Test Korean: {'event': {'p': 0.6143970655662541, 'r': 0.7562076749435666, 'f1': 0.6779661016949152}, 'argument': {'p': 0.7009721713881902, 'r': 0.6711891933380462, 'f1': 0.6857574602537009}, 'combined': 0.4649203120364074, 'epoch': 36}
Dev Russian: {'event': {'p': 0.5908607863974495, 'r': 0.7403462050599201, 'f1': 0.657210401891253}, 'argument': {'p': 0.6753343381490062, 'r': 0.6564785667679172, 'f1': 0.665772972705764}, 'combined': 0.43755292296028936, 'epoch': 36}
Test Russian: {'event': {'p': 0.6021245774987929, 'r': 0.70372460496614, 'f1': 0.6489721571688786}, 'argument': {'p': 0.7059198302048739, 'r': 0.664812540265734, 'f1': 0.6847497960252206}, 'combined': 0.444383552247437, 'epoch': 36}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7011784511784511, 'r': 0.6258452291510143, 'f1': 0.6613735609368797}, 'combined': 0.48185788011115527, 'epoch': 36}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6794871794871795, 'r': 0.49417249417249415, 'f1': 0.572199730094467}, 'combined': 0.3753630229419704, 'epoch': 36}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5967444434621995, 'r': 0.5854851143402712, 'f1': 0.5910611630482737}, 'combined': 0.34416219620532384, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 37
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 05:58:26.745255: step: 2/1531, loss: 6.739881791872904e-05
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2023-01-22 05:58:31.595381: step: 14/1531, loss: 0.004569028504192829
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2023-01-22 06:19:15.674676: step: 3062/1531, loss: 0.06727363914251328
==================================================
Loss: 0.058
--------------------
Dev Chinese: {'event': {'p': 0.5566502463054187, 'r': 0.7523302263648469, 'f1': 0.6398640996602492}, 'argument': {'p': 0.6866200222505435, 'r': 0.665287174738424, 'f1': 0.6757852842847551}, 'combined': 0.4324107424925104, 'epoch': 37}
Test Chinese: {'event': {'p': 0.5725288831835686, 'r': 0.755079006772009, 'f1': 0.6512533463129715}, 'argument': {'p': 0.711561168468201, 'r': 0.6750279943824176, 'f1': 0.6928133023108677}, 'combined': 0.4511969815000929, 'epoch': 37}
Dev Korean: {'event': {'p': 0.5670731707317073, 'r': 0.7430093209054593, 'f1': 0.6432276657060518}, 'argument': {'p': 0.6847802862466504, 'r': 0.6648883389337485, 'f1': 0.6746877249165599}, 'combined': 0.43397781037860567, 'epoch': 37}
Test Korean: {'event': {'p': 0.6153140761118753, 'r': 0.7573363431151241, 'f1': 0.6789779913989374}, 'argument': {'p': 0.7044042946658072, 'r': 0.6677838879094808, 'f1': 0.6856054359384622}, 'combined': 0.46551100178569, 'epoch': 37}
Dev Russian: {'event': {'p': 0.5961742826780021, 'r': 0.7470039946737683, 'f1': 0.6631205673758865}, 'argument': {'p': 0.6863008834325054, 'r': 0.6504455760567085, 'f1': 0.6678923595474626}, 'combined': 0.442893160409133, 'epoch': 37}
Test Russian: {'event': {'p': 0.6050217286335103, 'r': 0.7071106094808126, 'f1': 0.6520947176684881}, 'argument': {'p': 0.7124896940014386, 'r': 0.662551138072122, 'f1': 0.6866135850139717}, 'combined': 0.44773709186703436, 'epoch': 37}
Chinese: {'event': {'p': 0.5813953488372093, 'r': 0.9259259259259259, 'f1': 0.7142857142857142}, 'argument': {'p': 0.6839225589225587, 'r': 0.6104432757325318, 'f1': 0.6450972608177847}, 'combined': 0.46078375772698904, 'epoch': 37}
Korean: {'event': {'p': 0.6451612903225806, 'r': 0.6349206349206349, 'f1': 0.64}, 'argument': {'p': 0.6645687645687646, 'r': 0.4746919746919747, 'f1': 0.5538073038073038}, 'combined': 0.3544366744366744, 'epoch': 37}
Russian: {'event': {'p': 0.5348837209302325, 'r': 0.6388888888888888, 'f1': 0.5822784810126581}, 'argument': {'p': 0.5864537310905694, 'r': 0.5421930721403377, 'f1': 0.5634555455576059}, 'combined': 0.3280880391854413, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5866099893730075, 'r': 0.7350199733688415, 'f1': 0.652482269503546}, 'argument': {'p': 0.683379993265429, 'r': 0.6512681580459081, 'f1': 0.6669377678635541}, 'combined': 0.43516506839324093, 'epoch': 7}
Test for Russian: {'event': {'p': 0.6045388701110574, 'r': 0.7065462753950339, 'f1': 0.6515742909185532}, 'argument': {'p': 0.705324143744743, 'r': 0.644532966246494, 'f1': 0.673559681492538}, 'combined': 0.438874171859827, 'epoch': 7}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.6208250623010348, 'r': 0.5388292993556152, 'f1': 0.576928340724194}, 'combined': 0.3505387386678647, 'epoch': 7}
******************************
Epoch: 38
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 06:45:29.045201: step: 3062/1531, loss: 0.0010851634433493018
==================================================
Loss: 0.049
--------------------
Dev Chinese: {'event': {'p': 0.5556650246305419, 'r': 0.7509986684420772, 'f1': 0.638731596828992}, 'argument': {'p': 0.6860719684176639, 'r': 0.6677304490307755, 'f1': 0.6767769618883877}, 'combined': 0.43227882956404373, 'epoch': 38}
Test Chinese: {'event': {'p': 0.5738125802310655, 'r': 0.7567720090293454, 'f1': 0.6527135556096374}, 'argument': {'p': 0.7038636292728926, 'r': 0.6738667564817324, 'f1': 0.6885386368302975}, 'combined': 0.4494185018201163, 'epoch': 38}
Dev Korean: {'event': {'p': 0.5630081300813008, 'r': 0.7376830892143809, 'f1': 0.6386167146974063}, 'argument': {'p': 0.6880226983826696, 'r': 0.6635574500366966, 'f1': 0.6755686487998285}, 'combined': 0.43142943104911236, 'epoch': 38}
Test Korean: {'event': {'p': 0.6139385602934434, 'r': 0.7556433408577878, 'f1': 0.6774601568429043}, 'argument': {'p': 0.6952821232562831, 'r': 0.6669610591725241, 'f1': 0.6808271934588552}, 'combined': 0.4612332972635504, 'epoch': 38}
Dev Russian: {'event': {'p': 0.5876726886291179, 'r': 0.7363515312916112, 'f1': 0.6536643026004727}, 'argument': {'p': 0.6897670998248798, 'r': 0.6557609219821522, 'f1': 0.6723342836467331}, 'combined': 0.4394809206343302, 'epoch': 38}
Test Russian: {'event': {'p': 0.607436021245775, 'r': 0.7099322799097065, 'f1': 0.6546968514181629}, 'argument': {'p': 0.7016694848775216, 'r': 0.6624714324606996, 'f1': 0.6815072883639964}, 'combined': 0.4461806759104385, 'epoch': 38}
Chinese: {'event': {'p': 0.5697674418604651, 'r': 0.9074074074074074, 'f1': 0.7}, 'argument': {'p': 0.7285353535353536, 'r': 0.614137240170298, 'f1': 0.6664628346242696}, 'combined': 0.46652398423698865, 'epoch': 38}
Korean: {'event': {'p': 0.6290322580645161, 'r': 0.6190476190476191, 'f1': 0.6240000000000001}, 'argument': {'p': 0.6225274725274725, 'r': 0.5122061482820977, 'f1': 0.5620039682539683}, 'combined': 0.3506904761904763, 'epoch': 38}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5787942218173965, 'r': 0.5678735761227286, 'f1': 0.5732818958953261}, 'combined': 0.3483231772528564, 'epoch': 38}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5876726886291179, 'r': 0.7363515312916112, 'f1': 0.6536643026004727}, 'argument': {'p': 0.6897670998248798, 'r': 0.6557609219821522, 'f1': 0.6723342836467331}, 'combined': 0.4394809206343302, 'epoch': 38}
Test for Russian: {'event': {'p': 0.607436021245775, 'r': 0.7099322799097065, 'f1': 0.6546968514181629}, 'argument': {'p': 0.7016694848775216, 'r': 0.6624714324606996, 'f1': 0.6815072883639964}, 'combined': 0.4461806759104385, 'epoch': 38}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5787942218173965, 'r': 0.5678735761227286, 'f1': 0.5732818958953261}, 'combined': 0.3483231772528564, 'epoch': 38}
******************************
Epoch: 39
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 06:50:59.779905: step: 2/1531, loss: 0.0006707107531838119
2023-01-22 06:51:00.619287: step: 4/1531, loss: 1.684309482574463
2023-01-22 06:51:01.419164: step: 6/1531, loss: 7.443924550898373e-05
2023-01-22 06:51:02.229632: step: 8/1531, loss: 0.00039324379758909345
2023-01-22 06:51:03.027503: step: 10/1531, loss: 0.13250544667243958
2023-01-22 06:51:03.839899: step: 12/1531, loss: 0.00014545641897711903
2023-01-22 06:51:04.664128: step: 14/1531, loss: 0.004227147903293371
2023-01-22 06:51:05.471867: step: 16/1531, loss: 0.004991590976715088
2023-01-22 06:51:06.262036: step: 18/1531, loss: 0.001377228181809187
2023-01-22 06:51:07.109627: step: 20/1531, loss: 2.886770380428061e-05
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2023-01-22 06:51:11.142407: step: 30/1531, loss: 1.0198968648910522
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2023-01-22 06:51:14.466893: step: 38/1531, loss: 0.014654970727860928
2023-01-22 06:51:15.272794: step: 40/1531, loss: 1.6581209365540417e-06
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2023-01-22 06:51:17.703494: step: 46/1531, loss: 0.0021113462280482054
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==================================================
Loss: 0.054
--------------------
Dev Chinese: {'event': {'p': 0.5566502463054187, 'r': 0.7523302263648469, 'f1': 0.6398640996602492}, 'argument': {'p': 0.6909225606830013, 'r': 0.6474588854990381, 'f1': 0.6684849859239789}, 'combined': 0.42773954365464106, 'epoch': 39}
Test Chinese: {'event': {'p': 0.5738125802310655, 'r': 0.7567720090293454, 'f1': 0.6527135556096374}, 'argument': {'p': 0.7225776340612962, 'r': 0.6784174159957636, 'f1': 0.699801546531134}, 'combined': 0.4567699556574596, 'epoch': 39}
Dev Korean: {'event': {'p': 0.5691056910569106, 'r': 0.7456724367509987, 'f1': 0.6455331412103748}, 'argument': {'p': 0.6867842926662289, 'r': 0.64937774077809, 'f1': 0.6675574087729974}, 'combined': 0.4309304310234912, 'epoch': 39}
Test Korean: {'event': {'p': 0.6143970655662541, 'r': 0.7562076749435666, 'f1': 0.6779661016949152}, 'argument': {'p': 0.7138185538325776, 'r': 0.6684774975938914, 'f1': 0.6904044037594865}, 'combined': 0.46807078220982135, 'epoch': 39}
Dev Russian: {'event': {'p': 0.594048884165781, 'r': 0.7443408788282291, 'f1': 0.6607565011820331}, 'argument': {'p': 0.6869042865210074, 'r': 0.6307168319861277, 'f1': 0.6576125555168462}, 'combined': 0.4345217713166868, 'epoch': 39}
Test Russian: {'event': {'p': 0.607436021245775, 'r': 0.7099322799097065, 'f1': 0.6546968514181629}, 'argument': {'p': 0.7212913597833425, 'r': 0.6656918174667099, 'f1': 0.6923771882644003}, 'combined': 0.4532971651504635, 'epoch': 39}
Chinese: {'event': {'p': 0.5465116279069767, 'r': 0.8703703703703703, 'f1': 0.6714285714285714}, 'argument': {'p': 0.7279346866725507, 'r': 0.6196468820435762, 'f1': 0.6694399350649349}, 'combined': 0.4494810992578848, 'epoch': 39}
Korean: {'event': {'p': 0.6129032258064516, 'r': 0.6031746031746031, 'f1': 0.608}, 'argument': {'p': 0.7221264367816091, 'r': 0.530168776371308, 'f1': 0.6114355231143551}, 'combined': 0.3717527980535279, 'epoch': 39}
Russian: {'event': {'p': 0.5116279069767442, 'r': 0.6111111111111112, 'f1': 0.5569620253164557}, 'argument': {'p': 0.5801235515845317, 'r': 0.5035034598658199, 'f1': 0.5391047146038072}, 'combined': 0.30026085370338623, 'epoch': 39}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.688226379178257, 'r': 0.6484732309224732, 'f1': 0.6677586801692382}, 'combined': 0.4250060908891415, 'epoch': 24}
Test for Chinese: {'event': {'p': 0.5721009841677364, 'r': 0.7545146726862303, 'f1': 0.6507666098807495}, 'argument': {'p': 0.706200312861297, 'r': 0.6720178439362461, 'f1': 0.6886851828144444}, 'combined': 0.4481733216952602, 'epoch': 24}
Chinese: {'event': {'p': 0.5930232558139535, 'r': 0.9444444444444444, 'f1': 0.7285714285714286}, 'argument': {'p': 0.7048353432282004, 'r': 0.6524095738971772, 'f1': 0.6776099437043643}, 'combined': 0.4936872446988941, 'epoch': 24}
--------------------
Dev for Korean: {'event': {'p': 0.5599593495934959, 'r': 0.7336884154460719, 'f1': 0.6351585014409222}, 'argument': {'p': 0.6896507732882537, 'r': 0.6511144093236706, 'f1': 0.6698287839104278}, 'combined': 0.4254474466105426, 'epoch': 24}
Test for Korean: {'event': {'p': 0.6125630444750114, 'r': 0.7539503386004515, 'f1': 0.6759423222868707}, 'argument': {'p': 0.6960448984453177, 'r': 0.6631701988585358, 'f1': 0.6792099861634491}, 'combined': 0.45910677536775507, 'epoch': 24}
Korean: {'event': {'p': 0.6612903225806451, 'r': 0.6507936507936508, 'f1': 0.656}, 'argument': {'p': 0.6979923302503948, 'r': 0.5620197983834347, 'f1': 0.6226694169140212}, 'combined': 0.40847113749559794, 'epoch': 24}
--------------------
Dev for Russian: {'event': {'p': 0.5876726886291179, 'r': 0.7363515312916112, 'f1': 0.6536643026004727}, 'argument': {'p': 0.6897670998248798, 'r': 0.6557609219821522, 'f1': 0.6723342836467331}, 'combined': 0.4394809206343302, 'epoch': 38}
Test for Russian: {'event': {'p': 0.607436021245775, 'r': 0.7099322799097065, 'f1': 0.6546968514181629}, 'argument': {'p': 0.7016694848775216, 'r': 0.6624714324606996, 'f1': 0.6815072883639964}, 'combined': 0.4461806759104385, 'epoch': 38}
Russian: {'event': {'p': 0.5581395348837209, 'r': 0.6666666666666666, 'f1': 0.6075949367088608}, 'argument': {'p': 0.5787942218173965, 'r': 0.5678735761227286, 'f1': 0.5732818958953261}, 'combined': 0.3483231772528564, 'epoch': 38}